diff --git a/Working_Data/Question_5b_results.docx b/Working_Data/Question_5b_results.docx new file mode 100644 index 000000000..d4c30a524 Binary files /dev/null and b/Working_Data/Question_5b_results.docx differ diff --git a/Working_Data/Random Search_search_results.png b/Working_Data/Random Search_search_results.png new file mode 100644 index 000000000..d686f949a Binary files /dev/null and b/Working_Data/Random Search_search_results.png differ diff --git a/Working_Data/adls_labs_at5424.md b/Working_Data/adls_labs_at5424.md new file mode 100644 index 000000000..7c9e4b257 --- /dev/null +++ b/Working_Data/adls_labs_at5424.md @@ -0,0 +1,185 @@ +## Tutorial 1 (Lab 0): Introduction + +### Run-Time Error +Here is the run-time error encountered during the session: + +```plaintext +RuntimeError: Tried to erase Node bert_embeddings_dropout but it still had 6 users in the graph: +{getattr_2: None, size_4: None, bert_encoder_layer_0_attention_self_query: None, +bert_encoder_layer_0_attention_self_key: None, bert_encoder_layer_0_attention_self_value: None, +add_7: None}! +``` + +## Tutorial 2 (Lab 0): Lora Finetune + +### Removing attention_mask, labels from hf_input_names and its effect on the graphs: +The graph was created with and without the extra information and compared. + +Firstly, having no labels meant that at the end of the process, there is no cross-entropy calculated and viewed (so 4 blocks are removed). This is becuase the ground truth labels are required for loss calculations and not having them therefore means no losses can be calculated. Secondly, when there is no attention_mask specified, the model calls an extra block called getattr_1 after the input, instead of having a seperate input attention_mask block. When no mask is specified, more information from the model is used as an input to the masking process, implying that the mask is created based on the input information, whilst the external mask would be used for manually choosing which information to mask or not. + + +## Tutorial 3 (Lab 1): QAT +The following combinations of widths were tested: (4,2) (8,4) (12,8) (16,8) (20,10) (24,12) (28,14) (32,16). + +![Fixed point width vs highest achieved accuracy](fixed_point_width_vs_accuracy.png) +![PTQ and QAT comparison vs highest achieved accuracy](ptq_vs_qat_accuracy.png) + +It is clear that quantisation-aware training is much more effective in improving accuracy than just quantising after training, as it allows the model to adapt to the lower precision during training, reducing the loss that would otherwise be encoutered when quantisation is applied at the end of the processs. +The model that offered a strong balance between maximising accuracy and amount of quantisation was with fixed width 16 (16,8) + +## Tutorial 4 (Lab 1): Pruning +A range of sparisty levels were then applied, ranging from 0.1 to 0.9. Each sparsity level was tested with both the random and the l1-norm method. For each combination, 5 epochs of training were ran to allow the accuracies reached to closely match their potential if many more epochs of training were done. + +![Sparsity vs highest achieved accuracy](highest_accuracy_by_sparsity.png) +![Random vs L1-Norm comparison](pruning_accuracy_by_sparsity.png) + +L1-norm performed better than random in all cases and allowed for more drastic pruning. + +## Tutorial 5 (Lab 2): Nas Optuna +Each sampler was given the same search space, and 10 iterations to find the optimal setup. Each search space setup combination was trained for 3 epochs to allow the accuracies reached to stablise and represent the effectiveness of the search-space input more truly. +![Random vs Grid vs TPE search method comparison](combined_optuna_results.png) + +TPE found the highest accuracy combination the fastest and reached the highest accuracy, therefore was the best search method. +TPE was then used in part b, and compression-aware search ran and tested. + +![Effects of compression and post-compression fine-tuning](compression_aware_results.png) + +No compression eventually performed the best, mainly due to the compression being quite severe, but the compression aware training method reach similar accuracy levels to the non-compressed model with a much smaller model size. + +## Tutorial 6 (Lab 3): Mixed Precision Search +![Number of trails vs maximum achieved accuracy](optuna_search_results.png) + +TPE Sampler was used to search for the optimal configuration, which was found on the 3rd iteration. +The search was then extended to contain the following quantised configuartions for each layer: + +- torch.nn.Linear (no quantisation) +- LinearInteger, +- LinearMinifloatDenorm +- LinearMinifloatIEEE +- LinearLog +- LinearBlockFP +- LinearBlockLog +- LinearBinary +- LinearBinaryScaling + +(I wasn't able to use LinearBlockMinifloat without errors and LinearBinaryResidualSign had not been implemented yet so these were ommited.) I initially used the Optuna sampler (TPE in this case) to search for the optimal layer types which yielded the following results. + +![Number of trails vs maximum achieved accuracy](extended_optuna_search_results_2_curves.png) + +Iteration 2 (the highest accuracy iteration) used LinearMinifloatIEEE. After realising seperate curves for each precision were wanted, the code was rewritten to do 5 iterations of 3 epochs for each layer type. Here are these results. + +![Maximum achieved accuracy after 5 iterations for each precision layer type](optuna_combined_precision_progress.png) +The LinearLog and LinearBlockLog were both found to be completely inneffective (maybe implemented incorrectly), with LinearBinary and LinearBinaryScaling found not much higher in accuracy achieved. + +To see the trends in the rest of the results, here is a zoomed in view of these, showing that eventually LinearInteger performed best and even surpassed the full precision version. +![Best performing precision layer types](optuna_combined_precision_progress_zoomed.png) + +## Lab 4: ADLS Software Stream +### Part 1 +The pre-compiled model showed a longer processing time of 24.5413s compared to the JIT-compiled model with 15.3287s. This could be down to a few reasons that were tested by modifying the code. + +- Variations in time could be introduced by different backend compilation methods, which are optimised for different purposes. The compilers already available to test were inductor, cudagraphs and eager, with the default compiler being inductor offering a trade-off between efficiency and memory overhead. +- The timing method seemed to change its results quite a lot depending on if the script run was the first time or had been previously done already. This indicates some degree of warming-up was required for the precompiled method, potentially due to memory bottlenecks on the first runthrough. A likely cause of this is that the first run-through includes tracing and graph transformations that later ones do not. +- In addition, the lower number of iterations chosen in the code (n=5) meant that the results were more susceptible to initial memory overhead changes than in consequent iterations, reliability of the timing tests could be improved with more iterations. + +To test how much the timings were affected by number of itertaions, CPU/GPU, compiler methods, the following combinations were ran (the Colab environment was reset between every 5 or 20 runs to reset the environment, accomodating for potential warm-up effects). Cudagraphs was ommited from CPU runs since it requires GPU. + +5 runs on "cpu": +- Original model: 15.3287 s +- Optimised model: 24.5413 s +- Optimised model (inductor): 10.8946 s +- Optimised model (eager): 14.0508 s + +20 runs on "cpu": +- Original model: 15.0116 s +- Optimised model: 14.1991 s +- Optimised model (inductor): 11.3416 s +- Optimised model (eager): 13.3609 s + +5 runs on "cuda": +- Original model: 0.2519 s +- Optimised model: 5.0008 s +- Optimised model (inductor): 0.1103 s +- Optimised model (eager): 0.2530 s +- Optimised model (cudagraphs): 0.9695 s + +20 runs on "cuda": +- Original model: 0.1099 s +- Optimised model: 0.4991 s +- Optimised model (inductor): 0.1129 s +- Optimised model (eager): 0.1394 s +- Optimised model (cudagraphs): 0.3148 s + +20 runs but ignoring first 10 on "cuda": +- Original model: 0.0950 s +- Optimised model: 0.1106 s +- Optimised model (inductor): 0.1106 s +- Optimised model (eager): 0.0952 s +- Optimised model (cudagraphs): 0.1577 s + +100 runs but ignoring first 10 on "cuda": +- Original model: 0.0950 s +- Optimised model: 0.1120 s +- Optimised model (inductor): 0.1128 s +- Optimised model (eager): 0.0968 s +- Optimised model (cudagraphs): 0.0990 s + +The main takeaways from this is that: +- significant initial overhead in all cases due to tracing and graph transformations +- low number of iterations (n=5) skew results due to this, therefore more needed to stabilise. +- chaning the device from cpu to cuda allowed the GPU to be used much more effectively, showing the biggest improvement in performance. +- overall, the pre-compiled method did not show improvements indicating that the optimisation under the hood was well purposed for the already optimised ResNet18. Potentially for more custom models, this would not be the case. + +### Part 2 +For 5 iterations: +- Naive SDPA time (cpu): 0.033738 s +- Fused SDPA time (cpu): 0.020238 s +- Naive SDPA time (cuda): 0.000379 s +- Fused SDPA time (cuda): 0.000047 s + +For 20 iterations: +- Naive SDPA time (cpu): 0.026931 s +- Fused SDPA time (cpu): 0.020990 s +- Naive SDPA time (cuda): 0.000140 s +- Fused SDPA time (cuda): 0.000022 s + +For 100 iterations: +- Naive SDPA time (cpu): 0.025309 s +- Fused SDPA time (cpu): 0.020964 s +- Naive SDPA time (cuda): 0.000202 s +- Fused SDPA time (cuda): 0.000044 s + +The fused SDPA kernel outperformed the naive implementation in every case and device type, most significantly on CUDA. On CPU, the fused version showed around 20-25% increase in speed, whilst on GPU, the fused version was 500-10000% faster than the naive version. This shows that the kernel fusion on CPUs was still limited by CPU memory bandwidth and its lack of parallelism, whilst the GPU case was able to take advantage of fused kernels, thanks to the reduced memory overhead removing the memory bottleneck. + +### Part 3: +Answer 3a: MXINT8 benefits custom hardware if both activaton and weights are quantised in the same method for a few reasons: +- Consistent data types means that fusing mutliple operations, e.g. matrix multiplication and then an activation layer, can be done more easily without extra data type conversions that woudl otherwise require additional memory reads/writes. If all the data types are consistent, many layers can be fused into one highly optimised kernel which is beneficial for larger GPU based models. +- Using MXINT8 for both also means that there are no intermediate tensors being stored, reducing memory requirements. This is beneficial since memory bandwith is often a limiting factor in custom hardware methods. +- There are also dedicated MXINT8 hardware units that are optimised for certain data types, such as INT8 multiply-accumulate (MAC) units. Consistency then allows these hardwares to be better used and optimised. +- Lastly having more efficient processes means less power consumption overall, reducing costs, hardware weardown and maintencance, increases working life etc. + +Answer 3b: The rold of the dont_need_abs variable and bias_variable is to correctly scale the 'value' part of the number, so that when the exponent is then applied to it, it is scaled correctly to represent the desired number. This methodology is required since MXINT has no implicit leading bit for the mantissa. For example, if a 6-bit mantissa number (bit count starts from 0) is SXXX_XXXX, so the left-most X is the 6th bit, this 6th bit determines whether the mantissa is in the range of 0-63 or 64-127. Similar to scientific number format, if the value is smaller than 64, it also contains some scaling information which should be contained already by the exponent. Therefore the value has to be normalised to within the correct range, so that its job is just to represent the value, and then to be scaled by the exponent. If the scaling information (due to too small a value) was retained and then the exponent applied, the resulting number would be some 2^integer out in order of magnitude. To implement this, the 6th bit is checked if its 1 or 0. If its 1, nothing is changed (dont_need_abs = True), if it is 0, this means the values of mantissa need to shifted in position to the left until the 6th bit is 1. This is done by adding a 2^num to the value, depending on the number of position shifts required. The number of shifts required to scale correctly can be calculated by the equation: bias_variable = 2^(exp - 127) * C, where exp is the original MXINT8 exponent value for that group. + +Answer 3c: The cta_tiler partitions the data in a memory-efficient way. It allows a large input tensor or data structure to have smaller sub-tensors or components to be extracted and processed in seperate units (such as threads, warps and thread blocks) and then reformed into a new large tensor correctly, retaining the indexing structure. This allows for more efficient parrallelism and more efficient/higher accuracy memory/cache access. + +The cta_tiler dynamically adjusts the block sizes being sent for processing based on the incoming group size. The larger the group size, the larger BLK_M, but the smaller BLK_K is - changing the sub-tensor shape. This helps to maintain a consitent computational workload per tile since roughly the same amount of data is being sent to each thread in the Cooperative Thread Arrays (CTAs). This is how it allows memory requirements stay consistent and stable during processing. + +layout_sX partitions the threads in a threadblock by applying the make_layout command from CuTe. It takes in a shape (e.g. a matrix of 8 by 8) and then creates a layout object, which is essential for mapping from coordinate space(s) to an index space. This is what ensures that the larger tensor can be reconstructed correctly after tiling. The layout object, just like cta_tiler, dynamically changes the shape of the sub-tensor based on the input group size (since the shape input to make_layout is also BLK_M and BLK_K), mainting consistent amounts of data per each tile and therefore processing thread. + +Answer 3d: +Theres a few reasons why the saved GPU memory is not exactly (32 - (4+8/32))/32 = 86.7% The first is that the code actually uses MXINT8 not MXINT4, meaning the above equation is slightly incorrect. The correct theroetical savings should be (32 - (8 + 8/32))/32 = 74.2%, since the mantissa should be 8 bits not 4. Even with this change, the theoretical savings are not reached. This is due to the quantisation process only being applied to certain layer types. The following code block shows that only linear layers are quantised, and any others (such as activation layers) are left in full precision. + +```plaintext +for layer_name, layer in model.named_modules(): + if not isinstance(layer, torch.nn.Linear): + continue + if "classifier" in layer_name: + continue + layer.cuda() + layer_q = QLinearPacked.build_from_linear(layer, group_size=mxint8_group_size) + set_layer_by_name(model, layer_name, layer_q) + del layer + torch.cuda.empty_cache() +``` + +Therefore, even though the quantised weights take up far less space, the overall memory saving is reduced to about 66.4% instead of the upped theoretical bound 74.2% due to non-quantised layers also being used by the GPU. diff --git a/Working_Data/adls_labs_at5424.zip b/Working_Data/adls_labs_at5424.zip new file mode 100644 index 000000000..f675fbfed Binary files /dev/null and b/Working_Data/adls_labs_at5424.zip differ diff --git a/Working_Data/combined_optuna_results.png b/Working_Data/combined_optuna_results.png new file mode 100644 index 000000000..36abcb19e Binary files /dev/null and b/Working_Data/combined_optuna_results.png differ diff --git a/Working_Data/compression_aware_results.png b/Working_Data/compression_aware_results.png new file mode 100644 index 000000000..cc309f17e Binary files /dev/null and b/Working_Data/compression_aware_results.png differ diff --git a/Working_Data/edited-colab-files/lab4-software.ipynb b/Working_Data/edited-colab-files/lab4-software.ipynb new file mode 100644 index 000000000..90304dbdc --- /dev/null +++ b/Working_Data/edited-colab-files/lab4-software.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"xFjabqAuyAjs"},"source":["
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Lab 4 for Advanced Deep Learning Systems (ADLS) - Software Stream

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\n"," ELEC70109/EE9-AML3-10/EE9-AO25\n","
\n","\t\tWritten by\n"," Aaron Zhao, Cheng Zhang, and Pedro Gimenes \n","

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"]},{"cell_type":"markdown","metadata":{"id":"yx7MI5aIyAju"},"source":["# General introduction\n","\n","In this lab, you will learn how to optimize performance for torch based models.\n","\n","We will cover the following topics:\n","1. Automatic performance tuning using existing high-level flows (eg. `torch.compile`), understand its main building blocks and how it works.\n","2. Manual performance enhancement with techniques such as kernel fusion.\n","3. Manual performance enhancement with low-level custom kernels."]},{"cell_type":"code","source":["!git clone https://github.com/tonytarizzo/mase.git\n","%cd mase\n","!python -m pip install -e . -vvv\n","%cd src"],"metadata":{"id":"KXFbwTi0yDUR"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"RuxioVmuyAjv"},"source":["## torch.compile\n","\n","`torch.compile` makes PyTorch models run faster by optimizing the model and the input data. It is a just-in-time compiler that optimizes the model and the input data for the specific hardware. It is a part of the PyTorch ecosystem.\n","\n","JIT compilation is a technique that converts the Python code into machine code at runtime. This technique is used to improve the performance of the Python code. JIT compilation is used in many programming languages, including Python, Java, and C#. JIT compiler typically continuously analyses the code being executed and identifies parts of the code where the speedup gained from compilation or recompilation would outweigh the overhead of compiling that code.\n"]},{"cell_type":"markdown","metadata":{"id":"JfLinYgxyAjv"},"source":["We try to use `torch.compile` to optimize the performance of some arbitrary functions and also a model. The usage of `torch.compile` is very simple. You just need to add `torch.compile` as a decorator before the function or model you want to optimize, or you wrap it on top of the function or model.\n","\n","We first instantiate the helper functions below:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p8D0jS9yyAjv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1738849496589,"user_tz":0,"elapsed":36296,"user":{"displayName":"Tony","userId":"05291496024874036816"}},"outputId":"21b0c3e6-ce05-464f-c3ba-6bbfb0ddc91d"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/timm/models/helpers.py:7: FutureWarning: Importing from timm.models.helpers is deprecated, please import via timm.models\n"," warnings.warn(f\"Importing from {__name__} is deprecated, please import via timm.models\", FutureWarning)\n"]}],"source":["import torch\n","import time\n","from chop.models import get_model\n","from chop.dataset import get_dataset_info\n","\n","def timed_gpu(fn):\n"," start = torch.cuda.Event(enable_timing=True)\n"," end = torch.cuda.Event(enable_timing=True)\n"," start.record()\n"," result = fn()\n"," end.record()\n"," torch.cuda.synchronize()\n"," return result, start.elapsed_time(end) / 1000\n","\n","def timed_cpu(fn):\n"," start = time.time()\n"," result = fn()\n"," return result, time.time() - start\n","\n","def get_data():\n"," return torch.randn(128, 3, 224, 224)\n","\n","def time_model(fn, n=1000, device=\"cpu\"):\n"," times = []\n"," data = get_data().to(device)\n"," for _ in range(n):\n"," if device == \"cpu\":\n"," _, t = timed_cpu(lambda: fn(data.cpu()))\n"," else:\n"," _, t = timed_gpu(lambda: fn(data))\n"," times.append(t)\n"," avg_time = sum(times) / len(times)\n"," return avg_time"]},{"cell_type":"markdown","metadata":{"id":"i7zLvUqxyAjw"},"source":["Now, define the neural network. We're using a model which can be used to perform image classification on the ImageNet dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4b_z4RyGyAjw"},"outputs":[],"source":["cifar10_info = get_dataset_info(\"imagenet\")\n","model = get_model(\"resnet18\", pretrained=True, dataset_info=cifar10_info)\n","image = torch.randn(64, 3, 224, 224)\n","\n","opt_model = torch.compile(model)\n","opt_model_ind = torch.compile(model, backend=\"inductor\") # Default\n","opt_model_eag = torch.compile(model, backend=\"eager\")\n","opt_model_cud = torch.compile(model, backend=\"cudagraphs\")"]},{"cell_type":"markdown","metadata":{"id":"Ut_bRP8RyAjx"},"source":["Now, we can inspect the runtime of the model."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YM91ktGYyAjx","outputId":"bdc7d71e-76df-477f-8670-974e6d417c53","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1738795254878,"user_tz":0,"elapsed":41317,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["['cudagraphs', 'inductor', 'onnxrt', 'openxla', 'tvm']\n","Original model: 0.1301 s\n","Optimized model: 1.2589 s\n","Optimized model (inductor): 0.1096 s\n","Optimized model (eager): 0.1445 s\n","Optimized model (cudagraphs): 0.2674 s\n"]}],"source":["from torch._dynamo import list_backends\n","print(list_backends())\n","\n","device = \"cuda\"\n","n = 20\n","\n","model.eval()\n","opt_model.eval()\n","opt_model_ind.eval()\n","opt_model_eag.eval()\n","opt_model_cud.eval()\n","\n","model.to(device)\n","opt_model.to(device)\n","opt_model_ind.to(device)\n","opt_model_eag.to(device)\n","opt_model_cud.to(device)\n","\n","avg_t = time_model(model, n=n, device=device)\n","opt_avg_t = time_model(opt_model, n=n, device=device)\n","opt_avg_t_ind = time_model(opt_model_ind, n=n, device=device)\n","opt_avg_t_eag = time_model(opt_model_eag, n=n, device=device)\n","opt_avg_t_cud = time_model(opt_model_cud, n=n, device=device)\n","\n","print(f\"Original model: {avg_t:.4f} s\")\n","print(f\"Optimized model: {opt_avg_t:.4f} s\")\n","print(f\"Optimized model (inductor): {opt_avg_t_ind:.4f} s\")\n","print(f\"Optimized model (eager): {opt_avg_t_eag:.4f} s\")\n","print(f\"Optimized model (cudagraphs): {opt_avg_t_cud:.4f} s\")"]},{"cell_type":"code","source":["def time_model_v2(fn, n=20, device=\"cpu\"):\n"," \"\"\"Measures execution time only for iterations 10-20, ignoring warm-up overhead.\"\"\"\n"," times = []\n"," data = get_data().to(device)\n","\n"," # Warm-up: Ignore first 9 runs\n"," for _ in range(9):\n"," with torch.no_grad():\n"," fn(data)\n","\n"," # Measure only iterations 10-20\n"," for _ in range(10, n+1):\n"," if device == \"cpu\":\n"," _, t = timed_cpu(lambda: fn(data.cpu()))\n"," else:\n"," _, t = timed_gpu(lambda: fn(data))\n"," times.append(t)\n","\n"," # Compute average over measured iterations\n"," avg_time = sum(times) / len(times)\n"," return avg_time"],"metadata":{"id":"Pr1o2DNHPuQ7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["device = \"cuda\"\n","n = 100\n","\n","# Set models to eval mode\n","model.eval()\n","opt_model.eval()\n","opt_model_ind.eval()\n","opt_model_eag.eval()\n","opt_model_cud.eval()\n","\n","# Move models to appropriate device\n","model.to(device)\n","opt_model.to(device)\n","opt_model_ind.to(device)\n","opt_model_eag.to(device)\n","opt_model_cud.to(device)\n","\n","# Measure execution time using time_model_v2\n","avg_t = time_model_v2(model, n=n, device=device)\n","opt_avg_t = time_model_v2(opt_model, n=n, device=device)\n","opt_avg_t_ind = time_model_v2(opt_model_ind, n=n, device=device)\n","opt_avg_t_eag = time_model_v2(opt_model_eag, n=n, device=device)\n","opt_avg_t_cud = time_model_v2(opt_model_cud, n=n, device=device)\n","\n","# Print results\n","print(f\"Original model: {avg_t:.4f} s\")\n","print(f\"Optimized model: {opt_avg_t:.4f} s\")\n","print(f\"Optimized model (inductor): {opt_avg_t_ind:.4f} s\")\n","print(f\"Optimized model (eager): {opt_avg_t_eag:.4f} s\")\n","print(f\"Optimized model (cudagraphs): {opt_avg_t_cud:.4f} s\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rwM97njoP80f","executionInfo":{"status":"ok","timestamp":1738795409680,"user_tz":0,"elapsed":53002,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"2b4bffa2-5e5e-4b1f-85e7-8eb77e6a5038"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Original model: 0.0950 s\n","Optimized model: 0.1120 s\n","Optimized model (inductor): 0.1128 s\n","Optimized model (eager): 0.0968 s\n","Optimized model (cudagraphs): 0.0990 s\n"]}]},{"cell_type":"markdown","metadata":{"id":"-m5-fcTayAjx"},"source":["**❓Question** We can see, very likely, maybe it varies on your machine, that the runtime of the optimized model is actually slower. There are a few reasons for this, but these are left as an exercise for the reader to investigate.\n","\n","The core idea about `torch.compile` is that it is an automatic optimization tool. There are actually three main building blocks in `torch.compile`:\n","\n","- TorchDynamo: `torch._dynamo` actually captures the PyTorch graph through CPython (https://github.com/pytorch/pytorch/tree/main/torch/_dynamo).\n","- TorchInductor: `torch._inductor` (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) can be seen as the backend of `torch.compile`, which is responsible for providing the kernel implementation of different operators in the PyTorch graph, it actually mainly uses OpenAI's Triton for implementing these kernels (https://github.com/pytorch/pytorch/tree/main/torch/_inductor/kernel).\n","- AOT Autograd: This allows you to capture the whole graph, including the backward pass, ahead of time!\n","\n","TorchInductor actually also makes use of the `fx.graph` to pattern match code, as shown [here](https://github.com/pytorch/pytorch/tree/main/torch/_inductor/fx_passes)."]},{"cell_type":"markdown","metadata":{"id":"dEVsQdUbyAjx"},"source":["## Kernel Fusion"]},{"cell_type":"markdown","metadata":{"id":"r7AgS7C4yAjy"},"source":["Fused kernels are advantageous as they can reduce both the number of memory accesses and number of kernel launches. This can be particularly useful when you have a lot of small operations that can be fused together.\n","\n","### A simple example\n","\n","Assuming we have a simple module consiting of a linear layer and a relu activation function,\n","\n","```python\n","import torch.nn as nn\n","\n","class MyLayer(nn.Module):\n"," def __init__(self, in_features, out_features):\n"," super(MyLayer, self).__init__()\n"," self.linear = nn.Linear(in_features, out_features)\n"," self.relu = nn.ReLU()\n"," \n"," def forward(self, x):\n"," y1 = self.linear(x)\n"," y2 = self.relu(y1)\n"," return y2\n","```\n","\n","Note that both the linear layer (`torch.nn.functional.linear`) and ReLU layer (`torch.nn.functional.relu`) are built-in PyTorch operations. Here is what happens when this module runs on a GPU:\n","\n","- The kernel for the linear layer is launched on the GPU:\n"," - Tiles of the input tensor `x` and weights `W` are **loaded from GPU's global memory** to shared memory\n"," - Matrix multiplication runs with the `x` and `W` tiles, and the partial results are accumulated in registers until the entire output tile is completed.\n"," - The output tile is **written back** to the global memory (`y1`).\n","- Similarly, the kernel for the ReLU layer is then launched on the GPU:\n"," - Tiles of `y1` is **loaded from the global memory**.\n"," - ReLU is performed on `y1` tiles,\n"," - The results are **written back to the global memory**.\n","\n","Everytime we call a kernel, we have to load the data from the global memory, and later write the results back. Because of the limited bandwidth of the global memory, one way to improve the performance is to reduce the number of memory accesses. This is where fused kernels come in. A fused kernel of linear and relu works like this:\n","\n","- When the kernel for the fused operation is launched on the GPU:\n"," - Tiles of the input tensor `x` and weights `W` are **loaded from GPU's global memory** to shared memory\n"," - The matrix multiplications runs on with `x` and `W` tiles, and the partial results are accumulated in registers until the entire output tile is completed.\n"," - *ReLU is performed on the output tile*\n"," - The results are **written back to the global memory**.\n","\n","Fusing these two kernels saves the one read and one write between the GPU's (relatively) slow global memory and fast cache memory, especially considering that ReLU (relu(x)=max(0, x)) is a memory-bouded operation.\n","\n","\n","### SDPA example\n","The following example demonstrates a fused kernel to optimize the performance of the scaled dot product attention (SDPA). SDPA denotes the attention operations in Transformer, consisting of two GEMMs with a softmax operation in between. PyTorch 2.0+ offers a fused kernel for SDPA.\n","\n","Here we show the naive implementation of SDPA and also call the fused kernel for comparison."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WJy9bk5FyAjy"},"outputs":[],"source":["import math\n","import torch\n","import torch.nn.functional as F\n","\n","class ScaledDotProductAttention(torch.nn.Module):\n"," def __init__(self):\n"," super(ScaledDotProductAttention, self).__init__()\n","\n"," def forward(self, query, key, value):\n"," scale_factor = 1 / math.sqrt(query.size(-1))\n"," score = query @ key.transpose(-2, -1) * scale_factor\n"," attn = F.softmax(score, -1)\n"," context = attn @ value\n"," return context\n","\n","class ScaledDotProductAttentionFused(torch.nn.Module):\n"," def forward(self, query, key, value):\n"," return F.scaled_dot_product_attention(query, key, value)"]},{"cell_type":"code","source":["import time\n","\n","def time_sdpa(module, query, key, value, n=10):\n"," \"\"\"Measures average execution time for SDPA methods.\"\"\"\n"," times = []\n"," for _ in range(n):\n"," start = time.time()\n"," _ = module(query, key, value)\n"," times.append(time.time() - start)\n","\n"," return sum(times) / len(times)\n","\n","device = \"cuda\" # Change to \"cuda\" to test on GPU\n","dtype = torch.float16 if device == \"cuda\" else torch.float32 # Use float16 for CUDA\n","\n","query = torch.randn(32, 8, 128, 64, dtype=dtype, device=device)\n","key = torch.randn(32, 8, 128, 64, dtype=dtype, device=device)\n","value = torch.randn(32, 8, 128, 64, dtype=dtype, device=device)\n","\n","# Instantiate models\n","naive_sdpa = ScaledDotProductAttention().to(device)\n","fused_sdpa = ScaledDotProductAttentionFused().to(device)\n","\n","# Run timing tests\n","naive_time = time_sdpa(naive_sdpa, query, key, value, n=100)\n","fused_time = time_sdpa(fused_sdpa, query, key, value, n=100)\n","\n","print(f\"Naive SDPA time ({device}): {naive_time:.6f} s\")\n","print(f\"Fused SDPA time ({device}): {fused_time:.6f} s\")"],"metadata":{"id":"PLfg7GBjer7-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1738850793237,"user_tz":0,"elapsed":33,"user":{"displayName":"Tony","userId":"05291496024874036816"}},"outputId":"f4a1c2cc-20c6-4e46-99fd-34ac90e12ae6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Naive SDPA time (cuda): 0.000202 s\n","Fused SDPA time (cuda): 0.000044 s\n"]}]},{"cell_type":"markdown","metadata":{"id":"ckIUhEFSyAjy"},"source":["The first implementation makes use of a number of functions to compute the scaled dot product attention. The second implementation uses a fused kernel provided by Pytorch to compute the scaled dot product attention.\n","\n","Now we can do a very simple test to check their functional equivalence."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HymgYLIgyAjy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1738795282059,"user_tz":0,"elapsed":853,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"dea2734d-b67c-4f35-c054-0992fdd77067"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(1., dtype=torch.float16) tensor(1., dtype=torch.float16)\n"]}],"source":["device = \"cpu\"\n","query = torch.ones(32, 8, 128, 64, dtype=torch.float16, device=device)\n","key = torch.ones(32, 8, 128, 64, dtype=torch.float16, device=device)\n","value = torch.ones(32, 8, 128, 64, dtype=torch.float16, device=device)\n","\n","y1 = ScaledDotProductAttention()(query, key, value)\n","y2 = ScaledDotProductAttentionFused()(query, key, value)\n","print(y1[0, 0, 0, 0], y2[0, 0, 0, 0])"]},{"cell_type":"markdown","metadata":{"id":"WD0AJZXFyAjy"},"source":["The method behind fused SPDA is detailed in the [FlashAttention paper](https://arxiv.org/abs/2205.14135). FlashAttention loads Q, K, and V tiles from global memory, and computes the attention scores and the weighted sum of values in a single kernel. Note that the softmax is a row reduction operation, thus FlashAttention use a trick called \"online-softmax\", which records row max and row sum of partial results and scales the partial results before accumulation. Online-softmax allows the fusion of the GEMM-softmax-GEMM operation into a single kernel, saving the memory bandwidth and improving the performance losslessly. FlashAttention also optimizes the backward propagation computation of the attention layer. If you are interested in the details, you can check the paper series of FlashAttention, and [Triton implementation of FlashAttention](https://triton-lang.org/main/getting-started/tutorials/06-fused-attention.html) (Triton provides a Python-based programming environment for writing DNN compute kernels on GPU hardware without having to write CUDA code)."]},{"cell_type":"markdown","metadata":{"id":"YUelWkSIyAjy"},"source":["## Custom Kernels\n","\n","Custom kernels are a powerful way to optimize the performance of your code.\n","\n","When you run a PyTorch built-in function, e.g., `torch.matmul`, PyTorch will call the corresponding kernel function wrapped in its backend ATen to execute the operation. These built-in kernels may come from different libraries, such as Intel MKL, NVIDIA cuBLAS, or PyTorch's own implementation. PyTorch provides a lot of built-in kernels for different operations. However, sometimes you may want to write your own custom kernel to support a specific operation that PyTorch does not natively provide.\n","\n","### Quantization and Dequantization\n","In the following, we show *a custom kernel for dequantization*. Dequantization is the process of converting quantized values back to floating-point values.\n","\n","- Here is an example of 4-bit fixed-point numbers with radix point placed at the most significant bit (0b.0000). Quantizing an FP32 number to 4-bit fixed-point means finding the closest 4-bit fixed-point number. This process introduces quantization error, e.g., 0.0621687 will be rounded to 0.0625 (with an error of 0.000103313), but a previously 32-bit number can now be represented with only 4 bits, and potentially accelerate the computation if the hardware supports 4-bit fixed-point operations.\n","\n"," ```bash\n"," | FP32 | 4-bit fixed-point |\n"," | ---------- | ------------------------ |\n"," | 0.0 | 0.0 (0b0000) |\n"," | 0.0625 | 0.0625 (0b0001) |\n"," | 0.125 | 0.125 (0b0010) |\n"," | 0.1875 | 0.1875 (0b0011) |\n"," | 0.25 | 0.25 (0b0100) |\n"," | ... | ... |\n"," ```\n","\n","- Dequantization is the reverse process of quantization. It converts the quantized values back to floating-point values (without fixing the quantization error).\n","\n","GPU is still a common choice of running DNN workloads. DNN Quantization methods for GPUs can be generally categorized into two types:\n","(a) Weight-only quantization, (b) Weight-and-activation quantization. The kernel in this tutorial is for the former, which only quantizes weights to save GPU memory.\n","\n","### MXINT Format\n","\n","MXINT is a number format of vectors between floating-point and fixed-point, proposed by Microsoft ([ref](https://www.microsoft.com/en-us/research/publication/with-shared-microexponents-a-little-shifting-goes-a-long-way/)) and standardized by the industry for AI facility ([ref](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf)).\n","\n","MXINT takes the following form with `group_size` as the number of mantissas in a group sharing the same 8-bit exponent:\n","\n","```text\n","Exp |- Mantissa 1\n"," |- Mantissa 2\n"," |- ...\n"," |- Mantissa group_size\n","```\n","The mantissa in MXINT is signed fixed-point with the radix point placed after the 2nd leftmost bit, and exp is an 8-bit unsigned integer biased by 127.\n","\n","```text\n","Exp: eeee_eeee\n","Mantissa (8-bit in this example): si.ii_iiii\n","```\n","\n","For example, an example of an MXINT4 (4 denotes the mantissa bitwidth) vector with `group_size=4` looks like this:\n","\n","```text\n","EXP = 0b0111_1111\n","MANTISSA = [0b11.00]\n"," [0b01.00]\n"," [0b00.10]\n"," [0b00.01] \n","```\n","\n","The four mantissas share the same exponent.\n","\n","To dequantize this MXINT4 vector, we scale each fixed-point value by the shared exponent:\n","\n","- The decimal value of EXP is E = 2^(EXP-127) = 2^(127-127) = 1\n","- Then we scale each signed fixed-point value by E, i.e., `E .* MANTISSA`\n","```text\n"," [0b11.00] [-1.0 ] [-1.0]\n"," E .* MANTISSA = 1 .* [0b01.00] = 1 .* [ 1.0 ] = [ 1.0]\n"," [0b00.10] [ 0.5 ] [ 0.5]\n"," [0b00.11] [ 0.25] [ 0.25]\n","```\n","\n","The effective bitwidth of this MXINT4 vector is (4 + 8/4) = 6 bits. If we increase the group size to 32, the effective bitwidth will be (4 + 8 / 32) = 4.25 bits. Compared to 4-bit fixed-point number that only represents 16 values, MXINT4 with group_size = 32 can represent 2^8 * 16 = 4096 values, reducing the quantization error.\n","\n","❓ **Question**: How does MXINT8 benefit custom hardware if both the activation and weights in a linear layer are quantized to MXINT8?\n","\n","Now We show a custom kernel for dequantizing MXINT8 numbers, and show how it saves GPU memory with minimal loss of model performance.\n"]},{"cell_type":"markdown","metadata":{"id":"IL11Nwn5yAjz"},"source":["### Custom Kernel for MXINT8 Dequantization.\n","\n","#### Preamble\n","**Why dequantization kernel?**\n","\n","We target saving GPU memory by quantizing weights to approximated 8-bit numbers. This means model weights are stored in the form of MXINT8 in GPU global memory. When running a specific layer, the GPU loads the MXINT8 weights, dequantizes them to high-precision (BFloat16 this in kernel) and writes back for the following computation. Once computation of this layer is done, the dequantized weights are discarded. This saves GPU memory by storing weights in a more compact form.\n","\n","\n","**The following tutorial is more involved**\n","- CUDA-capable GPU is required.\n","- If developing on your own Windows laptop, WSL2 is highly recommended.\n","- The dev env needs setup with PyTorch, CUDA, and CMake.\n","- The kernel requires basic knowledge of C++, CUDA programming and CUTLASS CUTE.\n"," - CUTLASS is a CUDA template library for GEMM. CUTE is a part of CUTLASS for data/thread layout transformation.\n"," - CUTLASS repo: https://github.com/NVIDIA/cutlass\n"," - CUTE-related docs: https://github.com/NVIDIA/cutlass/tree/main/media/docs/cute\n","\n","**MASE-CUDA**\n","- The source codes of MXINT8 dequantization kernel can be found in [MASE-CUDA](https://github.com/DeepWok/mase-cuda).\n","- To setup the dev environment for custom kernels, please refer to the [README.md](https://github.com/DeepWok/mase-cuda/tree/master) and [this file](https://github.com/DeepWok/mase-cuda/blob/master/docs/beginner.md). This dev env is for building the kernel and Python package wheel. Once the wheel is built, you may install it in the mase environment and use it to quantize models.\n","\n","#### MXINT8 Dequantization Kernel\n","\n","The following kernels dequantize MXINT8 data to [BFloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format).\n","\n","##### CPU kernel\n","Usually before implementing CUDA kernels, we need to check the correctness of our understanding. This can be done by writing a CPU or Python version of the algorithm, which is easy to debug. [`mase_cuda::mxint8::dequantize::dequantize1d_host`](https://github.com/DeepWok/mase-cuda/blob/master/src/csrc/mxint/dequantize.cuh) is the C++ version of the dequantization algorithm, and [`mase_cuda.mxint8.dequantize.dequantize1d_simulated`](https://github.com/DeepWok/mase-cuda/blob/master/src/mase_cuda/mxint8/dequantize.py) is the PyTorch-simulated version of the algorithm. Here is the core part of the C++ version:\n","\n","```cpp\n"," for (int i = 0; i < M; ++i) {\n"," auto sign = static_cast(hX[i] & 0x80) << 8;\n"," auto exp = static_cast(hScales[i / group_size]) << 7;\n"," auto mantissa_abs = abs(hX[i]);\n"," auto frac = static_cast((mantissa_abs & 0x3F) << 1);\n"," auto out = cutlass::bfloat16_t::bitcast(sign | exp | frac);\n"," auto dont_need_abs = bool(mantissa_abs & 0x40);\n"," auto bias = cutlass::bfloat16_t::bitcast(sign | exp | uint16_t(0));\n"," y[i] = dont_need_abs ? out : out - bias;\n"," }\n","```\n","\n","For each mantissa element, the loop\n","- splits the sign bit from the mantissa, upcasts it to 16-bit and shifts it to the correct position.\n","- upcasts the scale to 16-bit and shifts it to the correct position.\n","- converts the int8 mantissa to BFloat16's fraction part.\n","\n","❓ **Question**: What is the purpose of the `dont_need_abs` variable and the `bias` variable? Note that unlike IEEE Floating-Point, MXINT has no implicit leading bit for the mantissa.\n","\n","##### GPU kernel\n","The CUDA kernel codes can be found at [`mase_cuda::mxint8::dequantize:dequantize1d_device`](https://github.com/DeepWok/mase-cuda/blob/master/src/csrc/mxint/dequantize.cuh). This kernel treats 1D data as a matrix of shape `[num_groups, group_size]` using `group_tiler`. Then it loads tiles of MXINT8 data from global memory to shared memory, dequantizes them using the algorithm above, and write the dequantized BFloat16 number back to global memory. This kernel leverages CUTE for partitioning data for copy, and partitioning threads in a threadblock for computation. Besides, it creates predication masks to avoid out-of-bound accesses.\n","\n","❓ **Question (Challenging)**: How does `cta_tiler` partition the data for copy? You may find the documentation of `local_tile` in CUTE helpful ([ref](https://github.com/NVIDIA/cutlass/blob/main/media/docs/cute/02_layout_algebra.md))\n","\n","❓ **Question (Challenging)**: How does `layout_sX` partition the threads in a threadblock for computation? You may find the documentation of `local_partition` in CUTE helpful ([ref](https://github.com/NVIDIA/cutlass/blob/main/media/docs/cute/02_layout_algebra.md))\n","\n","##### Testing the kernel\n","\n","After implementing the CUDA kernel, we can compare its output to the CPU version to check the correctness. A simple test to check the correctness of the CUDA kernel can be found [here](https://github.com/DeepWok/mase-cuda/blob/master/test/cu/mxint/dequantize/test_mxint8_dequantize1d.cu). We first generate random MXINT8 data, dequantize them using the CPU version, and then compare the results with the GPU version.\n","\n","You may run the following just command to build and run the test executable. Before that, you may need to change the `TORCH_CUDA_ARCH_LIST` variable in the [justfile](https://github.com/DeepWok/mase-cuda/blob/master/justfile) to match your GPU's [compute capability](https://developer.nvidia.com/cuda-gpus).\n","```bash\n","# build the test executable\n","$ just --set CU_BUILD_TARGETS test_mxint8_dequantize1d build-cu-test\n","# run the test executable\n","$ ./build/test/cu/mxint/dequantize/test_mxint8_dequantize1d\n","Usage: ./build/test/cu/mxint/dequantize/test_mxint8_dequantize1d [m] [group_size] [is_random]\n","m=4096, group_size=128, num_groups=32, is_random=0\n","PASSED\n","# or you can specify the arguments\n","$ ./build/test/cu/mxint/dequantize/test_mxint8_dequantize1d 2048 32 1\n","Usage: ./build/test/cu/mxint/dequantize/test_mxint8_dequantize1d [m] [group_size] [is_random]\n","m=2048, group_size=32, num_groups=64, is_random=1\n","PASSED\n","```\n","\n","##### Binding the kernel to PyTorch\n","\n","Many DeepLearning packages run on Python, and PyTorch is one of the most popular ones. To use the CUDA kernel in PyTorch, we need to\n","- Wrap the kernels with LibTorch API in C++. The wrapper function `mase_cuda::mxint8::dequantize::dequantize1d` can be found [here](https://github.com/DeepWok/mase-cuda/blob/master/src/csrc/mxint/dequantize.cuh). This wrapper takes PyTorch Tensors, and calls the CPU kernel or the CUDA kernel based on the device type of the input tensors, and returns the dequantized BFloat16 Tensors.\n","- Build the wrapper as an extension module (named `mase_cuda_ext` in this case) of `mase_cuda`. These steps uses PyBind11 and PyTorch C++ Extension. Here are the related files:\n"," - [bind.cu](https://github.com/DeepWok/mase-cuda/blob/master/src/csrc/bind.cu)\n"," - [setup.py](https://github.com/DeepWok/mase-cuda/blob/master/setup.py)\n","- Build `mase_cuda` package! You can build the package by running the following command:\n"," ```bash\n"," # the wheel file will be generated in the dist folder\n"," $ just-build-py\n"," # install the wheel file\n"," $ pip install dist/mase_cuda-0.0.1-cp311-cp311-linux_x86_64.whl\n"," ```\n","\n","#### Playing with the kernel\n","\n","The dequantization is now available in Python package `mase_cuda_ext`, and the main package `mase_cuda` has a Python wrapper over it.\n","\n","##### Latency profiling: GPU vs CPU\n","\n","Function [`test_ext_dequantize1d_latency`](https://github.com/DeepWok/mase-cuda/blob/master/test/py/mxint8/dequantize/test_dequantize1d.py) compares the latency of the CPU version and the GPU version of the dequantization kernel. This test case can be triggered by running the following command:\n","\n","📝 **Note** Remember to reduce the element number `num_elements` in `test_ext_dequantize1d_latency` before running it as too many elements may run out of CPU/GPU memory!!!\n","\n","```bash\n","$ just --set PYTEST_PATTERN test_ext_dequantize1d_latency test-py-pattern\n","```\n","\n","Here is an example output of the test case:\n","\n","```text\n","test/py/mxint8/dequantize/test_dequantize1d.py::test_ext_dequantize1d_latency\n","----------------------------------------------------------------------------- live log call ------------------------------------------------------------------------------\n","INFO test_dequantize1d:test_dequantize1d.py:203\n","+-----------+------------+------------------------+------------------------+---------------------+\n","| m | group_size | latency_cpu | latency_gpu | GPU speedup |\n","+-----------+------------+------------------------+------------------------+---------------------+\n","| 1024 | 8 | 9.584426879882813e-06 | 3.673119996674359e-05 | 0.2609342163762836 |\n","| 1024 | 16 | 4.9591064453125e-06 | 1.1745599983260035e-05 | 0.4222097170327847 |\n","| 1024 | 32 | 3.993511199951172e-06 | 1.1219199933111667e-05 | 0.3559532964703628 |\n","| 1024 | 64 | 3.826618194580078e-06 | 1.118720006197691e-05 | 0.3420532549146055 |\n","| 1024 | 128 | 3.933906555175781e-06 | 1.1347199836745858e-05 | 0.34668522734891255 |\n","| 1024 | 256 | 3.921985626220703e-06 | 1.0571199934929609e-05 | 0.3710066643675507 |\n","| 1024 | 512 | 3.7789344787597655e-06 | 1.0598399955779315e-05 | 0.35655707413637566 |\n","| 2097152 | 8 | 0.00988675355911255 | 1.6137600084766744e-05 | 612.653276024931 |\n","| 2097152 | 16 | 0.00951402187347412 | 1.6275200061500074e-05 | 584.571731070765 |\n","...\n","| 234881024 | 8 | 1.0949357390403747 | 0.0018697983980178834 | 585.5902648120154 |\n","| 234881024 | 16 | 1.0813238978385926 | 0.0017637632012367248 | 613.0777062818775 |\n","| 234881024 | 32 | 1.0706217408180236 | 0.0017168064057826995 | 623.6123870529959 |\n","| 234881024 | 64 | 1.0728538513183594 | 0.0017273023962974547 | 621.1152451464588 |\n","| 234881024 | 128 | 1.062897527217865 | 0.0017135215997695922 | 620.3000460343113 |\n","| 234881024 | 256 | 1.0750348925590516 | 0.0017249839961528779 | 623.2144152969729 |\n","| 234881024 | 512 | 1.0646217107772826 | 0.0011905567944049836 | 894.2216917164034 |\n","+-----------+------------+------------------------+------------------------+---------------------+\n","PASSED [100%]\n","```\n","\n","GPU is much faster than CPU if the data size is large enough.\n"]},{"cell_type":"markdown","metadata":{"id":"GfVxDd4eyAjz"},"source":["##### Saved GPU memory\n","\n","Let's use the dequantization kernel in a real model. `mase_cuda` has a custom layer `mase_cuda.mxint8.linear.QLinearPacked` for demostration (source code [here](https://github.com/DeepWok/mase-cuda/blob/master/src/mase_cuda/mxint8/linear.py)).\n","\n","We can compress the linear layers in a model with `QLinearPacked` and inspect the GPU memory usage. Install the built mase-cuda wheel in mase env (**not the dev env for kernel building**) and run the following codes using Python in mase env .\n","\n","\n","- First let's just run a simple sentiment analysis model in FP32. **Please copy the code below to a new Python file and run it instead of running it in the notebook.** This example runs a Deberta model which classifies the emotion of a sentence into one of the 6 categories: `anger`, `fear`, `joy`, `love`, `sadness`, `surprise`.\n","\n"," ```python\n"," import torch\n"," from transformers import AutoModelForSequenceClassification, AutoTokenizer\n"," \n"," model_name = \"AnkitAI/deberta-xlarge-base-emotions-classifier\"\n"," # if you meet OOM error, try this smaller model, but the quantization effect may not be obvious later\n"," # model_name = \"AnkitAI/deberta-v3-small-base-emotions-classifier\"\n"," model = AutoModelForSequenceClassification.from_pretrained(model_name).cuda().eval()\n"," tokenizer = AutoTokenizer.from_pretrained(model_name)\n"," label2emotion = {idx: emotion for emotion, idx in model.config.label2id.items()}\n"," \n"," \n"," # Example usage\n"," @torch.no_grad()\n"," def predict_emotion(model, tokenizer, text):\n"," inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=128)\n"," inputs = {k: v.cuda() for k, v in inputs.items()}\n"," outputs = model(**inputs)\n"," logits = outputs.logits\n"," predictions = logits.argmax(dim=1)\n"," predictions = label2emotion[predictions.item()]\n"," top3_values, top3_indices = torch.topk(logits, 3)\n"," top3_values = top3_values.cpu().tolist()\n"," top3_indices = top3_indices.cpu().tolist()\n"," return predictions, (top3_values, top3_indices)\n"," \n"," \n"," text = \"I'm so happy with the results!\"\n"," emotion, top3 = predict_emotion(model, tokenizer, text)\n"," \n"," print(\"Index to Emotion Mapping:\", label2emotion)\n"," print(\"Input text:\", text)\n"," print(\"Detected Emotion:\", emotion)\n"," print(f\"top3 logits: {top3[0]}, top3 indices: {top3[1]}\")\n"," ```\n","\n"," This code will output the detected emotion of the input text and the top 3 logits and their indices.\n"," \n"," \n","- Then let's compress the model with MXINT8 and inspect the GPU memory usage. **Please copy the code below to a new Python file and run it instead of running it in the notebook.**\n","\n"," ```python\n"," import torch\n"," from transformers import AutoModelForSequenceClassification, AutoTokenizer\n"," \n"," from mase_cuda.mxint8.linear import QLinearPacked\n"," \n"," init_memory = torch.cuda.memory_allocated() # in bytes\n"," model_name = \"AnkitAI/deberta-xlarge-base-emotions-classifier\"\n"," model = AutoModelForSequenceClassification.from_pretrained(model_name, torch_dtype=torch.float32).cuda().eval()\n"," tokenizer = AutoTokenizer.from_pretrained(model_name)\n"," label2emotion = {idx: emotion for emotion, idx in model.config.label2id.items()}\n"," \n"," mxint8_group_size = 32\n"," assert model.config.hidden_size % mxint8_group_size == 0\n"," assert model.config.intermediate_size % mxint8_group_size == 0\n"," \n"," text = \"I'm so happy with the results!\"\n"," \n"," \n"," # Example usage\n"," @torch.no_grad()\n"," def predict_emotion(model, tokenizer, text):\n"," inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=128)\n"," inputs = {k: v.cuda() for k, v in inputs.items()}\n"," outputs = model(**inputs)\n"," logits = outputs.logits\n"," predictions = logits.argmax(dim=1)\n"," predictions = label2emotion[predictions.item()]\n"," top3_values, top3_indices = torch.topk(logits, 3)\n"," top3_values = top3_values.cpu().tolist()\n"," top3_indices = top3_indices.cpu().tolist()\n"," return predictions, (top3_values, top3_indices)\n"," \n"," \n"," # check the GPU memory usage of FP32 model\n"," torch.cuda.reset_peak_memory_stats()\n"," emotion_fp32, top3_fp32 = predict_emotion(model, tokenizer, text)\n"," peak_memory_fp32 = torch.cuda.max_memory_allocated() - init_memory # in bytes\n"," \n"," \n"," def set_layer_by_name(module: torch.nn.Module, name: str, new_layer: torch.nn.Module):\n"," \"\"\"\n"," Replace a layer (`new_layer`) in a model (`module`) by its `name`.\n"," \"\"\"\n"," levels = name.split(\".\")\n"," if len(levels) > 1:\n"," mod_ = module\n"," for l_idx in range(len(levels) - 1):\n"," if levels[l_idx].isdigit():\n"," mod_ = mod_[int(levels[l_idx])]\n"," else:\n"," mod_ = getattr(mod_, levels[l_idx])\n"," setattr(mod_, levels[-1], new_layer)\n"," else:\n"," setattr(module, name, new_layer)\n"," \n"," \n"," for layer_name, layer in model.named_modules():\n"," if not isinstance(layer, torch.nn.Linear):\n"," continue\n"," if \"classifier\" in layer_name:\n"," continue\n"," layer.cuda()\n"," layer_q = QLinearPacked.build_from_linear(layer, group_size=mxint8_group_size)\n"," set_layer_by_name(model, layer_name, layer_q)\n"," del layer\n"," torch.cuda.empty_cache()\n"," \n"," torch.cuda.empty_cache()\n"," torch.cuda.reset_peak_memory_stats()\n"," emotion_mxint8, top3_mxint8 = predict_emotion(model, tokenizer, text)\n"," peak_memory_mxint8 = torch.cuda.max_memory_allocated() - init_memory # in bytes\n"," \n"," print(f\"FP32 model peak memory: {peak_memory_fp32/1024**2:.4f} MB\")\n"," print(f\"PF32 prediction: {emotion_fp32}\")\n"," print(f\"FP32 top3 logits: {top3_fp32[0]}, indices: {top3_fp32[1]}\")\n"," \n"," print(f\"MXINT8 model peak memory: {peak_memory_mxint8/1024**2:.4f} MB\")\n"," print(f\"MXINT8 prediction: {emotion_mxint8}\")\n"," print(f\"MXINT8 top3 logits: {top3_mxint8[0]}, indices: {top3_mxint8[1]}\")\n"," ```\n"," \n"," Here is an example output of the code:\n"," \n"," ```text\n"," FP32 model peak memory: 2906.1997 MB\n"," PF32 prediction: joy\n"," FP32 top3 logits: [[7.345229148864746, -1.485020399093628, -1.6403968334197998]], indices: [[1, 4, 0]]\n"," MXINT8 model peak memory: 976.1616 MB\n"," MXINT8 prediction: joy\n"," MXINT8 top3 logits: [[7.350158214569092, -1.488326072692871, -1.6497581005096436]], indices: [[1, 4, 0]]\n"," ```\n"," \n"," The MXINT8 **saves GPU memory by 66.4%** without affecting the final model prediction (\"joy\" in this case). Besides, the output logits are very close to the FP32 model.\n"," \n"," ❓ **Question**: Why the saved GPU memory is not exactly (32 - (4+8/32))/32 = 86.7%?"]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.4"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Working_Data/edited-colab-files/tutorial_1_introduction_to_mase.ipynb b/Working_Data/edited-colab-files/tutorial_1_introduction_to_mase.ipynb new file mode 100644 index 000000000..4821765d7 --- /dev/null +++ b/Working_Data/edited-colab-files/tutorial_1_introduction_to_mase.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"xNaO9yCHrdxP"},"source":["# Tutorial 1: Introduction to the Mase IR, MaseGraph and Torch FX passes"]},{"cell_type":"code","source":["!git clone https://github.com/tonytarizzo/mase.git\n","%cd mase\n","!python -m pip install -e . -vvv\n","%cd src"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9kIt2lJErgT3","executionInfo":{"status":"ok","timestamp":1737997679783,"user_tz":0,"elapsed":124382,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"5a7212f4-ac98-42cd-a3e4-d3b24d0234fb"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n"," Skipping link: none of the wheel's tags (pp36-none-any, pp37-none-any, pp38-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5f/f6/c3e89e719845eec391b6c9c56c839bc5a23ba005c18a5a0168ce564b2166/coverage-6.4.1-pp36.pp37.pp38-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/69/5f/65fd3b42b37b87f8f7b01f0e6c2c127a9e5f6d45d02d21a3832e426d6b37/coverage-6.4.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/1a/8b2f6aabf828e9795855001848ce72370819fffca883714eb25aa6d00b38/coverage-6.4.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/0e/e60fbc65bc054d904984ccd2a52c122fd291c682b5713b37dbb978f853bb/coverage-6.4.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/e7/dbbfe7288d846b372229794d7c5f48d55e519d30e1419febea7b1e71478c/coverage-6.4.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/3d/d79b2b927e7cb8dded2a003d22348b918509fe1238ff796a0b2fde9b3718/coverage-6.4.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible tags): https://files.pythonhosted.org/packages/c4/8d/5ec7d08f4601d2d792563fe31db5e9322c306848fec1e65ec8885927f739/coverage-6.5.0-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/89/a2/cbf599e50bb4be416e0408c4cf523c354c51d7da39935461a9687e039481/coverage-6.5.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/b0/3639d84ee8a900da0cf6450ab46e22517e4688b6cec0ba8ab6f8166103a2/coverage-6.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/24/c2ffb7777863c805df8761eac1e9c2f176c2abf1e1195234517eafb69d68/coverage-7.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/aa/c78014c8292ce3167fe283206904c9a9e16c0f033e3a24282f50a81dc04a/coverage-7.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/28/bead5a1d515eaf4c31de05ea929b5a0b0a115871675f0fdfe8634340d9a7/coverage-7.3.3-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/36/30b477648b55611cb904e1917b7f61e57532178f3f2bdc70dad3f5ab321c/coverage-7.3.3-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/8c/341543ac858e042e9c92ae028d6185a8cc8a9e4991d95dd92076e981b4dd/coverage-7.3.3-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/a1/29191b24dd60b6beff4138aa36d6c4ff26cc12993a1235e0335db62357ab/coverage-7.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/04/fe2f41c59462d36b36fb701ef1c58ce8bfd0a106ec2d4f475f28009c74b4/coverage-7.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/09/0f5df6dc74e87dc074280397496456f5feb90e1f9e720da41dc5784e557e/coverage-7.3.4-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d4/bf/71d35a6c79bccabf57e6900d173f11e5c037fbbfb343bfe354d2d9fb21d9/coverage-7.3.4-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2e/d3/d139c9f942ce17ebf2f84bc0c4b754f52628f3ee0556254e5800ab30b0cb/coverage-7.3.4-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0e/3c/29bb1fb05168711116d475f42c4b12831f2a1ffff29158baf158988c675d/coverage-7.3.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/11/94/60cf0c41caa55cd3c4f81f8d1aade5753ba68e46f92efec2801a0fdcdf8d/coverage-7.3.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/74/6b97d9e14c87613d2598ec0181affb2b23cbc4dcc149941e1ee7f44f9dee/coverage-7.3.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/98/4cceb22713b2787f1f2aa5aaf4842fc19dbdc20a9530691ae0a56dbf6451/coverage-7.3.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/64/9e0d36d9aa055fdad87d66ab47a6686318498e2142c5e3e3e72d5483ccfd/coverage-7.3.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible tags): https://files.pythonhosted.org/packages/50/5a/d727fcd2e0fc3aba61591b6f0fe1e87865ea9b6275f58f35810d6f85b05b/coverage-7.4.3-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a3/36/b5ae380c05f58544a40ff36f87fa1d6e45f5c2f299335586aac140c341ce/coverage-7.4.3-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/48/5ae1ccf4601500af0ca36eba0a2c1f1796e58fb7495de6da55ed43e13e5f/coverage-7.4.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7d/8f/2665744d223dcea532b1cf3a9edd236632f54fd3925b9b464f1d03b4421e/coverage-7.4.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1a/15/ae47f23bfd557364e731ad2ed182331ba72e8c063b806ba317cd327e73cc/coverage-7.4.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/09/91be1d04914deea7dd0e2f3e94d925c23e9b81ce23b0da014f1ff07dd772/coverage-7.4.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8b/c7/54cde44ebed02848db20d67388d0f82db1b65eca09d48181df71fbd81cf5/coverage-7.4.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/78/ab/39feda43fbd0ca46f695b36bfe1f6836efce9657e81889bb0dcc55fb1745/coverage-7.4.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/63/98e5a6b7ed1bfca874729ee309cc49a6d6658ab9e479a2b6d223ccc96e03/coverage-7.5.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/e4/d3c67a0a092127b8a3dffa2f75334a8cdb2cefc99e3d75a7f42cf1ff98a9/coverage-7.5.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/ad/12b1200490adb704ec7b1b9c37ec5bd748b30afcb9645de33ce18cc82a16/coverage-7.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/af/7bb0b0bd5e67217e086a0bba7633be38313358f02b4b6c0b7e5b68459dcb/coverage-7.5.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible tags): https://files.pythonhosted.org/packages/38/3d/9f9469f445789a170cb5bef3ad02ae9084ddd689f938797aa8ee793db404/coverage-7.5.4-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/d8/b7bde23a5e94cfc1a45effad2dd4c45dc111c515f71c522986dd8ded31a1/coverage-7.5.4-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/99/49/0e8c8e8f9f7ea87ed94ddce70cdfe49224b13857ef3cbdb65a5eb29bba6f/coverage-7.5.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c6/1c/bd6d46e44ddb2affc73271d22cba263d9e5d8a0afd593a5de62dbd1380cd/coverage-7.5.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/ee/87e1285608cb69c44a0da9864434818fc53a0a95ec45f112f623a6578ee9/coverage-7.5.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/f4/04e66966ed63a8d5df537de416658622d1c7a5e057183ebbac327fc6b881/coverage-7.6.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/83/812e55211d64eebe69e36d2c3561d0e2873e7efcd7ddbc510883510dd0f9/coverage-7.6.0-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/9e/5221ceb428d6e0189ed6039fec8400a788f77c6ad41b2db9f0ce48ec317c/coverage-7.6.0-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/35/915fa75c39c53b45f19d06bedb199d06d27728a81a2aff073efd9f6ddeef/coverage-7.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b4/47/7a36b59a530c5eb4f62f4d92f0f379a7347470ecde933cd1b1f4ccddcb7f/coverage-7.6.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible tags): https://files.pythonhosted.org/packages/7e/61/eb7ce5ed62bacf21beca4937a90fe32545c91a3c8a42a30c6616d48fc70d/coverage-7.6.1-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7d/73/041928e434442bd3afde5584bdc3f932fb4562b1597629f537387cec6f3d/coverage-7.6.1-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c7/c8/6ca52b5147828e45ad0242388477fdb90df2c6cbb9a441701a12b3c71bc8/coverage-7.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from 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compatible tags): https://files.pythonhosted.org/packages/d3/a8/08d7b38e6ff8df52331c83130d0ab92d9c9a8b5462f9e99c9f051a4ae206/coverage-7.6.1-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/6a/9cf96839d3147d55ae713eb2d877f4d777e7dc5ba2bce227167d0118dfe8/coverage-7.6.1-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/e4/7ff20d6a0b59eeaab40b3140a71e38cf52547ba21dbcf1d79c5a32bba61b/coverage-7.6.1-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8b/61/a7a6a55dd266007ed3b1df7a3386a0d760d014542d72f7c2c6938483b7bd/coverage-7.6.1-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/fa/13a6f56d72b429f56ef612eb3bc5ce1b75b7ee12864b3bd12526ab794847/coverage-7.6.1-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/df/5f2cd6048d44a54bb5f58f8ece4efbc5b686ed49f8bd8dbf41eb2a6a687f/coverage-7.6.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/18/7c53887643d921faa95529643b1b33e60ebba30ab835c8b5abd4e54d946b/coverage-7.6.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of 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compatible tags): https://files.pythonhosted.org/packages/f2/cb/ccb23c084d7f581f770dc7ed547dc5b50763334ad6ce26087a9ad0b5b26d/coverage-7.6.2-cp312-cp312-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/ab/58de9e2f94e4dc91b84d6e2705aa1e9d5447a2669fe113b4bbce6d2224a1/coverage-7.6.2-cp312-cp312-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/dc/8be87b9ed5dbd4892b603f41088b41982768e928734e5bdce67d2ddd460a/coverage-7.6.2-cp312-cp312-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/94/3b8f3abf88b7c451f97fd14c98f536bcee364e74250d928d57cc97c38ddd/coverage-7.6.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d5/4b/57f95e41a10525002f524f3dbd577a3a9871d67998f8a8eb192fe697dc7b/coverage-7.6.2-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c7/b9/cdf158e7991e2287bcf9082670928badb73d310047facac203ff8dcd5ff3/coverage-7.6.4-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/87/31/9c0cf84f0dfcbe4215b7eb95c31777cdc0483c13390e69584c8150c85175/coverage-7.6.4-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/ed/a38401079ad320ad6e054a01ec2b61d270511aeb3c201c80e99c841229d5/coverage-7.6.4-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) 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cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c0/70/6b0627e5bd68204ee580126ed3513140b2298995c1233bd67404b4e44d0e/coverage-7.6.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/eb/634d7dfab24ac3b790bebaf9da0f4a5352cbc125ce6a9d5c6cf4c6cae3c7/coverage-7.6.4-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/69/d4f3a4101171f32bc5b3caec8ff94c2c60f700107a6aaef7244b2c166793/coverage-7.6.4-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c2/4d/2dede4f7cb5a70fb0bb40a57627fddf1dbdc6b9c1db81f7c4dcdcb19e2f4/coverage-7.6.4-cp313-cp313-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3f/f9/d86368ae8c79e28f1fb458ebc76ae9ff3e8bd8069adc24e8f2fed03c58b7/coverage-7.6.4-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/c5/b4cc3c3f64622c58fbfd4d8b9a7a8ce9d355f172f91fcabbba1f026852f6/coverage-7.6.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/86/14c42e60b70a79b26099e4d289ccdfefbc68624d096f4481163085aa614c/coverage-7.6.4-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of 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compatible tags): https://files.pythonhosted.org/packages/f3/8c/6312d241fe7cbd1f0cade34a62fea6f333d1a261255d76b9a87074d8703c/coverage-7.6.4-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ce/5f/fef33dfd05d87ee9030f614c857deb6df6556b8f6a1c51bbbb41e24ee5ac/coverage-7.6.4-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/64/6a984b6e92e1ea1353b7ffa08e27f707a5e29b044622445859200f541e8c/coverage-7.6.4-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/45/44/3f64f38f6faab8a0cfd2c6bc6eb4c6daead246b97cf5f8fc23bf3788f841/coverage-7.6.4-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/11/4c465a5f98656821e499f4b4619929bd5a34639c466021740ecdca42aa30/coverage-7.6.4-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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compatible tags): https://files.pythonhosted.org/packages/3d/ba/fc6ac9f45ed6759dbf2b7fc517b45a81c71a66ac2751acece68579a4b611/coverage-7.6.5-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/ec/86a1111bb73968e1b8038244bbef5a5a51dc6e8d32825f6e0602f9ac31bf/coverage-7.6.5-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/c0/0f49c4ca3256aca7d19dc72979ff077c1c7e1a273569f9a842d6cb7d76af/coverage-7.6.5-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/41/fa5c68976dc331910d7f9f4ec693f46769a0b4bac808bd756bb6bcedfbb7/coverage-7.6.6-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4c/00/8f97089558401032a5a048cc7c81094d81ed5850c2d7584f81e1c9975656/coverage-7.6.6-cp313-cp313-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b7/8e/32fd0a880ddc926149962c3ff5a7778bc82bfb47ac2172e17ec6aec8b78a/coverage-7.6.6-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7f/3f/daa89d9bd6e4ad0fed80a5beac48dd9f6cc7ab0f188d7e77a78c35ded6af/coverage-7.6.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/2b/7ea9b2e7e65c422436deb20207c959483b782d030f15c7ee2d807bc8355c/coverage-7.6.6-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of 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compatible tags): https://files.pythonhosted.org/packages/a0/4e/d6c04af25061d8a10bf5f385abe21809f8f7b3302d01f31feabda876bf71/coverage-7.6.6-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/93/152ee79dbebb6d5589aaff243db3ebbde66b701671c69cc437c0230f9555/coverage-7.6.6-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/cf/2b7a4e600680ec3ba8b04012584484b7a8c7f6bac02a41a534f861932acd/coverage-7.6.6-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/27/8623dc736318a4758fb34211bdc8b13aeda1bd43b17994e83608c3104b41/coverage-7.6.6-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/24/e30af5a53fb4fa8c3f183ec3f53568ddfc123d34cfa964195614c4f59fc1/coverage-7.6.6-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3e/18/cb5b88349d4aa2f41ec78d65f92ea32572b30b3f55bc2b70e87578b8f434/coverage-7.6.9-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/26/9abab6539d2191dbda2ce8c97b67d74cbfc966cc5b25abb880ffc7c459bc/coverage-7.6.9-cp313-cp313-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/da/d49f19402240c93453f606e660a6676a2a1fbbaa6870cc23207790aa9697/coverage-7.6.9-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/da/e6/93bb9bf85497816082ec8da6124c25efa2052bd4c887dd3b317b91990c9e/coverage-7.6.9-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/df/65/6a824b9406fe066835c1274a9949e06f084d3e605eb1a602727a27ec2fe3/coverage-7.6.9-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of 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compatible tags): https://files.pythonhosted.org/packages/15/05/ec9d6080852984f7163c96984444e7cd98b338fd045b191064f943ee1c08/coverage-7.6.9-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0a/d8/775937670b93156aec29f694ce37f56214ed7597e1a75b4083ee4c32121c/coverage-7.6.9-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/58/88551cb7fdd5ec98cb6044e8814e38583436b14040a5ece15349c44c8f7c/coverage-7.6.9-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/19/4d5d3ae66938a7dcb2f58cef3fa5386f838f469575b0bb568c8cc9e3a33d/coverage-7.6.9-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/0b/4ee8a7821f682af9ad440ae3c1e379da89a998883271f088102d7ca2473d/coverage-7.6.9-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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link https://files.pythonhosted.org/packages/5b/d2/c25011f4d036cf7e8acbbee07a8e09e9018390aee25ba085596c4b83d510/coverage-7.6.9.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:>=3.9), version: 7.6.9\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/12/2a2a923edf4ddabdffed7ad6da50d96a5c126dae7b80a33df7310e329a1e/coverage-7.6.10-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/49/6985dbca9c7be3f3cb62a2e6e492a0c88b65bf40579e16c71ae9c33c6b23/coverage-7.6.10-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Found link https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9), version: 7.6.10\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a6/e2/6622f3b70f5f5b59f705e680dae6db64421af05a5d1e389afd24dae62e5b/coverage-7.6.10-cp311-cp311-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/03/0334a79b26ecf59958f2fe9dd1f5ab3e2f88db876f5071933de39af09647/coverage-7.6.10-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d7/45/8a707f23c202208d7b286d78ad6233f50dcf929319b664b6cc18a03c1aae/coverage-7.6.10-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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Found link https://files.pythonhosted.org/packages/5c/38/1f376dbf601ac89aea8a123fde7c616ae66eadca96a8afc32860b225be9f/execnet-1.0.3.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.3\n"," Found link https://files.pythonhosted.org/packages/ab/41/de81d1491e67e7922e0e42eaee1fde668f8fb2ad06157585bc13ee130a00/execnet-1.0.4.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.4\n"," Found link https://files.pythonhosted.org/packages/12/bf/eea2ed3bc852c36a141eba731a50078535253e6cc1129869af79180b2263/execnet-1.0.5.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.5\n"," Found link https://files.pythonhosted.org/packages/25/ce/badede1a9758ab81376d9f1f147ef458ad74a4059f46ef845e29f2a70678/execnet-1.0.6.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.6\n"," Found link https://files.pythonhosted.org/packages/e4/fe/e0917a2160d4ac962f9d4f79edc026a8fb78748c9c2b97de899c2756dc39/execnet-1.0.7.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.7\n"," 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https://files.pythonhosted.org/packages/99/f6/1963d06df2a3ae483c5dc6ed292b472f8eb1764b0b6415eafa894d938e7c/execnet-1.8.0.tar.gz (from https://pypi.org/simple/execnet/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 1.8.0\n"," Found link https://files.pythonhosted.org/packages/41/85/0cd8c8670ea330150be3427f9d279143e766839e83545f77572749b6d8c3/execnet-1.8.1-py2.py3-none-any.whl (from https://pypi.org/simple/execnet/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 1.8.1\n"," Found link https://files.pythonhosted.org/packages/7f/0e/85d74f3544d44fe0fc6a643c236210fbd9179e5b735ace90a2d452c38bd0/execnet-1.8.1.tar.gz (from https://pypi.org/simple/execnet/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 1.8.1\n"," Found link https://files.pythonhosted.org/packages/81/c0/3072ecc23f4c5e0a1af35e3a222855cfd9c80a1a105ca67be3b6172637dd/execnet-1.9.0-py2.py3-none-any.whl (from 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Found link https://files.pythonhosted.org/packages/66/c6/14bd4bd2db833ebc90ebdaf4af21ea6e672e92725e03bc91683e2e08af8d/execnet-2.1.0.tar.gz (from https://pypi.org/simple/execnet/) (requires-python:>=3.8), version: 2.1.0\n"," Found link https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl (from https://pypi.org/simple/execnet/) (requires-python:>=3.8), version: 2.1.1\n"," Found link https://files.pythonhosted.org/packages/bb/ff/b4c0dc78fbe20c3e59c0c7334de0c27eb4001a2b2017999af398bf730817/execnet-2.1.1.tar.gz (from https://pypi.org/simple/execnet/) (requires-python:>=3.8), version: 2.1.1\n","Skipping link: not a file: https://pypi.org/simple/execnet/\n","Given no hashes to check 4 links for project 'execnet': discarding no candidates\n","Collecting execnet>=2.1 (from pytest-xdist->mase-tools==1.0.0)\n"," Obtaining dependency information for execnet>=2.1 from 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Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/ae/ffc5a0ef134e2fe1322048b8bedf4b981103a60168a068c1c70c75e582ab/ale_py-0.7rc0-cp37-cp37m-win_amd64.whl (from https://pypi.org/simple/ale-py/) (requires-python:>=3.6)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e9/b6/6f095ab723d92d6ce77e1f11bcdf108e91a8d2184971df53e554c1d930ba/ale_py-0.7rc0-cp38-cp38-macosx_10_15_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:>=3.6)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://files.pythonhosted.org/packages/c6/c9/9cd84cbd5816aa8bee5fd5a00f857efd636ec30586848d571b67baf0b868/Mako-1.3.3-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.3\n"," Found link https://files.pythonhosted.org/packages/0a/dc/48e8853daf4b32748d062ce9cd47a744755fb60691ebc211ca689b849c1c/Mako-1.3.3.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.3\n"," Found link https://files.pythonhosted.org/packages/54/0c/9292a640ecc1dd92b5f32ec642a12999d9437068c083eeba6bda8419f827/Mako-1.3.4-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.4\n"," Found link https://files.pythonhosted.org/packages/71/4f/bfe962d3716a82509a45d66bb549acde002815eae68bda1f33b3a713b21d/Mako-1.3.4.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.4\n"," Found link https://files.pythonhosted.org/packages/03/62/70f5a0c2dd208f9f3f2f9afd103aec42ee4d9ad2401d78342f75e9b8da36/Mako-1.3.5-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.5\n"," Found link https://files.pythonhosted.org/packages/67/03/fb5ba97ff65ce64f6d35b582aacffc26b693a98053fa831ab43a437cbddb/Mako-1.3.5.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.5\n"," Found link https://files.pythonhosted.org/packages/48/22/bc14c6f02e6dccaafb3eba95764c8f096714260c2aa5f76f654fd16a23dd/Mako-1.3.6-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.6\n"," Found link https://files.pythonhosted.org/packages/fa/0b/29bc5a230948bf209d3ed3165006d257e547c02c3c2a96f6286320dfe8dc/mako-1.3.6.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.6\n"," Found link https://files.pythonhosted.org/packages/a7/d8/c516e830071b849ad60ee1026315bb8566381ac648ac8bda314569b4b7e2/Mako-1.3.7-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.7\n"," Found link https://files.pythonhosted.org/packages/5a/27/5af876b41cebd9d76fa8333b83ef9121726893f725952022edd194a1671e/mako-1.3.7.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.7\n"," Found link https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.8\n"," Found link https://files.pythonhosted.org/packages/5f/d9/8518279534ed7dace1795d5a47e49d5299dd0994eed1053996402a8902f9/mako-1.3.8.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.8\n","Skipping link: not a file: https://pypi.org/simple/mako/\n","Given no hashes to check 94 links for project 'mako': discarding no candidates\n","Collecting Mako (from alembic>=1.5.0->optuna->mase-tools==1.0.0)\n"," Obtaining dependency information for Mako from https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-1lo7z6j5\n"," Looking up \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata HTTP/1.1\" 200 2896\n"," Downloading Mako-1.3.8-py3-none-any.whl.metadata (2.9 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from 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aiohttp->datasets->mase-tools==1.0.0) (6.1.0)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets->mase-tools==1.0.0) (0.2.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets->mase-tools==1.0.0) (1.18.3)\n","Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.11/dist-packages (from gitdb<5,>=4.0.1->GitPython->mase-tools==1.0.0) (5.0.2)\n","1 location(s) to search for versions of farama-notifications:\n","* https://pypi.org/simple/farama-notifications/\n","Fetching project page and analyzing links: https://pypi.org/simple/farama-notifications/\n","Getting page https://pypi.org/simple/farama-notifications/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/farama-notifications/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/farama-notifications/ HTTP/1.1\" 200 1112\n","Updating cache with response from \"https://pypi.org/simple/farama-notifications/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/farama-notifications/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/93/3d/faf80234f72568a89ebf99d893ae30a9bc0d7226fca406e251bb41e283ee/Farama-Notifications-0.0.1.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/f8/f1/5ae419e5d38ea81c9e9df42c1320cf329b2f0197dd4c06a86c9971305744/Farama_Notifications-0.0.1-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/00/a4/22126dbc9c448912b0ea773bef2d60b595b6295e8880d7eb47ddd8a9c071/Farama-Notifications-0.0.2.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/99/65/eddbd8b13d7bfd5dcdfea3eb46458b052e27abfc85d37c4c79ded99c1ea0/Farama_Notifications-0.0.2-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/2e/2c/8384832b7a6b1fd6ba95bbdcae26e7137bb3eedc955c42fd5cdcc086cfbf/Farama-Notifications-0.0.4.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.4\n","Skipping link: not a file: https://pypi.org/simple/farama-notifications/\n","Given no hashes to check 6 links for project 'farama-notifications': discarding no candidates\n","Collecting farama-notifications>=0.0.1 (from 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/usr/local/lib/python3.11/dist-packages (from jsonschema>=3.2.0->sphinx-needs>=4->mase-tools==1.0.0) (0.22.3)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py~=3.0->myst_parser->mase-tools==1.0.0) (0.1.2)\n","Given no hashes to check 2 links for project 'dill': discarding no candidates\n","Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.11/dist-packages (from nbclient->myst-nb->mase-tools==1.0.0) (6.1.12)\n","Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.11/dist-packages (from nbclient->myst-nb->mase-tools==1.0.0) (5.7.2)\n","Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.11/dist-packages (from nbformat>=5.0->myst-nb->mase-tools==1.0.0) (2.21.1)\n","Given no hashes to check 2 links for project 'scipy': discarding no candidates\n","Requirement already satisfied: qdldl in /usr/local/lib/python3.11/dist-packages (from osqp>=0.6.2->cvxpy->mase-tools==1.0.0) (0.1.7.post5)\n","Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.11/dist-packages (from pexpect>4.3->ipython->mase-tools==1.0.0) (0.7.0)\n","Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=2.6->wandb->mase-tools==1.0.0) (0.7.0)\n","Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=2.6->wandb->mase-tools==1.0.0) (2.27.2)\n","Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from pydata-sphinx-theme>=0.15.2->sphinx-book-theme->mase-tools==1.0.0) (4.12.3)\n","Requirement already satisfied: Babel in /usr/local/lib/python3.11/dist-packages (from pydata-sphinx-theme>=0.15.2->sphinx-book-theme->mase-tools==1.0.0) (2.16.0)\n","1 location(s) to search for versions of accessible-pygments:\n","* https://pypi.org/simple/accessible-pygments/\n","Fetching project page and analyzing links: https://pypi.org/simple/accessible-pygments/\n","Getting page https://pypi.org/simple/accessible-pygments/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/accessible-pygments/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/accessible-pygments/ HTTP/1.1\" 200 1674\n","Updating cache with response from \"https://pypi.org/simple/accessible-pygments/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/accessible-pygments/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/cf/59/65a3e553fcfbe5bc5d5f861e92256484b56d2a14a633a1413c38dd71f0d5/accessible-pygments-0.0.1.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/a4/00/536a9d9ad7930a582e7320ad80b6ea7179d63d2e42c6e23abccde9b4a1ca/accessible_pygments-0.0.1-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/a2/41/0154c24518c00b98a7ce44493bbddc276ff4db70286b4008fb53cb9d2e69/accessible-pygments-0.0.2.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/87/57/75d37069faa4bcfb0b2d4fc768b84bb2fb2234c64dc9518e1b7ecafe683f/accessible_pygments-0.0.2-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/c1/dc/7b23dca20691e69f5a43b2a4b06ff755f72b8f4e87de9c69c78d4e644c74/accessible-pygments-0.0.3.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.3\n"," Found link https://files.pythonhosted.org/packages/b3/12/8b34b4ecf56bd4d30b100c0eed43ebe30394eeb063998a9b739dc8d46281/accessible_pygments-0.0.3-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.3\n"," Found link https://files.pythonhosted.org/packages/b2/50/7055ebd9b7928eca202768bcf5f8f69d8d69dec1767c956c08f055c5b31e/accessible-pygments-0.0.4.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/20/d7/45cfa326d945e411c7e02764206845b05f8f5766aa7ebc812ef3bc4138cd/accessible_pygments-0.0.4-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/) (requires-python:>=3.9), version: 0.0.5\n"," Found link 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requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (2.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (2024.12.14)\n","Requirement already satisfied: sphinxcontrib-applehelp>=1.0.7 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-devhelp>=1.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-htmlhelp>=2.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.1.0)\n","Requirement already satisfied: sphinxcontrib-jsmath>=1.0.1 in 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https://files.pythonhosted.org/packages/90/df/88bff450f333114680698dc4aac7506ff7cab164b794461906de31998665/humanfriendly-4.18-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 4.18\n"," Found link https://files.pythonhosted.org/packages/26/71/e7daf57e819a70228568ff5395fdbc4de81b63067b93167e07825fcf0bcf/humanfriendly-4.18.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 4.18\n"," Found link https://files.pythonhosted.org/packages/ad/97/688c749720d8107dd2895e3f7fa8c3716ec36715a0a9c1be58dd97fd2db0/humanfriendly-5.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 5.0\n"," Found link https://files.pythonhosted.org/packages/89/69/cb0c76f27ce221735efa65c84336358d808c31957cb0149fcfcc939dbc7b/humanfriendly-5.0.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 5.0\n"," Found link https://files.pythonhosted.org/packages/b0/dd/37a08313472fee1d92b158f771f41bdb5e962160f9bce07d446da15ee833/humanfriendly-6.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 6.0\n"," Found link https://files.pythonhosted.org/packages/f9/c3/32e12a9816f863dbfe6c1c88e888af81b9a2c7c50de6c5d47ea35e958b31/humanfriendly-6.0.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 6.0\n"," Found link https://files.pythonhosted.org/packages/1e/dc/8aac3921fd2a03978542a22eb7485b63c8e85ca75a4d3bb904fab82cf032/humanfriendly-6.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 6.1\n"," Found link https://files.pythonhosted.org/packages/43/12/fef23e1b47c7a6fcd9d15e12bdca745e686d4f52affe494b5fa90eb31efe/humanfriendly-6.1.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 6.1\n"," Found link https://files.pythonhosted.org/packages/2f/26/c6f5fca266afcb7586f460ae83f585def604a762608d12de86b78b59cb73/humanfriendly-7.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.0\n"," Found link https://files.pythonhosted.org/packages/c8/14/5a86809cbb17c4c4adf61c037a13ff087acec890cc3b3c0e7211997a397d/humanfriendly-7.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.0\n"," Found link https://files.pythonhosted.org/packages/f7/71/5cf23906339bd1574e84b1c0f1a17f761694fdeef93b8fa0c3cb436e527e/humanfriendly-7.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.1\n"," Found link https://files.pythonhosted.org/packages/b4/3c/ac93d2a50e3ae656f018e3e48edf2e6b849f938e25eebb7c74164d514315/humanfriendly-7.1.tar.gz (from 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link https://files.pythonhosted.org/packages/10/f2/174a19d9cf49fb9418584ae7f667957d3215b624396dd38278c79670a55b/humanfriendly-7.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.2\n"," Found link https://files.pythonhosted.org/packages/af/b7/798372f9ddf5429c69a36cb940ee1da7e6f37192c058db7ccb5d85af72d4/humanfriendly-7.3-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.3\n"," Found link https://files.pythonhosted.org/packages/27/32/32f678f41b2e0e7c59ae8916562d6d106d8a0c1aa55cabad377fe5c60b61/humanfriendly-7.3.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.3\n"," Found link https://files.pythonhosted.org/packages/19/a4/6deadfb0ed130b14628eff1cd39dc0e58c8dce9eddf46e2754060fe7f38b/humanfriendly-8.0-py2.py3-none-any.whl 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https://files.pythonhosted.org/packages/8e/2d/2f1b0a780b8c948c06c74c8c80e68ac354da52397ba432a1c5ac1923c3af/humanfriendly-8.2-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.2\n"," Found link https://files.pythonhosted.org/packages/6c/19/8e3b4c6fa7cca4788817db398c05274d98ecc6a35e0eaad2846fde90c863/humanfriendly-8.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.2\n"," Found link https://files.pythonhosted.org/packages/79/59/239f467c4bafad82ad1fdb0d2a16384d8a5e18d5a3197b4840473531e50e/humanfriendly-9.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.0\n"," Found link https://files.pythonhosted.org/packages/1f/6c/bddbe81bb6b0f03088069ef3a7346be5218cb0689025fa6c6c8dc1886da2/humanfriendly-9.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.0\n"," Found link https://files.pythonhosted.org/packages/93/66/363d01a81da2108a5cf446daf619779f06d49a0c4426dd02b40734f10e2f/humanfriendly-9.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.1\n"," Found link https://files.pythonhosted.org/packages/31/0e/a2e882aaaa0a378aa6643f4bbb571399aede7dbb5402d3a1ee27a201f5f3/humanfriendly-9.1.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.1\n"," Found link https://files.pythonhosted.org/packages/92/7e/a06472f484fa589933f39bfb41a7b849ca49f6d8e4fdfe978e27f0e3075c/humanfriendly-9.2-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.2\n"," Found link https://files.pythonhosted.org/packages/24/ca/f3a75b50d978872f6551d72c9c76890d68c84f3ba210cdba5f409587a2fc/humanfriendly-9.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.2\n"," Found link https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 10.0\n"," Found link https://files.pythonhosted.org/packages/cc/3f/2c29224acb2e2df4d2046e4c73ee2662023c58ff5b113c4c1adac0886c43/humanfriendly-10.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 10.0\n","Skipping link: not a file: https://pypi.org/simple/humanfriendly/\n","Given no hashes to check 6 links for project 'humanfriendly': discarding no 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beautifulsoup4->pydata-sphinx-theme>=0.15.2->sphinx-book-theme->mase-tools==1.0.0) (2.6)\n","Created temporary directory: /tmp/pip-unpack-crag90w4\n","Looking up \"https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl HTTP/1.1\" 200 4533541\n","Downloading pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl (4.5 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/4.5 MB\u001b[0m \u001b[31m125.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from 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MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/scipy/) (requires-python:>=3.10) to /tmp/pip-unpack-crag90w4/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl HTTP/1.1\" 200 71889\n","Downloading bitstring-4.3.0-py3-none-any.whl (71 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/71.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl (from https://pypi.org/simple/bitstring/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/bitstring-4.3.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 286125\n","Downloading bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (286 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/286.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.1/286.1 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/bitarray/) to /tmp/pip-unpack-crag90w4/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl HTTP/1.1\" 200 2634328\n","Downloading sphinx_needs-4.2.0-py3-none-any.whl (2.6 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m45.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl (from https://pypi.org/simple/sphinx-needs/) (requires-python:<4,>=3.9) to /tmp/pip-unpack-crag90w4/sphinx_needs-4.2.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl HTTP/1.1\" 200 27162\n","Downloading attr_dot_dict-0.1.0-py3-none-any.whl (27 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl (from https://pypi.org/simple/attr-dot-dict/) (requires-python:>=3.7) to /tmp/pip-unpack-crag90w4/attr_dot_dict-0.1.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c9/9b/2db8045b45844665c720dcfe292fdaf2e49825810c0103e1191515fc101a/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\"https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl (from https://pypi.org/simple/colorlog/) (requires-python:>=3.6) to /tmp/pip-unpack-crag90w4/colorlog-6.9.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl HTTP/1.1\" 200 480647\n","Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m22.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl (from https://pypi.org/simple/datasets/) (requires-python:>=3.9.0) to /tmp/pip-unpack-crag90w4/datasets-3.2.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl HTTP/1.1\" 200 590617\n","Downloading emoji-2.14.1-py3-none-any.whl (590 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/590.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m590.6/590.6 kB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl (from https://pypi.org/simple/emoji/) (requires-python:>=3.7) to /tmp/pip-unpack-crag90w4/emoji-2.14.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl HTTP/1.1\" 200 84010\n","Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl (from https://pypi.org/simple/evaluate/) (requires-python:>=3.8.0) to /tmp/pip-unpack-crag90w4/evaluate-0.4.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl HTTP/1.1\" 200 11034\n","Downloading ghp_import-2.1.0-py3-none-any.whl (11 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl (from https://pypi.org/simple/ghp-import/) to /tmp/pip-unpack-crag90w4/ghp_import-2.1.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl HTTP/1.1\" 200 12130\n","Downloading ipdb-0.13.13-py3-none-any.whl (12 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl (from https://pypi.org/simple/ipdb/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*) to /tmp/pip-unpack-crag90w4/ipdb-0.13.13-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl HTTP/1.1\" 200 1078141\n","Downloading kornia-0.8.0-py2.py3-none-any.whl (1.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl (from https://pypi.org/simple/kornia/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/kornia-0.8.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl HTTP/1.1\" 200 815241\n","Downloading lightning-2.5.0.post0-py3-none-any.whl (815 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/815.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m28.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl (from https://pypi.org/simple/lightning/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/lightning-2.5.0.post0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl HTTP/1.1\" 200 80281\n","Downloading myst_nb-1.1.2-py3-none-any.whl (80 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl (from https://pypi.org/simple/myst-nb/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/myst_nb-1.1.2-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl HTTP/1.1\" 200 84563\n","Downloading myst_parser-4.0.0-py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.6/84.6 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl (from https://pypi.org/simple/myst-parser/) (requires-python:>=3.10) to /tmp/pip-unpack-crag90w4/myst_parser-4.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/onnx/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl HTTP/1.1\" 200 84477\n","Downloading onnxconverter_common-1.14.0-py2.py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl (from https://pypi.org/simple/onnxconverter-common/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/onnxconverter_common-1.14.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl HTTP/1.1\" 200 162128\n","Downloading protobuf-3.20.2-py2.py3-none-any.whl (162 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/162.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.1/162.1 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl (from https://pypi.org/simple/protobuf/) (requires-python:>=3.7) to /tmp/pip-unpack-crag90w4/protobuf-3.20.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl HTTP/1.1\" 200 13331703\n","Downloading onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (13.3 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m11.8/13.3 MB\u001b[0m \u001b[31m90.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m52.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (from https://pypi.org/simple/onnxruntime/) to /tmp/pip-unpack-crag90w4/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl HTTP/1.1\" 200 424070\n","Downloading optimum-1.23.3-py3-none-any.whl (424 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.1/424.1 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl (from https://pypi.org/simple/optimum/) (requires-python:>=3.7.0) to /tmp/pip-unpack-crag90w4/optimum-1.23.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl HTTP/1.1\" 200 383372\n","Downloading optuna-4.2.0-py3-none-any.whl (383 kB)\n","\u001b[?25l 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\"https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl (from https://pypi.org/simple/pynvml/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/pynvml-12.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl HTTP/1.1\" 200 22949\n","Downloading pytest_cov-6.0.0-py3-none-any.whl (22 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl (from https://pypi.org/simple/pytest-cov/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/pytest_cov-6.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl HTTP/1.1\" 200 23491\n","Downloading pytest_html-4.1.1-py3-none-any.whl (23 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl (from https://pypi.org/simple/pytest-html/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/pytest_html-4.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e3/ac/c428c66241a144617a8af7a28e2e055e1438d23b949b62ac4b401a69fb79/pytest_profiling-1.8.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl HTTP/1.1\" 200 10171\n","Downloading pytest_sugar-1.0.0-py3-none-any.whl (10 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl (from https://pypi.org/simple/pytest-sugar/) to /tmp/pip-unpack-crag90w4/pytest_sugar-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl HTTP/1.1\" 200 46108\n","Downloading pytest_xdist-3.6.1-py3-none-any.whl (46 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/46.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.1/46.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl (from https://pypi.org/simple/pytest-xdist/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/pytest_xdist-3.6.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/02/df/0c7e4582b74264fe2179e78fcdeb9313f680d40ffe1dd4b078da5a2cbf82/pytorch_lightning-2.5.0.post0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/02/df/0c7e4582b74264fe2179e78fcdeb9313f680d40ffe1dd4b078da5a2cbf82/pytorch_lightning-2.5.0.post0-py3-none-any.whl HTTP/1.1\" 200 819282\n","Downloading pytorch_lightning-2.5.0.post0-py3-none-any.whl (819 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/819.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/02/df/0c7e4582b74264fe2179e78fcdeb9313f680d40ffe1dd4b078da5a2cbf82/pytorch_lightning-2.5.0.post0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m819.3/819.3 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/02/df/0c7e4582b74264fe2179e78fcdeb9313f680d40ffe1dd4b078da5a2cbf82/pytorch_lightning-2.5.0.post0-py3-none-any.whl (from https://pypi.org/simple/pytorch-lightning/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/pytorch_lightning-2.5.0.post0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl HTTP/1.1\" 200 90058\n","Downloading pytorch_nlp-0.5.0-py3-none-any.whl (90 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/90.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\"https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl HTTP/1.1\" 200 4237451\n","Downloading sphinx_glpi_theme-0.6-py2.py3-none-any.whl (4.2 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m3.8/4.2 MB\u001b[0m \u001b[31m119.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m63.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl (from https://pypi.org/simple/sphinx-glpi-theme/) to /tmp/pip-unpack-crag90w4/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl HTTP/1.1\" 200 7655561\n","Downloading sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl (7.7 MB)\n","\u001b[2K 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/tmp/pip-unpack-crag90w4/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl HTTP/1.1\" 200 101700\n","Downloading tensorboardX-2.6.2.2-py2.py3-none-any.whl (101 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/101.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.7/101.7 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl (from https://pypi.org/simple/tensorboardx/) to /tmp/pip-unpack-crag90w4/tensorboardX-2.6.2.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2102024\n","Downloading ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m52.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl HTTP/1.1\" 200 233565\n","Downloading alembic-1.14.1-py3-none-any.whl (233 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/233.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.6/233.6 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl (from https://pypi.org/simple/alembic/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/alembic-1.14.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl (from https://pypi.org/simple/dill/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/dill-0.3.8-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl HTTP/1.1\" 200 40612\n","Downloading execnet-2.1.1-py3-none-any.whl (40 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/40.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.6/40.6 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl (from https://pypi.org/simple/execnet/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/execnet-2.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl HTTP/1.1\" 200 179253\n","Downloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/179.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl (from https://pypi.org/simple/fsspec/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/fsspec-2024.9.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl HTTP/1.1\" 200 958120\n","Downloading gymnasium-1.0.0-py3-none-any.whl (958 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/958.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m958.1/958.1 kB\u001b[0m \u001b[31m37.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/gymnasium-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl HTTP/1.1\" 200 1572278\n","Downloading jedi-0.19.2-py2.py3-none-any.whl (1.6 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m46.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl (from https://pypi.org/simple/jedi/) (requires-python:>=3.6) to /tmp/pip-unpack-crag90w4/jedi-0.19.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/64/6b/67b87da9d36bff9df7d0efbd1a325fa372a43be7158effaf43ed7b22341d/jupyter_cache-1.0.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\"https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2050393\n","Downloading kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m53.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/kornia-rs/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl HTTP/1.1\" 200 28356\n","Downloading lightning_utilities-0.11.9-py3-none-any.whl (28 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl (from https://pypi.org/simple/lightning-utilities/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/lightning_utilities-0.11.9-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl HTTP/1.1\" 200 143519\n","Downloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/143.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K 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immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl (from https://pypi.org/simple/mypy-extensions/) (requires-python:>=3.5) to /tmp/pip-unpack-crag90w4/mypy_extensions-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl HTTP/1.1\" 200 44442\n","Downloading nvidia_ml_py-12.570.86-py3-none-any.whl (44 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl (from https://pypi.org/simple/nvidia-ml-py/) to /tmp/pip-unpack-crag90w4/nvidia_ml_py-12.570.86-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl HTTP/1.1\" 200 31191\n","Downloading pathspec-0.12.1-py3-none-any.whl (31 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl (from https://pypi.org/simple/pathspec/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/pathspec-0.12.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl HTTP/1.1\" 200 6723264\n","Downloading pydata_sphinx_theme-0.16.1-py3-none-any.whl (6.7 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m5.3/6.7 MB\u001b[0m \u001b[31m134.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from 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cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl HTTP/1.1\" 200 4244\n","Downloading requests_file-2.1.0-py2.py3-none-any.whl (4.2 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl (from https://pypi.org/simple/requests-file/) to /tmp/pip-unpack-crag90w4/requests_file-2.1.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl HTTP/1.1\" 200 8215\n","Downloading sphinx_data_viewer-0.1.5-py3-none-any.whl (8.2 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl (from https://pypi.org/simple/sphinx-data-viewer/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/sphinx_data_viewer-0.1.5-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl HTTP/1.1\" 200 121104\n","Downloading sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (121 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/121.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.1/121.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (from https://pypi.org/simple/sphinxcontrib-jquery/) (requires-python:>=2.7) to /tmp/pip-unpack-crag90w4/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl HTTP/1.1\" 200 927305\n","Downloading torchmetrics-1.6.1-py3-none-any.whl (927 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/927.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m927.3/927.3 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl (from https://pypi.org/simple/torchmetrics/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/torchmetrics-1.6.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 4211516\n","Downloading cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m129.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m62.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/cocotb/) (requires-python:>=3.6) to /tmp/pip-unpack-crag90w4/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl HTTP/1.1\" 200 46018\n","Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/46.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl (from https://pypi.org/simple/coloredlogs/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*) to /tmp/pip-unpack-crag90w4/coloredlogs-15.0.1-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl HTTP/1.1\" 200 8670\n","Downloading find_libpython-0.4.0-py3-none-any.whl (8.7 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl (from https://pypi.org/simple/find-libpython/) to /tmp/pip-unpack-crag90w4/find_libpython-0.4.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl HTTP/1.1\" 200 34763\n","Downloading gprof2dot-2024.6.6-py2.py3-none-any.whl (34 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl (from https://pypi.org/simple/gprof2dot/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/gprof2dot-2024.6.6-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl (from https://pypi.org/simple/stable-baselines3/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/stable_baselines3-2.5.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 194822\n","Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/xxhash/) (requires-python:>=3.7) to /tmp/pip-unpack-crag90w4/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl HTTP/1.1\" 200 2511\n","Downloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/) to /tmp/pip-unpack-crag90w4/Farama_Notifications-0.0.4-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl HTTP/1.1\" 200 86794\n","Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*) to /tmp/pip-unpack-crag90w4/humanfriendly-10.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl HTTP/1.1\" 200 1395903\n","Downloading accessible_pygments-0.0.5-py3-none-any.whl (1.4 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m49.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/) (requires-python:>=3.9) to /tmp/pip-unpack-crag90w4/accessible_pygments-0.0.5-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl HTTP/1.1\" 200 78569\n","Downloading Mako-1.3.8-py3-none-any.whl (78 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/78.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8) to /tmp/pip-unpack-crag90w4/Mako-1.3.8-py3-none-any.whl\n","Building wheels for collected packages: sphinx-test-reports, sphinxcontrib-plantuml, cocotb-bus\n"," Created temporary directory: /tmp/pip-wheel-4tb4cbyv\n"," Destination directory: /tmp/pip-wheel-4tb4cbyv\n"," Running command Building wheel for sphinx-test-reports (pyproject.toml)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/sphinxcontrib\n"," copying sphinxcontrib/__init__.py -> build/lib/sphinxcontrib\n"," creating build/lib/tests\n"," copying tests/test_test_ctest_file.py -> build/lib/tests\n"," copying tests/test_needs_linking.py -> build/lib/tests\n"," copying tests/test_custom_types.py -> build/lib/tests\n"," copying tests/test_basic_doc.py -> build/lib/tests\n"," copying tests/conftest.py -> build/lib/tests\n"," copying tests/test_env.py -> build/lib/tests\n"," copying tests/test_test_file.py -> build/lib/tests\n"," copying tests/test_custom_template.py -> build/lib/tests\n"," copying tests/test_suites.py -> build/lib/tests\n"," copying tests/test_junit_parser.py -> build/lib/tests\n"," copying tests/__init__.py -> build/lib/tests\n"," copying tests/test_json_parser.py -> build/lib/tests\n"," creating build/lib/docs\n"," copying docs/conf.py -> build/lib/docs\n"," creating build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/environment.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/jsonparser.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/test_reports.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/junitparser.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/exceptions.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/__init__.py -> build/lib/sphinxcontrib/test_reports\n"," creating build/lib/sphinxcontrib/test_reports/functions\n"," copying sphinxcontrib/test_reports/functions/__init__.py -> build/lib/sphinxcontrib/test_reports/functions\n"," creating build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_results.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_file.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_suite.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_report.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_env.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_case.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_common.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/__init__.py -> build/lib/sphinxcontrib/test_reports/directives\n"," creating build/lib/tests/doc_test/custom_tr_template\n"," copying tests/doc_test/custom_tr_template/conf.py -> build/lib/tests/doc_test/custom_tr_template\n"," creating build/lib/tests/doc_test/custom_tr_utf8_template\n"," copying tests/doc_test/custom_tr_utf8_template/conf.py -> build/lib/tests/doc_test/custom_tr_utf8_template\n"," creating build/lib/tests/doc_test/basic_doc\n"," copying tests/doc_test/basic_doc/conf.py -> build/lib/tests/doc_test/basic_doc\n"," creating build/lib/tests/doc_test/env_report_doc_default\n"," copying tests/doc_test/env_report_doc_default/conf.py -> build/lib/tests/doc_test/env_report_doc_default\n"," creating build/lib/tests/doc_test/needs_linking\n"," copying tests/doc_test/needs_linking/conf.py -> build/lib/tests/doc_test/needs_linking\n"," creating build/lib/tests/doc_test/json_parser\n"," copying tests/doc_test/json_parser/conf.py -> build/lib/tests/doc_test/json_parser\n"," creating build/lib/tests/doc_test/many_testsuites_doc\n"," copying tests/doc_test/many_testsuites_doc/conf.py -> build/lib/tests/doc_test/many_testsuites_doc\n"," creating build/lib/tests/doc_test/testsuites_doc\n"," copying tests/doc_test/testsuites_doc/conf.py -> build/lib/tests/doc_test/testsuites_doc\n"," creating build/lib/tests/doc_test/env_report_doc_raw\n"," copying tests/doc_test/env_report_doc_raw/conf.py -> build/lib/tests/doc_test/env_report_doc_raw\n"," creating build/lib/tests/doc_test/custom_types\n"," copying tests/doc_test/custom_types/conf.py -> build/lib/tests/doc_test/custom_types\n"," creating build/lib/tests/doc_test/env_report_warnings\n"," copying tests/doc_test/env_report_warnings/conf.py -> build/lib/tests/doc_test/env_report_warnings\n"," creating build/lib/tests/doc_test/env_report_doc\n"," copying tests/doc_test/env_report_doc/conf.py -> build/lib/tests/doc_test/env_report_doc\n"," creating build/lib/tests/doc_test/custom_tr_koi8_template\n"," copying tests/doc_test/custom_tr_koi8_template/conf.py -> build/lib/tests/doc_test/custom_tr_koi8_template\n"," creating build/lib/tests/doc_test/pytest_6_2\n"," copying tests/doc_test/pytest_6_2/conf.py -> build/lib/tests/doc_test/pytest_6_2\n"," creating build/lib/tests/doc_test/doc_test_file\n"," copying tests/doc_test/doc_test_file/conf.py -> build/lib/tests/doc_test/doc_test_file\n"," creating build/lib/tests/doc_test/doc_test_ctest_file\n"," copying tests/doc_test/doc_test_ctest_file/conf.py -> build/lib/tests/doc_test/doc_test_ctest_file\n"," creating build/lib/tests/doc_test/json_parser_complex\n"," copying tests/doc_test/json_parser_complex/conf.py -> build/lib/tests/doc_test/json_parser_complex\n"," creating build/lib/docs/ub_theme\n"," copying docs/ub_theme/conf.py -> build/lib/docs/ub_theme\n"," copying docs/ub_theme/__init__.py -> build/lib/docs/ub_theme\n"," running egg_info\n"," writing sphinx_test_reports.egg-info/PKG-INFO\n"," writing dependency_links to sphinx_test_reports.egg-info/dependency_links.txt\n"," writing requirements to sphinx_test_reports.egg-info/requires.txt\n"," writing top-level names to sphinx_test_reports.egg-info/top_level.txt\n"," reading manifest file 'sphinx_test_reports.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," adding license file 'LICENSE'\n"," adding license file 'AUTHORS'\n"," writing manifest file 'sphinx_test_reports.egg-info/SOURCES.txt'\n"," creating build/lib/sphinxcontrib/test_reports/css\n"," copying sphinxcontrib/test_reports/css/common.css -> build/lib/sphinxcontrib/test_reports/css\n"," copying sphinxcontrib/test_reports/directives/test_report_template.txt -> build/lib/sphinxcontrib/test_reports/directives\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," creating build/bdist.linux-x86_64/wheel\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/environment.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/css\n"," copying build/lib/sphinxcontrib/test_reports/css/common.css -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/css\n"," copying build/lib/sphinxcontrib/test_reports/jsonparser.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/functions\n"," copying build/lib/sphinxcontrib/test_reports/functions/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/functions\n"," copying build/lib/sphinxcontrib/test_reports/test_reports.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_results.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_file.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_suite.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_report.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_env.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_case.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_common.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_report_template.txt -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/junitparser.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/exceptions.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib\n"," creating build/bdist.linux-x86_64/wheel/tests\n"," copying build/lib/tests/test_test_ctest_file.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_needs_linking.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_custom_types.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_basic_doc.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/conftest.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_env.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_test_file.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_custom_template.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_suites.py -> build/bdist.linux-x86_64/wheel/./tests\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_template\n"," copying build/lib/tests/doc_test/custom_tr_template/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_utf8_template\n"," copying build/lib/tests/doc_test/custom_tr_utf8_template/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_utf8_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/basic_doc\n"," copying build/lib/tests/doc_test/basic_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/basic_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc_default\n"," copying build/lib/tests/doc_test/env_report_doc_default/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc_default\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/needs_linking\n"," copying build/lib/tests/doc_test/needs_linking/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/needs_linking\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/json_parser\n"," copying build/lib/tests/doc_test/json_parser/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/json_parser\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/many_testsuites_doc\n"," copying build/lib/tests/doc_test/many_testsuites_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/many_testsuites_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/testsuites_doc\n"," copying build/lib/tests/doc_test/testsuites_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/testsuites_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc_raw\n"," copying build/lib/tests/doc_test/env_report_doc_raw/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc_raw\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_types\n"," copying build/lib/tests/doc_test/custom_types/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_types\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_warnings\n"," copying build/lib/tests/doc_test/env_report_warnings/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_warnings\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc\n"," copying build/lib/tests/doc_test/env_report_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_koi8_template\n"," copying build/lib/tests/doc_test/custom_tr_koi8_template/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_koi8_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/pytest_6_2\n"," copying build/lib/tests/doc_test/pytest_6_2/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/pytest_6_2\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/doc_test_file\n"," copying build/lib/tests/doc_test/doc_test_file/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/doc_test_file\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/doc_test_ctest_file\n"," copying build/lib/tests/doc_test/doc_test_ctest_file/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/doc_test_ctest_file\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/json_parser_complex\n"," copying build/lib/tests/doc_test/json_parser_complex/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/json_parser_complex\n"," copying build/lib/tests/test_junit_parser.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_json_parser.py -> build/bdist.linux-x86_64/wheel/./tests\n"," creating build/bdist.linux-x86_64/wheel/docs\n"," copying build/lib/docs/conf.py -> build/bdist.linux-x86_64/wheel/./docs\n"," creating build/bdist.linux-x86_64/wheel/docs/ub_theme\n"," copying build/lib/docs/ub_theme/conf.py -> build/bdist.linux-x86_64/wheel/./docs/ub_theme\n"," copying build/lib/docs/ub_theme/__init__.py -> build/bdist.linux-x86_64/wheel/./docs/ub_theme\n"," running install_egg_info\n"," Copying sphinx_test_reports.egg-info to build/bdist.linux-x86_64/wheel/./sphinx_test_reports-1.1.0-py3.11.egg-info\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/sphinx_test_reports-1.1.0.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-4tb4cbyv/.tmp-wtv78rvr/sphinx_test_reports-1.1.0-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'docs/conf.py'\n"," adding 'docs/ub_theme/__init__.py'\n"," adding 'docs/ub_theme/conf.py'\n"," adding 'sphinxcontrib/__init__.py'\n"," adding 'sphinxcontrib/test_reports/__init__.py'\n"," adding 'sphinxcontrib/test_reports/environment.py'\n"," adding 'sphinxcontrib/test_reports/exceptions.py'\n"," adding 'sphinxcontrib/test_reports/jsonparser.py'\n"," adding 'sphinxcontrib/test_reports/junitparser.py'\n"," adding 'sphinxcontrib/test_reports/test_reports.py'\n"," adding 'sphinxcontrib/test_reports/css/common.css'\n"," adding 'sphinxcontrib/test_reports/directives/__init__.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_case.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_common.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_env.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_file.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_report.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_report_template.txt'\n"," adding 'sphinxcontrib/test_reports/directives/test_results.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_suite.py'\n"," adding 'sphinxcontrib/test_reports/functions/__init__.py'\n"," adding 'tests/__init__.py'\n"," adding 'tests/conftest.py'\n"," adding 'tests/test_basic_doc.py'\n"," adding 'tests/test_custom_template.py'\n"," adding 'tests/test_custom_types.py'\n"," adding 'tests/test_env.py'\n"," adding 'tests/test_json_parser.py'\n"," adding 'tests/test_junit_parser.py'\n"," adding 'tests/test_needs_linking.py'\n"," adding 'tests/test_suites.py'\n"," adding 'tests/test_test_ctest_file.py'\n"," adding 'tests/test_test_file.py'\n"," adding 'tests/doc_test/basic_doc/conf.py'\n"," adding 'tests/doc_test/custom_tr_koi8_template/conf.py'\n"," adding 'tests/doc_test/custom_tr_template/conf.py'\n"," adding 'tests/doc_test/custom_tr_utf8_template/conf.py'\n"," adding 'tests/doc_test/custom_types/conf.py'\n"," adding 'tests/doc_test/doc_test_ctest_file/conf.py'\n"," adding 'tests/doc_test/doc_test_file/conf.py'\n"," adding 'tests/doc_test/env_report_doc/conf.py'\n"," adding 'tests/doc_test/env_report_doc_default/conf.py'\n"," adding 'tests/doc_test/env_report_doc_raw/conf.py'\n"," adding 'tests/doc_test/env_report_warnings/conf.py'\n"," adding 'tests/doc_test/json_parser/conf.py'\n"," adding 'tests/doc_test/json_parser_complex/conf.py'\n"," adding 'tests/doc_test/many_testsuites_doc/conf.py'\n"," adding 'tests/doc_test/needs_linking/conf.py'\n"," adding 'tests/doc_test/pytest_6_2/conf.py'\n"," adding 'tests/doc_test/testsuites_doc/conf.py'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/AUTHORS'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/LICENSE'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/METADATA'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/WHEEL'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/top_level.txt'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for sphinx-test-reports (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sphinx-test-reports: filename=sphinx_test_reports-1.1.0-py3-none-any.whl size=72216 sha256=9da6ac8cd2af736dd63340fb88a240f8fea23bfc00196cada79cbd76deffdbf4\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-b_h578be/wheels/03/57/7d/6598421a14084c6221fe9832bbec1c5adc68218c00d43af21c\n"," Created temporary directory: /tmp/pip-wheel-erpveaqf\n"," Destination directory: /tmp/pip-wheel-erpveaqf\n"," Running command Building wheel for sphinxcontrib-plantuml (pyproject.toml)\n"," /tmp/pip-build-env-noq0nnoe/overlay/local/lib/python3.11/dist-packages/setuptools/dist.py:701: SetuptoolsDeprecationWarning: The namespace_packages parameter is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please replace its usage with implicit namespaces (PEP 420).\n","\n"," See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages for details.\n"," ********************************************************************************\n","\n"," !!\n"," ep.load()(self, ep.name, value)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/sphinxcontrib\n"," copying sphinxcontrib/plantuml.py -> build/lib/sphinxcontrib\n"," copying sphinxcontrib/__init__.py -> build/lib/sphinxcontrib\n"," running egg_info\n"," writing sphinxcontrib_plantuml.egg-info/PKG-INFO\n"," writing dependency_links to sphinxcontrib_plantuml.egg-info/dependency_links.txt\n"," writing namespace_packages to sphinxcontrib_plantuml.egg-info/namespace_packages.txt\n"," writing requirements to sphinxcontrib_plantuml.egg-info/requires.txt\n"," writing top-level names to sphinxcontrib_plantuml.egg-info/top_level.txt\n"," reading manifest file 'sphinxcontrib_plantuml.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'README'\n"," warning: no files found matching 'CHANGES.*'\n"," adding license file 'LICENSE'\n"," writing manifest file 'sphinxcontrib_plantuml.egg-info/SOURCES.txt'\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," copying sphinxcontrib/plantuml.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib\n"," Skipping installation of build/bdist.linux-x86_64/wheel/./sphinxcontrib/__init__.py (namespace package)\n"," running install_egg_info\n"," Copying sphinxcontrib_plantuml.egg-info to build/bdist.linux-x86_64/wheel/./sphinxcontrib_plantuml-0.30-py3.11.egg-info\n"," Installing build/bdist.linux-x86_64/wheel/./sphinxcontrib_plantuml-0.30-py3.11-nspkg.pth\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib_plantuml-0.30.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-erpveaqf/.tmp-k2bmpz8h/sphinxcontrib_plantuml-0.30-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'sphinxcontrib_plantuml-0.30-py3.11-nspkg.pth'\n"," adding 'sphinxcontrib/plantuml.py'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/LICENSE'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/METADATA'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/WHEEL'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/namespace_packages.txt'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/top_level.txt'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for sphinxcontrib-plantuml (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sphinxcontrib-plantuml: filename=sphinxcontrib_plantuml-0.30-py3-none-any.whl size=11594 sha256=a76a2fa84df1ae38177eb113561f3b2c76838f8ef5dc5273254dec37f608a345\n"," Stored in directory: /root/.cache/pip/wheels/ba/14/11/6573783b9c1e0a196a172550621e2d39a6cabb19b0ade3e4f5\n"," Created temporary directory: /tmp/pip-wheel-vfqy1wz8\n"," Destination directory: /tmp/pip-wheel-vfqy1wz8\n"," Running command Building wheel for cocotb-bus (pyproject.toml)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/cocotb_bus\n"," copying src/cocotb_bus/_version.py -> build/lib/cocotb_bus\n"," copying src/cocotb_bus/bus.py -> build/lib/cocotb_bus\n"," copying src/cocotb_bus/scoreboard.py -> build/lib/cocotb_bus\n"," copying src/cocotb_bus/__init__.py -> build/lib/cocotb_bus\n"," creating build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/avalon.py -> build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/xgmii.py -> build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/__init__.py -> build/lib/cocotb_bus/monitors\n"," creating build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/amba.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/avalon.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/opb.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/xgmii.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/__init__.py -> build/lib/cocotb_bus/drivers\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," creating build/bdist.linux-x86_64/wheel\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus\n"," copying build/lib/cocotb_bus/_version.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/avalon.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/xgmii.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/__init__.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/bus.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," copying build/lib/cocotb_bus/scoreboard.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/amba.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/avalon.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/opb.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/xgmii.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/__init__.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/__init__.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," running install_egg_info\n"," running egg_info\n"," writing src/cocotb_bus.egg-info/PKG-INFO\n"," writing dependency_links to src/cocotb_bus.egg-info/dependency_links.txt\n"," writing requirements to src/cocotb_bus.egg-info/requires.txt\n"," writing top-level names to src/cocotb_bus.egg-info/top_level.txt\n"," reading manifest file 'src/cocotb_bus.egg-info/SOURCES.txt'\n"," adding license file 'LICENSE'\n"," writing manifest file 'src/cocotb_bus.egg-info/SOURCES.txt'\n"," Copying src/cocotb_bus.egg-info to build/bdist.linux-x86_64/wheel/./cocotb_bus-0.2.1-py3.11.egg-info\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus-0.2.1.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-vfqy1wz8/.tmp-9b0lcvvj/cocotb_bus-0.2.1-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'cocotb_bus/__init__.py'\n"," adding 'cocotb_bus/_version.py'\n"," adding 'cocotb_bus/bus.py'\n"," adding 'cocotb_bus/scoreboard.py'\n"," adding 'cocotb_bus/drivers/__init__.py'\n"," adding 'cocotb_bus/drivers/amba.py'\n"," adding 'cocotb_bus/drivers/avalon.py'\n"," adding 'cocotb_bus/drivers/opb.py'\n"," adding 'cocotb_bus/drivers/xgmii.py'\n"," adding 'cocotb_bus/monitors/__init__.py'\n"," adding 'cocotb_bus/monitors/avalon.py'\n"," adding 'cocotb_bus/monitors/xgmii.py'\n"," adding 'cocotb_bus-0.2.1.dist-info/LICENSE'\n"," adding 'cocotb_bus-0.2.1.dist-info/METADATA'\n"," adding 'cocotb_bus-0.2.1.dist-info/WHEEL'\n"," adding 'cocotb_bus-0.2.1.dist-info/top_level.txt'\n"," adding 'cocotb_bus-0.2.1.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for cocotb-bus (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for cocotb-bus: filename=cocotb_bus-0.2.1-py3-none-any.whl size=34915 sha256=f6e31c90236bd4805cf8416d704bd0a175066d3cc178fdd90c0e995dbcf471ab\n"," Stored in directory: /root/.cache/pip/wheels/f3/40/c4/456859501d450830880eb944793719ae31e972bdb1ef60f64d\n","Successfully built sphinx-test-reports sphinxcontrib-plantuml cocotb-bus\n","Installing collected packages: sphinx-glpi-theme, nvidia-ml-py, find-libpython, farama-notifications, bitarray, xxhash, scipy, pytorch-nlp, pynvml, pybind11, protobuf, Pillow, pathspec, mypy-extensions, Mako, lightning-utilities, kornia_rs, jedi, humanfriendly, gymnasium, gprof2dot, fsspec, execnet, emoji, dill, coverage, colorlog, cocotb, bitstring, attr-dot-dict, ale-py, accessible-pygments, tensorboardx, requests-file, pytest-xdist, pytest-sugar, pytest-profiling, pytest-metadata, onnx, multiprocess, ghp-import, coloredlogs, cocotb-bus, black, alembic, sphinxcontrib-plantuml, sphinxcontrib-jquery, sphinx-data-viewer, pytest-html, pytest-cov, pydata-sphinx-theme, optuna, onnxruntime, onnxconverter-common, myst_parser, ipdb, torchmetrics, stable-baselines3, sphinx-rtd-theme, sphinx-needs, sphinx-book-theme, kornia, datasets, sphinx-test-reports, pytorch-lightning, optimum, evaluate, lightning, jupyter-cache, myst-nb, mase-tools\n","\n","\n","\n"," changing mode of /usr/local/bin/find_libpython to 755\n","\n","\n","\n"," Attempting uninstall: scipy\n"," Found existing installation: scipy 1.13.1\n"," Uninstalling scipy-1.13.1:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy-1.13.1.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy-1.13.1.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy.libs\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy.libs/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy/\n"," Successfully uninstalled scipy-1.13.1\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/pybind11-config to 755\n"," Attempting uninstall: protobuf\n"," Found existing installation: protobuf 4.25.5\n"," Uninstalling protobuf-4.25.5:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/google/~upb\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/google/_upb/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/google/~rotobuf\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/google/protobuf/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~rotobuf-4.25.5.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/protobuf-4.25.5.dist-info/\n"," Successfully uninstalled protobuf-4.25.5\n","\n"," Attempting uninstall: Pillow\n"," Found existing installation: pillow 11.1.0\n"," Uninstalling pillow-11.1.0:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~IL\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/PIL/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~illow-11.1.0.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/pillow-11.1.0.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~illow.libs\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/pillow.libs/\n"," Successfully uninstalled pillow-11.1.0\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/mako-render to 755\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/humanfriendly to 755\n","\n","\n"," changing mode of /usr/local/bin/gprof2dot to 755\n"," Attempting uninstall: fsspec\n"," Found existing installation: fsspec 2024.10.0\n"," Uninstalling fsspec-2024.10.0:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~sspec-2024.10.0.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/fsspec-2024.10.0.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~sspec\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/fsspec/\n"," Successfully uninstalled fsspec-2024.10.0\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/coverage to 755\n"," changing mode of /usr/local/bin/coverage-3.11 to 755\n"," changing mode of /usr/local/bin/coverage3 to 755\n","\n","\n"," changing mode of /usr/local/bin/cocotb-config to 755\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/backend-test-tools to 755\n"," changing mode of /usr/local/bin/check-model to 755\n"," changing mode of /usr/local/bin/check-node to 755\n","\n","\n"," changing mode of /usr/local/bin/ghp-import to 755\n","\n"," changing mode of /usr/local/bin/coloredlogs to 755\n","\n","\n"," changing mode of /usr/local/bin/black to 755\n"," changing mode of /usr/local/bin/blackd to 755\n","\n"," changing mode of /usr/local/bin/alembic to 755\n","\n","\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/optuna to 755\n","\n"," changing mode of /usr/local/bin/onnxruntime_test to 755\n","\n","\n"," changing mode of /usr/local/bin/myst-anchors to 755\n"," changing mode of /usr/local/bin/myst-docutils-demo to 755\n"," changing mode of /usr/local/bin/myst-docutils-html to 755\n"," changing mode of /usr/local/bin/myst-docutils-html5 to 755\n"," changing mode of /usr/local/bin/myst-docutils-latex to 755\n"," changing mode of /usr/local/bin/myst-docutils-pseudoxml to 755\n"," changing mode of /usr/local/bin/myst-docutils-xml to 755\n"," changing mode of /usr/local/bin/myst-inv to 755\n","\n"," changing mode of /usr/local/bin/ipdb3 to 755\n","\n","\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/datasets-cli to 755\n","\n","\n","\n"," changing mode of /usr/local/bin/optimum-cli to 755\n","\n"," changing mode of /usr/local/bin/evaluate-cli to 755\n","\n"," changing mode of /usr/local/bin/fabric to 755\n"," changing mode of /usr/local/bin/lightning to 755\n","\n"," changing mode of /usr/local/bin/jcache to 755\n","\n"," changing mode of /usr/local/bin/mystnb-docutils-html to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-html5 to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-latex to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-pseudoxml to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-xml to 755\n"," changing mode of /usr/local/bin/mystnb-quickstart to 755\n"," changing mode of /usr/local/bin/mystnb-to-jupyter to 755\n"," Running setup.py develop for mase-tools\n"," Running command python setup.py develop\n"," running develop\n"," /usr/local/lib/python3.11/dist-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please avoid running ``setup.py`` and ``easy_install``.\n"," Instead, use pypa/build, pypa/installer or other\n"," standards-based tools.\n","\n"," See https://github.com/pypa/setuptools/issues/917 for details.\n"," ********************************************************************************\n","\n"," !!\n"," easy_install.initialize_options(self)\n"," /usr/local/lib/python3.11/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please avoid running ``setup.py`` directly.\n"," Instead, use pypa/build, pypa/installer or other\n"," standards-based tools.\n","\n"," See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n"," ********************************************************************************\n","\n"," !!\n"," self.initialize_options()\n"," running egg_info\n"," creating src/mase_tools.egg-info\n"," writing src/mase_tools.egg-info/PKG-INFO\n"," writing dependency_links to src/mase_tools.egg-info/dependency_links.txt\n"," writing requirements to src/mase_tools.egg-info/requires.txt\n"," writing top-level names to src/mase_tools.egg-info/top_level.txt\n"," writing manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," reading manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," adding license file 'LICENSE'\n"," writing manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.11/dist-packages/mase-tools.egg-link (link to src)\n"," Adding mase-tools 1.0.0 to easy-install.pth file\n","\n"," Installed /root/mase/src\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n","tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\n","gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.14.1 which is incompatible.\n","grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.2 which is incompatible.\n","tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed Mako-1.3.8 Pillow-10.4.0 accessible-pygments-0.0.5 ale-py-0.10.1 alembic-1.14.1 attr-dot-dict-0.1.0 bitarray-3.0.0 bitstring-4.3.0 black-24.10.0 cocotb-1.9.2 cocotb-bus-0.2.1 coloredlogs-15.0.1 colorlog-6.9.0 coverage-7.6.10 datasets-3.2.0 dill-0.3.8 emoji-2.14.1 evaluate-0.4.3 execnet-2.1.1 farama-notifications-0.0.4 find-libpython-0.4.0 fsspec-2024.9.0 ghp-import-2.1.0 gprof2dot-2024.6.6 gymnasium-1.0.0 humanfriendly-10.0 ipdb-0.13.13 jedi-0.19.2 jupyter-cache-1.0.1 kornia-0.8.0 kornia_rs-0.1.8 lightning-2.5.0.post0 lightning-utilities-0.11.9 mase-tools-1.0.0 multiprocess-0.70.16 mypy-extensions-1.0.0 myst-nb-1.1.2 myst_parser-4.0.0 nvidia-ml-py-12.570.86 onnx-1.17.0 onnxconverter-common-1.14.0 onnxruntime-1.20.1 optimum-1.23.3 optuna-4.2.0 pathspec-0.12.1 protobuf-3.20.2 pybind11-2.13.6 pydata-sphinx-theme-0.16.1 pynvml-12.0.0 pytest-cov-6.0.0 pytest-html-4.1.1 pytest-metadata-3.1.1 pytest-profiling-1.8.1 pytest-sugar-1.0.0 pytest-xdist-3.6.1 pytorch-lightning-2.5.0.post0 pytorch-nlp-0.5.0 requests-file-2.1.0 scipy-1.14.1 sphinx-book-theme-1.1.3 sphinx-data-viewer-0.1.5 sphinx-glpi-theme-0.6 sphinx-needs-4.2.0 sphinx-rtd-theme-3.0.2 sphinx-test-reports-1.1.0 sphinxcontrib-jquery-4.1 sphinxcontrib-plantuml-0.30 stable-baselines3-2.5.0 tensorboardx-2.6.2.2 torchmetrics-1.6.1 xxhash-3.5.0\n","Removed build tracker: '/tmp/pip-build-tracker-b5ctjdnb'\n","/root/mase/src\n"]}]},{"cell_type":"markdown","metadata":{"id":"CmHqI5mTrdxQ"},"source":["In this tutorial, we'll see how to import a model into Mase by generating a compute graph using the [MaseGraph](https://github.com/DeepWok/mase/blob/adls_2024/src/chop/ir/graph/mase_graph.py) API and how to start optimizing models using analysis and transform passes. First, we'll import a pretrained model directly from [HuggingFace Transformers](https://github.com/huggingface/transformers). For this example, we'll use Bert for sequence classification. You can read the [Bert paper](https://arxiv.org/abs/1810.04805) for information regarding the architecture.\n","\n","We get a warning saying that some weights were not initialized, since only the weights in the decoder are pretrained and included in the HuggingFace Hub. When we use the [AutoModelForSequenceClassification](https://huggingface.co/docs/transformers/model_doc/auto) API, a classification head is added at the end of the model, with randomly initialized weights."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iY8Z7-pBrdxR","executionInfo":{"status":"ok","timestamp":1737997731943,"user_tz":0,"elapsed":423,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"69092d0b-b962-4baf-efe0-363ddb38c2e8"},"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["BertForSequenceClassification(\n"," (bert): BertModel(\n"," (embeddings): BertEmbeddings(\n"," (word_embeddings): Embedding(30522, 128, padding_idx=0)\n"," (position_embeddings): Embedding(512, 128)\n"," (token_type_embeddings): Embedding(2, 128)\n"," (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (encoder): BertEncoder(\n"," (layer): ModuleList(\n"," (0-1): 2 x BertLayer(\n"," (attention): BertAttention(\n"," (self): BertSdpaSelfAttention(\n"," (query): Linear(in_features=128, out_features=128, bias=True)\n"," (key): Linear(in_features=128, out_features=128, bias=True)\n"," (value): Linear(in_features=128, out_features=128, bias=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (output): BertSelfOutput(\n"," (dense): Linear(in_features=128, out_features=128, bias=True)\n"," (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," (intermediate): BertIntermediate(\n"," (dense): Linear(in_features=128, out_features=512, bias=True)\n"," (intermediate_act_fn): GELUActivation()\n"," )\n"," (output): BertOutput(\n"," (dense): Linear(in_features=512, out_features=128, bias=True)\n"," (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," )\n"," (pooler): BertPooler(\n"," (dense): Linear(in_features=128, out_features=128, bias=True)\n"," (activation): Tanh()\n"," )\n"," )\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (classifier): Linear(in_features=128, out_features=2, bias=True)\n",")\n"]}],"source":["from transformers import AutoModelForSequenceClassification\n","\n","model = AutoModelForSequenceClassification.from_pretrained(\"prajjwal1/bert-tiny\")\n","\n","print(model)"]},{"cell_type":"markdown","metadata":{"id":"ukmzVMB6rdxS"},"source":["## Generate an FX graph for the model"]},{"cell_type":"markdown","metadata":{"id":"xbsOYldjrdxS"},"source":["To import a model into Mase, we need to generate a compute graph. In the Machine Learning community, there are several ways of capturing and representing a compute graph, such as [ONNX](https://onnx.ai/), [Torchscript](https://pytorch.org/docs/stable/jit.html), [MLIR](https://mlir.llvm.org/), [TVM](https://tvm.apache.org/), etc. Mase relies on [Torch FX](https://pytorch.org/docs/stable/fx.html), which has the following features and benefits:\n","\n","- **High-level IR**: unlike `LLVM` or `MLIR`, `FX` offers a high-level representation of the computation which enables fast optimizations.\n","\n","- **Pytorch native**: every operator in the FX graph correlates to a Python object or callable, meaning we can transform and optimize the graph, then simply regenerate the Python code required to run it. Unlike ONNX, there is no requirement for a dedicated runtime: all you need is Python.\n","\n","When you call `MaseGraph(model)`, the [MaseTracer](https://github.com/DeepWok/mase/blob/main/src/chop/ir/graph/mase_graph.py) class runs a forward pass of the model with `Proxy` objects instead of the regular `Tensor` objects. These Proxies record every operation performed on them, which is then used to generate the compute graph. The following cell generates the graph and generates a drawing of the result."]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jPJZsq6hrdxS","executionInfo":{"status":"ok","timestamp":1737997744969,"user_tz":0,"elapsed":10805,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"8eee5729-f513-4869-cb40-8a426cae5e63"},"outputs":[{"output_type":"stream","name":"stderr","text":["`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n"]}],"source":["from chop import MaseGraph\n","\n","mg = MaseGraph(model)\n","mg.draw(\"bert-base-uncased.svg\")"]},{"cell_type":"markdown","metadata":{"id":"rTyDvzN1rdxT"},"source":["There are 6 different types of nodes in an FX graph: `placeholder`, `get_attr`, `call_function`, `call_module`, `call_method`, `output`. Each node has several associated attributes, such as `name`, `args`/`kwargs` and `target`. These have different contents and meaning depending on the node type. We provide a summary below, but for more details, see the [FX documentation](https://pytorch.org/docs/stable/fx.html).\n","\n","- **placeholder**: represents a function input, which can be a `Tensor` or another Python object.\n","\n","- **get_attr**: retrieves a parameter from the Pytorch module hierarchy. `target` is the fully-qualified string name of the parameter’s position in the module hierarchy.\n","\n","- **call_function**: applies a free function to some values. `target` is a handle to the Python callable. `args` and `kwargs` represent the arguments to the function, following the Python calling convention.\n","\n","- **call_module**: applies a module in the module hierarchy’s `forward()` method with the given arguments. `target` is the fully-qualified string name of the module in the module hierarchy to call.\n","\n","- **call_method**: calls a method on a value. `target` is the string name of the method to apply to the self argument.\n","\n","- **output**: contains the output of the traced function in its args[0] attribute. This corresponds to the `return` statement in the Graph printout.\n","\n","You may be wondering the difference between `call_function`, `call_method` and `call_module` nodes: `call_function` nodes can have arbitrary Python callable as targets, while the target for `call_method` nodes must be a `Tensor` class method. `call_module` nodes refer to `torch.nn.Module` objects which must be included in the Pytorch module hierarchy. For example, the Pytorch ReLU activation function can be seen any of these node types:\n","\n"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"KNksgT-KrdxT","executionInfo":{"status":"ok","timestamp":1737997770603,"user_tz":0,"elapsed":227,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}}},"outputs":[],"source":["import torch\n","\n","random_tensor = torch.randn(2, 2)\n","\n","function_relu = torch.relu(random_tensor)\n","method_relu = random_tensor.relu()\n","module_relu = torch.nn.ReLU()(random_tensor)\n","\n","assert torch.equal(function_relu, method_relu)\n","assert torch.equal(function_relu, module_relu)"]},{"cell_type":"markdown","metadata":{"id":"B438Hq7grdxU"},"source":["Open the generated SVG file (you may find this [VSCode extension](https://marketplace.visualstudio.com/items?itemName=SimonSiefke.svg-preview) useful) and inspect each node. If you can't generate the image, we show below a segment of the graph that corresponds to the first attention layer of the Bert encoder. If you also inspect the [Bert implementation](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) in the HuggingFace repository, you can see how each node in the generated graph corresponds to lines in the Python code. For example, the `bert_encoder_layer_0_attention_self_` nodes correspond to the calls to the Query/Key/Value linear layers defined in the `BertSelfAttention` class. You can also see how not every piece of code has an associated node in the graph - when the code is being symbolically traced, parts of the code that aren't executed (for example, if statements which never yield `True`) don't interact with the `Proxy` objects, hence they're not included in the graph.\n","\n","\"drawing\""]},{"cell_type":"markdown","metadata":{"id":"HmEVbad5rdxU"},"source":["## Understanding the Mase IR"]},{"cell_type":"markdown","metadata":{"id":"tlSw3U3rrdxU"},"source":["As previously mentioned, the Mase IR is built on top of Torch FX. However, the FX operator associated with each node in the graph refers broadly to Python semantics, such as how to execute code generation for a transformed graph. For example, when the FX code generator encounters a `call_function` node, it would know to generate code equivalent to `node.target(*node.args, **node.kwargs)`, while for `call_method` nodes, the code would correspond to `getattr(node.args[0], node.target)(*args[1:], **kwargs)`. However, beyond code generation, the FX IR has no information regarding the workload being executed by the graph - that's where the Mase IR comes in.\n","\n","As described in previous publications, the major benefit of the Mase IR is in offering a common abstraction layer for both hardware and software workloads (see [here](https://arxiv.org/abs/2307.15517), [here](https://openreview.net/forum?id=Z7v6mxNVdU)). You can find a list of Mase operators under the [IR definition file](https://github.com/DeepWok/mase/blob/main/src/chop/ir/common.py). You can see that most operators correspond strongly with either Pytorch or ONNX operators. Each operator is also associated with a node type, which can be one of the following.\n","\n","- ``module_related_func``: includes functions under ``torch.nn.functional`` and the ``torch.nn.Module`` that wraps them. For example, ``torch.nn.functional.relu`` and ``torch.nn.ReLU`` both fall under this category.\n","\n","- ``module``: a MASE module is a subclass of ``torch.nn.Module`` that does not have corresponding ``torch.nn.functional`` counterpart. For example, ``torch.nn.BatchNorm2D`` is a MASE module because ``torch.nn.functional.batch_norm_2d`` does not exist.\n","\n","- ``builtin_func``: MASE builtin_func includes functions under ``torch`` that are not ``torch.nn.functional`` and ``torch.nn.Module``, such as ``torch.cat`` and ``torch.bmm``.\n","\n","The following types are also present, which have the same meaning as in Torch FX.\n","\n","- ``placeholder``: input node of a MASEGraph.\n","\n","- ``get_attr``: represents the attribute of a MASE module.\n","\n","- ``output``: equivalent to the return statement in the forward function."]},{"cell_type":"markdown","metadata":{"id":"51VcIQkyrdxU"},"source":["## Understanding the Pass System"]},{"cell_type":"markdown","metadata":{"id":"c5HM8hqvrdxV"},"source":["If you have worked with compilers, you might be familiar with the concept of a pass, which is a function that iterates over each node in the graph to perform some task. In Mase, there are two categories of passes: analysis and transform passes.\n","\n","- **Analysis passes**: extract some information about each node, annotate nodes with relevant data, and generate payloads to be used by subsequent passes.\n","- **Transform passes**: change the topology of the graph by inserting, removing or replacing nodes.\n","\n","All passes, whether analysis or transform, have the following structure. Every pass accepts a dictionary `pass_args` containing required arguments, and outputs a tuple of the output graph (which can be annotated or transformed) and a `pass_outputs` dictionary. A pass doesn't need to use any arguments or generate any outputs (other than the output graph), however the argument and return signatures must follow this standard such that passes can be chained together.\n","\n","```python\n","\n","def dummy_pass(mg, pass_args={}):\n"," \n"," # ... do some setup\n"," pass_outputs = {}\n","\n"," for node in mg.fx_graph.nodes:\n"," # ... do stuff\n","\n"," return mg, pass_outputs\n","\n","```\n","\n","Next, we'll show how to run some analysis passes required to raise the generated FX graph to the Mase IR. Then, we'll come back to see how to write some simple analysis passes to do useful things."]},{"cell_type":"markdown","metadata":{"id":"IYJpCOIWrdxV"},"source":["## Raising the FX graph to the Mase IR"]},{"cell_type":"markdown","metadata":{"id":"t_CKLFTrrdxV"},"source":["To convert the simple FX graph we generated into the Mase IR, we must run the following analysis passes, which annotate each node with relevant metadata. Note that metadata follows under three categories: `common`, `hardware` and `software`. Hardware metadata is used for generating FPGA accelerators in the [emit Verilog toolflow](https://github.com/DeepWok/mase/tree/adls_2024/src/chop/passes/graph/transforms/verilog) (see Lab 4), while software metadata is used by passes such as [autosharding](https://github.com/DeepWok/mase/tree/main/src/chop/passes/graph/analysis/autosharding), which automatically finds a model parallelism configuration in a GPU cluster. Common metadata is generally required by all workflows in Mase.\n","\n","- **init_metadata_analysis_pass**: initializes a `MaseMetadata` object for each node in the graph, which behaves like a dictionary and is stored under `node.meta[\"mase\"]`. Each metadata instance has the following structure, which is empty at initialization. See [here](https://deepwok.github.io/mase/modules/api/analysis/init_metadata.html) for details on the implementation.\n","\n","```python\n"," node.meta[\"mase\"] = {\n"," \"common\": {},\n"," \"hardware\": {},\n"," \"software\": {},\n"," }\n","```\n","\n","- **add_common_metadata_analysis_pass**: populates the `node.meta[\"mase\"][\"common\"]` dictionary by executing the following two steps. See [here](https://deepwok.github.io/mase/modules/api/analysis/add_metadata.html) for details on the implementation.\n"," - **Operator inference**: determine the operator associated with each node in the graph from its fx operator and target, and annotate under `node.meta[\"mase\"][\"common\"][\"mase_op\"]`\n"," - **Shape Propagation**: similarly to the [Interpreter Pattern](https://pytorch.org/docs/stable/fx.html#the-interpreter-pattern) in the FX documentation, this involves running a forward pass of the entire model with a provided dummy input, and observing the Tensor metadata (shape, data type, stride, etc) of each argument and result for every node in the graph. This is then annotated under `node.meta[\"mase\"][\"common\"][\"args\"]` and `node.meta[\"mase\"][\"common\"][\"results\"]`.\n","\n","The `add_common_metadata_analysis_pass` requires a dummy Tensor input to run the shape propagation step. In the following cell, we show how this can be done using the HuggingFace tokenizer, to which we pass two truthful statements."]},{"cell_type":"code","execution_count":11,"metadata":{"id":"eKhMITFwrdxV","executionInfo":{"status":"ok","timestamp":1737997786241,"user_tz":0,"elapsed":1050,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["b8ec0a7066f84e93aeb3eb985cb5e7bb","a49d67e90a4d4bd29b6115856a0a7d64","1a20a51448c940cab7f8a59e54c4acfb","b79d443ee74e44dba2280f529fd32bda","43577dcebb3d4499a02f9e9b0775efeb","ce3fec3d1bf440de8ea71af03dda15e3","619cf7fe27c646958d3dda2b54968832","f41413bdc6ef4b26bfa355bc7474ff59","f1bdfc37cc844b728e76f6849b309c59","0e74422e0dff42ee8044a7bb4b3596e2","726869e25ce547c19281972be7e991f2","e0c341e9011944aeb90f7b31d2b04392","87272bdce72e4c6aa5f779d7aabcb29d","281a69d78d5d44d28b58f00b8e4919c1","658106823d2e41cfae192d16cadee7a6","4a5f89dd03414a7896af509d67714435","1608d3448fc848e8a7eb7b32105002b4","c3e99dc791d04f5b8d6dc616a62d90d6","747aa89eafd842e3bffa0fbdaec48483","10f06682ed8140c78cbded1aee315ec7","b8a43217cb2c4e2093b8c823db6fa997","ee30a304fbe7433580b3ae3c71121e64","c6216acd6d474bd2a9878e3a7698456e","ed950caa08e244c5b5b6a8d49b3ed056","350591fe804c42a8b831ad35e9577f90","4b82ebe04cae4ccd9d9f9b9366d9eb22","ef71b550573a4380b5ce7880f47b72b0","ef2c6259c31b45438681f16f7f860867","d468f6e3acdf46c683c5ea44d3973e33","3075a054af6a407c8b6d6545364a521d","43d9c39499374986a2607afda26c8f3d","5f956fde69b545eab2abb2318d8092cf","bb3bf9d0774a4bb5beee090f120eadd6","2d71d8ccb6f44b9ea054604a6eab5ac4","66e15b7d4aab43d58b00876e0f4b7065","ad7ce9b150a94c6699b372d532e1cbe9","fe373efbfc59457599363e8826b0db6c","45e7ad1cd71547c5a968cb0bafcc47d3","0e9ab5bf3a4a410984b0dd665253d4e4","db9c90c945c6488899a5360b3d55d4b5","a4bf91d34b3b45afae90fb221eb818c0","f9c3df65343f431cb9af9b9c54b934f5","ee728ab9ff824641a2597e36a79cab6c","2f2c2b6f7ac6409b97566fe257a8d508"]},"outputId":"a93cf21a-cb87-489f-d588-d35f23b77668"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 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..., -1.8623e+00,\n"," -1.1230e+00, 3.5013e-01],\n"," [ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., 6.4975e-01,\n"," -7.3862e-01, -2.1510e+00],\n"," ...,\n"," [ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00],\n"," [ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00],\n"," [ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]], grad_fn=)\n","tensor([[[[ 3.8307e-01, 1.8836e-02, -1.0314e+00, ..., -8.0955e-01,\n"," -2.8557e-01, -2.3318e-01],\n"," [-9.9661e-02, 3.1722e-01, -3.0517e-01, ..., 6.4335e-01,\n"," -3.1830e-01, -1.7296e+00]],\n","\n"," [[ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., -7.9549e-01,\n"," -6.9279e-01, -1.8082e-01],\n"," [-9.9201e-01, 9.4938e-01, 4.4198e-02, ..., 1.5973e+00,\n"," 1.1907e+00, -9.0454e-01]],\n","\n"," [[-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -1.8905e+00,\n"," 1.6226e-01, -1.2953e+00],\n"," [-7.0312e-01, -6.4926e-01, -5.0913e-01, ..., -5.3627e-01,\n"," -1.0456e+00, -1.6301e+00]],\n","\n"," ...,\n","\n"," [[-1.7466e-01, 3.0707e-02, 7.5225e-01, ..., -1.9281e+00,\n"," 1.1489e+00, -2.4530e-01],\n"," [-7.6226e-02, 8.5814e-01, -1.5467e+00, ..., -1.3217e+00,\n"," -1.3415e+00, -3.8328e-01]],\n","\n"," [[ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -2.4168e+00,\n"," 3.3385e-01, -6.2115e-02],\n"," [-1.6390e+00, -1.6085e-01, -1.9118e+00, ..., -1.6600e-01,\n"," -6.5011e-01, 2.1798e-02]],\n","\n"," [[-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., -1.0481e+00,\n"," -2.7634e+00, 2.2741e-01],\n"," [-1.4603e+00, 3.1239e-02, 3.8892e-01, ..., 1.3441e-03,\n"," -1.6754e+00, 3.1771e-01]]],\n","\n","\n"," [[[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., -7.8511e-01,\n"," -3.2510e-01, -2.1300e-01],\n"," [-3.9893e-01, 6.1469e-01, -3.9206e-01, ..., 6.4928e-01,\n"," -3.2944e-01, -2.4185e+00]],\n","\n"," [[ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.6488e+00,\n"," -8.6854e-01, -8.0783e-01],\n"," [-2.1516e+00, -2.4247e-01, -8.1713e-01, ..., -1.8623e+00,\n"," -1.1230e+00, 3.5013e-01]],\n","\n"," [[ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., -1.3121e+00,\n"," -6.7044e-01, -1.1324e+00],\n"," [ 4.3930e-01, 3.4082e-01, -1.2243e+00, ..., 6.4975e-01,\n"," -7.3862e-01, -2.1510e+00]],\n","\n"," ...,\n","\n"," [[ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 8.5980e-01,\n"," 3.2796e-01, -1.9442e+00],\n"," [-8.9502e-01, 9.7357e-01, 8.5447e-01, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00]],\n","\n"," [[ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., -1.5965e+00,\n"," -3.9380e-01, -4.3585e-01],\n"," [-2.2103e+00, 4.4127e-01, 1.1554e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00]],\n","\n"," [[ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4396e-02, -6.9033e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3792e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5677e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01],\n"," ...,\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01],\n"," [-1.0678e+00, 9.0884e-02, -4.5400e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6669e-03, -5.7118e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [ 4.5120e-01, -5.8034e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3362e-01, 1.5919e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1642e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6174e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3792e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5677e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4396e-02, -6.9033e-02],\n"," [-1.0678e+00, 9.0884e-02, -4.5400e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6669e-03, -5.7118e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8034e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3362e-01, 1.5919e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1642e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6174e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n"]}],"source":["import torch\n","import chop.passes as passes # type: ignore\n","from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n","\n","dummy_input = tokenizer(\n"," [\n"," \"AI may take over the world one day\",\n"," \"This is why you should learn ADLS\",\n"," ],\n"," return_tensors=\"pt\",\n",")\n","\n","mg, _ = passes.init_metadata_analysis_pass(mg)\n","mg, _ = passes.add_common_metadata_analysis_pass(\n"," mg,\n"," pass_args={\n"," \"dummy_in\": dummy_input,\n"," \"add_value\": False,\n"," },\n",")"]},{"cell_type":"markdown","metadata":{"id":"lwrKcjJardxV"},"source":["## Example: writing an analysis pass"]},{"cell_type":"markdown","metadata":{"id":"o7bKNgyBrdxV"},"source":["Writing an analysis pass is often simple - in the following example, we implement a pass which counts the number of dropout layers in the graph. We also show how to use the `get_logger` API from `chop.tools` to provide information about the graph to the user at runtime."]},{"cell_type":"code","execution_count":12,"metadata":{"id":"IeL316FUrdxV","executionInfo":{"status":"ok","timestamp":1737997789703,"user_tz":0,"elapsed":209,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"20596dfb-ff48-442f-ee2c-d8ff50ab684a"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.embeddings.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.0.attention.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.0.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.1.attention.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.1.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mDropout count is: 6\u001b[0m\n"]}],"source":["from chop.tools import get_logger # type: ignore\n","\n","logger = get_logger(\"mase_logger\")\n","logger.setLevel(\"INFO\")\n","\n","\n","def count_dropout_analysis_pass(mg, pass_args={}):\n","\n"," dropout_modules = 0\n"," dropout_functions = 0\n","\n"," for node in mg.fx_graph.nodes:\n"," if node.op == \"call_module\" and \"dropout\" in node.target:\n"," logger.info(f\"Found dropout module: {node.target}\")\n"," dropout_modules += 1\n"," else:\n"," logger.debug(f\"Skipping node: {node.target}\")\n","\n"," return mg, {\"dropout_count\": dropout_modules + dropout_functions}\n","\n","\n","mg, pass_out = count_dropout_analysis_pass(mg)\n","\n","logger.info(f\"Dropout count is: {pass_out['dropout_count']}\")"]},{"cell_type":"markdown","metadata":{"id":"oOlMbR0rrdxW"},"source":["## Example: writing a transform pass\n"]},{"cell_type":"markdown","metadata":{"id":"UVxXLNl8rdxW"},"source":["In this example, we delete all dropout nodes from the graph. Dropout is a useful training technique, but it doesn't have any effect on the activations at inference time, hence these nodes can be removed to simplify the graph. Transform passes may involve deleting, inserting, or replacing nodes in the graph. When doing this, we must carefully handle the arguments to ensure the graph topology is valid after transformation. Before erasing the dropout nodes, we must first find all other nodes that take the output of the dropout node as arguments, by running `node.replace_all_uses_with`. Without doing this, there would still be nodes that require arguments that no longer exist.\n","\n","> **Task**: Delete the call to `replace_all_uses_with` to verify that FX will report a RuntimeError.\n","\n","Finally, we rerun the analysis pass previously implemented to recount the number of dropout modules, and verify this is now zero."]},{"cell_type":"code","execution_count":13,"metadata":{"id":"OEKB6i5drdxW","executionInfo":{"status":"ok","timestamp":1737997792644,"user_tz":0,"elapsed":240,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c637ac6c-99d3-4ded-c15a-a8373c5e5ceb"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.embeddings.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.0.attention.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.0.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.1.attention.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.1.output.dropout\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: dropout\u001b[0m\n"]}],"source":["import torch.fx as fx\n","\n","\n","def remove_dropout_transform_pass(mg, pass_args={}):\n","\n"," for node in mg.fx_graph.nodes:\n"," if node.op == \"call_module\" and \"dropout\" in node.target:\n"," logger.info(f\"Removing dropout module: {node.target}\")\n","\n"," # Replace all users of the dropout node with its parent node\n"," parent_node = node.args[0]\n"," logger.debug(f\"This dropout module has parent node: {parent_node}\")\n"," node.replace_all_uses_with(parent_node)\n","\n"," # Erase the dropout node\n"," mg.fx_graph.erase_node(node)\n"," else:\n"," logger.debug(f\"Skipping node: {node.target}\")\n","\n"," return mg, {}\n","\n","\n","mg, _ = remove_dropout_transform_pass(mg)\n","mg, pass_out = count_dropout_analysis_pass(mg)\n","\n","assert pass_out[\"dropout_count\"] == 0"]},{"cell_type":"markdown","metadata":{"id":"9L0jbBbrrdxW"},"source":["## Exporting the MaseGraph"]},{"cell_type":"markdown","metadata":{"id":"iA0TdKv7rdxW"},"source":["You can export the transformed MaseGraph to be shared and used in future tutorials, by running the `mg.export()` command."]},{"cell_type":"code","execution_count":14,"metadata":{"id":"LMuVuQ-nrdxW","executionInfo":{"status":"ok","timestamp":1737997797888,"user_tz":0,"elapsed":213,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"db8cb5d9-72d6-4d83-879f-94adcd2449b5"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /root/tutorial_1.pt, /root/tutorial_1.mz\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /root/tutorial_1.pt\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /root/tutorial_1.mz\u001b[0m\n"]}],"source":["from pathlib import Path\n","\n","mg.export(f\"{Path.home()}/tutorial_1\")"]},{"cell_type":"markdown","metadata":{"id":"iIY_FkMerdxW"},"source":["After exporting, you can pick up where you left off by running the `MaseGraph.from_checkpoint` constructor."]},{"cell_type":"code","execution_count":15,"metadata":{"id":"aiveDbm3rdxW","executionInfo":{"status":"ok","timestamp":1737997800523,"user_tz":0,"elapsed":182,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"50700d8d-7688-409e-cb3f-917a53b36794"},"outputs":[{"output_type":"stream","name":"stderr","text":["/root/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n"]}],"source":["new_mg = 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/ea/34/5a4f7a48da3be173273cd9b866c998eb59e234da2ee4a30c1068e85c0e99/coverage-6.4.2.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 6.4.2\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/55/23/ec5b740dfe79e9a5972873997d85e3d0b1d08b6d17fe69034a04916ec9d0/coverage-6.4.3-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b9/1f/5704ce67cb66bc22e78e0e071ca1f8d136bb0d5abfc76fecaa24264323fd/coverage-6.4.3-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/89/e17f519b13d1c62a154485efdf547ca98d4179a4c3d97c5dbae7fb1c5fb5/coverage-6.4.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/84/461934c8d3fe5f864ad35402de9fab652a6f6531752fbcb4101ed3985bac/coverage-6.4.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/37/bd2cf47fe1de3b6b0fcbc6c9bbce5266a0d71c02c13244a08881a35cbdce/coverage-6.4.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/be/f8148ab0a34f180851a1751ed72c935a8f32d922037f34c3aa406fd70dc9/coverage-6.4.3-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e1/32/e4c1970794ff62fdf94182a68ebe9e8a96f4720659c0687122ca57087624/coverage-6.4.3-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a2/2d/f392e2d59f7ab0d0c6d6a6ced8bc63440c5e2e1b425545df95064fdb53d6/coverage-6.4.3-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/46/b3/f31486b5e098c2a03f09b8033c3bd9df2ea63d7f6861dc55228cc4851321/coverage-6.4.3-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/0d/97f88053fa47b5736a5c80ce32757ab0b37d0b541fff5bf15bfda010b0f2/coverage-6.4.3-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/99/6f461039a3d1754604084a535965ad4e6ae6bbad4b191deaac22b5c02cf1/coverage-6.4.3-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/ff/bd5f5401d25b53dcf6e4da2e46cfb0e139bf3ddbf3468179af0319774e83/coverage-6.4.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4d/d4/d4b511312729be53ada212792315f179d721b317053e7d7fa8644c3df005/coverage-6.4.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/a5/f90ebe1d1f30b19fe185c784318e380e34cf6cbb11775f09399efd7bdfda/coverage-6.4.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/32/c5/5e5833660ae06ecd7713ede0586329a827ed00d8b023513b6cee83713208/coverage-6.4.3-cp37-cp37m-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/43/0e/47365f030698de2da986e4bdfba404a3613cba1db123d55fd3e923be0c00/coverage-6.4.3-cp37-cp37m-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/b5/f82e90416eed8bbb705f4ba3a1c5b1c1773060093e545af49e112e110518/coverage-6.4.3-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/5a/7df57ee2e70584a10a1355793fe2081a74b19753ecf5cdc0fdcf7a11dfb3/coverage-6.4.3-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e3/82/82612958a937aaef699d8e7846a8f4302d3f63008990c83c6f62ce29632e/coverage-6.4.3-cp37-cp37m-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/52/7056ae02fd4955ba1abe83ac72d33511d5fd2e41c0a906695aa558da6e7b/coverage-6.4.3-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7c/2a/de52bb9187885bbf90fd89462d81ab28f9f40d090623491b80a8528a6910/coverage-6.4.3-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/57/fba4c25b70bdb40c992733d48d3740800dca1b927ec6a2b05d9fc992078f/coverage-6.4.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/b1/4120b124e57d7da46a180b2065ca0061efa76ea83b1e8cd07989c8f7c401/coverage-6.4.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/da/4f32b46ee7dc189aef756bcbf60c3ecdf18128b5c9bccfbde4a10fd38f55/coverage-6.4.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7a/26/df8dfdc984e2b528cd93328ac48ee076b595da6c56a24c2fcab152238f07/coverage-6.4.3-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/d6/448634e5818fda257cf64dfb004528e042138625c600b895a3c4e1f49eb5/coverage-6.4.3-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/9b/b80e3fc42f6942077551dbc593227c06354b5db446760e67ec6b61aa503d/coverage-6.4.3-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/02/98/9fb7c1c92f4436117f345bcbbaaaf37af6730b991383b97353110dcdf1f8/coverage-6.4.3-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/50/ab9f27b02307d11ecb184efedd9e649459d6e17dd86bcf1025c52d5eb095/coverage-6.4.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5a/1a/d1df308a7d7c8077664a8e70e1a2cbca7d54497aa38f4d35b6178e6134fc/coverage-6.4.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a5/6a/5a201c1b85c5c406c5ee5be4d17c223ad71a5f77937fe9a680b02e6a1fb3/coverage-6.4.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/25/b2/26c5bf1497eaf14bbd9d9b93bcb4a947a58c2b57233799b2700f415ab7c9/coverage-6.4.3-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/1a/ea125bdde30f3d5089eda690de51ee07f1f8851c2e9b50c0ee10e3f1acda/coverage-6.4.3-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/da/ab/64dbfdae362c6e2696c07d3ec19d4c00b10ca85a5430d7669e24fd58a6f4/coverage-6.4.3-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/af/cb876acef3823eb5af58ab5b9cc271a7054f5111d6ea669f0b6193b19707/coverage-6.4.3-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e2/db/2dcb6b3f1b91bc682e11f0231a5f7093ec2ddc847ad34ce87bab85eb3858/coverage-6.4.3-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp36-none-any, pp37-none-any, pp38-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/df/83/194a9e576faebbf371338a461af5f5464cf7ec3c80ed616cd7f9f23a5040/coverage-6.4.3-pp36.pp37.pp38-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/72/bf/3896381becfd8ee66be4d4b65fbf354d91a51a0306b8bc8cddd91a3ae679/coverage-6.4.3.tar.gz (from https://pypi.org/simple/coverage/) 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https://files.pythonhosted.org/packages/9a/ef/e122a8292d1f1c74fc544a8c577287bbc4601fb933748751994e347eb51c/coverage-6.4.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/66/0d/8f215abaf11f9a9aa8de5bba713f5c6c225cdd9b8bebe5f0174ff69e7b73/coverage-6.4.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e9/01/2633d1b187b316796bc115301a4d164eada99a23a6060b36b95549db283b/coverage-6.4.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/70/c3c2f1398334786028a25215eaf3311149bd0763d3e742809a43410153f4/coverage-6.4.4-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6a/9a/013fbcffcb0d41d37a3d11e7117c07ea641a36808781e2886fc47a63f166/coverage-6.4.4-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2a/20/0adc2c879dcd66b7ab7200525f5e777f17f4dc5b62fa2c37cfb00edfe906/coverage-6.4.4-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3f/df/f17eca5cc2784f5918ba592090778cd5c1cd5afb0bfb3bc768fc4f4a9429/coverage-6.4.4-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/6f/21d6a8f335e7d6e04cbad9af28df49efcfa7ba1d10a6f8065a61ab474210/coverage-6.4.4-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/d1/f795dd86aec5c7d2d50df62e5878df0a4339d28ff2db59c6bf5915124d7a/coverage-6.4.4-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/13/a14a2d54bff51896f0ce15341e17afea444616d6dc785bb3f8cc4725ca78/coverage-6.4.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ff/3b/b3f7aabd4045d9f8bda678fdc7d9b41055ac134756aa2c02b1e8f417ca30/coverage-6.4.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/a4/c4/1984348570df42a746d94b7378952d025ded04d63cc9d81433538f5c1670/coverage-6.4.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 6.4.4\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/21/c55f5529e9bbcf3c029790ca3cea3b18bae3dd897f7461b5f2165053ee35/coverage-6.4.4-cp311-cp311-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/b7/d047c9817aa3ac7244fa12d47e4ab19521976fc3ee7e62be63c87f3a0eb2/coverage-6.4.4-cp311-cp311-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/71/7d6d62e7fe7f41300693cf170980f4ba044dadf2448c6e42ad4493785df9/coverage-6.4.4-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/d3/ccdc9245b7e1e26c5b0fb5cbd23f6875a0f13fd5234397078b3600778f3e/coverage-6.4.4-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f9/55/071ebac3ba137ca63a001673e4d2b71bd4ddea2c1f963b7bbeb517cc7067/coverage-6.4.4-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/dd/81d47bfc7d8ac747530fec9f8e6d905722c22d446164dc7b82dc84a6dd60/coverage-6.4.4-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/f7/677f4b843767eda94c5f6e91b5a65a81117e1c234845db2ff080eed1eb63/coverage-6.4.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6d/de/8cac8a7a0b267ca624e26a42f76b91e64a6e552d8a385650f507e2bb735a/coverage-6.4.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/5b/55a40be355a47dcccbe584f3dd0ab876638b9f0dafdf489f931ccfc0aef5/coverage-6.4.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/d3/78e1c001e260223ea18a013d33aaa62262b3de3b673045dea2711bcb91ee/coverage-6.4.4-cp37-cp37m-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e2/e8/c803da1ca88ca49ebdb926b0e07c4b4c413e670320e8656acfa99df4a78b/coverage-6.4.4-cp37-cp37m-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/80/ec/7c93af6468c697cb118a3113a72e0a30143227c1a2cf5111ca958dd429db/coverage-6.4.4-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/98/34/246d4f6d9de9b376dc1291a3af638d228336b58687ec8781b2d2449a3c21/coverage-6.4.4-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/32/91c1969685b6c9ec1f162d9ad2ccd138392e1f004721e1ad075c6b31ed69/coverage-6.4.4-cp37-cp37m-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6d/fd/5714bf6cc7023ad0e8cd0b157e9f7a44582911e67f6972df4c97d94dea32/coverage-6.4.4-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/20/3dd5643cee8ff50233b65e793f9354e7683e7ec3559776f155084c5a2f50/coverage-6.4.4-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/69/5f/65fd3b42b37b87f8f7b01f0e6c2c127a9e5f6d45d02d21a3832e426d6b37/coverage-6.4.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/1a/8b2f6aabf828e9795855001848ce72370819fffca883714eb25aa6d00b38/coverage-6.4.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/0e/e60fbc65bc054d904984ccd2a52c122fd291c682b5713b37dbb978f853bb/coverage-6.4.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/e7/dbbfe7288d846b372229794d7c5f48d55e519d30e1419febea7b1e71478c/coverage-6.4.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/3d/d79b2b927e7cb8dded2a003d22348b918509fe1238ff796a0b2fde9b3718/coverage-6.4.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1c/59/1d3dae9d3821b81dc17b06438c6918060388457272af20856d42a8bb30b7/coverage-6.4.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/99/e69b196431c25d7fa845bdd8265a0f111d3e036c4b1cac7a98cb4e5a07b9/coverage-6.4.4-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/08/e4/9a7556b46a646520bda96f53c3fe691c9c5f7ebd0b64fc6aea8e829b75e7/coverage-6.4.4-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f9/f5/d3bea1146b7ee22323b667abc029e3be962a162e036e9d30459e6d554a8a/coverage-6.4.4-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/24/b1/9c3189a189f0ccfd57d0cbbc822852eee2c9bf678531a9433c6c249939d1/coverage-6.4.4-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a4/13/bf1041d937fab2c714eff6a6a16e4e964858ff2e8ea9e8e7acacb3d315e8/coverage-6.4.4-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp36-none-any, pp37-none-any, pp38-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5b/97/c129634655c5b099092debd466ca50e97c189f0e5639490e9d8b141942f6/coverage-6.4.4-pp36.pp37.pp38-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/79/f3/8c1af7233f874b5df281397e2b96bedf58dc440bd8c6fdbf93a4436c517a/coverage-6.4.4.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 6.4.4\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c4/8d/5ec7d08f4601d2d792563fe31db5e9322c306848fec1e65ec8885927f739/coverage-6.5.0-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/89/a2/cbf599e50bb4be416e0408c4cf523c354c51d7da39935461a9687e039481/coverage-6.5.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/b0/3639d84ee8a900da0cf6450ab46e22517e4688b6cec0ba8ab6f8166103a2/coverage-6.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/f3/c6025ba30f2ce21d20d5332c3819880fe8afdfc008c2e2f9c075c7b67543/coverage-6.5.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/7d/d5211ea782b193ab8064b06dc0cc042cf1a4ca9c93a530071459172c550f/coverage-6.5.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/10/9e/68e384940179713640743a010ac7f7c813d1087c8730a9c0bdfa73bdffd7/coverage-6.5.0-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2f/8b/ca3fe3cfbd66d63181f6e6a06b8b494bb327ba8222d2fa628b392b9ad08a/coverage-6.5.0-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c0/18/2a0a9b3c29376ce04ceb7ca2948559dad76409a2c9b3f664756581101e16/coverage-6.5.0-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/11/9e/7afba355bdabc550b3b2669e3432e71aec87d79400372d7686c09aab0acf/coverage-6.5.0-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ae/a3/f45cb5d32de0751863945d22083c15eb8854bb53681b2e792f2066c629b9/coverage-6.5.0-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/cf/455930004231fa87efe8be06d13512f34e070ddfee8b8bf5a050cdc47ab3/coverage-6.5.0-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/f3/5cbd79cf4cd059c80b59104aca33b8d05af4ad5bf5b1547645ecee716378/coverage-6.5.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/89/58/5ec19b43a6511288511f64fc4763d95af8403f5926e7e4556e6b29b03a26/coverage-6.5.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4b/66/6e588f5dfc93ccedd06d6785c8143f17bb92b89247d50128d8789e9588d0/coverage-6.5.0-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ff/27/339089b558672f04e62d0cd2d49b9280270bad3bc95de24e7eb03deb4638/coverage-6.5.0-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e6/24/7fe8ededb4060dd8c3f1d86cb624fcb3452f66fbef5051ed7fab126c5c0c/coverage-6.5.0-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/a6/af54588e2091693026df94b09106ee10dcbcdc8c9b2c3989149e6e44a324/coverage-6.5.0-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/85/03/9dcc8b7e269cfeaf5519d433d841a7d78f283c5fb016385d4690e1aedfc1/coverage-6.5.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6b/ba/ef67c1e859b8ddd8cafb81199986ff702efcd4ee5d373670a0bc0a293d1f/coverage-6.5.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/7f/13f5d58f5ca41182d7020af5257c8fd08ddf33921d2a28cf66753571c278/coverage-6.5.0-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/32/40/e2b1ffa42028365e3465d1340e7d390d096fc992dec2c80e4afed6361e83/coverage-6.5.0-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/3b/cd68cb278c4966df00158811ec1e357b9a7d132790c240fc65da57e10013/coverage-6.5.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/02/7a/a45f3958442d50b9a930a62f0dba9ee502521213ebd016203c2890ea212f/coverage-6.5.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bd/a0/e263b115808226fdb2658f1887808c06ac3f1b579ef5dda02309e0d54459/coverage-6.5.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a3/a0/4c59586df0511b18f7b59593672a4baadacef8f393024052d59c6222477c/coverage-6.5.0-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/6b/7efeeffc7559150a705931b2144b936042c561e63ef248f0e0d9f4523d74/coverage-6.5.0-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/fb/11982f5faf2990d4d9159e01a12bbf0a7d7873893d4d2e2acec012ad69ae/coverage-6.5.0-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e9/f0/3be949bd129237553714149b1909d34c94137ca4b86e036bc7060431da18/coverage-6.5.0-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/06/f1/5177428c35f331f118e964f727f79e3a3073a10271a644c8361d3cea8bfd/coverage-6.5.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/52/c08080405329326a7ff16c0dfdb4feefaa8edd7446413df67386fe1bbfe0/coverage-6.5.0-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/63/e9/f23e8664ec4032d7802a1cf920853196bcbdce7b56408e3efe1b2da08f3c/coverage-6.5.0-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/18/95/27f80dcd8273171b781a19d109aeaed7f13d78ef6d1e2f7134a5826fd1b4/coverage-6.5.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/0f/012a7370aaf61123a222b34b657dedc63e03ce2af8d064ac5c5afe14f29c/coverage-6.5.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6b/f2/919f0fdc93d3991ca074894402074d847be8ac1e1d78e7e9e1c371b69a6f/coverage-6.5.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/d9/b367c52cb1297414ba967e38fe9b5338ee4700a2d1592fc78532dc9f882f/coverage-6.5.0-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/58/2c/213861cec1d9f6451d29c0b1838769b558f6a8c6844b001f6e98c37c4b1b/coverage-6.5.0-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/e8/e712b61abf1282ce3ac9826473ab4b245a4319303cce2e4115a8de1435f2/coverage-6.5.0-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/17/e1d54e0e5a1e82dea1b1d9463dfe347ded58037beda00d326f943a9ef2d4/coverage-6.5.0-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/08/a88a9f3a11bb2d97c7a6719535a984b009728433838fbc65766488867c80/coverage-6.5.0-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp36-none-any, pp37-none-any, pp38-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6e/e6/b31a4b2aa9489da59b35ee0ea4259d6fe9b321a1eaa6492f19342d03d53b/coverage-6.5.0-pp36.pp37.pp38-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/5c/66/38d1870cb7cf62da49add1d6803fdbcdef632b2808b5c80bcac35b7634d8/coverage-6.5.0.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 6.5.0\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8e/89/b008167da073505b48022cd7f31422e000790ca149aba516db8785b958ed/coverage-6.6.0b1-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b7/3f/37130a2ecd87a4ebcffba3feca18c869085ea3aef0c1c2dd9abad970d6fe/coverage-6.6.0b1-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4e/b0/209b46ce40cf51d9f975bd3f6aec95aa8f19010befe5533b07bf5f5b8d14/coverage-6.6.0b1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/c6/b791b6ccae376850722677e5860a3b052636ec62577b58070390dabbe00d/coverage-6.6.0b1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fa/6c/c23b975041e7364327f99e3ce13ceb713a225c23292c5750112a9a3dbd03/coverage-6.6.0b1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/8d/71587f5b48b65f61f7c1bbadf401531bdb15fb37aa14a61cdf167c03d43e/coverage-6.6.0b1-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/88/9019610e165158386dcccf13547bcf34ffdc4e41de64d7895bbd0785840d/coverage-6.6.0b1-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/d0/4f34cad9ed299e088220f1ab8eeb58633596f1e72d02416a0f14f869a9cd/coverage-6.6.0b1-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/e6/c8251d87788a0e90efd42248952b5512b5b62f0a49aaa89f1e24367441ac/coverage-6.6.0b1-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/e7/fa5b82c5d4d725774fa41864c83dc9b61b6b1d1746b99260f77b392c79f8/coverage-6.6.0b1-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/29/7c/53356399bcd36b5b7386e6467c8b43ed06bd5e70a4ac1579b2aa8a605d2d/coverage-6.6.0b1-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/aa/87de6a97b897e8b1a175af61b35045f031ef615a65830279b4e2df48cc30/coverage-6.6.0b1-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/b6/7fd711bfa3d99e61a98db90720a30c605bcf4a15f659192b409b79493c92/coverage-6.6.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/5f/e0c5be474a8df75b4bda53d28841bdda9e751f8f1495c70a10055e05ea57/coverage-6.6.0b1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/17/17/61dfb11a076f5a7170ede61af83b388d13ffe2dfee7813202061cc97c69e/coverage-6.6.0b1-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/78/a2bae852655536de0a111d9be360e6a01913c43dcdb4010394b5a5bf47e1/coverage-6.6.0b1-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/a7/0fbdf0b14f0de38b05221e87ebb4d3a8d237d0c1cc433f275c1b78bf692f/coverage-6.6.0b1-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2e/1e/f95a147a7ede29b4d350dfaa4cb5011a447811609cca42d25b47c2c2b9e4/coverage-6.6.0b1-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ef/15/3aa7955344f1035887d8f2cb0568857d19a254db5ae27aba98afd3d1c50a/coverage-6.6.0b1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/be/5b/e28f196183a0f9233069d71ca38104c7bdcb807e373cb246ed88c9822e8c/coverage-6.6.0b1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fb/cc/532b8aa5b8a07715c65a4b1dbe01f51c92866dac7b72578e1d5d0624fac3/coverage-6.6.0b1-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/b4/122a07c62a6e8f140168427c23bc0bb47fd0c07b8bab141f42df2fad40ff/coverage-6.6.0b1-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/e7/986075c52a0d5c1f9a1efca44c82ec258ce558230e511431b25478872a97/coverage-6.6.0b1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d5/2e/87c5c639a7b5ed61b8817c244a6dc6d72e04b34065f38f7491f3bcf8e63b/coverage-6.6.0b1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/88/0c/d492c5988770745b63f7a71662fbd4c03d588cf36d644db73c541c9c76ad/coverage-6.6.0b1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/29/52a8efedf1494ecd258394fc85554092aaccc00a40c96eeadcfa1e50c927/coverage-6.6.0b1-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/8e/caa6affa70458cfd7afc27b9a36b3dbf72d69708d2aac25c0b60533408f7/coverage-6.6.0b1-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/1f/2e515e9859fbd651f4ede1531509749a244786b298653a2f8b31aca4b1c7/coverage-6.6.0b1-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/66/9d/b80bcfd4b927f0345a63f4e49fcb4417ffc37de9925c786a4d663ca56fc6/coverage-6.6.0b1-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/36/a95e57ebc54a205544baeeea962787454607a091e7517503517470d22071/coverage-6.6.0b1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/96/a7/1391bcdda295f07e3a3abcde3314b9934c611cd3ce08790c59e47842ede3/coverage-6.6.0b1-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a5/34/1fdae95bc6b139eb1a014591a2088a9ecacb4e0fbcea6225d5d0d50f4960/coverage-6.6.0b1-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/4b/e0795c1e71ff23681eadd1b6eb58e1eb393a7286d04cfb5ea3b76121006e/coverage-6.6.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6e/4a/1a6451604738b7e141b80d168615822d94bf934c61c5d8249852178883e1/coverage-6.6.0b1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/23/e067ac39110f32d3f34c5cd854766c415ae5501e3e6d1a6a02b5ba81dda1/coverage-6.6.0b1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/09/8c/0f1b2611626bb968a7b342f7a564f52f4dc56b41cd8fb9badaab09b44872/coverage-6.6.0b1-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2c/76/41efdd50058e92bb9433626d2a6d8fbe6b8c4598ef5e1a914975816f0b15/coverage-6.6.0b1-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e2/2b/075b7493c1a265f11a55fcc6d9da3b3221fd5b6173fb6c72b3171fd2604e/coverage-6.6.0b1-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/28/ef254811149440babef3c4485b2471a1749ac6af3b46aad5f5610bbefbe8/coverage-6.6.0b1-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/ad/013407a76559fe374e51ccac0ad87b9d230f90ea57142084db38b8d10d7a/coverage-6.6.0b1-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp36-none-any, pp37-none-any, pp38-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/f3/4b922d40092ac10173398807d6006ccab608760572439454c53d8c667127/coverage-6.6.0b1-pp36.pp37.pp38-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/20/ae/0d1fc36601ea434b60152ac496f6e85ef9fd183d37f4a2e6ffa2736032ab/coverage-6.6.0b1.tar.gz (from https://pypi.org/simple/coverage/) 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(from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/84/25/17cf3f1ffc24fb9e5fb09bbd9aeee0bdb7eb944e5a0059be16cc9918f1a3/coverage-7.0.0b1-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4f/4b/d1afb84fa4f1c583def60a4594bedbea12a216df08424005c33422aa1c5a/coverage-7.0.0b1-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e1/c0/0abe76b6227b2fce8f7d9c8bad913a0826e40b40afa1116badb3b468811a/coverage-7.0.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/60/388190367dafec8ca84be2153c45f3731f5096e1611e1493017c99698f5b/coverage-7.0.0b1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/de/e4/d0627c7090be20afee25111603677488f431a73b53da1c0c623db1f7129b/coverage-7.0.0b1-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b7/b2/158b7906e20552bc0683c036de05c645749f724064e96242f7253f8417c7/coverage-7.0.0b1-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6d/8a/36868cfe42af66241d20db37b90be2d87c051eb14ec2ffc31e161f37e759/coverage-7.0.0b1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2d/8a/620bba7dcc4793805a1a5581bcf387aa63202c15f7c14f1fcc21dbf672fa/coverage-7.0.0b1-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/06/67407cbc999d8bb3fa9af28109b3f39e830ea8479d8af17cdcfc6fc96874/coverage-7.0.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/28/74/13b677422f78a6d2f71a45931a315656b62357a0d43e55559f9e1030cbe1/coverage-7.0.0b1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5a/a0/5b854874d2a81e1de8a0b9dc6efef0bc2a3dbd7afbcf043bddf5010bdbf9/coverage-7.0.0b1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/98/50/747d60125cb9de723e500a9cfa624e04b7233a92949c24bd12485e96373a/coverage-7.0.0b1-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d9/5a/7b0ae57beeeb5f2a311322f8e7257f6b35658429efeec826ea5c9437d8d5/coverage-7.0.0b1-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp36-none-any, pp37-none-any, pp38-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9b/6f/3a7f025e48b9317868a9f2f5b65e92a696a55a3fbf8be30f5f8d35d3c72e/coverage-7.0.0b1-pp36.pp37.pp38-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/08/78/bc6f090064994bf0dfe181328f09d53f0820f531056f2fe8d067d3382c45/coverage-7.0.0b1.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/57/62/d409dcb9b1e62b8c020f89ecf4625efe39c18b37f2c6bb0fc8395cf5b7b8/coverage-7.0.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3a/03/f168aa18978a7205f6b192d9c7fb7ffc1c730d8bb336ba6a0947d3a9e8ff/coverage-7.0.0-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/ee/b7a833d77d945825bc91830b8cba3134db249381d82466e28a3f64e96588/coverage-7.0.0-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bd/56/8782b01ee6a0270d36f2ac1bbb44607a8e89b511f9f17726cd51a4de7ca0/coverage-7.0.0-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/47/b3ac5218cf361244e1f8f30d2f15fb27d130e071597ef7fb0fd01658ea98/coverage-7.0.0-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/89/0a47337a9b5bff4fe70347e1f294758314d9c58e40a029a135f6104bb591/coverage-7.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/b4/f6d815f0a93546640568caf0b6d51bb35d34110ce9880faacf11e75e49de/coverage-7.0.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/41/2951960e830903bf7131de46577d638643e9563d6002dc4da7cc1b70291d/coverage-7.0.0-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/73/3f/b17db3a6b34b7291aa1174e8a28eda786e161216d2cebd30b6d9450e1822/coverage-7.0.0-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3b/4e/43e66f987144d7969bddd86641a697cb4b9cd11ee6699d76cfc62f3ed6b2/coverage-7.0.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/83/b6b937a4270aeb3da076bc95f852405cad0f7b0b286a7c8c23a143ff309c/coverage-7.0.0-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/e4/00cda2a8565a3d66432c38221a19b71c9deb42091dfff6f6b9eb27c513e1/coverage-7.0.0-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/a4/4b95e12bebe9c40008fa7227748bc98128766f914f4e52bec616923641ee/coverage-7.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/87/d5ea5193bc695d9b6c9b5bc8064c38a49f9f5347e2015933a91d3d938774/coverage-7.0.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/6c/459e2514cfe6638bfd3781d608270fc22525bff1f1453d5b4d7d4b65f21b/coverage-7.0.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/c5/22fa097c1836a593df4b72ba91165861be38069c68bd800ac417239f7d23/coverage-7.0.0-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/82/42/2b19d05dc7dad7d677c5139a1a3fd25efd9e72252fe37a0c0fe92261143b/coverage-7.0.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cf/b2/7ad2d89ded04ad994c71bb804fbd4c081b30dd9011ddfcbabbca08b51001/coverage-7.0.0-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/96/58/0d69c42aa871ce2fe5f43ab11d6ff40e6155151697ba843443ddd5a8398e/coverage-7.0.0-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/5a/9932e6769d4dd777df926b52b7067e34e9c10e41131a5ed2c33b7b43c905/coverage-7.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/74/46222b13f03d5eb0f03fa16cfaa7da110c7f5884df4813d1a9f213e6fc5b/coverage-7.0.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/85/4402defff3fe5e6e11d7e15d24221a9cd7283ff18d099a71b4672940537b/coverage-7.0.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3b/a6/49d2b4ef11880a8a659d360d8c74fd942779fefee5ff17534e02ff2d4d3d/coverage-7.0.0-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/b3/1157fd44ed5fe8da352efd945afd05bde1bfa3f3bca90b22d07b393224fd/coverage-7.0.0-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1a/74/d9864e6837d783b54921fb3fa90001b7fcf8b1b000a1da3380a430f176a7/coverage-7.0.0-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2c/6f/abe4ecf858210a762950952c9fdf24751155dd649be7edb8a34f3e2a736a/coverage-7.0.0-cp39-cp39-win32.whl (from 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compatible tags): https://files.pythonhosted.org/packages/46/68/bb0bd60fb5b9991ba13268a9fda24dd7de0d9e229f9a51e1bec7900109fc/coverage-7.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/7e/9c8a9260a3c64d3c6b74ade620de142df04c3014e3cf39cddc620850359e/coverage-7.0.1-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3f/d3/7fcf9c6cd8254b7b41d0bde1cbed3ad3cdbbc8093509e72d11f4b59ecf6d/coverage-7.0.1-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/04/92/44680ce5ace94160640152718fc51bcad0c7b5f2f43cfdc5bb155ded1d13/coverage-7.0.1-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/62/76/73c7ce4d88738e318048184ec6468f6f9f11d9cefa0abbe6a43d6f19e006/coverage-7.0.1-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bb/58/72842bad4e52715f2a8e1080ccec4d741f9fd9fc7230a53bdfb62e230212/coverage-7.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/34/919612dce1b0257aaa027e5b376be8d5774e59dec3a4a3213fa073dbb6c1/coverage-7.0.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/6f/0691c418fe852e7bec48da3991c8c8da96bd05bcafeeca693344a27185a3/coverage-7.0.1-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/bb/84ffbf15f543f5d361cd8093d71cbba22ee84e9368575735e4b9ea0fc28c/coverage-7.0.1-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cd/8b/cd1dc46b1217daf1633a7ad65c4eeee43bd1609f3a1cdfaf70705f6b0f5d/coverage-7.0.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/82/65aeaeee270d1445283eed47e19aa658edb7508e10ab9300310f10908e85/coverage-7.0.1-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/a2/591422241441e771af23a4814641f9981cbd90cb47497d8db70fae5a9ccd/coverage-7.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/52/49/2cd9c6e52dd19ff4df24cf023c976a69d61acd717371eb7d39b38a84d9fd/coverage-7.0.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/ca/93b14ca361f7f47cf253d5781cc8baef0d08c8e899cb2f81f1774250fc68/coverage-7.0.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/b1/3386a84f5884444985956630b240b2d5b0e1da8c1fb2cd7b48522e4f0ad0/coverage-7.0.1-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/10/badaf53b78cea43cfe5ddb53b13d9c2aabbbc20c70aaf5ff7ce13b9803b0/coverage-7.0.1-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/3a/14bf920c7dc39489942390ef461dc6217820695a008d66ee07af13e8e86c/coverage-7.0.1-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3e/6c/b0dc426df5661dae5457c1ba179b52180deac9d0684b869779df7eecf8be/coverage-7.0.1-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/27/e1/8bd04d9b25de2438728115375a481af95ff9d9ebce303b283631316c63ff/coverage-7.0.1-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/5c/4cbc291792898d8bde2129e69915067e14df0160691386c219a8a5cb43b3/coverage-7.0.1-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/2b/69/721e5ebcb178d6d6d54e345bd9a5b8ff2884359e9f507853ada02edb4fda/coverage-7.0.1.tar.gz (from https://pypi.org/simple/coverage/) 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https://files.pythonhosted.org/packages/7d/55/880f1d732bcafb772d46c5facb3b3b87c6b539e8ebda2344e83770f40d09/coverage-7.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/9d/54f365c01defafab9d88ec38a8a4dcf9b16f3237e983eb09760d857f9967/coverage-7.0.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/24/6d9e8d62cbd4e353d467dd048a7ff6b7239d2c8b4b58638779e1619431ad/coverage-7.0.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/f6/1f01093deb86efb9e15391e462c69d5785f766937294c8d16f95129db669/coverage-7.0.2-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a4/9a/9e7cb4a5390468b7fccfabad65b0bba1f327b6afe05e56db35d6edbd2c5b/coverage-7.0.2-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/91/4bfa3c085d2e2ff75dc03161dc624355ef708eb4ddddc56c75972523b24f/coverage-7.0.2-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d2/f3/31a7a060b3efd61ca99d4decba765c0e79f4fd3cd1a34744b7d94229d751/coverage-7.0.2-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/04/3cc02fe4dfcab77cc5b7c68ca21c0e551e973cdf1a82017d4dbaf417e8cf/coverage-7.0.2-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/53/42eb142c8a9dc849bb3040d9859c8aa67ae5cf855654ed7835a2e1afee12/coverage-7.0.2-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d7/54/6e9f4fa7b79fd2c3f52b0bea0758b8365b106577cb02b71f085d6999acce/coverage-7.0.2-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/52/c4e43f57d6e3ff7012e1cdb1ebf898f3920ee4eafb3a7553f543d1476f30/coverage-7.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dd/f2/e8c9f73410995cfa34105ecf8f449b921a29103154c2a106921d687dc977/coverage-7.0.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/8e/92670c972f46040b2edabf6f5c922c7577ba66f8d92f4ad87f93faa1291d/coverage-7.0.2-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/e0/ba9ae2108e52fe3032e3fff1492a2addcdd1909f1884338a72592a9836e9/coverage-7.0.2-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/65/f12892e49b1b62bfb7449d996a557c2181245a919970c7639ede7eb933ec/coverage-7.0.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/45/b247c0b3c11ec5860096135164b294ebf70e4298847ac15bd5700976b992/coverage-7.0.2-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2e/ac/2aafb853ec212ac5aad94c7c3cff1e4b9b3a951d9f73e970c9eb3f563ef9/coverage-7.0.2-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dd/63/55143c5960b7538198a32527574e9563020b0d06e63c7a6f81a2c1a6eb5b/coverage-7.0.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/13/803bc7ac5a0fb9d60a2235bd4211d2b279bb42347016fb3936912a843fa8/coverage-7.0.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/b9/2d71e5686542ff85d90198a0eaa6739c9e83d5ca25ba5cdac958616e5a8b/coverage-7.0.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/48/ef/1e43346358926196c8aff847e0e9be41c051e1d320ecce8693f5e1f502e8/coverage-7.0.2-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/86/71c50d085c56f91fb165d30dad9126a2a0268ef5b32d4e5dbd634c3172d8/coverage-7.0.2-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/14/b6/0fde8ebfd50024f2822c0a605012fb224df14f5362c04f7b7a456c470fe6/coverage-7.0.2-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/a6/cdb6fe04757f0a33261d4de4c17eb1a5fb19d795ed8636aade8137a8bae1/coverage-7.0.2-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/1b/8deccb12e1441fdf987914e6065aa52b90c831c68e7f61080a339afbe667/coverage-7.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/58/3c4e65ebc803e04324a4dbff41db57e83f4a8ad90b39b9a2fb08cb042ad7/coverage-7.0.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a2/d8/a32ec00f1800daad2827318e2a2352f5075609477b5d02bf269739eea7bb/coverage-7.0.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/60/d1/027f8a7f2c69e97918607e564e1e9e0d6f94fb883435b98fd25d339c97e0/coverage-7.0.2-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/40/c3d3eba1431db0699ec28a77b5dcf7bf0d08ddb3de8a5164e2437163132b/coverage-7.0.2-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/31/fca89bbc2fe0b0f14fd83e26c5627911fdeef1b3c5d7029a414fd7a16b33/coverage-7.0.2-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3b/9c/3fbdb7bc51181c20b55231f1e2a49f03312ef844c36eb782b1928ba62615/coverage-7.0.2-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/d1/f395423d1d582be1fa8ef94f57ed073ab943f59243e5266753e4b57802bd/coverage-7.0.2-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/af/3821d6ad46b02fea611584d6ba10c2f17568ffe1479bc9b98e2a16f403bf/coverage-7.0.2-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/30/fe/88a94d564b65b2a0e224feb133941cab82d86d5833f3e7f4240d6e100b09/coverage-7.0.2.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/67/12/8f94385a685219f9a8a75f7fa8f23a8efca8796c3ca3ffd58bbf54998f29/coverage-7.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/70/928d9f9e072495395dd7515134401d64bb487d1df06a21e6536ca8fc49cc/coverage-7.0.3-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fb/25/6c7905d1ceaa16c9b6a21ceb7f0c01d39ccdfb0148970fe6685556f41d72/coverage-7.0.3-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/35/e2e99137911886622094a02289e48009300f0e5715bbf5daf6708c340d77/coverage-7.0.3-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0e/93/b0da21b17a02b55393732815f2120ddaf5cece9204f54641b82bbc9f724a/coverage-7.0.3-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/6a/c7c9c7e4852399bcd81ea78e77bb64dc334758291da4b1db80c195d09500/coverage-7.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/44/35ed6093a904d4156d9b2b108c615adbd0a713fafafcadbfbd578550c5c3/coverage-7.0.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e1/c4/dccbe2119d27dd85569c79f12373f112415b35b52908572381942f4ad745/coverage-7.0.3-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1f/07/26158834ce148c92749cdaf4f0648acf89cac85f8624a4eabecd04df1e90/coverage-7.0.3-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/16/cb79e97d2aabf1ec111cd95a24bb3c59e6caab1b3664c5c65afad1a9d5fc/coverage-7.0.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/79/10/34ac320a220d0a16d5043e34d0261a277d7bad10ed5a622cee919700929c/coverage-7.0.3-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/66/20/bcd49f1002bff4045473ccb0bb87352b894e06a806736425fff84fa4563a/coverage-7.0.3-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c3/67/353255b32c4d90b6f42d59c83618cb5e9cf991540a1a2475bc4b01ae6ea6/coverage-7.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/19/a3073a479340d6db973ff3a04c77fb589d6a6b84b42a63dc92332dfb6a0e/coverage-7.0.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1f/c5/bd07be8aa2c7c464e5d031488f541285f3b8d78c201f55e224d7f3be1b0a/coverage-7.0.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6f/f6/dde49dab49cc938be6300f4a191c081731a812efa7cba119894efdc05e4f/coverage-7.0.3-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/60/ae/607164d214cfec6070616ca247bbda209abe7f6331987f3c8a3ceb08ac07/coverage-7.0.3-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/d2/f7b78142aedfcf79d97afb5ff51df9fb84e1ee11e2fcae6a7bd278ac16df/coverage-7.0.3-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bc/03/b92455a8f62db385507aae1312a5e8315e9bca16088e2ce43e4bc72516dc/coverage-7.0.3-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/7f/4bdc79332222d6d62357cfbee4a0d8c357454408d396080689f19f07a89d/coverage-7.0.3-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/b0/50e610b8d74819e230e80d4c43d69a97287c3c0809934fccfb1a7be8c333/coverage-7.0.3-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8b/39/1ede73247e41225503425c155c42c2a374065f75adbcad5438e0ef760b5a/coverage-7.0.3-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/69/46c4db005e1f49b147cee49058e9e7e39879bf3ecbc5d8c8436713a9a5d8/coverage-7.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b4/01/2f5d689fc1ccd1416441913ab0f40875d930b9b0851d56829b31736700cf/coverage-7.0.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/09/ca/a4d22b391c69f9482b6036b5117ca6e881d8ce246a24755941dd0b55519a/coverage-7.0.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/b4/066648986b94cef4542c12c6180d66245aa2f48eadbffcbc18bccad5a557/coverage-7.0.3-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/8b/56e5cb65e44b39a52ad5dd7a09be1b700f14fdb3d8f540ef80bdbd8569b2/coverage-7.0.3-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/cc/69d82341eeb66b6b3f85f669c84d7f21a31ca16d65c58d70ac9566514d39/coverage-7.0.3-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/12/1801a9cbb8759b3d925d555363c1eb7aed7253b844f5778a8a6a3ea00cb7/coverage-7.0.3-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/13/f4871b3e3275a9da50bfb263498d8f7a5ab9626eed702e17a48e8281577d/coverage-7.0.3-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/04/b1/9e26e085fe2be92542ac89925a24f5fabe9e8198248e2b94edf024ab55df/coverage-7.0.3-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/0a/ee/8e01ae96d0872572db225803ffd0bcdf7d4408faa4ae6bd92955f2e504c1/coverage-7.0.3.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/76/72/46ba89752b9ffd56e5cd91ef7add689361739738c9b5d89f984c6dbb7132/coverage-7.0.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/84/c3/2c2f8ec4a6074c8dd1a3b3a003094c911bd4683f8f6a7aa494973313a792/coverage-7.0.4-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/3d/84d27f31a70efc522ac632f5bee1171d3b1fbaa5ca7b1447624990a68886/coverage-7.0.4-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2d/16/6dbe8360baa181f8d66ed3e7b0479bfa798a5dbda395836b6f4e0470141f/coverage-7.0.4-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/46/8c/faf7ce633c9315114de042629842a55b46d6fd7762197a9dc9cb7b0558d1/coverage-7.0.4-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/e8/1775d3fac8d036119045a07cd53850d8fc40caecec898a19dec7686e0c69/coverage-7.0.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/f6/35d24b5ccf9206c2d1b0dc0af41eb419bf736ffebefeef53ddf1a6a55b93/coverage-7.0.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/2f/9022993a307d5dba85f4d718076d1184b815effbb4c6f2c6cdab842b0b13/coverage-7.0.4-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/92/e0f4526cfa11ff23ea3985d6310954c9648affd0ba35b80b9e76ce49f2e1/coverage-7.0.4-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/9e/d0ebe8531c303a97ab088845bbca2d3167439876c76ea89024b6d63cc1cf/coverage-7.0.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cf/46/e2127054b5498d1c2ef9469f651f93c82c5ef4d8ac1082eb586c3984a85f/coverage-7.0.4-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/7c/3981bf2d56c998c7a7a3b0222bb0b9759f85ce3e93f5d912e39e9806814b/coverage-7.0.4-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4f/bf/6cb83ba9e521afe2265baa334dc05ff76ca8bbdf5868d89e415802ac3ed8/coverage-7.0.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/3f/7fd4944e9b4b86b64a7f3a8984d5ebc9208822b3f0a7f0fcf5dcc64ef721/coverage-7.0.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/d0/72f989a9ddcd883399d2310d6e389e2f2745e4f1d60eed335195c1ddf4dc/coverage-7.0.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/57/0d6d69a7cb8928c349e391a71cc829af03c87e04f40e04be5ca09ed34a43/coverage-7.0.4-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/d1/0938625917f607b0c7eea5a7656b9c30f980dd563753eb0bb0b9b8566f30/coverage-7.0.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/20/12b13fff64a9a858d3a8b1df4e9bc6426b00ec7d1da54d158a652b8aa6a9/coverage-7.0.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/85/10/e81502c3bf38d139e5b4d911425ea311712dc1535f8bec46513ef866efc1/coverage-7.0.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/22/6e5f0f5337cd230324a0e0b285b0ff76dd2775b1355057b4f62a0234de09/coverage-7.0.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8e/13/c972de92f72fb65046afb7c3b74c900fe4df21f4396161aa6c875d568c98/coverage-7.0.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d0/7c/54f5be261001ece15b35c14c36688f5ea54ccd04989f770a0bd9f28c45f9/coverage-7.0.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ac/cd/0ff4965534a3c14129403c52d238b8df657ef9787963946451f5af24791c/coverage-7.0.4-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/33/63/e2b6eaef1bb4a0231cd427a65f10b8c5ca85681057e8dca31a999d8d165d/coverage-7.0.4-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dd/62/1c5f83ee3e948e50275f5f24ab6d799e5ddc193edb687bcd486cd64aab5e/coverage-7.0.4-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/7b/df40b177325acf2e69f9a234ffc092de36133ffceb8ee78c53f94b645db0/coverage-7.0.4-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3f/a5/ec59fdfb92557cbf7365dad04ffe3b24f98192748a11598fc195d641bed3/coverage-7.0.4-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/b6/cd4e385a2fefde609077da9eeb9748a3362d6a6e0b4fb6ba0801902475fc/coverage-7.0.4-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/54/29/6eaa9d6e92adeb724f49fd6d132325a0cc38196bb186837af8c819628799/coverage-7.0.4.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/01/01/8d17427fbeb0ee132111c543bfeea5ab403d6869812cf6085adc18debecf/coverage-7.0.5-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/71/5e/55d1d143327306f793fce0cb34b2a4ab0d5a1559d632ca5e327a9f9bb848/coverage-7.0.5-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/e0/1b5b5bf33eaba010395864415aaa5c036a3fb10780cc2c92b3a8e51f035b/coverage-7.0.5-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c0/7f/7875a6e6a2522e8b7bc683be78714badd4ac8b3d90b69fc2cadc20ca4d52/coverage-7.0.5-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4c/f1/c8c99bb9902df7a01ac80ca850a0fbf2c5d2bf25755841ffdb5d72c1b068/coverage-7.0.5-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c6/81/e01309e5ba812a767bfd3ef692a619ee459af4d9313e7244305561aa2341/coverage-7.0.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/55/40719c5b7b7503144ffcf55ead8d37c6e8ec2269293a11c5d7654caa5228/coverage-7.0.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/75/fa4b613e2d4d8b0773ac21061cedd29a7a158ab9139c932011fdafc0cbc3/coverage-7.0.5-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/38/150ae9c3ee2f9544a1c1c50a78c98364d4e6ce9025cd647b739ec3b776b7/coverage-7.0.5-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/0c/c1f48457a7df79c53299d56e1632d1907cc8548fe158b2c839ad8e456584/coverage-7.0.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8b/e4/ca474e48fe69f2307cf9f2bb775a86130fad765b38ba9b402469d44b7893/coverage-7.0.5-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/ba/da1510983720f5196f4c8fe01489b6ce9c4fa661fe9aa9375611e639a8d7/coverage-7.0.5-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/02/d6/c095845877f235b52a7113e08e750a9210feabf059b71133e9349eccb836/coverage-7.0.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/66/c9/08ed37cdf2101a6832dfe99307fdbf6d9d5abc2070105ee061b7385a1f80/coverage-7.0.5-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ac/9a/5a0e4c17f105eb04caa7cd4e524a734badb6198c36617ac124002a68aa3b/coverage-7.0.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ff/ff/cfb9b03d3673d24aae2a40397d812cde590e36f5e4aad3efa946910965c3/coverage-7.0.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/4b/25fffab188056faf56071b38b72e8e5ae74c7f7f5f8302a764f9f915512c/coverage-7.0.5-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/87/65a9ac379bd840950efd5d69cf97584a4f21ef939c3d05a74edbd308aa2b/coverage-7.0.5-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/84/b3/992a6b222b14c99e6d4aa9f448c670a5f614648597499de6ddc11be839e3/coverage-7.0.5.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/f8/f6/c978a4f888393779725b64a1b1de5137a30b00d8a017be3074c225827d1b/coverage-7.1.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/97/5c6ceef429c0b38d1f5db700697f7f40c379ba498a3778f7365a64d8b03d/coverage-7.1.0-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/d7/215ea44cbed1a15663d0a88620bedd13d6d1d9287c55c0af1a0f07170e2b/coverage-7.1.0-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ef/92/edd214c7099c76a003238a342daf78621a04a9a8f37ce5dc61f3e4e91410/coverage-7.1.0-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/76/1a39d67eed8dd260f1fc94423309eb3eb809150554062c8938403c891deb/coverage-7.1.0-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/42/9e3920032dbe70ec83bf60672d28ff764fb7ad49bac060411a68a54b8758/coverage-7.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/c8/de630d1b872cc65f1878536e00fc0a1b610508f018ad90f957b171de06a6/coverage-7.1.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9d/1f/0332d1a22abe7462e6bf12906c95dc1b89ad288611a891f9189fb2e62678/coverage-7.1.0-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/70/bf/001c2d03855f137ab5d6c67992ea724e231f75756e3360d247bd84cfe231/coverage-7.1.0-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/f6/7ad07d231c09a5dad2813457c9f102780e1049f8019fbe78c4a9a024d7f0/coverage-7.1.0-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3e/f1/385b7fc2c7902d70f807b985010f955ffa5da4f74d5f32cd5317dd749f74/coverage-7.1.0-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/7a/849efa68d38db7ed6e4794de122dc9558d420b84715d2604f9bec5dbbda5/coverage-7.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/45/cf/45b4083971818aaf96dfb8a49d3a793fc012a154e20ab0a85d162029860c/coverage-7.1.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/40/d7c2594c6960e144202b95cf1e756a60a6847f15624cd9004d53f4fb8f46/coverage-7.1.0-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/78/ce/04337e09985687238b4b57403786a4a87814fe6035013f65359134c77c6c/coverage-7.1.0-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/5d/9a24dc820aa16eccda21ccdef823510bca3997901230f610ef5153eb915e/coverage-7.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/bb/a5c7cd34be5d589f6bdc6b81b052e3ac5a56a8cba5d75d9c17a6ab36f564/coverage-7.1.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4e/a0/a8002c51ce13ad68db9d30c71282b3166186ab022d978c3f707b18dce6bd/coverage-7.1.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/36/0779051758526614eddd6ddfdb53764c6f2c3d58e89c80a04bef021c88d7/coverage-7.1.0-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/c7/9272d6094a8aea80d244b105429ad5953795344415f10538c089184daf27/coverage-7.1.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/b5/dc65a49335dd78e1def7e0ec84bd144ba442b74e29a7dd236c551bd8b6bf/coverage-7.1.0-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/be/bf/217ad144ffb569b73d83e18eb794fedd9926cf636a9df2629de191e7c3ae/coverage-7.1.0-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cb/8f/2f5c5f2dd93d90d03e246aa84ba5e756929cd2fa15890af97ba0c4f84ddf/coverage-7.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/45/a0/d3986ff9a585e8053893c05606be3a812ff7407869d1006c8abbba5e6179/coverage-7.1.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d7/77/7e63d1b143df33195b3c468953aa87613324483adebb240d28486b4f3ac5/coverage-7.1.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/eb/3c2096bfcca48d5966a38c3fe8e144599cb8cb0fb46accae29072a6121f0/coverage-7.1.0-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/28/dc2b4d89a5a043ae6010bd02c6b93574d6031218f466a5e02686c4ee2187/coverage-7.1.0-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/f1/1754166ef29b4fc4db8f0cc03007bfafea9c6fd7e4453ad04118a6264903/coverage-7.1.0-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c3/f4/447fae940c944263e9d58c9351021b2eff6b7b4452488b7eff9c27913c4a/coverage-7.1.0-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/c8/3700779abfa359ef9af9ab2c76cfd86f2b3e8446c32c4e136823684698d0/coverage-7.1.0-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/52/f9/155bfe715ae87f52b3cc15aca476dc8db91c8daca7419d6e5eee931cc21f/coverage-7.1.0-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/18/a0/bfa6c6ab7a5f0aeb69dd169d956ead54133f5bca68a5945c4569ea2c40b3/coverage-7.1.0.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/cc/2c/72780f4066863df9bf17eeb9b29512e6d9b12517151281e3c68e240a7e41/coverage-7.2.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/b6/6cb610cbe6c863759232ff44d2a586ed675f93c1aa92e27a4727495b0033/coverage-7.2.0-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/5c/03eee90122ce1d03ed9e42a957cf79d5fdcca041c353669120c46111e421/coverage-7.2.0-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2a/3f/1525a76966df0d1b178d7df4bd0614258445231bb5e352c5d335f6a2de22/coverage-7.2.0-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/62/21/f42eee5f622fdd31107ab32f90f06f39778b18e3a5568f961f3ddee32d1a/coverage-7.2.0-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/9f/860c2245ace2e1730cb05fb46722797b088b333273764ca4bd4afeceef76/coverage-7.2.0-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/3b/f592b63c496121075ef5fb203c6d687967d0a06342207d3365b254d6850b/coverage-7.2.0-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/a4/43d0896b544ad0cc4d5560efa5a5f1bf6599f7d30150680b403014a5901a/coverage-7.2.0-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/58/f0/147930d69bcbce173a9fccfa41da71f44f7e34f69ca21d5e031c74cb476e/coverage-7.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/73/28e4db1afe1b21172f628e6d41b8e9cca9f79e21ab6a76200825e376c80d/coverage-7.2.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/08/8b4e9ffc37109d3637dfc36e7326b857d63d58906f2125589a2d9ee4c708/coverage-7.2.0-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/82/d4/7af06e31148814c24c642541e78cf8b4f4add177a93912a978e8282b2f91/coverage-7.2.0-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/56/41/2b025985d50664bfbc3d17cc8aa09bc93f0964843bef383af39ef609b510/coverage-7.2.0-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/0b/c3811936659771157f9319f9c3819dad29ba1175a15ced23757b4f3bfe6a/coverage-7.2.0-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/96/ba59fd23a5bdfcaf3cf84ae9745e9bbdfb9c97740f2419890f1e90aadaff/coverage-7.2.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/65/e5e039404be4e4376f0ee5c8fb689bb7907a58420765372fb2dc7808c59c/coverage-7.2.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/96/5768010ab7d4cc5c88b505c21370a987361d958ac9ecc0fbed53737d66d1/coverage-7.2.0-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a2/e7/f8c1753a7e5bc837b1c90f0808815e4971d780e00d4bca94d2f5430b91e4/coverage-7.2.0-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/2d/63fe1106106b68b8ec9fd2e795c5061dc54e1beb3cd7f4c7d543c6a75749/coverage-7.2.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3e/02/09fab2e31b26d0429071e47a9902ea4b6010e1b617ef3ca4cc9af5471ec2/coverage-7.2.0-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/0f/95fb06de6c04ccdf03e18079f759d88a139d82bd48d264f0b68800afea87/coverage-7.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/fb/133f0dbd6f4a02ff5022be257f4517e2a63b3a2009dc6ea4d91ecbc12510/coverage-7.2.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/50/9da6e53741014b6e8bc221ab3866f820c71453af2285d99832ef2279a3e3/coverage-7.2.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/47/88262eb416a92ab5be64f1d260881d70739d9c57b2845ce727fad83fc381/coverage-7.2.0-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/9c/8d2542bb2779fd23584b5fbbc561f975478964cae34301a0dc438e237322/coverage-7.2.0-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e1/47/9b230e1ee67785058d98edf2fca51a14d9a8c56b5477279dfa5df20af835/coverage-7.2.0-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c4/60/94b3e5ff26cc42f39d27b2a42e0783a8acf3afe365b277a452a2e4fcd572/coverage-7.2.0-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/b7/ab2eeae8192f07f4440716e0bc9b28a96771000dfa95fae8492d326b43dc/coverage-7.2.0-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/9f/4bcabf35e6935c4e8e5e7c9e7206d3f32c97cfccc5ff4fc0f53050a85b46/coverage-7.2.0-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/10/44/cadfed76f14e5fd2a8754c4443af064f00237724ddf858a970116d6e80fc/coverage-7.2.0.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 7.2.0\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e4/ae/489a39a3615a23000f731ddd0a8d397f771b42ab2d0404fd6607f0a38c42/coverage-7.2.1-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f9/06/5f6555205d13f8811558b73fa37596519272fb077ad7f9faa4e4162a23a4/coverage-7.2.1-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/33/f3cf1fd69b2de5c9d63836c517bd0082fa6db53aa5068ae7df9acf115b9e/coverage-7.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/da/f6/6c8f89d02dd9fda71ec61a5baa4d210017f2eb0bb57d4dd513d98d875867/coverage-7.2.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f3/ff/0bf7a9497dc91e4b0f11656a50c95fd1e641d912a281a0b0921d20fa5760/coverage-7.2.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/59/7b/250aa653d48f6dccc553dd95739aac5ed7880ee99358b0d5aa0f13d1a338/coverage-7.2.1-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d4/94/fb0c114600331faead0abd61d8182fcc67030f0711dd5841d7c9a36cdaf0/coverage-7.2.1-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/94/91/c708f837c5550ff5447203e29f5273d9b20959fa48753bcfe58cd522fbef/coverage-7.2.1-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/77/84/475d9bee215c6d590621b6d28589b960410b30602a68434601fc1e4be746/coverage-7.2.1-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/04/a1900980b5c279fd986ae125a3a447ec0cfaa3e908202cca9c07532ecb9f/coverage-7.2.1-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/51/61/2bab4add265c0fcf0a4372ab9e647405f157a9c5cdcbab1e0b7b117f92fa/coverage-7.2.1-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2d/24/06ad2452717337ed45a928107fc5d91601a4a79692012ee86dc06782ab51/coverage-7.2.1-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4e/6b/7d9c6c23aa227b91bc2f85f197406b899a4469c6e0d182d499eb2a515e91/coverage-7.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/ff/bf04eeb95213c25a5ef718e1e70b6e476f4e6f48b00d62860f3a8facd3ef/coverage-7.2.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bf/4e/bb6008789e813f6930179757acdd409f0056e48ef687416bed819464a79c/coverage-7.2.1-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/0b/0b1b4e1f9cb014826c453c7841fd3f2a197606df62b9afb798b7ffaac074/coverage-7.2.1-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bb/2d/0011ba3d37d53a840728390a86120f7316ec059a656afe42386abc67bfaa/coverage-7.2.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/f9/5b9e843989ab7461e97f0cba987f78fb043adc8975e65af9eb9bda22c3a2/coverage-7.2.1-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/78/f93c316411bbeea137c154664bdbd5e274bbd6710679723ff42a3e7444aa/coverage-7.2.1-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/fb/3a9756915a126b138e69f2eef2118b64f126250e6e67d597821eb23dcaf2/coverage-7.2.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f5/70/9400b5c37f43d8e39527f9209cc7b1db96b5f16a6a3da2ccd8f75465d567/coverage-7.2.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/aa/b799631d7c990e777da1a6ba85ccf5194180c79d49f20a1acb1306a18a13/coverage-7.2.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/9f/9d5ffcca635f09cb92e66afb149996889ea8cb7408bec5e908d36686bf8a/coverage-7.2.1-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/f5/97bdecac0c23ec52d2ddf4cae5d6de3c69c4441f1b1e448ff7975f91d37b/coverage-7.2.1-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bc/d8/a06bc91226ff1940547934af0c514acd17510cf9b9a54b13046e5f2d9538/coverage-7.2.1-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/0a/a82fc6973f9fbb7e5b0412b72b81eda6124adf496e3682cc7577dacaa4df/coverage-7.2.1-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/33/441942d7dfcc2a12df0e5ca918a9855b1c1672b3ff351045658668d184f9/coverage-7.2.1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/39/5efa733d0508d31565faf2761998f2e0417bfbb5bf4269b64384c7f5031a/coverage-7.2.1-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7c/f6/3c1715c27e3251baa578b469f3c266b48623d3bce4ea08509a636e11447c/coverage-7.2.1-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2f/e3/15f85c7527577907ddf354f45ad212c3882012c92186d45baa553cff47e3/coverage-7.2.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/36/afdf9ae3cf36ad922599df32b5517f48be389761869907bf89670eecd362/coverage-7.2.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/a9/62aabc67971d2fd439474b05cfc25c852b28bb6dfe8082c5b665652899f5/coverage-7.2.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4a/80/44c734492221b7e065bd8a2856c3f2f4650664abad70aab5e22a9d71a984/coverage-7.2.1-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1c/89/b2f00ae4d1267aeed0026e68b17a6357afe52c1f773dff9c595da4b88977/coverage-7.2.1-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/51/e0a33466be7083709db9325167349ffeeedc419bb73864f72c8280e25aae/coverage-7.2.1-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/d1/fb1735d36b3be16ac900bda1d05a0de92dbd7a5de4ede4e3f9f27beac5bc/coverage-7.2.1-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/0c/9c463e24ac89408c13084ddaefdb7c2d55ed2da7c556214159b261df4737/coverage-7.2.1-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/de/989111fbf70f00912513912b3111072680154ec713b3f3d523636832965d/coverage-7.2.1-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/8d/4a/3518606d4b110df4f3e77bd52c241ae8a84c6dc74fac7c2a8e809449e541/coverage-7.2.1.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 7.2.1\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/dc/3fe8388df88c8dfa3d5771b1f746ef5bc92f0c718a10fcef3600199d00f7/coverage-7.2.2-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/a6/6b3aae5b60e1e2ad8feb807cc33c5d313511980c3ee5de4662384165cec9/coverage-7.2.2-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/de/effbd360724a67b8e8f7f297c4cffdd5510161aeb4de08b08e1fc6fbbd49/coverage-7.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/f1/cf01ea71b3c525f5cb4aa9956b5bf2d4e0ac59cdc1e2aead8e73374ab420/coverage-7.2.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/06/b0541156f46970339776c42dafe5d227710127025cb96e4bd3176422016f/coverage-7.2.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/32/82dea3a1e928f8f7d2cdf6f0c6498fd42c5e9d661de0191f8c47d3966306/coverage-7.2.2-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/11/da/b52e1f1492d6431ce0ebdd9da8744faf9f9fb75c3077165aca44c178f6e7/coverage-7.2.2-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/7d/380f87502e91d1da48eb5d0dead8cee4911eb19e2d4b60e3f1ee9c975c85/coverage-7.2.2-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/29/3f/e2cef2978bfd787da8e02892448098ecb4a5909970c08b6220d0867460c3/coverage-7.2.2-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/d4/d8ee18c995b806b3d0d38dc17ab5888de148244da5be8d3dfbd7cea6cd7e/coverage-7.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1b/21/e757c7147ff8ef4be811d5fb3731ec0936ba6689444fc20dff953536a243/coverage-7.2.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4f/ec/acf6816f7fd5e628746db61725f7a3f5eb16b3b55c919c885072c60d17fa/coverage-7.2.2-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/c8/efee5b6e98f1009ca92333884d17eb205acb7aad800dc9b2d91475aef2a4/coverage-7.2.2-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f5/6f/7811ed60d4088b6a54bb48d57b48f647e55c876ee9088e3fa123eb879673/coverage-7.2.2-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/83/db/85c50845c1ca6c83082decfed61a8d928903ef6d72f8a2c20a615118fc8a/coverage-7.2.2-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/d3/7c6d91fc5dd15496954e9a941a765fce70913314181289e441db0e5c6568/coverage-7.2.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/75/93/1821ffa10c91b0c4220f1b525f82b408ca49a605481d76687078cd251504/coverage-7.2.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/45/687fb10526b66642a479b8703cd5ea53030fc582e76e7236e7a6318fd660/coverage-7.2.2-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/bf/ae3344872e1a33e58481afaa1587e39a4e626fdaa69bd11c1454fd34e0d3/coverage-7.2.2-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/49/ef6f4681a90ad12b734e8fd85913dc310eb58bfc33eb95534bdfef073476/coverage-7.2.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/6b/30aa019a24ba3ba3487b6d61e468c898dcba77a153012f2032d72695ec13/coverage-7.2.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/50/b752e846395b17d3789b070b82ac54e88cc2e4f4d906b9f864074caf04b9/coverage-7.2.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/18/a9/98d8eddba099f79548527af0aa546c31c3d8a2cc9e5d1c59a10a584ffc6d/coverage-7.2.2-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/35/5a6ac4b81c41fe067af22a5e9ac440609a122c1ece220b8c565dfb27f205/coverage-7.2.2-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/57/629e70906739102dbd742bcd7484f5446dfaacbd6dc3f46f42a88a0677c5/coverage-7.2.2-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/f0/ca6217975c4d756cbf136abfab805b470424279b322f8c9f191c21898ee9/coverage-7.2.2-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/56/65/fedba1cb8d954823eded8e7b943749531f81060f8e4214fe9ee8dd1da5bc/coverage-7.2.2-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6f/e4/9576c24b37c941e70a4815cbbd44ff2779c83b821ab3a38022b6f530d61c/coverage-7.2.2-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/06/ab/1dd1a592ca3dff0c0d1da895c609f6b8b6b3092a2cc06f5494826b7689c8/coverage-7.2.2-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/12/51c5bdce0ad62a29bff429e99a9b2f23a0220cca89e768fc22694d4aeb4f/coverage-7.2.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/0c/6e0a65466f8008b2001c0354782753a880617b15dba857eb7e97928313fc/coverage-7.2.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/21/632047c45edb08f0eb51f9e8e49591d23c15ff078b32e0a88ddf99f4e8c2/coverage-7.2.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/70/74/7ceb96c38dac56e5894c3b0af6c4adfd0dd7f96521e8b829544d1dcec8fe/coverage-7.2.2-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d9/55/f45a1d08ad1299c5199f3cf1baaa02fcffe347fdec8ddacc484858700ef6/coverage-7.2.2-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4a/73/031f7380e2912f78801d28120091ba2dcf5bc0ad689e958d6b970ce67cb7/coverage-7.2.2-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1c/02/46d5dc331c9419d01e66c497f3974af895aaccf6a2a2695cfd6f83f1935f/coverage-7.2.2-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d4/cd/ef9d489e9c943636193de07e5089d84252c611572865e50e475495528070/coverage-7.2.2-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/92/cffba484a7a34ca08097f7991b72cbd85da4adc717c69c067bab2e1f612c/coverage-7.2.2-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/54/8a/db9d9cd24f96bb872eea151bb0d5c8cb6a96825b70a0cfaf07bceab2884d/coverage-7.2.2.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/7c/95/d28a93fb7bcaeeccb0c17137c14a69320dab6c6dfccceb561953a7b2c95f/coverage-7.2.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ef/b7/a59cdcb2533488c504af08216f6564cc15c048063a79a15e12e27351d2ca/coverage-7.2.3-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fa/19/883184f389f9867ecf14903477c504a65944f55c6520166c67d8799039ba/coverage-7.2.3-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/de/b3d461de0b93bf433d4edd156fb428947057810b4767bdec4c6bfc7885f1/coverage-7.2.3-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/3e/e105c3bc0360a050f17f817127d9b1ae4c44b85aa4280a94309dfab0e6e0/coverage-7.2.3-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b7/2d/184206a2347cf9aca35de9d7dfdbf602631ff8b7b1ef477f2ebc94d97df7/coverage-7.2.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/12/96f37d07e408cf732e69060a6f5dd5f06bc69fe7ef1690c765a283fcbc6f/coverage-7.2.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/6e/6ef8af39815037773c7529754f87189315dd7d6fcbb7079628561c6b8da6/coverage-7.2.3-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/92/42f0044c439197d7d66db89110b543e6a5c144f9dec816b3c022a3c07bb7/coverage-7.2.3-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/87/211e487beadd26338a417f34ccfad50389a5c6d2afa0801134adf61c1d45/coverage-7.2.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/14/d3878e9ee7da32f4ae1d438e1e8098faae19d8e1a3658e979360048a8ba4/coverage-7.2.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/c1/b1922c2899869db0579169d42d2771d6f9458d73c5945d1d552f5ef62893/coverage-7.2.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4e/66/37f16cc31c75b1583789fc24e799edcb79a2e7a020f8a4d8e2a72ff85950/coverage-7.2.3-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/aa/d52001aba60767e1376dadd1ff7ad9c5a6e78d1c4cf7f33f1050132d034a/coverage-7.2.3-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/08/9c/ae1212e2948b8ba1d1b1ebbda65421a4853a93e09301cc7ca7c25d8f8080/coverage-7.2.3-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/08/86/aa8edd6d9f0e145a1fbb7e3a221d0f4e51a753a907a7f669c57c29a25a36/coverage-7.2.3-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/87/c2/a09c5c47b721afb4d4c7e75fa4874ad39ce18ca9f93d3d7cd9a206da3a32/coverage-7.2.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/65/d8b365f35e8e7b8864f7779f3009f60f0e1e054d4a26af132da2e26bb5fe/coverage-7.2.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/2b/a5bfe2f7dfb059c3c1f46ff89ac97230121e38f9d970c3ecad78ce1e4aa8/coverage-7.2.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/63/d8/dab638f9c3a538987f8f17b2481dc327effd3604da8200577f4ed0c1665b/coverage-7.2.3-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8e/a6/b8b852e58f8eea2d8eaf1acc00219fa936a8642e197d2e074997710ccb1f/coverage-7.2.3-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/70/f7e96c52aa4aefd4e0c1f74f9d72043a550f330ffb0a00b6c4e30173b064/coverage-7.2.3-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/68/505c0b18ebdda489bcc5dedb8db876352556a8ff8e115a2de05735cbf3af/coverage-7.2.3-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/d5/d52b046cb9fd09f408c3a0a06ba3fa3f5c8ab053ddd6368fe6dd33d3a7a7/coverage-7.2.3-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/54/e8/5da2bffb2490365aa84429b4b8a2ec7fb7658e92a8bae6f2b8fb5d006930/coverage-7.2.3-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/62/53/42d3382a915e49ae9e682eb6e3d29b8dcb90ae253d03efef1d5ec14b2f0a/coverage-7.2.3.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/0e/e7/38dd6426c67363776e9a6eb2f042b0886c49ec410d8624db5762f30512f8/coverage-7.2.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/98/9eed386500c61635a81349c44ffa66ae349263ee097813f2e23099cb2118/coverage-7.2.4-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/09/4f/8ac26650d6660c998e0a63fb5064b553bdc44453385ab30fdd7a9bad64a8/coverage-7.2.4-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/c0/7ed44485b65ec20c288bfb3454cf447db8bba44139a69897578c021e1e54/coverage-7.2.4-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/4e/3ab119213f5a839fcb452aecb0cf5da81897064bf77b5f8d3a71dba6126e/coverage-7.2.4-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/55/1f/f17e1a966ffad5c2d55f0571eec7d28392167cc127661bc15d3230392c8e/coverage-7.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/02/fd/951d7d2fccb671d944b14c41c5486deaeb517daab31c97748b4bbf283e51/coverage-7.2.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/b0/aed6e87045b7d5ed8b81be7d38a0bd4271ca3875bc16b7d1ab5d214fc767/coverage-7.2.4-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/22/624c94d5f627b8604d5e968beaedbfcc6348256aec2b96f956ff2d7105bb/coverage-7.2.4-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/82/3e45dbfd86db66a80e5ef987c2090bcfcfb77ac430b559d909ac87db8e8d/coverage-7.2.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a2/cd/7099da5c9ea82f7a7b6a40442931590223e4f75c16db436d116e0847acd1/coverage-7.2.4-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/f9/69f809d1bd0feb327af3c0387b99acf1235f73296b90b4d53ccaf0ee9a40/coverage-7.2.4-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/57/661f509b6b8998c217ebedb9c7d133cfa0dfe9a0ed5195bdedcbc7b2c833/coverage-7.2.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bf/c2/cd5dff48efd56d2eacde8f7db23134c635add5da297691532b58183384f9/coverage-7.2.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/80/d7/95e068c3515776abfc50c4756d91eb96e9182fc80ee44b1da2fdb657fd02/coverage-7.2.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/16/512a8ca46f2f507df054baae7145767675868df556b51550223df000e418/coverage-7.2.4-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fb/0a/c380834dd02d42ddb6803abd82b75706ecf23990d69e387b78858476d227/coverage-7.2.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/52/c5/beb71090ff401e598cadb2983ddaa700e4f219d8282256f941c40dbb749a/coverage-7.2.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/58/27/f6861fc6c82eb241c650716b764e58bd5c5625e25fa5566c662efc7d829a/coverage-7.2.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/f1/78f9d594d21ede0f18611594d7d97bb630bdfa2df0d6c9600a2c7b2c96d9/coverage-7.2.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/a4/86f077c64cb90a370abd85d1542832f0dd65436ea7076786c1a43626d69c/coverage-7.2.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/48/caf375b028fb93a31bbe09e1642ac9d162747875caf67e9d62a57c5c70d0/coverage-7.2.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c7/c5/edb9fd3f0a5f14a0ca7545b9306e257f56003c4f7ebd094526c7e3c2c498/coverage-7.2.4-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/af/bf12c1476783d98fabea143f517a85bfe6666c39863e7fea4e033fd27c8e/coverage-7.2.4-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8e/d6/71a30e191124cca6a42ff49390ae906b3a16104e8879234239b45bdeab0d/coverage-7.2.4-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/75/21/5a01c67dc139a0ef5bd691f00d7cdb831a1de53a1493e46537949252f821/coverage-7.2.4-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/df/e6/f5b983af00fc64ff53b50cdf9356d19b6c0d79b2ebc79dbcdb47c5afbc0a/coverage-7.2.4-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/9c/55401902070a7d0d7cdd880ca24fb7d813cf151934e41981c6d5588422d5/coverage-7.2.4-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/78/3e/8e2d27ae0ec92d009c2906d21f15f5936cfddc5bb5660f5bb50c521c4114/coverage-7.2.4.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/a8/e0/ea6fe3d440ec24b3f1802b177fa92f69d9df7e3ab11d93bd510258588725/coverage-7.2.5-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/d0/11395768fa7eca73643f4ea177883fdc28a68a1fb7f9102fa6ee180efc44/coverage-7.2.5-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6a/ec/6b12580715aae877303c79e025a7591cad83fb76b4f18eef99e78784e064/coverage-7.2.5-cp310-cp310-musllinux_1_1_i686.whl (from 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9b/a4/ed7f5dc3160fcb3d1f86a8f74c59404aa3d47ad71448a9b9d441df6812c7/coverage-7.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/52/5ffdff100e10385c452ad36ea063ac51b192228a5bd5b21529b96b339833/coverage-7.2.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/62/fc/4f9be6c0d7fe460990d05ffd2316683bfcd8a94758dde26d2da84487a8af/coverage-7.2.5-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/ee/67f851378e163e5323671c21d42bf6d059a16c058fbf10338cdf2170c90b/coverage-7.2.5-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/97/f6173b9937ebab116a5a899aa79c1009141eb894911b65a83cd8cd5c0d1f/coverage-7.2.5-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9c/51/2500745ec2f95703016a71cc0686bed494cdad32de5d1ebaa33dd1d2b187/coverage-7.2.5-cp37-cp37m-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ef/7a/78d45957963d4a85301ece73cb46352e59dbf17a38d4412542b074cbae87/coverage-7.2.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5f/79/da677806f4745a3c9c11ef9c2a2d4fe969975948dcb158fa967623c3f67c/coverage-7.2.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/9c/c79d54b85a00014f466bc967166c9c495e7f06747d26fbd4fec531fb69dd/coverage-7.2.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/5c/8aec846dd51d4d374ea87689f24b3ee93b023020160141456f51986aac54/coverage-7.2.5-cp37-cp37m-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/de/ab8f77c21d8ad1682d6db9220e5e1f941a9490d2c1bb1baf50f12f4bee64/coverage-7.2.5-cp37-cp37m-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/51/7b/cfcea378640bf97d728b56f5926136062abea89f9a617524a57753f364d8/coverage-7.2.5-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c3/88/3d53153687a291cbbcb968d542b3cdd8574246d863fa6aeaad1afb3dc529/coverage-7.2.5-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/53/3b2c1fb37a990f1a8987e000e7a74ab93646ad287b562f4d5bc113e0665f/coverage-7.2.5-cp37-cp37m-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/54/5ef3171884fff597781f21f683ddb9dd2c35f05af2947aaec0b6d183dbce/coverage-7.2.5-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/f2/a54848d2582917d9a132e0adaa74f0e067191079fa66149c8a431bdfb184/coverage-7.2.5-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/55/67/abfcaf4034db803b31f1d012d0e2f532d87a2d10954cb8ede1eb23079496/coverage-7.2.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/0a/2ea958a9e8571df2f030f9e3d47add9a9372f06cb2bb01e1e0285383ed53/coverage-7.2.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/ef/dfbcd7e5687559cbd04933985707cdeb811c3815336a8bd0ae1077d9f11c/coverage-7.2.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/c6/7f263714255cda41443a6f741654499e2f2ef9925b57aa4e159a81785edc/coverage-7.2.5-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/f7/cfa587948bfd61c6a50ea0c208a98ae92b2368ccbef39621bae02cc49e91/coverage-7.2.5-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/59/6f310c6b5e71a3dd374af186eb9b1380f0f34b684e805812ca1e93874748/coverage-7.2.5-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/3c/8c6745ff0c58707a70607ccc63f27b3437494fa98715181a40c44388d789/coverage-7.2.5-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/67/b5/6b5751966ffc4da52b0b68aa60de105ff0666f6e99a58e2beea614df399c/coverage-7.2.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/93/c992d89b11661ab4121c7837a5c6cf3e2eabbbbae668132b5d6b8ad95c41/coverage-7.2.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2c/22/82903d8d343bf6e174e566a4cf3f767315db4dd52a17d7f0255bff7e56c7/coverage-7.2.5-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/af/d56666ff64d15d753bbe6c4db8bba76c244be0b5a51061f2ede35d72dc62/coverage-7.2.5-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/81/72/340c4bc7fc32d1148c483c07561850b95113277cdf3e7d44183ebfd372e8/coverage-7.2.5-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/1c/b67259cb5dfe9a94eb65d542d8190cffc609f2d387038228b4f31a3e486b/coverage-7.2.5-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/28/ae/2d01daabe5dd2652be564f8c49d51b553b1f05a263a1c58e74accb400030/coverage-7.2.5-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/97/eb8cdc5a82947efd330c13fd17f7985c20135d7fe7263652cc37481298a7/coverage-7.2.5-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/c7/28ed5ec209f79197b0f9a0b74d1b0d34b513f744eda2e828376cfb13a49f/coverage-7.2.5-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/3d/87/ac5bb366221fe53c55f5ea83b14d476834703cbb395dcc335a92742737c3/coverage-7.2.5.tar.gz (from https://pypi.org/simple/coverage/) 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compatible tags): https://files.pythonhosted.org/packages/09/cd/8d4acce078204146233ec355c8cf59f39fabf9d207bedc710d4f4cb430b1/coverage-7.2.6-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/4c/75f9e7d5dc8606d058ec621e47d9e7eb9d4773210afcc9a1d31533d469b3/coverage-7.2.6-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/da/2a/323413d28a11306dba90cc3a88e9faf46b1cfe718566d0fb0cd490d79488/coverage-7.2.6-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/e7/e1888ec5aa7e973706a6a241499e7f7000e4793221bda681ff17b22fb1e6/coverage-7.2.6-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/77/f4/ff5e1dc67ef1f064246f9661349f62c09fe6a949dcd058725db45a8bac72/coverage-7.2.6-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e8/31/9f2ef68dd8020f5ef935999693fe200403adfbf52a5f848065bb79e74971/coverage-7.2.6-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7a/9a/5bfdec784c0398f7e71c4c76f821ec47f4cca6afa7af5fd8c563174d95fc/coverage-7.2.6-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4f/27/8aef369c12595749dc58c5be31c69ab25b4e5e56a211653088089e12e7b4/coverage-7.2.6-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/86/cf/55db38787ad0ae32ee155931a54eca2ab94eabdbff983c48e44d35abc493/coverage-7.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/e9/9920ea959b1ae5f12c3c386172ed9872c26029a7c8244ae62a3ea6e79f58/coverage-7.2.6-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9c/be/edd6e7dc15f26aca5d9e21b98a18c165ab04174776a2a97fe75ca5c58d97/coverage-7.2.6-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/80/db/826ce2bfc8a5f6cc881e413d8ef6cbf3144a1252ca1016278999ffee9506/coverage-7.2.6-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/05/71/780900f3a35fc3a462e6b3c843d8010c035904bf3d91c2e4f23b6c919ac4/coverage-7.2.6-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/82/49a007ca3ec7c6685f6c9d350aed5533a4ca9d82307a20f75b50aee1693a/coverage-7.2.6-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/d7/41ea3d37deff20135e0f5e8780684d91d0e60a7b2be337b8403abc5d56b9/coverage-7.2.6-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/34/97f3abe04d6558343e7f84f592cffa296e0891c7450a848894e33b2442ff/coverage-7.2.6-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/87/5b/6481e014a46dd718fb3ae88960a2738c9d7122c977eb1e2674740239f090/coverage-7.2.6-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/82/7fc7bfa0d586f88e3bebeb28699893c8d45c4fdca31e728224e690eb2c85/coverage-7.2.6-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ae/61/6b8b90203ade6886a07aaf4a296a976032f954ef395a838fb21e279320cc/coverage-7.2.6-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/ae/1c03b506f2ddb1a83ade47fbb385fbbd3fd41361ce3a164e946b16c68141/coverage-7.2.6-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/83/e8/8fa548c0b9cfc4eabf1859babd1a0a1c124d378d7a4d3de1ad52276ed224/coverage-7.2.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/96/66/f19d825894741e072f3c0321241688fffa89ed42d8cd7cb997c45c9c9a5a/coverage-7.2.6-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2e/64/04cf0cdab209c36e405e237c8f65e8b88beefd71a8f1f2321e1ff537c39e/coverage-7.2.6-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bd/1f/d9def4f2b72ad43db50d7131eb40c9cdd17bf4242587b1df2e1e02ee6fd8/coverage-7.2.6-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/81/f7/a54092963ef5432fd59fe87051f2d0a1fe234cc89743b9a3a4e97eb6f96e/coverage-7.2.6-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0a/a5/c292296237ca45eb4ff79bc71397296997f1b49a2b314d2ae29cd785d0b0/coverage-7.2.6-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/f6/2cd73d75fc43c0fd964c9d9d8933f409d0ed4fd46f0fc05e9266fc255938/coverage-7.2.6-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/91/f99141e5f17dc18cda76d7024dfbc80a205a889c9788ce82cffe25a17b09/coverage-7.2.6-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/95/6205f4b8bde42eefbbb9ecad885d42d7aab274904de0cc7d174790c74b48/coverage-7.2.6-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/fc/6d/e8658433ce675a34ac82167ce8b890b1c020dcb6bacc7a0e4505af82bfaa/coverage-7.2.6.tar.gz (from https://pypi.org/simple/coverage/) 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https://files.pythonhosted.org/packages/b8/9d/926fce7e03dbfc653104c2d981c0fa71f0572a9ebd344d24c573bd6f7c4f/coverage-7.2.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/3a/67f5d18f911abf96857f6f7e4df37ca840e38179e2cc9ab6c0b9c3380f19/coverage-7.2.7-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b4/bd/1b2331e3a04f4cc9b7b332b1dd0f3a1261dfc4114f8479bebfcc2afee9e8/coverage-7.2.7-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2b/86/3dbf9be43f8bf6a5ca28790a713e18902b2d884bc5fa9512823a81dff601/coverage-7.2.7-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/e8/469ed808a782b9e8305a08bad8c6fa5f8e73e093bda6546c5aec68275bff/coverage-7.2.7-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/29/8f/4fad1c2ba98104425009efd7eaa19af9a7c797e92d40cd2ec026fa1f58cb/coverage-7.2.7-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/94/4e/d4e46a214ae857be3d7dc5de248ba43765f60daeb1ab077cb6c1536c7fba/coverage-7.2.7-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/95/16eed713202406ca0a37f8ac259bbf144c9d24f9b8097a8e6ead61da2dbb/coverage-7.2.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/49/4d487e2ad5d54ed82ac1101e467e8994c09d6123c91b2a962145f3d262c2/coverage-7.2.7-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/5f/d2bd0f02aa3c3e0311986e625ccf97fdc511b52f4f1a063e4f37b624772f/coverage-7.2.7-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ba/92/69c0722882643df4257ecc5437b83f4c17ba9e67f15dc6b77bad89b6982e/coverage-7.2.7-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/81/b108a60bc758b448c151e5abceed027ed77a9523ecbc6b8a390938301841/coverage-7.2.7-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/5c/d9760ac497c41f9c4841f5972d0edf05d50cad7814e86ee7d133ec4a0ac8/coverage-7.2.7-cp312-cp312-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/69/8c/26a95b08059db1cbb01e4b0e6d40f2e9debb628c6ca86b78f625ceaf9bab/coverage-7.2.7-cp312-cp312-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_i686, cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686, cp37-cp37m-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/67/a2/6fa66a50e6e894286d79a3564f42bd54a9bd27049dc0a63b26d9924f0aa3/coverage-7.2.7-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-manylinux1_x86_64, cp37-cp37m-manylinux2014_x86_64, cp37-cp37m-manylinux_2_17_x86_64, cp37-cp37m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/d6/53e999ec1bf7498ca4bc5f3b8227eb61db39068d2de5dcc359dec5601b5a/coverage-7.2.7-cp37-cp37m-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e9/40/383305500d24122dbed73e505a4d6828f8f3356d1f68ab6d32c781754b81/coverage-7.2.7-cp37-cp37m-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0e/bc/7e3a31534fabb043269f14fb64e2bb2733f85d4cf39e5bbc71357c57553a/coverage-7.2.7-cp37-cp37m-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c6/fc/be19131010930a6cf271da48202c8cc1d3f971f68c02fb2d3a78247f43dc/coverage-7.2.7-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/28/d7/9a8de57d87f4bbc6f9a6a5ded1eaac88a89bf71369bb935dac3c0cf2893e/coverage-7.2.7-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/e4/e6182e4697665fb594a7f4e4f27cb3a4dd00c2e3d35c5c706765de8c7866/coverage-7.2.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/e3/f552d5871943f747165b92a924055c5d6daa164ae659a13f9018e22f3990/coverage-7.2.7-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/55/49f65ccdd4dfd6d5528e966b28c37caec64170c725af32ab312889d2f857/coverage-7.2.7-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/31/340428c238eb506feb96d4fb5c9ea614db1149517f22cc7ab8c6035ef6d9/coverage-7.2.7-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dd/ce/97c1dd6592c908425622fe7f31c017d11cf0421729b09101d4de75bcadc8/coverage-7.2.7-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/de/a3/5a98dc9e239d0dc5f243ef5053d5b1bdcaa1dee27a691dfc12befeccf878/coverage-7.2.7-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4a/fb/78986d3022e5ccf2d4370bc43a5fef8374f092b3c21d32499dee8e30b7b6/coverage-7.2.7-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c3/1c/6b3c9c363fb1433c79128e0d692863deb761b1b78162494abb9e5c328bc0/coverage-7.2.7-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/88/da/495944ebf0ad246235a6bd523810d9f81981f9b81c6059ba1f56e943abe0/coverage-7.2.7-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/0c/3dfeeb1006c44b911ee0ed915350db30325d01808525ae7cc8d57643a2ce/coverage-7.2.7-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/af/5964b8d7d9a5c767785644d9a5a63cacba9a9c45cc42ba06d25895ec87be/coverage-7.2.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d9/1d/cd467fceb62c371f9adb1d739c92a05d4e550246daa90412e711226bd320/coverage-7.2.7-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/57/e4f8ad64d84ca9e759d783a052795f62a9f9111585e46068845b1cb52c2b/coverage-7.2.7-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/88/8b/b0d9fe727acae907fa7f1c8194ccb6fe9d02e1c3e9001ecf74c741f86110/coverage-7.2.7-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/66/2e/c99fe1f6396d93551aa352c75410686e726cd4ea104479b9af1af22367ce/coverage-7.2.7-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bb/e9/88747b40c8fb4a783b40222510ce6d66170217eb05d7f46462c36b4fa8cc/coverage-7.2.7-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b1/d5/a8e276bc005e42114468d4fe03e0a9555786bc51cbfe0d20827a46c1565a/coverage-7.2.7-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/0c/4a848ae663b47f1195abcb09a951751dd61f80b503303b9b9d768e0fd321/coverage-7.2.7-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Skipping link: none of the wheel's tags (pp37-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/67/fb/b3b1d7887e1ea25a9608b0776e480e4bbc303ca95a31fd585555ec4fff5a/coverage-7.2.7-pp37.pp38.pp39-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7)\n"," Found link https://files.pythonhosted.org/packages/45/8b/421f30467e69ac0e414214856798d4bc32da1336df745e49e49ae5c1e2a8/coverage-7.2.7.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.7), version: 7.2.7\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3a/58/9c4bb389ccc0ba9f9337d7e2f313a96dacbd2647e774cdc43de4325186d4/coverage-7.3.0-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/3b/5bdcbd8c64abf4eb1d61addf11754ad5883f3bda1d612cc843cbb3958902/coverage-7.3.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0c/e5/724283de5799ce58e5efd5f1989919f115d9db273baa98befd99827c80cf/coverage-7.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a5/40/2e6791d3bca7734d6c2f42527aa9656630215a8324b459ae21bb98905251/coverage-7.3.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/02/f24041262825e15425fc77ecc63555cf3741d3eec3ed06acdd4bdd636a9b/coverage-7.3.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2f/60/6fb960383f9159f67ba08924de6f8ac75aac6107c67dc9c6a533e0fccd3e/coverage-7.3.0-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/09/7e/2f686c461ca6f28d32b248ec369c387798ec7e28a4525b2f79988c3f8164/coverage-7.3.0-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/1f/a8132477bd5ca4f7e372c7d01bf8e844db6c0805f18d3d0e0b913e6cc22e/coverage-7.3.0-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e6/b0/e15a3acf0dce6215afcd2186f53fd534d2b456208e078409431b9e70445a/coverage-7.3.0-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4d/bd/1c3e5ccc7372fa7b65b294017444ef7b3040016349a3762c561ad271375a/coverage-7.3.0-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a6/c5/c94da7b5ee14a0e7b046b2d59b50fe37d50ae78046e3459639961d3dccf5/coverage-7.3.0-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/56/61/0bc551ef5e4cd459c34e769969b080d667ea9b2b3265819d4ae1f8d07702/coverage-7.3.0-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7a/6b/f16c757f34adaf76413b061ff412d599958a299dba5dfb9371e5567b77d9/coverage-7.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/62/b9/de6fc3a608b4c0438b96e120fe83304d39b6be640b14363004843602118d/coverage-7.3.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/ad/1559ab85952a47531004f9a32bcac51f9755e9541fb03eae42a9358e00dd/coverage-7.3.0-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/32/5a/d8e474e01fde6511bf8354df005248aeb2e3a71dacfe1624fbc2916a15f4/coverage-7.3.0-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/05/1d/45d448cfa9cdf7aea9ec49711a143c82afc793e9542f9ba9e3f5b83c4d4d/coverage-7.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/43/29bb5ceabd87bdff07ac29333a68828f210e7c2e928c85464e9264f7a8df/coverage-7.3.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/be/ed/cdeeb2a3c7803b98fb452f624bed905929508030dbdae8ea69930d555cda/coverage-7.3.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/83/010ea70978c6deabc64542572dcddd43f27dfec2807fd853621702783fa2/coverage-7.3.0-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/83/a61789658d2441b848dec999b58ce453309f88dc1c62a723a04b8c8141c5/coverage-7.3.0-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4a/6b/4873ec302c4b7dc493620a6f82b5421262c5c4d1485544e93cf79ad7b989/coverage-7.3.0-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/5c/58e39b974c30b5d7d9b326162ddf303feac270cbc58166f0fbabee3bd124/coverage-7.3.0-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/49/e0/2ab75b127e7948520562ad9047bcc3d1a186409f906083d5435799cc3332/coverage-7.3.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/5e/0278a108b3fed55a4c9d6f21e1fc0ed3c081e18da2a9c716cffbb75c2d20/coverage-7.3.0-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/6f/ebfba55e56aaf256dcae2b8e52a3ec3f1212a11a6d16cf9367d547a862b9/coverage-7.3.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/2f/1cb5dd04bb1aa3c6740e750f4c3fbc35013a38dbfd3394d57ff610da949d/coverage-7.3.1-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4a/6e/29862fdd3783cfbb99cfac1fa8ae65fbcb031e5c431184fe0fdd472a890c/coverage-7.3.1-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f3/c8/2fa541357143ff9461db094f802d5880112212f8ec24dd8773c0c3f1c90b/coverage-7.3.1-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/f9/05634b5d28218dc9d6c150588468e314685e1166c05c3cbe9b2983a2e060/coverage-7.3.1-cp311-cp311-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/b7/cfbc4d13e64b855bce829ce84c4c0828885cf045367c15f591b4edab8c1d/coverage-7.3.1-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/b7/006256429deb514fe8e304d7817b89f093dcb22eace9b3e6aa1cfda0cf75/coverage-7.3.1-cp311-cp311-win32.whl 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https://files.pythonhosted.org/packages/f1/5a/b670d8a1f07a6d57278d37e765250b5a7d3e2d1b8904fcc56420c16d2828/coverage-7.3.1-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d4/fb/ee6e9351694f97e33b19b70b040d90f43d1ca7ae9fbc140e66c7f9161a7b/coverage-7.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2d/1a/a46dbcfbedb581988637c75a7f467a7f8dfe684b0a975736c5acf78524e9/coverage-7.3.1-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/a4/015c3ab8f87b38a9f6aae0f0bee8f7cdfb0fe2ed0cdd084e58946ae03d35/coverage-7.3.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b1/d7/0823053928dea2dffa5d0ec49f02a083c8ebeed3f053aab4010723461610/coverage-7.3.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/43/b9a7f83f1061fe6a54b3a58dfd6b49071cfd6394a476075c800a7bccaf20/coverage-7.3.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/4e/16cbc39925e52fc1073df36dd8af29e12e570a32c89c9d356698b04ff75b/coverage-7.3.1-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3a/dc/4acc34fc50ffc9c253541b5b11764422f36e079ed277a8914e0e081af429/coverage-7.3.1-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/ec/3637baa306c317c3fa28b6fe1072fe6ea53f43e0d15fed149250113274d5/coverage-7.3.1-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/08/8a/c35faee85da0444b6b17547dc908d1f7f74e4087c8dc1bb073bc6170e312/coverage-7.3.1-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/80/464823e327596913e8d14c3d62d6185fc95ba09fa703633d7f4233694058/coverage-7.3.1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/21/3b/bff94691f95b248ba0be37dccae75de32e4f69b6444ebfe9946aecf150e5/coverage-7.3.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/b0/5eeb9f805d4c1a29efbe95265a8d8bf87c345d64be820c40543e9282898b/coverage-7.3.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/60/9dc10827724d37aa71b61f73fadb901a1c7841bdb57523e1970946fb95be/coverage-7.3.1-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/07/e146ddf53b7761cb2040932c562b3af28c80ac2e043a4d5179e648b9f20a/coverage-7.3.1-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/a2/552abe631e1293dbf7d679f602f75135e26f33f1537b8dc1229f3ebba624/coverage-7.3.2-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/60/9965e385c8492ace75c127a388c652de36d0fef6c36df7ab85accb77bc8b/coverage-7.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ef/c3/51159a4a3331556b231bd0241e16c6d3b374c1da4dedf35614a27905c598/coverage-7.3.2-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/17/2b/209c93095f9fc441396f131eb154d87c22c8b5056c795fcd529fcf5d1d0b/coverage-7.3.2-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/6b/4d3b9ce8b79378f960e3b74bea4569daf6bd3e1d562a15c9ce4d40be182c/coverage-7.3.2-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/df/9283cc3e33342995971c30be0e32d05f680f1ad2a25e20eb9e5b978e56a9/coverage-7.3.2-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/65/c8/9a592fe3d81cd67c3fa4f2b3ff2110c472da0176669405780c515e253ec1/coverage-7.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/bd/1915390d15093f8dfbb9c3b338f6da64be7d052e6a0e527e610f56d0d0ac/coverage-7.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/bc/01/bf243cf5c926681b35d0c6aa9a3b33da35ab65323c4a593d386b08a0249e/coverage-7.3.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.3.2\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f0/f4/069a73ba15fad98930a23ce35ddd12b45b5a29ca8c065076b1f4c5475192/coverage-7.3.2-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/52/07/215f1373eeded0ba34fedc7d61ff72ae771705692dfead54e74a97bbbfbb/coverage-7.3.2-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/54/0a/4e9781bd1814eee3545b451bb680e9338dfecf9406316c0d2d148fd34d8b/coverage-7.3.2-cp312-cp312-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/c7/6723b42ee2776e5a7a5288aadf9562a69b67aa32260e4692dcfab5944fb5/coverage-7.3.2-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/82/23/ca8de5b2629fb9891d2fd154054f79625e0f1fc141a348a834e0f131c3d8/coverage-7.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/49/e2/a7a6451ca4e1e6676288632a948d7438e2c71206fc6c01328e7eb58f571d/coverage-7.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/f7/066ac34162f711a18abf90b6c2f277a976c9804c548bcd81f543e0996b27/coverage-7.3.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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(cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/24/c2ffb7777863c805df8761eac1e9c2f176c2abf1e1195234517eafb69d68/coverage-7.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/aa/c78014c8292ce3167fe283206904c9a9e16c0f033e3a24282f50a81dc04a/coverage-7.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f1/e7/6d778d717d178c8c73103e2c467f3c8d8ebc9cacb825ebe3f3cf05e7c6df/coverage-7.3.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6d/70/5e8f300eefd7d494538b906a5d6e891c77ff1f37696dcf6cf86379462e0a/coverage-7.3.2-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7e/82/21e62bd33819427143a3c8368581c54dcf74fa5e7c0e7328239655c32062/coverage-7.3.2-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/4a/3b4400eb8e4e1455175370e2f10d1d388caba28123bb4870b7c0065764ea/coverage-7.3.2-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/96/b4/0a4981518f349ee00871cc4b403bc12f4e5385093dc7f25ad94508eeac5d/coverage-7.3.2-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/20/3aec615a2f1f401dc70b96396a7b82b80a71f49d4258fd5bb7edcfd68be8/coverage-7.3.2-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/25/26/b1e4533eb8e6eb80a61e442a61f86c55a962a6693114053a790870f1d37f/coverage-7.3.2-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/57/44/ecd5442163c53f333bfcd2e7f428457a68b008a4b65d436a64b1db362451/coverage-7.3.2.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.3.2\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/a2/2abbc6fe2f2f79a0fab8e09945ca203b1e0e7a615033e8758559952f393f/coverage-7.3.3-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a6/05/7df10c5f37d61d54b0978ba439ce214da1ff1a0f17c50723d6679745612e/coverage-7.3.3-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/1d/0881cba71253f77f624ee9744b43bab54131a38c87dda3ea0baead257625/coverage-7.3.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/5d/7b3dc974fb0119f495591cb86772239ef1ebfec9195814f4d048f4fae71c/coverage-7.3.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/71/d3/c2f1e16271de67ba87a1d2d2875aaaec305f29dc503be7f91b3fb01f9028/coverage-7.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/d1/79318319cb9188610d5620cb714a9ad81b75ceaf2c6d7906786c85cd1695/coverage-7.3.3-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/70/a1/14fabc6df7d5e99a6eb2ee6fd7dc8bb1434bd9573c9eba63fbbffd3940c5/coverage-7.3.3-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/5a/d0ea42098f29b3656e4708db5c01fb615976e83a55a4f1e020c8dd76849d/coverage-7.3.3-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/53/b696fe3048910267d7fc68a82c76e4e3fcfe3a50b1c2a1f579d14eeaa7d0/coverage-7.3.3-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/38/9d/25e653f68239d797eeb0f064ed0a781950d33e0cc27a8f77a56e5c841c1c/coverage-7.3.3-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0a/6d/70e76cbc35e927d7c978dcceafd237dadb75ed75590c3624d993539dd3d3/coverage-7.3.3-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/89/0e/069f47850f6b247038c7426b2181eb099d5342c7c05f8f3f87b99896dc97/coverage-7.3.3-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/71/10/21a9ab8a705f7323e66d875d15568dda1673a4613eb5867e24f9a43e04c0/coverage-7.3.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/71/3878d5ab345d7011bc20910bac92ccc92a9a48ca054ab38696baac7dae03/coverage-7.3.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4a/11/0591f1934e87e5212f925f777d5fe5873707953aeab6e2bb20a8a7673411/coverage-7.3.3-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2f/a5/6d843867cc138c0881b879f88a0af0c75bb3050f154ec9abc1fe45b08377/coverage-7.3.3-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/1d/21e264146b8c012c419d1cd2e84b3ef53ed3f84a8a2119ab9366228e2e09/coverage-7.3.3-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d2/8b/113b8c7e7f7c258a0a7054f66d2afa1d974d22a8b220e43ddd45bfa1cabb/coverage-7.3.3-cp312-cp312-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b1/56/99c484d06e851e77da7b2fb777fa43e40cdb773fda7829d5be57fb7ff869/coverage-7.3.3-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/1c/1dbab8cafa43b81c90d0c8566443c354313384a69c9b1f9aa2d7d0f293b9/coverage-7.3.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/67/044b75ef13cd9e3455676c53b769e0dabfacdac5b6decf15d88293bb3b63/coverage-7.3.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fb/dd/85c0289975ea609d3f82ecb5b7ed827af8c51518440ce5e56b509765915e/coverage-7.3.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/56/09/b7f1c8abee26d0c79d5b78a5048cfc55a945b86a73892fabc3c2b3a7f99c/coverage-7.3.3-cp312-cp312-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/87/a1/37c34343655558a8bd095b07a769026cf1ec2020ae36de292f7d03f85caa/coverage-7.3.3-cp312-cp312-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/03/733db9c235927b5d70fae61b7055fb3ced4c2725eac237ba4ef8fa560ecc/coverage-7.3.3-cp312-cp312-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0e/e0/e529e363057e911f5232b4887e23f497ae71ddedbfd612040425245880f2/coverage-7.3.3-cp312-cp312-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/58/d5/1b35ef4c4f4dc1b9c61be243c1a3e50adffae1a1635375bfd1eb3da0fcd8/coverage-7.3.3-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1c/54/2c907897dbd8d20faffe373fbc06ed6847a22a722eb121ab0c184b8f9330/coverage-7.3.3-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/38/4d/732ce9bc04d5fc1c95ec94037ec567a2346e38b68e13f87d3ea5e26953c0/coverage-7.3.3-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4e/c5/2920b629cbcb90b00d2105bf87670748cbeb55c4dd5a3a329337bd31494b/coverage-7.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/51/8d/9d6407fc2cf53ad956ac779af4b64adcd9340ea63d46b66fda4e0e457a2a/coverage-7.3.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/19/432d0e438546b17fe20bed3a26f447ba7bfb24fc3414b61b6f545994a9dc/coverage-7.3.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/04/f63c0c05fcc74818c03a671ff01ad04dc14a76c96a8721f178e3b581cc31/coverage-7.3.3-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/5f/7697843e13046cabbf492b62ea6b1a9fa679a41ddd04fdf540e16a615182/coverage-7.3.3-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/76/9583ba7f54fb679c3d2899656a860acabbe3159f26c44c566d022c3b68ce/coverage-7.3.3-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/29/1e/2d540d4f3b53212f67cf109509ab76c787e881e04017429e6cdc4183fb38/coverage-7.3.3-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/28/bead5a1d515eaf4c31de05ea929b5a0b0a115871675f0fdfe8634340d9a7/coverage-7.3.3-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/36/30b477648b55611cb904e1917b7f61e57532178f3f2bdc70dad3f5ab321c/coverage-7.3.3-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/8c/341543ac858e042e9c92ae028d6185a8cc8a9e4991d95dd92076e981b4dd/coverage-7.3.3-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/a1/29191b24dd60b6beff4138aa36d6c4ff26cc12993a1235e0335db62357ab/coverage-7.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://files.pythonhosted.org/packages/82/bb/f73492b1d983749c4ad092b4996e86d6a026599f28c726a14f02b5a6bed8/coverage-7.3.3-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/77/1c/e67e6b1f9edb2177c122f8490ff451ff26407cf66e1420e1a5cac645d4c8/coverage-7.3.3-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/12/c5/126ce69491f8158a96025298f2f1451ddcf290c12e91ac89e16ad93b0808/coverage-7.3.3-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/59/78/6478c55544b57e6243f048de0d640a9364849a7d110d051cfc85d46ff299/coverage-7.3.3-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/75/6f/a3f541a23e939cb9d18c8f02c56a0cc454b7922ef71a8dc0ac0550c375db/coverage-7.3.3-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bc/82/08952e3b0287ac4036bf9f9e31d0736629f4b8494c0d33c906a2c8f83139/coverage-7.3.3-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/cf/79/b8ed9dbd011d2c9c7e0273f59daae4645469ab4ab09a23e1033e352978a6/coverage-7.3.3.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.3.3\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/69/80f691610763e9221923476485ec8fd1983d57c4087d6bf02e2ade9912b2/coverage-7.3.4-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b4/de/c103a40d50b3850646c706d99139239951cb7662330f8f6db8cecca88cc5/coverage-7.3.4-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/02/78/2cca0586281bb9498f0d0e48d694747a01391e1907f3967a33ddd363db91/coverage-7.3.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/7f/f70d54d07424cd9d7afd8e00865c17fdea4956683a07f0e039e0ca9aa563/coverage-7.3.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/94/1b/bd597a07755b233822760c9998d603218ca4b3151b6d5048e9fd0a5bf572/coverage-7.3.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/59/be/ebfd136a05284d78540f543d50dd2222d6a1f6b1806cbdedafe8e9a195e6/coverage-7.3.4-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/9d/b8b85b3a3dcf06c176f7167cb312f6bfbcf57f3eef12c4dc1039e7a0c299/coverage-7.3.4-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3f/a8/ed5862e71cb9b0f3c85e9f527e1aadcc70720d8fb551b30bd60c1df15513/coverage-7.3.4-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/6d/27a0d933cb167808bbfd2f6686230b9d3db96500a837fe06e9fb9c483c6b/coverage-7.3.4-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/97/f0e980ae1a423fb779f62eb5b9a1ac0298b7edc0d6ef5e463f5da59e8676/coverage-7.3.4-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/85/3c0390ec821c88704382ed539d69b525fce3c3be44e1504107231573be96/coverage-7.3.4-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/83/d6916f789eb6295856ab57babcd1ee639003c7db1b08cd84504a83c04c72/coverage-7.3.4-cp311-cp311-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/72/aa/0fcbb87d7f2bc2efccfe07c374fb187faa018310bf94858071a2ae15c678/coverage-7.3.4-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/68/f35107fffd24622c0c7e3a4e2d69006a79c299064ef5781c65bf10c09b75/coverage-7.3.4-cp311-cp311-win32.whl 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cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/25/4819620dcbb7015f9507004c1365b06119d12984a0d7085fc1fa464caa12/coverage-7.3.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/93/d675de0a14b49d0d53413567ece7f72aea87ee13ecd254539d2a29fbf57a/coverage-7.3.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/5b/0c8844480ca9fe7d5397019fb64b9b4bd45e011d55f67d17ca1aabd2209f/coverage-7.3.4-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/98/15/931850561f1ac0e29fbc2c916912e0a3bd611660add4db68b5fcd95c6705/coverage-7.3.4-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/49/6f8bfa205b9a86d6b6204e03044ec1fa30f4595d454a391b9743ff224850/coverage-7.3.4-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/64/9e0d36d9aa055fdad87d66ab47a6686318498e2142c5e3e3e72d5483ccfd/coverage-7.3.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d4/a2/56abff1081a0d7d536d2bc21fba0fdf3df30839a930880a2d028b33c5f5f/coverage-7.3.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/7b/8437bd2ba2f6ffa7228febba69011e1774a8d3bf3ad5a1a6f56b3a851177/coverage-7.3.4-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/80/028617e26e4286ee5678110b0a46daba740532da048be30e2e69f3015bf2/coverage-7.3.4-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/9c/7dffc6f9a6467ce260f03cbf11c21bf5b740fd668b2e57118e23014aa0f4/coverage-7.4.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/4d/cda0f5ab752b95734b8831136871032a716f8505d52d533e90e98e16b3ab/coverage-7.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/18/03a276a0f4f7847f50d21b1e9a5c715438c0ae03afd4a944c2123bee3621/coverage-7.4.0-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2a/9b/2a17ca3f7c59e77d18b6a829ef8fa4bff7690c1e77035e6a67b4531bc484/coverage-7.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6e/0c/dfbc0437e55850bafd7a6e11899da610e44ceceb9943991f4f9f2433986b/coverage-7.4.0-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/62/1a77a23d2565850ccf4dc885906100b30ebd753f8e8106736c961dfe80aa/coverage-7.4.0-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/29/29/683527d6ce0c6fcfb2d46b3cc4fb3d5bf10fad89588f338b6ffec6203300/coverage-7.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/37/81916e4220c96ca15d8b8c38d6be68cf83b0fe7f70e22846b0e4d52fed91/coverage-7.4.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/9a/825705f435ef469c780045746c725f974ca8b059380df28b6331995a2ae1/coverage-7.4.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/d1/3fc27235187d5cfc4ab8739de8da7ec8fc447b7d29136fdee31fa2951652/coverage-7.4.0-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/87/71/0d90c4cda220c1f20f0eeaa997633eb1ec0bcaf5d8250c299d0f27a5885d/coverage-7.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2b/cc/bf2bfaf953a47d9771383cb87ce2985ec3d4aad445fb88143c24d9839079/coverage-7.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/10/97/ca7dec2d9a1262bc0dbfb757989444fec8cde908083b15fb3339210aa7b8/coverage-7.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d7/95/5f7e7f7d46d671d1b81e36ef6439798645ed042c1ffd116ded89897b254d/coverage-7.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c3/92/f2d89715c3397e76fe365b1ecbb861d1279ff8d47d23635040a358bc75dc/coverage-7.4.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/c8/a0f6a2fe09bcada89661eca3c79ee71ad33acfa2ee141b4e300a7281563d/coverage-7.4.1-cp312-cp312-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/8b/41ff7b01b18fac13af654b9c398b4c3cf40ae19f02e9633ea49898799f85/coverage-7.4.1-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/75/a4abb6a0d1d4814fbcf8d9e552fd08b579236d8f5c5bb4cfd8a566c43612/coverage-7.4.1-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/4e/66a3821f6fc8a28d07740d9115fdacffb7e7d61431b9ae112bacde846327/coverage-7.4.1-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2a/12/89d5f08eb9be53910e3b9b2d02dd932f9b50bac10281272cdbaf8dee58d9/coverage-7.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/46/4d/9d6a7081c31d1388bff379250ab3ab0c873330c8139c07e8f4b6df61fe65/coverage-7.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/b9/49b1028a69b1e9476db7508705fc67a1218ece54af07b87339eac1b5600a/coverage-7.4.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/54/4c/e2d59855d36921e3025380f75e110e672bb8500a5e5832af59b65a218ee4/coverage-7.4.1-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/72/31/a8d0a018aceecf8b2728f924c0a2d1c07c36be611301db1843538315dca8/coverage-7.4.1-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/e3/87ee5c1250934d42038680c41c04bac813025913c460c761859b04dcbff7/coverage-7.4.1-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/c2/6e58ed50a5dc87ef347b675a1c9157e42080881edf4516acd35775427566/coverage-7.4.1-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/6e/6ff67d0f7514bf2dd119f2fef7374f7c853dae042129286d69eebb431028/coverage-7.4.1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/ae/0d439dc9adc0111ffbed38149d73ddf34f7a8768e377020181e624cf2634/coverage-7.4.1-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/96/38/474912cc19b0ba5ad4cb0bcfdc1fdec177c16cbb5df61db3c28d59ce33f7/coverage-7.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/a7/bbf7b34f874e03ba43beb5d837b519582478fb76e25f416547dc9a816b8a/coverage-7.4.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/ef/9132aee1ec59ba00ba43ee59dfe757569fadcab493f36e1706c8a078fd34/coverage-7.4.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/12/edc15be6f760302f4045e795efe6180e9c605c5147a2e16b6f51aa665479/coverage-7.4.2-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/38/60/9588687af3e716958be97c5774f557f113186a5aa729a8dc145109ef7d3f/coverage-7.4.2-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/8d/f05079f37bfce291eeae88ed2fd88f739501aae46a85dbbdcc91a1544d5b/coverage-7.4.2-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/23/060aee28dfb1d35e3d71da2bdfa7d9f6f0c45a1d7266465f73e22853241a/coverage-7.4.2-cp311-cp311-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/11/c8/44c176d82053128014d6d5311cad00a036a47f28af39e193780edf377e82/coverage-7.4.2-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bc/c1/fc8b48d919c9ccf93aa2b339bee2b8ba91f27c28379e8cc383a04a443755/coverage-7.4.2-cp311-cp311-win32.whl 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/48/b4/3aa6b6d53f71d7f3178283df8d11bbe691975a15496af628cae3cf0c98ea/coverage-7.4.2-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/d0/28d3fe029cc0a6f1dbdd8b22eefa2a69c5b36f960d34f085e802339f34d8/coverage-7.4.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, 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pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/06/c6/1f1a2cf9886d31ee24af4b697d0f75f1362ec027215a00f231654f665720/coverage-7.4.2-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/d0/9ca7448917d7add2eeb24f4e2068ed1e2272b8354b4f11c83fcbbe06b429/coverage-7.4.2-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2e/3a/16b3a77f3c3ef3f30cadf0bede3a91ed14f8bc9b3f75b8e5a7ad193eab34/coverage-7.4.2-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/cc/ce9ba987f177054cb29c9fef5fbdc4ff69c47ac136b6ddee2bc5c2d93bda/coverage-7.4.2-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/79/b2/a23ef2ff36b935a3eb9c31e991ea99526e121b177ffef1960086b10695a4/coverage-7.4.2-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b8/96/1bbfe9c580685b5e029b21c42aef008b7c21646fc907032d8590c05b3c3d/coverage-7.4.2-cp39-cp39-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1f/c0/6f160690aa24c9e4cb6b985145bef7af4d2f543f3d4541d1bf6dfa85341e/coverage-7.4.2-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/10/3911dbc8d2f81b43a02cc387823e1d59f2e496bbcf1aa238a636dacaabf3/coverage-7.4.2-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/58/2c/7d977d8771790f27065dcc76d54dbacca6e6cdb9642d61a26e7c308898be/coverage-7.4.2-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/bf/4f/a873791c3caa6c064a05332a5954f73b7ad6cfd6c1a1368aef1dd4ff1cfe/coverage-7.4.2.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.4.2\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/5a/d727fcd2e0fc3aba61591b6f0fe1e87865ea9b6275f58f35810d6f85b05b/coverage-7.4.3-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a3/36/b5ae380c05f58544a40ff36f87fa1d6e45f5c2f299335586aac140c341ce/coverage-7.4.3-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9e/48/5ae1ccf4601500af0ca36eba0a2c1f1796e58fb7495de6da55ed43e13e5f/coverage-7.4.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/a1/161102d2e26fde2d878d68cc1ed303758dc7b01ee14cc6aa70f5fd1b910d/coverage-7.4.3-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/af/1510df1132a68ca876013c0417ca46836252e43871d2623b489e4339c980/coverage-7.4.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/2c/dafb1ae6b813225a63d9675222ea69f0ddd22062fc2af7f86eefc4458323/coverage-7.4.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/a9/1a/e2120233177b3e2ea9dcfd49a050748060166c74792b2b1db4a803307da4/coverage-7.4.3-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.4.3\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9d/d8/111ec1a65fef57ad2e31445af627d481f660d4a9218ee5c774b45187812a/coverage-7.4.3-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/eb/28416f1721a3b7fa28ea499e8a6f867e28146ea2453839c2bca04a001eeb/coverage-7.4.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/02/255bc1d1c0c82836faca0479dab142b5ecede2c76dfa6867a247d263bc47/coverage-7.4.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2f/db/70900f10b85a66f761a3a28950ccd07757d51548b1d10157adc4b9415f15/coverage-7.4.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4c/22/a7c0955a0c9cdfbf9cb0950f78f32a6afc7006e93162ed2cc182fbed1167/coverage-7.4.3-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1c/0d/1b06023f0df5b803effa41c3456caa2e81438fc90a2c2c37588254b97b98/coverage-7.4.3-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/3f/cde6fd2e4cc447bd24e3dc2e79abd2e0fba67ac162996253d3505f8efef4/coverage-7.4.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/37/fa86779a6813e695b48fc696b3d12b4742cde51ecba8ffb55dd7363f9580/coverage-7.4.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/ad/effc12b8f72321cb847c5ba7f4ea7ce3e5c19c641f6418131f8fb0ab2f61/coverage-7.4.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9b/08/796cb089ce66da0453aed1b4a36be02c6e0e08207dd77d8bfd37cb1c6952/coverage-7.4.3-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/f1/0c7cc82978de5585e8d5e1e09cc1b0272c2e27d759c755b4541f25f022de/coverage-7.4.3-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/56/cc/7b355af846b024e845e7beb58d277088c5a27590975f96666229a4d7bb6d/coverage-7.4.3-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/d2/e2/f2d313169e0ecf1b46295b3ddf28a6818d02c1b047413f38b6325823cb2b/coverage-7.4.3.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.4.3\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/45/f4/10bf725621aeec5cc2fa1bc73021f5ba1ac01bcbf2c7278d8d34e1df6457/coverage-7.4.4-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/10/1e/f676e1655d10bf59a6cb8de0601b7ea3c252c764782a3c2263f6d6bbcf28/coverage-7.4.4-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/93/41/e6e9dbb322f3c93aba7bc519b9c62846d923d7b57398bdd7eda3f0acdd11/coverage-7.4.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/4e/feff6d115dcc239e5850570ca2ea27a243c8a69596e7f1dabe54a6102d89/coverage-7.4.4-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/32/829d0e709fa699dc4e498fa77a561d25fc57954ba32466279952b98f0836/coverage-7.4.4-cp310-cp310-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7e/60/62a8c190d20bf605c89a000fd6d41e3563b5792e7275b12eeefe6803b473/coverage-7.4.4-cp310-cp310-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5a/52/3641a452e1afa686094910287a8d8a472aafa09f833b2716c2c11c5cabb4/coverage-7.4.4-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/9b/d0a8c02209f17549ce2283829b7be2b4eaef8bc7c7e0d8016774e73d54c0/coverage-7.4.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/86/d5d971283ef625391595d79321d3f9bef09dcaa0537db665fb0d4f445c7d/coverage-7.4.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/ce/98e90709f9879d5834d04b49b86736118a78d848a9162333aa659c6442a7/coverage-7.4.4-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/79/9dceb3847177d3bed1df3dd25a7672cc634369bc3cb6d2eed57ed6366a86/coverage-7.4.4-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/6d/e142c823e5d4b24481f990da4cf9d2d577a6f4e1fb6faf39d9a4e42b1d43/coverage-7.4.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/1a/105f0139df6a2adbcaa0c110711a46dbd9f59e93a09ca15a97d59c2564f2/coverage-7.4.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/98/79/185cb42910b6a2b2851980407c8445ac0da0750dff65e420e86f973c8396/coverage-7.4.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/12/2303d1c543a11ea060dbc7144ed3174fc09107b5dd333649415c95ede58b/coverage-7.4.4-cp312-cp312-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/32/d4/60b1071c35bd3828590483ae0f8531f07b77d737e2c81dc51887c03bf890/coverage-7.4.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0a/4f/0e04c34df68716b90bedf8b791c684d6a54cab92fbc9ca2c236a8ca268e6/coverage-7.4.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/6a/7eebb71ebdf5e56b6da69e5ca8f05b743e054ce9d4dfd440dbcb3f9be0f0/coverage-7.4.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/8e/6df9cfab2eb2c5d8e634a18ade3451b587fd75a434366982bdcbefc125e6/coverage-7.4.4-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/9c/bd573c65cf554b9979241c575916897e27107a70205b2fbe71218eaa24c4/coverage-7.4.4-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/60/6b/7ac6da198b2c22fc6ba53e479cc800ec230bc7a40c14ed62358d7f1c809f/coverage-7.4.4-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f9/43/9d3ed7750d2f8a9f7d0d4682fe87ed07080c44f56a8a16a5d4d87c81c278/coverage-7.4.4-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7d/8f/2665744d223dcea532b1cf3a9edd236632f54fd3925b9b464f1d03b4421e/coverage-7.4.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1a/15/ae47f23bfd557364e731ad2ed182331ba72e8c063b806ba317cd327e73cc/coverage-7.4.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/bf/d5/f809d8b630cf4c11fe490e20037a343d12a74ec2783c6cdb5aee725e7137/coverage-7.4.4.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.4.4\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/db/08d54dbc12fdfe5857b06105fd1235bdebb7da7c11cd1a0fae936556162a/coverage-7.5.0-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/ff/02c4bcff1025b4a788aa3933e1cd1474d79de43e0d859273b3319ef43cd3/coverage-7.5.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/b1/7820a8ef62adeebd37612af9d2369f4467a3bc2641dea1243450def5489e/coverage-7.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2c/0e/23a388f3ce16c5ea01a454fef6a9039115abd40b748027d4fef18b3628a7/coverage-7.5.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/81/e871b0d58ca5d6cc27d00b2f668ce09c4643ef00512341f3a592a81fb6cd/coverage-7.5.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/cb/42a6d34d5840635394f1e172aaa0e7cbd9346155e5004a8ee75d8e434c6b/coverage-7.5.0-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4c/26/b276e0c70cba5059becce2594a268a2731d5b4f2386e9a6afdf37ffa3d44/coverage-7.5.0-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/71/cf/964bb667ea37d64b25f04d4cfaf6232cdb7a6472e1f4a4faf0459ddcec40/coverage-7.5.0-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1a/be/b6422a1422381704dd015cc23e503acd1a44a6bdc4e59c75f8c6a2b24151/coverage-7.5.0-cp311-cp311-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9b/93/e8231000754d4a31fe9a6c550f6a436eacd2e50763ba2b418f10b2308e45/coverage-7.5.0-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/6f/eb5aae80bf9d01d0f293121d4caa660ac968da2cb967f82547a7b5e8d65b/coverage-7.5.0-cp311-cp311-win32.whl 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5b/c3/ce852a9f7b6855dd0409e4fa58d13b1b6ed947188c9753fa057254ea7b4d/coverage-7.5.0-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/b4/12b81ad556b4a03c501d4ee83452193deafcc1ab9ebe6f3dbdda42d331a6/coverage-7.5.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6b/03/cbc3665ee7178b00ea09a07c2e71c99774928760e04beebb26350658b4b0/coverage-7.5.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bb/e0/79df954b854d595d63edc061f714a1cf506e30412620025ba240807bd658/coverage-7.5.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/1d/8a6cdd1ac5c5f2456629abbbf6d5a5b761930d72fa341b7ad69d345d4f54/coverage-7.5.0-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7a/d8/3adb547c71cabb9b9cef6c7f8663bc7c912f2c646ccded7a5f5311e1db68/coverage-7.5.0-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2f/75/2034dcd84ab85dcfc7dafa1ef2ed2ae4deedca6ef0a60cf0fb1a1fe62fe3/coverage-7.5.0-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/51/7f/12c3e036daea8aed4a415d5f5e31188ccdfb8fcf65f1dd5ee99290fee284/coverage-7.5.0-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/4f/87ec7da194229cee7c71d837399d8cd9f57a75103b3e4b95a170e985ce9e/coverage-7.5.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/e4/d3c67a0a092127b8a3dffa2f75334a8cdb2cefc99e3d75a7f42cf1ff98a9/coverage-7.5.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/12/7f/9b787ffc31bc39aa9e98c7005b698e7c6539bd222043e4a9c83b83c782a2/coverage-7.5.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/ee/9998a0d855cad5f8e04062f7428b83c34aa643e5df468409593a480d5585/coverage-7.5.0-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/94/1e348cd4445404c588ec8199adde0b45727b1d7989d8fb097d39c93e3da5/coverage-7.5.0-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/81/f00ce7ef95479085feb01fa9e352b2b5b2b9d24767acf2266d6267a6dba9/coverage-7.5.0-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/52/d3/3ec80acdd57a0d6a1111b978ade388824f37126446fd6750d38bfaca949c/coverage-7.5.0.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.5.0\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/73/04/9fdec61663f956e5f46f358a8cebae92c2e82699feaabe5d676e5fcf003d/coverage-7.5.1-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/99/1085837b2ee333095740ba0553b93d8298ccac8a426ffec19557e98a383a/coverage-7.5.1-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/4d/7c687b394b7f86c91cdf62a37b17de7ba483b6114aa077bdde260f652ae2/coverage-7.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/17/d1/8dfa3e3ab88cf832faf64bee9b0e4c7e51865b75a2ca44e20bd09bb74440/coverage-7.5.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/bd/e2/9da6c61f71f769e63122ac9f029dcafe5fe50e1e4043f8dd950daf58290f/coverage-7.5.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.5.1\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/9f/8db7f64de1cbc925ec7b4033c8d21979ec4acb0209e6b77f5cdfbdd83e05/coverage-7.5.1-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/de/e2/b5508991eb49f723ac0c4e31e7603fa4ce3dce7f93aa9fd2f262a5688e52/coverage-7.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9a/41/fa31297336a19844e4be4df2a2b31842be9d7a418b8912529c3d056fad25/coverage-7.5.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3f/4f/fcad903698f02ac0d7501432449db12e15fbe5ecfbc01e363eb752c65cbd/coverage-7.5.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e3/23/b8a91185caa3cc6072924648bacf166cb746c4175fb7371e1d0af8e26493/coverage-7.5.1-cp312-cp312-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/fd/732f683ee88c26f282e0e991975a58cbb1866821ef24d14d8a04043f63f2/coverage-7.5.1-cp312-cp312-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/a9/69aeb5196e0af01cd87ba43860871a2161811520ea1f6ab109847277228c/coverage-7.5.1-cp312-cp312-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0c/0a/f2ed640e2000be5866e0c4daafb73aed1ea370937689488e8c672e93c4a4/coverage-7.5.1-cp312-cp312-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/5b/0cc691159bfb24db1f862b4147080c8788b5626ac89c430b4bbc22347df9/coverage-7.5.1-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b7/51/162b1a5f14a3ecc477e9313be87b9d7ec2ecbcba2bd1a4be5bae37bfdc3c/coverage-7.5.1-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/32/94/9b0ad427654df8e895209b7963daa9daf1c04f9d33e9309559cb2546de92/coverage-7.5.1-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b1/3c/5ee1d6a766f5a1f26e488c11c17ac104f69ab11b4f45b7f11e3e926a17d6/coverage-7.5.1-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/e0/7aaee2781f7282ce4ffd90d9a761e66db41f78585e80553a7090fbcb83f0/coverage-7.5.1-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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(cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/dc/4e0ce431c96b1db1548f80d4a4123fd7c584765c6b46e66ab96410325b26/coverage-7.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/3f/7e0898438cbbec0ca3d915dc08b75e1cfbe3bab27727be7081bfd1f70e74/coverage-7.5.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/50/b7d6f236c20334b0378ed88078e830640a64ad8eb9f11f818b2af34d00c0/coverage-7.5.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cb/90/f7293f507c3ec7523d98fc03412052f8bdc081a9199a779ad2ab8faeda0d/coverage-7.5.1-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/14/d7/d13c2e7ee932dd3d1b51bd3e7d822bf0f6bb069268ab8efe52052a68af01/coverage-7.5.2-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/0e/4d8deba914195402c4dc1863aa3b7490ca383a90a4e56b2d8e1ef67709f0/coverage-7.5.2-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/d3/48/b806bc980a0f57cbe9d4bb2b76ad302c81c0f6affb30f6e8dd9d99d1555f/coverage-7.5.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/c2/fb3e6dc3c12f065e28af01645297b4a4040fd3034b6a65f722bd97661685/coverage-7.5.2-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/ba/50ee333d7dd67c8cb377e92c20a32ee714a3babb08046c508dfc38675144/coverage-7.5.2-cp310-cp310-musllinux_1_1_i686.whl (from 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/a6/b915c9d419cf7638cffe93e79b15ec9f2c15178b9de327ad8fdfc96e7601/coverage-7.5.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/e4/6c151663f181b2e6470afdf8f78c66628b67a5c64d5531c62ec18c622192/coverage-7.5.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/29/3d/b46d6ddc60cb7f8b23c0731deb4b01a5ce19c06827d61e873f4f146052d3/coverage-7.5.2-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cf/49/92a1239eabefeda958922f0a441e7473313ad540daf8e85e6a99c83b0b59/coverage-7.5.2-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f5/ee/2dfd97c3c13935076614239b1738e5df1dde67139f8e73504fdd89b493a8/coverage-7.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c7/01/3792e9932e6037d03526db3d5c45409b65483632fdb426ff49065818c21d/coverage-7.5.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/2e/f52bd5573fdf4b527ed0255ec8efc5fae77f5d0b086c41abae6eb175b426/coverage-7.5.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/59/c9/e89dfe1c4931835562b75a5a16cdf2759b51f0092ce2df046a3db840a32a/coverage-7.5.2-cp312-cp312-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/b5/14e3b639737af69b9e602afab04dbc319b76cac36e3b03ede4c92170484f/coverage-7.5.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a4/1f/467d6b174cdeb7b1064009cba2aa3af20ae41dc4ebab34124c6931f05167/coverage-7.5.2-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5b/94/6583a32719fdfee296493d0234ff3a2a4bbb4fee11afccddb0f00a96c1fa/coverage-7.5.2-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/30/cdb45952ead7b12b0bab33546d40fba1a6371a1599884c816b89441c3a6b/coverage-7.5.2-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/0d/12/3adf1c2c148abc2658eb69c9e7e796d4734f63a0e262c52ab78a5404688c/coverage-7.5.2.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.5.2\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/c5/2636e073f656ccda6a6bffbc5b1803c4d64075dedaae176b7a616278f68d/coverage-7.5.3-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f3/7a/8cb117c4d51f599d797e626fa7ac85d1572fb8d98e746eb5c1465af168f3/coverage-7.5.3-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/70/1647300bf3a2517125a5d851470174323c1fead93c53208d755a2f4b1618/coverage-7.5.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/80/97/47151c4a3a664146a76649c8184afd5e26fa156c104443def877f5e06b75/coverage-7.5.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/10/77/9d65122b73f2cfa14744286adc3c1f88bdef49ffd628076429ae59e8fa21/coverage-7.5.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/c7/d4a0b38a9e70f5864b01a7aae430078e326ea73c435e1166f58c1abe4ff9/coverage-7.5.3-cp310-cp310-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bc/a6/65a534068389098e248836193a663d512d0806e1a1f9f1114466585d4dcc/coverage-7.5.3-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/77/6fb0a12edb21c02541a8fa59c5c51fb49f929b03aa9c773478136440b521/coverage-7.5.3-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/7b/c7b26c4824a58ef4511cd314e816b53573fbb25dc987c20a5bed6d44926b/coverage-7.5.3-cp311-cp311-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/1d/45299174011063aa2fcfe1dc4a25aae2722decf611fb0ee38fab4d18f62e/coverage-7.5.3-cp311-cp311-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/65/f6/debe265565e253663dbe6ab49ab4422f16849b09276e43858b54b68037e4/coverage-7.5.3-cp311-cp311-win32.whl 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/a7/e590f15018ea15ba9eab4fd5727e4481b46c733b92566bd1e03c448d002f/coverage-7.5.3-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/84/35/93175835425eb07001b81eb8cffdd7128ca12b9e0942c7c6b64f4410c7b6/coverage-7.5.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, 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pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/ab/ffbd1c5299c65765c1035c2d06ac765161ff169108f4a0eec58aa45c880b/coverage-7.5.3-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/58/35390178ede0daf35ca688c7ee3bab98b2f727863c286d5993979876098c/coverage-7.5.3-cp38-cp38-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ce/1a/6000c78c7f0944a4030675ef27f563494f0bf189bb0aa9147080a3db0de3/coverage-7.5.3-cp38-cp38-musllinux_1_1_x86_64.whl (from https://pypi.org/simple/coverage/) 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(requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/10/ad84d019b8d72e92fb908a0524a190f7212062f89360099907b7a812dc65/coverage-7.5.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/e0/0e30ca5c6c5bcae86df9583c30807ff26e0b991e76f266b81224410663e4/coverage-7.5.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/93/3b/dbca2db3a60ed8543a0c815bf83875bf549e02fe6df4baa736216dfb33ae/coverage-7.5.3-cp39-cp39-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/66/6f06c86471c79ebff4a300390b5fe1d3b2b9101995075e48d096092ddb6d/coverage-7.5.3-cp39-cp39-musllinux_1_1_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_1_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/c9/7953a450762a62abbaf0a65b67fe406b3b515a755139b3db662da441ac87/coverage-7.5.3-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/6c/a5/62ae2dc1850feabb74207a422d00893f451ee0950e52792eb208970a30b1/coverage-7.5.3.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.5.3\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/38/3d/9f9469f445789a170cb5bef3ad02ae9084ddd689f938797aa8ee793db404/coverage-7.5.4-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/d8/b7bde23a5e94cfc1a45effad2dd4c45dc111c515f71c522986dd8ded31a1/coverage-7.5.4-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/99/49/0e8c8e8f9f7ea87ed94ddce70cdfe49224b13857ef3cbdb65a5eb29bba6f/coverage-7.5.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b8/69/a3bdace4d667f592b7730c0d636ac9ff9195f678fb4e61b5469b91e49919/coverage-7.5.4-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cd/bd/8515e955724baab11e8220a3872dc3d1c895b841b281ac8865834257ae2e/coverage-7.5.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/d5/f4f9d2d86e3bd0c3ae761e2511c4033abcdce1de8f1926f8e7c98952540d/coverage-7.5.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/1e/62/e33595d35c9fa7cbcca5df2c3745b595532ec94b68c49ca2877629c4aca1/coverage-7.5.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.5.4\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2c/63/4f781db529b585a6ef3860ea01390951b006dbea9ada4ea3a3d830e325f4/coverage-7.5.4-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/50/c5aadf036078072f31d8f1ae1a6000cc70f3f6cf652939c2d77551174d77/coverage-7.5.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/a6/57c42994b1686461c7b0b29de3b6d3d60c5f23a656f96460f9c755a31506/coverage-7.5.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/88/52/7054710a881b09d295e93b9889ac204c241a6847a8c05555fc6e1d8799d5/coverage-7.5.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://files.pythonhosted.org/packages/35/b3/27fbdf02d2e561d68a4e53522c83f4f2756aea5886c73880a96b8afdeaae/coverage-7.5.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/dd/b29cc90e643af35acd9ddc99c520b7bcd34ec5c13830f5ef956dd6d6b6a2/coverage-7.5.4-cp38-cp38-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/da/a3dbe8d7bfa6da354ed63691d2a1c3ec3afb058125ed578647fdf8396aa5/coverage-7.5.4-cp38-cp38-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/86/d4/b3863e938d1b95b3f34bcf7fa9772b66f40fff0819193749e92a528ebfba/coverage-7.5.4-cp38-cp38-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c3/65/4a3ae9bfb1eaec6898e34a8b283c7cc36d07c034f9abf40e494db616a090/coverage-7.5.4-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/55/f38b087d950693b90034abfeefe825f9fda142d3c7469750d5141ab28a9b/coverage-7.5.4-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/1f/b6c0725454c49b88c0229cdbb22435a90e94521149ea1d068da1d17906d7/coverage-7.5.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/f5/3e13e18a4e42fbb7734c1919255841b7fda188babc57e0fcad3c2e5a080e/coverage-7.5.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c6/1c/bd6d46e44ddb2affc73271d22cba263d9e5d8a0afd593a5de62dbd1380cd/coverage-7.5.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/01/ee/87e1285608cb69c44a0da9864434818fc53a0a95ec45f112f623a6578ee9/coverage-7.5.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c4/b4/0cbc18998613f8caaec793ad5878d2450382dfac80e65d352fb7cd9cc1dc/coverage-7.5.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/03/3968f2d60208c4332bb12c2d25fdfdbbd9a5c7a2a64b4ed1050b20a9dc3f/coverage-7.5.4-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/d0/1e2bae9d17c7c8757b75a9b9c7bf35083a84fcf5361802bb6da911aa7089/coverage-7.5.4-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/93/9eabf10ab089b9b9dcb026d84e70a3114054b75f2d37fd7e61642da775a1/coverage-7.5.4-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9d/f9/8a6cf39d151765f6adaa2808032fda07f57fe4ba658deed11bf1b6c65f11/coverage-7.5.4-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/3a/e75878173e3f5ef21c04b96c535b5e0d10195e5fa28a842b781d339c3df9/coverage-7.5.4-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7a/c3/a5b06a07b68795018f47b5d69b523ad473ac9ee66be3c22c4d3e5eadd91e/coverage-7.5.4-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/ef/05/31553dc038667012853d0a248b57987d8d70b2d67ea885605f87bcb1baba/coverage-7.5.4.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.5.4\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/f5/8cd12ebaf9b9364bef89928d9023cd053e2af3cb34d40bddd847f775230c/coverage-7.6.0-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/0f/36d4b27a0f937fd0ab4928dd8f87880f0d6ee9ae7201cbfd97e1ba4e207c/coverage-7.6.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/b1/7b/5ddf82c7bac217d2343efbd36657a3d848a8670f983767ffe2d3212b76bc/coverage-7.6.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f9/f6/538947ef46e671dd1110ce2f2ef0764441d4feecbfffc1b03f79d7ef1c92/coverage-7.6.0-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/a9/077c9ab033bb3e04dee9cf6c93755a4e891300cfb410bdee286fd194392c/coverage-7.6.0-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/41/2262e64043db1a685f7d4d27eb0fa90c77ce777f76c623a8cdadffdde375/coverage-7.6.0-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/36/714d2f31a822e3ea52e6a3dbf1e07690298cd420bc207f9cb178b371a2d7/coverage-7.6.0-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f9/cf/77b3fb7e132cbae1f112458399157f67f69d5264a9e5a6eae3b91e580b79/coverage-7.6.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/aa/da317fae6b6057428c72f1023a61d57a79a049f03bc4cbe6ffb7cb61e698/coverage-7.6.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/80/88/649d1047bfcbf51227726bfa47073e3b5b94310acbd66f6457c274f74391/coverage-7.6.0-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/41/eeba3fca0f4b6820184572c50d911959f5d108ec5f8e55fd30f0981d5209/coverage-7.6.0-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0e/2a/f62d42a48449b26cfdf940661cf28bccc27e199dc0e956c738a6b1c942af/coverage-7.6.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0c/a2/d7c0988df525298b2c19c482cec27f76bbeba6c3ed7f85d9f79d8996e509/coverage-7.6.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/aa/0419103c357bfd95a65d7b2e2249f9f1d79194241c5e87819cd81d36b96c/coverage-7.6.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2a/ce/375f8fbbabc51e3dfce91355460912c930a4e241ffbafc1f3a35f43ac90b/coverage-7.6.0-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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(cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cb/db/242c44433c5d342c8bf83864131e56af8c1c1ea5645a825b1800c19ad9bf/coverage-7.6.0-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4f/04/c4cf62adfd21ecd6104b1f5a82ed2d945e5da6c55f45e6a75ef610cd8d00/coverage-7.6.0-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/79/df2c1a652ff472ac53aa0147d42d7b3f12a513d9e97883c187f862b44148/coverage-7.6.0-cp38-cp38-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/60/47f81e7c2ff127ea02019dbecc21bd72cbfc2ae6611ad96aea380bd857c1/coverage-7.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a3/f8/b16f61f5370b63f0a66d9ae7ecfde5baa11852549432253ff2c05dbd2ebd/coverage-7.6.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/86/dd0bc2bbfec8c6d420b4040aa1c7c9fd35f60a398f9a20517783243a10dd/coverage-7.6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/68/bbf243320c0c4349cf4c02e7c96c0d8607403b0ea389022c67cef23aaa57/coverage-7.6.0-cp38-cp38-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/87/7c/8e0f3d3566f3b858d1b5b2bac295d5959de03377932faa411d534738fe04/coverage-7.6.0-cp38-cp38-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/69/6d/fcf2591aa8c1e57b38d0b61adee303232068733351478117b7d68a0817a6/coverage-7.6.0-cp38-cp38-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/a0/5625fadcba0ad2ec9bc56b64cb7bd4c841d4ecb31e01147d5c4e9dfa7a40/coverage-7.6.0-cp38-cp38-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/f4/04e66966ed63a8d5df537de416658622d1c7a5e057183ebbac327fc6b881/coverage-7.6.0-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/83/812e55211d64eebe69e36d2c3561d0e2873e7efcd7ddbc510883510dd0f9/coverage-7.6.0-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/9e/5221ceb428d6e0189ed6039fec8400a788f77c6ad41b2db9f0ce48ec317c/coverage-7.6.0-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/41/35/915fa75c39c53b45f19d06bedb199d06d27728a81a2aff073efd9f6ddeef/coverage-7.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b4/47/7a36b59a530c5eb4f62f4d92f0f379a7347470ecde933cd1b1f4ccddcb7f/coverage-7.6.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/ee/94cb9454a4573c40e20075823f8e6ce8494cd0995a8f8e8ab20d9d4a9f62/coverage-7.6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/c8/60c0b10a867081c8dd5faa457eddd8b955b03d2a74ad2b0ad4491afd93e9/coverage-7.6.0-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c9/6e/4008f7f8d8559863c16c88f50201db531a021438d0898aa4711686639316/coverage-7.6.0-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp38-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/69/2b79b6b37c57cd05c85b76ec5ceabf7e091ab0f4986dfefaddbb468881c0/coverage-7.6.0-pp38.pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/64/c8/a94ce9e17756aed521085ae716d627623374d34f92c1daf7162272ecb030/coverage-7.6.0.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.6.0\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7e/61/eb7ce5ed62bacf21beca4937a90fe32545c91a3c8a42a30c6616d48fc70d/coverage-7.6.1-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7d/73/041928e434442bd3afde5584bdc3f932fb4562b1597629f537387cec6f3d/coverage-7.6.1-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c7/c8/6ca52b5147828e45ad0242388477fdb90df2c6cbb9a441701a12b3c71bc8/coverage-7.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d5/da/9ac2b62557f4340270942011d6efeab9833648380109e897d48ab7c1035d/coverage-7.6.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/23/9e2c114d0178abc42b6d8d5281f651a8e6519abfa0ef460a00a91f80879d/coverage-7.6.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/7e/a0230756fb133343a52716e8b855045f13342b70e48e8ad41d8a0d60ab98/coverage-7.6.1-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/28/7c/3753c8b40d232b1e5eeaed798c875537cf3cb183fb5041017c1fdb7ec14e/coverage-7.6.1-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/e3/818a2b2af5b7573b4b82cf3e9f137ab158c90ea750a8f053716a32f20f06/coverage-7.6.1-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/fb/4532b0b0cefb3f06d201648715e03b0feb822907edab3935112b61b885e2/coverage-7.6.1-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5a/25/af337cc7421eca1c187cc9c315f0a755d48e755d2853715bfe8c418a45fa/coverage-7.6.1-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/5f/67af7d60d7e8ce61a4e2ddcd1bd5fb787180c8d0ae0fbd073f903b3dd95d/coverage-7.6.1-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/1c/aa1efa6459d822bd72c4abc0b9418cf268de3f60eeccd65dc4988553bd8d/coverage-7.6.1-cp311-cp311-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fb/c8/521c698f2d2796565fe9c789c2ee1ccdae610b3aa20b9b2ef980cc253640/coverage-7.6.1-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7d/30/033e663399ff17dca90d793ee8a2ea2890e7fdf085da58d82468b4220bf7/coverage-7.6.1-cp311-cp311-win32.whl 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b9/67/e1413d5a8591622a46dd04ff80873b04c849268831ed5c304c16433e7e30/coverage-7.6.1-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/14/5b/9dec847b305e44a5634d0fb8498d135ab1d88330482b74065fcec0622224/coverage-7.6.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/b7/35760a67c168e29f454928f51f970342d23cf75a2bb0323e0f07334c85f3/coverage-7.6.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f7/95/d2fd31f1d638df806cae59d7daea5abf2b15b5234016a5ebb502c2f3f7ee/coverage-7.6.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/6f/f82f9a500c7c5722368978a5390c418d2a4d083ef955309a8748ecaa8920/coverage-7.6.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_i686, cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686, cp38-cp38-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a6/94/d3055aa33d4e7e733d8fa309d9adf147b4b06a82c1346366fc15a2b1d5fa/coverage-7.6.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e4/6e/885bcd787d9dd674de4a7d8ec83faf729534c63d05d51d45d4fa168f7102/coverage-7.6.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/63/df50120a7744492710854860783d6819ff23e482dee15462c9a833cc428a/coverage-7.6.1-cp38-cp38-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/1c/96cf86b70b69ea2b12924cdf7cabb8ad10e6130eab8d767a1099fbd2a44f/coverage-7.6.1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/d3/d54c5aa83268779d54c86deb39c1c4566e5d45c155369ca152765f8db413/coverage-7.6.1-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/f7/08/7e37f82e4d1aead42a7443ff06a1e406aabf7302c4f00a546e4b320b994c/coverage-7.6.1.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.8), version: 7.6.1\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/14/fb75c01b8427fb567c90ce920c90ed2bd314ad6960d54e8b377928607fd1/coverage-7.6.2-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/93/b4/dcbf15f5583507415d0a78ce206e19d76699f1161e8b1ff6e1a21e9f9743/coverage-7.6.2-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/ee/57d607e14479fb760721ea1784608ade532665934bd75f260b250dc6c877/coverage-7.6.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/e1/cd263fd750fdb115aab11a086e3584d99d46fca1f201b5493cc3972aea28/coverage-7.6.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/3b/a1623d50fcd6ba532cef0c3c1059eec2a08a311676ffa84dbe4beb2b8a33/coverage-7.6.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b1/a6/8f3b3fd1f9b9400f3df38a7159362622546e2d951cc4984cf4617d0fd4d7/coverage-7.6.2-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/d1/6b354c2cd52e0244944c097aaa71896869878df999f5f8e75fcd37eaf0f3/coverage-7.6.2-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a5/29/72da824da4182f518b054c21552b7ed2473a4e4c6ac616298209808a1a5c/coverage-7.6.2-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/bd/760a599c08c882d97382855264586bba2604901029c3f6bec5710477ae81/coverage-7.6.2-cp311-cp311-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/83/de/41c3b90a779e473ae1ca325542aa5fa5464b7d2061288e9c22ba5f1deaa3/coverage-7.6.2-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/90/61fe2721b9a9d9446e6c3ca33b6569e81d2a9a795ddfe786a66bf54035b7/coverage-7.6.2-cp311-cp311-win32.whl 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of the wheel's tags (cp312-cp312-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6c/dc/8be87b9ed5dbd4892b603f41088b41982768e928734e5bdce67d2ddd460a/coverage-7.6.2-cp312-cp312-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/3a/3f44e55273a58bfb39b87ad76541bbb81d14de916b034fdb39971cc99ffe/coverage-7.6.2-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ae/99/c9676a75b57438a19c5174dfcf39798b42728ad56650497286379dc0c2c3/coverage-7.6.2-cp313-cp313-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e0/de/820ecb42e892049c5f384430e98b35b899da3451dd0cdb2f867baf26abfa/coverage-7.6.2-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dd/59/81fc7ad855d65eeb68fe9e7809cbb339946adb07be7ac32d3fc24dc17bd7/coverage-7.6.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cd/a7/865de3eb9e78ffbf7afd92f86d2580b18edfb6f0481bd3c39b205e05a762/coverage-7.6.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/94/3b8f3abf88b7c451f97fd14c98f536bcee364e74250d928d57cc97c38ddd/coverage-7.6.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d5/4b/57f95e41a10525002f524f3dbd577a3a9871d67998f8a8eb192fe697dc7b/coverage-7.6.2-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/99/c9/9fbe5b841628e1d9030c8044844afef4f4735586289eb9237eeb5b97f0d7/coverage-7.6.2-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/43/0d/2200a0d447e30de94d48e4851c04d8dce37340815e7eda27457a7043c037/coverage-7.6.2-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/8a/106c66faafb4a87002b698769d6de3c4db0b6c29a7aeb72de13b893c333e/coverage-7.6.2-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c4/f5/1b39e2faaf5b9cc7eed568c444df5991ce7ff7138e2e735a6801be1bdadb/coverage-7.6.2-cp313-cp313-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/79/a3/8dd4e6c09f5286094cd6c7edb115b3fbf06ad8304d45431722a4e3bc2508/coverage-7.6.2-cp313-cp313t-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8e/db/a9aa7009bbdc570a235e1ac781c0a83aa323cac6db8f8f13c2127b110978/coverage-7.6.2-cp313-cp313t-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux2014_aarch64, cp313-cp313t-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/54/08/d0962be62d4335599ca2ff3a48bb68c9bfb80df74e28ca689ff5f392087b/coverage-7.6.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_i686, cp313-cp313t-manylinux2014_i686, cp313-cp313t-manylinux_2_17_i686, cp313-cp313t-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a5/a2/158570aff1dd88b661a6c11281cbb190e8696e77798b4b2e47c74bfb2f39/coverage-7.6.2-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/fe/b00428cca325b6585ca77422e4f64d7d86a225b14664b98682ea501efb57/coverage-7.6.2-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/21/0a15fefc13039450bc45e7159f3add92489f004555eb7dab9c7ad4365dd0/coverage-7.6.2-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/de/b8/5c093526046a8450a7a3d62ad09517cf38e638f6b3ee9433dd6a73360501/coverage-7.6.2-cp313-cp313t-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/8b/542b607d2cff56e5a90a6948f5a9040b693761d2be2d3c3bf88957b02361/coverage-7.6.2-cp313-cp313t-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/82/2e9111aa5e59f42b332d387f64e3205c2263518d1e660154d0c9fc54390e/coverage-7.6.2-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9d/46/aabe4305cfc57cab4865f788ceceef746c422469720c32ed7a5b44e20f5e/coverage-7.6.2-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6a/a9/85d14426f2449252f302f12c1c2a957a0a7ae7f35317ca3eaa365e1d6453/coverage-7.6.2-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/71/ff/bc4d5697a55edf1ff077c47df5637ff4518ba2760ada82c142aca79ea3fe/coverage-7.6.2-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/65/1301721d09f5b58da9decfd62eb42eaef07fdb854dae904c3482e59cc309/coverage-7.6.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/ec/7a2f361485226e6934a8f5d1f6eef7e8b7faf228fb6107476fa584700a32/coverage-7.6.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2d/60/b23e61a372bef93c9d13d87efa2ea3a870130be498e5b81740616b6e6200/coverage-7.6.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/89/ec/4a56d9b310b2413987682ae3a858e30ea11d6f6d05366ecab4d73385fbef/coverage-7.6.2-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/77/31ecc00c525dea216d59090b807e9d1268a07d289f9dbe0cfc6795e33b68/coverage-7.6.2-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/02/3f84bdd286a9db9b816cb5ca0adfa001575f8e496ba39da26f0ded2f0849/coverage-7.6.2-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7c/34/158b73026cbc2d2b3a56fbc71d955c0eea52953e49de97f820b3060f62b9/coverage-7.6.2-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/05/4326e4ea071176f0bddc30b5a3555b48fa96c45a8f6a09b6c2e4041dfcc0/coverage-7.6.2-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9d/5c/88f15b7614ba9ed1dbb1c0bd2c9073184b96c2bead0b93199487b44d04b3/coverage-7.6.2-pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/9a/60/e781e8302e7b28f21ce06e30af077f856aa2cb4cf2253287dae9a593d509/coverage-7.6.2.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.2\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/c9/d0bb2e5720ea2182d8c22e15a74a4a0b5e8cfda2773dc8c524ff75a3f392/coverage-7.6.3-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/b1/cd2e55ed5e88f157b97be34c5a0689668f2a1126eb59e9f17f37d507b275/coverage-7.6.3-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/86/3b/f3bf339f2e1a30e81685bb2f90d7e4b3b50e102162ca83524e1f8ab0a6a9/coverage-7.6.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/d0/ede5afa443e324d0dc19f1b9a1f9ec836cd2755d1735104ef6e8ab65dd8f/coverage-7.6.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/9b/84dde4f3307227cc69ed579ba49efc3f38cbe608793a93e163be801eb72c/coverage-7.6.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b8/0c/b3a330b57d8ce70b08e383005f948ad78397f569edb75cd9b9e7f3d29932/coverage-7.6.3-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/72/b5/c068657f338cb26dfa0c8c4cec210e56c416d3f5a1d124ebc6dc5c1c5c54/coverage-7.6.3-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/c9/a96b5c1865a5a84380bd8f74a27e9972c9f2f5b17c854662242e473c6afd/coverage-7.6.3-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/bb/c05f8597095e713c73d2b9c142a914ee6bfea096afcbb37923a0d956c9b2/coverage-7.6.3-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/78/4c/2705183ff384b1612170b70fb716dcd24941f9c71b02860f6bbdf7f2f780/coverage-7.6.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/00/ada23862b99bf25218a74a116011982e20d1d4740fe4ad009c08f1090a5b/coverage-7.6.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/5d/be9f27811e767b8bd8b5836c5f05708263cc1f2c24d8244f36a5f7baee75/coverage-7.6.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/0f/0b61bfa7aada98936cc433c8bd2e9f90c3fdc02607bdade04ffa1b8f83ba/coverage-7.6.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/d4/83dfe1cdabf7f574217ce431c8506c80c6afa5c4056b1159282ba54bb6f2/coverage-7.6.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/05/0a/f53ab3bf80bcf6a17bece8851d4687cf7e424505cf4ce7a3c4cb41b52d96/coverage-7.6.3-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/e8/9a18a78779e5c9d51c8c60de4e9e06c91a03f529fa5b31993b85f364a114/coverage-7.6.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/d5/6100bb2b104365634068aad82ec332663a32c7572f4bbe24825e79ecb712/coverage-7.6.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d8/11/7e5ac48885f4fed8edb4624425b60405c96c5cf92c2260305eeb6d179897/coverage-7.6.3-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/6c/4943c562bc8f541dbc466181c615743fe7987433b9ac00301b4f2c8bce60/coverage-7.6.3-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/49/cb/44402ef105d8a77849fa019b975c9e35b184498ec7a6070c30deaad47fab/coverage-7.6.3-cp313-cp313-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/c4/1e9b42abe9d381585d6f9384bbfbfce464234261e3e331ab98eeef3fc11a/coverage-7.6.3-cp313-cp313t-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ad/dd/29fb9c6b94a52da04613e5005d0a8875e57ca76b570d2625964fd92d7fab/coverage-7.6.3-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/17/efb9ca2a5f9ccf8af267ff2c02ad976a2dc29f9b3c63209e2a89588d7f95/coverage-7.6.3-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/cf/ec610fdae6cacd6be3cc5ddf66207b4a6770eb3e9b5b4902bd34356c4da3/coverage-7.6.3-cp39-cp39-macosx_10_9_x86_64.whl (from 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cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/38/d3/36e07e96cadb8c6c68226dfee8643e518483951885e61f565d15cd2882fa/coverage-7.6.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/5b/8a4029ab21352e397dbfe5515699f9f7f0018f0838f4ecbb84b848a0df99/coverage-7.6.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/83/81/2238abf2467435bb6200f6347e9fe190747f9821b27c9433f0b978d5d030/coverage-7.6.3-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/07/a5d8e24d89458784eee903800983ff34fb54fcdaf4cdec05b98f3a9aec30/coverage-7.6.3-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/39/f44344dd77c535f3264a6e6e95bef4e0bd53366e1eb82ce6980a17f1b0c4/coverage-7.6.3-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/67/a5/b8ff75aba81839c7a85f764f15214076b9e24655e196a63bc5e2053fa040/coverage-7.6.3-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/c0/4346b0a287b72c917ca9cbc7230d73035082ab2b5692830929967917035e/coverage-7.6.3-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/53/ed/a38401079ad320ad6e054a01ec2b61d270511aeb3c201c80e99c841229d5/coverage-7.6.4-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/e7/c3ad33b179ab4213f0d70da25a9c214d52464efa11caeab438592eb1d837/coverage-7.6.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/36/91/fc02e8d8e694f557752120487fd982f654ba1421bbaa5560debf96ddceda/coverage-7.6.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/cc/57/cb08f0eda0389a9a8aaa4fc1f9fec7ac361c3e2d68efd5890d7042c18aa3/coverage-7.6.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.4\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/27/03/932c2c5717a7fa80cd43c6a07d3177076d97b79f12f40f882f9916db0063/coverage-7.6.4-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d5/3f/0af47dcb9327f65a45455fbca846fe96eb57c153af46c4754a3ba678938a/coverage-7.6.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8a/3c/37a9d81bbd4b23bc7d46ca820e16174c613579c66342faa390a271d2e18b/coverage-7.6.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c0/70/6b0627e5bd68204ee580126ed3513140b2298995c1233bd67404b4e44d0e/coverage-7.6.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags 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compatible tags): https://files.pythonhosted.org/packages/7f/f8/4436a643631a2fbab4b44d54f515028f6099bfb1cd95b13cfbf701e7f2f2/coverage-7.6.4-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/49/50/1571810ddd01f99a0a8be464a4ac8b147f322cd1e8e296a1528984fc560b/coverage-7.6.4-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f3/8c/6312d241fe7cbd1f0cade34a62fea6f333d1a261255d76b9a87074d8703c/coverage-7.6.4-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ce/5f/fef33dfd05d87ee9030f614c857deb6df6556b8f6a1c51bbbb41e24ee5ac/coverage-7.6.4-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/64/6a984b6e92e1ea1353b7ffa08e27f707a5e29b044622445859200f541e8c/coverage-7.6.4-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/60/ce5a9e942e9543783b3db5d942e0578b391c25cdd5e7f342d854ea83d6b7/coverage-7.6.4-cp313-cp313-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/78/53/6719677e92c308207e7f10561a1b16ab8b5c00e9328efc9af7cfd6fb703e/coverage-7.6.4-cp313-cp313t-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fa/dd/7054928930671fcb39ae6a83bb71d9ab5f0afb733172543ced4b09a115ca/coverage-7.6.4-cp313-cp313t-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux2014_aarch64, cp313-cp313t-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/7d/fd656ddc2b38301927b9eb3aae3fe827e7aa82e691923ed43721fd9423c9/coverage-7.6.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_i686, cp313-cp313t-manylinux2014_i686, cp313-cp313t-manylinux_2_17_i686, cp313-cp313t-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/d0/eb9a3cc2100b83064bb086f18aedde3afffd7de6ead28f69736c00b7f302/coverage-7.6.4-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/45/44/3f64f38f6faab8a0cfd2c6bc6eb4c6daead246b97cf5f8fc23bf3788f841/coverage-7.6.4-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/11/4c465a5f98656821e499f4b4619929bd5a34639c466021740ecdca42aa30/coverage-7.6.4-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f1/96/ebecda2d016cce9da812f404f720ca5df83c6b29f65dc80d2000d0078741/coverage-7.6.4-cp313-cp313t-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/d9/3d820c00066ae55d69e6d0eae11d6149a5ca7546de469ba9d597f01bf2d7/coverage-7.6.4-cp313-cp313t-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8f/c3/4fa1eb412bb288ff6bfcc163c11700ff06e02c5fad8513817186e460ed43/coverage-7.6.4-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7e/77/03fc2979d1538884d921c2013075917fc927f41cd8526909852fe4494112/coverage-7.6.4-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fb/27/7efede2355bd1417137246246ab0980751b3ba6065102518a2d1eba6a278/coverage-7.6.4-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f3/94/594af55226676d078af72b329372e2d036f9ba1eb6bcf1f81debea2453c7/coverage-7.6.4-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d5/13/19de1c5315b22795dd67dbd9168281632424a344b648d23d146572e42c2b/coverage-7.6.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/26/8fba01ce9f376708c7efed2761cea740f50a1b4138551886213797a4cecd/coverage-7.6.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/66/4db60266551b89e820b457bc3811a3c5eaad3c1324cef7730c468633387a/coverage-7.6.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2a/9b/7b33f0892fccce50fc82ad8da76c7af1731aea48ec71279eef63a9522db7/coverage-7.6.4-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/49/6ff9c4e8a67d9014e1c434566e9169965f970350f4792a0246cd0d839442/coverage-7.6.4-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/81/f9/c9d330dec440676b91504fcceebca0814718fa71c8498cf29d4e21e9dbfc/coverage-7.6.4-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/d9/605517a023a0ba8eb1f30d958f0a7ff3a21867b07dcb42618f862695ca0e/coverage-7.6.4-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/79/2626903efa84e9f5b9c8ee6972de8338673fdb5bb8d8d2797740bf911027/coverage-7.6.4-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cc/56/e1d75e8981a2a92c2a777e67c26efa96c66da59d645423146eb9ff3a851b/coverage-7.6.4-pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/52/12/3669b6382792783e92046730ad3327f53b2726f0603f4c311c4da4824222/coverage-7.6.4.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.4\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a4/8d/9032831bd5bc57029bdbe4f30f25471c10d94dad75b000db9e01c5e968d5/coverage-7.6.5-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/23/03350a59c103c953e9b5a31825c7931c2619dc23113f9cc958afd0cd1306/coverage-7.6.5-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/25/39/647e879b1a444a45426725616967d2eafed923f91dd7443fe845712d6a04/coverage-7.6.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/62/37/288f73e76752c8ec104f16bfe56027cda7e62c4ed010100469749ad8fe91/coverage-7.6.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/9d/cbc76b46b1c60b04869977701c1507b394b90cb3af0bb794af7b62586f8b/coverage-7.6.5-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0b/40/9eccf12f1e593c0ed35d7a1dacbb571616d3a9c63b3f201235792e892cef/coverage-7.6.5-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a8/18/1a994116f24b2a59724e1efc610189d918d13710facc1734eddac9c4e5a6/coverage-7.6.5-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/72/3e7e55f3fd7e87973fb0ad2b62952a73f8e33c85a7def4ec43a783a7217e/coverage-7.6.5-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/2f/e4cf3b0857172d331e857f7941c8b9d596e775d01b6b71dff83d8c47f32e/coverage-7.6.5-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/bc/a10e5a77b48fc7924131644ca773cc79a8d7835894fa4bed02c71f6cce0d/coverage-7.6.5-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/18/96/5a5a956865710b21ffa32613f1dfc6fb55c060c41d5b89b8b26787b4a737/coverage-7.6.5-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/a5/782c0dad1f202000a3772662a5f7b14076a2c42f745058ae7f236f667fe5/coverage-7.6.5-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/90/62e376f76801db8b3e067f6b48e49aec236187c922186cffebdbc964b935/coverage-7.6.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c5/16/330a12336ac048d6d83ebf8086cea0ecdeb5d2520cd90c057c4f69e21680/coverage-7.6.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d9/9e/389c4966c7021770361bb73ca86787870076ac971e218d4f133cc764bc7d/coverage-7.6.5-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/32/7c3f0a59c2dbbef8c8df6827e93f76cbd0ba0924af2cb47e86c02df0208b/coverage-7.6.5-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/82/0fb1fc8986706cc9cfcba018d734f4d26c9bb6c51c6b8fccbf362dfebbcb/coverage-7.6.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cd/bb/8c77cba6dac838fc35dacde176be6e80556446b233fa5f628c2240a83c2d/coverage-7.6.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/ac/57e435de18c45be75cf641b14e5c272f76ae0f06d8e0aa80070827bd7c49/coverage-7.6.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/cd/ea1fba2f701b299fbd79a05cab7ab399f98222f05223f33793d2010224f8/coverage-7.6.5-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/96/3b/b9b2dcb5f714668ba49cbb83558402edd5607c15bd93e283c6b09b85c501/coverage-7.6.5-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b8/27/dc4d32110317187b96edb1045ea41cac09911b9b836d241f031c296a3bb4/coverage-7.6.5-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/ba/fc6ac9f45ed6759dbf2b7fc517b45a81c71a66ac2751acece68579a4b611/coverage-7.6.5-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/ec/86a1111bb73968e1b8038244bbef5a5a51dc6e8d32825f6e0602f9ac31bf/coverage-7.6.5-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/c0/0f49c4ca3256aca7d19dc72979ff077c1c7e1a273569f9a842d6cb7d76af/coverage-7.6.5-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/43/9f/4e0ca79b45afcc529bd7bf7800359625ac67b9525659f1a44230d1e38ae5/coverage-7.6.5-cp313-cp313-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/96/b02d7c3a7464f98ad358609b9125562d2b135cf898f1f7b06321468d1e7e/coverage-7.6.5-cp313-cp313t-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/d7/ffb0c961618dd3f922a9e5b39aee5018a60a98b1f1b1267b7c2e6d81d787/coverage-7.6.5-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/b1/de22418d59bf2b60f5eccb268954bbdd9107b3b7523d970cfd8ba3867e65/coverage-7.6.5-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/9c/1389871ed26c89d6ec57439c9c2ea800477d84ba08803ac1822a5d8413c7/coverage-7.6.5-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/83/35/334c9427c5c2a943decd4098382940869bb8fc8061fd468efbfd2498cd3e/coverage-7.6.5-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/08/98/e18bbfa589b808479d2cb22c9e331f85d4bcf21bed30fe852709bd5ba3a6/coverage-7.6.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cf/b5/d8c5fb710e0e5ecd3325a06c843a819c8b035ac8786078dd99969a8f11af/coverage-7.6.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d9/86/17834bb3637fc4efb438b58029835df228166dbece1c5c405c3933844d80/coverage-7.6.5-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/ae/0ecb341ed874babe3eb5c3093eff30e0ba17423a8c6e4b82d6262779580f/coverage-7.6.5-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/80/01/d6c1c4ee7915d9f659087f92df72d68a7fd324574a9841f316e2beba9105/coverage-7.6.5-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/8e/2b358fef70d73a86e43ba24a9bb9ccadef6ef02bdb658609d48549414ee3/coverage-7.6.5-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) 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of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/28/3740285727ce7d86ef3ac6550adda95c54cec677e963607fff55fad2b339/coverage-7.6.6-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/65/90/156a33aad31affc5d183bd33b72e950b8be7702b4dbce69da930ce6f559e/coverage-7.6.6-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/67/b8/6740fd64039083b650e6a12a037c8b7c98ec0c8285f10af899bd33877fa0/coverage-7.6.6-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/9f/f40b103b99a82ceeb21cef1832101d8ed00fad3687ae9221c88717566f68/coverage-7.6.6-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/02/be27391242a57b83ec187fb1d2f6ecc1b57dec0b3d68635db9d890407e62/coverage-7.6.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/13/8a/921231a331aaebf12abf705bcfe29530600ca8d690a2bc81e3657a42b08a/coverage-7.6.6-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/9c/40/8a4f7ff31fba2cc781cbe0dd77172ab417501c31f8f5719fabab62d6bed2/coverage-7.6.6-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.6\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4c/9a/2aead9cbde0abd1855760def4aded900d2b21a601eb52ce40a7cea6a3807/coverage-7.6.6-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/77/97/36f5837574d78646faede04fcc707eaf6bff728fd167b5e36399a080f558/coverage-7.6.6-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6b/a6/f453d7a17f0a9a585f6177169e5ef717e975d90267e2abefbd8d144f2b1b/coverage-7.6.6-cp312-cp312-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/2b/fd2f2d614d0f0e8b9eee80c3932096faf3332f53455d73cdaa262757f431/coverage-7.6.6-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f0/af/28fd8d78b53308a4bf3fb6d674409571d470a2bc14f220e122be10bcd56c/coverage-7.6.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cb/46/c5288703d171896995d0415459dee86efebbf8ccb4f0ceab63837167d6f8/coverage-7.6.6-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/42/20/4a3db349bba990757a6f8363856b8fdb99918bd1792ca762580a3cde4d96/coverage-7.6.6-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags 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compatible tags): https://files.pythonhosted.org/packages/01/a4/6d937964a5bac9d5883b6b17d5884d8afbf7430cff29e918ebe3ff77bfdf/coverage-7.6.6-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/75/9c/e2fa3c41f327e7fa221a33b579b6747bb72c5fbee6fbe3a345fc8c24dc54/coverage-7.6.6-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/4e/d6c04af25061d8a10bf5f385abe21809f8f7b3302d01f31feabda876bf71/coverage-7.6.6-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/93/152ee79dbebb6d5589aaff243db3ebbde66b701671c69cc437c0230f9555/coverage-7.6.6-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/cf/2b7a4e600680ec3ba8b04012584484b7a8c7f6bac02a41a534f861932acd/coverage-7.6.6-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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wheel's tags (cp313-cp313t-manylinux2014_aarch64, cp313-cp313t-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5d/83/e9461501bfb08665abb5cdf89c09bd7b9c92498ef5122a3cfa72e1e41e98/coverage-7.6.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_i686, cp313-cp313t-manylinux2014_i686, cp313-cp313t-manylinux_2_17_i686, cp313-cp313t-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d0/e8/edc4ca760e52aae60ced2890b9a963152773c058fa83a8e294860127c079/coverage-7.6.6-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/27/8623dc736318a4758fb34211bdc8b13aeda1bd43b17994e83608c3104b41/coverage-7.6.6-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/24/e30af5a53fb4fa8c3f183ec3f53568ddfc123d34cfa964195614c4f59fc1/coverage-7.6.6-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/6d/d704777fc077a0b381688216d19cb097b7f6c9206ac3b22cc4dcc413a501/coverage-7.6.6-cp313-cp313t-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/11/d8/1c1fdf64ceed326744a3ef0791de40296778b9a1c55d14d979500823c28d/coverage-7.6.6-cp313-cp313t-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6a/de/027a728ef45000f7dc04e907cc9fce77086c15b3765aaffd7e5720bbad4f/coverage-7.6.6-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e2/e3/ed0b73dfb7f15975587cb387e770791ff8f7ed341fd419e9da710805a8e8/coverage-7.6.6-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7e/55/73507d114ffe03e85fc7824c826b6eec34069031680944ea9bb930bc0e5a/coverage-7.6.6-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c7/84/eb381496858e47d4c902005b625eccf583d62203dede3b441da832d82ca3/coverage-7.6.6-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f0/c9/b7e8beb69167e8012e0a1bb1131b92b9513a8cbd082673db10bd807fe546/coverage-7.6.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e1/b0/345a59e75e744f26975fa2a2410276472db3c37421899155aadcaf3066cd/coverage-7.6.6-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/80/90464a57a3edc897c9b1bcc3461240265baaf990b0709bb0d1c176b9d6b4/coverage-7.6.6-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/55/a4f934972057f5c2f6720b0a381d322a57a1d264258d8f6f02402747c66b/coverage-7.6.6-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f5/e3/5b24ea44ceecbd19ac7359d6f31d9b60e99dfe97ff97fb3968af4fcf63ea/coverage-7.6.6-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c2/16/4f6aa1b74a0fd4f1aef4dc312d2d065e474c7902e37a268febfd265edacb/coverage-7.6.6-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9a/7e/14575fd3cb01e71caea16847245c18557a83dd2117f3afb0ec6a88fb015b/coverage-7.6.6-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/58/f3334741c9c4d936185111b365e05b81bfd87d32d66f415f377ab0beafb1/coverage-7.6.6-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/5c/a6cad05d51dfc961cca3ee9af4d505cde662844b503c1b97a628f15565dd/coverage-7.6.6-pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/82/72/016d875d65253ed07db4d570b4834a8b9c872882e16cff3d3d22141d2d48/coverage-7.6.6.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.6\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/62/c9/84898713e61208ddbe71b991d8f311d9ca175629ce5f1a46018acc643572/coverage-7.6.7-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f0/69/7dfd65f0e284617f72d974f6dfedc7bc16f86172e5bc6ebc8b63430263f3/coverage-7.6.7-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/bd/41/de07328d2e79916fcc6cd53a5a1d18d163483519ab95f7f60fe15276811c/coverage-7.6.7-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e4/cc/2a669319b1295e0c52e8cfbbb163b32188b62f3b0bbe7014ef402b24b7cf/coverage-7.6.7-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/71/a1bb90cb177358a2d364b3968a2069225f614d6824c3d959dee688ca0902/coverage-7.6.7-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/dc/87551219d3437214523d1c7de0a717bead7a3369ed9bae05a7fd2854476f/coverage-7.6.7-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/a4/d74ae3a3fb9e55fe5d9b811ce68a6bd8df3ae0a92c336acbc00075bc24fa/coverage-7.6.7-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/35/c9/d7a02a9654c41174fb99402c0fbd9583d0d2cb8714e7f948117fa7f919c4/coverage-7.6.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/11/64/6c43a0ec43e5ddc5e09b0b589e3fd31def05fc463920d084e5af35fe527d/coverage-7.6.7-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/df/14/38b7c081e86e845df1867143ddb6e05bf8395f60ab3923c023a56d97cca1/coverage-7.6.7-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8b/f3/af34f814ca3814f798878ae368b638edb91298595470614f5265f3f416fa/coverage-7.6.7-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ac/46/da1bd9a3a893f74f5ce85f35e2755fcb00a80ed21e18d300c54f64938b1c/coverage-7.6.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/12/af8e932496de1997bf4a36785d025ddac6427cbaf6954f26c2edaf21a58a/coverage-7.6.7-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/60/a2/23eb11eb60f825a84397cb94701d6f41d2e8e88ad7d0ba2b4339f38435fb/coverage-7.6.7-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/9e/63b318bc469308a32b0fbd6c80e2ea05dd7a2b7e840a46b3974843083a8c/coverage-7.6.7-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b8/07/afda6e10c50e3a8c21020c5c1d1b4f3d7eff1c190305cef2962adf8de018/coverage-7.6.7-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/85/43/bd1934b75e31f2a49665be6a6b7f8bfaff7266ba19721bdb90239f5e9ed7/coverage-7.6.7-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2b/19/7a70458c1624724086195b40628e91bc5b9ca180cdfefcc778285c49c7b2/coverage-7.6.7-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f3/2c/3dee671415ff13c05ca68243b2264fc95a5eea57697cffa7986b68b8f608/coverage-7.6.7-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/37/ad/e0d1228638711aeacacc98d1197af6226b6d062d12c81a6bcc17d3234533/coverage-7.6.7-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/90/95/6467e9d9765a63c7f142703a7f212f6af114bd73a6c1cffeb7ad7f003a86/coverage-7.6.7-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/7a/fc11a163f0fd6ce8539d0f1b565873fe6903b900214ff71b5d80d16154c3/coverage-7.6.7-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/91/58be3a56efff0c3481e48e2caa56d5d6f3c5c8d385bf4adbecdfd85484b0/coverage-7.6.7-cp313-cp313-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/7e/fed983809c2eccb09c5ddccfdb08efb7f2dd1ae3454dabf1c92c5a2e9946/coverage-7.6.7-cp313-cp313t-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/75/e5/d7772e56a7eace80e98ac39f2756d4b690fc0ce2384418174e02519a26a8/coverage-7.6.7-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2d/12/f2666e4e36b43221391ffcd971ab0c50e19439c521c2c87cd7e0b49ddba2/coverage-7.6.7-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4c/3d/5ee1ccc37d39e4c06194492e15cd6327d0a85b6c4f14c112fd19b65dc6a7/coverage-7.6.7-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c2/91/cfdf3c9f4c141d2172b5abd9631853144537d4849d00d08eff2b7e3a8318/coverage-7.6.7-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/67/6b0460017083bd9330d2d74e3b5aff3e85f9918c96ae8eae8135a262cc34/coverage-7.6.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/59/0c3a8a3f5ef007862774cb8d25580ba8cc3a60e79d2e0798efb117eaea7b/coverage-7.6.7-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cd/fc/68d19fb8688d976cb0da7713ca632ca5a5423c92aeae377161d9b888bb38/coverage-7.6.7-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9d/8a/e76da4084c59420f4f9fac8a5d4b08f0281774f56375c59e76e27eafdb8d/coverage-7.6.7-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/b7/cc00329039500147d3b5724ca412e6b5b8124da7c2865b673a09f04e71fa/coverage-7.6.7-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/af/1710b65f590d52c9c5f1a238142feb2ef1ff61915fa41531b372e920bee3/coverage-7.6.7-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/99/32773e1f26cbfe11a0cadc4a4163a2249f04e83f0b8def93d85c572d0628/coverage-7.6.7-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d7/ef/4b86263d312da7df483a84b69b4e0575fd777fb673fbef95a4df8a68a07c/coverage-7.6.7-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): 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https://files.pythonhosted.org/packages/18/43/b2cfd4413a5b64ab27c289228b0c45b4527d1b99381cc9d6a00bfd515da4/coverage-7.6.8-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8e/95/8b2fbb9d1a79277963b6095cd51a90fb7088cd3618faf75550038331f78b/coverage-7.6.8-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4d/d7/9e939508a39ef67605b715ca89c6522214aceb27c2db9152ae3ae1cf8626/coverage-7.6.8-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ba/e2/1c5fb52eafcffeebaa9db084bff47e7c3cf4f97db752226c232cee4d530b/coverage-7.6.8-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/31/6a56469609a252549dd4b090815428d5521edd4642440d987573a450c069/coverage-7.6.8-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/9f/e98211980f6e2f439e251737482aa77906c9b9c507824c71a2ce7eea0402/coverage-7.6.8-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/c7/8bab83fb9c20f7f8163c5a20dcb62d591b906a214a6dc6b07413074afc80/coverage-7.6.8-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/d6/00243df625f1b282bb25c83ce153ae2c06f8e7a796a8d833e7235337b4d9/coverage-7.6.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1e/07/faf04b3eeb55ffc2a6f24b65dffe6e0359ec3b283e6efb5050ea0707446f/coverage-7.6.8-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/43/23/c79e497bf4d8fcacd316bebe1d559c765485b8ec23ac4e23025be6bfce09/coverage-7.6.8-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.8\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/e5/791bae13be3c6451e32ef7af1192e711c6a319f3c597e9b218d148fd0633/coverage-7.6.8-cp311-cp311-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/05/c6/bbfdfb03aada601fb8993ced17468c8c8e0b4aafb3097026e680fabb7ce1/coverage-7.6.8-cp311-cp311-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/67/f9/f8e5a4b2ce96d1b0e83ae6246369eb8437001dc80ec03bb51c87ff557cd8/coverage-7.6.8-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f0/70/b05328901e4debe76e033717e1452d00246c458c44e9dbd893e7619c2967/coverage-7.6.8-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/70/55/1efa24f960a2fa9fbc44a9523d3f3c50ceb94dd1e8cd732168ab2dc41b07/coverage-7.6.8-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/ce/3edf581c8fe429ed8ced6e6d9ac693c25975ef9093413276dab6ed68a80a/coverage-7.6.8-cp312-cp312-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/09/9c/cf102ab046c9cf8895c3f7aadcde6f489a4b2ec326757e8c6e6581829b5e/coverage-7.6.8-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/39/06/42aa6dd13dbfca72e1fd8ffccadbc921b6e75db34545ebab4d955d1e7ad3/coverage-7.6.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a0/20/2932971dc215adeca8eeff446266a7fef17a0c238e881ffedebe7bfa0669/coverage-7.6.8-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ac/85/4323ece0cd5452c9522f4b6e5cc461e6c7149a4b1887c9e7a8b1f4e51146/coverage-7.6.8-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/77/52/b2537487d8f36241e518e84db6f79e26bc3343b14844366e35b090fae0d4/coverage-7.6.8-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7c/99/7f007762012186547d0ecc3d328da6b6f31a8c99f05dc1e13dcd929918cd/coverage-7.6.8-cp312-cp312-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/53/e9b5cf0682a1cab9352adfac73caae0d77ae1d65abc88975d510f7816389/coverage-7.6.8-cp312-cp312-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0c/50/054f0b464fbae0483217186478eefa2e7df3a79917ed7f1d430b6da2cf0d/coverage-7.6.8-cp312-cp312-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/df/d0/09ba870360a27ecf09e177ca2ff59d4337fc7197b456f22ceff85cffcfa5/coverage-7.6.8-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9a/84/6f0ccf94a098ac3d6d6f236bd3905eeac049a9e0efcd9a63d4feca37ac4b/coverage-7.6.8-cp313-cp313-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/2b/e3b3a3a12ebec738c545897ac9f314620470fcbc368cdac88cf14974ba20/coverage-7.6.8-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/d4/e4/a91e9bb46809c8b63e68fc5db5c4d567d3423b6691d049a4f950e38fbe9d/coverage-7.6.8-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/9c/9b99b0591ec4555b7292d271e005f27b465388ce166056c435b288db6a69/coverage-7.6.8-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a6/85/285c2df9a04bc7c31f21fd9d4a24d19e040ec5e2ff06e572af1f6514c9e7/coverage-7.6.8-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cb/a1/95ec8522206f76cdca033bf8bb61fff56429fb414835fc4d34651dfd29fc/coverage-7.6.8-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/ac/687e9ba5e6d0979e9dab5c02e01c4f24ac58260ef82d88d3b433b3f84f1e/coverage-7.6.8-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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wheel's tags (cp313-cp313t-manylinux2014_aarch64, cp313-cp313t-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9c/0d/33635fd429f6589c6e1cdfc7bf581aefe4c1792fbff06383f9d37f59db60/coverage-7.6.8-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_i686, cp313-cp313t-manylinux2014_i686, cp313-cp313t-manylinux_2_17_i686, cp313-cp313t-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/32/8a08da0e46f3830bbb9a5b40614241b2e700f27a9c2889f53122486443ed/coverage-7.6.8-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags 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show compatible tags): https://files.pythonhosted.org/packages/32/ee/53bdbf67760928c44b57b2c28a8c0a4bf544f85a9ee129a63ba5c78fdee4/coverage-7.6.8-cp313-cp313t-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/49/5a57910bd0af6d8e802b4ca65292576d19b54b49f81577fd898505dee075/coverage-7.6.8-cp313-cp313t-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bd/37/e450c9f6b297c79bb9858407396ed3e084dcc22990dd110ab01d5ceb9770/coverage-7.6.8-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/44/79/7d0c7dd237c6905018e2936cd1055fe1d42e7eba2ebab3c00f4aad2a27d7/coverage-7.6.8-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/2e/db/5c7008bcd8858c2dea02702ef0fee761f23780a6be7cd1292840f3e165b1/coverage-7.6.8-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1c/30/e1be5b6802baa55967e83bdf57bd51cd2763b72cdc591a90aa0b465fffee/coverage-7.6.8-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/df/19c0e12f9f7b976cd7b92ae8200d26f5b6cd3f322d17ac7b08d48fbf5bc5/coverage-7.6.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e8/7a/a80b0c4fb48e8bce92bcfe3908e47e6c7607fb8f618a4e0de78218e42d9b/coverage-7.6.8-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/0e/1a4ecee734d70b78fc458ff611707f804605721467ef45fc1f1a684772ad/coverage-7.6.8-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/24/42/6eadd73adc0163cb18dee4fef80baefeb3faa11a1e217a2db80e274e784d/coverage-7.6.8-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/68/5f/10b825f39ecfe6fc5ee3120205daaa0950443948f0d0a538430f386fdf58/coverage-7.6.8-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/56/72/ad92bdad934de103e19a128a349ef4a0560892fd33d62becb1140885e44c/coverage-7.6.8-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/1d/d61d9b2d17628c4db834e9650b776663535b4258d0dc204ec475188b5b2a/coverage-7.6.8-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/d1/ef43852a998c41183dbffed4ab0dd658f9975d570c6106ea43fdcb5dcbf4/coverage-7.6.8-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/32/df/0d2476121cd0bfb9ca2413efe02289c474b82c4b134863bef4b89ec7bcfa/coverage-7.6.8-pp39.pp310-none-any.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/ab/75/aecfd0a3adbec6e45753976bc2a9fed62b42cea9a206d10fd29244a77953/coverage-7.6.8.tar.gz (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.8\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/49/f3/f830fb53bf7e4f1d5542756f61d9b740352a188f43854aab9409c8cdeb18/coverage-7.6.9-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4e/e3/ea5632a3a6efd00ab0a791adc0f3e48512097a757ee7dcbee5505f57bafa/coverage-7.6.9-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e4/ae/18ff8b5580e27e62ebcc888082aa47694c2772782ea7011ddf58e377e98f/coverage-7.6.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_i686, cp310-cp310-manylinux2014_i686, cp310-cp310-manylinux_2_17_i686, cp310-cp310-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/6a/52/57030a8d15ab935624d298360f0a6704885578e39f7b4f68569e59f5902d/coverage-7.6.9-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux1_x86_64, cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64, cp310-cp310-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d0/c5/4466602195ecaced298d55af1e29abceb812addabefd5bd9116a204f7bab/coverage-7.6.9-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/1c/55552c3009b7bf96732e36548596ade771c87f89cf1f5a8e3975b33539b5/coverage-7.6.9-cp310-cp310-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bb/7d/da3dca6878701182ea42c51df47a47c80eaef2a76f5aa3e891dc2a8cce3f/coverage-7.6.9-cp310-cp310-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/28/cc/39de85ac1d5652bc34ff2bee39ae251b1fdcaae53fab4b44cab75a432bc0/coverage-7.6.9-cp310-cp310-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/2b/7eb011a9378911088708f121825a71134d0c15fac96972a0ae7a8f5a4049/coverage-7.6.9-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5b/35/c3f40a2269b416db34ce1dedf682a7132c26f857e33596830fa4deebabf9/coverage-7.6.9-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b1/91/b3dc2f7f38b5cca1236ab6bbb03e84046dd887707b4ec1db2baa47493b3b/coverage-7.6.9-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/2b/53fd6cb34d443429a92b3ec737f4953627e38b3bee2a67a3c03425ba8573/coverage-7.6.9-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/f2/68edb1e6826f980a124f21ea5be0d324180bf11de6fd1defcf9604f76df0/coverage-7.6.9-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/83/8fec0ee68c2c4a5ab5f0f8527277f84ed6f2bd1310ae8a19d0c5532253ab/coverage-7.6.9-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/33/6d/31f6ab0b4f0f781636075f757eb02141ea1b34466d9d1526dbc586ed7078/coverage-7.6.9-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7d/fb/e14c38adebbda9ed8b5f7f8e03340ac05d68d27b24397f8d47478927a333/coverage-7.6.9-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a4/11/a782af39b019066af83fdc0e8825faaccbe9d7b19a803ddb753114b429cc/coverage-7.6.9-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/60/52/b16af8989a2daf0f80a88522bd8e8eed90b5fcbdecf02a6888f3e80f6ba7/coverage-7.6.9-cp312-cp312-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/79/6b7826fca8846c1216a113227b9f114ac3e6eacf168b4adcad0cb974aaca/coverage-7.6.9-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/07/0bc73da0ccaf45d0d64ef86d33b7d7fdeef84b4c44bf6b85fb12c215c5a6/coverage-7.6.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/71/8a/9761f409910961647d892454687cedbaccb99aae828f49486734a82ede6e/coverage-7.6.9-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, 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https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/da/e6/93bb9bf85497816082ec8da6124c25efa2052bd4c887dd3b317b91990c9e/coverage-7.6.9-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/df/65/6a824b9406fe066835c1274a9949e06f084d3e605eb1a602727a27ec2fe3/coverage-7.6.9-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_x86_64, cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64, cp313-cp313-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/79/6c7a800913a9dd23ac8c8da133ebb556771a5a3d4df36b46767b1baffd35/coverage-7.6.9-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/e7/834d530293fdc8a63ba8ff70033d5182022e569eceb9aec7fc716b678a39/coverage-7.6.9-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/05/ec9d6080852984f7163c96984444e7cd98b338fd045b191064f943ee1c08/coverage-7.6.9-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0a/d8/775937670b93156aec29f694ce37f56214ed7597e1a75b4083ee4c32121c/coverage-7.6.9-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/58/88551cb7fdd5ec98cb6044e8814e38583436b14040a5ece15349c44c8f7c/coverage-7.6.9-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b7/12/cfbf49b95120872785ff8d56ab1c7fe3970a65e35010c311d7dd35c5fd00/coverage-7.6.9-cp313-cp313-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7c/68/c1cb31445599b04bde21cbbaa6d21b47c5823cdfef99eae470dfce49c35a/coverage-7.6.9-cp313-cp313t-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): 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https://files.pythonhosted.org/packages/ab/9c/0aaac011aef95a93ef3cb2fba3fde30bc7e68a6635199ed469b1f5ea355a/coverage-7.6.9-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f8/19/4d5d3ae66938a7dcb2f58cef3fa5386f838f469575b0bb568c8cc9e3a33d/coverage-7.6.9-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/0d/1e4a48d289429d38aae3babdfcadbf35ca36bdcf3efc8f09b550a845bdb5/coverage-7.6.9-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/26/74/b0729f196f328ac55e42b1e22ec2f16d8bcafe4b8158a26ec9f1cdd1d93e/coverage-7.6.9-cp313-cp313t-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/93/fe/8873d88999b8e4b0d8150df554d72d6943b3938bba328fcb5422572cfd84/coverage-7.6.9-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/23/c1/5dc48dfe3714a6ae9d2cd128a9df39570e46d3831f19a9be84011e767209/coverage-7.6.9-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e8/08/5644e101c823f0b18aa5c408037c2438fad05e6eb9f9e6581459aa0bfb92/coverage-7.6.9-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/02/995c019c0a2d70188d4d8184a0376eb28fcfb759981bb0e9961b463344fd/coverage-7.6.9-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/d3/48ce8c9a89c7013f89ec7e01402e7a136a2e849c8f8664ea7f17b225295c/coverage-7.6.9-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/d2/11ac147bd76cc5d8a6254c9a9b6beaab51c3532ba0abdfaf669bf48d2c67/coverage-7.6.9-cp39-cp39-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/18/cb/6e35c5766041737f14c31ad02b5404ae6ec05d4e17ccffd69f6d99431e0a/coverage-7.6.9-cp39-cp39-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ff/62/5de767f225e09ce959b71d1f3efc9e86e1c3de1fded85886bf705248905d/coverage-7.6.9-cp39-cp39-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/65/72/bacb4b4c9da226e2343aa4bfebcb2bc008eda2f28aa913474aef27bfc397/coverage-7.6.9-cp39-cp39-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f4/4d/096d19dbd8998c9aaf8798078dd884f65652eb891fe7b0e657b5ac07411d/coverage-7.6.9-cp39-cp39-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (pp310-none-any, pp39-none-any) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f2/db/67770cceb4a64d3198bf2aa49946f411b85ec6b0a9b489e61c8467a4253b/coverage-7.6.10-cp310-cp310-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/27/e8bfc43f5345ec2c27bc8a1fa77cdc5ce9dcf954445e11f14bb70b889d14/coverage-7.6.10-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/85/d2/5e175fcf6766cf7501a8541d81778fd2f52f4870100e791f5327fd23270b/coverage-7.6.10-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4b/6f/06db4dc8fca33c13b673986e20e466fd936235a6ec1f0045c3853ac1b593/coverage-7.6.10-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0d/62/c6a0cf80318c1c1af376d52df444da3608eafc913b82c84a4600d8349472/coverage-7.6.10-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-manylinux1_i686, cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686, cp311-cp311-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a3/59/750adafc2e57786d2e8739a46b680d4fb0fbc2d57fbcb161290a9f1ecf23/coverage-7.6.10-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Found link https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9), version: 7.6.10\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/fe/45af5c82389a71e0cae4546413266d2195c3744849669b0bab4b5f2c75da/coverage-7.6.10-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/db/11/3f8e803a43b79bc534c6a506674da9d614e990e37118b4506faf70d46ed6/coverage-7.6.10-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/86/77/19d09ea06f92fdf0487499283b1b7af06bc422ea94534c8fe3a4cd023641/coverage-7.6.10-cp312-cp312-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b6/67/5479b9f2f99fcfb49c0d5cf61912a5255ef80b6e80a3cddba39c38146cf4/coverage-7.6.10-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_aarch64, cp312-cp312-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/d1/febf59030ce1c83b7331c3546d7317e5120c5966471727aa7ac157729c4b/coverage-7.6.10-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_i686, cp312-cp312-manylinux2014_i686, cp312-cp312-manylinux_2_17_i686, cp312-cp312-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/4b/7e/5ac4c90192130e7cf8b63153fe620c8bfd9068f89a6d9b5f26f1550f7a26/coverage-7.6.10-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux1_x86_64, cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/03/0334a79b26ecf59958f2fe9dd1f5ab3e2f88db876f5071933de39af09647/coverage-7.6.10-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d7/45/8a707f23c202208d7b286d78ad6233f50dcf929319b664b6cc18a03c1aae/coverage-7.6.10-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/66/02/603ce0ac2d02bc7b393279ef618940b4a0535b0868ee791140bda9ecfa40/coverage-7.6.10-cp312-cp312-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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(cp313-cp313-macosx_10_13_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/25/6d/31883d78865529257bf847df5789e2ae80e99de8a460c3453dbfbe0db069/coverage-7.6.10-cp313-cp313-macosx_10_13_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/70/22/3f2b129cc08de00c83b0ad6252e034320946abfc3e4235c009e57cfeee05/coverage-7.6.10-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): 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show compatible tags): https://files.pythonhosted.org/packages/9a/0b/7797d4193f5adb4b837207ed87fecf5fc38f7cc612b369a8e8e12d9fa114/coverage-7.6.10-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/65/4d/6f83ca1bddcf8e51bf8ff71572f39a1c73c34cf50e752a952c34f24d0a60/coverage-7.6.10-cp313-cp313-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/9d/2470df6aa146aff4c65fee0f87f58d2164a67533c771c9cc12ffcdb865d5/coverage-7.6.10-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/08/dd/723fef5d901e6a89f2507094db66c091449c8ba03272861eaefa773ad95c/coverage-7.6.10-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/f7/64d3298b2baf261cb35466000628706ce20a82d42faf9b771af447cd2b76/coverage-7.6.10-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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wheel's tags (cp313-cp313t-manylinux2014_aarch64, cp313-cp313t-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/85/7d/125a5362180fcc1c03d91850fc020f3831d5cda09319522bcfa6b2b70be7/coverage-7.6.10-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_i686, cp313-cp313t-manylinux2014_i686, cp313-cp313t-manylinux_2_17_i686, cp313-cp313t-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a9/9c/4358bf3c74baf1f9bddd2baf3756b54c07f2cfd2535f0a47f1e7757e54b3/coverage-7.6.10-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags 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show compatible tags): https://files.pythonhosted.org/packages/08/40/61158b5499aa2adf9e37bc6d0117e8f6788625b283d51e7e0c53cf340530/coverage-7.6.10-cp313-cp313t-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/50/69/b3f2416725621e9f112e74e8470793d5b5995f146f596f133678a633b77e/coverage-7.6.10-cp313-cp313t-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3c/6e/fe899fb937657db6df31cc3e61c6968cb56d36d7326361847440a430152e/coverage-7.6.10-cp313-cp313t-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9)\n"," Skipping link: 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Found link https://files.pythonhosted.org/packages/03/ba/bda83d96bed99276a0b142baeb9a4b8b3183c0d3ae2d5470a0e2b1ebfe40/pytest_metadata-3.1.0.tar.gz (from https://pypi.org/simple/pytest-metadata/) (requires-python:\u003e=3.8), version: 3.1.0\n"," Found link https://files.pythonhosted.org/packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl (from https://pypi.org/simple/pytest-metadata/) (requires-python:\u003e=3.8), version: 3.1.1\n"," Found link https://files.pythonhosted.org/packages/a6/85/8c969f8bec4e559f8f2b958a15229a35495f5b4ce499f6b865eac54b878d/pytest_metadata-3.1.1.tar.gz (from https://pypi.org/simple/pytest-metadata/) (requires-python:\u003e=3.8), version: 3.1.1\n","Skipping link: not a file: https://pypi.org/simple/pytest-metadata/\n","Given no hashes to check 14 links for project 'pytest-metadata': discarding no candidates\n","Collecting pytest-metadata\u003e=2.0.0 (from pytest-html-\u003emase-tools==1.0.0)\n"," 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https://pypi.org/simple/gprof2dot/), version: 2017.9.19\n"," Found link https://files.pythonhosted.org/packages/fd/77/3158821acc45cc26d5815d4250275d03c254572ff53c0388af8df168ce78/gprof2dot-2019.11.30.tar.gz (from https://pypi.org/simple/gprof2dot/), version: 2019.11.30\n"," Found link https://files.pythonhosted.org/packages/0f/80/11d3ec1703cc61606ddc68851747d1d1df3700d5e6c2b559af6dbeb398c5/gprof2dot-2021.2.21.tar.gz (from https://pypi.org/simple/gprof2dot/), version: 2021.2.21\n"," Found link https://files.pythonhosted.org/packages/1e/96/2735e0238be958d0a0b578fdfb46ff175869e462351805faab5ce4cae2d7/gprof2dot-2022.7.29-py2.py3-none-any.whl (from https://pypi.org/simple/gprof2dot/) (requires-python:\u003e=2.7), version: 2022.7.29\n"," Found link https://files.pythonhosted.org/packages/ab/0b/fc056b26a90c1836aa6c6e1332372dc13050d384f017e388131854ead8cf/gprof2dot-2022.7.29.tar.gz (from https://pypi.org/simple/gprof2dot/) (requires-python:\u003e=2.7), version: 2022.7.29\n"," Found link 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Found link https://files.pythonhosted.org/packages/5c/38/1f376dbf601ac89aea8a123fde7c616ae66eadca96a8afc32860b225be9f/execnet-1.0.3.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.3\n"," Found link https://files.pythonhosted.org/packages/ab/41/de81d1491e67e7922e0e42eaee1fde668f8fb2ad06157585bc13ee130a00/execnet-1.0.4.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.4\n"," Found link https://files.pythonhosted.org/packages/12/bf/eea2ed3bc852c36a141eba731a50078535253e6cc1129869af79180b2263/execnet-1.0.5.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.5\n"," Found link https://files.pythonhosted.org/packages/25/ce/badede1a9758ab81376d9f1f147ef458ad74a4059f46ef845e29f2a70678/execnet-1.0.6.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.6\n"," Found link https://files.pythonhosted.org/packages/e4/fe/e0917a2160d4ac962f9d4f79edc026a8fb78748c9c2b97de899c2756dc39/execnet-1.0.7.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.7\n"," Found link https://files.pythonhosted.org/packages/09/12/ce658610f5ff1d3e0a93dce159f1aec19bd058855e5d16cb1ae24b820236/execnet-1.0.8.tar.gz (from https://pypi.org/simple/execnet/), version: 1.0.8\n"," Found link https://files.pythonhosted.org/packages/2b/3a/07fd0c5e14199cdf5be625be248a571d9dc5e4b252d13f69ceb29bf26d99/execnet-1.0.9.zip (from https://pypi.org/simple/execnet/), version: 1.0.9\n"," Found link https://files.pythonhosted.org/packages/4e/71/6d732758c4d89562bcc749bf606f638dcf0e7945a828f09a1ce2755df242/execnet-1.1.zip (from https://pypi.org/simple/execnet/), version: 1.1\n"," Found link https://files.pythonhosted.org/packages/e6/41/7c8ef35e651cedc3714623fc3acc24681ef5e2a86e79494d25a6fdc17b25/execnet-1.2.0.tar.gz (from https://pypi.org/simple/execnet/), version: 1.2.0\n"," Found link https://files.pythonhosted.org/packages/1a/77/f390aa1ee13b03ba16fb7fc0829c1caa9fccba01b7017b1a0c52f846440b/execnet-1.3.0-py2.py3-none-any.whl (from https://pypi.org/simple/execnet/), version: 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https://files.pythonhosted.org/packages/75/a5/1b3fbfb73905b40f7418f5b12356d990d7f5c427d2f5dac53b2910a81c2b/ale_py-0.10.1-cp311-cp311-macosx_10_15_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/2e/93a0c1eeeafa4db76a2a7ae800ae69802365e0ba6300618a23b1791adb79/ale_py-0.10.1-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Found link https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8), version: 0.10.1\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/55/f0/9471eee05e95818fbbea1e3aca93d1eeeeabcc64f66f444bf36d9556846c/ale_py-0.10.1-cp311-cp311-win_amd64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/cb/618e97c6fa9ca94c996bcc22299d6c0a0461a39a5d42755085c3d29d4f42/ale_py-0.10.1-cp312-cp312-macosx_10_15_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/3d/cd/75033786eacb9d495c5aeddc425394a9d37104afa2980152504fb594ee61/ale_py-0.10.1-cp312-cp312-macosx_11_0_arm64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1a/08/d1fdfd9e8d0b0609d9ed047830a8c076a1aafc037213da36051a7ca47d54/ale_py-0.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp312-cp312-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/16/ef/f71fcf7d202ad1546638f5535bd169e71cd143354efbdfe7ae22a767b5ca/ale_py-0.10.1-cp312-cp312-win_amd64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b3/b9/deceed80853a22d7f81c989986f5771b174c129c1aa60acd94b3d7b3ff42/ale_py-0.10.1-cp313-cp313-macosx_10_15_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/95/a0/ced7cb40f44afe86f287e257aee8f09758bbc0e6b91c78f554a1d0963be7/ale_py-0.10.1-cp313-cp313-macosx_11_0_arm64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_x86_64, cp313-cp313-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b0/a6/f05688bed7fd7d64611a8c72225212dd971e32d39ac9e1c299bf3d4e85c2/ale_py-0.10.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/ale-py/) 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Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5f/ab/6412519ca0181a3c1ae66889105784d52d323210f8f142fd224e1fecb34d/ale_py-0.10.1-cp39-cp39-macosx_10_15_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1f/78/c351f1f6cd97670fe25a96c7d08746b92b41201737f4ff4cc35866194eea/ale_py-0.10.1-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): 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ale-py\u003e=0.9.0 (from stable-baselines3[extra]-\u003emase-tools==1.0.0)\n"," Obtaining dependency information for ale-py\u003e=0.9.0 from https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-890nc8tf\n"," Looking up \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata HTTP/1.1\" 200 7594\n"," Downloading ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.6 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https://files.pythonhosted.org/packages/03/62/70f5a0c2dd208f9f3f2f9afd103aec42ee4d9ad2401d78342f75e9b8da36/Mako-1.3.5-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.5\n"," Found link https://files.pythonhosted.org/packages/67/03/fb5ba97ff65ce64f6d35b582aacffc26b693a98053fa831ab43a437cbddb/Mako-1.3.5.tar.gz (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.5\n"," Found link https://files.pythonhosted.org/packages/48/22/bc14c6f02e6dccaafb3eba95764c8f096714260c2aa5f76f654fd16a23dd/Mako-1.3.6-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.6\n"," Found link https://files.pythonhosted.org/packages/fa/0b/29bc5a230948bf209d3ed3165006d257e547c02c3c2a96f6286320dfe8dc/mako-1.3.6.tar.gz (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.6\n"," Found link https://files.pythonhosted.org/packages/a7/d8/c516e830071b849ad60ee1026315bb8566381ac648ac8bda314569b4b7e2/Mako-1.3.7-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.7\n"," Found link https://files.pythonhosted.org/packages/5a/27/5af876b41cebd9d76fa8333b83ef9121726893f725952022edd194a1671e/mako-1.3.7.tar.gz (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.7\n"," Found link https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.8\n"," Found link https://files.pythonhosted.org/packages/5f/d9/8518279534ed7dace1795d5a47e49d5299dd0994eed1053996402a8902f9/mako-1.3.8.tar.gz (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8), version: 1.3.8\n","Skipping link: not a file: https://pypi.org/simple/mako/\n","Given no hashes to check 94 links for project 'mako': discarding no candidates\n","Collecting Mako (from alembic\u003e=1.5.0-\u003eoptuna-\u003emase-tools==1.0.0)\n"," Obtaining dependency information for Mako from https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-iu4dszw7\n"," Looking up \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata HTTP/1.1\" 200 2896\n"," Downloading Mako-1.3.8-py3-none-any.whl.metadata (2.9 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl.metadata\"\n"," etag object cached for 1209600 seconds\n"," Caching due to etag\n"," Created temporary directory: /tmp/pip-metadata-y0yjs4f7\n","Requirement already satisfied: aiohappyeyeballs\u003e=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (2.4.4)\n","Requirement already satisfied: aiosignal\u003e=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (1.3.2)\n","Requirement already satisfied: attrs\u003e=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (24.3.0)\n","Requirement already satisfied: frozenlist\u003e=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (1.5.0)\n","Requirement already satisfied: multidict\u003c7.0,\u003e=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (6.1.0)\n","Requirement already satisfied: propcache\u003e=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (0.2.1)\n","Requirement already satisfied: yarl\u003c2.0,\u003e=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp-\u003edatasets-\u003emase-tools==1.0.0) (1.18.3)\n","Requirement already satisfied: smmap\u003c6,\u003e=3.0.1 in /usr/local/lib/python3.11/dist-packages (from gitdb\u003c5,\u003e=4.0.1-\u003eGitPython-\u003emase-tools==1.0.0) (5.0.2)\n","1 location(s) to search for versions of farama-notifications:\n","* https://pypi.org/simple/farama-notifications/\n","Fetching project page and analyzing links: https://pypi.org/simple/farama-notifications/\n","Getting page https://pypi.org/simple/farama-notifications/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/farama-notifications/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/farama-notifications/ HTTP/1.1\" 200 1112\n","Updating cache with response from \"https://pypi.org/simple/farama-notifications/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/farama-notifications/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/93/3d/faf80234f72568a89ebf99d893ae30a9bc0d7226fca406e251bb41e283ee/Farama-Notifications-0.0.1.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/f8/f1/5ae419e5d38ea81c9e9df42c1320cf329b2f0197dd4c06a86c9971305744/Farama_Notifications-0.0.1-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/00/a4/22126dbc9c448912b0ea773bef2d60b595b6295e8880d7eb47ddd8a9c071/Farama-Notifications-0.0.2.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/99/65/eddbd8b13d7bfd5dcdfea3eb46458b052e27abfc85d37c4c79ded99c1ea0/Farama_Notifications-0.0.2-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/2e/2c/8384832b7a6b1fd6ba95bbdcae26e7137bb3eedc955c42fd5cdcc086cfbf/Farama-Notifications-0.0.4.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.4\n","Skipping link: not a file: https://pypi.org/simple/farama-notifications/\n","Given no hashes to check 6 links for project 'farama-notifications': discarding no candidates\n","Collecting farama-notifications\u003e=0.0.1 (from gymnasium\u003c1.1.0,\u003e=0.29.1-\u003estable-baselines3[extra]-\u003emase-tools==1.0.0)\n"," Obtaining dependency information for farama-notifications\u003e=0.0.1 from https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-65akmsup\n"," Looking up \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata HTTP/1.1\" 200 558\n"," Downloading Farama_Notifications-0.0.4-py3-none-any.whl.metadata (558 bytes)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata\"\n"," etag object cached for 1209600 seconds\n"," Caching due to etag\n"," Created temporary directory: /tmp/pip-metadata-_bt48fbh\n","Requirement already satisfied: parso\u003c0.9.0,\u003e=0.8.4 in /usr/local/lib/python3.11/dist-packages (from jedi\u003e=0.16-\u003eipython-\u003emase-tools==1.0.0) (0.8.4)\n","Requirement already satisfied: MarkupSafe\u003e=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2-\u003etorch-\u003emase-tools==1.0.0) (3.0.2)\n","Requirement already satisfied: jsonschema-specifications\u003e=2023.03.6 in /usr/local/lib/python3.11/dist-packages (from jsonschema\u003e=3.2.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (2024.10.1)\n","Requirement already satisfied: referencing\u003e=0.28.4 in /usr/local/lib/python3.11/dist-packages (from jsonschema\u003e=3.2.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (0.36.1)\n","Requirement already satisfied: rpds-py\u003e=0.7.1 in /usr/local/lib/python3.11/dist-packages (from jsonschema\u003e=3.2.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (0.22.3)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py~=3.0-\u003emyst_parser-\u003emase-tools==1.0.0) (0.1.2)\n","Given no hashes to check 2 links for project 'dill': discarding no candidates\n","Requirement already satisfied: jupyter-client\u003e=6.1.12 in /usr/local/lib/python3.11/dist-packages (from nbclient-\u003emyst-nb-\u003emase-tools==1.0.0) (6.1.12)\n","Requirement already satisfied: jupyter-core!=5.0.*,\u003e=4.12 in /usr/local/lib/python3.11/dist-packages (from nbclient-\u003emyst-nb-\u003emase-tools==1.0.0) (5.7.2)\n","Requirement already satisfied: fastjsonschema\u003e=2.15 in /usr/local/lib/python3.11/dist-packages (from nbformat\u003e=5.0-\u003emyst-nb-\u003emase-tools==1.0.0) (2.21.1)\n","Given no hashes to check 2 links for project 'scipy': discarding no candidates\n","Requirement already satisfied: qdldl in /usr/local/lib/python3.11/dist-packages (from osqp\u003e=0.6.2-\u003ecvxpy-\u003emase-tools==1.0.0) (0.1.7.post5)\n","Requirement already satisfied: ptyprocess\u003e=0.5 in /usr/local/lib/python3.11/dist-packages (from pexpect\u003e4.3-\u003eipython-\u003emase-tools==1.0.0) (0.7.0)\n","Requirement already satisfied: annotated-types\u003e=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic\u003c3,\u003e=2.6-\u003ewandb-\u003emase-tools==1.0.0) (0.7.0)\n","Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic\u003c3,\u003e=2.6-\u003ewandb-\u003emase-tools==1.0.0) (2.27.2)\n","Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from pydata-sphinx-theme\u003e=0.15.2-\u003esphinx-book-theme-\u003emase-tools==1.0.0) (4.12.3)\n","Requirement already satisfied: Babel in /usr/local/lib/python3.11/dist-packages (from pydata-sphinx-theme\u003e=0.15.2-\u003esphinx-book-theme-\u003emase-tools==1.0.0) (2.16.0)\n","1 location(s) to search for versions of accessible-pygments:\n","* https://pypi.org/simple/accessible-pygments/\n","Fetching project page and analyzing links: https://pypi.org/simple/accessible-pygments/\n","Getting page https://pypi.org/simple/accessible-pygments/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/accessible-pygments/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/accessible-pygments/ HTTP/1.1\" 200 1674\n","Updating cache with response from \"https://pypi.org/simple/accessible-pygments/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/accessible-pygments/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/cf/59/65a3e553fcfbe5bc5d5f861e92256484b56d2a14a633a1413c38dd71f0d5/accessible-pygments-0.0.1.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/a4/00/536a9d9ad7930a582e7320ad80b6ea7179d63d2e42c6e23abccde9b4a1ca/accessible_pygments-0.0.1-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/a2/41/0154c24518c00b98a7ce44493bbddc276ff4db70286b4008fb53cb9d2e69/accessible-pygments-0.0.2.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/87/57/75d37069faa4bcfb0b2d4fc768b84bb2fb2234c64dc9518e1b7ecafe683f/accessible_pygments-0.0.2-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/c1/dc/7b23dca20691e69f5a43b2a4b06ff755f72b8f4e87de9c69c78d4e644c74/accessible-pygments-0.0.3.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.3\n"," Found link https://files.pythonhosted.org/packages/b3/12/8b34b4ecf56bd4d30b100c0eed43ebe30394eeb063998a9b739dc8d46281/accessible_pygments-0.0.3-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.3\n"," Found link https://files.pythonhosted.org/packages/b2/50/7055ebd9b7928eca202768bcf5f8f69d8d69dec1767c956c08f055c5b31e/accessible-pygments-0.0.4.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/20/d7/45cfa326d945e411c7e02764206845b05f8f5766aa7ebc812ef3bc4138cd/accessible_pygments-0.0.4-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/) (requires-python:\u003e=3.9), version: 0.0.5\n"," Found link https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz (from https://pypi.org/simple/accessible-pygments/) (requires-python:\u003e=3.9), version: 0.0.5\n","Skipping link: not a file: https://pypi.org/simple/accessible-pygments/\n","Given no hashes to check 10 links for project 'accessible-pygments': discarding no candidates\n","Collecting accessible-pygments (from pydata-sphinx-theme\u003e=0.15.2-\u003esphinx-book-theme-\u003emase-tools==1.0.0)\n"," Obtaining dependency information for accessible-pygments from https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-8heaoha_\n"," Looking up \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl.metadata HTTP/1.1\" 200 10286\n"," Downloading accessible_pygments-0.0.5-py3-none-any.whl.metadata (10 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from 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requests~=2.32-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (2024.12.14)\n","Requirement already satisfied: sphinxcontrib-applehelp\u003e=1.0.7 in /usr/local/lib/python3.11/dist-packages (from sphinx\u003c9,\u003e=7.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-devhelp\u003e=1.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx\u003c9,\u003e=7.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-htmlhelp\u003e=2.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx\u003c9,\u003e=7.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (2.1.0)\n","Requirement already satisfied: sphinxcontrib-jsmath\u003e=1.0.1 in /usr/local/lib/python3.11/dist-packages (from sphinx\u003c9,\u003e=7.0-\u003esphinx-needs\u003e=4-\u003emase-tools==1.0.0) (1.0.1)\n","Requirement already satisfied: sphinxcontrib-qthelp\u003e=1.0.6 in 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https://files.pythonhosted.org/packages/90/df/88bff450f333114680698dc4aac7506ff7cab164b794461906de31998665/humanfriendly-4.18-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 4.18\n"," Found link https://files.pythonhosted.org/packages/26/71/e7daf57e819a70228568ff5395fdbc4de81b63067b93167e07825fcf0bcf/humanfriendly-4.18.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 4.18\n"," Found link https://files.pythonhosted.org/packages/ad/97/688c749720d8107dd2895e3f7fa8c3716ec36715a0a9c1be58dd97fd2db0/humanfriendly-5.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 5.0\n"," Found link https://files.pythonhosted.org/packages/89/69/cb0c76f27ce221735efa65c84336358d808c31957cb0149fcfcc939dbc7b/humanfriendly-5.0.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 5.0\n"," Found link https://files.pythonhosted.org/packages/b0/dd/37a08313472fee1d92b158f771f41bdb5e962160f9bce07d446da15ee833/humanfriendly-6.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 6.0\n"," Found link https://files.pythonhosted.org/packages/f9/c3/32e12a9816f863dbfe6c1c88e888af81b9a2c7c50de6c5d47ea35e958b31/humanfriendly-6.0.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 6.0\n"," Found link https://files.pythonhosted.org/packages/1e/dc/8aac3921fd2a03978542a22eb7485b63c8e85ca75a4d3bb904fab82cf032/humanfriendly-6.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/), version: 6.1\n"," Found link https://files.pythonhosted.org/packages/43/12/fef23e1b47c7a6fcd9d15e12bdca745e686d4f52affe494b5fa90eb31efe/humanfriendly-6.1.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 6.1\n"," Found link https://files.pythonhosted.org/packages/2f/26/c6f5fca266afcb7586f460ae83f585def604a762608d12de86b78b59cb73/humanfriendly-7.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.0\n"," Found link https://files.pythonhosted.org/packages/c8/14/5a86809cbb17c4c4adf61c037a13ff087acec890cc3b3c0e7211997a397d/humanfriendly-7.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.0\n"," Found link https://files.pythonhosted.org/packages/f7/71/5cf23906339bd1574e84b1c0f1a17f761694fdeef93b8fa0c3cb436e527e/humanfriendly-7.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.1\n"," Found link https://files.pythonhosted.org/packages/b4/3c/ac93d2a50e3ae656f018e3e48edf2e6b849f938e25eebb7c74164d514315/humanfriendly-7.1.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.1\n"," Found link https://files.pythonhosted.org/packages/ba/b3/80b90fa705f8809c0a25d15c8cb7864e6e57ebad30048926fd9c3414f21d/humanfriendly-7.1.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.1.1\n"," Found link https://files.pythonhosted.org/packages/bb/ee/c85cca94adc8d6eca6d86802dbffcedf6de7645a69d07d9993a39783f79d/humanfriendly-7.1.1.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.1.1\n"," Found link https://files.pythonhosted.org/packages/f8/d4/f28089b2146f5dd175fb0f7a65f4c3db459f9cbecd0e38b3682021459aad/humanfriendly-7.2-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.2\n"," Found link https://files.pythonhosted.org/packages/10/f2/174a19d9cf49fb9418584ae7f667957d3215b624396dd38278c79670a55b/humanfriendly-7.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.2\n"," Found link https://files.pythonhosted.org/packages/af/b7/798372f9ddf5429c69a36cb940ee1da7e6f37192c058db7ccb5d85af72d4/humanfriendly-7.3-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.3\n"," Found link https://files.pythonhosted.org/packages/27/32/32f678f41b2e0e7c59ae8916562d6d106d8a0c1aa55cabad377fe5c60b61/humanfriendly-7.3.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 7.3\n"," Found link https://files.pythonhosted.org/packages/19/a4/6deadfb0ed130b14628eff1cd39dc0e58c8dce9eddf46e2754060fe7f38b/humanfriendly-8.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.0\n"," Found link https://files.pythonhosted.org/packages/53/1e/cde1153172d0d2bdf68845b8a52f8dd1bdd509f506d123a32a751a1bb0bd/humanfriendly-8.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.0\n"," Found link https://files.pythonhosted.org/packages/9d/25/417cfcd511782bc678c1285a365271bdbe9ec895fa69a4c7a294ae9586f5/humanfriendly-8.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.1\n"," Found link https://files.pythonhosted.org/packages/2e/d1/e0d8db85b71fc6e7d5be7d78bb5db64c63790aec45acef6578190d66c666/humanfriendly-8.1.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.1\n"," Found link https://files.pythonhosted.org/packages/8e/2d/2f1b0a780b8c948c06c74c8c80e68ac354da52397ba432a1c5ac1923c3af/humanfriendly-8.2-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.2\n"," Found link https://files.pythonhosted.org/packages/6c/19/8e3b4c6fa7cca4788817db398c05274d98ecc6a35e0eaad2846fde90c863/humanfriendly-8.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 8.2\n"," Found link https://files.pythonhosted.org/packages/79/59/239f467c4bafad82ad1fdb0d2a16384d8a5e18d5a3197b4840473531e50e/humanfriendly-9.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.0\n"," Found link https://files.pythonhosted.org/packages/1f/6c/bddbe81bb6b0f03088069ef3a7346be5218cb0689025fa6c6c8dc1886da2/humanfriendly-9.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.0\n"," Found link https://files.pythonhosted.org/packages/93/66/363d01a81da2108a5cf446daf619779f06d49a0c4426dd02b40734f10e2f/humanfriendly-9.1-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.1\n"," Found link https://files.pythonhosted.org/packages/31/0e/a2e882aaaa0a378aa6643f4bbb571399aede7dbb5402d3a1ee27a201f5f3/humanfriendly-9.1.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.1\n"," Found link https://files.pythonhosted.org/packages/92/7e/a06472f484fa589933f39bfb41a7b849ca49f6d8e4fdfe978e27f0e3075c/humanfriendly-9.2-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.2\n"," Found link https://files.pythonhosted.org/packages/24/ca/f3a75b50d978872f6551d72c9c76890d68c84f3ba210cdba5f409587a2fc/humanfriendly-9.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.2\n"," Found link https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 10.0\n"," Found link https://files.pythonhosted.org/packages/cc/3f/2c29224acb2e2df4d2046e4c73ee2662023c58ff5b113c4c1adac0886c43/humanfriendly-10.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 10.0\n","Skipping link: not a file: https://pypi.org/simple/humanfriendly/\n","Given no hashes to check 6 links for project 'humanfriendly': discarding no 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\"https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 MB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/scipy/) (requires-python:\u003e=3.10) to /tmp/pip-unpack-c6ofk08o/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl HTTP/1.1\" 200 71889\n","Downloading bitstring-4.3.0-py3-none-any.whl (71 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/71.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl (from https://pypi.org/simple/bitstring/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/bitstring-4.3.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 286125\n","Downloading bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (286 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/286.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.1/286.1 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/dc/46/25ebc667907736b2c5c84f4bd8260d9bece8b69719a33db5c3f3dcb281a5/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/bitarray/) to /tmp/pip-unpack-c6ofk08o/bitarray-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl HTTP/1.1\" 200 2634328\n","Downloading sphinx_needs-4.2.0-py3-none-any.whl (2.6 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m53.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl (from https://pypi.org/simple/sphinx-needs/) (requires-python:\u003c4,\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/sphinx_needs-4.2.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl HTTP/1.1\" 200 27162\n","Downloading attr_dot_dict-0.1.0-py3-none-any.whl (27 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl (from https://pypi.org/simple/attr-dot-dict/) (requires-python:\u003e=3.7) to /tmp/pip-unpack-c6ofk08o/attr_dot_dict-0.1.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c9/9b/2db8045b45844665c720dcfe292fdaf2e49825810c0103e1191515fc101a/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c9/9b/2db8045b45844665c720dcfe292fdaf2e49825810c0103e1191515fc101a/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (from https://pypi.org/simple/black/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl HTTP/1.1\" 200 11424\n","Downloading colorlog-6.9.0-py3-none-any.whl (11 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl (from https://pypi.org/simple/colorlog/) (requires-python:\u003e=3.6) to /tmp/pip-unpack-c6ofk08o/colorlog-6.9.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl HTTP/1.1\" 200 480647\n","Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl (from https://pypi.org/simple/datasets/) (requires-python:\u003e=3.9.0) to /tmp/pip-unpack-c6ofk08o/datasets-3.2.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl HTTP/1.1\" 200 590617\n","Downloading emoji-2.14.1-py3-none-any.whl (590 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/590.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m590.6/590.6 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl (from https://pypi.org/simple/emoji/) (requires-python:\u003e=3.7) to /tmp/pip-unpack-c6ofk08o/emoji-2.14.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl HTTP/1.1\" 200 84010\n","Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl (from https://pypi.org/simple/evaluate/) (requires-python:\u003e=3.8.0) to /tmp/pip-unpack-c6ofk08o/evaluate-0.4.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl HTTP/1.1\" 200 11034\n","Downloading ghp_import-2.1.0-py3-none-any.whl (11 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl (from https://pypi.org/simple/ghp-import/) to /tmp/pip-unpack-c6ofk08o/ghp_import-2.1.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl HTTP/1.1\" 200 12130\n","Downloading ipdb-0.13.13-py3-none-any.whl (12 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl (from https://pypi.org/simple/ipdb/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*) to /tmp/pip-unpack-c6ofk08o/ipdb-0.13.13-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl HTTP/1.1\" 200 1078141\n","Downloading kornia-0.8.0-py2.py3-none-any.whl (1.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m33.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl (from https://pypi.org/simple/kornia/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/kornia-0.8.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl HTTP/1.1\" 200 815241\n","Downloading lightning-2.5.0.post0-py3-none-any.whl (815 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/815.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m36.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl (from https://pypi.org/simple/lightning/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/lightning-2.5.0.post0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl HTTP/1.1\" 200 80281\n","Downloading myst_nb-1.1.2-py3-none-any.whl (80 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl (from https://pypi.org/simple/myst-nb/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/myst_nb-1.1.2-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl HTTP/1.1\" 200 84563\n","Downloading myst_parser-4.0.0-py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.6/84.6 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl (from https://pypi.org/simple/myst-parser/) (requires-python:\u003e=3.10) to /tmp/pip-unpack-c6ofk08o/myst_parser-4.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 16048190\n","Downloading onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.0 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m13.5/16.0 MB\u001b[0m \u001b[31m106.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.0/16.0 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/onnx/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl HTTP/1.1\" 200 84477\n","Downloading onnxconverter_common-1.14.0-py2.py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl (from https://pypi.org/simple/onnxconverter-common/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/onnxconverter_common-1.14.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl HTTP/1.1\" 200 162128\n","Downloading protobuf-3.20.2-py2.py3-none-any.whl (162 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/162.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.1/162.1 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl (from https://pypi.org/simple/protobuf/) (requires-python:\u003e=3.7) to /tmp/pip-unpack-c6ofk08o/protobuf-3.20.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl HTTP/1.1\" 200 13331703\n","Downloading onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (13.3 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m13.0/13.3 MB\u001b[0m \u001b[31m98.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m55.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (from https://pypi.org/simple/onnxruntime/) to /tmp/pip-unpack-c6ofk08o/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl HTTP/1.1\" 200 424070\n","Downloading optimum-1.23.3-py3-none-any.whl (424 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.1/424.1 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl (from https://pypi.org/simple/optimum/) (requires-python:\u003e=3.7.0) to /tmp/pip-unpack-c6ofk08o/optimum-1.23.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl HTTP/1.1\" 200 383372\n","Downloading optuna-4.2.0-py3-none-any.whl (383 kB)\n","\u001b[?25l 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\u001b[32m243.3/243.3 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/13/2f/0f24b288e2ce56f51c920137620b4434a38fd80583dbbe24fc2a1656c388/pybind11-2.13.6-py3-none-any.whl (from https://pypi.org/simple/pybind11/) (requires-python:\u003e=3.7) to /tmp/pip-unpack-c6ofk08o/pybind11-2.13.6-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl HTTP/1.1\" 200 26560\n","Downloading pynvml-12.0.0-py3-none-any.whl (26 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl (from https://pypi.org/simple/pynvml/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/pynvml-12.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl HTTP/1.1\" 200 22949\n","Downloading pytest_cov-6.0.0-py3-none-any.whl (22 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl (from https://pypi.org/simple/pytest-cov/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/pytest_cov-6.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl HTTP/1.1\" 200 23491\n","Downloading pytest_html-4.1.1-py3-none-any.whl (23 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl (from https://pypi.org/simple/pytest-html/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/pytest_html-4.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e3/ac/c428c66241a144617a8af7a28e2e055e1438d23b949b62ac4b401a69fb79/pytest_profiling-1.8.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl HTTP/1.1\" 200 10171\n","Downloading pytest_sugar-1.0.0-py3-none-any.whl (10 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl (from https://pypi.org/simple/pytest-sugar/) to /tmp/pip-unpack-c6ofk08o/pytest_sugar-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl HTTP/1.1\" 200 46108\n","Downloading pytest_xdist-3.6.1-py3-none-any.whl (46 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/46.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\"https://files.pythonhosted.org/packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl HTTP/1.1\" 200 90058\n","Downloading pytorch_nlp-0.5.0-py3-none-any.whl (90 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/90.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\"https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl HTTP/1.1\" 200 4237451\n","Downloading sphinx_glpi_theme-0.6-py2.py3-none-any.whl (4.2 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/4.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m81.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl (from https://pypi.org/simple/sphinx-glpi-theme/) to /tmp/pip-unpack-c6ofk08o/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl HTTP/1.1\" 200 7655561\n","Downloading sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl (7.7 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/7.7 MB\u001b[0m \u001b[31m139.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m85.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl (from https://pypi.org/simple/sphinx-rtd-theme/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl HTTP/1.1\" 200 101700\n","Downloading tensorboardX-2.6.2.2-py2.py3-none-any.whl (101 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/101.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.7/101.7 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl (from https://pypi.org/simple/tensorboardx/) to /tmp/pip-unpack-c6ofk08o/tensorboardX-2.6.2.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2102024\n","Downloading ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m68.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl HTTP/1.1\" 200 233565\n","Downloading alembic-1.14.1-py3-none-any.whl (233 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/233.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.6/233.6 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl (from https://pypi.org/simple/alembic/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/alembic-1.14.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 239750\n","Downloading coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/239.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.8/239.8 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl HTTP/1.1\" 200 116252\n","Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl (from https://pypi.org/simple/dill/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/dill-0.3.8-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl HTTP/1.1\" 200 40612\n","Downloading execnet-2.1.1-py3-none-any.whl (40 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/40.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.6/40.6 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl (from https://pypi.org/simple/execnet/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/execnet-2.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl HTTP/1.1\" 200 179253\n","Downloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/179.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl (from https://pypi.org/simple/fsspec/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/fsspec-2024.9.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl HTTP/1.1\" 200 958120\n","Downloading gymnasium-1.0.0-py3-none-any.whl (958 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/958.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m958.1/958.1 kB\u001b[0m \u001b[31m43.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/gymnasium-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl HTTP/1.1\" 200 1572278\n","Downloading jedi-0.19.2-py2.py3-none-any.whl (1.6 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m56.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl (from https://pypi.org/simple/jedi/) (requires-python:\u003e=3.6) to /tmp/pip-unpack-c6ofk08o/jedi-0.19.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/64/6b/67b87da9d36bff9df7d0efbd1a325fa372a43be7158effaf43ed7b22341d/jupyter_cache-1.0.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\"https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2050393\n","Downloading kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m68.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/kornia-rs/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl HTTP/1.1\" 200 28356\n","Downloading lightning_utilities-0.11.9-py3-none-any.whl (28 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl (from https://pypi.org/simple/lightning-utilities/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/lightning_utilities-0.11.9-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl HTTP/1.1\" 200 143519\n","Downloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/143.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K 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directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl (from https://pypi.org/simple/mypy-extensions/) (requires-python:\u003e=3.5) to /tmp/pip-unpack-c6ofk08o/mypy_extensions-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl HTTP/1.1\" 200 44442\n","Downloading nvidia_ml_py-12.570.86-py3-none-any.whl (44 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl (from https://pypi.org/simple/nvidia-ml-py/) to /tmp/pip-unpack-c6ofk08o/nvidia_ml_py-12.570.86-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl HTTP/1.1\" 200 31191\n","Downloading pathspec-0.12.1-py3-none-any.whl (31 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl (from https://pypi.org/simple/pathspec/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/pathspec-0.12.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl HTTP/1.1\" 200 6723264\n","Downloading pydata_sphinx_theme-0.16.1-py3-none-any.whl (6.7 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/6.7 MB\u001b[0m \u001b[31m110.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m68.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/pydata_sphinx_theme-0.16.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl HTTP/1.1\" 200 11428\n","Downloading pytest_metadata-3.1.1-py3-none-any.whl (11 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl (from https://pypi.org/simple/pytest-metadata/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/pytest_metadata-3.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl HTTP/1.1\" 200 4244\n","Downloading requests_file-2.1.0-py2.py3-none-any.whl (4.2 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl (from https://pypi.org/simple/requests-file/) to /tmp/pip-unpack-c6ofk08o/requests_file-2.1.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl HTTP/1.1\" 200 8215\n","Downloading sphinx_data_viewer-0.1.5-py3-none-any.whl (8.2 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl (from https://pypi.org/simple/sphinx-data-viewer/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/sphinx_data_viewer-0.1.5-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl HTTP/1.1\" 200 121104\n","Downloading sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (121 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/121.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.1/121.1 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (from https://pypi.org/simple/sphinxcontrib-jquery/) (requires-python:\u003e=2.7) to /tmp/pip-unpack-c6ofk08o/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl HTTP/1.1\" 200 927305\n","Downloading torchmetrics-1.6.1-py3-none-any.whl (927 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/927.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m927.3/927.3 kB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl (from https://pypi.org/simple/torchmetrics/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/torchmetrics-1.6.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 4211516\n","Downloading cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/4.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m68.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/cocotb/) (requires-python:\u003e=3.6) to /tmp/pip-unpack-c6ofk08o/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl HTTP/1.1\" 200 46018\n","Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/46.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl (from https://pypi.org/simple/coloredlogs/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*) to /tmp/pip-unpack-c6ofk08o/coloredlogs-15.0.1-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl HTTP/1.1\" 200 8670\n","Downloading find_libpython-0.4.0-py3-none-any.whl (8.7 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl (from https://pypi.org/simple/find-libpython/) to /tmp/pip-unpack-c6ofk08o/find_libpython-0.4.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl HTTP/1.1\" 200 34763\n","Downloading gprof2dot-2024.6.6-py2.py3-none-any.whl (34 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl (from https://pypi.org/simple/gprof2dot/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/gprof2dot-2024.6.6-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl (from https://pypi.org/simple/stable-baselines3/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/stable_baselines3-2.5.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 194822\n","Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/xxhash/) (requires-python:\u003e=3.7) to /tmp/pip-unpack-c6ofk08o/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl HTTP/1.1\" 200 2511\n","Downloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/) to /tmp/pip-unpack-c6ofk08o/Farama_Notifications-0.0.4-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl HTTP/1.1\" 200 86794\n","Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:\u003e=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*) to /tmp/pip-unpack-c6ofk08o/humanfriendly-10.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl HTTP/1.1\" 200 1395903\n","Downloading accessible_pygments-0.0.5-py3-none-any.whl (1.4 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m61.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/) (requires-python:\u003e=3.9) to /tmp/pip-unpack-c6ofk08o/accessible_pygments-0.0.5-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl HTTP/1.1\" 200 78569\n","Downloading Mako-1.3.8-py3-none-any.whl (78 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/78.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:\u003e=3.8) to /tmp/pip-unpack-c6ofk08o/Mako-1.3.8-py3-none-any.whl\n","Building wheels for collected packages: sphinx-test-reports, sphinxcontrib-plantuml, cocotb-bus\n"," Created temporary directory: /tmp/pip-wheel-ex383052\n"," Destination directory: /tmp/pip-wheel-ex383052\n"," Running command Building wheel for sphinx-test-reports (pyproject.toml)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/tests\n"," copying tests/test_env.py -\u003e build/lib/tests\n"," copying tests/test_test_file.py -\u003e build/lib/tests\n"," copying tests/test_basic_doc.py -\u003e build/lib/tests\n"," copying tests/test_custom_template.py -\u003e build/lib/tests\n"," copying tests/test_json_parser.py -\u003e build/lib/tests\n"," copying tests/test_junit_parser.py -\u003e build/lib/tests\n"," copying tests/__init__.py -\u003e build/lib/tests\n"," copying tests/test_test_ctest_file.py -\u003e build/lib/tests\n"," copying tests/test_needs_linking.py -\u003e build/lib/tests\n"," copying tests/conftest.py -\u003e build/lib/tests\n"," copying tests/test_custom_types.py -\u003e build/lib/tests\n"," copying tests/test_suites.py -\u003e build/lib/tests\n"," creating build/lib/sphinxcontrib\n"," copying sphinxcontrib/__init__.py -\u003e build/lib/sphinxcontrib\n"," creating build/lib/docs\n"," copying docs/conf.py -\u003e build/lib/docs\n"," creating build/lib/tests/doc_test/env_report_doc_raw\n"," copying tests/doc_test/env_report_doc_raw/conf.py -\u003e build/lib/tests/doc_test/env_report_doc_raw\n"," creating build/lib/tests/doc_test/custom_tr_koi8_template\n"," copying tests/doc_test/custom_tr_koi8_template/conf.py -\u003e build/lib/tests/doc_test/custom_tr_koi8_template\n"," creating build/lib/tests/doc_test/pytest_6_2\n"," copying tests/doc_test/pytest_6_2/conf.py -\u003e build/lib/tests/doc_test/pytest_6_2\n"," creating build/lib/tests/doc_test/custom_tr_template\n"," copying tests/doc_test/custom_tr_template/conf.py -\u003e build/lib/tests/doc_test/custom_tr_template\n"," creating build/lib/tests/doc_test/json_parser_complex\n"," copying tests/doc_test/json_parser_complex/conf.py -\u003e build/lib/tests/doc_test/json_parser_complex\n"," creating build/lib/tests/doc_test/json_parser\n"," copying tests/doc_test/json_parser/conf.py -\u003e build/lib/tests/doc_test/json_parser\n"," creating build/lib/tests/doc_test/custom_types\n"," copying tests/doc_test/custom_types/conf.py -\u003e build/lib/tests/doc_test/custom_types\n"," creating build/lib/tests/doc_test/many_testsuites_doc\n"," copying tests/doc_test/many_testsuites_doc/conf.py -\u003e build/lib/tests/doc_test/many_testsuites_doc\n"," creating build/lib/tests/doc_test/doc_test_file\n"," copying tests/doc_test/doc_test_file/conf.py -\u003e build/lib/tests/doc_test/doc_test_file\n"," creating build/lib/tests/doc_test/needs_linking\n"," copying tests/doc_test/needs_linking/conf.py -\u003e build/lib/tests/doc_test/needs_linking\n"," creating build/lib/tests/doc_test/env_report_doc_default\n"," copying tests/doc_test/env_report_doc_default/conf.py -\u003e build/lib/tests/doc_test/env_report_doc_default\n"," creating build/lib/tests/doc_test/basic_doc\n"," copying tests/doc_test/basic_doc/conf.py -\u003e build/lib/tests/doc_test/basic_doc\n"," creating build/lib/tests/doc_test/testsuites_doc\n"," copying tests/doc_test/testsuites_doc/conf.py -\u003e build/lib/tests/doc_test/testsuites_doc\n"," creating build/lib/tests/doc_test/env_report_doc\n"," copying tests/doc_test/env_report_doc/conf.py -\u003e build/lib/tests/doc_test/env_report_doc\n"," creating build/lib/tests/doc_test/doc_test_ctest_file\n"," copying tests/doc_test/doc_test_ctest_file/conf.py -\u003e build/lib/tests/doc_test/doc_test_ctest_file\n"," creating build/lib/tests/doc_test/custom_tr_utf8_template\n"," copying tests/doc_test/custom_tr_utf8_template/conf.py -\u003e build/lib/tests/doc_test/custom_tr_utf8_template\n"," creating build/lib/tests/doc_test/env_report_warnings\n"," copying tests/doc_test/env_report_warnings/conf.py -\u003e build/lib/tests/doc_test/env_report_warnings\n"," creating build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/exceptions.py -\u003e build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/jsonparser.py -\u003e build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/junitparser.py -\u003e build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/environment.py -\u003e build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/__init__.py -\u003e build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/test_reports.py -\u003e build/lib/sphinxcontrib/test_reports\n"," creating build/lib/sphinxcontrib/test_reports/functions\n"," copying sphinxcontrib/test_reports/functions/__init__.py -\u003e build/lib/sphinxcontrib/test_reports/functions\n"," creating build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_env.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_case.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_results.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_suite.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/__init__.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_report.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_file.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_common.py -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," creating build/lib/docs/ub_theme\n"," copying docs/ub_theme/conf.py -\u003e build/lib/docs/ub_theme\n"," copying docs/ub_theme/__init__.py -\u003e build/lib/docs/ub_theme\n"," running egg_info\n"," writing sphinx_test_reports.egg-info/PKG-INFO\n"," writing dependency_links to sphinx_test_reports.egg-info/dependency_links.txt\n"," writing requirements to sphinx_test_reports.egg-info/requires.txt\n"," writing top-level names to sphinx_test_reports.egg-info/top_level.txt\n"," reading manifest file 'sphinx_test_reports.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," adding license file 'LICENSE'\n"," adding license file 'AUTHORS'\n"," writing manifest file 'sphinx_test_reports.egg-info/SOURCES.txt'\n"," copying sphinxcontrib/test_reports/directives/test_report_template.txt -\u003e build/lib/sphinxcontrib/test_reports/directives\n"," creating build/lib/sphinxcontrib/test_reports/css\n"," copying sphinxcontrib/test_reports/css/common.css -\u003e build/lib/sphinxcontrib/test_reports/css\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," creating build/bdist.linux-x86_64/wheel\n"," creating build/bdist.linux-x86_64/wheel/tests\n"," copying build/lib/tests/test_env.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_test_file.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_basic_doc.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_custom_template.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_json_parser.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_junit_parser.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc_raw\n"," copying build/lib/tests/doc_test/env_report_doc_raw/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc_raw\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_koi8_template\n"," copying build/lib/tests/doc_test/custom_tr_koi8_template/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_koi8_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/pytest_6_2\n"," copying build/lib/tests/doc_test/pytest_6_2/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/pytest_6_2\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_template\n"," copying build/lib/tests/doc_test/custom_tr_template/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/json_parser_complex\n"," copying build/lib/tests/doc_test/json_parser_complex/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/json_parser_complex\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/json_parser\n"," copying build/lib/tests/doc_test/json_parser/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/json_parser\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_types\n"," copying build/lib/tests/doc_test/custom_types/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_types\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/many_testsuites_doc\n"," copying build/lib/tests/doc_test/many_testsuites_doc/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/many_testsuites_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/doc_test_file\n"," copying build/lib/tests/doc_test/doc_test_file/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/doc_test_file\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/needs_linking\n"," copying build/lib/tests/doc_test/needs_linking/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/needs_linking\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc_default\n"," copying build/lib/tests/doc_test/env_report_doc_default/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc_default\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/basic_doc\n"," copying build/lib/tests/doc_test/basic_doc/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/basic_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/testsuites_doc\n"," copying build/lib/tests/doc_test/testsuites_doc/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/testsuites_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc\n"," copying build/lib/tests/doc_test/env_report_doc/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/doc_test_ctest_file\n"," copying build/lib/tests/doc_test/doc_test_ctest_file/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/doc_test_ctest_file\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_utf8_template\n"," copying build/lib/tests/doc_test/custom_tr_utf8_template/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_utf8_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_warnings\n"," copying build/lib/tests/doc_test/env_report_warnings/conf.py -\u003e build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_warnings\n"," copying build/lib/tests/test_test_ctest_file.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_needs_linking.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/conftest.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_custom_types.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_suites.py -\u003e build/bdist.linux-x86_64/wheel/./tests\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/exceptions.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/jsonparser.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/junitparser.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/functions\n"," copying build/lib/sphinxcontrib/test_reports/functions/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/functions\n"," copying build/lib/sphinxcontrib/test_reports/environment.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_report_template.txt -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_env.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_case.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_results.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_suite.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_report.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_file.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_common.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/test_reports.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/css\n"," copying build/lib/sphinxcontrib/test_reports/css/common.css -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/css\n"," copying build/lib/sphinxcontrib/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib\n"," creating build/bdist.linux-x86_64/wheel/docs\n"," copying build/lib/docs/conf.py -\u003e build/bdist.linux-x86_64/wheel/./docs\n"," creating build/bdist.linux-x86_64/wheel/docs/ub_theme\n"," copying build/lib/docs/ub_theme/conf.py -\u003e build/bdist.linux-x86_64/wheel/./docs/ub_theme\n"," copying build/lib/docs/ub_theme/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./docs/ub_theme\n"," running install_egg_info\n"," Copying sphinx_test_reports.egg-info to build/bdist.linux-x86_64/wheel/./sphinx_test_reports-1.1.0-py3.11.egg-info\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/sphinx_test_reports-1.1.0.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-ex383052/.tmp-ix46zqfo/sphinx_test_reports-1.1.0-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'docs/conf.py'\n"," adding 'docs/ub_theme/__init__.py'\n"," adding 'docs/ub_theme/conf.py'\n"," adding 'sphinxcontrib/__init__.py'\n"," adding 'sphinxcontrib/test_reports/__init__.py'\n"," adding 'sphinxcontrib/test_reports/environment.py'\n"," adding 'sphinxcontrib/test_reports/exceptions.py'\n"," adding 'sphinxcontrib/test_reports/jsonparser.py'\n"," adding 'sphinxcontrib/test_reports/junitparser.py'\n"," adding 'sphinxcontrib/test_reports/test_reports.py'\n"," adding 'sphinxcontrib/test_reports/css/common.css'\n"," adding 'sphinxcontrib/test_reports/directives/__init__.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_case.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_common.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_env.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_file.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_report.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_report_template.txt'\n"," adding 'sphinxcontrib/test_reports/directives/test_results.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_suite.py'\n"," adding 'sphinxcontrib/test_reports/functions/__init__.py'\n"," adding 'tests/__init__.py'\n"," adding 'tests/conftest.py'\n"," adding 'tests/test_basic_doc.py'\n"," adding 'tests/test_custom_template.py'\n"," adding 'tests/test_custom_types.py'\n"," adding 'tests/test_env.py'\n"," adding 'tests/test_json_parser.py'\n"," adding 'tests/test_junit_parser.py'\n"," adding 'tests/test_needs_linking.py'\n"," adding 'tests/test_suites.py'\n"," adding 'tests/test_test_ctest_file.py'\n"," adding 'tests/test_test_file.py'\n"," adding 'tests/doc_test/basic_doc/conf.py'\n"," adding 'tests/doc_test/custom_tr_koi8_template/conf.py'\n"," adding 'tests/doc_test/custom_tr_template/conf.py'\n"," adding 'tests/doc_test/custom_tr_utf8_template/conf.py'\n"," adding 'tests/doc_test/custom_types/conf.py'\n"," adding 'tests/doc_test/doc_test_ctest_file/conf.py'\n"," adding 'tests/doc_test/doc_test_file/conf.py'\n"," adding 'tests/doc_test/env_report_doc/conf.py'\n"," adding 'tests/doc_test/env_report_doc_default/conf.py'\n"," adding 'tests/doc_test/env_report_doc_raw/conf.py'\n"," adding 'tests/doc_test/env_report_warnings/conf.py'\n"," adding 'tests/doc_test/json_parser/conf.py'\n"," adding 'tests/doc_test/json_parser_complex/conf.py'\n"," adding 'tests/doc_test/many_testsuites_doc/conf.py'\n"," adding 'tests/doc_test/needs_linking/conf.py'\n"," adding 'tests/doc_test/pytest_6_2/conf.py'\n"," adding 'tests/doc_test/testsuites_doc/conf.py'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/AUTHORS'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/LICENSE'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/METADATA'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/WHEEL'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/top_level.txt'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for sphinx-test-reports (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sphinx-test-reports: filename=sphinx_test_reports-1.1.0-py3-none-any.whl size=72216 sha256=6195a65dac5933fd9f83085998594cc31853b1ebd97c84f7a36049f3ffbcf9ff\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-e8csewb0/wheels/03/57/7d/6598421a14084c6221fe9832bbec1c5adc68218c00d43af21c\n"," Created temporary directory: /tmp/pip-wheel-6aq2kp32\n"," Destination directory: /tmp/pip-wheel-6aq2kp32\n"," Running command Building wheel for sphinxcontrib-plantuml (pyproject.toml)\n"," /tmp/pip-build-env-u7vnib3q/overlay/local/lib/python3.11/dist-packages/setuptools/dist.py:701: SetuptoolsDeprecationWarning: The namespace_packages parameter is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please replace its usage with implicit namespaces (PEP 420).\n","\n"," See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages for details.\n"," ********************************************************************************\n","\n"," !!\n"," ep.load()(self, ep.name, value)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/sphinxcontrib\n"," copying sphinxcontrib/__init__.py -\u003e build/lib/sphinxcontrib\n"," copying sphinxcontrib/plantuml.py -\u003e build/lib/sphinxcontrib\n"," running egg_info\n"," writing sphinxcontrib_plantuml.egg-info/PKG-INFO\n"," writing dependency_links to sphinxcontrib_plantuml.egg-info/dependency_links.txt\n"," writing namespace_packages to sphinxcontrib_plantuml.egg-info/namespace_packages.txt\n"," writing requirements to sphinxcontrib_plantuml.egg-info/requires.txt\n"," writing top-level names to sphinxcontrib_plantuml.egg-info/top_level.txt\n"," reading manifest file 'sphinxcontrib_plantuml.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'README'\n"," warning: no files found matching 'CHANGES.*'\n"," adding license file 'LICENSE'\n"," writing manifest file 'sphinxcontrib_plantuml.egg-info/SOURCES.txt'\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," Skipping installation of build/bdist.linux-x86_64/wheel/./sphinxcontrib/__init__.py (namespace package)\n"," copying sphinxcontrib/plantuml.py -\u003e build/bdist.linux-x86_64/wheel/./sphinxcontrib\n"," running install_egg_info\n"," Copying sphinxcontrib_plantuml.egg-info to build/bdist.linux-x86_64/wheel/./sphinxcontrib_plantuml-0.30-py3.11.egg-info\n"," Installing build/bdist.linux-x86_64/wheel/./sphinxcontrib_plantuml-0.30-py3.11-nspkg.pth\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib_plantuml-0.30.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-6aq2kp32/.tmp-2g0zmejb/sphinxcontrib_plantuml-0.30-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'sphinxcontrib_plantuml-0.30-py3.11-nspkg.pth'\n"," adding 'sphinxcontrib/plantuml.py'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/LICENSE'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/METADATA'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/WHEEL'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/namespace_packages.txt'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/top_level.txt'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for sphinxcontrib-plantuml (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sphinxcontrib-plantuml: filename=sphinxcontrib_plantuml-0.30-py3-none-any.whl size=11594 sha256=9a7dfa882901efd26d0152cfe9ac647178f075fb180acd7c2e107146ebadd728\n"," Stored in directory: /root/.cache/pip/wheels/ba/14/11/6573783b9c1e0a196a172550621e2d39a6cabb19b0ade3e4f5\n"," Created temporary directory: /tmp/pip-wheel-8u2s_oc6\n"," Destination directory: /tmp/pip-wheel-8u2s_oc6\n"," Running command Building wheel for cocotb-bus (pyproject.toml)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/cocotb_bus\n"," copying src/cocotb_bus/scoreboard.py -\u003e build/lib/cocotb_bus\n"," copying src/cocotb_bus/bus.py -\u003e build/lib/cocotb_bus\n"," copying src/cocotb_bus/_version.py -\u003e build/lib/cocotb_bus\n"," copying src/cocotb_bus/__init__.py -\u003e build/lib/cocotb_bus\n"," creating build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/__init__.py -\u003e build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/avalon.py -\u003e build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/xgmii.py -\u003e build/lib/cocotb_bus/monitors\n"," creating build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/opb.py -\u003e build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/__init__.py -\u003e build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/amba.py -\u003e build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/avalon.py -\u003e build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/xgmii.py -\u003e build/lib/cocotb_bus/drivers\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," creating build/bdist.linux-x86_64/wheel\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus\n"," copying build/lib/cocotb_bus/scoreboard.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/avalon.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/xgmii.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/bus.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," copying build/lib/cocotb_bus/_version.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," copying build/lib/cocotb_bus/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/opb.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/__init__.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/amba.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/avalon.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/xgmii.py -\u003e build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," running install_egg_info\n"," running egg_info\n"," writing src/cocotb_bus.egg-info/PKG-INFO\n"," writing dependency_links to src/cocotb_bus.egg-info/dependency_links.txt\n"," writing requirements to src/cocotb_bus.egg-info/requires.txt\n"," writing top-level names to src/cocotb_bus.egg-info/top_level.txt\n"," reading manifest file 'src/cocotb_bus.egg-info/SOURCES.txt'\n"," adding license file 'LICENSE'\n"," writing manifest file 'src/cocotb_bus.egg-info/SOURCES.txt'\n"," Copying src/cocotb_bus.egg-info to build/bdist.linux-x86_64/wheel/./cocotb_bus-0.2.1-py3.11.egg-info\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus-0.2.1.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-8u2s_oc6/.tmp-jp0b0p2z/cocotb_bus-0.2.1-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'cocotb_bus/__init__.py'\n"," adding 'cocotb_bus/_version.py'\n"," adding 'cocotb_bus/bus.py'\n"," adding 'cocotb_bus/scoreboard.py'\n"," adding 'cocotb_bus/drivers/__init__.py'\n"," adding 'cocotb_bus/drivers/amba.py'\n"," adding 'cocotb_bus/drivers/avalon.py'\n"," adding 'cocotb_bus/drivers/opb.py'\n"," adding 'cocotb_bus/drivers/xgmii.py'\n"," adding 'cocotb_bus/monitors/__init__.py'\n"," adding 'cocotb_bus/monitors/avalon.py'\n"," adding 'cocotb_bus/monitors/xgmii.py'\n"," adding 'cocotb_bus-0.2.1.dist-info/LICENSE'\n"," adding 'cocotb_bus-0.2.1.dist-info/METADATA'\n"," adding 'cocotb_bus-0.2.1.dist-info/WHEEL'\n"," adding 'cocotb_bus-0.2.1.dist-info/top_level.txt'\n"," adding 'cocotb_bus-0.2.1.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for cocotb-bus (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for cocotb-bus: filename=cocotb_bus-0.2.1-py3-none-any.whl size=34915 sha256=8dbae2067fae074e7fac6a48ff5d2e9ad9e20ff4ea5deed80c7f615afd4037a2\n"," Stored in directory: /root/.cache/pip/wheels/f3/40/c4/456859501d450830880eb944793719ae31e972bdb1ef60f64d\n","Successfully built sphinx-test-reports sphinxcontrib-plantuml cocotb-bus\n","Installing collected packages: sphinx-glpi-theme, nvidia-ml-py, find-libpython, farama-notifications, bitarray, xxhash, scipy, pytorch-nlp, pynvml, pybind11, protobuf, Pillow, pathspec, mypy-extensions, Mako, lightning-utilities, kornia_rs, jedi, humanfriendly, gymnasium, gprof2dot, fsspec, execnet, emoji, dill, coverage, colorlog, cocotb, bitstring, attr-dot-dict, ale-py, accessible-pygments, tensorboardx, requests-file, pytest-xdist, pytest-sugar, pytest-profiling, pytest-metadata, onnx, multiprocess, ghp-import, coloredlogs, cocotb-bus, black, alembic, sphinxcontrib-plantuml, sphinxcontrib-jquery, sphinx-data-viewer, pytest-html, pytest-cov, pydata-sphinx-theme, optuna, onnxruntime, onnxconverter-common, myst_parser, ipdb, torchmetrics, stable-baselines3, sphinx-rtd-theme, sphinx-needs, sphinx-book-theme, kornia, datasets, sphinx-test-reports, pytorch-lightning, optimum, evaluate, lightning, jupyter-cache, myst-nb, mase-tools\n","\n","\n","\n"," changing mode of /usr/local/bin/find_libpython to 755\n","\n","\n","\n"," Attempting uninstall: scipy\n"," Found existing installation: scipy 1.13.1\n"," Uninstalling scipy-1.13.1:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy-1.13.1.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy-1.13.1.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy.libs\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy.libs/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy/\n"," Successfully uninstalled scipy-1.13.1\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/pybind11-config to 755\n"," Attempting uninstall: protobuf\n"," Found existing installation: protobuf 4.25.5\n"," Uninstalling protobuf-4.25.5:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/google/~upb\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/google/_upb/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/google/~rotobuf\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/google/protobuf/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~rotobuf-4.25.5.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/protobuf-4.25.5.dist-info/\n"," Successfully uninstalled protobuf-4.25.5\n","\n"," Attempting uninstall: Pillow\n"," Found existing installation: pillow 11.1.0\n"," Uninstalling pillow-11.1.0:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~IL\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/PIL/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~illow-11.1.0.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/pillow-11.1.0.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~illow.libs\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/pillow.libs/\n"," Successfully uninstalled pillow-11.1.0\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/mako-render to 755\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/humanfriendly to 755\n","\n","\n"," changing mode of /usr/local/bin/gprof2dot to 755\n"," Attempting uninstall: fsspec\n"," Found existing installation: fsspec 2024.10.0\n"," Uninstalling fsspec-2024.10.0:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~sspec-2024.10.0.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/fsspec-2024.10.0.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~sspec\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/fsspec/\n"," Successfully uninstalled fsspec-2024.10.0\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/coverage to 755\n"," changing mode of /usr/local/bin/coverage-3.11 to 755\n"," changing mode of /usr/local/bin/coverage3 to 755\n","\n","\n"," changing mode of /usr/local/bin/cocotb-config to 755\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/backend-test-tools to 755\n"," changing mode of /usr/local/bin/check-model to 755\n"," changing mode of /usr/local/bin/check-node to 755\n","\n","\n"," changing mode of /usr/local/bin/ghp-import to 755\n","\n"," changing mode of /usr/local/bin/coloredlogs to 755\n","\n","\n"," changing mode of /usr/local/bin/black to 755\n"," changing mode of /usr/local/bin/blackd to 755\n","\n"," changing mode of /usr/local/bin/alembic to 755\n","\n","\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/optuna to 755\n","\n"," changing mode of /usr/local/bin/onnxruntime_test to 755\n","\n","\n"," changing mode of /usr/local/bin/myst-anchors to 755\n"," changing mode of /usr/local/bin/myst-docutils-demo to 755\n"," changing mode of /usr/local/bin/myst-docutils-html to 755\n"," changing mode of /usr/local/bin/myst-docutils-html5 to 755\n"," changing mode of /usr/local/bin/myst-docutils-latex to 755\n"," changing mode of /usr/local/bin/myst-docutils-pseudoxml to 755\n"," changing mode of /usr/local/bin/myst-docutils-xml to 755\n"," changing mode of /usr/local/bin/myst-inv to 755\n","\n"," changing mode of /usr/local/bin/ipdb3 to 755\n","\n","\n","\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/datasets-cli to 755\n","\n","\n","\n"," changing mode of /usr/local/bin/optimum-cli to 755\n","\n"," changing mode of /usr/local/bin/evaluate-cli to 755\n","\n"," changing mode of /usr/local/bin/fabric to 755\n"," changing mode of /usr/local/bin/lightning to 755\n","\n"," changing mode of /usr/local/bin/jcache to 755\n","\n"," changing mode of /usr/local/bin/mystnb-docutils-html to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-html5 to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-latex to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-pseudoxml to 755\n"," changing mode of /usr/local/bin/mystnb-docutils-xml to 755\n"," changing mode of /usr/local/bin/mystnb-quickstart to 755\n"," changing mode of /usr/local/bin/mystnb-to-jupyter to 755\n"," Running setup.py develop for mase-tools\n"," Running command python setup.py develop\n"," running develop\n"," /usr/local/lib/python3.11/dist-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please avoid running ``setup.py`` and ``easy_install``.\n"," Instead, use pypa/build, pypa/installer or other\n"," standards-based tools.\n","\n"," See https://github.com/pypa/setuptools/issues/917 for details.\n"," ********************************************************************************\n","\n"," !!\n"," easy_install.initialize_options(self)\n"," /usr/local/lib/python3.11/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please avoid running ``setup.py`` directly.\n"," Instead, use pypa/build, pypa/installer or other\n"," standards-based tools.\n","\n"," See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n"," ********************************************************************************\n","\n"," !!\n"," self.initialize_options()\n"," running egg_info\n"," creating src/mase_tools.egg-info\n"," writing src/mase_tools.egg-info/PKG-INFO\n"," writing dependency_links to src/mase_tools.egg-info/dependency_links.txt\n"," writing requirements to src/mase_tools.egg-info/requires.txt\n"," writing top-level names to src/mase_tools.egg-info/top_level.txt\n"," writing manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," reading manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," adding license file 'LICENSE'\n"," writing manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.11/dist-packages/mase-tools.egg-link (link to src)\n"," Adding mase-tools 1.0.0 to easy-install.pth file\n","\n"," Installed /content/mase/src\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n","gensim 4.3.3 requires scipy\u003c1.14.0,\u003e=1.7.0, but you have scipy 1.14.1 which is incompatible.\n","tensorflow-metadata 1.16.1 requires protobuf\u003c6.0.0dev,\u003e=4.25.2; python_version \u003e= \"3.11\", but you have protobuf 3.20.2 which is incompatible.\n","grpcio-status 1.62.3 requires protobuf\u003e=4.21.6, but you have protobuf 3.20.2 which is incompatible.\n","tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,\u003c5.0.0dev,\u003e=3.20.3, but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed Mako-1.3.8 Pillow-10.4.0 accessible-pygments-0.0.5 ale-py-0.10.1 alembic-1.14.1 attr-dot-dict-0.1.0 bitarray-3.0.0 bitstring-4.3.0 black-24.10.0 cocotb-1.9.2 cocotb-bus-0.2.1 coloredlogs-15.0.1 colorlog-6.9.0 coverage-7.6.10 datasets-3.2.0 dill-0.3.8 emoji-2.14.1 evaluate-0.4.3 execnet-2.1.1 farama-notifications-0.0.4 find-libpython-0.4.0 fsspec-2024.9.0 ghp-import-2.1.0 gprof2dot-2024.6.6 gymnasium-1.0.0 humanfriendly-10.0 ipdb-0.13.13 jedi-0.19.2 jupyter-cache-1.0.1 kornia-0.8.0 kornia_rs-0.1.8 lightning-2.5.0.post0 lightning-utilities-0.11.9 mase-tools-1.0.0 multiprocess-0.70.16 mypy-extensions-1.0.0 myst-nb-1.1.2 myst_parser-4.0.0 nvidia-ml-py-12.570.86 onnx-1.17.0 onnxconverter-common-1.14.0 onnxruntime-1.20.1 optimum-1.23.3 optuna-4.2.0 pathspec-0.12.1 protobuf-3.20.2 pybind11-2.13.6 pydata-sphinx-theme-0.16.1 pynvml-12.0.0 pytest-cov-6.0.0 pytest-html-4.1.1 pytest-metadata-3.1.1 pytest-profiling-1.8.1 pytest-sugar-1.0.0 pytest-xdist-3.6.1 pytorch-lightning-2.5.0.post0 pytorch-nlp-0.5.0 requests-file-2.1.0 scipy-1.14.1 sphinx-book-theme-1.1.3 sphinx-data-viewer-0.1.5 sphinx-glpi-theme-0.6 sphinx-needs-4.2.0 sphinx-rtd-theme-3.0.2 sphinx-test-reports-1.1.0 sphinxcontrib-jquery-4.1 sphinxcontrib-plantuml-0.30 stable-baselines3-2.5.0 tensorboardx-2.6.2.2 torchmetrics-1.6.1 xxhash-3.5.0\n","Removed build tracker: '/tmp/pip-build-tracker-udf3ge9g'\n","/content/mase/src\n"]}],"source":["!git clone https://github.com/tonytarizzo/mase.git\n","%cd mase\n","!python -m pip install -e . -vvv\n","%cd src"]},{"cell_type":"markdown","metadata":{"id":"tVWRL8XQwdcE"},"source":["When we import a pretrained transformer model from HuggingFace, we receive the encoder/decoder weights, which aren't that useful on their own - to perform a useful task such as sequence classification, we add a classification head on top of the model and train those weights on the required dataset. In this tutorial, we'll look at fine tuning a Bert model for sequence classification with two approaches. First, we'll attempt full Supervised Fine Tuning (SFT). Then, we'll use the Mase stack to add a [LoRA](https://arxiv.org/abs/2106.09685) adapter to the model. We'll look at the effect in memory requirement for training and the achieved accuracy."]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1737999478146,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"mib5buD2wdcF"},"outputs":[],"source":["checkpoint = \"DeepWokLab/bert-tiny\"\n","tokenizer_checkpoint = \"DeepWokLab/bert-tiny\"\n","dataset_name = \"imdb\""]},{"cell_type":"markdown","metadata":{"id":"ob9He1U1wdcG"},"source":["## Sentiment Analysis with the IMDb Dataset"]},{"cell_type":"markdown","metadata":{"id":"OhzrcnojwdcG"},"source":["The IMDB dataset, introduced in [this 2011 paper](https://aclanthology.org/P11-1015/) from Stanford, is commonly used for sentiment analysis in the Natural Language Processing (NLP) community. This is a collection of 50k movie reviews from the IMDb website, labelled as either \"positive\" or \"negative\". Here is an example of a positive review:\n","\n","\u003e I turned over to this film in the middle of the night and very nearly skipped right passed it. It was only because there was nothing else on that I decided to watch it. In the end, I thought it was great.\u003cbr /\u003e\u003cbr /\u003eAn interesting storyline, good characters, a clever script and brilliant directing makes this a fine film to sit down and watch. This was, in fact, the first I'd heard of this movie, but I would have been happy to have paid money to see this at the cinema.\u003cbr /\u003e\u003cbr /\u003eMy IMDB Rating : 8 out of 10\u003cbr /\u003e\u003cbr /\u003e\n","\n","And a negative review:\n","\n","\u003e its a totally average film with a few semi-alright action sequences that make the plot seem a little better and remind the viewer of the classic van dam films. parts of the plot don't make sense and seem to be added in to use up time. the end plot is that of a very basic type that doesn't leave the viewer guessing and any twists are obvious from the beginning. the end scene with the flask backs don't make sense as they are added in and seem to have little relevance to the history of van dam's character. not really worth watching again, bit disappointed in the end production, even though it is apparent it was shot on a low budget certain shots and sections in the film are of poor directed quality\n","\n","The dataset is available from HuggingFace through the ``datasets`` library. We use the `get_tokenized_dataset` utility in Mase to automatically tokenize it."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"executionInfo":{"elapsed":130542,"status":"ok","timestamp":1737999608687,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"hZ1RyFIMwdcG","outputId":"b19315e5-2f75-4d72-b172-c92cc41cbef9"},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for DeepWokLab/bert-tiny.\u001b[0m\n","/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n","Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n","You are not authenticated with the Hugging Face Hub in this notebook.\n","If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n"," warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d75e887ecee74b0b89b55569f2c6f2cf","version_major":2,"version_minor":0},"text/plain":["README.md: 0%| | 0.00/7.81k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ebb6f75e1601439d807c430fbc7add37","version_major":2,"version_minor":0},"text/plain":["train-00000-of-00001.parquet: 0%| | 0.00/21.0M [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"24ae44d57dd743208aee4221a9044b16","version_major":2,"version_minor":0},"text/plain":["test-00000-of-00001.parquet: 0%| | 0.00/20.5M [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ba0d3c9e9ed0432eab3a5d8a0e05866d","version_major":2,"version_minor":0},"text/plain":["unsupervised-00000-of-00001.parquet: 0%| | 0.00/42.0M [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"318bba4ac87a4d069e5e46f3ab01bd41","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0%| | 0/25000 [00:00\u003c?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"88defe1e93bc4f3599565fffa91a83e1","version_major":2,"version_minor":0},"text/plain":["Generating test split: 0%| | 0/25000 [00:00\u003c?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2352e086cb7e4e4eabf19a7a95ae66df","version_major":2,"version_minor":0},"text/plain":["Generating unsupervised split: 0%| | 0/50000 [00:00\u003c?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4f39d781d47349e9bdf06638e383790d","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/1.22k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"db619a1c336d4dd8b31c13fcde469e7f","version_major":2,"version_minor":0},"text/plain":["vocab.txt: 0%| | 0.00/232k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d44973f60ec647d88c25409f1f20939d","version_major":2,"version_minor":0},"text/plain":["tokenizer.json: 0%| | 0.00/711k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5c351d9856a64f8e90d05b92d3ecd0f9","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json: 0%| | 0.00/125 [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"922c9314ce9a4f829686e46251c37d52","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00\u003c?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3dae0a95c9a94d429d735459337509a1","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00\u003c?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7849ff957c4d424baadb48fc8c12f876","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/50000 [00:00\u003c?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["from chop.tools import get_tokenized_dataset # type: ignore\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")"]},{"cell_type":"markdown","metadata":{"id":"sZL6Mr27wdcH"},"source":["## Generate a MaseGraph with Custom Arguments"]},{"cell_type":"markdown","metadata":{"id":"dk4OUUxCwdcH"},"source":["By inspecting the implementation of the Bert model in HuggingFace, we can see the forward function has a signature similar to the following.\n","\n","```python\n"," class BertForSequenceClassification(BertPreTrainedModel):\n"," def __init__(self, config):\n"," super().__init__(config)\n"," self.bert = BertModel(config)\n"," ...\n","\n"," def forward(\n"," self,\n"," input_ids: Optional[torch.Tensor] = None,\n"," attention_mask: Optional[torch.Tensor] = None,\n"," token_type_ids: Optional[torch.Tensor] = None,\n"," position_ids: Optional[torch.Tensor] = None,\n"," head_mask: Optional[torch.Tensor] = None,\n"," inputs_embeds: Optional[torch.Tensor] = None,\n"," labels: Optional[torch.Tensor] = None,\n"," output_attentions: Optional[bool] = None,\n"," output_hidden_states: Optional[bool] = None,\n"," return_dict: Optional[bool] = None,\n"," ) -\u003e Union[Tuple[torch.Tensor], SequenceClassifierOutput]:\n"," ...\n","```\n","\n","By default, the MaseGraph constructor chooses to use the `input_ids` argument, ignoring the other optional arguments. However, you can specify which inputs to drive during symbolic tracing using the `hf_input_names` argument. In the following cell, we also drive the `attention_mask` and `labels` inputs. By specifying the `labels` argument, we include a `nn.CrossEntropyLoss` module at the end of the model to calculate the loss directly.\n","\n","\u003e **Task:** Remove the `attention_mask` and `labels` arguments from the `hf_input_names` list and re-run the following cell. Use `mg.draw()` to visualize the graph in each case. Can you see any changes in the graph topology? Can you explain why this happens?"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":0,"status":"ok","timestamp":1737999620288,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"rfqU8BJKwdcI","outputId":"04552249-8f25-4230-f5f7-e81a24c62258"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b958c1b1faa64497852ca73355391a6a","version_major":2,"version_minor":0},"text/plain":["config.json: 0%| | 0.00/622 [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4d576a692e2746e1afa01563f1638f1e","version_major":2,"version_minor":0},"text/plain":["model.safetensors: 0%| | 0.00/17.5M [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at DeepWokLab/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for DeepWokLab/bert-tiny.\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],\n","\n","\n"," [[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=\u003cNativeLayerNormBackward0\u003e)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 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-0.1589, ..., 0.0258, -0.0364, 0.9990]],\n","\n"," ...,\n","\n"," [[-0.6300, 0.1252, 0.3486, ..., 0.5692, -0.9417, -0.1399],\n"," [-0.0088, 0.1378, -0.4819, ..., -0.1261, 0.2908, 1.0980]],\n","\n"," [[-1.0593, -0.6395, -0.3343, ..., 0.0571, -0.7842, 0.1042],\n"," [-0.5584, 0.9932, -0.1105, ..., 0.2486, 0.4005, 0.6046]],\n","\n"," [[-0.2081, 0.2785, 0.0613, ..., -0.0353, -0.8389, -0.0026],\n"," [-0.4516, 0.8092, -0.0513, ..., 0.1182, 0.4294, 0.4781]]],\n","\n","\n"," [[[-0.6850, -0.2168, -0.6087, ..., 0.1402, -0.7267, -0.1502],\n"," [ 0.0907, 0.7927, -0.0524, ..., -0.4610, -0.4295, 0.4391]],\n","\n"," [[-0.2084, -0.1325, -0.1151, ..., 0.0938, -0.8963, 0.1296],\n"," [-0.2352, 1.0586, -0.1117, ..., -0.4943, -0.6546, 0.6617]],\n","\n"," [[-0.3665, 0.3408, -0.6133, ..., 0.1938, -0.6681, 0.5929],\n"," [ 0.8269, 1.7861, -1.1207, ..., -0.0885, 0.2791, 1.4721]],\n","\n"," ...,\n","\n"," [[-1.0985, -1.2861, -1.2531, ..., 0.3609, -1.4022, -0.4444],\n"," [-0.1038, 0.9623, -0.7062, ..., -0.3340, 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1.1685],\n"," [ 0.0717, -0.1039, 0.5084, ..., -2.1932, 0.0751, 2.8236],\n"," [-2.4774, 0.7563, -0.7502, ..., -2.1312, -0.0685, 0.8700]]],\n"," grad_fn=\u003cNativeLayerNormBackward0\u003e)\n","tensor([[[-0.0455, 0.6529, 0.6297, ..., 0.4139, -0.9381, 0.6769],\n"," [-3.1104, -3.7282, -2.3953, ..., -0.9155, -0.8280, -1.7070],\n"," [-0.9324, -2.9333, -2.3249, ..., -0.8455, -0.0326, -0.6998],\n"," ...,\n"," [-1.9000, -1.1028, -1.1281, ..., -0.2809, 2.0206, -1.0802],\n"," [-1.1088, -1.0420, -2.4026, ..., -0.4478, 0.7391, -0.0354],\n"," [ 0.7349, 0.6742, -2.6697, ..., -0.5114, 1.5155, 2.0246]],\n","\n"," [[-0.0799, 0.7813, 0.4918, ..., 0.6888, -0.7680, 0.9805],\n"," [-2.8720, -1.0602, -2.3610, ..., -2.1143, 0.9664, -1.1212],\n"," [-1.4705, -2.1384, -1.9955, ..., -0.9722, 1.5909, -0.1668],\n"," ...,\n"," [-2.9884, -1.1566, -2.5215, ..., 1.1460, 0.7120, -0.6320],\n"," [-3.3666, -0.7966, -3.3154, ..., 0.5316, 1.7058, 2.1950],\n"," [ 0.6626, 0.8537, -2.7251, ..., -0.0901, 1.5883, 2.3840]]],\n"," grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[-0.0455, 0.6529, 0.6297, ..., 0.4139, -0.9381, 0.6769],\n"," [-3.1104, -3.7282, -2.3953, ..., -0.9155, -0.8280, -1.7070],\n"," [-0.9324, -2.9333, -2.3249, ..., -0.8455, -0.0326, -0.6998],\n"," ...,\n"," [-1.9000, -1.1028, -1.1281, ..., -0.2809, 2.0206, -1.0802],\n"," [-1.1088, -1.0420, -2.4026, ..., -0.4478, 0.7391, -0.0354],\n"," [ 0.7349, 0.6742, -2.6697, ..., -0.5114, 1.5155, 2.0246]],\n","\n"," [[-0.0799, 0.7813, 0.4918, ..., 0.6888, -0.7680, 0.9805],\n"," [-2.8720, -1.0602, -2.3610, ..., -2.1143, 0.9664, -1.1212],\n"," [-1.4705, -2.1384, -1.9955, ..., -0.9722, 1.5909, -0.1668],\n"," ...,\n"," [-2.9884, -1.1566, -2.5215, ..., 1.1460, 0.7120, -0.6320],\n"," [-3.3666, -0.7966, -3.3154, ..., 0.5316, 1.7058, 2.1950],\n"," [ 0.6626, 0.8537, -2.7251, ..., -0.0901, 1.5883, 2.3840]]],\n"," grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[[-0.0455, 0.6529, 0.6297, ..., 1.0165, -1.6055, 0.0557],\n"," [-0.9141, -1.5101, -0.8415, ..., 0.4139, 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[-1.3725, -0.8212, 0.1984, ..., -1.8218, -1.4800, -0.2956],\n"," ...,\n"," [-0.5946, 0.5680, 0.8938, ..., -1.6653, 0.8218, 1.1902],\n"," [ 1.2800, 1.9566, 0.2540, ..., -1.2290, 0.5257, 1.2667],\n"," [ 1.3511, 1.3329, -0.0782, ..., -0.8454, -0.7400, 1.5966]]],\n"," grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[[-0.8947, 0.0412, -1.2359, ..., 1.5140, -1.9812, -2.5532],\n"," [-0.2951, -1.6086, -0.6381, ..., 0.4410, -0.3965, 0.0106]],\n","\n"," [[-1.9196, 0.3326, 0.8482, ..., -2.3348, 1.3935, 1.1452],\n"," [-0.5277, 0.1234, 0.7865, ..., -1.5790, -1.1817, -1.0156]],\n","\n"," [[-2.1664, 0.3959, 0.7476, ..., -2.0817, 0.2852, 0.8173],\n"," [-0.8414, 0.5154, -0.4553, ..., -2.1767, -0.6488, 0.1889]],\n","\n"," ...,\n","\n"," [[-1.6009, 0.4887, -0.4818, ..., -1.8165, 1.4764, 0.5091],\n"," [-0.6869, 0.4007, -1.5818, ..., -1.1268, 0.4111, 0.7892]],\n","\n"," [[-0.1528, 1.1728, -0.5164, ..., -1.3611, 1.0621, 1.1810],\n"," [-0.7595, -0.1699, -1.5305, ..., -0.4340, 0.1499, 1.6704]],\n","\n"," [[ 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grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[ 3.8307e-01, 1.8837e-02, -1.0314e+00, ..., 6.4335e-01,\n"," -3.1830e-01, -1.7296e+00],\n"," [ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., 1.5973e+00,\n"," 1.1907e+00, -9.0454e-01],\n"," [-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -5.3627e-01,\n"," -1.0456e+00, -1.6301e+00],\n"," ...,\n"," [-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.3217e+00,\n"," -1.3415e+00, -3.8328e-01],\n"," [ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -1.6600e-01,\n"," -6.5011e-01, 2.1798e-02],\n"," [-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., 1.3443e-03,\n"," -1.6754e+00, 3.1771e-01]],\n","\n"," [[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., 6.4928e-01,\n"," -3.2944e-01, -2.4185e+00],\n"," [ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.8623e+00,\n"," -1.1230e+00, 3.5013e-01],\n"," [ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., 6.4975e-01,\n"," -7.3862e-01, -2.1510e+00],\n"," ...,\n"," [ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00],\n"," [ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00],\n"," [ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]], grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[ 3.8307e-01, 1.8837e-02, -1.0314e+00, ..., 6.4335e-01,\n"," -3.1830e-01, -1.7296e+00],\n"," [ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., 1.5973e+00,\n"," 1.1907e+00, -9.0454e-01],\n"," [-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -5.3627e-01,\n"," -1.0456e+00, -1.6301e+00],\n"," ...,\n"," [-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.3217e+00,\n"," -1.3415e+00, -3.8328e-01],\n"," [ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -1.6600e-01,\n"," -6.5011e-01, 2.1798e-02],\n"," [-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., 1.3443e-03,\n"," -1.6754e+00, 3.1771e-01]],\n","\n"," [[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., 6.4928e-01,\n"," -3.2944e-01, -2.4185e+00],\n"," [ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.8623e+00,\n"," -1.1230e+00, 3.5013e-01],\n"," [ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., 6.4975e-01,\n"," -7.3862e-01, -2.1510e+00],\n"," ...,\n"," [ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00],\n"," [ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00],\n"," [ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]], grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[[ 3.8307e-01, 1.8837e-02, -1.0314e+00, ..., -8.0955e-01,\n"," -2.8557e-01, -2.3318e-01],\n"," [-9.9661e-02, 3.1722e-01, -3.0517e-01, ..., 6.4335e-01,\n"," -3.1830e-01, -1.7296e+00]],\n","\n"," [[ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., -7.9549e-01,\n"," -6.9279e-01, -1.8082e-01],\n"," [-9.9201e-01, 9.4938e-01, 4.4198e-02, ..., 1.5973e+00,\n"," 1.1907e+00, -9.0454e-01]],\n","\n"," [[-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -1.8905e+00,\n"," 1.6226e-01, -1.2953e+00],\n"," [-7.0312e-01, -6.4926e-01, -5.0913e-01, ..., -5.3627e-01,\n"," -1.0456e+00, -1.6301e+00]],\n","\n"," ...,\n","\n"," [[-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.9281e+00,\n"," 1.1489e+00, -2.4530e-01],\n"," [-7.6226e-02, 8.5814e-01, -1.5467e+00, ..., -1.3217e+00,\n"," -1.3415e+00, -3.8328e-01]],\n","\n"," [[ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -2.4168e+00,\n"," 3.3385e-01, -6.2115e-02],\n"," [-1.6390e+00, -1.6085e-01, -1.9118e+00, ..., -1.6600e-01,\n"," -6.5011e-01, 2.1798e-02]],\n","\n"," [[-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., -1.0481e+00,\n"," -2.7634e+00, 2.2741e-01],\n"," [-1.4603e+00, 3.1239e-02, 3.8892e-01, ..., 1.3443e-03,\n"," -1.6754e+00, 3.1771e-01]]],\n","\n","\n"," [[[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., -7.8511e-01,\n"," -3.2510e-01, -2.1300e-01],\n"," [-3.9893e-01, 6.1469e-01, -3.9206e-01, ..., 6.4928e-01,\n"," -3.2944e-01, -2.4185e+00]],\n","\n"," [[ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.6488e+00,\n"," -8.6854e-01, -8.0783e-01],\n"," [-2.1516e+00, -2.4247e-01, -8.1713e-01, ..., -1.8623e+00,\n"," -1.1230e+00, 3.5013e-01]],\n","\n"," [[ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., -1.3121e+00,\n"," -6.7044e-01, -1.1324e+00],\n"," [ 4.3930e-01, 3.4082e-01, -1.2243e+00, ..., 6.4975e-01,\n"," -7.3862e-01, -2.1510e+00]],\n","\n"," ...,\n","\n"," [[ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 8.5980e-01,\n"," 3.2796e-01, -1.9442e+00],\n"," [-8.9502e-01, 9.7357e-01, 8.5447e-01, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00]],\n","\n"," [[ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., -1.5965e+00,\n"," -3.9380e-01, -4.3585e-01],\n"," [-2.2103e+00, 4.4127e-01, 1.1554e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00]],\n","\n"," [[ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=\u003cViewBackward0\u003e)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01],\n"," ...,\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=\u003cScaledDotProductFlashAttentionForCpuBackward0\u003e)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=\u003cTransposeBackward0\u003e)\n","tensor([[ 0.1901, -0.2853],\n"," [ 0.3167, -0.4158]], grad_fn=\u003cAddmmBackward0\u003e)\n","tensor([1, 0])\n"]}],"source":["from transformers import AutoModelForSequenceClassification # type: ignore\n","\n","from chop import MaseGraph\n","import chop.passes as passes # type: ignore\n","\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","model.config.problem_type = \"single_label_classification\"\n","\n","mg = MaseGraph(\n"," model,\n"," hf_input_names=[\n"," \"input_ids\",\n"," \"attention_mask\",\n"," \"labels\",\n"," ],\n",")\n","\n","mg, _ = passes.init_metadata_analysis_pass(mg)\n","mg, _ = passes.add_common_metadata_analysis_pass(mg)\n","\n","mg.draw()"]},{"cell_type":"markdown","metadata":{"id":"4woTbqoIwdcI"},"source":["## Full Supervised Finetuning (SFT)"]},{"cell_type":"markdown","metadata":{"id":"nM8Awkj8wdcI"},"source":["Before training the model, let's inspect how many trainable parameters there are. If you're familiar with Keras, you might have used the `model.summary()` API before, but it's not as easy to do the same in Pytorch - luckily, Mase has a module-level pass with this functionality."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":0,"status":"ok","timestamp":1737999620233,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"7fGPINmnwdcI","outputId":"ac794ae9-a7c4-4cf1-da31-eb9a055e9c6c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------------------------------------+------------------------+\n","| Submodule | Trainable Parameters |\n","+=================================================+========================+\n","| bert | 4385920 |\n","+-------------------------------------------------+------------------------+\n","| bert.embeddings | 3972864 |\n","+-------------------------------------------------+------------------------+\n","| bert.embeddings.word_embeddings | 3906816 |\n","+-------------------------------------------------+------------------------+\n","| bert.embeddings.token_type_embeddings | 256 |\n","+-------------------------------------------------+------------------------+\n","| bert.embeddings.position_embeddings | 65536 |\n","+-------------------------------------------------+------------------------+\n","| bert.embeddings.LayerNorm | 256 |\n","+-------------------------------------------------+------------------------+\n","| bert.embeddings.dropout | 0 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder | 396544 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer | 396544 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0 | 198272 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention | 66304 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self | 49536 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.query | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.key | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.value | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output | 16768 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dense | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dropout | 0 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.LayerNorm | 256 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate | 66048 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate.dense | 66048 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output | 65920 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dense | 65664 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dropout | 0 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.LayerNorm | 256 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1 | 198272 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention | 66304 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self | 49536 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.query | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.key | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.value | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output | 16768 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dense | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dropout | 0 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.LayerNorm | 256 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate | 66048 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate.dense | 66048 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output | 65920 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dense | 65664 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dropout | 0 |\n","+-------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.LayerNorm | 256 |\n","+-------------------------------------------------+------------------------+\n","| bert.pooler | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.pooler.dense | 16512 |\n","+-------------------------------------------------+------------------------+\n","| bert.pooler.activation | 0 |\n","+-------------------------------------------------+------------------------+\n","| dropout | 0 |\n","+-------------------------------------------------+------------------------+\n","| classifier | 258 |\n","+-------------------------------------------------+------------------------+\n","| crossentropyloss_0 | 0 |\n","+-------------------------------------------------+------------------------+\n","\n","Total Trainable Parameters: 14480258\n"]}],"source":["from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore\n","\n","_, _ = report_trainable_parameters_analysis_pass(mg.model)"]},{"cell_type":"markdown","metadata":{"id":"deTsXkaQwdcJ"},"source":["From this, we can see the majority of the trainable parameters are in the `Embedding` layer. We don't need to train this, so we freeze those parameters in the cell below."]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1737999612339,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"FmneWl3_wdcJ"},"outputs":[],"source":["for param in mg.model.bert.embeddings.parameters():\n"," param.requires_grad = False"]},{"cell_type":"markdown","metadata":{"id":"4HmAbN1NwdcJ"},"source":["To train the model, we rely on the `Trainer` class from the `transformers` library, which makes it easy to set up a training loop with any hardware configuration. The `get_trainer` utility in Mase handles assigning the training arguments to the `Trainer` class for common use cases, such as in this tutorial."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":105},"executionInfo":{"elapsed":716,"status":"ok","timestamp":1737999613054,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"2hHOkNO0wdcJ","outputId":"ae2308f6-ad6c-4442-c7bd-59374563c629"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f20655509bb44c97b094319b03402345","version_major":2,"version_minor":0},"text/plain":["Downloading builder script: 0%| | 0.00/4.20k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]}],"source":["from chop.tools import get_trainer # type: ignore\n","\n","trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"qtEXunkXwdcJ"},"source":["Before running any fine tuning, let's see how the model performs out of the box. Without any fine-tuning, we can see the model just performs a random guess - there are two labels in the dataset, so this corresponds to an accuracy of around 50%."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":18648,"status":"ok","timestamp":1737999638913,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"SOKYlmBgwdcJ","outputId":"c6b96b89-b530-4900-80b2-57824be69e06"},"outputs":[{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='694' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [ 694/3125 00:05 \u003c 00:19, 124.13 it/s]\n"," \u003c/div\u003e\n"," "],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [3125/3125 00:24]\n"," \u003c/div\u003e\n"," "],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Evaluation accuracy: 0.5\n"]}],"source":["# Evaluate accuracy\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"uLakV-vTwdcJ"},"source":["Now, run the cell below to execute a single training epoch with the current setup."]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"elapsed":34802,"status":"ok","timestamp":1737999689774,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"},"user_tz":0},"id":"ncPlysLFwdcJ"},"outputs":[{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [3125/3125 00:50, Epoch 1/1]\n"," \u003c/div\u003e\n"," \u003ctable border=\"1\" class=\"dataframe\"\u003e\n"," \u003cthead\u003e\n"," \u003ctr style=\"text-align: left;\"\u003e\n"," \u003cth\u003eStep\u003c/th\u003e\n"," \u003cth\u003eTraining Loss\u003c/th\u003e\n"," \u003c/tr\u003e\n"," \u003c/thead\u003e\n"," \u003ctbody\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e500\u003c/td\u003e\n"," \u003ctd\u003e0.626000\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e1000\u003c/td\u003e\n"," \u003ctd\u003e0.507400\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e1500\u003c/td\u003e\n"," \u003ctd\u003e0.469200\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e2000\u003c/td\u003e\n"," \u003ctd\u003e0.430600\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e2500\u003c/td\u003e\n"," \u003ctd\u003e0.426000\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e3000\u003c/td\u003e\n"," \u003ctd\u003e0.420100\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003c/tbody\u003e\n","\u003c/table\u003e\u003cp\u003e"],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["TrainOutput(global_step=3125, training_loss=0.47749987548828127, metrics={'train_runtime': 50.8825, 'train_samples_per_second': 491.328, 'train_steps_per_second': 61.416, 'total_flos': 0.0, 'train_loss': 0.47749987548828127, 'epoch': 1.0})"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"k0sCZbjdwdcK"},"source":["Let's see how much accuracy we get after a single training epoch of full finetuning."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":37},"id":"Wb9iTUUXwdcK"},"outputs":[{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='6250' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [3125/3125 01:55]\n"," \u003c/div\u003e\n"," "],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Evaluation accuracy: 0.81968\n"]}],"source":["eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"yAour0q6wdcK"},"source":["We can now export the SFT version of the model to be used in later tutorials."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7mQ-XbrWwdcK"},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /root/tutorial_2_sft.pt, /root/tutorial_2_sft.mz\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /root/tutorial_2_sft.pt\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /root/tutorial_2_sft.mz\u001b[0m\n","\u001b[33mWARNING \u001b[0m \u001b[34mFailed to pickle call_function node: finfo\u001b[0m\n","\u001b[33mWARNING \u001b[0m \u001b[34mcannot pickle 'torch.finfo' object\u001b[0m\n","\u001b[33mWARNING \u001b[0m \u001b[34mFailed to pickle call_function node: getattr_2\u001b[0m\n","\u001b[33mWARNING \u001b[0m \u001b[34mcannot pickle 'torch.finfo' object\u001b[0m\n"]}],"source":["from pathlib import Path\n","\n","mg.export(f\"{Path.home()}/tutorial_2_sft\")"]},{"cell_type":"markdown","metadata":{"id":"nOUhltTqwdcK"},"source":["## Parameter Efficient Finetuning (PEFT) with LoRA"]},{"cell_type":"markdown","metadata":{"id":"_NBscZcRwdcK"},"source":["An alternative to full fine-tuning is Parameter Efficient Fine Tuning (PEFT), which uses a small number of trainable parameters to achieve similar performance. LoRA was proposed by a research team at Microsoft in 2021, as an efficient technique for PEFT.\n","\n","\u003cdiv style=\"text-align: center;\"\u003e\n"," \u003cimg src=\"imgs/lora_adapter.png\" alt=\"drawing\" width=\"400\"/\u003e\n","\u003c/div\u003e\n","\n","Consider the standard equation of a linear layer:\n","\n","$$\n","y = X W + b\n","$$\n","\n","The LoRA method involves replacing this with the following, where A and B are low-rank matrices. We freeze the $W$ parameters, and only allow the optimizer to train the parameters in $A$ and $B$.\n","\n","$$\n","y = X (W + AB) + b\n","$$\n","\n","This enables us to achieve accuracies comparable to full fine tuning, while only training a fraction of the parameters. See [the paper](https://arxiv.org/abs/2106.09685) for more details. We can inject the LoRA adapter into the existing model using the `insert_lora_adapter_transform_pass` pass in Mase, as follows."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"kF8kOIoNwdcK"},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_0_attention_self_query, target: bert.encoder.layer.0.attention.self.query with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_0_attention_self_key, target: bert.encoder.layer.0.attention.self.key with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_0_attention_self_value, target: bert.encoder.layer.0.attention.self.value with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_0_attention_output_dense, target: bert.encoder.layer.0.attention.output.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_0_intermediate_dense, target: bert.encoder.layer.0.intermediate.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_0_output_dense, target: bert.encoder.layer.0.output.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_1_attention_self_query, target: bert.encoder.layer.1.attention.self.query with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_1_attention_self_key, target: bert.encoder.layer.1.attention.self.key with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_1_attention_self_value, target: bert.encoder.layer.1.attention.self.value with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_1_attention_output_dense, target: bert.encoder.layer.1.attention.output.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_1_intermediate_dense, target: bert.encoder.layer.1.intermediate.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_encoder_layer_1_output_dense, target: bert.encoder.layer.1.output.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: bert_pooler_dense, target: bert.pooler.dense with LoRALinear module.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mReplaced node: classifier, target: classifier with LoRALinear module.\u001b[0m\n"]}],"source":["mg, _ = passes.insert_lora_adapter_transform_pass(\n"," mg,\n"," pass_args={\n"," \"rank\": 6,\n"," \"alpha\": 1.0,\n"," \"dropout\": 0.5,\n"," },\n",")"]},{"cell_type":"markdown","metadata":{"id":"zEBM_gZlwdcK"},"source":["Similar to before, let's report the number of trainable parameters."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"ZaucDN_2wdcK"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------------------------------------------------+------------------------+\n","| Submodule | Trainable Parameters |\n","+=====================================================+========================+\n","| bert | 439808 |\n","+-----------------------------------------------------+------------------------+\n","| bert.embeddings | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.embeddings.word_embeddings | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.embeddings.token_type_embeddings | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.embeddings.position_embeddings | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.embeddings.LayerNorm | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.embeddings.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder | 421888 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer | 421888 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0 | 210944 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention | 71936 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self | 53760 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.query | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.query.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.query.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.query.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.query.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.key | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.key.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.key.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.key.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.key.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.value | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.value.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.value.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.value.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.self.value.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output | 18176 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dense | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dense.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dense.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dense.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.attention.output.LayerNorm | 256 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate | 69376 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate.dense | 69376 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate.dense.linear | 65536 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate.dense.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate.dense.lora_b | 3072 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.intermediate.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output | 69632 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dense | 69376 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dense.linear | 65536 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dense.lora_a | 3072 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dense.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.0.output.LayerNorm | 256 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1 | 210944 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention | 71936 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self | 53760 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.query | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.query.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.query.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.query.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.query.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.key | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.key.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.key.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.key.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.key.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.value | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.value.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.value.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.value.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.self.value.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output | 18176 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dense | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dense.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dense.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dense.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.attention.output.LayerNorm | 256 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate | 69376 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate.dense | 69376 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate.dense.linear | 65536 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate.dense.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate.dense.lora_b | 3072 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.intermediate.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output | 69632 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dense | 69376 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dense.linear | 65536 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dense.lora_a | 3072 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dense.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.encoder.layer.1.output.LayerNorm | 256 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler.dense | 17920 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler.dense.linear | 16384 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler.dense.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler.dense.lora_b | 768 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler.dense.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| bert.pooler.activation | 0 |\n","+-----------------------------------------------------+------------------------+\n","| dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| classifier | 1036 |\n","+-----------------------------------------------------+------------------------+\n","| classifier.linear | 256 |\n","+-----------------------------------------------------+------------------------+\n","| classifier.lora_a | 768 |\n","+-----------------------------------------------------+------------------------+\n","| classifier.lora_b | 12 |\n","+-----------------------------------------------------+------------------------+\n","| classifier.dropout | 0 |\n","+-----------------------------------------------------+------------------------+\n","| crossentropyloss_0 | 0 |\n","+-----------------------------------------------------+------------------------+\n","\n","Total Trainable Parameters: 3169816\n"]}],"source":["_, _ = report_trainable_parameters_analysis_pass(mg.model)"]},{"cell_type":"markdown","metadata":{"id":"voMss4xjwdcL"},"source":["In this case, LoRA reduces the number of trainable parameters by $4.5\\times$! We'll run a few more training epochs and evaluate the resulting accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"l1H3RdaTwdcL"},"outputs":[{"name":"stderr","output_type":"stream","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [3125/3125 01:04, Epoch 1/1]\n"," \u003c/div\u003e\n"," \u003ctable border=\"1\" class=\"dataframe\"\u003e\n"," \u003cthead\u003e\n"," \u003ctr style=\"text-align: left;\"\u003e\n"," \u003cth\u003eStep\u003c/th\u003e\n"," \u003cth\u003eTraining Loss\u003c/th\u003e\n"," \u003c/tr\u003e\n"," \u003c/thead\u003e\n"," \u003ctbody\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e500\u003c/td\u003e\n"," \u003ctd\u003e0.435200\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e1000\u003c/td\u003e\n"," \u003ctd\u003e0.420400\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e1500\u003c/td\u003e\n"," \u003ctd\u003e0.416200\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e2000\u003c/td\u003e\n"," \u003ctd\u003e0.390400\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e2500\u003c/td\u003e\n"," \u003ctd\u003e0.395800\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003ctr\u003e\n"," \u003ctd\u003e3000\u003c/td\u003e\n"," \u003ctd\u003e0.395200\u003c/td\u003e\n"," \u003c/tr\u003e\n"," \u003c/tbody\u003e\n","\u003c/table\u003e\u003cp\u003e"],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [3125/3125 00:26]\n"," \u003c/div\u003e\n"," "],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Evaluation accuracy: 0.83732\n"]}],"source":["trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=1,\n",")\n","trainer.train()\n","\n","# Evaluate accuracy\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"bcFndhbpwdcL"},"source":["After training is finished, we can run the `fuse_lora_weights_transform_pass` pass to optimize the model for inference. This pass replaces each `LoRALinear` instance with an `nn.Linear` module, where the $AB$ product added to the original weights matrix. This incurs less kernel invocations when deploying the model, which reduces inference runtime."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"eZIOX3pUwdcL"},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.0.attention.self.query.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.0.attention.self.key.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.0.attention.self.value.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.0.attention.output.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.0.intermediate.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.0.output.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.1.attention.self.query.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.1.attention.self.key.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.1.attention.self.value.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.1.attention.output.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.1.intermediate.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.encoder.layer.1.output.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for bert.pooler.dense.\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mFusing LoRALinear weights for classifier.\u001b[0m\n"]},{"data":{"text/html":["\n"," \u003cdiv\u003e\n"," \n"," \u003cprogress value='6250' max='3125' style='width:300px; height:20px; vertical-align: middle;'\u003e\u003c/progress\u003e\n"," [3125/3125 01:03]\n"," \u003c/div\u003e\n"," "],"text/plain":["\u003cIPython.core.display.HTML object\u003e"]},"metadata":{},"output_type":"display_data"}],"source":["mg, _ = passes.fuse_lora_weights_transform_pass(mg)\n","eval_results = trainer.evaluate()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"Bbr3k-plwdcL"},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluation accuracy: 0.83732\n"]}],"source":["print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"6h8CPceCwdcL"},"source":["Finally, export the finetuned model to be used in future tutorials."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Ev4aIDIwdcL"},"outputs":[],"source":["from pathlib import Path\n","\n","mg.export(f\"{Path.home()}/tutorial_2_lora\")"]},{"cell_type":"markdown","metadata":{"id":"-o0ElDthwdcL"},"source":["## Conclusion"]},{"cell_type":"markdown","metadata":{"id":"tSL90HeQwdcL"},"source":["By adjusting the rank number of LoRA, we can control the trade-off between memory usage and fine-tuned accuracy. Such parameter-efficient fine-tuning techniques are very useful in the area of large language models (LLMs), where the memory requirement for training is a significant bottleneck."]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","name":"","version":""},"kernelspec":{"display_name":"Python 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Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d4/16/7732d4fceffc9d2aff7aaded3820cc1ed0bf83d534e19e69d21474ba9624/coverage-6.4.2-cp37-cp37m-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/f6/53/6353cec1305e478f003a52dc4ed42e05232af99faccd4dd300c5e687d5d1/coverage-6.4.2-cp38-cp38-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/18/72/757fe0070c5da9467835904e410b0e4aae2edf9f9ffd9e20285ff96af6fa/coverage-6.4.2-cp38-cp38-macosx_11_0_arm64.whl 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Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/84/90/97d0d88ffcbb9019cd6e8628b07d761522f04b5ea79b8ee14d18b7eeee06/coverage-6.4.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/55/58/6d11b1933a0fe5cae0ecfa21d1570dbd3290ed874512e20117858084533b/coverage-6.4.2-cp38-cp38-musllinux_1_1_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.7)\n"," Skipping link: none of the wheel's tags (cp38-cp38-musllinux_1_1_i686) are compatible (run pip debug --verbose 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8c/d9/e84edcf16e44e98b4e568d1cb1bfbeab43c91795af490a89c2ebcb370a7c/coverage-7.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/91/26/6bfe51c92ac314e3c75c7d1e61c357bd79d52982ef3c4e0006f21d301efd/coverage-7.3.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/aa/80/464823e327596913e8d14c3d62d6185fc95ba09fa703633d7f4233694058/coverage-7.3.1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/20/b6/e911ced41c17b7d53bb31aa695e7c24917a87d5e66bb4c640de015802a3f/coverage-7.3.1-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/34/1e/3010b80c346b7537de9e5633ad4ffa3abb756e856ee381a92e422c5df7ba/coverage-7.3.1-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/c7/e80c182c7014d4ba5f5a3d631c91b632e81b8a2e9db11013779c8b6fb11e/coverage-7.3.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/21/3b/bff94691f95b248ba0be37dccae75de32e4f69b6444ebfe9946aecf150e5/coverage-7.3.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/a1/29191b24dd60b6beff4138aa36d6c4ff26cc12993a1235e0335db62357ab/coverage-7.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/04/fe2f41c59462d36b36fb701ef1c58ce8bfd0a106ec2d4f475f28009c74b4/coverage-7.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible tags): https://files.pythonhosted.org/packages/f3/a6/f681656a888ab8e65406b5d957a0bc2b2eadca7196246a1c4ebbd276bb1a/coverage-7.4.0-cp310-cp310-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ee/9c/7dffc6f9a6467ce260f03cbf11c21bf5b740fd668b2e57118e23014aa0f4/coverage-7.4.0-cp310-cp310-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/47/4d/cda0f5ab752b95734b8831136871032a716f8505d52d533e90e98e16b3ab/coverage-7.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/6e/6ff67d0f7514bf2dd119f2fef7374f7c853dae042129286d69eebb431028/coverage-7.4.1-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9f/ae/0d439dc9adc0111ffbed38149d73ddf34f7a8768e377020181e624cf2634/coverage-7.4.1-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ce/e1/df16e7e353c2ba5a5b3e02a6bad7dbf1bc62d5b9cfe5c06ed0e31fc64122/coverage-7.4.1-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/86/25/6b70cb21b6e62158aab40a0e930361d4397f4ef4cbd2a04d3d01b6e4c5cf/coverage-7.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/cc/c4da6426501cdbad3b37edbeca7b485137f74a6030d5a974060d8369f898/coverage-7.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/05/d4/2d95ff69fc0a53df8fd2f35dcad8a2d73a7cfc059e1430fda8510afcf771/coverage-7.4.2-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0c/1c/1edb0c197839083b711b9c0ebe5cd065b4858085d18a1fa8df76fc6a28ad/coverage-7.4.2-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a2/4f/7ab8769631c703af44c487ba5f0df1801abfc2b6e930ce4add4d11592633/coverage-7.4.2-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/78/5d4c92a65850a5e19e9087b89f89091185300abf18facdc971af73181c2c/coverage-7.4.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b2/11/5e520d65fc4a897956fd2211149d535b41531d299049f0e49405e42540a5/coverage-7.4.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/dc/4e0ce431c96b1db1548f80d4a4123fd7c584765c6b46e66ab96410325b26/coverage-7.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ea/3f/7e0898438cbbec0ca3d915dc08b75e1cfbe3bab27727be7081bfd1f70e74/coverage-7.5.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5b/94/6583a32719fdfee296493d0234ff3a2a4bbb4fee11afccddb0f00a96c1fa/coverage-7.5.2-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/30/cdb45952ead7b12b0bab33546d40fba1a6371a1599884c816b89441c3a6b/coverage-7.5.2-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/60/62dfe607a05ee6a50958d9eff80144b1677070da9dfbb8914e64c81c4e02/coverage-7.5.2-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/03/ad/12b1200490adb704ec7b1b9c37ec5bd748b30afcb9645de33ce18cc82a16/coverage-7.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/19/af/7bb0b0bd5e67217e086a0bba7633be38313358f02b4b6c0b7e5b68459dcb/coverage-7.5.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e3/27/3b8ee8a2d96cf9f01a2add9bf100050878785aadc4c4bba5a67eb797dd95/coverage-7.5.3-cp38-cp38-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/57/99/ccb1e4f40688d793ac54407fc86d120c6816ae99b747b47d3e2cc1067457/coverage-7.5.3-cp39-cp39-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/65/af/3b0268da4cba77f3dda012bafe90b8cbf163713ac92b3cc237ff794bf277/coverage-7.5.3-cp39-cp39-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/30/ab/701129bdffd493d59070b238238d25ad8882f4967d44f7a399833a5585ae/coverage-7.5.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_i686, cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686, cp39-cp39-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e7/10/ad84d019b8d72e92fb908a0524a190f7212062f89360099907b7a812dc65/coverage-7.5.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, 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compatible tags): https://files.pythonhosted.org/packages/d3/a8/08d7b38e6ff8df52331c83130d0ab92d9c9a8b5462f9e99c9f051a4ae206/coverage-7.6.1-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d6/6a/9cf96839d3147d55ae713eb2d877f4d777e7dc5ba2bce227167d0118dfe8/coverage-7.6.1-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/74/e4/7ff20d6a0b59eeaab40b3140a71e38cf52547ba21dbcf1d79c5a32bba61b/coverage-7.6.1-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.8)\n"," Skipping link: none of the wheel's tags 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/40/ae/b0f027a7ea045e2c88f520a15ef73371cdf8ab544a109f7b8a9338d01e7d/coverage-7.6.3-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/77/8e5c0c6027ce0d06d0cb9569d372fb94247b5a49a7ef8bba288956696dcb/coverage-7.6.3-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/ca/0fe701e0bf0ba3062466ceeccb9857caa492886375bbf6eabeab118a4dd0/coverage-7.6.3-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) 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https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/76/f5/9e5b4cda520e07ff0e2bb61f6176cd9bf1a2a77c2f89caf8005ae9eba1d3/coverage-7.6.3-cp311-cp311-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/ad/b62d2f0367b5eba16aa950c9a4e691aaa59b153ba0d038ad15bd92b67ce2/coverage-7.6.3-cp311-cp311-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): 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cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d3/d4/83dfe1cdabf7f574217ce431c8506c80c6afa5c4056b1159282ba54bb6f2/coverage-7.6.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/05/0a/f53ab3bf80bcf6a17bece8851d4687cf7e424505cf4ce7a3c4cb41b52d96/coverage-7.6.3-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/b2/21/ff415e195eafc86ad6e3e6bdb04e6ebd2caa432d27ec261c0a6d849a171e/coverage-7.6.3-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/28/81/08c1c7d2ecc14036b6fb71433ce51fb0942b39ef2bbb654fda82e4e16004/coverage-7.6.3-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c6/50/912a47a6fa3582c6694e0acdb4c5cf1264950a400a3a9d35a5552e7a91cd/coverage-7.6.3-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9c/ba/b5722bec74017eaa1c5d35377f40a2a434e6c864cf3f1b46cddb62657642/coverage-7.6.3-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a1/d1/1264cc9ad5079439f438fd49080cbb57a8c6c589919872eaacdbc30d3b1a/coverage-7.6.3-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux2014_aarch64, cp313-cp313-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/92/ac/57e435de18c45be75cf641b14e5c272f76ae0f06d8e0aa80070827bd7c49/coverage-7.6.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-manylinux1_i686, cp313-cp313-manylinux2014_i686, cp313-cp313-manylinux_2_17_i686, cp313-cp313-manylinux_2_5_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/cd/ea1fba2f701b299fbd79a05cab7ab399f98222f05223f33793d2010224f8/coverage-7.6.5-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of 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compatible tags): https://files.pythonhosted.org/packages/3d/ba/fc6ac9f45ed6759dbf2b7fc517b45a81c71a66ac2751acece68579a4b611/coverage-7.6.5-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/64/ec/86a1111bb73968e1b8038244bbef5a5a51dc6e8d32825f6e0602f9ac31bf/coverage-7.6.5-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c1/c0/0f49c4ca3256aca7d19dc72979ff077c1c7e1a273569f9a842d6cb7d76af/coverage-7.6.5-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/9c/790038b13580bc65064825f2c419cbc01af6f623b3a7be717e5b2acd2fe4/coverage-7.6.5-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5c/fd/aa621b166f11e70df9b5e395a91d1311508df909ea1d9d2a12da44e533eb/coverage-7.6.5-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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compatible tags): https://files.pythonhosted.org/packages/a0/4e/d6c04af25061d8a10bf5f385abe21809f8f7b3302d01f31feabda876bf71/coverage-7.6.6-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/93/152ee79dbebb6d5589aaff243db3ebbde66b701671c69cc437c0230f9555/coverage-7.6.6-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/cf/2b7a4e600680ec3ba8b04012584484b7a8c7f6bac02a41a534f861932acd/coverage-7.6.6-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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(requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-manylinux1_x86_64, cp313-cp313t-manylinux2014_x86_64, cp313-cp313t-manylinux_2_17_x86_64, cp313-cp313t-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ed/27/8623dc736318a4758fb34211bdc8b13aeda1bd43b17994e83608c3104b41/coverage-7.6.6-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313t-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/24/e30af5a53fb4fa8c3f183ec3f53568ddfc123d34cfa964195614c4f59fc1/coverage-7.6.6-cp313-cp313t-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/31/31/6a56469609a252549dd4b090815428d5521edd4642440d987573a450c069/coverage-7.6.8-cp310-cp310-win_amd64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ab/9f/e98211980f6e2f439e251737482aa77906c9b9c507824c71a2ce7eea0402/coverage-7.6.8-cp311-cp311-macosx_10_9_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fd/c7/8bab83fb9c20f7f8163c5a20dcb62d591b906a214a6dc6b07413074afc80/coverage-7.6.8-cp311-cp311-macosx_11_0_arm64.whl (from https://pypi.org/simple/coverage/) 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cp312-cp312-manylinux2014_x86_64, cp312-cp312-manylinux_2_17_x86_64, cp312-cp312-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ac/85/4323ece0cd5452c9522f4b6e5cc461e6c7149a4b1887c9e7a8b1f4e51146/coverage-7.6.8-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/77/52/b2537487d8f36241e518e84db6f79e26bc3343b14844366e35b090fae0d4/coverage-7.6.8-cp312-cp312-musllinux_1_2_aarch64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp312-cp312-musllinux_1_2_i686) are compatible (run pip debug --verbose to show compatible tags): 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compatible tags): https://files.pythonhosted.org/packages/a6/85/285c2df9a04bc7c31f21fd9d4a24d19e040ec5e2ff06e572af1f6514c9e7/coverage-7.6.8-cp313-cp313-musllinux_1_2_i686.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-musllinux_1_2_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/cb/a1/95ec8522206f76cdca033bf8bb61fff56429fb414835fc4d34651dfd29fc/coverage-7.6.8-cp313-cp313-musllinux_1_2_x86_64.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags (cp313-cp313-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/ac/687e9ba5e6d0979e9dab5c02e01c4f24ac58260ef82d88d3b433b3f84f1e/coverage-7.6.8-cp313-cp313-win32.whl (from https://pypi.org/simple/coverage/) (requires-python:>=3.9)\n"," Skipping link: none of the wheel's tags 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https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.8), version: 0.14.4\n"," Found link https://files.pythonhosted.org/packages/de/47/1bc31c4bc8b395cd37d8ceaf720abe10cf64c857fb9ce55856a6dd958484/pydata_sphinx_theme-0.14.4.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.8), version: 0.14.4\n"," Found link https://files.pythonhosted.org/packages/65/ee/7c1553663ee739b7212cae1e035d02e54d8dd671e9a84e0c056d1dbb6f79/pydata_sphinx_theme-0.15.0rc0-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.15.0rc0\n"," Found link https://files.pythonhosted.org/packages/90/fa/f6aae9c6d4e7aaa6af63c27c308d694e3c72fc418d1e5c03ab458995a747/pydata_sphinx_theme-0.15.0rc0.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.15.0rc0\n"," Found link https://files.pythonhosted.org/packages/ee/d1/4d35f8cf0b065d25f99b5d7a9bc0349c1993538f4f8bbb04477d0e8d4051/pydata_sphinx_theme-0.15.1-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.15.1\n"," Found link https://files.pythonhosted.org/packages/13/a5/be6032e8fcd4355151899988b8bcebb2be78233a0ad92259ab0fc856e8fe/pydata_sphinx_theme-0.15.1.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.15.1\n"," Found link https://files.pythonhosted.org/packages/bc/ef/1700096a5c1d17c2d99332b0759c7ca70346aac917ceafa4c380f085d359/pydata_sphinx_theme-0.15.2-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.15.2\n"," Found link https://files.pythonhosted.org/packages/b8/46/69150af28bfce3dc7594c0b6b1f12143eff685b96a18747a821fd255c432/pydata_sphinx_theme-0.15.2.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 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https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.15.4\n"," Found link https://files.pythonhosted.org/packages/11/e1/efc23c146d100e0a28f830f46fc94d919d23a4af984a865a96eb10071d98/pydata_sphinx_theme-0.16.0rc0-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.0rc0\n"," Found link https://files.pythonhosted.org/packages/7b/78/4ff79793cc8d31271be05fd23af7b03940578b4c812a0a2388ffe02175c9/pydata_sphinx_theme-0.16.0rc0.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.0rc0\n"," Found link https://files.pythonhosted.org/packages/ba/92/38f384061e1361fac7092c35e932c0e08026fb9080bf3fbf05f4c3bb6bda/pydata_sphinx_theme-0.16.0-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.0\n"," Found link https://files.pythonhosted.org/packages/91/c3/5240f2a5dc0b4856655c003466f70aa50d676b1709e5b04f0bee296bbd28/pydata_sphinx_theme-0.16.0.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.0\n"," Found link https://files.pythonhosted.org/packages/ee/7a/06324796187659bbc28439cc214353b700041e05ff27389e137bdb597bf7/pydata_sphinx_theme-0.16.1rc0-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.1rc0\n"," Found link https://files.pythonhosted.org/packages/11/c9/f7f3b17c248a33de4621d03b8d1bd3a3a5a4055402eec6688495f727200d/pydata_sphinx_theme-0.16.1rc0.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.1rc0\n"," Found link https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.1\n"," Found link https://files.pythonhosted.org/packages/00/20/bb50f9de3a6de69e6abd6b087b52fa2418a0418b19597601605f855ad044/pydata_sphinx_theme-0.16.1.tar.gz (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9), version: 0.16.1\n","Skipping link: not a file: https://pypi.org/simple/pydata-sphinx-theme/\n","Given no hashes to check 10 links for project 'pydata-sphinx-theme': discarding no candidates\n","Collecting pydata-sphinx-theme>=0.15.2 (from sphinx-book-theme->mase-tools==1.0.0)\n"," Obtaining dependency information for pydata-sphinx-theme>=0.15.2 from https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-wonquyq7\n"," Looking up \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl.metadata HTTP/1.1\" 200 7549\n"," Downloading pydata_sphinx_theme-0.16.1-py3-none-any.whl.metadata (7.5 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl.metadata\"\n"," etag object cached for 1209600 seconds\n"," Caching due to etag\n"," Created temporary directory: /tmp/pip-metadata-0yuv_paf\n","Given no hashes to check 4 links for project 'sphinxcontrib-jquery': discarding no candidates\n","Requirement already satisfied: lxml in /usr/local/lib/python3.11/dist-packages (from sphinx-test-reports@ git+https://github.com/useblocks/sphinx-test-reports->mase-tools==1.0.0) (5.3.0)\n","Given no hashes to check 6 links for project 'sphinx-needs': discarding no candidates\n","1 location(s) to search for versions of gymnasium:\n","* https://pypi.org/simple/gymnasium/\n","Fetching project page and analyzing links: https://pypi.org/simple/gymnasium/\n","Getting page https://pypi.org/simple/gymnasium/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/gymnasium/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/gymnasium/ HTTP/1.1\" 200 3714\n","Updating cache with response from \"https://pypi.org/simple/gymnasium/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/gymnasium/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/70/41/bbde8c911e4aed580641ce130cedf604125f7472fed35a7906cb4cf2a9e2/gymnasium-0.0.1-py3-none-any.whl (from https://pypi.org/simple/gymnasium/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/53/2a/5d3fe2469206f0265010ca4d93e96d109c38fcbfbd64159116515ba4e2cb/gymnasium-0.26.1-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.6), version: 0.26.1\n"," Found link https://files.pythonhosted.org/packages/dc/18/eeebf30bd3174bb8e511cbba3dfd647071264a402d91208f0980345fcc22/gymnasium-0.26.1.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.6), version: 0.26.1\n"," Found link https://files.pythonhosted.org/packages/ec/f9/402710984dc16f3eb8d77c2aacde7126d35ad2a77be184cabc9493e97e6d/gymnasium-0.26.2-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.6), version: 0.26.2\n"," Found link https://files.pythonhosted.org/packages/79/a3/f4fab8e911cac64a9e62a735f103b8b637eb90e9b8c18dc04ea38a1161a0/gymnasium-0.26.2.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.6), version: 0.26.2\n"," Found link https://files.pythonhosted.org/packages/e7/d8/95bc547d57d7661b00809b0714dff7690a79cfafdbc4812c93790fea5add/Gymnasium-0.26.3-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.6), version: 0.26.3\n"," Found link https://files.pythonhosted.org/packages/d2/98/7453b05467682cd6461636fad4b15aa3ef975e0762719268bb9ec74681da/Gymnasium-0.26.3.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.6), version: 0.26.3\n"," Found link https://files.pythonhosted.org/packages/b5/07/3aeb35cdca3d283b95b9542a2a90207c281cdb9f1730d2aada407737b32f/gymnasium-0.27.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.27.0\n"," Found link https://files.pythonhosted.org/packages/db/75/3e10976ec93d2d2da03903658ba8a4c5dd31ddad707ead480661648976a4/gymnasium-0.27.0.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.27.0\n"," Found link https://files.pythonhosted.org/packages/35/2a/c63241f1ebc5c44c223d87ec06b08f3269f001dbddbea2fbdca7a85a47e3/gymnasium-0.27.1-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.27.1\n"," Found link https://files.pythonhosted.org/packages/59/d2/7aaa82d8e85c98d8190bfbbaaade146d90e63d9ec8be77af0370cc19ccd7/gymnasium-0.27.1.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.27.1\n"," Found link https://files.pythonhosted.org/packages/85/eb/f365f780a89061575dfb96db6584bb80699bcbe74fcd8c4cc093daea7cb9/gymnasium-0.28.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.28.0\n"," Found link https://files.pythonhosted.org/packages/b8/05/fe684232776f6b847211264819b464dae38a82c30df5171c764e2f4e1ae0/gymnasium-0.28.0.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.28.0\n"," Found link https://files.pythonhosted.org/packages/60/82/3762ef4555791a729ae554e13c011efe5e8347d7eba9ea5ed245a8d1b234/gymnasium-0.28.1-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.28.1\n"," Found link https://files.pythonhosted.org/packages/58/6a/c304954dc009648a21db245a8f56f63c8da8a025d446dd0fd67319726003/gymnasium-0.28.1.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.7), version: 0.28.1\n"," Found link https://files.pythonhosted.org/packages/3f/00/a728a4a8608213482fc38d76d842657d29b546f214e83801a044de074612/gymnasium-0.29.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 0.29.0\n"," Found link https://files.pythonhosted.org/packages/5a/40/77566cb53866adba5ab91f7d59e3d10c8d0cbab59ae0839b05f60c797b6c/gymnasium-0.29.0.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 0.29.0\n"," Found link https://files.pythonhosted.org/packages/a8/4d/3cbfd81ed84db450dbe73a89afcd8bc405273918415649ac6683356afe92/gymnasium-0.29.1-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 0.29.1\n"," Found link https://files.pythonhosted.org/packages/0d/f8/5699ddb3e1c4f6d97b8930e573074849b921da8374fccd141f0f3a9bd713/gymnasium-0.29.1.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 0.29.1\n"," Found link https://files.pythonhosted.org/packages/b2/81/0e0e046dee49c2e3de39ea1fc56ac6b1092ad9909ce5b870f6d75b92e3e7/gymnasium-1.0.0a1-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 1.0.0a1\n"," Found link https://files.pythonhosted.org/packages/15/7d/0e075ef950f887023ef36e7af77f2f97afd70011c63968e724beb66e9ad4/gymnasium-1.0.0a1.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 1.0.0a1\n"," Found link https://files.pythonhosted.org/packages/de/3c/84dd30bc10dfd7147b450eba71252da995ada6166bdb3899debc9b403cc9/gymnasium-1.0.0a2-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 1.0.0a2\n"," Found link https://files.pythonhosted.org/packages/82/07/d345f9519928c7b8851a580f2c1ae3773845f75d944e3f560dc92bb4b0ad/gymnasium-1.0.0a2.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 1.0.0a2\n"," Found link https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 1.0.0\n"," Found link https://files.pythonhosted.org/packages/4e/12/1047b8fdbfcdce74022048d916e844ad7e6e1114d81d26a7aed657e3a76d/gymnasium-1.0.0.tar.gz (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8), version: 1.0.0\n","Skipping link: not a file: https://pypi.org/simple/gymnasium/\n","Given no hashes to check 4 links for project 'gymnasium': discarding no candidates\n","Collecting gymnasium<1.1.0,>=0.29.1 (from stable-baselines3[extra]->mase-tools==1.0.0)\n"," Obtaining dependency information for gymnasium<1.1.0,>=0.29.1 from https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-kfabtf5z\n"," Looking up \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl.metadata HTTP/1.1\" 200 9457\n"," Downloading gymnasium-1.0.0-py3-none-any.whl.metadata (9.5 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl.metadata\"\n"," etag object cached for 1209600 seconds\n"," Caching due to etag\n"," Created temporary directory: /tmp/pip-metadata-ae__1bkd\n","Requirement already satisfied: pygame in /usr/local/lib/python3.11/dist-packages (from stable-baselines3[extra]->mase-tools==1.0.0) (2.6.1)\n","Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from stable-baselines3[extra]->mase-tools==1.0.0) (13.9.4)\n","1 location(s) to search for versions of ale-py:\n","* 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/usr/local/lib/python3.11/dist-packages (from tensorboard->mase-tools==1.0.0) (3.1.3)\n","Given no hashes to check 2 links for project 'protobuf': discarding no candidates\n","Given no hashes to check 3 links for project 'pillow': discarding no candidates\n","Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from wandb->mase-tools==1.0.0) (0.4.0)\n","Given no hashes to check 2 links for project 'protobuf': discarding no candidates\n","Requirement already satisfied: pydantic<3,>=2.6 in /usr/local/lib/python3.11/dist-packages (from wandb->mase-tools==1.0.0) (2.10.5)\n","Requirement already satisfied: sentry-sdk>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb->mase-tools==1.0.0) (2.20.0)\n","Requirement already satisfied: setproctitle in /usr/local/lib/python3.11/dist-packages (from wandb->mase-tools==1.0.0) (1.3.4)\n","1 location(s) to search for versions of mako:\n","* https://pypi.org/simple/mako/\n","Fetching project page and analyzing links: https://pypi.org/simple/mako/\n","Getting page https://pypi.org/simple/mako/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/mako/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/mako/ HTTP/1.1\" 200 11876\n","Updating cache with response from \"https://pypi.org/simple/mako/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/mako/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/fe/60/2b0d8cdc3d311e68cd5daa046704b00b4aa89db51a30d22fcfbf34cb1ea1/Mako-0.1.0.tar.gz (from https://pypi.org/simple/mako/), version: 0.1.0\n"," Found link https://files.pythonhosted.org/packages/26/76/5def15b515d84f1663c7e196574701416699add3dbc757e0d0f74897f369/Mako-0.1.1.tar.gz (from https://pypi.org/simple/mako/), version: 0.1.1\n"," Found link 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https://files.pythonhosted.org/packages/8c/2a/0cad9e137b04530bb76960f1df41c4b1ab94384910d44c56464bfc116f8c/Mako-0.7.1.tar.gz (from https://pypi.org/simple/mako/), version: 0.7.1\n"," Found link https://files.pythonhosted.org/packages/fa/8f/d901355a53ee1f4a2291580ddec7b7fdeba8fcaa45ded3e4241f26423f53/Mako-0.7.2.tar.gz (from https://pypi.org/simple/mako/), version: 0.7.2\n"," Found link https://files.pythonhosted.org/packages/9b/82/32d68c0fdfb14e1ecfab42c1fc9810fe927c91e877f9db3ea8a9ce448a76/Mako-0.7.3.tar.gz (from https://pypi.org/simple/mako/), version: 0.7.3\n"," Found link https://files.pythonhosted.org/packages/3c/63/777708d2a8ad7beb57cdf2cc45cf740ed33c4a5f5b139d725815700c04eb/Mako-0.8.0.tar.gz (from https://pypi.org/simple/mako/), version: 0.8.0\n"," Found link https://files.pythonhosted.org/packages/ef/2e/fcd7241a6f0270ec990a8fcdb2f5a44e0ba9d8daeb2c8ecbb2e0d75501da/Mako-0.8.1.tar.gz (from https://pypi.org/simple/mako/), version: 0.8.1\n"," Found link 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https://files.pythonhosted.org/packages/6e/01/45ab9f723a93e0ca75fba4d2c266bb041120cb4215eab94f7c78743ac7ed/Mako-1.2.0-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.7), version: 1.2.0\n"," Found link https://files.pythonhosted.org/packages/50/ec/1d687348f0954bda388bfd1330c158ba8d7dea4044fc160e74e080babdb9/Mako-1.2.0.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.7), version: 1.2.0\n"," Found link https://files.pythonhosted.org/packages/f1/01/62590c494bd3602e49b3536a5f9456f98e9a55178738bd2cb4811f0ebcdf/Mako-1.2.1-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.7), version: 1.2.1\n"," Found link https://files.pythonhosted.org/packages/ad/dd/34201dae727bb183ca14fd8417e61f936fa068d6f503991f09ee3cac6697/Mako-1.2.1.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.7), version: 1.2.1\n"," Found link 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https://files.pythonhosted.org/packages/03/62/70f5a0c2dd208f9f3f2f9afd103aec42ee4d9ad2401d78342f75e9b8da36/Mako-1.3.5-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.5\n"," Found link https://files.pythonhosted.org/packages/67/03/fb5ba97ff65ce64f6d35b582aacffc26b693a98053fa831ab43a437cbddb/Mako-1.3.5.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.5\n"," Found link https://files.pythonhosted.org/packages/48/22/bc14c6f02e6dccaafb3eba95764c8f096714260c2aa5f76f654fd16a23dd/Mako-1.3.6-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.6\n"," Found link https://files.pythonhosted.org/packages/fa/0b/29bc5a230948bf209d3ed3165006d257e547c02c3c2a96f6286320dfe8dc/mako-1.3.6.tar.gz (from https://pypi.org/simple/mako/) (requires-python:>=3.8), version: 1.3.6\n"," Found link 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aiohttp->datasets->mase-tools==1.0.0) (6.1.0)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets->mase-tools==1.0.0) (0.2.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets->mase-tools==1.0.0) (1.18.3)\n","Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.11/dist-packages (from gitdb<5,>=4.0.1->GitPython->mase-tools==1.0.0) (5.0.2)\n","1 location(s) to search for versions of farama-notifications:\n","* https://pypi.org/simple/farama-notifications/\n","Fetching project page and analyzing links: https://pypi.org/simple/farama-notifications/\n","Getting page https://pypi.org/simple/farama-notifications/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/farama-notifications/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry 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https://pypi.org/simple/farama-notifications/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/99/65/eddbd8b13d7bfd5dcdfea3eb46458b052e27abfc85d37c4c79ded99c1ea0/Farama_Notifications-0.0.2-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/2e/2c/8384832b7a6b1fd6ba95bbdcae26e7137bb3eedc955c42fd5cdcc086cfbf/Farama-Notifications-0.0.4.tar.gz (from https://pypi.org/simple/farama-notifications/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/), version: 0.0.4\n","Skipping link: not a file: https://pypi.org/simple/farama-notifications/\n","Given no hashes to check 6 links for project 'farama-notifications': discarding no candidates\n","Collecting farama-notifications>=0.0.1 (from gymnasium<1.1.0,>=0.29.1->stable-baselines3[extra]->mase-tools==1.0.0)\n"," Obtaining dependency information for farama-notifications>=0.0.1 from https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-enovupda\n"," Looking up \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl.metadata HTTP/1.1\" 200 558\n"," Downloading Farama_Notifications-0.0.4-py3-none-any.whl.metadata (558 bytes)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from 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/usr/local/lib/python3.11/dist-packages (from jsonschema>=3.2.0->sphinx-needs>=4->mase-tools==1.0.0) (0.22.3)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py~=3.0->myst_parser->mase-tools==1.0.0) (0.1.2)\n","Given no hashes to check 2 links for project 'dill': discarding no candidates\n","Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.11/dist-packages (from nbclient->myst-nb->mase-tools==1.0.0) (6.1.12)\n","Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.11/dist-packages (from nbclient->myst-nb->mase-tools==1.0.0) (5.7.2)\n","Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.11/dist-packages (from nbformat>=5.0->myst-nb->mase-tools==1.0.0) (2.21.1)\n","Given no hashes to check 2 links for project 'scipy': discarding no candidates\n","Requirement already satisfied: qdldl in /usr/local/lib/python3.11/dist-packages (from osqp>=0.6.2->cvxpy->mase-tools==1.0.0) (0.1.7.post5)\n","Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.11/dist-packages (from pexpect>4.3->ipython->mase-tools==1.0.0) (0.7.0)\n","Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=2.6->wandb->mase-tools==1.0.0) (0.7.0)\n","Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=2.6->wandb->mase-tools==1.0.0) (2.27.2)\n","Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from pydata-sphinx-theme>=0.15.2->sphinx-book-theme->mase-tools==1.0.0) (4.12.3)\n","Requirement already satisfied: Babel in /usr/local/lib/python3.11/dist-packages (from pydata-sphinx-theme>=0.15.2->sphinx-book-theme->mase-tools==1.0.0) (2.16.0)\n","1 location(s) to search for versions of accessible-pygments:\n","* https://pypi.org/simple/accessible-pygments/\n","Fetching project page and analyzing links: https://pypi.org/simple/accessible-pygments/\n","Getting page https://pypi.org/simple/accessible-pygments/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/accessible-pygments/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/accessible-pygments/ HTTP/1.1\" 200 1674\n","Updating cache with response from \"https://pypi.org/simple/accessible-pygments/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/accessible-pygments/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/cf/59/65a3e553fcfbe5bc5d5f861e92256484b56d2a14a633a1413c38dd71f0d5/accessible-pygments-0.0.1.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/a4/00/536a9d9ad7930a582e7320ad80b6ea7179d63d2e42c6e23abccde9b4a1ca/accessible_pygments-0.0.1-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.1\n"," Found link https://files.pythonhosted.org/packages/a2/41/0154c24518c00b98a7ce44493bbddc276ff4db70286b4008fb53cb9d2e69/accessible-pygments-0.0.2.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/87/57/75d37069faa4bcfb0b2d4fc768b84bb2fb2234c64dc9518e1b7ecafe683f/accessible_pygments-0.0.2-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.2\n"," Found link https://files.pythonhosted.org/packages/c1/dc/7b23dca20691e69f5a43b2a4b06ff755f72b8f4e87de9c69c78d4e644c74/accessible-pygments-0.0.3.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.3\n"," Found link https://files.pythonhosted.org/packages/b3/12/8b34b4ecf56bd4d30b100c0eed43ebe30394eeb063998a9b739dc8d46281/accessible_pygments-0.0.3-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.3\n"," Found link https://files.pythonhosted.org/packages/b2/50/7055ebd9b7928eca202768bcf5f8f69d8d69dec1767c956c08f055c5b31e/accessible-pygments-0.0.4.tar.gz (from https://pypi.org/simple/accessible-pygments/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/20/d7/45cfa326d945e411c7e02764206845b05f8f5766aa7ebc812ef3bc4138cd/accessible_pygments-0.0.4-py2.py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/), version: 0.0.4\n"," Found link https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/) (requires-python:>=3.9), version: 0.0.5\n"," Found link 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cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl.metadata HTTP/1.1\" 200 10286\n"," Downloading accessible_pygments-0.0.5-py3-none-any.whl.metadata (10 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl.metadata\"\n"," etag object cached for 1209600 seconds\n"," Caching due to etag\n"," Created temporary directory: /tmp/pip-metadata-9vgirhfk\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (3.4.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (2.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests~=2.32->sphinx-needs>=4->mase-tools==1.0.0) (2024.12.14)\n","Requirement already satisfied: sphinxcontrib-applehelp>=1.0.7 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-devhelp>=1.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-htmlhelp>=2.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.1.0)\n","Requirement already satisfied: sphinxcontrib-jsmath>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (1.0.1)\n","Requirement already satisfied: sphinxcontrib-qthelp>=1.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: sphinxcontrib-serializinghtml>=1.1.9 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.0.0)\n","Requirement already satisfied: snowballstemmer>=2.2 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (2.2.0)\n","Requirement already satisfied: alabaster>=0.7.14 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (1.0.0)\n","Requirement already satisfied: imagesize>=1.3 in /usr/local/lib/python3.11/dist-packages (from sphinx<9,>=7.0->sphinx-needs>=4->mase-tools==1.0.0) (1.4.1)\n","Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.11/dist-packages (from sqlalchemy>=1.4.2->optuna->mase-tools==1.0.0) (3.1.1)\n","Given no hashes to check 26 links for project 'lightning-utilities': discarding no candidates\n","1 location(s) to search for versions of humanfriendly:\n","* https://pypi.org/simple/humanfriendly/\n","Fetching project page and analyzing links: https://pypi.org/simple/humanfriendly/\n","Getting page https://pypi.org/simple/humanfriendly/\n","Found index url https://pypi.org/simple/\n","Looking up \"https://pypi.org/simple/humanfriendly/\" in the cache\n","Request header has \"max_age\" as 0, cache bypassed\n","No cache entry available\n","https://pypi.org:443 \"GET /simple/humanfriendly/ HTTP/1.1\" 200 22666\n","Updating cache with response from \"https://pypi.org/simple/humanfriendly/\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Fetched page https://pypi.org/simple/humanfriendly/ as application/vnd.pypi.simple.v1+json\n"," Found link https://files.pythonhosted.org/packages/ce/b7/86c1e0bf0ef9f0e34f150742b16783e1935d30a6d8efb64402518cf99368/humanfriendly-1.4.1.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.4.1\n"," Found link https://files.pythonhosted.org/packages/d9/40/34a93e935104ac4baee005f77e2f21873ca5fbc855415f632f85c733d69e/humanfriendly-1.4.2.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.4.2\n"," Found link https://files.pythonhosted.org/packages/c1/4e/5c968bec9278553e28e2a51d4077f5eb2c90d10d9f5f7daa62709fbe29b5/humanfriendly-1.5.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.5\n"," Found link https://files.pythonhosted.org/packages/4c/ac/6f47bd3ce955ce6a1fbe7e62805324f944170a89b8dcbc7ef8f72a978fd1/humanfriendly-1.6.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.6\n"," Found link https://files.pythonhosted.org/packages/70/5f/5beafe8e28314539f981efc33fbb460027e4e87f85401f3ed0e2eb87c7f7/humanfriendly-1.7.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.7\n"," Found link https://files.pythonhosted.org/packages/0c/c3/209263bb651f18ab14ff703738aba270b1872577d907a66bd610e7494836/humanfriendly-1.7.1.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.7.1\n"," Found link https://files.pythonhosted.org/packages/07/13/ae63ebeefde99a87034e3d30da076500a3e2206803e451c8190c1d1518f8/humanfriendly-1.8.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.8\n"," Found link https://files.pythonhosted.org/packages/0e/d6/a9fceb66374d0111ac2cc6743b231048815bf896464a3d5f065e16f2435d/humanfriendly-1.8.1.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.8.1\n"," Found link https://files.pythonhosted.org/packages/8d/20/87555eda1ede7bd8721bac94656818d000741c0ef06b85a4015f0ce3af61/humanfriendly-1.8.2.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.8.2\n"," Found link https://files.pythonhosted.org/packages/c2/77/bceaa84fea9c7572e1bb4582dcc1cc4ba1034156b4cfc948886ed51fe42c/humanfriendly-1.8.3.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.8.3\n"," Found link https://files.pythonhosted.org/packages/03/49/5e7114fca8e7f3c62bd910a850761096717b58c3545ff54297ff1a0be84e/humanfriendly-1.8.4.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.8.4\n"," Found link https://files.pythonhosted.org/packages/59/bb/7e7c541711a1c1b86ce155aa9e5f3aabc5ef93622ae8134f964a7baa33fc/humanfriendly-1.8.6.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.8.6\n"," Found link https://files.pythonhosted.org/packages/80/19/08be4a40d49afdb109fbbe6b181583f4d32e006ac363ca64af4581528391/humanfriendly-1.9.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.9\n"," Found link https://files.pythonhosted.org/packages/10/7c/bb2a517589edd3fb24ca5bfa1484e88be5210de18bce1f40273c94d51862/humanfriendly-1.9.1.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.9.1\n"," Found link https://files.pythonhosted.org/packages/d3/e5/05bb49679c078526a58d99476966b87b413a16b0978a0413deca112e2e33/humanfriendly-1.9.2.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.9.2\n"," Found link https://files.pythonhosted.org/packages/9d/2b/f04edd9e6c3802197818702b60e52c9fb4b1f49bd71544c97fb7064032e0/humanfriendly-1.9.3.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.9.3\n"," Found link https://files.pythonhosted.org/packages/d8/62/8b4d0bba161c9806199296ecec265b3c776edfa2b6fc2c7fad69d75feb0a/humanfriendly-1.9.4.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.9.4\n"," Found link https://files.pythonhosted.org/packages/88/4b/1ce6814307a9cb074c533f822028c5dc5687f0f62772bdb7af6a5aa97884/humanfriendly-1.9.6.tar.gz (from https://pypi.org/simple/humanfriendly/), version: 1.9.6\n"," Found link 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https://files.pythonhosted.org/packages/24/ca/f3a75b50d978872f6551d72c9c76890d68c84f3ba210cdba5f409587a2fc/humanfriendly-9.2.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 9.2\n"," Found link https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 10.0\n"," Found link https://files.pythonhosted.org/packages/cc/3f/2c29224acb2e2df4d2046e4c73ee2662023c58ff5b113c4c1adac0886c43/humanfriendly-10.0.tar.gz (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*), version: 10.0\n","Skipping link: not a file: https://pypi.org/simple/humanfriendly/\n","Given no hashes to check 6 links for project 'humanfriendly': discarding no candidates\n","Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime->mase-tools==1.0.0)\n"," Obtaining dependency information for humanfriendly>=9.1 from https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl.metadata\n"," Created temporary directory: /tmp/pip-unpack-q4xh2s6z\n"," Looking up \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl.metadata\" in the cache\n"," No cache entry available\n"," No cache entry available\n"," https://files.pythonhosted.org:443 \"GET /packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl.metadata HTTP/1.1\" 200 9201\n"," Downloading humanfriendly-10.0-py2.py3-none-any.whl.metadata (9.2 kB)\n"," Ignoring unknown cache-control directive: immutable\n"," Updating cache with response from \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl.metadata\"\n"," etag object cached for 1209600 seconds\n"," Caching due to etag\n"," Created temporary directory: /tmp/pip-metadata-1fa07jpi\n","Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib_metadata->myst-nb->mase-tools==1.0.0) (3.21.0)\n","Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.11/dist-packages (from ipykernel->myst-nb->mase-tools==1.0.0) (0.2.0)\n","Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.11/dist-packages (from ipykernel->myst-nb->mase-tools==1.0.0) (6.3.3)\n","Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.11/dist-packages (from jupyter-client>=6.1.12->nbclient->myst-nb->mase-tools==1.0.0) (24.0.1)\n","Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->pydata-sphinx-theme>=0.15.2->sphinx-book-theme->mase-tools==1.0.0) (2.6)\n","Created temporary directory: /tmp/pip-unpack-ojeyakbd\n","Looking up \"https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl HTTP/1.1\" 200 4533541\n","Downloading pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl (4.5 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/4.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from 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available\n","https://files.pythonhosted.org:443 \"GET /packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 41165244\n","Downloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m38.3/41.2 MB\u001b[0m \u001b[31m109.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 MB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/scipy/) (requires-python:>=3.10) to /tmp/pip-unpack-ojeyakbd/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl HTTP/1.1\" 200 71889\n","Downloading bitstring-4.3.0-py3-none-any.whl (71 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/71.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c3/e2/ffb466d2325eba94fcae18df609605b99d454b3855491d7bf1023c473911/bitstring-4.3.0-py3-none-any.whl (from https://pypi.org/simple/bitstring/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/bitstring-4.3.0-py3-none-any.whl\n","Looking up 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available\n","https://files.pythonhosted.org:443 \"GET /packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl HTTP/1.1\" 200 2634328\n","Downloading sphinx_needs-4.2.0-py3-none-any.whl (2.6 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m88.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/b1/ab/ad787682b6ff9437d5d2e01476d005d1d6304e1d99665d9a7dc2bb2bfecd/sphinx_needs-4.2.0-py3-none-any.whl (from https://pypi.org/simple/sphinx-needs/) (requires-python:<4,>=3.9) to /tmp/pip-unpack-ojeyakbd/sphinx_needs-4.2.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl HTTP/1.1\" 200 27162\n","Downloading attr_dot_dict-0.1.0-py3-none-any.whl (27 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/af/ab/3c717181e61cfa97b71e6e01d072725c2d323350b408ab43547d02bc19ad/attr_dot_dict-0.1.0-py3-none-any.whl (from https://pypi.org/simple/attr-dot-dict/) (requires-python:>=3.7) to /tmp/pip-unpack-ojeyakbd/attr_dot_dict-0.1.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c9/9b/2db8045b45844665c720dcfe292fdaf2e49825810c0103e1191515fc101a/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c9/9b/2db8045b45844665c720dcfe292fdaf2e49825810c0103e1191515fc101a/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (from https://pypi.org/simple/black/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/black-24.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl HTTP/1.1\" 200 11424\n","Downloading colorlog-6.9.0-py3-none-any.whl (11 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl (from https://pypi.org/simple/colorlog/) (requires-python:>=3.6) to /tmp/pip-unpack-ojeyakbd/colorlog-6.9.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl HTTP/1.1\" 200 480647\n","Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d7/84/0df6c5981f5fc722381662ff8cfbdf8aad64bec875f75d80b55bfef394ce/datasets-3.2.0-py3-none-any.whl (from https://pypi.org/simple/datasets/) (requires-python:>=3.9.0) to /tmp/pip-unpack-ojeyakbd/datasets-3.2.0-py3-none-any.whl\n","Looking up 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kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/91/db/a0335710caaa6d0aebdaa65ad4df789c15d89b7babd9a30277838a7d9aac/emoji-2.14.1-py3-none-any.whl (from https://pypi.org/simple/emoji/) (requires-python:>=3.7) to /tmp/pip-unpack-ojeyakbd/emoji-2.14.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl HTTP/1.1\" 200 84010\n","Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a2/e7/cbca9e2d2590eb9b5aa8f7ebabe1beb1498f9462d2ecede5c9fd9735faaf/evaluate-0.4.3-py3-none-any.whl (from https://pypi.org/simple/evaluate/) (requires-python:>=3.8.0) to /tmp/pip-unpack-ojeyakbd/evaluate-0.4.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl HTTP/1.1\" 200 11034\n","Downloading ghp_import-2.1.0-py3-none-any.whl (11 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl (from https://pypi.org/simple/ghp-import/) to /tmp/pip-unpack-ojeyakbd/ghp_import-2.1.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl HTTP/1.1\" 200 12130\n","Downloading ipdb-0.13.13-py3-none-any.whl (12 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl (from https://pypi.org/simple/ipdb/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*) to /tmp/pip-unpack-ojeyakbd/ipdb-0.13.13-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl HTTP/1.1\" 200 1078141\n","Downloading kornia-0.8.0-py2.py3-none-any.whl (1.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m37.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/f2/33/7721a4f69dd5f020c30de456d7b948fea8d3897d9f29a51f7538948ee7e2/kornia-0.8.0-py2.py3-none-any.whl (from https://pypi.org/simple/kornia/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/kornia-0.8.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl HTTP/1.1\" 200 815241\n","Downloading lightning-2.5.0.post0-py3-none-any.whl (815 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/815.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m36.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d4/54/2de887e964a561776ac49c5e715ca8a56138ea7e2b95325e0a79aa46a070/lightning-2.5.0.post0-py3-none-any.whl (from https://pypi.org/simple/lightning/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/lightning-2.5.0.post0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl HTTP/1.1\" 200 80281\n","Downloading myst_nb-1.1.2-py3-none-any.whl (80 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/04/45/cf78b2f09c46b36f486b75c34a8b48580e53b543bd9a467b3c7eb9054b70/myst_nb-1.1.2-py3-none-any.whl (from https://pypi.org/simple/myst-nb/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/myst_nb-1.1.2-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl HTTP/1.1\" 200 84563\n","Downloading myst_parser-4.0.0-py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.6/84.6 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/ca/b4/b036f8fdb667587bb37df29dc6644681dd78b7a2a6321a34684b79412b28/myst_parser-4.0.0-py3-none-any.whl (from https://pypi.org/simple/myst-parser/) (requires-python:>=3.10) to /tmp/pip-unpack-ojeyakbd/myst_parser-4.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 16048190\n","Downloading onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.0 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m15.7/16.0 MB\u001b[0m \u001b[31m134.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.0/16.0 MB\u001b[0m \u001b[31m66.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/onnx/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl HTTP/1.1\" 200 84477\n","Downloading onnxconverter_common-1.14.0-py2.py3-none-any.whl (84 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/6d/6a/9ed9fd4da34cb41fda35bc5cc9e990c605689db7de63ed84fedbca5a77f6/onnxconverter_common-1.14.0-py2.py3-none-any.whl (from https://pypi.org/simple/onnxconverter-common/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/onnxconverter_common-1.14.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl HTTP/1.1\" 200 162128\n","Downloading protobuf-3.20.2-py2.py3-none-any.whl (162 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/162.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.1/162.1 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/8b/e6/2a47ce2eba1aaf287380a44270da897ada03d118a55c19595ec7b4f0831f/protobuf-3.20.2-py2.py3-none-any.whl (from https://pypi.org/simple/protobuf/) (requires-python:>=3.7) to /tmp/pip-unpack-ojeyakbd/protobuf-3.20.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl HTTP/1.1\" 200 13331703\n","Downloading onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (13.3 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m9.9/13.3 MB\u001b[0m \u001b[31m94.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m70.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/11/ac/4120dfb74c8e45cce1c664fc7f7ce010edd587ba67ac41489f7432eb9381/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (from https://pypi.org/simple/onnxruntime/) to /tmp/pip-unpack-ojeyakbd/onnxruntime-1.20.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl HTTP/1.1\" 200 424070\n","Downloading optimum-1.23.3-py3-none-any.whl (424 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.1/424.1 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/48/33/97cf226c47e4cf5a79159668732038cdd6c0199c72782d5b5a0db54f9a2d/optimum-1.23.3-py3-none-any.whl (from https://pypi.org/simple/optimum/) (requires-python:>=3.7.0) to /tmp/pip-unpack-ojeyakbd/optimum-1.23.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl HTTP/1.1\" 200 383372\n","Downloading optuna-4.2.0-py3-none-any.whl (383 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/383.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m383.4/383.4 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/40/ff/018341c06188f9d9c0aa49672446d194f4170a0376516df4ec52bab4a7b4/optuna-4.2.0-py3-none-any.whl (from https://pypi.org/simple/optuna/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/optuna-4.2.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/13/2f/0f24b288e2ce56f51c920137620b4434a38fd80583dbbe24fc2a1656c388/pybind11-2.13.6-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/13/2f/0f24b288e2ce56f51c920137620b4434a38fd80583dbbe24fc2a1656c388/pybind11-2.13.6-py3-none-any.whl HTTP/1.1\" 200 243282\n","Downloading pybind11-2.13.6-py3-none-any.whl (243 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/243.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/13/2f/0f24b288e2ce56f51c920137620b4434a38fd80583dbbe24fc2a1656c388/pybind11-2.13.6-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m243.3/243.3 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/13/2f/0f24b288e2ce56f51c920137620b4434a38fd80583dbbe24fc2a1656c388/pybind11-2.13.6-py3-none-any.whl (from https://pypi.org/simple/pybind11/) (requires-python:>=3.7) to /tmp/pip-unpack-ojeyakbd/pybind11-2.13.6-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl HTTP/1.1\" 200 26560\n","Downloading pynvml-12.0.0-py3-none-any.whl (26 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/ed/df/f7cf07a65a96dd11d71f346f9c2863accdd4784da83af7181b067d556cbc/pynvml-12.0.0-py3-none-any.whl (from https://pypi.org/simple/pynvml/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/pynvml-12.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl HTTP/1.1\" 200 22949\n","Downloading pytest_cov-6.0.0-py3-none-any.whl (22 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl (from https://pypi.org/simple/pytest-cov/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/pytest_cov-6.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl HTTP/1.1\" 200 23491\n","Downloading pytest_html-4.1.1-py3-none-any.whl (23 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/c8/c7/c160021cbecd956cc1a6f79e5fe155f7868b2e5b848f1320dad0b3e3122f/pytest_html-4.1.1-py3-none-any.whl (from https://pypi.org/simple/pytest-html/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/pytest_html-4.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e3/ac/c428c66241a144617a8af7a28e2e055e1438d23b949b62ac4b401a69fb79/pytest_profiling-1.8.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl HTTP/1.1\" 200 10171\n","Downloading pytest_sugar-1.0.0-py3-none-any.whl (10 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl (from https://pypi.org/simple/pytest-sugar/) to /tmp/pip-unpack-ojeyakbd/pytest_sugar-1.0.0-py3-none-any.whl\n","Looking up 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\u001b[32m0.0/819.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/02/df/0c7e4582b74264fe2179e78fcdeb9313f680d40ffe1dd4b078da5a2cbf82/pytorch_lightning-2.5.0.post0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m819.3/819.3 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/02/df/0c7e4582b74264fe2179e78fcdeb9313f680d40ffe1dd4b078da5a2cbf82/pytorch_lightning-2.5.0.post0-py3-none-any.whl (from https://pypi.org/simple/pytorch-lightning/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/pytorch_lightning-2.5.0.post0-py3-none-any.whl\n","Looking up 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\u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2b/80/90574e2e82c955b9c6f6b77f7badb2cf2ef4ef77599e4343cced2d098681/sphinx_book_theme-1.1.3-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m430.1/430.1 kB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/2b/80/90574e2e82c955b9c6f6b77f7badb2cf2ef4ef77599e4343cced2d098681/sphinx_book_theme-1.1.3-py3-none-any.whl (from https://pypi.org/simple/sphinx-book-theme/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/sphinx_book_theme-1.1.3-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl HTTP/1.1\" 200 4237451\n","Downloading sphinx_glpi_theme-0.6-py2.py3-none-any.whl (4.2 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/4.2 MB\u001b[0m \u001b[31m89.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m54.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/e3/8b/ed9cf99ebbadf482c9f527d3fde75fbc8f1f7626beb926bca8c892eba8b0/sphinx_glpi_theme-0.6-py2.py3-none-any.whl (from https://pypi.org/simple/sphinx-glpi-theme/) to /tmp/pip-unpack-ojeyakbd/sphinx_glpi_theme-0.6-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/85/77/46e3bac77b82b4df5bb5b61f2de98637724f246b4966cfc34bc5895d852a/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl HTTP/1.1\" 200 7655561\n","Downloading sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl (7.7 MB)\n","\u001b[2K 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/tmp/pip-unpack-ojeyakbd/sphinx_rtd_theme-3.0.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl HTTP/1.1\" 200 101700\n","Downloading tensorboardX-2.6.2.2-py2.py3-none-any.whl (101 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/101.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.7/101.7 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl (from https://pypi.org/simple/tensorboardx/) to /tmp/pip-unpack-ojeyakbd/tensorboardX-2.6.2.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2102024\n","Downloading ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m44.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a6/a1/adcc29a2162364046de02c49e65310894c99fd623c1ca871ec1bff082e25/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/ale-py/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/ale_py-0.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl HTTP/1.1\" 200 233565\n","Downloading alembic-1.14.1-py3-none-any.whl (233 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/233.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.6/233.6 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/54/7e/ac0991d1745f7d755fc1cd381b3990a45b404b4d008fc75e2a983516fbfe/alembic-1.14.1-py3-none-any.whl (from https://pypi.org/simple/alembic/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/alembic-1.14.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 239750\n","Downloading coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/239.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2c/f8/ef009b3b98e9f7033c19deb40d629354aab1d8b2d7f9cfec284dbedf5096/coverage-7.6.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.8/239.8 kB\u001b[0m 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl (from https://pypi.org/simple/dill/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/dill-0.3.8-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl HTTP/1.1\" 200 40612\n","Downloading execnet-2.1.1-py3-none-any.whl (40 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/40.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.6/40.6 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl (from https://pypi.org/simple/execnet/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/execnet-2.1.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl HTTP/1.1\" 200 179253\n","Downloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/179.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/1d/a0/6aaea0c2fbea2f89bfd5db25fb1e3481896a423002ebe4e55288907a97a3/fsspec-2024.9.0-py3-none-any.whl (from https://pypi.org/simple/fsspec/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/fsspec-2024.9.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl HTTP/1.1\" 200 958120\n","Downloading gymnasium-1.0.0-py3-none-any.whl (958 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/958.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m958.1/958.1 kB\u001b[0m \u001b[31m45.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/de/85/f5039ce2df5f0789ff1240f08a59f3e8c92e4c5f99543b7aad7388532f7c/gymnasium-1.0.0-py3-none-any.whl (from https://pypi.org/simple/gymnasium/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/gymnasium-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl HTTP/1.1\" 200 1572278\n","Downloading jedi-0.19.2-py2.py3-none-any.whl (1.6 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m48.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl (from https://pypi.org/simple/jedi/) (requires-python:>=3.6) to /tmp/pip-unpack-ojeyakbd/jedi-0.19.2-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/64/6b/67b87da9d36bff9df7d0efbd1a325fa372a43be7158effaf43ed7b22341d/jupyter_cache-1.0.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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\"https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2050393\n","Downloading kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m48.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/eb/b9/46ffae8b1acfb00d08110440ce7ee00f0a92c0856829b76c0e10be394042/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/kornia-rs/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/kornia_rs-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl HTTP/1.1\" 200 28356\n","Downloading lightning_utilities-0.11.9-py3-none-any.whl (28 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/85/f3/1305321a12c984405e26fc64b5d521569e9872fb811f4aace8e168099160/lightning_utilities-0.11.9-py3-none-any.whl (from https://pypi.org/simple/lightning-utilities/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/lightning_utilities-0.11.9-py3-none-any.whl\n","Looking up 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/50/15/b56e50e8debaf439f44befec5b2af11db85f6e0f344c3113ae0be0593a91/multiprocess-0.70.16-py311-none-any.whl (from https://pypi.org/simple/multiprocess/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/multiprocess-0.70.16-py311-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl HTTP/1.1\" 200 4695\n","Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl (from https://pypi.org/simple/mypy-extensions/) (requires-python:>=3.5) to /tmp/pip-unpack-ojeyakbd/mypy_extensions-1.0.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl HTTP/1.1\" 200 44442\n","Downloading nvidia_ml_py-12.570.86-py3-none-any.whl (44 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d8/a8/ec37169be4e2b7063b9076ed3fe0661e87335fbca665eed3f48c415cb234/nvidia_ml_py-12.570.86-py3-none-any.whl (from https://pypi.org/simple/nvidia-ml-py/) to /tmp/pip-unpack-ojeyakbd/nvidia_ml_py-12.570.86-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl HTTP/1.1\" 200 31191\n","Downloading pathspec-0.12.1-py3-none-any.whl (31 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl (from https://pypi.org/simple/pathspec/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/pathspec-0.12.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl HTTP/1.1\" 200 6723264\n","Downloading pydata_sphinx_theme-0.16.1-py3-none-any.whl (6.7 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/6.7 MB\u001b[0m \u001b[31m107.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/e2/0d/8ba33fa83a7dcde13eb3c1c2a0c1cc29950a048bfed6d9b0d8b6bd710b4c/pydata_sphinx_theme-0.16.1-py3-none-any.whl (from https://pypi.org/simple/pydata-sphinx-theme/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/pydata_sphinx_theme-0.16.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET 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cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl HTTP/1.1\" 200 4244\n","Downloading requests_file-2.1.0-py2.py3-none-any.whl (4.2 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/d7/25/dd878a121fcfdf38f52850f11c512e13ec87c2ea72385933818e5b6c15ce/requests_file-2.1.0-py2.py3-none-any.whl (from https://pypi.org/simple/requests-file/) to /tmp/pip-unpack-ojeyakbd/requests_file-2.1.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl HTTP/1.1\" 200 8215\n","Downloading sphinx_data_viewer-0.1.5-py3-none-any.whl (8.2 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/57/60/5e902d53a5eb3ec7166a8eda602bd78d4b8671a73917567edc8f13a3b366/sphinx_data_viewer-0.1.5-py3-none-any.whl (from https://pypi.org/simple/sphinx-data-viewer/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/sphinx_data_viewer-0.1.5-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl HTTP/1.1\" 200 121104\n","Downloading sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (121 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/121.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.1/121.1 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/76/85/749bd22d1a68db7291c89e2ebca53f4306c3f205853cf31e9de279034c3c/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (from https://pypi.org/simple/sphinxcontrib-jquery/) (requires-python:>=2.7) to /tmp/pip-unpack-ojeyakbd/sphinxcontrib_jquery-4.1-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl HTTP/1.1\" 200 927305\n","Downloading torchmetrics-1.6.1-py3-none-any.whl (927 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/927.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m927.3/927.3 kB\u001b[0m \u001b[31m33.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/9d/e1/84066ff60a20dfa63f4d9d8ddc280d5ed323b7f06504dbb51c523b690116/torchmetrics-1.6.1-py3-none-any.whl (from https://pypi.org/simple/torchmetrics/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/torchmetrics-1.6.1-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 4211516\n","Downloading cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB)\n","\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/2d/49/591bbe8961cfb540b3b17fda8e07b19d7e0203a2e272706c71aba37426c0/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/cocotb/) (requires-python:>=3.6) to /tmp/pip-unpack-ojeyakbd/cocotb-1.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl HTTP/1.1\" 200 46018\n","Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/46.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl (from https://pypi.org/simple/coloredlogs/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*) to /tmp/pip-unpack-ojeyakbd/coloredlogs-15.0.1-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl HTTP/1.1\" 200 8670\n","Downloading find_libpython-0.4.0-py3-none-any.whl (8.7 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/1d/89/6b4624122d5c61a86e8aebcebd377866338b705ce4f115c45b046dc09b99/find_libpython-0.4.0-py3-none-any.whl (from https://pypi.org/simple/find-libpython/) to /tmp/pip-unpack-ojeyakbd/find_libpython-0.4.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl HTTP/1.1\" 200 34763\n","Downloading gprof2dot-2024.6.6-py2.py3-none-any.whl (34 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/ae/27/15c4d20871a86281e2bacde9e9f634225d1c2ed0db072f98acf201022411/gprof2dot-2024.6.6-py2.py3-none-any.whl (from https://pypi.org/simple/gprof2dot/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/gprof2dot-2024.6.6-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl HTTP/1.1\" 200 183933\n","Downloading stable_baselines3-2.5.0-py3-none-any.whl (183 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/183.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.9/183.9 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/81/ce/09ff2f03ba06b1355ca6b21554dbebbca5da4cb8ee6c1eb12e8151ab476b/stable_baselines3-2.5.0-py3-none-any.whl (from https://pypi.org/simple/stable-baselines3/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/stable_baselines3-2.5.0-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 194822\n","Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/d9/72/9256303f10e41ab004799a4aa74b80b3c5977d6383ae4550548b24bd1971/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/xxhash/) (requires-python:>=3.7) to /tmp/pip-unpack-ojeyakbd/xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","Looking up \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl HTTP/1.1\" 200 2511\n","Downloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n","Ignoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","Downloading link https://files.pythonhosted.org/packages/05/2c/ffc08c54c05cdce6fbed2aeebc46348dbe180c6d2c541c7af7ba0aa5f5f8/Farama_Notifications-0.0.4-py3-none-any.whl (from https://pypi.org/simple/farama-notifications/) to /tmp/pip-unpack-ojeyakbd/Farama_Notifications-0.0.4-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl HTTP/1.1\" 200 86794\n","Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (from https://pypi.org/simple/humanfriendly/) (requires-python:>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*) to /tmp/pip-unpack-ojeyakbd/humanfriendly-10.0-py2.py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl HTTP/1.1\" 200 1395903\n","Downloading accessible_pygments-0.0.5-py3-none-any.whl (1.4 MB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m58.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl (from https://pypi.org/simple/accessible-pygments/) (requires-python:>=3.9) to /tmp/pip-unpack-ojeyakbd/accessible_pygments-0.0.5-py3-none-any.whl\n","Looking up \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl\" in the cache\n","No cache entry available\n","No cache entry available\n","https://files.pythonhosted.org:443 \"GET /packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl HTTP/1.1\" 200 78569\n","Downloading Mako-1.3.8-py3-none-any.whl (78 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/78.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0mIgnoring unknown cache-control directive: immutable\n","Updating cache with response from \"https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl\"\n","etag object cached for 1209600 seconds\n","Caching due to etag\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading link https://files.pythonhosted.org/packages/1e/bf/7a6a36ce2e4cafdfb202752be68850e22607fccd692847c45c1ae3c17ba6/Mako-1.3.8-py3-none-any.whl (from https://pypi.org/simple/mako/) (requires-python:>=3.8) to /tmp/pip-unpack-ojeyakbd/Mako-1.3.8-py3-none-any.whl\n","Building wheels for collected packages: sphinx-test-reports, sphinxcontrib-plantuml, cocotb-bus\n"," Created temporary directory: /tmp/pip-wheel-6kzftpqf\n"," Destination directory: /tmp/pip-wheel-6kzftpqf\n"," Running command Building wheel for sphinx-test-reports (pyproject.toml)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/tests\n"," copying tests/test_env.py -> build/lib/tests\n"," copying tests/test_test_file.py -> build/lib/tests\n"," copying tests/test_basic_doc.py -> build/lib/tests\n"," copying tests/test_custom_template.py -> build/lib/tests\n"," copying tests/test_json_parser.py -> build/lib/tests\n"," copying tests/test_junit_parser.py -> build/lib/tests\n"," copying tests/__init__.py -> build/lib/tests\n"," copying tests/test_test_ctest_file.py -> build/lib/tests\n"," copying tests/test_needs_linking.py -> build/lib/tests\n"," copying tests/conftest.py -> build/lib/tests\n"," copying tests/test_custom_types.py -> build/lib/tests\n"," copying tests/test_suites.py -> build/lib/tests\n"," creating build/lib/sphinxcontrib\n"," copying sphinxcontrib/__init__.py -> build/lib/sphinxcontrib\n"," creating build/lib/docs\n"," copying docs/conf.py -> build/lib/docs\n"," creating build/lib/tests/doc_test/env_report_doc_raw\n"," copying tests/doc_test/env_report_doc_raw/conf.py -> build/lib/tests/doc_test/env_report_doc_raw\n"," creating build/lib/tests/doc_test/custom_tr_koi8_template\n"," copying tests/doc_test/custom_tr_koi8_template/conf.py -> build/lib/tests/doc_test/custom_tr_koi8_template\n"," creating build/lib/tests/doc_test/pytest_6_2\n"," copying tests/doc_test/pytest_6_2/conf.py -> build/lib/tests/doc_test/pytest_6_2\n"," creating build/lib/tests/doc_test/custom_tr_template\n"," copying tests/doc_test/custom_tr_template/conf.py -> build/lib/tests/doc_test/custom_tr_template\n"," creating build/lib/tests/doc_test/json_parser_complex\n"," copying tests/doc_test/json_parser_complex/conf.py -> build/lib/tests/doc_test/json_parser_complex\n"," creating build/lib/tests/doc_test/json_parser\n"," copying tests/doc_test/json_parser/conf.py -> build/lib/tests/doc_test/json_parser\n"," creating build/lib/tests/doc_test/custom_types\n"," copying tests/doc_test/custom_types/conf.py -> build/lib/tests/doc_test/custom_types\n"," creating build/lib/tests/doc_test/many_testsuites_doc\n"," copying tests/doc_test/many_testsuites_doc/conf.py -> build/lib/tests/doc_test/many_testsuites_doc\n"," creating build/lib/tests/doc_test/doc_test_file\n"," copying tests/doc_test/doc_test_file/conf.py -> build/lib/tests/doc_test/doc_test_file\n"," creating build/lib/tests/doc_test/needs_linking\n"," copying tests/doc_test/needs_linking/conf.py -> build/lib/tests/doc_test/needs_linking\n"," creating build/lib/tests/doc_test/env_report_doc_default\n"," copying tests/doc_test/env_report_doc_default/conf.py -> build/lib/tests/doc_test/env_report_doc_default\n"," creating build/lib/tests/doc_test/basic_doc\n"," copying tests/doc_test/basic_doc/conf.py -> build/lib/tests/doc_test/basic_doc\n"," creating build/lib/tests/doc_test/testsuites_doc\n"," copying tests/doc_test/testsuites_doc/conf.py -> build/lib/tests/doc_test/testsuites_doc\n"," creating build/lib/tests/doc_test/env_report_doc\n"," copying tests/doc_test/env_report_doc/conf.py -> build/lib/tests/doc_test/env_report_doc\n"," creating build/lib/tests/doc_test/doc_test_ctest_file\n"," copying tests/doc_test/doc_test_ctest_file/conf.py -> build/lib/tests/doc_test/doc_test_ctest_file\n"," creating build/lib/tests/doc_test/custom_tr_utf8_template\n"," copying tests/doc_test/custom_tr_utf8_template/conf.py -> build/lib/tests/doc_test/custom_tr_utf8_template\n"," creating build/lib/tests/doc_test/env_report_warnings\n"," copying tests/doc_test/env_report_warnings/conf.py -> build/lib/tests/doc_test/env_report_warnings\n"," creating build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/exceptions.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/jsonparser.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/junitparser.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/environment.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/__init__.py -> build/lib/sphinxcontrib/test_reports\n"," copying sphinxcontrib/test_reports/test_reports.py -> build/lib/sphinxcontrib/test_reports\n"," creating build/lib/sphinxcontrib/test_reports/functions\n"," copying sphinxcontrib/test_reports/functions/__init__.py -> build/lib/sphinxcontrib/test_reports/functions\n"," creating build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_env.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_case.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_results.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_suite.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/__init__.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_report.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_file.py -> build/lib/sphinxcontrib/test_reports/directives\n"," copying sphinxcontrib/test_reports/directives/test_common.py -> build/lib/sphinxcontrib/test_reports/directives\n"," creating build/lib/docs/ub_theme\n"," copying docs/ub_theme/conf.py -> build/lib/docs/ub_theme\n"," copying docs/ub_theme/__init__.py -> build/lib/docs/ub_theme\n"," running egg_info\n"," writing sphinx_test_reports.egg-info/PKG-INFO\n"," writing dependency_links to sphinx_test_reports.egg-info/dependency_links.txt\n"," writing requirements to sphinx_test_reports.egg-info/requires.txt\n"," writing top-level names to sphinx_test_reports.egg-info/top_level.txt\n"," reading manifest file 'sphinx_test_reports.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," adding license file 'LICENSE'\n"," adding license file 'AUTHORS'\n"," writing manifest file 'sphinx_test_reports.egg-info/SOURCES.txt'\n"," copying sphinxcontrib/test_reports/directives/test_report_template.txt -> build/lib/sphinxcontrib/test_reports/directives\n"," creating build/lib/sphinxcontrib/test_reports/css\n"," copying sphinxcontrib/test_reports/css/common.css -> build/lib/sphinxcontrib/test_reports/css\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," creating build/bdist.linux-x86_64/wheel\n"," creating build/bdist.linux-x86_64/wheel/tests\n"," copying build/lib/tests/test_env.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_test_file.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_basic_doc.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_custom_template.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_json_parser.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_junit_parser.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./tests\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc_raw\n"," copying build/lib/tests/doc_test/env_report_doc_raw/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc_raw\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_koi8_template\n"," copying build/lib/tests/doc_test/custom_tr_koi8_template/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_koi8_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/pytest_6_2\n"," copying build/lib/tests/doc_test/pytest_6_2/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/pytest_6_2\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_template\n"," copying build/lib/tests/doc_test/custom_tr_template/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/json_parser_complex\n"," copying build/lib/tests/doc_test/json_parser_complex/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/json_parser_complex\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/json_parser\n"," copying build/lib/tests/doc_test/json_parser/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/json_parser\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_types\n"," copying build/lib/tests/doc_test/custom_types/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_types\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/many_testsuites_doc\n"," copying build/lib/tests/doc_test/many_testsuites_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/many_testsuites_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/doc_test_file\n"," copying build/lib/tests/doc_test/doc_test_file/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/doc_test_file\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/needs_linking\n"," copying build/lib/tests/doc_test/needs_linking/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/needs_linking\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc_default\n"," copying build/lib/tests/doc_test/env_report_doc_default/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc_default\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/basic_doc\n"," copying build/lib/tests/doc_test/basic_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/basic_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/testsuites_doc\n"," copying build/lib/tests/doc_test/testsuites_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/testsuites_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_doc\n"," copying build/lib/tests/doc_test/env_report_doc/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_doc\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/doc_test_ctest_file\n"," copying build/lib/tests/doc_test/doc_test_ctest_file/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/doc_test_ctest_file\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/custom_tr_utf8_template\n"," copying build/lib/tests/doc_test/custom_tr_utf8_template/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/custom_tr_utf8_template\n"," creating build/bdist.linux-x86_64/wheel/tests/doc_test/env_report_warnings\n"," copying build/lib/tests/doc_test/env_report_warnings/conf.py -> build/bdist.linux-x86_64/wheel/./tests/doc_test/env_report_warnings\n"," copying build/lib/tests/test_test_ctest_file.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_needs_linking.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/conftest.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_custom_types.py -> build/bdist.linux-x86_64/wheel/./tests\n"," copying build/lib/tests/test_suites.py -> build/bdist.linux-x86_64/wheel/./tests\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/exceptions.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/jsonparser.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/junitparser.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/functions\n"," copying build/lib/sphinxcontrib/test_reports/functions/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/functions\n"," copying build/lib/sphinxcontrib/test_reports/environment.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," copying build/lib/sphinxcontrib/test_reports/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_report_template.txt -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_env.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_case.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_results.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_suite.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_report.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_file.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/directives/test_common.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/directives\n"," copying build/lib/sphinxcontrib/test_reports/test_reports.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib/test_reports/css\n"," copying build/lib/sphinxcontrib/test_reports/css/common.css -> build/bdist.linux-x86_64/wheel/./sphinxcontrib/test_reports/css\n"," copying build/lib/sphinxcontrib/__init__.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib\n"," creating build/bdist.linux-x86_64/wheel/docs\n"," copying build/lib/docs/conf.py -> build/bdist.linux-x86_64/wheel/./docs\n"," creating build/bdist.linux-x86_64/wheel/docs/ub_theme\n"," copying build/lib/docs/ub_theme/conf.py -> build/bdist.linux-x86_64/wheel/./docs/ub_theme\n"," copying build/lib/docs/ub_theme/__init__.py -> build/bdist.linux-x86_64/wheel/./docs/ub_theme\n"," running install_egg_info\n"," Copying sphinx_test_reports.egg-info to build/bdist.linux-x86_64/wheel/./sphinx_test_reports-1.1.0-py3.11.egg-info\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/sphinx_test_reports-1.1.0.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-6kzftpqf/.tmp-sj92pq6c/sphinx_test_reports-1.1.0-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'docs/conf.py'\n"," adding 'docs/ub_theme/__init__.py'\n"," adding 'docs/ub_theme/conf.py'\n"," adding 'sphinxcontrib/__init__.py'\n"," adding 'sphinxcontrib/test_reports/__init__.py'\n"," adding 'sphinxcontrib/test_reports/environment.py'\n"," adding 'sphinxcontrib/test_reports/exceptions.py'\n"," adding 'sphinxcontrib/test_reports/jsonparser.py'\n"," adding 'sphinxcontrib/test_reports/junitparser.py'\n"," adding 'sphinxcontrib/test_reports/test_reports.py'\n"," adding 'sphinxcontrib/test_reports/css/common.css'\n"," adding 'sphinxcontrib/test_reports/directives/__init__.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_case.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_common.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_env.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_file.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_report.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_report_template.txt'\n"," adding 'sphinxcontrib/test_reports/directives/test_results.py'\n"," adding 'sphinxcontrib/test_reports/directives/test_suite.py'\n"," adding 'sphinxcontrib/test_reports/functions/__init__.py'\n"," adding 'tests/__init__.py'\n"," adding 'tests/conftest.py'\n"," adding 'tests/test_basic_doc.py'\n"," adding 'tests/test_custom_template.py'\n"," adding 'tests/test_custom_types.py'\n"," adding 'tests/test_env.py'\n"," adding 'tests/test_json_parser.py'\n"," adding 'tests/test_junit_parser.py'\n"," adding 'tests/test_needs_linking.py'\n"," adding 'tests/test_suites.py'\n"," adding 'tests/test_test_ctest_file.py'\n"," adding 'tests/test_test_file.py'\n"," adding 'tests/doc_test/basic_doc/conf.py'\n"," adding 'tests/doc_test/custom_tr_koi8_template/conf.py'\n"," adding 'tests/doc_test/custom_tr_template/conf.py'\n"," adding 'tests/doc_test/custom_tr_utf8_template/conf.py'\n"," adding 'tests/doc_test/custom_types/conf.py'\n"," adding 'tests/doc_test/doc_test_ctest_file/conf.py'\n"," adding 'tests/doc_test/doc_test_file/conf.py'\n"," adding 'tests/doc_test/env_report_doc/conf.py'\n"," adding 'tests/doc_test/env_report_doc_default/conf.py'\n"," adding 'tests/doc_test/env_report_doc_raw/conf.py'\n"," adding 'tests/doc_test/env_report_warnings/conf.py'\n"," adding 'tests/doc_test/json_parser/conf.py'\n"," adding 'tests/doc_test/json_parser_complex/conf.py'\n"," adding 'tests/doc_test/many_testsuites_doc/conf.py'\n"," adding 'tests/doc_test/needs_linking/conf.py'\n"," adding 'tests/doc_test/pytest_6_2/conf.py'\n"," adding 'tests/doc_test/testsuites_doc/conf.py'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/AUTHORS'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/LICENSE'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/METADATA'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/WHEEL'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/top_level.txt'\n"," adding 'sphinx_test_reports-1.1.0.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for sphinx-test-reports (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sphinx-test-reports: filename=sphinx_test_reports-1.1.0-py3-none-any.whl size=72216 sha256=436a062eae5d50eb8ee5fa2389cf36b6d03b24106a38c80d9cbd0cad5e12fd2e\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-28x49y4j/wheels/03/57/7d/6598421a14084c6221fe9832bbec1c5adc68218c00d43af21c\n"," Created temporary directory: /tmp/pip-wheel-kqy0g4hi\n"," Destination directory: /tmp/pip-wheel-kqy0g4hi\n"," Running command Building wheel for sphinxcontrib-plantuml (pyproject.toml)\n"," /tmp/pip-build-env-p8hybwwb/overlay/local/lib/python3.11/dist-packages/setuptools/dist.py:701: SetuptoolsDeprecationWarning: The namespace_packages parameter is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please replace its usage with implicit namespaces (PEP 420).\n","\n"," See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages for details.\n"," ********************************************************************************\n","\n"," !!\n"," ep.load()(self, ep.name, value)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/sphinxcontrib\n"," copying sphinxcontrib/__init__.py -> build/lib/sphinxcontrib\n"," copying sphinxcontrib/plantuml.py -> build/lib/sphinxcontrib\n"," running egg_info\n"," writing sphinxcontrib_plantuml.egg-info/PKG-INFO\n"," writing dependency_links to sphinxcontrib_plantuml.egg-info/dependency_links.txt\n"," writing namespace_packages to sphinxcontrib_plantuml.egg-info/namespace_packages.txt\n"," writing requirements to sphinxcontrib_plantuml.egg-info/requires.txt\n"," writing top-level names to sphinxcontrib_plantuml.egg-info/top_level.txt\n"," reading manifest file 'sphinxcontrib_plantuml.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'README'\n"," warning: no files found matching 'CHANGES.*'\n"," adding license file 'LICENSE'\n"," writing manifest file 'sphinxcontrib_plantuml.egg-info/SOURCES.txt'\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," Skipping installation of build/bdist.linux-x86_64/wheel/./sphinxcontrib/__init__.py (namespace package)\n"," copying sphinxcontrib/plantuml.py -> build/bdist.linux-x86_64/wheel/./sphinxcontrib\n"," running install_egg_info\n"," Copying sphinxcontrib_plantuml.egg-info to build/bdist.linux-x86_64/wheel/./sphinxcontrib_plantuml-0.30-py3.11.egg-info\n"," Installing build/bdist.linux-x86_64/wheel/./sphinxcontrib_plantuml-0.30-py3.11-nspkg.pth\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/sphinxcontrib_plantuml-0.30.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-kqy0g4hi/.tmp-tjsng0rv/sphinxcontrib_plantuml-0.30-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'sphinxcontrib_plantuml-0.30-py3.11-nspkg.pth'\n"," adding 'sphinxcontrib/plantuml.py'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/LICENSE'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/METADATA'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/WHEEL'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/namespace_packages.txt'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/top_level.txt'\n"," adding 'sphinxcontrib_plantuml-0.30.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for sphinxcontrib-plantuml (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sphinxcontrib-plantuml: filename=sphinxcontrib_plantuml-0.30-py3-none-any.whl size=11594 sha256=d5453c1cfef7106d34008f60621e1b71e30967958c88fa9f91e5b2c50c3f3cf5\n"," Stored in directory: /root/.cache/pip/wheels/ba/14/11/6573783b9c1e0a196a172550621e2d39a6cabb19b0ade3e4f5\n"," Created temporary directory: /tmp/pip-wheel-ckfu1c9t\n"," Destination directory: /tmp/pip-wheel-ckfu1c9t\n"," Running command Building wheel for cocotb-bus (pyproject.toml)\n"," running bdist_wheel\n"," running build\n"," running build_py\n"," creating build/lib/cocotb_bus\n"," copying src/cocotb_bus/scoreboard.py -> build/lib/cocotb_bus\n"," copying src/cocotb_bus/bus.py -> build/lib/cocotb_bus\n"," copying src/cocotb_bus/_version.py -> build/lib/cocotb_bus\n"," copying src/cocotb_bus/__init__.py -> build/lib/cocotb_bus\n"," creating build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/__init__.py -> build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/avalon.py -> build/lib/cocotb_bus/monitors\n"," copying src/cocotb_bus/monitors/xgmii.py -> build/lib/cocotb_bus/monitors\n"," creating build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/opb.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/__init__.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/amba.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/avalon.py -> build/lib/cocotb_bus/drivers\n"," copying src/cocotb_bus/drivers/xgmii.py -> build/lib/cocotb_bus/drivers\n"," installing to build/bdist.linux-x86_64/wheel\n"," running install\n"," running install_lib\n"," creating build/bdist.linux-x86_64/wheel\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus\n"," copying build/lib/cocotb_bus/scoreboard.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/__init__.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/avalon.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/monitors/xgmii.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/monitors\n"," copying build/lib/cocotb_bus/bus.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," copying build/lib/cocotb_bus/_version.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," copying build/lib/cocotb_bus/__init__.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/opb.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/__init__.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/amba.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/avalon.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," copying build/lib/cocotb_bus/drivers/xgmii.py -> build/bdist.linux-x86_64/wheel/./cocotb_bus/drivers\n"," running install_egg_info\n"," running egg_info\n"," writing src/cocotb_bus.egg-info/PKG-INFO\n"," writing dependency_links to src/cocotb_bus.egg-info/dependency_links.txt\n"," writing requirements to src/cocotb_bus.egg-info/requires.txt\n"," writing top-level names to src/cocotb_bus.egg-info/top_level.txt\n"," reading manifest file 'src/cocotb_bus.egg-info/SOURCES.txt'\n"," adding license file 'LICENSE'\n"," writing manifest file 'src/cocotb_bus.egg-info/SOURCES.txt'\n"," Copying src/cocotb_bus.egg-info to build/bdist.linux-x86_64/wheel/./cocotb_bus-0.2.1-py3.11.egg-info\n"," running install_scripts\n"," creating build/bdist.linux-x86_64/wheel/cocotb_bus-0.2.1.dist-info/WHEEL\n"," creating '/tmp/pip-wheel-ckfu1c9t/.tmp-4dmfuvwe/cocotb_bus-0.2.1-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n"," adding 'cocotb_bus/__init__.py'\n"," adding 'cocotb_bus/_version.py'\n"," adding 'cocotb_bus/bus.py'\n"," adding 'cocotb_bus/scoreboard.py'\n"," adding 'cocotb_bus/drivers/__init__.py'\n"," adding 'cocotb_bus/drivers/amba.py'\n"," adding 'cocotb_bus/drivers/avalon.py'\n"," adding 'cocotb_bus/drivers/opb.py'\n"," adding 'cocotb_bus/drivers/xgmii.py'\n"," adding 'cocotb_bus/monitors/__init__.py'\n"," adding 'cocotb_bus/monitors/avalon.py'\n"," adding 'cocotb_bus/monitors/xgmii.py'\n"," adding 'cocotb_bus-0.2.1.dist-info/LICENSE'\n"," adding 'cocotb_bus-0.2.1.dist-info/METADATA'\n"," adding 'cocotb_bus-0.2.1.dist-info/WHEEL'\n"," adding 'cocotb_bus-0.2.1.dist-info/top_level.txt'\n"," adding 'cocotb_bus-0.2.1.dist-info/RECORD'\n"," removing build/bdist.linux-x86_64/wheel\n"," Building wheel for cocotb-bus (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for cocotb-bus: filename=cocotb_bus-0.2.1-py3-none-any.whl size=34915 sha256=21c842a34a3891d78eeaf279620d583904bab10db20fcbd7593776a862aff32b\n"," Stored in directory: /root/.cache/pip/wheels/f3/40/c4/456859501d450830880eb944793719ae31e972bdb1ef60f64d\n","Successfully built sphinx-test-reports sphinxcontrib-plantuml cocotb-bus\n","Installing collected packages: sphinx-glpi-theme, nvidia-ml-py, find-libpython, farama-notifications, bitarray, xxhash, scipy, pytorch-nlp, pynvml, pybind11, protobuf, Pillow, pathspec, mypy-extensions, Mako, lightning-utilities, kornia_rs, jedi, humanfriendly, gymnasium, gprof2dot, fsspec, execnet, emoji, dill, coverage, colorlog, cocotb, bitstring, attr-dot-dict, ale-py, accessible-pygments, tensorboardx, requests-file, pytest-xdist, pytest-sugar, pytest-profiling, pytest-metadata, onnx, multiprocess, ghp-import, coloredlogs, cocotb-bus, black, alembic, sphinxcontrib-plantuml, sphinxcontrib-jquery, sphinx-data-viewer, pytest-html, pytest-cov, pydata-sphinx-theme, optuna, onnxruntime, onnxconverter-common, myst_parser, ipdb, torchmetrics, stable-baselines3, sphinx-rtd-theme, sphinx-needs, sphinx-book-theme, kornia, datasets, sphinx-test-reports, pytorch-lightning, optimum, evaluate, lightning, jupyter-cache, myst-nb, mase-tools\n","\n","\n","\n"," changing mode of /usr/local/bin/find_libpython to 755\n","\n","\n","\n"," Attempting uninstall: scipy\n"," Found existing installation: scipy 1.13.1\n"," Uninstalling scipy-1.13.1:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy-1.13.1.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy-1.13.1.dist-info/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy.libs\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy.libs/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~cipy\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/scipy/\n"," Successfully uninstalled scipy-1.13.1\n","\n","\n","\n","\n"," changing mode of /usr/local/bin/pybind11-config to 755\n"," Attempting uninstall: protobuf\n"," Found existing installation: protobuf 4.25.5\n"," Uninstalling protobuf-4.25.5:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/google/~upb\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/google/_upb/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/google/~rotobuf\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/google/protobuf/\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~rotobuf-4.25.5.dist-info\n"," Removing file or directory /usr/local/lib/python3.11/dist-packages/protobuf-4.25.5.dist-info/\n"," Successfully uninstalled protobuf-4.25.5\n","\n"," Attempting uninstall: Pillow\n"," Found existing installation: pillow 11.1.0\n"," Uninstalling pillow-11.1.0:\n"," Created temporary directory: /usr/local/lib/python3.11/dist-packages/~IL\n"," Removing file or directory 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pypa/build, pypa/installer or other\n"," standards-based tools.\n","\n"," See https://github.com/pypa/setuptools/issues/917 for details.\n"," ********************************************************************************\n","\n"," !!\n"," easy_install.initialize_options(self)\n"," /usr/local/lib/python3.11/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n"," !!\n","\n"," ********************************************************************************\n"," Please avoid running ``setup.py`` directly.\n"," Instead, use pypa/build, pypa/installer or other\n"," standards-based tools.\n","\n"," See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n"," ********************************************************************************\n","\n"," !!\n"," self.initialize_options()\n"," running egg_info\n"," creating src/mase_tools.egg-info\n"," writing src/mase_tools.egg-info/PKG-INFO\n"," writing dependency_links to src/mase_tools.egg-info/dependency_links.txt\n"," writing requirements to src/mase_tools.egg-info/requires.txt\n"," writing top-level names to src/mase_tools.egg-info/top_level.txt\n"," writing manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," reading manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," adding license file 'LICENSE'\n"," writing manifest file 'src/mase_tools.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.11/dist-packages/mase-tools.egg-link (link to src)\n"," Adding mase-tools 1.0.0 to easy-install.pth file\n","\n"," Installed /content/mase/src\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n","gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.14.1 which is incompatible.\n","tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.2 which is incompatible.\n","grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.2 which is incompatible.\n","tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed Mako-1.3.8 Pillow-10.4.0 accessible-pygments-0.0.5 ale-py-0.10.1 alembic-1.14.1 attr-dot-dict-0.1.0 bitarray-3.0.0 bitstring-4.3.0 black-24.10.0 cocotb-1.9.2 cocotb-bus-0.2.1 coloredlogs-15.0.1 colorlog-6.9.0 coverage-7.6.10 datasets-3.2.0 dill-0.3.8 emoji-2.14.1 evaluate-0.4.3 execnet-2.1.1 farama-notifications-0.0.4 find-libpython-0.4.0 fsspec-2024.9.0 ghp-import-2.1.0 gprof2dot-2024.6.6 gymnasium-1.0.0 humanfriendly-10.0 ipdb-0.13.13 jedi-0.19.2 jupyter-cache-1.0.1 kornia-0.8.0 kornia_rs-0.1.8 lightning-2.5.0.post0 lightning-utilities-0.11.9 mase-tools-1.0.0 multiprocess-0.70.16 mypy-extensions-1.0.0 myst-nb-1.1.2 myst_parser-4.0.0 nvidia-ml-py-12.570.86 onnx-1.17.0 onnxconverter-common-1.14.0 onnxruntime-1.20.1 optimum-1.23.3 optuna-4.2.0 pathspec-0.12.1 protobuf-3.20.2 pybind11-2.13.6 pydata-sphinx-theme-0.16.1 pynvml-12.0.0 pytest-cov-6.0.0 pytest-html-4.1.1 pytest-metadata-3.1.1 pytest-profiling-1.8.1 pytest-sugar-1.0.0 pytest-xdist-3.6.1 pytorch-lightning-2.5.0.post0 pytorch-nlp-0.5.0 requests-file-2.1.0 scipy-1.14.1 sphinx-book-theme-1.1.3 sphinx-data-viewer-0.1.5 sphinx-glpi-theme-0.6 sphinx-needs-4.2.0 sphinx-rtd-theme-3.0.2 sphinx-test-reports-1.1.0 sphinxcontrib-jquery-4.1 sphinxcontrib-plantuml-0.30 stable-baselines3-2.5.0 tensorboardx-2.6.2.2 torchmetrics-1.6.1 xxhash-3.5.0\n","Removed build tracker: '/tmp/pip-build-tracker-ixxnp6zv'\n","/content/mase/src\n"]}]},{"cell_type":"markdown","metadata":{"id":"d2ulScxVoyFe"},"source":["In this tutorial, we'll build on top of Tutorial 2 by taking the Bert model fine tuned for sequence classification and running Mase's quantization pass. First, we'll run simple Post-Training Quantization (PTQ) and see how much accuracy drops. Then, we'll run some further training iterations of the quantized model (i.e. QAT) and see whether the accuracy of the trained quantized model approaches the accuracy of the original (full-precision) model."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7_oSe0y2oyFf"},"outputs":[],"source":["checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\""]},{"cell_type":"markdown","metadata":{"id":"4RABoJxeoyFg"},"source":["## Importing the model"]},{"cell_type":"markdown","metadata":{"id":"k6KFmYnfoyFg"},"source":["If you are starting from scratch, you can create a MaseGraph for Bert by running the following cell."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Ddnzd8eoyFg","executionInfo":{"status":"ok","timestamp":1738005607255,"user_tz":0,"elapsed":2044,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"2eb697fe-f1de-4586-803e-54e7a98a5ebf"},"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 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1.5897e+00, 1.3689e-01, -6.8915e-01, ..., -7.9549e-01,\n"," -6.9279e-01, -1.8082e-01],\n"," [-9.9201e-01, 9.4938e-01, 4.4198e-02, ..., 1.5973e+00,\n"," 1.1907e+00, -9.0454e-01]],\n","\n"," [[-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -1.8905e+00,\n"," 1.6226e-01, -1.2953e+00],\n"," [-7.0312e-01, -6.4926e-01, -5.0913e-01, ..., -5.3627e-01,\n"," -1.0456e+00, -1.6301e+00]],\n","\n"," ...,\n","\n"," [[-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.9281e+00,\n"," 1.1489e+00, -2.4530e-01],\n"," [-7.6226e-02, 8.5814e-01, -1.5467e+00, ..., -1.3217e+00,\n"," -1.3415e+00, -3.8328e-01]],\n","\n"," [[ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -2.4168e+00,\n"," 3.3385e-01, -6.2115e-02],\n"," [-1.6390e+00, -1.6085e-01, -1.9118e+00, ..., -1.6600e-01,\n"," -6.5011e-01, 2.1798e-02]],\n","\n"," [[-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., -1.0481e+00,\n"," -2.7634e+00, 2.2741e-01],\n"," [-1.4603e+00, 3.1239e-02, 3.8892e-01, ..., 1.3443e-03,\n"," -1.6754e+00, 3.1771e-01]]],\n","\n","\n"," [[[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., -7.8511e-01,\n"," -3.2510e-01, -2.1300e-01],\n"," [-3.9893e-01, 6.1469e-01, -3.9206e-01, ..., 6.4928e-01,\n"," -3.2944e-01, -2.4185e+00]],\n","\n"," [[ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.6488e+00,\n"," -8.6854e-01, -8.0783e-01],\n"," [-2.1516e+00, -2.4247e-01, -8.1713e-01, ..., -1.8623e+00,\n"," -1.1230e+00, 3.5013e-01]],\n","\n"," [[ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., -1.3121e+00,\n"," -6.7044e-01, -1.1324e+00],\n"," [ 4.3930e-01, 3.4082e-01, -1.2243e+00, ..., 6.4975e-01,\n"," -7.3862e-01, -2.1510e+00]],\n","\n"," ...,\n","\n"," [[ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 8.5980e-01,\n"," 3.2796e-01, -1.9442e+00],\n"," [-8.9502e-01, 9.7357e-01, 8.5447e-01, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00]],\n","\n"," [[ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., -1.5965e+00,\n"," -3.9380e-01, -4.3585e-01],\n"," [-2.2103e+00, 4.4127e-01, 1.1554e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00]],\n","\n"," [[ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01],\n"," ...,\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[-0.0073, -0.0693],\n"," [ 0.0201, -0.0920]], grad_fn=)\n","tensor([1, 0])\n"]}],"source":["from transformers import AutoModelForSequenceClassification\n","\n","from chop import MaseGraph\n","import chop.passes as passes\n","\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","model.config.problem_type = \"single_label_classification\"\n","\n","mg = MaseGraph(\n"," model,\n"," hf_input_names=[\n"," \"input_ids\",\n"," \"attention_mask\",\n"," \"labels\",\n"," ],\n",")\n","\n","mg, _ = passes.init_metadata_analysis_pass(mg)\n","mg, _ = passes.add_common_metadata_analysis_pass(mg)"]},{"cell_type":"markdown","metadata":{"id":"lQp3fcnEoyFi"},"source":["If you have previously ran the tutorial on LoRA Finetuning, run the following cell to import the fine tuned checkpoint."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hyjsuG_doyFi","executionInfo":{"status":"ok","timestamp":1738005610508,"user_tz":0,"elapsed":345,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"c6516faa-c010-4858-9801-f39a2eb337d7"},"outputs":[{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[33mWARNING \u001b[0m \u001b[34mNode finfo not found in loaded metadata.\u001b[0m\n","\u001b[33mWARNING \u001b[0m \u001b[34mNode getattr_2 not found in loaded metadata.\u001b[0m\n"]}],"source":["from pathlib import Path\n","from chop import MaseGraph\n","\n","mg = MaseGraph.from_checkpoint(f\"{Path.home()}/tutorial_2_lora\")"]},{"cell_type":"markdown","metadata":{"id":"SLeBypK9oyFi"},"source":["## Post-Training Quantization (PTQ)"]},{"cell_type":"markdown","metadata":{"id":"aLedZyYcoyFj"},"source":["Here, we simply quantize the model and evaluate the effect in its accuracy. First, let's evaluate the model accuracy before quantization (if you're coming from Tutorial 2, this should be the same as the post-LoRA evaluation accuracy). As seen in Tutorial 2, we can use the `get_tokenized_dataset` and `get_trainer` utilities to generate a HuggingFace `Trainer` instance for training and evaluation."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EAzry8eEoyFj"},"outputs":[],"source":["from chop.tools import get_tokenized_dataset, get_trainer\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n",")\n","\n","# Evaluate accuracy\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"jHLdPea8oyFj"},"source":["To run the quantization pass, we pass a quantization configuration dictionary as argument. This defines the quantization mode, numerical format and precision for each operator in the graph. We'll run the quantization in \"by type\" mode, meaning nodes are quantized according to their `mase_op`. Other modes include by name and by regex name. We'll quantize all activations, weights and biases in the model to fixed-point with the same precision. This may be sub-optimal, but works as an example. In future tutorials, we'll see how to run the `search` flow in `Mase` to find optimal quantization configurations to minimize accuracy loss."]},{"cell_type":"code","source":["import chop.passes as passes\n","\n","# Linear configs were manually changed per combination for the tutorial task\n","quantization_config = {\n"," \"by\": \"type\",\n"," \"default\": {\n"," \"config\": {\n"," \"name\": None,\n"," }\n"," },\n"," \"linear\": {\n"," \"config\": {\n"," \"name\": \"integer\",\n"," # data\n"," \"data_in_width\": 16,\n"," \"data_in_frac_width\": 8,\n"," # weight\n"," \"weight_width\": 16,\n"," \"weight_frac_width\": 8,\n"," # bias\n"," \"bias_width\": 16,\n"," \"bias_frac_width\": 8,\n"," }\n"," },\n","}\n","\n","mg, _ = passes.quantize_transform_pass(\n"," mg,\n"," pass_args=quantization_config,\n",")"],"metadata":{"id":"sQhebzbZMzMA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wEhQH6L8oyFj"},"source":["Let's evaluate the immediate effect of quantization on the model accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ciA2h0CXoyFk"},"outputs":[],"source":["trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n",")\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"xggbMpEHoyFk"},"source":["We can save the current checkpoint for future reference (optional)."]},{"cell_type":"code","source":["from pathlib import Path\n","\n","mg.export(f\"{Path.home()}/tutorial_3_ptq\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oJSKonvb_WUd","executionInfo":{"status":"ok","timestamp":1738005465491,"user_tz":0,"elapsed":4,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"9c4606ac-5f00-47e8-fda3-5b9115d09e59"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /root/tutorial_3_ptq.pt, /root/tutorial_3_ptq.mz\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /root/tutorial_3_ptq.pt\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /root/tutorial_3_ptq.mz\u001b[0m\n"]}]},{"cell_type":"markdown","metadata":{"id":"W4g3pDtloyFk"},"source":["## Quantization-Aware Training (QAT)"]},{"cell_type":"markdown","metadata":{"id":"5dDCZKmOoyFk"},"source":["You should have seen in the last section that quantization can lead to a significant drop in accuracy. Next, we'll run QAT to evaluate whether this performance gap can be reduced. To run QAT in Mase, all you need to do is include the model back in your training loop after running the quantization pass."]},{"cell_type":"code","source":["# Evaluate accuracy\n","trainer.train()\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":301},"id":"JmfCzyds_Ua9","executionInfo":{"status":"ok","timestamp":1738005581785,"user_tz":0,"elapsed":116297,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"7dec8884-c259-4dd4-c2cc-3f4be69affce"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:28]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Evaluation accuracy: 0.84468\n"]}]},{"cell_type":"markdown","source":["# Evaluate accuracy\n","trainer.train()\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"],"metadata":{"id":"mDnaw6Zi_R5R"}},{"cell_type":"markdown","metadata":{"id":"gk2_zepUoyFk"},"source":["We can see the accuracy of the quantized model can match (or sometimes exceed) the full precision model, with a much lower memory requirement to store the weights. Finally, save the final checkpoint for future tutorials."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JXR1MjHBoyFk","outputId":"842a02cf-887c-44ca-e1ec-4a1eb9613387","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1738005582149,"user_tz":0,"elapsed":368,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /root/tutorial_3_qat.pt, /root/tutorial_3_qat.mz\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /root/tutorial_3_qat.pt\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /root/tutorial_3_qat.mz\u001b[0m\n"]}],"source":["from pathlib import Path\n","\n","mg.export(f\"{Path.home()}/tutorial_3_qat\")"]},{"cell_type":"code","source":["### Plotting function for Lab 1, Part 1\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.ticker as mticker\n","\n","# (Full-Precision) First test is full precision model,\n","# (PTQ) Second test is quantised model accuracy evaluation (no retraining),\n","# (QAT) Third test is quantised model accuracy evaluation (with retraining)\n","\n","# The results were gathered manually and inputted below. Later test were done more programmatically\n","results = [\n"," {\n"," \"test_id\": 1,\n"," \"data_in_width\": 4,\n"," \"data_in_frac_width\": 2,\n"," \"weight_width\": 4,\n"," \"weight_frac_width\": 2,\n"," \"bias_width\": 4,\n"," \"bias_frac_width\": 2,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.5,\n"," \"qat_accuracy\": 0.5,\n"," },\n"," {\n"," \"test_id\": 2,\n"," \"data_in_width\": 8,\n"," \"data_in_frac_width\": 4,\n"," \"weight_width\": 8,\n"," \"weight_frac_width\": 4,\n"," \"bias_width\": 8,\n"," \"bias_frac_width\": 4,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.78388,\n"," \"qat_accuracy\": 0.84076,\n"," },\n"," {\n"," \"test_id\": 5,\n"," \"data_in_width\": 12,\n"," \"data_in_frac_width\": 8,\n"," \"weight_width\": 12,\n"," \"weight_frac_width\": 8,\n"," \"bias_width\": 12,\n"," \"bias_frac_width\": 8,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.83704,\n"," \"qat_accuracy\": 0.84412,\n"," },\n"," {\n"," \"test_id\": 6,\n"," \"data_in_width\": 16,\n"," \"data_in_frac_width\": 8,\n"," \"weight_width\": 16,\n"," \"weight_frac_width\": 8,\n"," \"bias_width\": 16,\n"," \"bias_frac_width\": 8,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.83796,\n"," \"qat_accuracy\": 0.84468,\n"," },\n"," {\n"," \"test_id\": 6,\n"," \"data_in_width\": 20,\n"," \"data_in_frac_width\": 10,\n"," \"weight_width\": 20,\n"," \"weight_frac_width\": 10,\n"," \"bias_width\": 20,\n"," \"bias_frac_width\": 10,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.83776,\n"," \"qat_accuracy\": 0.845,\n"," },\n"," {\n"," \"test_id\": 6,\n"," \"data_in_width\": 24,\n"," \"data_in_frac_width\": 12,\n"," \"weight_width\": 24,\n"," \"weight_frac_width\": 12,\n"," \"bias_width\": 24,\n"," \"bias_frac_width\": 12,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.83732,\n"," \"qat_accuracy\": 0.84488,\n"," },\n"," {\n"," \"test_id\": 7,\n"," \"data_in_width\": 28,\n"," \"data_in_frac_width\": 14,\n"," \"weight_width\": 28,\n"," \"weight_frac_width\": 14,\n"," \"bias_width\": 28,\n"," \"bias_frac_width\": 14,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.83736,\n"," \"qat_accuracy\": 0.84504,\n"," },\n"," {\n"," \"test_id\": 7,\n"," \"data_in_width\": 32,\n"," \"data_in_frac_width\": 16,\n"," \"weight_width\": 32,\n"," \"weight_frac_width\": 16,\n"," \"bias_width\": 32,\n"," \"bias_frac_width\": 16,\n"," \"full_precision_accuracy\": 0.83732,\n"," \"ptq_accuracy\": 0.83736,\n"," \"qat_accuracy\": 0.84492,\n"," }]\n","\n","def plot_results_separated(results):\n"," fixed_point_widths = [4, 8, 12, 16, 20, 24, 28, 32]\n"," ptq_accuracies = [res[\"ptq_accuracy\"] for res in results if res[\"ptq_accuracy\"] is not None]\n"," qat_accuracies = [res[\"qat_accuracy\"] for res in results if res[\"qat_accuracy\"] is not None]\n"," highest_accuracies = [max(ptq_accuracies[i], qat_accuracies[i]) for i in range(len(ptq_accuracies))]\n","\n"," # Plot 1: PTQ vs QAT\n"," plt.figure(figsize=(10, 6))\n"," plt.plot(\n"," fixed_point_widths[:len(ptq_accuracies)],\n"," ptq_accuracies,\n"," marker=\"s\",\n"," label=\"PTQ Accuracy\",\n"," linestyle=\"-.\",\n"," linewidth=2,\n"," alpha=0.8,\n"," )\n"," plt.plot(\n"," fixed_point_widths[:len(qat_accuracies)],\n"," qat_accuracies,\n"," marker=\"d\",\n"," label=\"QAT Accuracy\",\n"," linestyle=\":\",\n"," linewidth=2,\n"," alpha=0.8,\n"," )\n"," # Add labels, grid, and legend\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Fixed Point Width\", fontsize=12)\n"," plt.ylabel(\"Accuracy\", fontsize=12)\n"," plt.title(\"PTQ vs QAT Accuracy by Fixed Point Width\", fontsize=14, pad=15)\n"," plt.legend(fontsize=10, loc=\"lower right\")\n","\n"," # Format y-axis to show percentages\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.0f}%\"))\n","\n"," # Add value labels on the points\n"," for x, y in zip(fixed_point_widths[:len(ptq_accuracies)], ptq_accuracies):\n"," plt.text(x, y + 0.002, f\"{y*100:.2f}%\", fontsize=8, ha=\"center\")\n"," for x, y in zip(fixed_point_widths[:len(qat_accuracies)], qat_accuracies):\n"," plt.text(x, y + 0.002, f\"{y*100:.2f}%\", fontsize=8, ha=\"center\")\n","\n"," plt.tight_layout()\n"," plt.savefig(\"ptq_vs_qat_accuracy.png\", dpi=300)\n"," plt.show()\n","\n"," # Plot 2: Highest Accuracy vs Fixed Width\n"," plt.figure(figsize=(10, 6))\n"," plt.plot(\n"," fixed_point_widths[:len(highest_accuracies)],\n"," highest_accuracies,\n"," marker=\"o\",\n"," label=\"Highest Accuracy\",\n"," linestyle=\"--\",\n"," linewidth=2,\n"," )\n","\n"," # Add labels, grid, and legend\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Fixed Point Width\", fontsize=12)\n"," plt.ylabel(\"Accuracy\", fontsize=12)\n"," plt.title(\"Highest Accuracy by Fixed Point Width\", fontsize=14, pad=15)\n","\n"," # Format y-axis to show percentages\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.0f}%\"))\n","\n"," # Add value labels on the points\n"," for x, y in zip(fixed_point_widths[:len(highest_accuracies)], highest_accuracies):\n"," plt.text(x, y + 0.002, f\"{y*100:.2f}%\", fontsize=8, ha=\"center\")\n","\n"," plt.tight_layout()\n"," plt.savefig(\"fixed_point_width_vs_accuracy.png\", dpi=300)\n"," plt.show()\n","\n","plot_results_separated(results)\n"],"metadata":{"id":"7_hgoVipd7TC"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.11"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Working_Data/edited-colab-files/tutorial_4_pruning.ipynb b/Working_Data/edited-colab-files/tutorial_4_pruning.ipynb new file mode 100644 index 000000000..7ba6bf3e8 --- /dev/null +++ b/Working_Data/edited-colab-files/tutorial_4_pruning.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"Q2rZEga7NzOx"},"source":["# Tutorial 4: Unstructured Pruning on Bert"]},{"cell_type":"code","source":["!git clone https://github.com/tonytarizzo/mase.git\n","%cd mase\n","!python -m pip install -e . -vvv\n","%cd src"],"metadata":{"id":"h0g_y_S-N36S"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p4JDteW3NzO0"},"source":["Pruning is a technique used to reduce the size and complexity of neural networks by removing unnecessary parameters (weights and connections) or structural components (neurons, filters, or layers). The goal is to create a smaller, more efficient model that maintains most of the original model's performance. The following benefits can be seen from pruning neural networks:\n","\n","- **Reduce model size**: Deep neural networks often have millions of parameters, leading to large storage requirements.\n","\n","- **Decrease inference time**: Fewer parameters mean fewer computations, resulting in faster predictions.\n","\n","- **Improve generalization**: Removing unnecessary connections can help prevent overfitting.\n","\n","- **Energy efficiency**: Smaller models require less energy to run, which is crucial for edge devices and mobile applications.\n","\n","Structured pruning removes entire structures (e.g., channels, filters, or layers) from the network, while unstructured pruning removes individual weights or connections from the network, regardless of their location. In this tutorial, we'll build on top of Tutorial 3 by taking the quantized Bert model and running Mase's unstructured pruning pass. After pruning, we'll run further fine tuning iterations to retain sequence classification accuracy in the pruned model."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rWIWYcoHNzO1"},"outputs":[],"source":["checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\""]},{"cell_type":"markdown","metadata":{"id":"U3bdp4fdNzO1"},"source":["## Importing the model"]},{"cell_type":"markdown","metadata":{"id":"t3R0YG77NzO2"},"source":["If you are starting from scratch, you can create a MaseGraph for Bert by running the following cell."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["12ebdda9297d44cab3581e641980aefd","48480354294b41189a7f5a731e416d63","533eff032590429396a43073d1ce2989","25ef39e38f44411cb217bca7e63ce8fb","08b74fc57d4440ecb7a3d8194db8b835","4175611411ef4f4cb9c9c9cc6cd621dc","db5081a3b72f4425a421fc905554cf58","e56635fa20124cb0b926641a3b316d42","961b7bf2d37f4b2297414b4a48bbc476","80e7c9947d60491f895cc1e1d9b1a41d","b59c229cb69148aa8c034128788d0bbd","d110b3c4a2b94818886642d449a851b1","5cc47ea749374c3493a8478c155c6888","4148d7bc496944c29102226c4605bbb6","759103f4065b4f5fae539467b4b8417d","bdc9dbccf8ba4993899d92ca96efe0c1","c9047a581c964491907ce1628977c8e1","dabb1b81c65b468c97eb6ed9ca699865","48f9a774d70d4ae58fe3fd4e9816519f","91109c9d901c49749080fe46bef3e029","55f4bc88401744598d5e81f54600bcb0","9a217bb22f0e4101b3eefa05cf90fa34","d03678e81ea5401c93b0b2f676417dee","9d4a468967354531a191ed8e67a054de","9518a864d095409590bc3a4aa3160694","1fe36016520246e3adea53d0c8cb407c","f5fdb34295884aff912a7a1e9e9602d9","2658a8848b5148a8b3dd3f82f2aa6453","fdc034a46c6141a490d810a5e21f046d","a05d0f86bdb84116bfa5bb5d0bc62818","5ee0ad4e1c304cdeaf876fa8ebc53ede","24b90031ba444fb0a90d0eb51d5155a7","c221168f5eb6431cae5f44ee030e3f6b"]},"id":"_TN2_x3nNzO2","executionInfo":{"status":"ok","timestamp":1738015896210,"user_tz":0,"elapsed":28831,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"4aeb843d-a81d-4912-f4a4-3e9689c1314f"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/285 [00:00)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -2.0138e+00,\n"," 4.5529e-01, -7.8171e-01],\n"," [ 1.1969e+00, 1.6337e+00, 2.5047e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00]],\n","\n"," [[ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -2.2501e-01,\n"," 7.2290e-02, -1.8290e+00],\n"," [ 8.9952e-01, 1.0029e+00, 7.4536e-04, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00]],\n","\n"," ...,\n","\n"," [[ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., 1.1867e+00,\n"," -1.3561e+00, 6.5158e-01],\n"," [ 9.5466e-01, 4.5887e-01, 7.8078e-01, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00]],\n","\n"," [[ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 6.5467e-01,\n"," -6.8451e-01, 6.5081e-01],\n"," [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]],\n","\n","\n"," [[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., 1.7508e+00,\n"," -3.6708e-01, -1.3251e+00],\n"," [ 7.9208e-01, -1.3537e-01, 2.3756e-01, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00]],\n","\n"," [[-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -9.6646e-01,\n"," -4.8876e-01, -1.4426e+00],\n"," [ 1.0250e+00, -6.9093e-01, -1.2734e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00]],\n","\n"," ...,\n","\n"," [[ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n"," 1.0394e+00, 4.2402e-01],\n"," [-4.7386e-01, 2.6401e+00, 1.7024e+00, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01]],\n","\n"," [[ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -6.1665e-01,\n"," 2.7627e-01, -1.2083e+00],\n"," [ 9.3395e-01, -9.7541e-01, -2.5442e-02, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]]], grad_fn=)\n","tensor([[[-0.1709, 0.5230, -0.8713, ..., -1.3382, 0.5892, 0.4026],\n"," [-0.5842, 0.9588, 1.5642, ..., -1.0731, -0.7330, 0.3132],\n"," [-0.8601, -1.3756, 0.5042, ..., -0.0476, 0.2650, 1.2150],\n"," ...,\n"," [ 0.0520, 1.1719, -1.5471, ..., -0.7894, 0.1419, 1.6964],\n"," [ 0.7654, -1.5053, -0.4142, ..., -1.4622, -0.8975, 1.4576],\n"," [-1.2008, -0.6008, -1.4608, ..., -1.2105, -0.4289, 0.3827]],\n","\n"," [[-0.1709, 0.5230, -0.8713, ..., -1.3382, 0.5892, 0.4026],\n"," [-1.3806, 0.2626, -0.5207, ..., -1.6714, -0.0554, 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...,\n","\n"," [[ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 8.5980e-01,\n"," 3.2796e-01, -1.9442e+00],\n"," [-8.9502e-01, 9.7357e-01, 8.5447e-01, ..., 1.7637e+00,\n"," 9.1331e-01, -1.7033e+00]],\n","\n"," [[ 1.5909e+00, -1.4922e+00, 1.0060e+00, ..., -1.5965e+00,\n"," -3.9380e-01, -4.3585e-01],\n"," [-2.2103e+00, 4.4127e-01, 1.1554e+00, ..., 9.8096e-01,\n"," 8.6736e-01, -2.3894e+00]],\n","\n"," [[ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 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-6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[-0.0581, 0.0628],\n"," [-0.0348, -0.0095]], grad_fn=)\n","tensor([1, 0])\n"]}],"source":["from transformers import AutoModelForSequenceClassification\n","\n","from chop import MaseGraph\n","import chop.passes as passes\n","\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","model.config.problem_type = \"single_label_classification\"\n","\n","mg = MaseGraph(\n"," model,\n"," hf_input_names=[\n"," \"input_ids\",\n"," \"attention_mask\",\n"," \"labels\",\n"," ],\n",")\n","\n","mg, _ = passes.init_metadata_analysis_pass(mg)\n","mg, _ = passes.add_common_metadata_analysis_pass(mg)"]},{"cell_type":"markdown","metadata":{"id":"8mVFUEFGNzO2"},"source":["If you have previously ran the tutorial on Quantization-Aware Training (QAT), run the following cell to import the fine tuned checkpoint."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1UO6Eb1oNzO3","executionInfo":{"status":"ok","timestamp":1738019373435,"user_tz":0,"elapsed":41500,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"b9fe4b9c-66a9-48d4-aa47-a13a13237083"},"outputs":[{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n"]}],"source":["from pathlib import Path\n","from chop import MaseGraph\n","\n","mg = MaseGraph.from_checkpoint(f\"{Path.home()}/tutorial_3_qat\")"]},{"cell_type":"markdown","metadata":{"id":"p_tE5zWNNzO3"},"source":["## Unstructured Pruning"]},{"cell_type":"markdown","metadata":{"id":"ej_TQOeONzO3"},"source":["Before running pruning, let's evaluate the model accuracy on the IMDb dataset. If you're coming from Tutorial, this would be the same as the accuracy after Quantization Aware Training (QAT). If you've just initialized the model, this will likely be a random guess (i.e. around 50%), in which case pruning wouldn't have a significant effect on the accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m5-5HBuSNzO3","executionInfo":{"status":"ok","timestamp":1738016008375,"user_tz":0,"elapsed":76442,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/","height":608,"referenced_widgets":["597fa58ce9134f2b8245d8ac20c48d22","f5f924c5b7c442fc9ab25961cc7be86c","6b74fbf533c94a7bbfc6a97680ba456b","5529f846763c4bb9879645fca0ab9aad","4c96730bfa1a4906a11f6cfa64d5afec","1acfc364619d449c9ef72d03a11a5548","7096b6422e8a4251bd0bb8981cb2d4b8","4c8c7f40c2094ee681e550a85e543762","c3ee39089706433989b974cbcfd75809","a4adb983b9084f4c9c067365bdbfb73e","b3e2f0fb96e541db9144dfcf3a984fc8","2b520de171d34694a18977f2d24b7e5d","48e77351c5f6489ab82ad43660830779","cf38894a5f60493cbe3b38e5d65251e0","89e9f7195f114ba6b0a80c6f35c379c0","720ea211ccb744a588e50c2da5f80064","69bf6e7a40f24d968207b3fb87377588","5ffa0379b3054705b83db46436a2c45d","09917c83ae364aa49d0edca735a0f685","c0a9dffa3f52445cb6ae7120abbcf7db","fddc2c431d054e4b921b6845892eb91c","fae0e7dcf889464980c889155856a9ce","510756aac67440dc9b8d7b24093e7cc3","39d7da33faca4a348de61ad1cb73340b","9f4088e3ad09460cbd69425d16859c49","a86eeb6248ae471e807b88ee69e193b3","59593c6533fd4e54b698116c14d80380","3e5cdcabea7e4527852c8c7841d05a09","e0f7869a1cb14ce1a5fc76ef25f9d778","6e1cd7fd6f2a461080a721d22d6bfcff","efd926cf14a94adbb86eed7faa76b008","acf29cbe6b914001966ac99e74fb4059","9cf514e2c7b348abbf1b9746ec6efa06","68e424afc751494a9318d3106dff197e","88650c3fd6c74c91b1a10b0b56db5e9a","99fa514476ea48ea93ee34f3b78ad146","49643a4c5d7c4b0aad279a38585b8fc6","de119bc7369a4ca6b4ea110a3bdc6e7e","ddd888215bc34f52adc07bf1f9c331ed","105e325411e842dfa9546736f0267582","6fa4b35b61da446c8d66567dc73dcf64","d908ff1010754f63a675f563771d0ed6","7db3ab56ca614c56bfab2c9b88c1519d","5988122ddb2a4e3d867bd4cabf2bb9f3","4839679c9e3e4f05be4ca06a17e7826e","940e414f75524857b248b9a89c712a14","16d5f246da9946f899ef8a0977a1d2f6","aa655659f71d485786c06eb433357ec0","c4f56ee00f5548eaa2e9e0604c876cae","de82d6b3bced41c681c39b7ebd7b7aa2","a0e25816414048adb92d3a67cd236170","e7a1af0af31e4eda9a8c2b9311d6ee75","3ede65a3e6114b428b2e1bbc6076b1fc","a89ac33ba5e548a49a140b9fe4be71e6","6ee5d3bc86d94f5b899875c8047ca4a5","a5874510a843424ba5c00da6b6862227","5fdb2533f01d4968bac86b1809814356","2dd21cfb40df4c2ebdb004803f48f72a","decb096c72994bd69492a7139212834e","820f5d6cf87242d8bf6eb380642f7700","721064a3e71449f2944dc37d639c5043","4e7ee706f23a4a6c8b81bb69fce8f805","18258667cccd4fc1bcb220793e1d1a9a","a5172069f2be428bb14ce46057233079","f0b7f2a608694305b97fdae8e6703a1a","92a10268e0184ffd96d72f8aa71ba253","c0ee689b9fe449aeafb8b9babfd7e4ea","9ca296b4dbce437280441d79ff168f68","cd3ee71c32f54b9ba24fd66f8f34ef6e","89292aaca02a43aaa02825700db69d4f","ed9b178013744a46960696257b2f6ee4","ed9578c80b70400298375b6ab580163d","71ea418bfd6447949eb5ad5513c140a2","9f587f589d384aab9ab8b668754baeb0","06ed72d689024833ba79bd706bb0bdd3","c291766768824e3b96da7ba5eee6bc9a","02292c7c0ba44ea091b3a1c5d381cee7","16f129a731ee4dfc9e5787ea154192f6","3f8a85eae6284d82b9321c3ce5ed960a","ad4a091b25f74760b53cc18bcaf54089","27ecf8abd9c349b4aa3fd206be20e11e","c6f559707d154156b88fdbff2dedac10","9fdaf8268fb54dd2a6c030dc6e0b6baf","9364e8eb70a24375b00f3c0b81733883","de2f014509264e0199acc78bd594cdd5","0f637ae8d0ba420e8c2bf2a8155501bb","b5247f45f966431bae605acdc1c89193","7fc5a73db56548c5be462cd2d43829c7","8ea1451b3a4244a094ea4924764c6fbc","5a2f5b2af1854f6ba0e20032848bf93f","756a5134ba5347d695920faa126b5cf0","3b5a552baf484abfa40ef435030bc730","d2436f8fccd748b284262438794463ee","df1ac48c0ace4594a20b1e8c26577e11","4ab0ed98c58349d289755b4bb77310c6","acec1f103b3f475b9b809256d3a31733","b51f2543ad244df3ba44d39555515e76","bd5f726c9dd44f24a0204db8a14935f6","d01eb20b72314235818a50b3ecbd06cc","ccf30cbc69874b83b12be20c2f7bae30","44e1679cfb7d46ddbfce7d19381abcc5","fe97f26f4d464cf788acb420d0a1c30f","b141505140404104b0038eb1785bff9e","5539e3a6ea474d2bb2b12882d068bdb0","e3dfee174def4f5fb340738be8cfe7bb","5e8a2cda666d44efae227f08b9768b0a","d354f57320914c809a557a9ded737ed0","68c7471a884e4b2d81a6f8546c1268d2","229c4035202a406d92f3ad8846a024f3","4608e37d28e44c40be16c416452844c9","8b48f7f0ef1a4d9f8848608de45a8efb","0a1e05c04fe944ab86301988755f8c1d","28d1092a08bd45dea93571fc144a615b","6ce87d478fb14bac9c4ad7ffdec0c246","0915a59a613e43b88fd896d92fddb4c6","e70fabd1579541418b46362bddbfd4df","8fc482b8591b4d5f85ea6b159f52c2c8","361e2bfd1b8b4d749768155410470bd6","c3e1df1d0ee8424bbf560214afd260a8","30dfac4094a24b47b5246bcfad0d5570","88d7a11b105d425ab500611b04f7f7cf","fddadd3ae0e9402ca49a9c58584f6e52","3ac85edb600a42999eff9e0f5100563c","18013b1d9eb1415cb44364309c93e731","5229d52449874ff59be9c66e647069b6","090b01e454d840f7bc2ec9349e60cf4f","08faef40e08f41f9bbb365373730ecd3","c4c70eb1c5ea46a88f993eba9eef6922","41e5cba6330a4356985e7d0c821bea9f","3b9b6927f6624e269923f09e794fd56b","824451d69a6e4122985aa9819f56a889","1192a1c33ce14ab2b828d836ab798a29","065573de85864b4f89f142385dd2ad7c","88b0fd795cd444bca60828620266884b","1c5213ccf4214bb6afa6c5a2c5c3dfb6","999f0279caec433581373c44d1a2927c","755a2f014df34df39714d6aa3c1701f7","0745a9ab361f4b7480ced1b769c3a4b4","d607b60a75d14b6da47f423501db0f1a","631671f301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\u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0%| | 0.00/7.81k [00:00"],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Evaluation accuracy: 0.84472\n"]}],"source":["from chop.tools import get_tokenized_dataset, get_trainer\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n",")\n","\n","# Evaluate accuracy\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"vsb3K6A-NzO3"},"source":["To run the pruning pass, we pass the following pruning configuration dictionary, which defines the following parameters.\n","\n","- **Sparsity**: a value between 0 and 1, expressing the proportion of elements in the model that should be pruned (i.e. set to 0).\n","\n","- **Method**: several pruning methods are supported, including ``Random`` and ``L1-Norm``.\n","\n","- **Scope**: defines whether to consider each weight/activation tensor individually (``local``) or all tensors in the model (``global``) when obtaining statistics for pruning (e.g. absolute value threshold for pruning)\n","\n","We'll start by running random pruning with local scope, at a fixed sparsity. This may be suboptimal, but in future tutorials we'll see how to find optimal pruning and quantization configurations for a given model on a specified dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LeDlNMTWNzO4","executionInfo":{"status":"ok","timestamp":1738016008828,"user_tz":0,"elapsed":455,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"265b3f49-3931-4057-e538-33aa6ed860f0"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]}],"source":["import chop.passes as passes\n","\n","pruning_config = {\n"," \"weight\": {\n"," \"sparsity\": 0.2,\n"," \"method\": \"l1-norm\",\n"," \"scope\": \"local\",\n"," },\n"," \"activation\": {\n"," \"sparsity\": 0.2,\n"," \"method\": \"l1-norm\",\n"," \"scope\": \"local\",\n"," },\n","}\n","\n","mg, _ = passes.prune_transform_pass(mg, pass_args=pruning_config)"]},{"cell_type":"markdown","metadata":{"id":"3i6lFfsiNzO4"},"source":["Let's evaluate the effect of pruning on accuracy. It's likely to observe drops of around 10% or more."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LpANkOuWNzO4","executionInfo":{"status":"ok","timestamp":1738016059464,"user_tz":0,"elapsed":50638,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/","height":111},"outputId":"0d0800c2-9e59-41a7-a0f0-b8d62c99c139"},"outputs":[{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Evaluation accuracy: 0.8402\n"]}],"source":["trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=5,\n",")\n","\n","# Evaluate accuracy\n","eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"markdown","metadata":{"id":"BPBrRBEtNzO4"},"source":["To overcome the drop in accuracy, we'll run a few finetuning epochs. This allows the model to adapt to the new pruning mask."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XivOeZosNzO4","executionInfo":{"status":"ok","timestamp":1738016428952,"user_tz":0,"elapsed":368711,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"8b02a6d2-d007-4eaf-dda1-4938b2286734"},"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.384800
10000.382400
15000.395400
20000.377300
25000.377600
30000.394400
35000.389100
40000.375000
45000.350400
50000.360100
55000.370100
60000.362900
65000.346500
70000.362700
75000.356600
80000.349500
85000.376700
90000.354000
95000.361300
100000.343000
105000.357700
110000.346000
115000.337000
120000.349900
125000.343000
130000.330400
135000.336800
140000.333000
145000.349000
150000.338300
155000.353400

"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=15625, training_loss=0.3591796530761719, metrics={'train_runtime': 369.4186, 'train_samples_per_second': 338.37, 'train_steps_per_second': 42.296, 'total_flos': 0.0, 'train_loss': 0.3591796530761719, 'epoch': 5.0})"]},"metadata":{},"execution_count":8}],"source":["trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"PkXLQluHNzO4"},"source":["Let's evaluate the model accuracy after finetuning. We should see that the accuracy is reverted back to the original level."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R4MF-N2ZNzO4","executionInfo":{"status":"ok","timestamp":1738016485118,"user_tz":0,"elapsed":56176,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/","height":55},"outputId":"e5bd414e-71de-41b5-fbd1-7520791429a2"},"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 07:55]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Evaluation accuracy: 0.85688\n"]}],"source":["eval_results = trainer.evaluate()\n","print(f\"Evaluation accuracy: {eval_results['eval_accuracy']}\")"]},{"cell_type":"code","source":["### Full combined code for Lab 1\n","from transformers import AutoModelForSequenceClassification\n","from chop import MaseGraph\n","import chop.passes as passes\n","from pathlib import Path\n","from chop.tools import get_tokenized_dataset, get_trainer\n","\n","sparsity_levels = [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n","methods = [\"l1-norm\", \"random\"]\n","\n","results = [\n"," {\n"," \"sparsity\": sparsity,\n"," \"l1_initial_pruned_accuracy\": None,\n"," \"l1_fine_tuned_accuracy\": None,\n"," \"rand_initial_pruned_accuracy\": None,\n"," \"rand_fine_tuned_accuracy\": None,\n"," }\n"," for sparsity in sparsity_levels\n","]\n","\n","for iter, sparsity in enumerate(sparsity_levels):\n"," for method in methods:\n"," checkpoint = \"prajjwal1/bert-tiny\"\n"," tokenizer_checkpoint = \"bert-base-uncased\"\n"," dataset_name = \"imdb\"\n","\n"," model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n"," model.config.problem_type = \"single_label_classification\"\n","\n"," mg = MaseGraph(\n"," model,\n"," hf_input_names=[\n"," \"input_ids\",\n"," \"attention_mask\",\n"," \"labels\",\n"," ],\n"," )\n","\n"," mg, _ = passes.init_metadata_analysis_pass(mg)\n"," mg, _ = passes.add_common_metadata_analysis_pass(mg)\n","\n"," mg = MaseGraph.from_checkpoint(f\"{Path.home()}/tutorial_3_qat\")\n","\n"," dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n"," )\n","\n"," trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," )\n","\n"," # Evaluate initial accuracy\n"," eval_results = trainer.evaluate()\n"," initial_accuracy = eval_results[\"eval_accuracy\"]\n"," print(f\"Initial accuracy (method={method}, sparsity={sparsity}): {initial_accuracy}\")\n","\n"," # Apply pruning\n"," pruning_config = {\n"," \"weight\": {\n"," \"sparsity\": sparsity,\n"," \"method\": method,\n"," \"scope\": \"local\",\n"," },\n"," \"activation\": {\n"," \"sparsity\": sparsity,\n"," \"method\": method,\n"," \"scope\": \"local\",\n"," },\n"," }\n","\n"," mg, _ = passes.prune_transform_pass(mg, pass_args=pruning_config)\n","\n"," trainer = get_trainer(\n"," model=mg.model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=5,\n"," )\n","\n"," # Evaluate pruned accuracy\n"," eval_results = trainer.evaluate()\n"," pruned_accuracy = eval_results[\"eval_accuracy\"]\n"," print(f\"Pruned accuracy (method={method}, sparsity={sparsity}): {pruned_accuracy}\")\n","\n"," # Fine-tune and evaluate\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n"," fine_tuned_accuracy = eval_results[\"eval_accuracy\"]\n"," print(f\"Fine-tuned accuracy (method={method}, sparsity={sparsity}): {fine_tuned_accuracy}\")\n","\n"," # Update results\n"," for result in results:\n"," if result[\"sparsity\"] == sparsity:\n"," if method == \"l1-norm\":\n"," result[\"l1_initial_pruned_accuracy\"] = pruned_accuracy\n"," result[\"l1_fine_tuned_accuracy\"] = fine_tuned_accuracy\n"," elif method == \"random\":\n"," result[\"rand_initial_pruned_accuracy\"] = pruned_accuracy\n"," result[\"rand_fine_tuned_accuracy\"] = fine_tuned_accuracy\n","\n","for result in results:\n"," print(result)\n"],"metadata":{"id":"-CSEZJIzTmGt","executionInfo":{"status":"ok","timestamp":1738062302206,"user_tz":0,"elapsed":42928775,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["da9f9aae79c04330805ca1c1845a7f5f","1eb0355b634e4aeba9a46dd5a50ae6a1","874d64a02d544b7da4066d79a49e98ed","e43d9f170ff94f1ca0fe6f2a4c4dced2","e86bce094a6d417dbfa567d4e468760b","88fa546f26ce4c1785f6a89bc2238c10","2b494e4144f94d458b87008f1ac989f7","51b4b50dcad04840bbecba9ca158ab20","ce44748e68054f5ba4ccd24145de0414","0e13e4abccd546af8ceff4fd44f7d799","857e2ed69476480585ce28b009ec401e","994d6a0c62564f54ab6f7486456f0326","797c8617b63745599e99cecdca3e85eb","b5f2c9d38c2249b8abc6019af35383c0","5267deb6e4334ee0b583b539742bc892","5722a776c10c44efb2a496abdadd45b9","777255ceae8d4eb39cb61cfcf6faffa8","dd36db9b96d74a13a04f864d59c792b1","81cc3a438220447a837e84d20b2ea67e","efe067db0b034f67a58a4da1b63caa67","9974d1a765134a5586f933b9399298e2","95b67e31855e40ae9aff27493afaa894","439984cc6282495bbcb345117b4abe6f","08ade5e3005f4e74a474ea5384008831","5d754643ce094ba5a515d55b5de5be73","530dc49db68c4120bf96256fd93f3646","545ebc69d39d42bf92e20e17175df3a5","d7f39ef0919f48cd93b2d7d34f069205","6e7ddcf3c7754f5b93147a02e4d1e1ad","b66663b198174af4a9665fb1b1e4cb98","edf996781646457fbb2b0c6ce7b72432","d9a785f315484a89acd29dc276efbcdd","9b3734bcf95d40bb829f34ee08b8a5ed","5ea116471cef49f39048999dfe6e2d9c","7c9eda18df734713ba733456420cd948","d9e7921b327d499e8dd725229674e9f1","8c2624e7b2824432a56e1180bbe65067","c0baf708597d44409f0279f0c3e3a7f3","36b27385c9d4483d90babd2f66acb847","8e231fa0f7e641a2a45fd97ca1eef02d","3bee09ad4b3f41fa91a996d1cf59fecb","132d7340b297450dac9be7ad4a959ff4","963a2e05bffe48d5afc8015a82c4a80e","a290004b18ea40d2b4e08cab2c1b153b","be99505b1e494211a1121cb2bb42b8bf","be90543e9f52437890a2f2d4b3aebe93","252d11ee6e8640fda2cae8072a78ba1f","53b2c0e81208489b8b5e4dfbd71c0efb","6495b1b2e02a4480b3a5341c97036391","2696ebb24e4c4bc3aad9df76d5e3d039","7d9f7aea1c0c4c3f891d6d02cf8d6a54","0247500916d1409282bb5e3d12658192","8352d22b0c3b4e30b8609180487244d4","7b551cb439b04671bd64bcb2c88b601b","455adbb8beba472489ffbfc9c46a8bea","98e8c02345c1479080291eacbd0826ef","b42160c9c83f42b69443f39e1b9fdbf7","da3debd493224234af31a97488a5f17c","89d4270a662a4a66b695f434c5712ab2","e7e654aed38341768bc7ba95e1facd05","94858a2436ce42bfa5f6f968c8f1af98","c969db7b43ce4fa6a2de30310fde877f","44b8c0e0370c402c885f8855b69d99a9","8a820b82249e456d93b40e70ec82f7fd","4cf8b9b554e940f7ade5bf4157f2a8ec","c48d078382b94793a718cd24bc7d838b","531967bcaf3042118f7fa1060789ab20","2a9cd64f21f843259113f0851631c29b","c59e2cefa0de43589727b6c6f0d823d1","14c563d5b9fc4e2cb8c95971bff500cf","ea5a5fc75c834e9398f2743aaf8a5d6e","bee1050e32c9454fae57eb14904213ad","7ae1a5c0d9b041aa894fa3783381ed38","b492b57bad244e74bd74d3a6aaf29ddd","8cb2a0e4d2b34b91aef130e67c085ff4","54b02d8189ab4a419f21ea19b879d0ce","2576778a199046a8881cdcb77b9c967c","9445f63fc322454b80cfc63567475b07","001d2406fb6e4d7588443b2565a2b45b","f8d3d0432fc64aa2a879766d4390efa2","a3e459f5476640d8bae73f26eb5c9103","afb93aadb1f44f56bfb42ee678f42ea8","f23654b383b448dca1cd72f1957834c6","e3b3b5a798b94174b828e27f51974658","17fbd0fddf49466b84a0eb1ba449ef49","98fa9ec6481a43558675f082fbaab373","242ecc6d99c44e84b6a24c198baeedc7","0a852ea0afb04242b0e6682b99e96aa4","c24759abc8274eafb3f36637b7277595","5b8da6ebae834673979b0c5aaf529eb1","3f60841ce6d64f11b67bade8b9594b37","928a6b7513e9438f99a56a89af887f49","1748051f781747e1b5ae89e8643bb412","1fb852753a9e4805b970e25fa6e5ee4e","a8aaff419ebf4602a7da7cb087f5ab22","c0a3405546bc4743a7afe41aeddd368d","7dec3c58063140e287e3c088fd3424c3","f9db8c248a99466883f0db9ddad2960d","68307d5055994f25b619409bc53b309d","9ae366b37c904c54942e483db770e7c4","5db0c7edf26145249650738d6b249157","79248651867a4e30b8ff4c55fab066b4","785172341124487eabb03228255ff44f","97ecd33f9d984ebc9b49a6c0d0d04589","c2f739219325497dab6905314174a2b6","1f8ec4e4ae5c43238335dc00f0078f10","f2d12629302b4bdcace53e671b3a4f3b","3f78b655fdb7410b83b17fca65e5d59d","406039e6bbcc4f269dd2bc1b9f557bcc","9c11b51c052646fb9dfcdbad40319afc","a260c6f24bbf4c54935c3e00d4f9cda0","c4c30df8f3fe4ee58926e32cf4b21a1b","feb127f34c454a878fe56f0d1b667782","755b654f19f740478fb0104feb0115d0","a9cf468fcd2046de9bd70e443f5c6c54","fa6e5229e6174b30901d7da80607c7bd","9be5276374b747a3826761e174197b80","df104223177e44bdbb3112d8b899043e","b139dbc66c994f3f90abf99522f7ebaa","7daede5cd69641d59d63a2cdd2800224","5854ef6755074339b6b5eacf172ab045","4813e83378324b1ba48a8e404c87129c","33146d09bc034ad984dcbbb9b7c2091d","fc79f3e8cae8433f872a2f1803269ae8","98ccacbf24d04f44b493d8876ebae445","2e9a0cef305d483a9cdbb6e535eea681","1d09cdab5615437ea93574f959b65ef8","406cb901665d48f88f175f009243e427","8316e8fc1c7c4a8b9c20fcf50881ca70","72b4a6125d98422a92a1fe64e0a564da","cde2c170817f452ea9a3b4969432f52d","121bf5613de24d12a30ba62e7600c4ed","5244f4ea0401422b8973e707d7d978d2","31d7a40269fe44ad9fd4c3dda419065c","75d60812fa604984a2d62c7f8d0aaada","7a24d5183dff438c9414e6b8917e50d3","eae3c06ed4414db6bee7ee153f771255","1abac9885a74430aa80b885de4fb8497","29c05c3e26fe44b19632d0d048670cec","898e9275a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UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/285 [00:00)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 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-1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01],\n"," ...,\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[-0.5153, -0.2656],\n"," [-0.4542, -0.2613]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0%| | 0.00/7.81k [00:00"],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.2): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.2): 0.83992\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.359700
10000.364100
15000.374500
20000.361900
25000.371000
30000.381300
35000.389100
40000.381200
45000.353000
50000.365000
55000.378400
60000.364600
65000.351200
70000.360900
75000.357000
80000.357900
85000.376800
90000.361600
95000.362700
100000.344600
105000.359500
110000.355900
115000.343000
120000.351400
125000.338600
130000.338600
135000.358100
140000.338900
145000.349300
150000.340300
155000.349700

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.2): 0.85832\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/25000 [00:00"],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.2): 0.6308\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.565900
10000.480100
15000.472000
20000.449700
25000.459400
30000.441700
35000.460700
40000.437700
45000.427100
50000.421200
55000.441600
60000.425000
65000.410100
70000.431900
75000.423400
80000.419900
85000.432600
90000.427700
95000.436000
100000.411600
105000.419300
110000.426600
115000.406300
120000.416300
125000.404600
130000.417600
135000.415000
140000.404500
145000.412200
150000.412200
155000.415200

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.2): 0.8232\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 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0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/50000 [00:00"],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.3): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.3): 0.83004\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:27, Epoch 5/5]\n","
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StepTraining Loss
5000.367700
10000.365900
15000.383700
20000.361100
25000.375800
30000.384200
35000.390600
40000.379300
45000.353200
50000.367100
55000.382100
60000.370400
65000.353800
70000.372300
75000.361600
80000.356200
85000.379500
90000.357100
95000.371700
100000.348000
105000.360200
110000.364100
115000.343800
120000.360400
125000.347500
130000.346700
135000.365500
140000.343200
145000.355900
150000.348300
155000.352400

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.3): 0.85504\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -2.0138e+00,\n"," 4.5529e-01, -7.8171e-01],\n"," [ 1.1969e+00, 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1030, 0.2155],\n"," [0.0341, 0.0664]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.3): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.3): 0.54652\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.673100
10000.590900
15000.509800
20000.481800
25000.488800
30000.471900
35000.476100
40000.461800
45000.458400
50000.449300
55000.463300
60000.449100
65000.439600
70000.448000
75000.451300
80000.447200
85000.462200
90000.450200
95000.465800
100000.434400
105000.443300
110000.446400
115000.433100
120000.440800
125000.429600
130000.439900
135000.442300
140000.434100
145000.448200
150000.433700
155000.451700

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.3): 0.80812\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01],\n"," ...,\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1950, 0.2717],\n"," [0.2753, 0.2599]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:31]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.4): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [3125/3125 09:36]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.4): 0.80796\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:26, Epoch 5/5]\n","
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StepTraining Loss
5000.396300
10000.384200
15000.392800
20000.370100
25000.387300
30000.389700
35000.396000
40000.393600
45000.362300
50000.368500
55000.386700
60000.378700
65000.364600
70000.374200
75000.368000
80000.362900
85000.388400
90000.366300
95000.381100
100000.363800
105000.370700
110000.369300
115000.352600
120000.363800
125000.359700
130000.359100
135000.375700
140000.359000
145000.363600
150000.356100
155000.363800

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.4): 0.8468\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 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0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.4): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.4): 0.50972\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 57:11, Epoch 5/5]\n","
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StepTraining Loss
5000.691100
10000.688900
15000.680600
20000.652900
25000.590800
30000.549100
35000.537700
40000.518900
45000.500000
50000.500500
55000.515000
60000.499600
65000.487800
70000.502100
75000.495100
80000.489600
85000.500000
90000.478200
95000.504800
100000.478700
105000.484000
110000.485200
115000.470100
120000.486500
125000.459500
130000.477100
135000.471500
140000.466600
145000.477100
150000.479900
155000.486700

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.4): 0.78696\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -2.0138e+00,\n"," 4.5529e-01, -7.8171e-01],\n"," [ 1.1969e+00, 1.6337e+00, 2.5047e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00]],\n","\n"," [[ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -2.2501e-01,\n"," 7.2290e-02, -1.8290e+00],\n"," [ 8.9952e-01, 1.0029e+00, 7.4536e-04, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00]],\n","\n"," ...,\n","\n"," [[ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., 1.1867e+00,\n"," -1.3561e+00, 6.5158e-01],\n"," [ 9.5466e-01, 4.5887e-01, 7.8078e-01, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00]],\n","\n"," [[ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 6.5467e-01,\n"," -6.8451e-01, 6.5081e-01],\n"," [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]],\n","\n","\n"," [[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., 1.7508e+00,\n"," -3.6708e-01, -1.3251e+00],\n"," [ 7.9208e-01, -1.3537e-01, 2.3756e-01, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00]],\n","\n"," [[-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -9.6646e-01,\n"," -4.8876e-01, -1.4426e+00],\n"," [ 1.0250e+00, -6.9093e-01, -1.2734e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00]],\n","\n"," ...,\n","\n"," [[ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n"," 1.0394e+00, 4.2402e-01],\n"," [-4.7386e-01, 2.6401e+00, 1.7024e+00, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01]],\n","\n"," [[ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -6.1665e-01,\n"," 2.7627e-01, -1.2083e+00],\n"," [ 9.3395e-01, -9.7541e-01, -2.5442e-02, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1950, 0.2717],\n"," [0.2753, 0.2599]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.5): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [3125/3125 09:34]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.5): 0.76728\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:22, Epoch 5/5]\n","
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StepTraining Loss
5000.447600
10000.416100
15000.408600
20000.396400
25000.405400
30000.405600
35000.415400
40000.401100
45000.382300
50000.382200
55000.396600
60000.390600
65000.376400
70000.394900
75000.382400
80000.376700
85000.403500
90000.375800
95000.394300
100000.374300
105000.385700
110000.386500
115000.372400
120000.380800
125000.368600
130000.374300
135000.388800
140000.370100
145000.378200
150000.379800
155000.383500

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.5): 0.84148\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],\n","\n","\n"," [[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 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4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, 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-3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1030, 0.2155],\n"," [0.0341, 0.0664]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.5): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.5): 0.5066\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 1:01:26, Epoch 5/5]\n","
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StepTraining Loss
5000.692800
10000.694200
15000.692900
20000.692200
25000.691600
30000.692600
35000.691200
40000.688300
45000.687000
50000.682100
55000.676600
60000.658400
65000.635400
70000.614000
75000.593600
80000.580000
85000.579700
90000.556200
95000.572000
100000.549400
105000.561200
110000.548900
115000.549000
120000.560100
125000.539400
130000.548700
135000.538800
140000.544900
145000.543500
150000.543900
155000.544100

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.5): 0.74436\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 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0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.6): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.6): 0.563\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:25, Epoch 5/5]\n","
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StepTraining Loss
5000.551900
10000.458500
15000.446200
20000.422900
25000.423300
30000.422000
35000.441100
40000.420200
45000.405900
50000.406000
55000.415000
60000.411800
65000.400300
70000.408400
75000.407400
80000.402800
85000.425300
90000.401800
95000.414700
100000.407800
105000.407800
110000.413200
115000.394300
120000.401300
125000.393900
130000.399700
135000.403600
140000.392400
145000.402000
150000.400900
155000.402500

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.6): 0.82284\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -2.0138e+00,\n"," 4.5529e-01, -7.8171e-01],\n"," [ 1.1969e+00, 1.6337e+00, 2.5047e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00]],\n","\n"," [[ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -2.2501e-01,\n"," 7.2290e-02, -1.8290e+00],\n"," [ 8.9952e-01, 1.0029e+00, 7.4536e-04, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00]],\n","\n"," ...,\n","\n"," [[ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., 1.1867e+00,\n"," -1.3561e+00, 6.5158e-01],\n"," [ 9.5466e-01, 4.5887e-01, 7.8078e-01, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00]],\n","\n"," [[ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 6.5467e-01,\n"," -6.8451e-01, 6.5081e-01],\n"," [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]],\n","\n","\n"," [[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., 1.7508e+00,\n"," -3.6708e-01, -1.3251e+00],\n"," [ 7.9208e-01, -1.3537e-01, 2.3756e-01, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00]],\n","\n"," [[-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -9.6646e-01,\n"," -4.8876e-01, -1.4426e+00],\n"," [ 1.0250e+00, -6.9093e-01, -1.2734e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00]],\n","\n"," ...,\n","\n"," [[ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n"," 1.0394e+00, 4.2402e-01],\n"," [-4.7386e-01, 2.6401e+00, 1.7024e+00, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01]],\n","\n"," [[ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -6.1665e-01,\n"," 2.7627e-01, -1.2083e+00],\n"," [ 9.3395e-01, -9.7541e-01, -2.5442e-02, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1030, 0.2155],\n"," [0.0341, 0.0664]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.6): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.6): 0.5046\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 1:02:06, Epoch 5/5]\n","
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StepTraining Loss
5000.693200
10000.694200
15000.693300
20000.693300
25000.693600
30000.694000
35000.693800
40000.692900
45000.692900
50000.693000
55000.693500
60000.692600
65000.693700
70000.693100
75000.693400
80000.692600
85000.693300
90000.692600
95000.692400
100000.692500
105000.692900
110000.692300
115000.692200
120000.693000
125000.692200
130000.692700
135000.692300
140000.692400
145000.692500
150000.691400
155000.691600

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.6): 0.52732\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01],\n"," ...,\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," ...,\n"," [ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01]],\n","\n"," [[-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1950, 0.2717],\n"," [0.2753, 0.2599]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.7): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [3125/3125 09:43]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.7): 0.54052\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:26, Epoch 5/5]\n","
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StepTraining Loss
5000.651800
10000.603800
15000.557400
20000.508000
25000.485500
30000.469400
35000.479300
40000.463000
45000.446800
50000.449500
55000.451800
60000.441900
65000.443900
70000.442200
75000.437800
80000.438000
85000.459300
90000.421000
95000.447400
100000.423600
105000.430700
110000.439300
115000.423300
120000.432700
125000.421400
130000.431400
135000.435300
140000.420200
145000.428800
150000.434100
155000.426800

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.7): 0.8098\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 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0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.7): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.7): 0.50292\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 1:01:39, Epoch 5/5]\n","
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StepTraining Loss
5000.693200
10000.693500
15000.693400
20000.693400
25000.693300
30000.693300
35000.693500
40000.692900
45000.693200
50000.693300
55000.693500
60000.692900
65000.693400
70000.693100
75000.693300
80000.693200
85000.693500
90000.693200
95000.693100
100000.693200
105000.693200
110000.693500
115000.693200
120000.693300
125000.693100
130000.693300
135000.693400
140000.693400
145000.693000
150000.693000
155000.693000

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.7): 0.50436\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -2.0138e+00,\n"," 4.5529e-01, -7.8171e-01],\n"," [ 1.1969e+00, 1.6337e+00, 2.5047e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00]],\n","\n"," [[ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -2.2501e-01,\n"," 7.2290e-02, -1.8290e+00],\n"," [ 8.9952e-01, 1.0029e+00, 7.4536e-04, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00]],\n","\n"," ...,\n","\n"," [[ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., 1.1867e+00,\n"," -1.3561e+00, 6.5158e-01],\n"," [ 9.5466e-01, 4.5887e-01, 7.8078e-01, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00]],\n","\n"," [[ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 6.5467e-01,\n"," -6.8451e-01, 6.5081e-01],\n"," [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]],\n","\n","\n"," [[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., 1.7508e+00,\n"," -3.6708e-01, -1.3251e+00],\n"," [ 7.9208e-01, -1.3537e-01, 2.3756e-01, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00]],\n","\n"," [[-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -9.6646e-01,\n"," -4.8876e-01, -1.4426e+00],\n"," [ 1.0250e+00, -6.9093e-01, -1.2734e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00]],\n","\n"," ...,\n","\n"," [[ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n"," 1.0394e+00, 4.2402e-01],\n"," [-4.7386e-01, 2.6401e+00, 1.7024e+00, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01]],\n","\n"," [[ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -6.1665e-01,\n"," 2.7627e-01, -1.2083e+00],\n"," [ 9.3395e-01, -9.7541e-01, -2.5442e-02, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1950, 0.2717],\n"," [0.2753, 0.2599]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.8): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [3125/3125 09:53]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.8): 0.50076\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:38, Epoch 5/5]\n","
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StepTraining Loss
5000.689500
10000.684900
15000.679100
20000.671000
25000.666800
30000.658000
35000.649600
40000.637600
45000.627600
50000.612700
55000.612000
60000.593700
65000.585400
70000.572300
75000.569700
80000.561000
85000.568500
90000.550500
95000.551400
100000.538100
105000.531800
110000.530300
115000.524900
120000.524500
125000.522100
130000.524700
135000.523500
140000.510900
145000.521600
150000.516000
155000.513300

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.8): 0.76204\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, 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-3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1030, 0.2155],\n"," [0.0341, 0.0664]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.8): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.8): 0.49896\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 1:03:13, Epoch 5/5]\n","
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StepTraining Loss
5000.693200
10000.693300
15000.693100
20000.693000
25000.693100
30000.693200
35000.693100
40000.693100
45000.693200
50000.693100
55000.693100
60000.693300
65000.693300
70000.693200
75000.693300
80000.693100
85000.693200
90000.693100
95000.693100
100000.693100
105000.693100
110000.693200
115000.693100
120000.693300
125000.693000
130000.693100
135000.693200
140000.693100
145000.693200
150000.693100
155000.693200

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.8): 0.504\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 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0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=l1-norm, sparsity=0.9): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [3125/3125 09:46]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=l1-norm, sparsity=0.9): 0.50312\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 07:29, Epoch 5/5]\n","
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StepTraining Loss
5000.692500
10000.693000
15000.693100
20000.692900
25000.692900
30000.692100
35000.691500
40000.691300
45000.691400
50000.690500
55000.691500
60000.691000
65000.690900
70000.688800
75000.690000
80000.689300
85000.689700
90000.689200
95000.689200
100000.688400
105000.687600
110000.688800
115000.689000
120000.687700
125000.689000
130000.687800
135000.686800
140000.687700
145000.688500
150000.687100
155000.688500

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=l1-norm, sparsity=0.9): 0.56104\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n","\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n"," [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n","\n","\n"," [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n","tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n"," [0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.4170, 0.1046, -0.1551],\n"," [-2.6940, 0.6198, -0.4564, ..., -1.4367, -1.5705, -3.1260],\n"," [-1.7524, 0.8535, -0.2155, ..., -0.5222, -1.2430, -1.7199],\n"," ...,\n"," [-0.0347, 0.7446, 1.4462, ..., -1.1578, -2.6197, 0.2612],\n"," [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n"," [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n"," grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00],\n"," [ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00],\n"," ...,\n"," [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00],\n"," [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]],\n","\n"," [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01],\n"," [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00],\n"," [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00],\n"," ...,\n"," [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01],\n"," [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01],\n"," [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]], grad_fn=)\n","tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -2.0138e+00,\n"," 4.5529e-01, -7.8171e-01],\n"," [ 1.1969e+00, 1.6337e+00, 2.5047e-01, ..., -8.1892e-01,\n"," 1.1978e+00, 2.1808e+00]],\n","\n"," [[ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -2.2501e-01,\n"," 7.2290e-02, -1.8290e+00],\n"," [ 8.9952e-01, 1.0029e+00, 7.4536e-04, ..., -6.3025e-01,\n"," -1.5967e-01, 1.3284e+00]],\n","\n"," ...,\n","\n"," [[ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., 1.1867e+00,\n"," -1.3561e+00, 6.5158e-01],\n"," [ 9.5466e-01, 4.5887e-01, 7.8078e-01, ..., -3.0983e-01,\n"," -1.2971e-01, 1.1265e+00]],\n","\n"," [[ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 6.5467e-01,\n"," -6.8451e-01, 6.5081e-01],\n"," [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3174e+00,\n"," 2.2258e-01, 8.8157e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., -1.6159e+00,\n"," 7.2677e-02, 1.1724e-01]]],\n","\n","\n"," [[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n"," -3.5687e-01, -2.6793e-01],\n"," [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n"," 1.8900e-01, 2.8282e-01]],\n","\n"," [[-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., 1.7508e+00,\n"," -3.6708e-01, -1.3251e+00],\n"," [ 7.9208e-01, -1.3537e-01, 2.3756e-01, ..., -5.9181e-01,\n"," 1.4373e+00, 2.4267e+00]],\n","\n"," [[-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -9.6646e-01,\n"," -4.8876e-01, -1.4426e+00],\n"," [ 1.0250e+00, -6.9093e-01, -1.2734e+00, ..., -5.9773e-01,\n"," -3.0482e-01, 1.4038e+00]],\n","\n"," ...,\n","\n"," [[ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n"," 1.0394e+00, 4.2402e-01],\n"," [-4.7386e-01, 2.6401e+00, 1.7024e+00, ..., -1.1508e+00,\n"," 7.5490e-01, 8.2911e-01]],\n","\n"," [[ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -6.1665e-01,\n"," 2.7627e-01, -1.2083e+00],\n"," [ 9.3395e-01, -9.7541e-01, -2.5442e-02, ..., -1.5233e+00,\n"," -6.0733e-01, 3.3097e-01]],\n","\n"," [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n"," -3.2756e-01, -6.3130e-01],\n"," [ 8.6773e-01, 2.0996e-01, -3.4332e-01, ..., 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4.2857e-01, ..., -1.1366e+00,\n"," -2.8409e+00, 4.6711e-01],\n"," [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n"," -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," ...,\n"," [ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02]],\n","\n"," [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 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-8.2075e-01, -1.1496e+00],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00],\n"," ...,\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]],\n"," grad_fn=)\n","tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n"," -6.5765e-01, -1.4711e-01],\n"," [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3793e-03,\n"," -5.2249e-01, -4.2095e-01]],\n","\n"," [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n"," -2.7815e-01, -2.6628e-01],\n"," [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n"," -4.1385e-01, -1.3744e+00]],\n","\n"," [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n"," -4.3860e-01, -2.1582e-01],\n"," [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n"," -1.6626e-01, -7.0940e-01]],\n","\n"," ...,\n","\n"," [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n"," 6.3807e-01, -8.3788e-02],\n"," [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n"," -6.9561e-01, -8.5675e-01]],\n","\n"," [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n"," 4.1589e-01, -1.8024e-01],\n"," [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n"," -1.0965e+00, -4.8097e-01]],\n","\n"," [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n"," 8.4397e-02, -6.9034e-02],\n"," [-1.0678e+00, 9.0885e-02, -4.5401e-02, ..., 1.5424e-01,\n"," -1.1762e+00, -2.9385e-01]]],\n","\n","\n"," [[[ 4.9445e-01, 9.6663e-03, -5.7117e-01, ..., -9.7640e-01,\n"," -9.0948e-01, -1.3761e-01],\n"," [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n"," -8.2075e-01, -1.1496e+00]],\n","\n"," [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n"," -3.9088e-01, -3.1635e-01],\n"," [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n"," -5.5070e-01, -1.8440e+00]],\n","\n"," [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n"," -4.7996e-01, -4.5624e-01],\n"," [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n"," -4.6589e-01, -2.0454e+00]],\n","\n"," ...,\n","\n"," [[ 3.3361e-01, 1.5917e-03, -1.0108e+00, ..., -1.5704e+00,\n"," -3.9079e-01, -2.1742e-01],\n"," [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n"," 3.1643e-01, -2.0784e+00]],\n","\n"," [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n"," 1.3298e-01, -1.5481e+00],\n"," [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n"," -1.0588e+00, -7.6781e-01]],\n","\n"," [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n"," -4.1355e-01, -5.8558e-01],\n"," [-1.6402e+00, 3.1250e-01, -6.6103e-04, ..., 6.6493e-01,\n"," -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n","tensor([[0.1030, 0.2155],\n"," [0.0341, 0.0664]], grad_fn=)\n","tensor([1, 0])\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," loaded_model = torch.load(f)\n","\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_0_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_query\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_key\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_self_value\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_attention_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_intermediate_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_encoder_layer_1_output_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: bert_pooler_dense\u001b[0m\n","\u001b[32mINFO \u001b[0m \u001b[34mPruning module: classifier\u001b[0m\n"]},{"output_type":"stream","name":"stdout","text":["Initial accuracy (method=random, sparsity=0.9): 0.84468\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Pruned accuracy (method=random, sparsity=0.9): 0.50224\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
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"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Fine-tuned accuracy (method=random, sparsity=0.9): 0.5034\n","{'sparsity': 0.2, 'l1_initial_pruned_accuracy': 0.83992, 'l1_fine_tuned_accuracy': 0.85832, 'rand_initial_pruned_accuracy': 0.6308, 'rand_fine_tuned_accuracy': 0.8232}\n","{'sparsity': 0.3, 'l1_initial_pruned_accuracy': 0.83004, 'l1_fine_tuned_accuracy': 0.85504, 'rand_initial_pruned_accuracy': 0.54652, 'rand_fine_tuned_accuracy': 0.80812}\n","{'sparsity': 0.4, 'l1_initial_pruned_accuracy': 0.80796, 'l1_fine_tuned_accuracy': 0.8468, 'rand_initial_pruned_accuracy': 0.50972, 'rand_fine_tuned_accuracy': 0.78696}\n","{'sparsity': 0.5, 'l1_initial_pruned_accuracy': 0.76728, 'l1_fine_tuned_accuracy': 0.84148, 'rand_initial_pruned_accuracy': 0.5066, 'rand_fine_tuned_accuracy': 0.74436}\n","{'sparsity': 0.6, 'l1_initial_pruned_accuracy': 0.563, 'l1_fine_tuned_accuracy': 0.82284, 'rand_initial_pruned_accuracy': 0.5046, 'rand_fine_tuned_accuracy': 0.52732}\n","{'sparsity': 0.7, 'l1_initial_pruned_accuracy': 0.54052, 'l1_fine_tuned_accuracy': 0.8098, 'rand_initial_pruned_accuracy': 0.50292, 'rand_fine_tuned_accuracy': 0.50436}\n","{'sparsity': 0.8, 'l1_initial_pruned_accuracy': 0.50076, 'l1_fine_tuned_accuracy': 0.76204, 'rand_initial_pruned_accuracy': 0.49896, 'rand_fine_tuned_accuracy': 0.504}\n","{'sparsity': 0.9, 'l1_initial_pruned_accuracy': 0.50312, 'l1_fine_tuned_accuracy': 0.56104, 'rand_initial_pruned_accuracy': 0.50224, 'rand_fine_tuned_accuracy': 0.5034}\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import matplotlib.ticker as mticker\n","\n","# Printed results were imported here\n","results = [\n"," {\n"," \"sparsity\": 0.1,\n"," \"l1_initial_pruned_accuracy\": 0.84368,\n"," \"l1_fine_tuned_accuracy\": 0.8604,\n"," \"rand_initial_pruned_accuracy\": 0.73676,\n"," \"rand_fine_tuned_accuracy\": 0.83952\n"," },\n"," {\n"," \"sparsity\": 0.2,\n"," \"l1_initial_pruned_accuracy\": 0.83992,\n"," \"l1_fine_tuned_accuracy\": 0.85832,\n"," \"rand_initial_pruned_accuracy\": 0.6308,\n"," \"rand_fine_tuned_accuracy\": 0.8232\n"," },\n"," {\n"," \"sparsity\": 0.3,\n"," \"l1_initial_pruned_accuracy\": 0.83004,\n"," \"l1_fine_tuned_accuracy\": 0.85504,\n"," \"rand_initial_pruned_accuracy\": 0.54652,\n"," \"rand_fine_tuned_accuracy\": 0.80812\n"," },\n"," {\n"," \"sparsity\": 0.4,\n"," \"l1_initial_pruned_accuracy\": 0.80796,\n"," \"l1_fine_tuned_accuracy\": 0.8468,\n"," \"rand_initial_pruned_accuracy\": 0.50972,\n"," \"rand_fine_tuned_accuracy\": 0.78696\n"," },\n"," {\n"," \"sparsity\": 0.5,\n"," \"l1_initial_pruned_accuracy\": 0.76728,\n"," \"l1_fine_tuned_accuracy\": 0.84148,\n"," \"rand_initial_pruned_accuracy\": 0.5066,\n"," \"rand_fine_tuned_accuracy\": 0.74436\n"," },\n"," {\n"," \"sparsity\": 0.6,\n"," \"l1_initial_pruned_accuracy\": 0.563,\n"," \"l1_fine_tuned_accuracy\": 0.82284,\n"," \"rand_initial_pruned_accuracy\": 0.5046,\n"," \"rand_fine_tuned_accuracy\": 0.52732\n"," },\n"," {\n"," \"sparsity\": 0.7,\n"," \"l1_initial_pruned_accuracy\": 0.54052,\n"," \"l1_fine_tuned_accuracy\": 0.8098,\n"," \"rand_initial_pruned_accuracy\": 0.50292,\n"," \"rand_fine_tuned_accuracy\": 0.50436\n"," },\n"," {\n"," \"sparsity\": 0.8,\n"," \"l1_initial_pruned_accuracy\": 0.50076,\n"," \"l1_fine_tuned_accuracy\": 0.76204,\n"," \"rand_initial_pruned_accuracy\": 0.49896,\n"," \"rand_fine_tuned_accuracy\": 0.504\n"," },\n"," {\n"," \"sparsity\": 0.9,\n"," \"l1_initial_pruned_accuracy\": 0.50312,\n"," \"l1_fine_tuned_accuracy\": 0.56104,\n"," \"rand_initial_pruned_accuracy\": 0.50224,\n"," \"rand_fine_tuned_accuracy\": 0.5034\n"," }\n","]\n","\n","def plot_pruning_results_separated(results):\n"," # Extract sparsity levels\n"," sparsities = [res[\"sparsity\"] for res in results]\n","\n"," # Extract accuracies\n"," l1_initial = [res[\"l1_initial_pruned_accuracy\"] for res in results]\n"," l1_fine_tuned = [res[\"l1_fine_tuned_accuracy\"] for res in results]\n"," rand_initial = [res[\"rand_initial_pruned_accuracy\"] for res in results]\n"," rand_fine_tuned = [res[\"rand_fine_tuned_accuracy\"] for res in results]\n","\n"," # Calculate highest achieved accuracy per sparsity\n"," highest_accuracies = [\n"," max(filter(None, [l1_init, l1_fine, rand_init, rand_fine]))\n"," for l1_init, l1_fine, rand_init, rand_fine in zip(l1_initial, l1_fine_tuned, rand_initial, rand_fine_tuned)\n"," ]\n","\n"," # Plot 1: Highest achieved accuracy per sparsity\n"," plt.figure(figsize=(10, 6))\n"," plt.plot(\n"," sparsities,\n"," highest_accuracies,\n"," marker=\"o\",\n"," linestyle=\"--\",\n"," linewidth=2,\n"," label=\"Highest Accuracy\",\n"," )\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Sparsity\", fontsize=12)\n"," plt.ylabel(\"Accuracy\", fontsize=12)\n"," plt.title(\"Highest Achieved Accuracy by Sparsity\", fontsize=14, pad=15)\n"," plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x*100:.0f}%\"))\n"," for x, y in zip(sparsities, highest_accuracies):\n"," plt.text(x, y + 0.005, f\"{y*100:.1f}%\", fontsize=8, ha=\"center\")\n"," plt.tight_layout()\n"," plt.savefig(\"highest_accuracy_by_sparsity.png\", dpi=300)\n"," plt.show()\n","\n"," # Plot 2: Random and L1-Norm accuracy curves\n"," plt.figure(figsize=(10, 6))\n"," rand_accuracies = [\n"," max(filter(None, [rand_init, rand_fine]))\n"," for rand_init, rand_fine in zip(rand_initial, rand_fine_tuned)\n"," ]\n"," l1_accuracies = [\n"," max(filter(None, [l1_init, l1_fine]))\n"," for l1_init, l1_fine in zip(l1_initial, l1_fine_tuned)\n"," ]\n"," plt.plot(\n"," sparsities,\n"," rand_accuracies,\n"," marker=\"s\",\n"," linestyle=\"-.\",\n"," linewidth=2,\n"," label=\"Random Pruning\",\n"," )\n"," plt.plot(\n"," sparsities,\n"," l1_accuracies,\n"," marker=\"d\",\n"," linestyle=\":\",\n"," linewidth=2,\n"," label=\"L1-Norm Pruning\",\n"," )\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Sparsity\", fontsize=12)\n"," plt.ylabel(\"Accuracy\", fontsize=12)\n"," plt.title(\"Effect of Sparsity on Pruning Accuracy\", fontsize=14, pad=15)\n"," plt.legend(fontsize=10, loc=\"lower left\")\n"," plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x*100:.0f}%\"))\n"," for x, y in zip(sparsities, rand_accuracies):\n"," plt.text(x, y + 0.005, f\"{y*100:.1f}%\", fontsize=8, ha=\"center\")\n"," for x, y in zip(sparsities, l1_accuracies):\n"," plt.text(x, y + 0.005, f\"{y*100:.1f}%\", fontsize=8, ha=\"center\")\n"," plt.tight_layout()\n"," plt.savefig(\"pruning_accuracy_by_sparsity.png\", dpi=300)\n"," plt.show()\n","\n","# Call the function\n","plot_pruning_results_separated(results)"],"metadata":{"id":"w47lNYtgeJj-"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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\ No newline at end of file diff --git a/Working_Data/edited-colab-files/tutorial_5_nas_optuna.ipynb b/Working_Data/edited-colab-files/tutorial_5_nas_optuna.ipynb new file mode 100644 index 000000000..c48fbee60 --- /dev/null +++ b/Working_Data/edited-colab-files/tutorial_5_nas_optuna.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"7DydA7nIqfRn"},"source":["# Tutorial 5: Neural Architecture Search (NAS) with Mase and Optuna"]},{"cell_type":"markdown","metadata":{"id":"R2aRJzOUqfRq"},"source":["In this tutorial, we'll see how Mase can be integrated with Optuna, the popular hyperparameter optimization framework, to search for a Bert model optimized for sequence classification on the IMDb dataset. We'll take the Optuna-generated model and import it into Mase, then run the CompressionPipeline to prepare the model for edge deployment by quantizing and pruning its weights.\n","\n","As we'll see, running Architecture Search with Mase/Optuna involves the following steps.\n","\n","1. **Define the search space**: this is a dictionary containing the range of values for each parameter at each layer in the model.\n","\n","2. **Write the model constructor**: this is a function which uses Optuna utilities to sample a model from the search space, and constructs the model using transformers from_config class method.\n","\n","3. **Write the objective function**: this function calls on the model constructor defined in Step 2 and defines the training/evaluation setup for each search iteration.\n","\n","4. **Go!** Choose an Optuna sampler, create a study and launch the search."]},{"cell_type":"code","source":["!git clone https://github.com/tonytarizzo/mase.git\n","%cd mase\n","!python -m pip install -e . -vvv\n","%cd src"],"metadata":{"id":"6jpiGuBkqjCI"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"j2Zk7wOxqfRr"},"outputs":[],"source":["checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\""]},{"cell_type":"markdown","metadata":{"id":"bVufzaS4qfRs"},"source":["First, fetch the dataset using the `get_tokenized_dataset` utility."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":610,"referenced_widgets":["1d55b6eca3454c13a1a4600f98c137d5","2efa9bd3a98041bc8f9f6008a27f6b3d","101fb2858f1f4e16aa0685b96617602c","368f8d55bf6548f7bb7bbf173908219a","e164b3a3d3024ee5af35e9806a656b7b","db8a587589c7490b81325ef078a3bac2","350123e8b3c1459e93ffec4740e81c6e","5f2c284e24634a47a39068fa3267ea71","d6a3d501de074dfd83c73db7b4226fa4","52a653a253a3467c9d22e459fabc81cc","41d14fc46d7b4b7b9eee599aa6025dcd","8531adfebe734aa993e8e3e8682fc041","d7d5130b486a467bb874ab301ea6d2ea","c41c5abf4931434b9728d7163fcd4c0d","1e9a6229d2fd4d9ea8823f8124222cf6","39fcf6742d3645a98e9a68e1f368646d","6b9bb476b57f4b36a922821a0f29be44","65ca1d430e664b29b8a3950ab835e3b7","2537a4d68bb64e569ab4fd341e1d5da6","fa57368075884e73a3ac765f6947ef60","7b08f6fb89484c3cab4b32a163fdab04","17ef2bd427d84a4799fe60494e9d80d3","315eac70e820433990b9985d04d914c8","9cfb7b11764e4bc39203e5757a54571b","21ffcffe702941b8b289b7c07571369f","8c2df7867dda448f860b64b8958f5853","2d1adfc4dfa54e91ab273fa17e5c39a5","31b6cbc2c7b0493795fbd8b8848432d3","c11cee74e91444bf93d50c193bf97cd5","e258545ad6684eb1bee19313a460aef8","0b420d56be1244c3b9c7e62b7d452497","8134d9b20f1448f1ae6ac92801df473f","1222e5a4ce5040f0b913fbc36657174b","4579e49b5a20466cae36c1de2ca1d461","7705c1e8312642b3be9d7cd313d15a90","9891800707f64a869498ddd1f9daf188","296efb81b12047518742be2ff3199e43","ae705d9e96ed4b309b1a5c279bad613c","c5f8e88d22ca444a8d86374a8d065f4f","fccfdf89139a4bbda20282fe44bb9e58","68867f94013b4d739a0fe60b05ac4656","d331551901784aafa2945332c9690139","c81414a488c3414088b1463672b94911","490a6609d04b4e1485d92c2620c36cb3","a180999cde5d4e8faa722bcbb5d7c77d","2b1137227071447a98791ae1304260f5","1bc34894efeb41b2923ac6e8b829026c","3559bf53c7fc4e9292bff7f4c325c558","e9e660958792448996a23b1dd7f34aed","94e8065d3d7f4359840718cfc0ac0b76","d4aad28a1aa543bf895b3d70c1cb0aaa","500f5929ef8e490fa2f2bee7ed9550ec","c70e1ba4467743129c83844b0ac7e7d4","c1cf48ceec8346db911331811fd2da0d","24ebd1e0b45e4ca0b167a3eaa86f6ff2","1fd052aaf12c472c9c2575617efe51cd","f35bd50c4d9549feb197b332e7919d7a","c996037372c54458b0be08be15099988","92b2532554ed47b890684e1bd4653540","78fa821b998749dd9b62f189844bbda6","60e1fad2fbab4b059b7068df7205e974","1c282627c25e42359ba7f6023d6da7fa","c9a239dcc5244be599c793c1ae8aa8cd","50ad4c3e48d142e899f8d07ccd04c90a","34b1e37d0ea142489a87c222799cc52a","5a9b45c6ba834ac9af0f1c4cde87b87e","4bc7b14bce554d068dde710a6dc74a5e","22649d0a0dc84cc3bba283edbb08feaa","eb82e80362e44a06b7979c97a327f845","ff293637dfc642e38174778e04353e6e","ff8404f84b894629922a79fdd5eb3a7a","cf0f4368bde943e1acebe4ef59e96627","aa0c9d1e72bc42ada936146241d95c0b","b16b6c35210a4f5eba4059016c997f17","e0835a67cb9e451f928eb7bfe18bf55d","c1efb4feeeae4b62bbc12d2fec16c295","dff7d5ff1dc4424e960dc37a6023ad29","bfeba6d577194e5bb1a565a43bbbf08a","4c83670bbf014f1ab116eae657782c4b","8a5a5ed6ab7f45348dd280423bbf9c8d","9bef16ca9538448a9d731625b0272658","f2d77d8f87dd43ed9b046d9e3cb68707","4d574bf47f554ce8b3fefcbccccaf634","9759b0969f8e4bc38854077edf632f21","f8e389e4f9b449d3a93f9654ed4ec005","7c910a000fa44cbdae82fe83d3be8ee1","fa28ff80207e4b25aa81c638f3078c4c","a3a4e21c3eba40aea3a4f4bb87e39b31","fa9778b1283b4219abaacbc62f20daef","f48711b9132b49beabc1c15ca3ecc4a1","14d152ed45cf4278a12787ab8975534d","820f40c6c2694617829c859f927e36e9","4d67d1088c6141ebb38b3a124fa72d75","bfd6aadb20a24da8b31d5fe92e51aea9","9b59ed5586ea4fbb818b88ee425ee6dd","e56c25fa241b4170a729b1c4f18727fa","1f9585de3d104ad9b0191fea40e47d16","1da7f9ce85b64a4fbc264ffca0391aec","e1a4f60e5b014578942503d3c8b5c59e","be21715af1504819a46987e2500420fd","42b8fdf4169e4a45aa5d64a8065fb3c7","025d0074e47c4c9283f36658c37b1f79","c38531adccf04a7f9c4176deeeeb1444","c03247b007b0436e9d9191c5a2452cb3","72977759b39b47068b2cafa8285a45f0","68e8469b7b2a47f98c47eeb377d5324c","4b69442dc945425f92fa5aa1b1f49a7f","8a56bcaeb37341e382cc1679d763d7cc","ab6218ae64264020b341ff63630c11d7","bbfd4c3a2f0e46f294090603f5f3d30c","d54f578ff8a346b587ed2a830e70e579","0ba8de6f9baa47e894574aacc6a4e795","283a5d9c0b5b43e7a8e18cd0fc263d44","04a6b1953c784b6e80290be07ae6cce2","c508b2b7dd394714b8997cffb7055bab","519c0eb9b66349aab8fe8bcf3e17a99e","b9ac3357693d4716bfd973d0ceb38490","dda246d524f14319b2aac4004b3a5197","ac5d6fa3ad154751a2b37c5904748394","7eaaff24d7004727b4cebb733ee07505","e2c3fd20c7394e2298edfc6be929a253","a256020d917b43ab92a286c17c42f38b","26c08e083b4d4e11adc0c230b4a7017a","041a691dc47445a9a03fa4eb943942c7","f5ebbc1012d3432fae75580378bb3715","14375e7d4d374ab08d2793dfca1cc772","6c752f37bcb94f85becab7446ad1ac10","6754e2c7d6634243bd3435ab20db1b85","1ed5e27b76f44e9cb30f4fa920443aa3","60fec928a7d043bbba25f8962a666d12","0a3c59020e87431ea2e9bfe3ea510353","170753cf5cf545879e68fba4945f861a","bdf379efe9cc47809b2a685405b5a5df","8c86e08ce9cb4108b3bc4d270fa66d03","730fee4b7acc40dbb4e8eee97ec2c040","0b86ca173da74e5f92644dd4120d739f","44eeb157b4734f0abe6fab89a10e744d","fd61f6548eaa4f5f9b72388c37a9ae2d","e86920e43af447fd8832e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Tarizzo","userId":"03279709671483876992"}},"outputId":"0201f3f6-c9e8-4e4c-a78b-b6af7d3261e6"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0%| | 0.00/7.81k [00:00 current_max:\n"," current_max = t.value\n"," running_max_accuracies.append(current_max)\n","\n"," return study, running_max_accuracies\n","\n","def save_study_results_to_csv(study, filename):\n"," \"\"\"\n"," Saves each trial's results into a CSV, including:\n"," - trial number\n"," - objective value (accuracy)\n"," - parameters\n"," - model config parameters\n"," \"\"\"\n"," rows = []\n"," for t in study.trials:\n"," row = {\n"," \"trial_number\": t.number,\n"," \"accuracy\": t.value,\n"," }\n"," # Merge in parameter key-value pairs directly\n"," row.update(t.params)\n","\n"," # Add model config if it exists in user attributes\n"," if \"model\" in t.user_attrs:\n"," model_config = t.user_attrs[\"model\"].config.to_dict()\n"," for key, value in model_config.items():\n"," row[f\"config_{key}\"] = value\n","\n"," rows.append(row)\n","\n"," df = pd.DataFrame(rows)\n"," df.to_csv(filename, index=False)\n"," print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n","\n","if __name__ == \"__main__\":\n"," random_sampler = RandomSampler()\n"," random_study, random_max_curve = run_study_and_get_curve(\n"," sampler=random_sampler,\n"," n_trials=10,\n"," study_name=\"bert-random-study\",\n"," )\n","\n"," best_random_model = random_study.best_trial.user_attrs[\"model\"].cpu()\n"," with open(\"best_random_model.pkl\", \"wb\") as f:\n"," dill.dump(best_random_model, f)\n","\n"," save_study_results_to_csv(random_study, \"random_study_trials.csv\")"],"metadata":{"id":"9JzrYU3pyDRZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Part 5a, full code for grid method ###\n","import torch.nn as nn\n","from chop.nn.modules import Identity\n","from transformers import AutoConfig, AutoModelForSequenceClassification\n","from chop.tools.utils import deepsetattr\n","from chop.tools import get_tokenized_dataset, get_trainer\n","import optuna\n","from optuna.samplers import GridSampler, TPESampler, RandomSampler\n","import matplotlib.pyplot as plt\n","import dill\n","from pathlib import Path\n","import pandas as pd\n","\n","# Load starting point from checkpoint\n","checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\"\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","grid_search_space = {\n"," \"num_layers\": [2, 4, 8],\n"," \"num_heads\": [2, 4, 8, 16],\n"," \"hidden_size\": [128, 192, 256, 384, 512],\n"," \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n"," \"linear_layer_choices\": [\"linear\", \"identity\"],\n","}\n","\n","def construct_model(trial):\n"," # Loads the default config from a small BERT checkpoint\n"," config = AutoConfig.from_pretrained(checkpoint)\n","\n"," # Map parameters to config\n"," config.num_hidden_layers = trial.suggest_categorical(\n"," \"num_layers\", grid_search_space[\"num_layers\"]\n"," )\n"," config.num_attention_heads = trial.suggest_categorical(\n"," \"num_heads\", grid_search_space[\"num_heads\"]\n"," )\n"," config.hidden_size = trial.suggest_categorical(\n"," \"hidden_size\", grid_search_space[\"hidden_size\"]\n"," )\n"," config.intermediate_size = trial.suggest_categorical(\n"," \"intermediate_size\", grid_search_space[\"intermediate_size\"]\n"," )\n","\n"," model = AutoModelForSequenceClassification.from_config(config)\n","\n"," # Handle linear layer choice\n"," linear_choice = trial.suggest_categorical(\n"," \"linear_layer_choices\", grid_search_space[\"linear_layer_choices\"]\n"," )\n"," if linear_choice == \"identity\":\n"," for name, layer in model.named_modules():\n"," if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n"," deepsetattr(model, name, Identity())\n","\n"," return model\n","\n","def objective(trial):\n"," # Builds the model given the trial input\n"," trial_model = construct_model(trial)\n","\n"," trainer = get_trainer(\n"," model=trial_model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n","\n"," # Train, evaluate and return\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n"," trial.set_user_attr(\"model\", trial_model)\n"," return eval_results[\"eval_accuracy\"]\n","\n","def run_study_and_get_curve(sampler, n_trials=None, study_name=\"study\"):\n"," \"\"\"\n"," Runs an Optuna study with the provided sampler and returns:\n"," - the study object\n"," - a list of best accuracies up to each trial (running max)\n"," \"\"\"\n"," study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=study_name,\n"," sampler=sampler,\n"," )\n","\n"," study.optimize(\n"," objective,\n"," n_trials=n_trials,\n"," timeout=60 * 60 * 24,\n"," show_progress_bar=False,\n"," )\n","\n"," # Retains the maximum accuracies reached\n"," running_max_accuracies = []\n"," current_max = 0.0\n"," for t in study.trials:\n"," if t.value is not None and t.value > current_max:\n"," current_max = t.value\n"," running_max_accuracies.append(current_max)\n","\n"," return study, running_max_accuracies\n","\n","def save_study_results_to_csv(study, filename):\n"," \"\"\"\n"," Saves each trial's results into a CSV, including:\n"," - trial number\n"," - objective value (accuracy)\n"," - parameters\n"," - model config parameters\n"," \"\"\"\n"," rows = []\n"," for t in study.trials:\n"," row = {\n"," \"trial_number\": t.number,\n"," \"accuracy\": t.value,\n"," }\n"," # Merge in parameter key-value pairs directly\n"," row.update(t.params)\n","\n"," # Add model config if it exists in user attributes\n"," if \"model\" in t.user_attrs:\n"," model_config = t.user_attrs[\"model\"].config.to_dict()\n"," for key, value in model_config.items():\n"," row[f\"config_{key}\"] = value\n","\n"," rows.append(row)\n","\n"," df = pd.DataFrame(rows)\n"," df.to_csv(filename, index=False)\n"," print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n","\n","if __name__ == \"__main__\":\n"," grid_sampler = GridSampler(\n"," search_space={\n"," \"num_layers\": grid_search_space[\"num_layers\"],\n"," \"num_heads\": grid_search_space[\"num_heads\"],\n"," \"hidden_size\": grid_search_space[\"hidden_size\"],\n"," \"intermediate_size\": grid_search_space[\"intermediate_size\"],\n"," \"linear_layer_choices\": grid_search_space[\"linear_layer_choices\"],\n"," }\n"," )\n","\n"," grid_study, grid_max_curve = run_study_and_get_curve(\n"," sampler=grid_sampler,\n"," n_trials=10,\n"," study_name=\"bert-grid-study\",\n"," )\n","\n"," best_grid_model = grid_study.best_trial.user_attrs[\"model\"].cpu()\n"," with open(\"best_grid_model.pkl\", \"wb\") as f:\n"," dill.dump(best_grid_model, f)\n","\n"," save_study_results_to_csv(grid_study, \"grid_study_trials.csv\")"],"metadata":{"id":"WLBgJEB5DcCb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Part 5a, full code for TPE method ###\n","import torch.nn as nn\n","from chop.nn.modules import Identity\n","from transformers import AutoConfig, AutoModelForSequenceClassification\n","from chop.tools.utils import deepsetattr\n","from chop.tools import get_tokenized_dataset, get_trainer\n","import optuna\n","from optuna.samplers import GridSampler, TPESampler, RandomSampler\n","import matplotlib.pyplot as plt\n","import dill\n","from pathlib import Path\n","import pandas as pd\n","\n","# Load starting point from checkpoint\n","checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\"\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","tpe_search_space = {\n"," \"num_layers\": [2, 4, 8],\n"," \"num_heads\": [2, 4, 8, 16],\n"," \"hidden_size\": [128, 192, 256, 384, 512],\n"," \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n"," \"linear_layer_choices\": [\"linear\", \"identity\"],\n","}\n","\n","def construct_model(trial):\n"," # Loads the default config from a small BERT checkpoint\n"," config = AutoConfig.from_pretrained(checkpoint)\n","\n"," # Map parameters to config\n"," config.num_hidden_layers = trial.suggest_categorical(\n"," \"num_layers\", tpe_search_space[\"num_layers\"]\n"," )\n"," config.num_attention_heads = trial.suggest_categorical(\n"," \"num_heads\", tpe_search_space[\"num_heads\"]\n"," )\n"," config.hidden_size = trial.suggest_categorical(\n"," \"hidden_size\", tpe_search_space[\"hidden_size\"]\n"," )\n"," config.intermediate_size = trial.suggest_categorical(\n"," \"intermediate_size\", tpe_search_space[\"intermediate_size\"]\n"," )\n","\n"," model = AutoModelForSequenceClassification.from_config(config)\n","\n"," # Handle linear layer choice\n"," linear_choice = trial.suggest_categorical(\n"," \"linear_layer_choices\", tpe_search_space[\"linear_layer_choices\"]\n"," )\n"," if linear_choice == \"identity\":\n"," for name, layer in model.named_modules():\n"," if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n"," deepsetattr(model, name, Identity())\n","\n"," return model\n","\n","def objective(trial):\n"," # Builds the model given the trial input\n"," trial_model = construct_model(trial)\n","\n"," trainer = get_trainer(\n"," model=trial_model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n","\n"," # Train, evaluate and return\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n"," trial.set_user_attr(\"model\", trial_model)\n"," return eval_results[\"eval_accuracy\"]\n","\n","def run_study_and_get_curve(sampler, n_trials=None, study_name=\"study\"):\n"," \"\"\"\n"," Runs an Optuna study with the provided sampler and returns:\n"," - the study object\n"," - a list of best accuracies up to each trial (running max)\n"," \"\"\"\n"," study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=study_name,\n"," sampler=sampler,\n"," )\n","\n"," study.optimize(\n"," objective,\n"," n_trials=n_trials,\n"," timeout=60 * 60 * 24,\n"," show_progress_bar=False,\n"," )\n","\n"," # Retains the maximum accuracies reached\n"," running_max_accuracies = []\n"," current_max = 0.0\n"," for t in study.trials:\n"," if t.value is not None and t.value > current_max:\n"," current_max = t.value\n"," running_max_accuracies.append(current_max)\n","\n"," return study, running_max_accuracies\n","\n","def save_study_results_to_csv(study, filename):\n"," \"\"\"\n"," Saves each trial's results into a CSV, including:\n"," - trial number\n"," - objective value (accuracy)\n"," - parameters\n"," - model config parameters\n"," \"\"\"\n"," rows = []\n"," for t in study.trials:\n"," row = {\n"," \"trial_number\": t.number,\n"," \"accuracy\": t.value,\n"," }\n"," # Merge in parameter key-value pairs directly\n"," row.update(t.params)\n","\n"," # Add model config if it exists in user attributes\n"," if \"model\" in t.user_attrs:\n"," model_config = t.user_attrs[\"model\"].config.to_dict()\n"," for key, value in model_config.items():\n"," row[f\"config_{key}\"] = value\n","\n"," rows.append(row)\n","\n"," df = pd.DataFrame(rows)\n"," df.to_csv(filename, index=False)\n"," print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n","\n","if __name__ == \"__main__\":\n"," tpe_sampler = TPESampler()\n","\n"," tpe_study, tpe_max_curve = run_study_and_get_curve(\n"," sampler=tpe_sampler,\n"," n_trials=10,\n"," study_name=\"bert-tpe-study\",\n"," )\n","\n"," best_tpe_model = tpe_study.best_trial.user_attrs[\"model\"].cpu()\n"," with open(\"best_tpe_model.pkl\", \"wb\") as f:\n"," dill.dump(best_tpe_model, f)\n","\n"," save_study_results_to_csv(tpe_study, \"tpe_study_trials.csv\")"],"metadata":{"id":"VrTOwoZ6EO47"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Helper function that downloaded all CSVs when running finished, in case Colab was disconnected for inactivity.\n","import glob\n","from google.colab import files\n","\n","def download_all_csvs():\n"," \"\"\"\n"," Find all CSV files in the directory structure and download them to the local machine.\n"," Handles errors gracefully and provides feedback for each file.\n"," \"\"\"\n"," try:\n"," # Find all CSV files (searches recursively)\n"," csv_files = glob.glob(\"**/*.csv\", recursive=True)\n","\n"," if not csv_files:\n"," print(\"No CSV files found.\")\n"," return\n","\n"," print(f\"Found {len(csv_files)} CSV file(s):\")\n"," for file in csv_files:\n"," print(f\"- {file}\")\n","\n"," # Download each CSV file\n"," for file in csv_files:\n"," try:\n"," print(f\"Downloading {file}...\")\n"," files.download(file)\n"," except Exception as e:\n"," print(f\"Error downloading {file}: {e}\")\n","\n"," print(\"All CSV files processed.\")\n","\n"," except Exception as e:\n"," print(f\"An error occurred: {e}\")\n","\n","download_all_csvs()\n"],"metadata":{"id":"WPgeq_78er72"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Plotting Function for printing the grid, random and TPE results ###\n","import matplotlib.pyplot as plt\n","import matplotlib.ticker as mticker\n","import pandas as pd\n","\n","def plot_combined_optuna_results(csv_paths):\n"," plt.figure(figsize=(10, 6))\n","\n"," for csv_path in csv_paths:\n"," data = pd.read_csv(csv_path)\n"," accuracies = data[\"accuracy\"].tolist()\n","\n"," running_max = []\n"," current_max = float('-inf')\n"," for acc in accuracies:\n"," current_max = max(current_max, acc)\n"," running_max.append(current_max)\n","\n"," # Determine the label based on the file name\n"," if 'random' in csv_path.lower():\n"," label = 'Random Search'\n"," elif 'grid' in csv_path.lower():\n"," label = 'Grid Search'\n"," elif 'tpe' in csv_path.lower():\n"," label = 'TPE Search'\n"," else:\n"," label = 'Unknown Search Method'\n","\n"," # Plot the running maximum for the current search method\n"," plt.plot(\n"," range(len(running_max)),\n"," running_max,\n"," marker=\"o\",\n"," linestyle=\"--\",\n"," linewidth=2,\n"," label=label,\n"," )\n","\n"," # Grid and labels\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Number of Trials\", fontsize=12)\n"," plt.ylabel(\"Maximum Achieved Accuracy\", fontsize=12)\n"," plt.title(\"Comparison of Maximum Achieved Accuracy by Search Method\", fontsize=14, pad=15)\n","\n"," # Format y-axis as percentage\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.1f}%\"))\n","\n"," # Legend and layout\n"," plt.legend(fontsize=10, loc=\"lower right\")\n"," plt.tight_layout()\n"," plt.savefig('combined_optuna_results.png', dpi=300)\n"," plt.show()\n","\n","csv_paths = [\n"," 'random_study_trials.csv',\n"," 'grid_study_trials.csv',\n"," 'tpe_study_trials.csv',\n","]\n","\n","plot_combined_optuna_results(csv_paths)\n"],"metadata":{"id":"K7hYdedcwU5s"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Part 5b, Initial Implementation ###\n","import torch\n","from chop.tools import get_tokenized_dataset\n","import torch.nn as nn\n","from chop.nn.modules import Identity\n","from transformers import AutoConfig, AutoModelForSequenceClassification\n","from chop.tools.utils import deepsetattr\n","from chop.tools import get_trainer\n","import optuna\n","from optuna.samplers import GridSampler, RandomSampler, TPESampler\n","from pathlib import Path\n","import dill\n","from chop.pipelines import CompressionPipeline\n","from chop import MaseGraph\n","import chop.passes as passes\n","import inspect\n","\n","\n","def construct_model(trial):\n"," # Loads the default config from a small BERT checkpoint\n"," config = AutoConfig.from_pretrained(checkpoint)\n","\n"," # Map parameters to config\n"," config.num_hidden_layers = trial.suggest_categorical(\n"," \"num_layers\", search_space[\"num_layers\"]\n"," )\n"," config.num_attention_heads = trial.suggest_categorical(\n"," \"num_heads\", search_space[\"num_heads\"]\n"," )\n"," config.hidden_size = trial.suggest_categorical(\n"," \"hidden_size\", search_space[\"hidden_size\"]\n"," )\n"," config.intermediate_size = trial.suggest_categorical(\n"," \"intermediate_size\", search_space[\"intermediate_size\"]\n"," )\n","\n"," model = AutoModelForSequenceClassification.from_config(config)\n","\n"," linear_choice = trial.suggest_categorical(\n"," \"linear_layer_choices\", search_space[\"linear_layer_choices\"]\n"," )\n"," if linear_choice == \"identity\":\n"," for name, layer in model.named_modules():\n"," if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n"," deepsetattr(model, name, Identity())\n","\n"," return model\n","\n","def objective(trial):\n"," # Define the model and ensure it is on the correct device\n"," device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"," print(f\"[INFO] Model is being initialized on: {device}\")\n","\n"," model = construct_model(trial).to(device)\n"," print(f\"[INFO] Model moved to: {next(model.parameters()).device}\")\n","\n"," original_forward = model.forward\n"," model.config.use_cache = False\n","\n"," # Set up the trainer\n"," trainer = get_trainer(\n"," model=model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n","\n"," print(\"[INFO] Starting initial training...\")\n"," trainer.train()\n","\n"," # Move model to CPU after training\n"," model.cpu()\n"," print(f\"[INFO] Model moved to: {next(model.parameters()).device} after training\")\n","\n"," mg = MaseGraph(\n"," model,\n"," hf_input_names=[\n"," \"input_ids\",\n"," \"attention_mask\",\n"," \"labels\",\n"," ],\n"," )\n","\n"," mg, _ = passes.init_metadata_analysis_pass(mg)\n"," mg, _ = passes.add_common_metadata_analysis_pass(mg)\n"," pipe = CompressionPipeline()\n","\n"," quantization_config = {\n"," \"by\": \"type\",\n"," \"default\": {\"config\": {\"name\": None}},\n"," \"linear\": {\n"," \"config\": {\n"," \"name\": \"integer\",\n"," \"data_in_width\": 8,\n"," \"data_in_frac_width\": 4,\n"," \"weight_width\": 8,\n"," \"weight_frac_width\": 4,\n"," \"bias_width\": 8,\n"," \"bias_frac_width\": 4,\n"," }\n"," },\n"," }\n","\n"," pruning_config = {\n"," \"weight\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n"," \"activation\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n"," }\n","\n"," print(\"[INFO] Applying compression pipeline (quantization & pruning)...\")\n"," mg, _ = pipe(\n"," mg,\n"," pass_args={\n"," \"quantize_transform_pass\": quantization_config,\n"," \"prune_transform_pass\": pruning_config,\n"," },\n"," )\n","\n"," compressed_model = mg.model.to(device)\n"," print(f\"[INFO] Compressed model class: {compressed_model.__class__}\")\n"," print(f\"[INFO] Compressed model moved to: {next(compressed_model.parameters()).device}\")\n","\n"," print(\"[INFO] Reassigning original forward to the compressed model...\")\n"," print_model_forward_info(compressed_model, label=\"Model AFTER compression\")\n","\n"," # Continue training the compressed model\n"," trainer = get_trainer(\n"," model=compressed_model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3, # Post-compression training phase\n"," )\n","\n"," print(\"[INFO] Starting post-compression training...\")\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n","\n"," print(f\"[INFO] Final evaluation accuracy: {eval_results['eval_accuracy']:.4f}\")\n"," trial.set_user_attr(\"model\", model)\n"," return eval_results[\"eval_accuracy\"]\n","\n","\n","def main_run():\n"," global checkpoint, tokenizer, dataset, search_space\n","\n"," checkpoint = \"prajjwal1/bert-tiny\"\n"," tokenizer_checkpoint = \"bert-base-uncased\"\n"," dataset_name = \"imdb\"\n","\n"," dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n"," )\n","\n"," sampler = TPESampler()\n","\n"," search_space = {\n"," \"num_layers\": [2, 4, 8],\n"," \"num_heads\": [2, 4, 8, 16],\n"," \"hidden_size\": [128, 192, 256, 384, 512],\n"," \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n"," \"linear_layer_choices\": [\"linear\", \"identity\"],\n"," }\n","\n"," study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=\"bert-tiny-compression-aware-study\",\n"," sampler=sampler,\n"," )\n","\n"," print('Reached!')\n"," study.optimize(\n"," objective,\n"," n_trials=3,\n"," timeout=60 * 60 * 24,\n"," )\n","\n"," # Save the best model\n"," try:\n"," model = study.best_trial.user_attrs[\"model\"].cpu()\n"," except AttributeError: # If `.cpu()` fails, fetch without it\n"," model = study.best_trial.user_attrs[\"model\"]\n","\n"," with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"wb\") as f:\n"," dill.dump(model, f)\n","\n","main_run()"],"metadata":{"id":"CKH5fY9WBOGJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def load_results_from_csv(filepath):\n"," return pd.read_csv(filepath)\n","\n","# Load the results\n","csv_filepath = \"formatted_training_results.csv\"\n","results_df = load_results_from_csv(csv_filepath)\n","\n","# Function to plot the compression-aware results\n","def plot_compression_aware_results(results_df):\n"," # Separate results by complexity type\n"," no_compression = results_df[results_df[\"complexity\"] == \"No Compression\"]\n"," compression_no_post = results_df[results_df[\"complexity\"] == \"Compression-aware (No Post-Training)\"]\n"," compression_with_post = results_df[results_df[\"complexity\"] == \"Compression-aware (Post-Training)\"]\n","\n"," # Helper to compute running max\n"," def get_running_max(values):\n"," running_max = []\n"," current_max = float('-inf')\n"," for val in values:\n"," current_max = max(current_max, val)\n"," running_max.append(current_max)\n"," return running_max\n","\n"," # Compute running max for each scenario\n"," no_compression_max = get_running_max(no_compression[\"value\"].tolist())\n"," compression_no_post_max = get_running_max(compression_no_post[\"value\"].tolist())\n"," compression_with_post_max = get_running_max(compression_with_post[\"value\"].tolist())\n","\n"," # Trials count\n"," trials_no_compression = no_compression[\"trial\"].tolist()\n"," trials_compression_no_post = compression_no_post[\"trial\"].tolist()\n"," trials_compression_with_post = compression_with_post[\"trial\"].tolist()\n","\n"," # Plot the results\n"," plt.figure(figsize=(10, 6))\n","\n"," # No Compression\n"," plt.plot(\n"," trials_no_compression,\n"," no_compression_max,\n"," marker=\"o\",\n"," linestyle=\"--\",\n"," linewidth=2,\n"," label=\"No Compression\",\n"," )\n","\n"," # Compression-aware (No Post-Training)\n"," plt.plot(\n"," trials_compression_no_post,\n"," compression_no_post_max,\n"," marker=\"s\",\n"," linestyle=\"-.\",\n"," linewidth=2,\n"," label=\"Compression-aware (No Post-Training)\",\n"," )\n","\n"," # Compression-aware (Post-Training)\n"," plt.plot(\n"," trials_compression_with_post,\n"," compression_with_post_max,\n"," marker=\"d\",\n"," linestyle=\":\",\n"," linewidth=2,\n"," label=\"Compression-aware (Post-Training)\",\n"," )\n","\n"," # Format the plot\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Number of Trials\", fontsize=12)\n"," plt.ylabel(\"Maximum Achieved Accuracy\", fontsize=12)\n"," plt.title(\"Compression-Aware Search Results\", fontsize=14, pad=15)\n","\n"," # Format y-axis as percentage\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.1f}%\"))\n","\n"," # Add annotations for each point\n"," for i, val in zip(trials_no_compression, no_compression_max):\n"," plt.text(i, val, f\"{val*100:.1f}%\", fontsize=8, ha=\"center\", va=\"bottom\")\n"," for i, val in zip(trials_compression_no_post, compression_no_post_max):\n"," plt.text(i, val, f\"{val*100:.1f}%\", fontsize=8, ha=\"center\", va=\"bottom\")\n"," for i, val in zip(trials_compression_with_post, compression_with_post_max):\n"," plt.text(i, val, f\"{val*100:.1f}%\", fontsize=8, ha=\"center\", va=\"bottom\")\n","\n"," # Show legend\n"," plt.legend()\n"," plt.tight_layout()\n"," plt.savefig(\"compression_aware_results.png\", dpi=300)\n"," plt.show()\n","\n","plot_compression_aware_results(results_df)"],"metadata":{"id":"qEe7X92jwr8o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Part 5b, Full Implementation ###\n","import torch\n","from chop.tools import get_tokenized_dataset\n","import torch.nn as nn\n","from chop.nn.modules import Identity\n","from transformers import AutoConfig, AutoModelForSequenceClassification\n","from chop.tools.utils import deepsetattr\n","from chop.tools import get_trainer\n","import optuna\n","from optuna.samplers import TPESampler\n","from pathlib import Path\n","import dill\n","from chop.pipelines import CompressionPipeline\n","from chop import MaseGraph\n","import chop.passes as passes\n","import inspect\n","import csv\n","\n","def construct_model(trial):\n"," # Loads the default config from a small BERT checkpoint\n"," config = AutoConfig.from_pretrained(checkpoint)\n","\n"," # Map parameters to config\n"," config.num_hidden_layers = trial.suggest_categorical(\n"," \"num_layers\", search_space[\"num_layers\"]\n"," )\n"," config.num_attention_heads = trial.suggest_categorical(\n"," \"num_heads\", search_space[\"num_heads\"]\n"," )\n"," config.hidden_size = trial.suggest_categorical(\n"," \"hidden_size\", search_space[\"hidden_size\"]\n"," )\n"," config.intermediate_size = trial.suggest_categorical(\n"," \"intermediate_size\", search_space[\"intermediate_size\"]\n"," )\n","\n"," model = AutoModelForSequenceClassification.from_config(config)\n","\n"," linear_choice = trial.suggest_categorical(\n"," \"linear_layer_choices\", search_space[\"linear_layer_choices\"]\n"," )\n"," if linear_choice == \"identity\":\n"," for name, layer in model.named_modules():\n"," if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n"," deepsetattr(model, name, Identity())\n","\n"," return model\n","\n","def objective(trial):\n"," # Define the model and ensure it is on the correct device\n"," device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"," print(f\"[INFO] Model is being initialized on: {device}\")\n","\n"," model = construct_model(trial).to(device)\n"," print(f\"[INFO] Model moved to: {next(model.parameters()).device}\")\n","\n"," # Keep a copy of the original forward if needed later\n"," original_forward = model.forward\n"," model.config.use_cache = False\n","\n"," # 1) Training Only (No Compression)\n"," print(\"[INFO] Starting initial training (no compression)...\")\n"," trainer_no_comp = get_trainer(\n"," model=model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n"," trainer_no_comp.train()\n"," eval_results_no_comp = trainer_no_comp.evaluate()\n"," no_comp_acc = eval_results_no_comp[\"eval_accuracy\"]\n"," print(f\"[INFO] Accuracy (no compression): {no_comp_acc:.4f}\")\n","\n"," # Move model to CPU after initial training\n"," model.cpu()\n"," print(f\"[INFO] Model moved to: {next(model.parameters()).device} after initial training\")\n","\n"," # 2) Apply Compression, Evaluate with No Additional Training\n"," print(\"[INFO] Building MaseGraph and applying compression pipeline...\")\n"," mg = MaseGraph(\n"," model,\n"," hf_input_names=[\"input_ids\", \"attention_mask\", \"labels\"],\n"," )\n"," mg, _ = passes.init_metadata_analysis_pass(mg)\n"," mg, _ = passes.add_common_metadata_analysis_pass(mg)\n","\n"," pipe = CompressionPipeline()\n"," quantization_config = {\n"," \"by\": \"type\",\n"," \"default\": {\"config\": {\"name\": None}},\n"," \"linear\": {\n"," \"config\": {\n"," \"name\": \"integer\",\n"," \"data_in_width\": 8,\n"," \"data_in_frac_width\": 4,\n"," \"weight_width\": 8,\n"," \"weight_frac_width\": 4,\n"," \"bias_width\": 8,\n"," \"bias_frac_width\": 4,\n"," }\n"," },\n"," }\n"," pruning_config = {\n"," \"weight\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n"," \"activation\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n"," }\n","\n"," mg, _ = pipe(\n"," mg,\n"," pass_args={\n"," \"quantize_transform_pass\": quantization_config,\n"," \"prune_transform_pass\": pruning_config,\n"," },\n"," )\n","\n"," compressed_model = mg.model.to(device)\n"," print(f\"[INFO] Compressed model moved to: {next(compressed_model.parameters()).device}\")\n","\n"," print(\"[INFO] Evaluating compression result (no post-training)...\")\n"," trainer_comp_no_post = get_trainer(\n"," model=compressed_model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=1, # Dummy trainer, doesn't actually train, no train() call\n"," )\n","\n"," eval_results_no_post = trainer_comp_no_post.evaluate()\n"," compression_no_post_acc = eval_results_no_post[\"eval_accuracy\"]\n"," print(f\"[INFO] Accuracy (compression, no post-training): {compression_no_post_acc:.4f}\")\n","\n"," # 3) Continue Training the Compressed Model (Post-Compression Training)\n"," print(\"[INFO] Starting post-compression training...\")\n"," trainer_comp_post = get_trainer(\n"," model=compressed_model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3, # Post-compression training phase\n"," )\n"," trainer_comp_post.train()\n"," eval_results_post_train = trainer_comp_post.evaluate()\n"," compression_post_acc = eval_results_post_train[\"eval_accuracy\"]\n"," print(f\"[INFO] Accuracy (compression + post-training): {compression_post_acc:.4f}\")\n","\n"," # Set the model as an attribute for later use (the final compressed & post-trained model)\n"," trial.set_user_attr(\"model\", compressed_model)\n","\n"," # Store scenario accuracies for CSV\n"," trial.set_user_attr(\"no_compression_acc\", no_comp_acc)\n"," trial.set_user_attr(\"compression_no_post_acc\", compression_no_post_acc)\n"," trial.set_user_attr(\"compression_post_acc\", compression_post_acc)\n","\n"," # Return the final accuracy after compression + post-training\n"," return compression_post_acc\n","\n","def main_run():\n"," global checkpoint, tokenizer, dataset, search_space\n","\n"," checkpoint = \"prajjwal1/bert-tiny\"\n"," tokenizer_checkpoint = \"bert-base-uncased\"\n"," dataset_name = \"imdb\"\n","\n"," dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n"," )\n","\n"," sampler = TPESampler()\n","\n"," search_space = {\n"," \"num_layers\": [2, 4, 8],\n"," \"num_heads\": [2, 4, 8, 16],\n"," \"hidden_size\": [128, 192, 256, 384, 512],\n"," \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n"," \"linear_layer_choices\": [\"linear\", \"identity\"],\n"," }\n","\n"," study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=\"bert-tiny-compression-aware-study\",\n"," sampler=sampler,\n"," )\n","\n"," print(\"[INFO] Optimisation started...\")\n"," study.optimize(\n"," objective,\n"," n_trials=10,\n"," timeout=60 * 60 * 24,\n"," )\n","\n"," # Write results of all trials into a CSV file\n"," results_path = \"results.csv\"\n"," with open(results_path, mode=\"w\", newline=\"\") as f:\n"," writer = csv.writer(f)\n"," writer.writerow([\"trial_number\", \"no_compression_acc\", \"compression_no_post_acc\", \"compression_post_acc\"])\n"," for t in study.trials:\n"," writer.writerow([\n"," t.number,\n"," t.user_attrs.get(\"no_compression_acc\", None),\n"," t.user_attrs.get(\"compression_no_post_acc\", None),\n"," t.user_attrs.get(\"compression_post_acc\", None),\n"," ])\n","\n"," print(f\"[INFO] Results written to {results_path}\")\n","\n"," # Save the best model\n"," try:\n"," best_model = study.best_trial.user_attrs[\"model\"].cpu()\n"," except AttributeError:\n"," best_model = study.best_trial.user_attrs[\"model\"]\n","\n"," with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"wb\") as f:\n"," dill.dump(best_model, f)\n","\n","main_run()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["3e68fba565584d298666bd1dbb4a755a","f9a112e96d464a27916505d8bf87081f","fd415b4d294c4124bb496dff17f5ecb4","e6522dff3fbb4d07a2f4aabd96c08f4a","e6f196d2eaa54ce4af1d36b9352c6f8f","c0608f0ca9494631b72cc8a889a0ac67","93aef6d998134470bec10d4ff1c55416","9104b386d0464844897914941ad94631","0f089911fd2e4ceebd9f4456f8308f45","3c2b737bdd9449ea9887fbb88782f736","413dd0a2f172412cb7c23a0487dec5e5","9aed195082fa4ccfba75bbe021dde399","3d76ebaeac294679a715c0ed1c5a9f1f","69102c68ad6a46f4912b0614a54fe5c7","a8a28c83cc44461f8661396568b63f19","deec568dbf1b4cb69b8a5a16ac623aa1","7a15acfac92b4fa5910a442a79c251c2","7ee05099fcae44c2928e40fbdbac1c2e","35e85c06e88f4b5fb8418a26abf3e5f0","2ac2158b6857418d8c34958a0590e04f","ff101c25243a4d6fb39c80c7e31fc673","9de607f7151147bdba84ff781dbdd332","c200dfb5ae4a432c8efb93eff7a77e83","575f6a640dc04af6962645caf83aa1dc","b5ddc6664d524f5e9a881905a5a969f8","45493c1410a04679a369f49322b3b521","cdc37c3d62ce453cb637485ef6c5903c","96fea978e6124c298f2e6eaffe4990f4","ee4b8f6df2c34fc6a23f7d2c4a0e6d1e","4c3be3583d944dfb890ac60a93c6002b","9f4e44c7eed6425fac1a0703ff9c0abd","9e56197fec3d49b6a7598cbff5606187","27c5a99f5e084ecca2d92c9ad9598fff","ac401d227e204e9db7e76ab8c29420c7","40b339e7c5c041679ad5d4c0caa04990","cc3d169c6dfe4bdda30f4292ee2c00ed","70a5e3f2c94541b795f80db04457b281","a7f7fc16b7e54685976b5f7f4d1ed0e9","41e84b8b88234ea7974de2761b68f4dc","7ab5246ea3d248d4828a1f8d65b07039","cafd1d3ec5714aee8e931141adf9e531","5bffbec7a9934c5f8011247cadc97d31","1f2edd35296347078e7f1ee535363726","5f8eac8416224cdaaf68e4ccdeda2db6"],"output_embedded_package_id":"1BeJb03SNaskHdCsVuHYV-ZWl-MDdoEfR"},"id":"6JEGXGICa_ko","executionInfo":{"status":"error","timestamp":1738309181273,"user_tz":0,"elapsed":26878254,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"18955bd7-c522-4ad1-91f2-a04b095f0396"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import matplotlib.ticker as mticker\n","import pandas as pd\n","\n","# Function to load results from CSV\n","def load_results_from_csv(filepath):\n"," return pd.read_csv(filepath)\n","\n","# Load the results\n","csv_filepath = \"formatted_training_results.csv\"\n","results_df = load_results_from_csv(csv_filepath)\n","\n","def plot_compression_aware_results(results):\n"," # Separate results by complexity type\n"," no_compression = [res for res in results if res[\"complexity\"] == \"No Compression\"]\n"," compression_no_post = [res for res in results if res[\"complexity\"] == \"Compression-aware (No Post-Training)\"]\n"," compression_with_post = [res for res in results if res[\"complexity\"] == \"Compression-aware (Post-Training)\"]\n","\n"," # Helper to compute running max\n"," def get_running_max(data):\n"," running_max = []\n"," current_max = float('-inf')\n"," for res in data:\n"," current_max = max(current_max, res[\"value\"])\n"," running_max.append(current_max)\n"," return running_max\n","\n"," # Compute running max for each scenario\n"," no_compression_max = get_running_max(no_compression)\n"," compression_no_post_max = get_running_max(compression_no_post)\n"," compression_with_post_max = get_running_max(compression_with_post)\n","\n"," # Trials count\n"," trials_no_compression = list(range(len(no_compression_max)))\n"," trials_compression_no_post = list(range(len(compression_no_post_max)))\n"," trials_compression_with_post = list(range(len(compression_with_post_max)))\n","\n"," # Plot the results\n"," plt.figure(figsize=(10, 6))\n","\n"," # No Compression\n"," plt.plot(\n"," trials_no_compression,\n"," no_compression_max,\n"," marker=\"o\",\n"," linestyle=\"--\",\n"," linewidth=2,\n"," label=\"No Compression\",\n"," )\n","\n"," # Compression-aware (No Post-Training)\n"," plt.plot(\n"," trials_compression_no_post,\n"," compression_no_post_max,\n"," marker=\"s\",\n"," linestyle=\"-.\",\n"," linewidth=2,\n"," label=\"Compression-aware (No Post-Training)\",\n"," )\n","\n"," # Compression-aware (Post-Training)\n"," plt.plot(\n"," trials_compression_with_post,\n"," compression_with_post_max,\n"," marker=\"d\",\n"," linestyle=\":\",\n"," linewidth=2,\n"," label=\"Compression-aware (Post-Training)\",\n"," )\n","\n"," # Format the plot\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Number of Trials\", fontsize=12)\n"," plt.ylabel(\"Maximum Achieved Accuracy\", fontsize=12)\n"," plt.title(\"Compression-Aware Search Results\", fontsize=14, pad=15)\n","\n"," # Format y-axis as percentage\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.1f}%\"))\n","\n"," # Add annotations for each point\n"," for i, val in enumerate(no_compression_max):\n"," plt.text(i, val, f\"{val*100:.1f}%\", fontsize=8, ha=\"center\", va=\"bottom\")\n"," for i, val in enumerate(compression_no_post_max):\n"," plt.text(i, val, f\"{val*100:.1f}%\", fontsize=8, ha=\"center\", va=\"bottom\")\n","\n","plot_compression_aware_results(results_p2)"],"metadata":{"id":"TkFdO709w1uo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Helper function to download models automatically ###\n","from google.colab import files\n","\n","def download_best_model():\n"," best_model_path = \"tutorial_5_best_model.pkl\"\n","\n"," try:\n"," print(f\"Attempting to download {best_model_path}...\")\n"," files.download(best_model_path)\n"," print(f\"Successfully downloaded {best_model_path}.\")\n"," except Exception as e:\n"," print(f\"Error downloading {best_model_path}: {e}\")\n","\n","download_best_model()"],"metadata":{"id":"n--7pucfbGGo"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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\ No newline at end of file diff --git a/Working_Data/edited-colab-files/tutorial_6_mixed_precision_search.ipynb b/Working_Data/edited-colab-files/tutorial_6_mixed_precision_search.ipynb new file mode 100644 index 000000000..7836f56f7 --- /dev/null +++ b/Working_Data/edited-colab-files/tutorial_6_mixed_precision_search.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"j0fcaK5J23Tv"},"source":["# Tutorial 6: Mixed Precision Quantization Search with Mase and Optuna"]},{"cell_type":"code","source":["!git clone https://github.com/tonytarizzo/mase.git\n","%cd mase\n","!python -m pip install -e . -vvv\n","%cd src"],"metadata":{"id":"9wkmNTxH3QMk"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pWng0Iib23Tx"},"source":["In this tutorial, we'll see how Mase can be integrated with Optuna, the popular hyperparameter optimization framework, to search for a Bert model optimized for sequence classification on the IMDb dataset. We'll take the Optuna-generated model and import it into Mase, then run the CompressionPipeline to prepare the model for edge deployment by quantizing and pruning its weights.\n","\n","As we'll see, running Architecture Search with Mase/Optuna involves the following steps.\n","\n","1. **Define the search space**: this is a dictionary containing the range of values for each parameter at each layer in the model.\n","\n","2. **Write the model constructor**: this is a function which uses Optuna utilities to sample a model from the search space, and constructs the model using transformers from_config class method.\n","\n","3. **Write the objective function**: this function calls on the model constructor defined in Step 2 and defines the training/evaluation setup for each search iteration.\n","\n","4. **Go!** Choose an Optuna sampler, create a study and launch the search."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m_AI0p_A23Ty"},"outputs":[],"source":["checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\""]},{"cell_type":"markdown","metadata":{"id":"i3IEa6zR23Tz"},"source":["## Importing the model"]},{"cell_type":"markdown","metadata":{"id":"48ROCJ8A23Tz"},"source":["If you are starting from scratch, you can load the Bert checkpoint directly from HuggingFace."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IOevC2JL23Tz"},"outputs":[],"source":["from transformers import AutoModel\n","\n","base_model = AutoModel.from_pretrained(checkpoint)"]},{"cell_type":"markdown","metadata":{"id":"KEu43dxD23T0"},"source":["If you have previously ran the tutorial on Neural Architecture Search (NAS), run the following cell to import the best model obtained from the search process."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hXd6fMtg23T0"},"outputs":[],"source":["from pathlib import Path\n","import dill\n","\n","with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"rb\") as f:\n"," base_model = dill.load(f)"]},{"cell_type":"markdown","metadata":{"id":"AXW7Gu-_23T0"},"source":["First, fetch the dataset using the `get_tokenized_dataset` utility."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yXqYCB5V23T0","colab":{"base_uri":"https://localhost:8080/","height":67,"referenced_widgets":["3ac7ad73b7df420cb60f64e17f0474aa","83c44f6c2eff42f6ae8a5439675abc96","029ae597f25c4bf7adf2a95b3b100883","4534ad94bdbe4ff885813ea5ec43bab3","093620b1d0c241049b9c8efec4c82508","23fe52aa8e364a49806f561a2ac62a20","40343d7154454b9fbb3dd79787828990","1af18a9d8da84f81aa76c924d8f12070","077b0d3e710b440081a3357893f93447","84172a151a7c404db96f800b6b4f422a","4b52112bfb254fae86ef3c2a520fcbf0"]},"executionInfo":{"status":"ok","timestamp":1738340119502,"user_tz":0,"elapsed":39730,"user":{"displayName":"Tony","userId":"05291496024874036816"}},"outputId":"09ebc937-f922-4ef2-9a9e-9b89023896eb"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/25000 [00:00 which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/4.20k [00:00, 'encoder.layer.0.attention.self.key_type': , 'encoder.layer.0.attention.self.value_type': , 'encoder.layer.0.attention.output.dense_type': , 'encoder.layer.0.intermediate.dense_type': , 'encoder.layer.0.output.dense_type': , 'encoder.layer.1.attention.self.query_type': , 'encoder.layer.1.attention.self.key_type': , 'encoder.layer.1.attention.self.value_type': , 'encoder.layer.1.attention.output.dense_type': , 'encoder.layer.1.intermediate.dense_type': , 'encoder.layer.1.output.dense_type': , 'pooler.dense_type': } because of the following error: TypeError(\"BertModel.forward() got an unexpected keyword argument 'labels'\").\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.11/dist-packages/optuna/study/_optimize.py\", line 197, in _run_trial\n"," value_or_values = func(trial)\n"," ^^^^^^^^^^^\n"," File \"\", line 18, in objective\n"," trainer.train()\n"," File \"/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\", line 2164, in train\n"," return inner_training_loop(\n"," ^^^^^^^^^^^^^^^^^^^^\n"," File \"/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\", line 2524, in _inner_training_loop\n"," tr_loss_step = self.training_step(model, inputs, num_items_in_batch)\n"," ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n"," File \"/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\", line 3654, in training_step\n"," loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)\n"," ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n"," File \"/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\", line 3708, in compute_loss\n"," outputs = model(**inputs)\n"," ^^^^^^^^^^^^^^^\n"," File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n"," return self._call_impl(*args, **kwargs)\n"," ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n"," File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n"," return forward_call(*args, **kwargs)\n"," ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n","TypeError: BertModel.forward() got an unexpected keyword argument 'labels'\n","[W 2025-01-31 16:15:28,782] Trial 0 failed with value None.\n"]},{"output_type":"error","ename":"TypeError","evalue":"BertModel.forward() got an unexpected keyword argument 'labels'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m study.optimize(\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/optuna/study/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mnested\u001b[0m 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\u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2163\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2164\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 2165\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2166\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2522\u001b[0m )\n\u001b[1;32m 2523\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2524\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m if (\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3653\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3654\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3656\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m 3706\u001b[0m \u001b[0mloss_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"num_items_in_batch\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3707\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mloss_kwargs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3708\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3709\u001b[0m \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3710\u001b[0m \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1735\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1738\u001b[0m \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1745\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1746\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1749\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: BertModel.forward() got an unexpected keyword argument 'labels'"]}],"source":["import optuna\n","\n","study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=\"bert-tiny-nas-study\",\n"," sampler=sampler,\n",")\n","\n","study.optimize(\n"," objective,\n"," n_trials=1,\n"," timeout=60 * 60 * 24,\n",")"]},{"cell_type":"code","source":["from pathlib import Path\n","import dill\n","\n","print(Path.home())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pw_vPXi054cj","executionInfo":{"status":"ok","timestamp":1738587966772,"user_tz":0,"elapsed":126,"user":{"displayName":"Tony","userId":"05291496024874036816"}},"outputId":"8c701628-1c25-4c63-f4ba-1ea86ad38240"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/root\n"]}]},{"cell_type":"code","source":["### Part 6a Full Code ###\n","import pandas as pd\n","from transformers import AutoModel\n","from chop.tools import get_tokenized_dataset\n","import torch\n","from chop.nn.quantized.modules.linear import LinearInteger\n","from chop.tools.utils import deepsetattr\n","from copy import deepcopy\n","from chop.tools import get_trainer\n","import optuna\n","from optuna.samplers import TPESampler\n","from pathlib import Path\n","import dill\n","\n","checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\"\n","\n","with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"rb\") as f:\n"," base_model = dill.load(f)\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","search_space = {\n"," \"linear_layer_choices\": [\n"," torch.nn.Linear,\n"," LinearInteger,\n"," ],\n","}\n","\n","def construct_model(trial):\n"," # Fetch the model\n"," trial_model = deepcopy(base_model)\n","\n"," # Quantize layers according to optuna suggestions\n"," for name, layer in trial_model.named_modules():\n"," if isinstance(layer, torch.nn.Linear):\n"," new_layer_cls = trial.suggest_categorical(\n"," f\"{name}_type\", search_space[\"linear_layer_choices\"]\n"," )\n"," if new_layer_cls == torch.nn.Linear:\n"," continue\n","\n"," kwargs = {\n"," \"in_features\": layer.in_features,\n"," \"out_features\": layer.out_features,\n"," }\n","\n"," if new_layer_cls == LinearInteger:\n"," kwargs[\"config\"] = {\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", [8, 16, 32]),\n"," \"data_in_frac_width\": trial.suggest_categorical(f\"{name}_data_in_frac_width\", [2, 4, 8]),\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", [8, 16, 32]),\n"," \"weight_frac_width\": trial.suggest_categorical(f\"{name}_weight_frac_width\", [2, 4, 8]),\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", [8, 16, 32]),\n"," \"bias_frac_width\": trial.suggest_categorical(f\"{name}_bias_frac_width\", [2, 4, 8]),\n"," }\n","\n"," new_layer = new_layer_cls(**kwargs)\n"," new_layer.weight.data = layer.weight.data.clone()\n","\n"," deepsetattr(trial_model, name, new_layer)\n","\n"," return trial_model\n","\n","def objective(trial):\n"," model = construct_model(trial)\n"," trainer = get_trainer(\n"," model=model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n"," trial.set_user_attr(\"model\", model)\n"," return eval_results[\"eval_accuracy\"]\n","\n","sampler = TPESampler()\n","study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=\"bert-tiny-nas-study\",\n"," sampler=sampler,\n",")\n","study.optimize(objective, n_trials=10, timeout=60 * 60 * 24)\n","\n","# Save results to CSV for plotting later\n","results_df = pd.DataFrame({\n"," \"trial_number\": [t.number for t in study.trials],\n"," \"accuracy\": [t.value for t in study.trials]\n","})\n","results_df.to_csv(\"optuna_results.csv\", index=False)\n"],"metadata":{"id":"WfcCrO7v74Zd","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["c1320287e571446a8ee97a72b19800e5","1f1e79c3a46d4601838a01b8a948bf4f","412b69b8fce740a782c2ad271bd278e9","bc02799013a44546a145965ad0776a92","5de309d0d350497b9cadd8520397c8fb","5faab9c0be414283b579f884fb532f7d","0119ed8cbbf7400fb48509facde7b857","b29757cf16384807a682eb66ad2c21ca","d53ccf42c6ea4be3aa87858846f7c32b","96a8c92d993c4900830e7c079b4f8c44","fd0d227c9e0a44aeb625cde73ff6dac8","4bd70509e719409f83a4b3f9b958f90d","6fb72b8af8924da0be333b10400fd210","52d60a0a5dfd42a9905ac8696e479208","8c3e166775f7417aa1b1ed378b0a0bf2","647cb1ad76c24de387b8846ff87c1d85","4fb6a0e7801e43b0a5c81263caec90eb","3e2b00c66809445c945994bb58887183","f5852a7905ec4b50b0249ce586acd5ee","cea7c42eee5d42ef98ac2f79968ff3f6","181c85c6ef884ac0a58beb932ecd0baf","18e7e502ecde47e1a1cd980a88c12282"]},"executionInfo":{"status":"ok","timestamp":1738594337019,"user_tz":0,"elapsed":6083309,"user":{"displayName":"Tony","userId":"05291496024874036816"}},"outputId":"7ca71329-9109-4000-b33d-37ca53cf3b11"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/25000 [00:00 which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/4.20k [00:00"],"text/html":["\n","

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StepTraining Loss
5000.693000
10000.689900
15000.625900
20000.420100
25000.354700
30000.380200
35000.314000
40000.265500
45000.264800
50000.296100
55000.300000
60000.287400
65000.240000
70000.220700
75000.241200
80000.213400
85000.232900
90000.230200
95000.214100
100000.162100
105000.210700
110000.183500
115000.173700
120000.189000
125000.176800
130000.158500
135000.140400
140000.139100
145000.160800
150000.161900
155000.161700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 13:21:58,632] Trial 0 finished with value: 0.87348 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_weight_frac_width': 2, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_bias_frac_width': 8, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_data_in_frac_width': 2, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_frac_width': 2, 'bert.pooler.dense_bias_width': 16, 'bert.pooler.dense_bias_frac_width': 8, 'classifier_type': }. Best is trial 0 with value: 0.87348.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:28, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.700900
10000.691900
15000.521600
20000.397400
25000.378000
30000.439800
35000.357800
40000.330600
45000.320700
50000.355300
55000.374600
60000.351600
65000.284400
70000.262800
75000.284900
80000.231100
85000.275800
90000.280600
95000.246500
100000.188800
105000.219600
110000.199600
115000.204400
120000.204600
125000.207000
130000.169100
135000.149200
140000.152100
145000.164100
150000.153500
155000.159800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:47]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 13:32:16,612] Trial 1 finished with value: 0.85444 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.pooler.dense_type': , 'classifier_type': , 'classifier_data_in_width': 8, 'classifier_data_in_frac_width': 8, 'classifier_weight_width': 32, 'classifier_weight_frac_width': 4, 'classifier_bias_width': 8, 'classifier_bias_frac_width': 4}. Best is trial 0 with value: 0.87348.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:17, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.693100
10000.693000
15000.628700
20000.468600
25000.356400
30000.388000
35000.313900
40000.281200
45000.263200
50000.298000
55000.303300
60000.289800
65000.249500
70000.229000
75000.248100
80000.219800
85000.236300
90000.241600
95000.217400
100000.173600
105000.201900
110000.177900
115000.174100
120000.184000
125000.187900
130000.162200
135000.141100
140000.144000
145000.157500
150000.157200
155000.163400

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 13:42:20,525] Trial 2 finished with value: 0.87484 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 4, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_bias_frac_width': 4, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_data_in_frac_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_frac_width': 2, 'bert.pooler.dense_bias_width': 16, 'bert.pooler.dense_bias_frac_width': 2, 'classifier_type': }. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:23, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.325000
10000.316700
15000.355300
20000.353500
25000.329500
30000.374300
35000.281000
40000.249600
45000.231000
50000.271400
55000.280500
60000.274600
65000.226800
70000.196700
75000.233000
80000.174100
85000.213300
90000.213000
95000.187300
100000.141300
105000.160000
110000.144800
115000.144500
120000.155200
125000.156400
130000.123300
135000.084200
140000.098100
145000.120400
150000.121200
155000.118000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:45]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 13:52:32,448] Trial 3 finished with value: 0.8642 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 4, 'bert.encoder.layer.1.output.dense_type': , 'bert.pooler.dense_type': , 'classifier_type': }. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:09, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.357300
10000.385000
15000.400100
20000.414300
25000.406900
30000.464000
35000.351800
40000.316900
45000.298000
50000.312600
55000.349200
60000.333000
65000.274600
70000.236900
75000.251100
80000.206300
85000.264800
90000.236700
95000.225900
100000.162900
105000.193000
110000.173600
115000.164900
120000.177200
125000.180400
130000.140600
135000.122100
140000.118400
145000.137300
150000.130900
155000.137300

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:43]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 14:02:27,724] Trial 4 finished with value: 0.8636 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_bias_frac_width': 2, 'bert.pooler.dense_type': , 'classifier_type': , 'classifier_data_in_width': 8, 'classifier_data_in_frac_width': 8, 'classifier_weight_width': 16, 'classifier_weight_frac_width': 8, 'classifier_bias_width': 16, 'classifier_bias_frac_width': 4}. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","WARNING:evaluate.loading:Using the latest cached version of the module from /root/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-metric--accuracy/f887c0aab52c2d38e1f8a215681126379eca617f96c447638f751434e8e65b14 (last modified on Mon Feb 3 13:11:32 2025) since it couldn't be found locally at evaluate-metric--accuracy, or remotely on the Hugging Face Hub.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:22, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.700800
10000.696100
15000.636200
20000.346300
25000.321000
30000.338600
35000.279000
40000.256100
45000.241800
50000.258500
55000.265200
60000.257100
65000.217000
70000.194900
75000.184400
80000.177400
85000.193500
90000.205400
95000.173300
100000.132700
105000.156500
110000.143900
115000.150300
120000.142800
125000.148200
130000.120400
135000.095600
140000.105700
145000.118900
150000.125100
155000.130700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:47]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 14:13:01,499] Trial 5 finished with value: 0.85048 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 4, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_bias_frac_width': 4, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_data_in_frac_width': 8, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_frac_width': 4, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_frac_width': 2, 'classifier_type': }. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:10, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.693100
10000.693300
15000.627000
20000.380400
25000.329800
30000.367900
35000.291700
40000.259000
45000.255500
50000.287900
55000.296100
60000.272200
65000.234600
70000.207700
75000.220100
80000.191200
85000.224100
90000.232300
95000.199500
100000.138200
105000.173100
110000.174500
115000.149100
120000.162000
125000.178800
130000.141000
135000.106300
140000.122500
145000.133400
150000.128400
155000.134200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:45]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 14:22:58,894] Trial 6 finished with value: 0.8592 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_weight_frac_width': 2, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_bias_frac_width': 2, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_data_in_frac_width': 4, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_weight_frac_width': 2, 'bert.pooler.dense_bias_width': 16, 'bert.pooler.dense_bias_frac_width': 4, 'classifier_type': }. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:00, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.333800
10000.309800
15000.328100
20000.326100
25000.319100
30000.367700
35000.277400
40000.248800
45000.228700
50000.279400
55000.282900
60000.264300
65000.235300
70000.198400
75000.228300
80000.167800
85000.196600
90000.208800
95000.194400
100000.143400
105000.173800
110000.152600
115000.140500
120000.163600
125000.162000
130000.126500
135000.102900
140000.110000
145000.124200
150000.127400
155000.130800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:42]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 14:32:44,311] Trial 7 finished with value: 0.86852 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.output.dense_type': , 'bert.pooler.dense_type': , 'classifier_type': }. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 09:04, Epoch 5/5]\n","
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StepTraining Loss
5000.332400
10000.307700
15000.336400
20000.329300
25000.307600
30000.356300
35000.274600
40000.255500
45000.218100
50000.267100
55000.283400
60000.259800
65000.229400
70000.195500
75000.219100
80000.171300
85000.203800
90000.219300
95000.184600
100000.146900
105000.173400
110000.146600
115000.143300
120000.150100
125000.162700
130000.131800
135000.097700
140000.098400
145000.132000
150000.132300
155000.128600

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:43]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 14:42:34,162] Trial 8 finished with value: 0.8678 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_weight_frac_width': 2, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.output.dense_type': , 'bert.pooler.dense_type': , 'classifier_type': , 'classifier_data_in_width': 32, 'classifier_data_in_frac_width': 8, 'classifier_weight_width': 32, 'classifier_weight_frac_width': 4, 'classifier_bias_width': 32, 'classifier_bias_frac_width': 2}. Best is trial 2 with value: 0.87484.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [15625/15625 08:57, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.405600
10000.327600
15000.350600
20000.355900
25000.332700
30000.375300
35000.279000
40000.270200
45000.245100
50000.297700
55000.309000
60000.288600
65000.244400
70000.226600
75000.250300
80000.196500
85000.222300
90000.235300
95000.210800
100000.170000
105000.204100
110000.172100
115000.165000
120000.195300
125000.170900
130000.168100
135000.136800
140000.142700
145000.167100
150000.155700
155000.156000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:42]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 14:52:16,484] Trial 9 finished with value: 0.87176 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_bias_frac_width': 2, 'bert.pooler.dense_type': , 'classifier_type': , 'classifier_data_in_width': 32, 'classifier_data_in_frac_width': 4, 'classifier_weight_width': 8, 'classifier_weight_frac_width': 8, 'classifier_bias_width': 16, 'classifier_bias_frac_width': 2}. Best is trial 2 with value: 0.87484.\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import matplotlib.ticker as mticker\n","import pandas as pd\n","\n","def plot_optuna_search_results(csv_filepath):\n"," # Read the CSV file containing the trial results\n"," results_df = pd.read_csv(csv_filepath)\n","\n"," # Ensure the trials are in order (by trial_number)\n"," results_df = results_df.sort_values(by='trial_number')\n","\n"," # Compute the running maximum accuracy up to each trial\n"," running_max = []\n"," current_max = float('-inf')\n"," for acc in results_df['accuracy']:\n"," current_max = max(current_max, acc)\n"," running_max.append(current_max)\n"," results_df['running_max'] = running_max\n","\n"," # Create the plot\n"," plt.figure(figsize=(10, 6))\n"," plt.plot(results_df['trial_number'], results_df['running_max'],\n"," marker='o', linestyle='-', linewidth=2)\n","\n"," # Label the axes and the plot\n"," plt.xlabel(\"Number of Trials\", fontsize=12)\n"," plt.ylabel(\"Maximum Achieved Accuracy\", fontsize=12)\n"," plt.title(\"Optuna Search Results\", fontsize=14, pad=15)\n"," plt.grid(True, linestyle=\"--\", alpha=0.5)\n","\n"," # Format the y-axis to display percentages\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.1f}%\"))\n","\n"," # Annotate each point with its percentage value\n"," for trial, acc in zip(results_df['trial_number'], results_df['running_max']):\n"," plt.text(trial, acc, f\"{acc*100:.1f}%\", fontsize=8, ha='center', va='bottom')\n","\n"," plt.tight_layout()\n"," plt.savefig(\"optuna_search_results.png\", dpi=300)\n"," plt.show()\n","\n","plot_optuna_search_results(\"optuna_results.csv\")"],"metadata":{"id":"6EEBN8bNxFsD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### Part 6b Extended Search Version 1 Full Code ###\n","import pandas as pd\n","from transformers import AutoModel\n","from chop.tools import get_tokenized_dataset\n","import torch\n","from chop.tools.utils import deepsetattr\n","from copy import deepcopy\n","from chop.tools import get_trainer\n","import optuna\n","from optuna.samplers import TPESampler\n","from chop.nn.quantized.modules.linear import (\n"," LinearInteger,\n"," LinearMinifloatDenorm,\n"," LinearMinifloatIEEE,\n"," LinearLog,\n"," LinearBlockFP,\n"," # LinearBlockMinifloat,\n"," LinearBlockLog,\n"," LinearBinary,\n"," LinearBinaryScaling,\n"," # LinearBinaryResidualSign, # Excluded because it is not implemented.\n",")\n","from pathlib import Path\n","import dill\n","\n","# These choices were the search space used in the configs for each layer type.\n","width_choices = [8, 16, 32]\n","frac_width_choices = [2, 4, 8]\n","\n","# Load model and dataset\n","checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\"\n","\n","with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"rb\") as f:\n"," base_model = dill.load(f)\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","search_space = {\n"," \"linear_layer_choices\": [\n"," torch.nn.Linear,\n"," LinearInteger,\n"," LinearMinifloatDenorm,\n"," LinearMinifloatIEEE,\n"," LinearLog,\n"," LinearBlockFP,\n"," LinearBlockLog,\n"," LinearBinary,\n"," LinearBinaryScaling,\n"," ],\n","}\n","\n","def get_config_for_layer(trial, name, layer_type):\n"," if layer_type == LinearInteger:\n"," return {\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_frac_width\": trial.suggest_categorical(f\"{name}_data_in_frac_width\", frac_width_choices),\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_frac_width\": trial.suggest_categorical(f\"{name}_weight_frac_width\", frac_width_choices),\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_frac_width\": trial.suggest_categorical(f\"{name}_bias_frac_width\", frac_width_choices),\n"," }\n"," elif layer_type in [LinearMinifloatDenorm, LinearMinifloatIEEE]:\n"," return {\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_width\": trial.suggest_categorical(f\"{name}_data_in_exponent_width\", [3, 4, 5]),\n"," \"data_in_exponent_bias\": trial.suggest_categorical(f\"{name}_data_in_exponent_bias\", [0, 1, 2]),\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_width\": trial.suggest_categorical(f\"{name}_weight_exponent_width\", [3, 4, 5]),\n"," \"weight_exponent_bias\": trial.suggest_categorical(f\"{name}_weight_exponent_bias\", [0, 1, 2]),\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_width\": trial.suggest_categorical(f\"{name}_bias_exponent_width\", [3, 4, 5]),\n"," \"bias_exponent_bias\": trial.suggest_categorical(f\"{name}_bias_exponent_bias\", [0, 1, 2]),\n"," }\n"," elif layer_type == LinearLog:\n"," return {\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_bias\": trial.suggest_categorical(f\"{name}_data_in_exponent_bias\", [0, 1, 2]),\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_bias\": trial.suggest_categorical(f\"{name}_weight_exponent_bias\", [0, 1, 2]),\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_bias\": trial.suggest_categorical(f\"{name}_bias_exponent_bias\", [0, 1, 2]),\n"," }\n"," elif layer_type == LinearBlockFP:\n"," return {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_width\": 5,\n"," \"weight_exponent_bias\": 15,\n"," \"weight_block_size\": [16],\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_width\": 5,\n"," \"data_in_exponent_bias\": 15,\n"," \"data_in_block_size\": [16],\n"," \"data_in_skip_first_dim\": True,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_width\": 5,\n"," \"bias_exponent_bias\": 15,\n"," \"bias_block_size\": [16],\n"," }\n"," elif layer_type == LinearBlockMinifloat:\n"," return {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_width\": 5,\n"," \"weight_exponent_bias_width\": 15,\n"," \"weight_block_size\": [16],\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_width\": 5,\n"," \"data_in_exponent_bias_width\": 15,\n"," \"data_in_block_size\": [16],\n"," \"data_in_skip_first_dim\": True,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_width\": 5,\n"," \"bias_exponent_bias_width\": 15,\n"," \"bias_block_size\": [16],\n"," \"width\": 5, # Added due to error message\n"," \"exponent_width\": 15,\n"," \"exponent_bias_width\": 15,\n"," }\n"," elif layer_type == LinearBlockLog:\n"," return {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_bias_width\": 0,\n"," \"weight_block_size\": [16],\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_bias_width\": 0,\n"," \"data_in_block_size\": [16],\n"," \"data_in_skip_first_dim\": True,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_bias_width\": 0,\n"," \"bias_block_size\": [16],\n"," }\n"," elif layer_type == LinearBinary:\n"," return {\n"," \"weight_stochastic\": False,\n"," \"weight_bipolar\": True,\n"," }\n"," elif layer_type == LinearBinaryScaling:\n"," return {\n"," \"data_in_stochastic\": False,\n"," \"bias_stochastic\": False,\n"," \"weight_stochastic\": False,\n"," \"data_in_bipolar\": True,\n"," \"bias_bipolar\": True,\n"," \"weight_bipolar\": True,\n"," \"binary_training\": True,\n"," }\n"," else:\n"," return {}\n","\n","def construct_model(trial):\n"," trial_model = deepcopy(base_model)\n"," # Replace each torch.nn.Linear layer with a quantised version if selected.\n"," for name, layer in trial_model.named_modules():\n"," if isinstance(layer, torch.nn.Linear):\n"," new_layer_cls = trial.suggest_categorical(\n"," f\"{name}_type\", search_space[\"linear_layer_choices\"]\n"," )\n"," if new_layer_cls == torch.nn.Linear:\n"," continue # Keep the original layer if no quantisation is selected.\n"," kwargs = {\n"," \"in_features\": layer.in_features,\n"," \"out_features\": layer.out_features,\n"," }\n"," kwargs[\"config\"] = get_config_for_layer(trial, name, new_layer_cls)\n"," new_layer = new_layer_cls(**kwargs)\n"," new_layer.weight.data = layer.weight.data.clone()\n"," deepsetattr(trial_model, name, new_layer)\n"," return trial_model\n","\n","def objective(trial):\n"," model = construct_model(trial)\n"," trainer = get_trainer(\n"," model=model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n"," trial.set_user_attr(\"model\", model)\n"," return eval_results[\"eval_accuracy\"]\n","\n","sampler = TPESampler()\n","study = optuna.create_study(\n"," direction=\"maximize\",\n"," study_name=\"bert-tiny-nas-study\",\n"," sampler=sampler,\n",")\n","study.optimize(objective, n_trials=10, timeout=60 * 60 * 24)\n","\n","# Save trial number, accuracy and parameters to CSV\n","results_df = pd.DataFrame({\n"," \"trial_number\": [t.number for t in study.trials],\n"," \"accuracy\": [t.value for t in study.trials],\n"," \"params\": [t.params for t in study.trials]\n","})\n","results_df.to_csv(\"extended_optuna_results.csv\", index=False)\n"],"metadata":{"id":"Msv_3ZyrEdOc","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1738617930083,"user_tz":0,"elapsed":9661557,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"f3c141fd-20e1-4d42-8706-aed0a1430618"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","[I 2025-02-03 18:44:31,887] A new study created in memory with name: bert-tiny-nas-study\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.693900
10000.693400
15000.695800
20000.694100
25000.692800
30000.694700
35000.693900
40000.694900
45000.693500
50000.694700
55000.693000
60000.693500
65000.693600
70000.694100
75000.694500
80000.693700
85000.694400
90000.693700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 19:00:08,539] Trial 0 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 1, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_width': 5, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_width': 4, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_bias': 1, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_bias': 0, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_bias': 2, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_data_in_exponent_bias': 2, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_weight_exponent_bias': 0, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_width': 5, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.self.key_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_exponent_width': 5, 'bert.encoder.layer.1.attention.self.key_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_exponent_width': 5, 'bert.encoder.layer.1.attention.output.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_exponent_width': 5, 'bert.encoder.layer.1.attention.output.dense_bias_exponent_bias': 0, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_weight_exponent_bias': 1, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_bias_exponent_bias': 1, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_data_in_exponent_bias': 2, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_exponent_bias': 0, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_exponent_bias': 2, 'classifier_type': , 'classifier_data_in_width': 8, 'classifier_data_in_exponent_width': 4, 'classifier_data_in_exponent_bias': 0, 'classifier_weight_width': 8, 'classifier_weight_exponent_width': 5, 'classifier_weight_exponent_bias': 1, 'classifier_bias_width': 16, 'classifier_bias_exponent_width': 5, 'classifier_bias_exponent_bias': 2}. Best is trial 0 with value: 0.5.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.391200
10000.361900
15000.356600
20000.362000
25000.316300
30000.386400
35000.298800
40000.259100
45000.257300
50000.292000
55000.292500
60000.288600
65000.248200
70000.230100
75000.255700
80000.198700
85000.232900
90000.242400

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:58]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 19:15:57,337] Trial 1 finished with value: 0.87904 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_exponent_width': 5, 'bert.encoder.layer.0.attention.self.key_data_in_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_exponent_width': 4, 'bert.encoder.layer.0.attention.self.key_weight_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.self.key_bias_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 1, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 4, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 4, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_data_in_exponent_width': 5, 'bert.encoder.layer.1.output.dense_data_in_exponent_bias': 2, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_weight_exponent_width': 3, 'bert.encoder.layer.1.output.dense_weight_exponent_bias': 2, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_bias_exponent_width': 3, 'bert.encoder.layer.1.output.dense_bias_exponent_bias': 2, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_data_in_exponent_width': 4, 'bert.pooler.dense_data_in_exponent_bias': 1, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_exponent_width': 3, 'bert.pooler.dense_weight_exponent_bias': 2, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_exponent_width': 5, 'bert.pooler.dense_bias_exponent_bias': 1, 'classifier_type': , 'classifier_data_in_width': 16, 'classifier_data_in_exponent_width': 4, 'classifier_data_in_exponent_bias': 0, 'classifier_weight_width': 32, 'classifier_weight_exponent_width': 5, 'classifier_weight_exponent_bias': 0, 'classifier_bias_width': 8, 'classifier_bias_exponent_width': 5, 'classifier_bias_exponent_bias': 2}. Best is trial 1 with value: 0.87904.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 09:22, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.420400
10000.326800
15000.346300
20000.334800
25000.324400
30000.363100
35000.281300
40000.261200
45000.240800
50000.282800
55000.288200
60000.270800
65000.241100
70000.224500
75000.238500
80000.186500
85000.214700
90000.228200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:01]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 19:27:23,307] Trial 2 finished with value: 0.87968 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.self.query_data_in_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_weight_exponent_width': 4, 'bert.encoder.layer.0.attention.self.query_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.self.query_bias_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 0, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_bias': 2, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_bias': 0, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_exponent_width': 3, 'bert.encoder.layer.1.attention.output.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_exponent_width': 3, 'bert.encoder.layer.1.attention.output.dense_weight_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.output.dense_bias_exponent_bias': 1, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.output.dense_type': , 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_data_in_exponent_width': 5, 'bert.pooler.dense_data_in_exponent_bias': 1, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_weight_exponent_width': 3, 'bert.pooler.dense_weight_exponent_bias': 1, 'bert.pooler.dense_bias_width': 16, 'bert.pooler.dense_bias_exponent_width': 4, 'bert.pooler.dense_bias_exponent_bias': 0, 'classifier_type': , 'classifier_data_in_width': 32, 'classifier_data_in_exponent_width': 5, 'classifier_data_in_exponent_bias': 2, 'classifier_weight_width': 8, 'classifier_weight_exponent_width': 3, 'classifier_weight_exponent_bias': 1, 'classifier_bias_width': 16, 'classifier_bias_exponent_width': 4, 'classifier_bias_exponent_bias': 2}. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 13:48, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.647800
10000.396200
15000.379400
20000.383200
25000.385700
30000.394400
35000.303000
40000.275600
45000.250500
50000.295500
55000.282800
60000.273200
65000.231500
70000.199500
75000.235200
80000.172000
85000.200000
90000.205800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 03:29]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 19:44:42,961] Trial 3 finished with value: 0.87412 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 1, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 5, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 5, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_exponent_width': 4, 'bert.encoder.layer.1.intermediate.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_exponent_width': 3, 'bert.encoder.layer.1.intermediate.dense_weight_exponent_bias': 1, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_exponent_width': 3, 'bert.encoder.layer.1.intermediate.dense_bias_exponent_bias': 0, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_data_in_exponent_width': 5, 'bert.encoder.layer.1.output.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_exponent_width': 4, 'bert.encoder.layer.1.output.dense_weight_exponent_bias': 1, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_bias_exponent_width': 4, 'bert.encoder.layer.1.output.dense_bias_exponent_bias': 2, 'bert.pooler.dense_type': , 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_bias_width': 16, 'classifier_type': , 'classifier_data_in_width': 16, 'classifier_data_in_exponent_bias': 0, 'classifier_weight_width': 16, 'classifier_weight_exponent_bias': 2, 'classifier_bias_width': 16, 'classifier_bias_exponent_bias': 1}. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 08:33, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.701400
10000.695400
15000.699300
20000.692800
25000.685400
30000.633400
35000.528900
40000.374500
45000.322200
50000.329700
55000.358800
60000.349500
65000.279100
70000.273400
75000.277200
80000.260200
85000.293300
90000.273200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:43]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 19:55:01,697] Trial 4 finished with value: 0.87172 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 1, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_type': , 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_data_in_exponent_bias': 2, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_exponent_bias': 1, 'bert.pooler.dense_bias_width': 8, 'bert.pooler.dense_bias_exponent_bias': 2, 'classifier_type': }. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 11:35, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.693100
30000.693100
35000.693100
40000.693100
45000.693100
50000.693100
55000.693100
60000.693100
65000.693100
70000.693100
75000.693100
80000.693100
85000.693100
90000.693100

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:40]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 20:09:19,066] Trial 5 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 4, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_width': 5, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_width': 5, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_bias': 1, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_width': 4, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_width': 3, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_bias': 0, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_width': 4, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_bias': 1, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 5, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_bias_frac_width': 8, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_data_in_frac_width': 4, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_frac_width': 2, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_frac_width': 8, 'classifier_type': , 'classifier_weight_width': 8, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 14:59, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.695100
10000.694300
15000.694200
20000.693400
25000.692400
30000.693500
35000.693900
40000.695100
45000.693000
50000.693600
55000.692700
60000.692800
65000.694000
70000.693500
75000.693500
80000.693200
85000.693700
90000.693200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 03:51]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 20:28:13,479] Trial 6 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_width': 5, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.self.key_weight_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.self.key_bias_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_data_in_exponent_bias': 0, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_exponent_bias': 2, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_exponent_bias': 2, 'classifier_type': , 'classifier_data_in_width': 16, 'classifier_data_in_exponent_width': 3, 'classifier_data_in_exponent_bias': 2, 'classifier_weight_width': 8, 'classifier_weight_exponent_width': 4, 'classifier_weight_exponent_bias': 0, 'classifier_bias_width': 32, 'classifier_bias_exponent_width': 4, 'classifier_bias_exponent_bias': 1}. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 18:10, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.737500
10000.698100
15000.696800
20000.694400
25000.679900
30000.532400
35000.481300
40000.436600
45000.385000
50000.373200
55000.351100
60000.335200
65000.312000
70000.304800
75000.323700
80000.308300
85000.319300
90000.288100

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 03:59]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 20:50:24,442] Trial 7 finished with value: 0.73012 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_bias': 2, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_width': 4, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_width': 3, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_bias': 0, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_width': 5, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_bias': 0, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_data_in_exponent_width': 5, 'bert.encoder.layer.0.output.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_weight_exponent_width': 3, 'bert.encoder.layer.0.output.dense_weight_exponent_bias': 0, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_bias_exponent_width': 5, 'bert.encoder.layer.0.output.dense_bias_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 5, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_exponent_bias': 2, 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_exponent_width': 3, 'bert.encoder.layer.1.intermediate.dense_data_in_exponent_bias': 2, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_exponent_width': 3, 'bert.encoder.layer.1.intermediate.dense_weight_exponent_bias': 1, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_exponent_width': 3, 'bert.encoder.layer.1.intermediate.dense_bias_exponent_bias': 1, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_data_in_exponent_width': 4, 'bert.pooler.dense_data_in_exponent_bias': 1, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_exponent_width': 5, 'bert.pooler.dense_weight_exponent_bias': 0, 'bert.pooler.dense_bias_width': 8, 'bert.pooler.dense_bias_exponent_width': 4, 'bert.pooler.dense_bias_exponent_bias': 1, 'classifier_type': }. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 15:55, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.700400
10000.696000
15000.696300
20000.695100
25000.696500
30000.696900
35000.695500
40000.693700
45000.693800
50000.695100
55000.693900
60000.694100
65000.692900
70000.694600
75000.694000
80000.694500
85000.692900
90000.694000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 04:06]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 21:10:28,192] Trial 8 finished with value: 0.50216 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_weight_exponent_bias': 0, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_exponent_bias': 2, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_width': 5, 'bert.encoder.layer.0.attention.self.value_data_in_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_exponent_width': 5, 'bert.encoder.layer.0.attention.self.value_weight_exponent_bias': 1, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_exponent_width': 5, 'bert.encoder.layer.0.attention.self.value_bias_exponent_bias': 0, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_bias': 1, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_bias': 1, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_bias': 2, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_exponent_bias': 1, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_exponent_bias': 0, 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_width': 3, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_exponent_width': 3, 'bert.encoder.layer.1.attention.self.key_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.self.key_bias_exponent_bias': 2, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_exponent_bias': 0, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_exponent_bias': 1, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_type': , 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_bias_width': 32, 'classifier_type': , 'classifier_data_in_width': 8, 'classifier_data_in_exponent_width': 5, 'classifier_data_in_exponent_bias': 1, 'classifier_weight_width': 32, 'classifier_weight_exponent_width': 4, 'classifier_weight_exponent_bias': 2, 'classifier_bias_width': 8, 'classifier_bias_exponent_width': 4, 'classifier_bias_exponent_bias': 1}. Best is trial 2 with value: 0.87968.\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/usr/local/lib/python3.11/dist-packages/optuna/distributions.py:515: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n"," warnings.warn(message)\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [9375/9375 12:07, Epoch 3/3]\n","
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StepTraining Loss
5000.639700
10000.570900
15000.551900
20000.484800
25000.480100
30000.444500
35000.397800
40000.377700
45000.365100
50000.350000
55000.366500
60000.361500
65000.307600
70000.304600
75000.299600
80000.263700
85000.274100
90000.280900

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:52]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 21:25:29,505] Trial 9 finished with value: 0.86984 and parameters: {'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_width': 4, 'bert.encoder.layer.0.attention.output.dense_data_in_exponent_bias': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_width': 4, 'bert.encoder.layer.0.attention.output.dense_weight_exponent_bias': 0, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_width': 3, 'bert.encoder.layer.0.attention.output.dense_bias_exponent_bias': 2, 'bert.encoder.layer.0.intermediate.dense_type': , 'bert.encoder.layer.0.output.dense_type': , 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_data_in_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_exponent_width': 4, 'bert.encoder.layer.1.attention.self.query_weight_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_exponent_width': 3, 'bert.encoder.layer.1.attention.self.query_bias_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_exponent_bias': 1, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_exponent_bias': 0, 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.encoder.layer.1.intermediate.dense_type': , 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_exponent_bias': 0, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_exponent_bias': 2, 'bert.encoder.layer.1.output.dense_type': , 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_data_in_exponent_width': 4, 'bert.encoder.layer.1.output.dense_data_in_exponent_bias': 0, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_exponent_width': 4, 'bert.encoder.layer.1.output.dense_weight_exponent_bias': 2, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_bias_exponent_width': 5, 'bert.encoder.layer.1.output.dense_bias_exponent_bias': 0, 'bert.pooler.dense_type': , 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_data_in_exponent_width': 5, 'bert.pooler.dense_data_in_exponent_bias': 0, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_exponent_width': 4, 'bert.pooler.dense_weight_exponent_bias': 0, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_exponent_width': 4, 'bert.pooler.dense_bias_exponent_bias': 1, 'classifier_type': }. Best is trial 2 with value: 0.87968.\n"]}]},{"cell_type":"code","source":["### Part 6b Extended Search Version 2 Full Code ###\n","import random\n","import dill\n","from copy import deepcopy\n","from pathlib import Path\n","import torch\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import optuna\n","from optuna.samplers import TPESampler\n","from transformers import AutoModel\n","from chop.tools import get_tokenized_dataset, get_trainer\n","from chop.tools.utils import deepsetattr\n","from torch import nn\n","from chop.nn.quantized.modules.linear import (\n"," LinearInteger,\n"," LinearMinifloatDenorm,\n"," LinearMinifloatIEEE,\n"," LinearLog,\n"," LinearBlockFP,\n"," # LinearBlockMinifloat,\n"," LinearBlockLog,\n"," LinearBinary,\n"," LinearBinaryScaling,\n"," # LinearBinaryResidualSign, # Excluded because it is not implemented.\n",")\n","\n","checkpoint = \"prajjwal1/bert-tiny\"\n","tokenizer_checkpoint = \"bert-base-uncased\"\n","dataset_name = \"imdb\"\n","\n","model = AutoModel.from_pretrained(checkpoint)\n","with open(\"tutorial_5_best_model.pkl\", \"rb\") as f:\n"," base_model = dill.load(f)\n","\n","dataset, tokenizer = get_tokenized_dataset(\n"," dataset=dataset_name,\n"," checkpoint=tokenizer_checkpoint,\n"," return_tokenizer=True,\n",")\n","\n","precision_choices = [\n"," nn.Linear,\n"," LinearInteger,\n"," LinearMinifloatDenorm,\n"," LinearMinifloatIEEE,\n"," LinearLog,\n"," LinearBlockFP,\n"," LinearBlockLog,\n"," LinearBinary,\n"," LinearBinaryScaling,\n","]\n","\n","# These choices were the search space used in the configs for each layer type.\n","width_choices = [8, 16, 32]\n","frac_width_choices = [2, 4, 8]\n","\n","def construct_model_fixed(trial, chosen_precision):\n"," trial_model = deepcopy(base_model)\n"," # Replace each torch.nn.Linear layer with a quantised version if selected.\n","\n"," for name, layer in trial_model.named_modules():\n"," if isinstance(layer, nn.Linear):\n"," if chosen_precision == nn.Linear:\n"," continue\n"," kwargs = {\n"," \"in_features\": layer.in_features,\n"," \"out_features\": layer.out_features,\n"," }\n","\n"," if chosen_precision == LinearInteger:\n"," config = {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_frac_width\": trial.suggest_categorical(f\"{name}_weight_frac_width\", frac_width_choices),\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_frac_width\": trial.suggest_categorical(f\"{name}_data_in_frac_width\", frac_width_choices),\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_frac_width\": trial.suggest_categorical(f\"{name}_bias_frac_width\", frac_width_choices),\n"," \"floor\": False,\n"," }\n"," elif chosen_precision in [LinearMinifloatDenorm, LinearMinifloatIEEE]:\n"," config = {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_width\": 5,\n"," \"weight_exponent_bias\": 15,\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_width\": 5,\n"," \"data_in_exponent_bias\": 15,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_width\": 5,\n"," \"bias_exponent_bias\": 15,\n"," }\n"," elif chosen_precision == LinearLog:\n"," config = {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_bias\": 0,\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_bias\": 0,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_bias\": 0,\n"," }\n"," elif chosen_precision == LinearBlockFP:\n"," config = {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_width\": 5,\n"," \"weight_exponent_bias\": 15,\n"," \"weight_block_size\": [16], # kept fixed for now.\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_width\": 5,\n"," \"data_in_exponent_bias\": 15,\n"," \"data_in_block_size\": [16],\n"," \"data_in_skip_first_dim\": True,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_width\": 5,\n"," \"bias_exponent_bias\": 15,\n"," \"bias_block_size\": [16],\n"," }\n"," elif chosen_precision == LinearBlockLog:\n"," config = {\n"," \"weight_width\": trial.suggest_categorical(f\"{name}_weight_width\", width_choices),\n"," \"weight_exponent_bias_width\": 0,\n"," \"weight_block_size\": [16],\n"," \"data_in_width\": trial.suggest_categorical(f\"{name}_data_in_width\", width_choices),\n"," \"data_in_exponent_bias_width\": 0,\n"," \"data_in_block_size\": [16],\n"," \"data_in_skip_first_dim\": True,\n"," \"bias_width\": trial.suggest_categorical(f\"{name}_bias_width\", width_choices),\n"," \"bias_exponent_bias_width\": 0,\n"," \"bias_block_size\": [16],\n"," }\n"," elif chosen_precision == LinearBinary:\n"," config = {\n"," \"weight_stochastic\": False,\n"," \"weight_bipolar\": True,\n"," }\n"," elif chosen_precision == LinearBinaryScaling:\n"," config = {\n"," \"data_in_stochastic\": False,\n"," \"bias_stochastic\": False,\n"," \"weight_stochastic\": False,\n"," \"data_in_bipolar\": True,\n"," \"bias_bipolar\": True,\n"," \"weight_bipolar\": True,\n"," \"binary_training\": True,\n"," }\n"," else:\n"," config = {}\n","\n"," new_layer = chosen_precision(**kwargs, config=config)\n"," new_layer.weight.data = layer.weight.data.clone()\n"," if layer.bias is not None:\n"," new_layer.bias.data = layer.bias.data.clone()\n"," deepsetattr(trial_model, name, new_layer)\n","\n"," return trial_model\n","\n","def objective(trial, chosen_precision):\n"," model = construct_model_fixed(trial, chosen_precision)\n"," trainer = get_trainer(\n"," model=model,\n"," tokenized_dataset=dataset,\n"," tokenizer=tokenizer,\n"," evaluate_metric=\"accuracy\",\n"," num_train_epochs=3,\n"," )\n"," trainer.train()\n"," eval_results = trainer.evaluate()\n"," trial.set_user_attr(\"model\", model)\n","\n"," trial.set_user_attr(\"precision_type\", chosen_precision.__name__ if chosen_precision != nn.Linear else \"FullPrecision\")\n"," return eval_results[\"eval_accuracy\"]\n","\n","def run_study_for_precision(chosen_precision, n_trials=5):\n"," sampler = TPESampler()\n"," study_name = f\"study_{chosen_precision.__name__}\" if chosen_precision != nn.Linear else \"study_FullPrecision\"\n"," study = optuna.create_study(direction=\"maximize\", study_name=study_name, sampler=sampler)\n","\n"," # Optimize the study with a lambda that fixes the chosen precision.\n"," study.optimize(lambda trial: objective(trial, chosen_precision),\n"," n_trials=n_trials,\n"," timeout=60 * 60 * 24)\n","\n"," # Gather results into a DataFrame.\n"," results = []\n"," for t in sorted(study.trials, key=lambda t: t.number):\n"," results.append({\n"," \"trial_number\": t.number,\n"," \"trial_accuracy\": t.value,\n"," \"precision_type\": t.user_attrs.get(\"precision_type\", \"Unknown\")\n"," })\n"," df = pd.DataFrame(results)\n"," csv_name = f\"optuna_results_{chosen_precision.__name__ if chosen_precision != nn.Linear else 'no_quantisation'}.csv\"\n"," df.to_csv(csv_name, index=False)\n"," print(f\"Results for precision {chosen_precision.__name__ if chosen_precision != nn.Linear else 'no_quantisation'} saved to {csv_name}\")\n"," return df\n","\n","all_results = []\n","\n","for prec in precision_choices:\n"," print(f\"\\nRunning study for precision type: {prec.__name__ if prec != nn.Linear else 'no_quantisation'}\")\n"," df_prec = run_study_for_precision(prec, n_trials=5)\n","\n"," df_prec[\"precision_type\"] = prec.__name__ if prec != nn.Linear else \"no_quantisation\"\n"," all_results.append(df_prec)\n","\n"," df_prec = df_prec.sort_values(\"trial_number\")\n"," trial_nums = df_prec[\"trial_number\"].tolist()\n"," accuracies = df_prec[\"trial_accuracy\"].tolist()\n"," cum_best = []\n"," current_best = -float(\"inf\")\n"," for acc in accuracies:\n"," current_best = max(current_best, acc)\n"," cum_best.append(current_best)\n"," plt.figure(figsize=(6,4))\n"," plt.plot(trial_nums, cum_best, marker=\"o\", label=prec.__name__ if prec != nn.Linear else \"No Quantisation\")\n"," plt.xlabel(\"Trial Number\")\n"," plt.ylabel(\"Maximum Achieved Accuracy\")\n"," plt.title(f\"Optimization Progress for {prec.__name__ if prec != nn.Linear else 'No Quantisation'}\")\n"," plt.legend()\n"," plt.grid(True)\n"," plt.savefig(f\"optuna_progress_{prec.__name__ if prec != nn.Linear else 'no_quantisation'}.png\", dpi=300)\n"," plt.show()\n","\n","combined_df = pd.concat(all_results, ignore_index=True)\n","\n","plt.figure(figsize=(10, 6))\n","\n","for precision, group in combined_df.groupby(\"precision_type\"):\n"," group = group.sort_values(\"trial_number\")\n"," trial_nums = group[\"trial_number\"].tolist()\n"," accuracies = group[\"trial_accuracy\"].tolist()\n","\n"," cum_best = []\n"," current_best = -float(\"inf\")\n"," for acc in accuracies:\n"," current_best = max(current_best, acc)\n"," cum_best.append(current_best)\n"," plt.plot(trial_nums, cum_best, marker=\"o\", label=precision)\n","\n","plt.xlabel(\"Trial Number\")\n","plt.ylabel(\"Maximum Achieved Accuracy\")\n","plt.title(\"Comparing maximum achieved accuracy for each precision type (5 trails each, 3 epochs per trail)\")\n","plt.legend(title=\"Precision Type\")\n","plt.grid(True)\n","plt.savefig(\"extended_optuna_combined_precision_progress.png\")\n","plt.show()\n","\n","# Save the combined results to CSV.\n","combined_df.to_csv(\"optuna_combined_results.csv\", index=False)\n","print(\"Combined results saved to optuna_combined_results.csv\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"xOuzRmBsf9OJ","executionInfo":{"status":"error","timestamp":1738647373357,"user_tz":0,"elapsed":27104861,"user":{"displayName":"Antonio Tarizzo","userId":"03279709671483876992"}},"outputId":"e7c843c3-2cda-48a1-9a86-e4686dfa4dc3"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n","[I 2025-02-03 21:27:16,619] A new study created in memory with name: study_FullPrecision\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\n","Running study for precision type: no_quantisation\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
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StepTraining Loss
5000.344200
10000.309400
15000.347200
20000.325800
25000.320100
30000.376600
35000.283100
40000.249000
45000.225600
50000.274900
55000.275200
60000.241900

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

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\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:31:24,069] Trial 0 finished with value: 0.87852 and parameters: {}. Best is trial 0 with value: 0.87852.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
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StepTraining Loss
5000.344100
10000.309100
15000.331800
20000.343100
25000.324100
30000.371700
35000.278000
40000.241900
45000.228100
50000.279400
55000.269700
60000.242700

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

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\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:35:35,860] Trial 1 finished with value: 0.8764 and parameters: {}. Best is trial 0 with value: 0.87852.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:27, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.344200
10000.309400
15000.348100
20000.339900
25000.315900
30000.373900
35000.272800
40000.247200
45000.224900
50000.275400
55000.270600
60000.247400

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:39]\n","
\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:39:43,466] Trial 2 finished with value: 0.87616 and parameters: {}. Best is trial 0 with value: 0.87852.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:26, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.344200
10000.309400
15000.347000
20000.336000
25000.327700
30000.376900
35000.280200
40000.248600
45000.223200
50000.275800
55000.274700
60000.247500

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:38]\n","
\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:43:49,501] Trial 3 finished with value: 0.87632 and parameters: {}. Best is trial 0 with value: 0.87852.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:29, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.344200
10000.309400
15000.347200
20000.331500
25000.320800
30000.370500
35000.280100
40000.246300
45000.218700
50000.277700
55000.270500
60000.239900

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:39]\n","
\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:47:59,758] Trial 4 finished with value: 0.87668 and parameters: {}. Best is trial 0 with value: 0.87852.\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Results for precision no_quantisation saved to optuna_results_no_quantisation.csv\n"]},{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:48:00,411] A new study created in memory with name: study_LinearInteger\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\n","Running study for precision type: LinearInteger\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
\n"," \n"," \n"," [6250/6250 04:01, Epoch 2/2]\n","
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StepTraining Loss
5000.422700
10000.367100
15000.357600
20000.365300
25000.325000
30000.377400
35000.313700
40000.276500
45000.257300
50000.300800
55000.323000
60000.299400

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:51]\n","
\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:52:54,819] Trial 0 finished with value: 0.87264 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_bias_frac_width': 2, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_weight_frac_width': 4, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_data_in_frac_width': 8, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_frac_width': 4, 'classifier_weight_width': 16, 'classifier_weight_frac_width': 4, 'classifier_data_in_width': 16, 'classifier_data_in_frac_width': 2, 'classifier_bias_width': 8, 'classifier_bias_frac_width': 4}. Best is trial 0 with value: 0.87264.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:55, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.684000
30000.550700
35000.424200
40000.361600
45000.329000
50000.350200
55000.353000
60000.364400

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:51]\n","
\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 21:57:43,423] Trial 1 finished with value: 0.85892 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 4, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_weight_frac_width': 2, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_bias_frac_width': 8, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_frac_width': 2, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_data_in_frac_width': 2, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_frac_width': 2, 'classifier_weight_width': 32, 'classifier_weight_frac_width': 8, 'classifier_data_in_width': 16, 'classifier_data_in_frac_width': 2, 'classifier_bias_width': 16, 'classifier_bias_frac_width': 2}. Best is trial 0 with value: 0.87264.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
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StepTraining Loss
5000.425800
10000.361700
15000.354400
20000.352200
25000.325500
30000.370800
35000.307600
40000.275600
45000.266200
50000.283200
55000.325200
60000.310400

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:51]\n","
\n"," "],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[I 2025-02-03 22:02:32,337] Trial 2 finished with value: 0.86988 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_bias_frac_width': 4, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_weight_frac_width': 4, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_data_in_frac_width': 4, 'bert.pooler.dense_bias_width': 16, 'bert.pooler.dense_bias_frac_width': 8, 'classifier_weight_width': 16, 'classifier_weight_frac_width': 8, 'classifier_data_in_width': 8, 'classifier_data_in_frac_width': 4, 'classifier_bias_width': 32, 'classifier_bias_frac_width': 8}. Best is trial 0 with value: 0.87264.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"data":{"text/html":["\n","
\n"," \n"," \n"," [3032/6250 01:54 < 02:01, 26.46 it/s, Epoch 0.97/2]\n","
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StepTraining Loss
5000.389300
10000.318800
15000.335100
20000.344100
25000.327200
30000.371200

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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StepTraining Loss
5000.389300
10000.318800
15000.335100
20000.344100
25000.327200
30000.371200
35000.284100
40000.264500
45000.240600
50000.278500
55000.294700
60000.271000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:51]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:07:22,161] Trial 3 finished with value: 0.87984 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 4, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 2, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 4, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_weight_frac_width': 4, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_bias_frac_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_frac_width': 8, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_data_in_frac_width': 8, 'bert.pooler.dense_bias_width': 32, 'bert.pooler.dense_bias_frac_width': 4, 'classifier_weight_width': 32, 'classifier_weight_frac_width': 8, 'classifier_data_in_width': 16, 'classifier_data_in_frac_width': 2, 'classifier_bias_width': 16, 'classifier_bias_frac_width': 8}. Best is trial 3 with value: 0.87984.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:56, Epoch 2/2]\n","
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StepTraining Loss
5000.693100
10000.693200
15000.693200
20000.693200
25000.693100
30000.675800
35000.625900
40000.563900
45000.461900
50000.388400
55000.360300
60000.360800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:51]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:12:12,013] Trial 4 finished with value: 0.86192 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_frac_width': 2, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_frac_width': 2, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_frac_width': 4, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_frac_width': 2, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_frac_width': 4, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_frac_width': 2, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_weight_frac_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_data_in_frac_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_frac_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_frac_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_frac_width': 4, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_frac_width': 4, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_frac_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_frac_width': 2, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_frac_width': 4, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_frac_width': 2, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_weight_frac_width': 4, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_data_in_frac_width': 2, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_bias_frac_width': 4, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_weight_frac_width': 4, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_data_in_frac_width': 4, 'bert.pooler.dense_bias_width': 8, 'bert.pooler.dense_bias_frac_width': 4, 'classifier_weight_width': 16, 'classifier_weight_frac_width': 2, 'classifier_data_in_width': 8, 'classifier_data_in_frac_width': 8, 'classifier_bias_width': 8, 'classifier_bias_frac_width': 8}. Best is trial 3 with value: 0.87984.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearInteger saved to optuna_results_LinearInteger.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:12:12,467] A new study created in memory with name: study_LinearMinifloatDenorm\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearMinifloatDenorm\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.332100
10000.325500
15000.339900
20000.343200
25000.321600
30000.381000
35000.278300
40000.262200
45000.229900
50000.280900
55000.283300
60000.246900

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:23:20,292] Trial 0 finished with value: 0.87764 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 8, 'classifier_data_in_width': 32, 'classifier_bias_width': 16}. Best is trial 0 with value: 0.87764.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 08:04, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.331900
10000.320200
15000.348900
20000.345800
25000.321200
30000.388000
35000.284100
40000.250500
45000.233300
50000.277500
55000.281500
60000.247300

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:51]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:34:18,054] Trial 1 finished with value: 0.8764 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 8, 'classifier_data_in_width': 32, 'classifier_bias_width': 16}. Best is trial 0 with value: 0.87764.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 08:08, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.332200
10000.316500
15000.336200
20000.341500
25000.332300
30000.375100
35000.273900
40000.253800
45000.230000
50000.279000
55000.283000
60000.250500

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:53]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:45:21,133] Trial 2 finished with value: 0.878 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 8, 'classifier_data_in_width': 32, 'classifier_bias_width': 8}. Best is trial 2 with value: 0.878.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 08:07, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.320800
10000.341600
15000.348300
20000.351200
25000.319900
30000.390200
35000.290400
40000.250800
45000.232400
50000.288000
55000.287900
60000.246300

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:53]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 22:56:23,431] Trial 3 finished with value: 0.8756 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 16, 'classifier_data_in_width': 16, 'classifier_bias_width': 8}. Best is trial 2 with value: 0.878.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 08:04, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.336000
10000.312400
15000.338200
20000.344100
25000.322500
30000.367400
35000.271500
40000.248400
45000.225600
50000.272400
55000.267900
60000.251600

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 02:52]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 23:07:21,156] Trial 4 finished with value: 0.87752 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 8, 'classifier_weight_width': 32, 'classifier_data_in_width': 16, 'classifier_bias_width': 32}. Best is trial 2 with value: 0.878.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearMinifloatDenorm saved to optuna_results_LinearMinifloatDenorm.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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2025-02-03 23:07:21,605] A new study created in memory with name: study_LinearMinifloatIEEE\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearMinifloatIEEE\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 11:52, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.322500
10000.311900
15000.333900
20000.338800
25000.334700
30000.371100
35000.282900
40000.246800
45000.227400
50000.277100
55000.275300
60000.243600

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 04:40]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 23:23:55,921] Trial 0 finished with value: 0.87636 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 16, 'classifier_data_in_width': 32, 'classifier_bias_width': 8}. Best is trial 0 with value: 0.87636.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 11:53, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.336200
10000.325900
15000.343400
20000.342500
25000.330800
30000.373500
35000.276800
40000.249500
45000.222100
50000.279900
55000.274500
60000.247300

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 04:40]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 23:40:31,958] Trial 1 finished with value: 0.87652 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 8, 'classifier_weight_width': 8, 'classifier_data_in_width': 16, 'classifier_bias_width': 16}. Best is trial 1 with value: 0.87652.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 11:54, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.328100
10000.319700
15000.338300
20000.336100
25000.318200
30000.374600
35000.277200
40000.243500
45000.217500
50000.270400
55000.276500
60000.237500

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 04:40]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-03 23:57:08,073] Trial 2 finished with value: 0.87708 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 8, 'classifier_weight_width': 8, 'classifier_data_in_width': 16, 'classifier_bias_width': 8}. Best is trial 2 with value: 0.87708.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 11:52, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.321300
10000.328300
15000.331900
20000.339000
25000.319100
30000.372600
35000.268000
40000.244800
45000.221800
50000.274600
55000.281900
60000.237700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 04:39]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:13:41,961] Trial 3 finished with value: 0.87684 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 8, 'classifier_weight_width': 8, 'classifier_data_in_width': 16, 'classifier_bias_width': 8}. Best is trial 2 with value: 0.87708.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 11:53, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.332600
10000.317600
15000.333700
20000.344200
25000.317600
30000.375400
35000.286300
40000.242400
45000.225600
50000.268300
55000.269900
60000.242800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 04:40]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:30:17,319] Trial 4 finished with value: 0.87672 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 32, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 2 with value: 0.87708.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearMinifloatIEEE saved to optuna_results_LinearMinifloatIEEE.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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aalf9Hfv6+TJkyiKwowZM5gxY0ap/cjIyKBhw4a8/fbbPPPMMzz66KO0atWKXr16MXToUM3pA1tbW9555x1ee+016tevz+OPP87TTz/NsGHDcHNzK7Xt8lT0dShhZWVFo0aN9N6Pvuzs7HTmrd39vjhx4gQ3btwo83ROyaRYKF5iYfr06WzdulXnj5IbN25oPW7YsOE9J2e/+OKLTJkyhZ9//pnPP/+cp59++p5/FN3tft/793L27FmaNWumc/q5efPmmudL/PTTT/znP/9h//79WnPZDLFGVOfOnSs02bdZs2YVuprOwcHBZK66e5AkkRE6nJycaNCgAX/99Ve59f766y8aNmyIo6NjufUM8YEv68qGssoVRblnmx9++CFffPEFq1evxt/fX+u51atXM3z4cPr378/UqVNxdXXF0tKSmJgYTp06pXf8d6ro63E/fYPiSzEr8qV251+cVe3ufZVMCp0yZQohISGlblOSKHXu3JlTp07x/fffs3nzZj799FM++OADli1bxqhRowCYNGkSoaGhfPfdd/zyyy/MmDGDmJgYtm7dqnOpqqHZ2to+kKu9KnKVj1qtxtXVlc8//7zU50sSoevXr9OlSxccHR15++238fb2xs7Ojr179/Lvf/9ba9IuVOy90qBBA7p27cr777/Pjh07dK5Uqoj7fe8bym+//Ua/fv3o3LkzS5YsoUGDBlhbWxMfH8+aNWseaCyibJLIiFI9/fTT/Pe//+X333+nU6dOOs//9ttvnDlzhrFjx+o8d+LECa2/vE+ePIlardaabGnsFW9/++03pkyZwqRJk0q9umTdunU0adKE9evXa8VaclVECX360bhxY9RqNSdOnND85QeQnp7O9evXady4cSV6YnglcRw7dkwzYbPEsWPHNM+X/Hvy5EmdNkorK03JpfvW1tYVSrqcnZ0ZMWIEI0aM4NatW3Tu3JmZM2dqEhkoHgF87bXXeO211zhx4gT+/v68//77rF69ukIxlajo62CKvL292bJlCx07diw3+UhKSuLKlSusX7+ezp07a8pLrmyrrLCwMEaNGkXt2rVLXf/E2Bo3bsxff/2FWq3WSj5LTqWVHNtvvvkGOzs7fvnlF621bko7/WPs77SHmcyREaWaOnUq9vb2jB07litXrmg9d/XqVcaNG0eNGjWYOnWqzraLFy/Werxo0SIArbVLHBwcjLYE/MWLFxkwYACdOnXSuSqjRMlfhHf+Bbhr1y6Sk5O16pVcmVKRvpR8ocfGxmqVL1iwAEBzBYSxtWvXDldXV5YtW6Y1lP7zzz9z5MgRTZzu7u60atWKVatWcevWLU297du3c/DgwQrty9XVla5du/Lxxx9z8eJFneczMzM1/7/7fVizZk2aNm2qiTEnJ0dnwTVvb29q1aqlc3l7RVT0dTBFAwYMoKioiNmzZ+s8V1hYqHm/lvY+z8/PZ8mSJfe1/+eff57o6GiWLFliEusE3a1Pnz5cunSJtWvXasoKCwtZtGgRNWvWpEuXLkDx66NSqbQuRT9z5kypK/ga8zvtYScjMqJUzZo1Y+XKlQwePBg/Pz+dlX0vX77MF198UeolhqmpqfTr149evXqRnJzM6tWrCQsLo02bNpo6gYGBbNmyhQULFuDu7o6Xl5dmom5VmzBhApmZmbz++ut8+eWXWs+1bt2a1q1b8/TTT7N+/XqeffZZ+vbtS2pqKsuWLaNFixZav7Tt7e1p0aIFa9eu5dFHH8XZ2ZlWrVrRqlUrnf22adOG8PBwPvnkE82QfkpKCitXrqR///48+eSTVd73irC2tuadd95hxIgRdOnShUGDBmkuO/b09GTy5MmaunPnzuWZZ56hY8eOjBgxgmvXrvHRRx/RqlUrrdepPIsXL6ZTp074+fkxevRomjRpQnp6OsnJyZw7d44DBw4AxRNAu3btSmBgIM7Ozvz555+sW7eOiIgIAI4fP85TTz3FgAEDaNGiBVZWVnz77bekp6fz4osvVunrUFlxcXFs2rRJp/zOy38ro0uXLowdO5aYmBj2799Pz549sba25sSJE3z99dd8+OGHPP/88wQHB1OnTh3Cw8OZMGECKpWKzz777L5P4Tg5OZW7houxjRkzho8//pjhw4ezZ88ePD09WbduHTt27CA2NlYzp6dv374sWLCAXr16ERYWRkZGBosXL6Zp06Y6p94r8522bt26Ulf27dGjh9al7Xv37i11RNHb21vrIogbN26UOfJYrRfKM+IVU8IM/PXXX8qgQYOUBg0aKNbW1oqbm5syaNAg5eDBgzp1Sy4pPHz4sPL8888rtWrVUurUqaNERERoXb6qKIpy9OhRpXPnzoq9vb0CaC5bLOtyzNIuuwWU8ePHa5WVXK545yW4d18GXd5liiWXUavVamXu3LlK48aNFVtbWyUgIED56aeflPDwcK3LihVFUXbu3KkEBgYqNjY2Wm2Udtl3QUGBMmvWLMXLy0uxtrZWPDw8lGnTpmld5lpen+++jLQspb02dyvtctg7rV27VgkICFBsbW0VZ2dnZfDgwcq5c+d06n355ZeKr6+vYmtrq7Rq1Ur54YcflOeee07x9fXV1CntuNzp1KlTyrBhwxQ3NzfF2tpaadiwofL0008r69at09T5z3/+o7Rv316pXbu2Ym9vr/j6+ipz5sxR8vPzFUVRlMuXLyvjx49XfH19FQcHB8XJyUkJCgpSvvrqq3u+XuVdDl2R1yE8PFxxcHC4537u3l9ZP//880+Zl1+Xtp+ylhj45JNPlMDAQMXe3l6pVauW4ufnp7z++uvKhQsXNHV27NihPP7444q9vb3i7u6uvP7665rlDe68tPnOS6zvVt5zJfS5/Loi7/37ufxaURQlPT1dGTFihFKvXj3FxsZG8fPz03qtSyxfvlxp1qyZYmtrq/j6+irx8fGlvt73+k6r6OXXd/bpXpdf33m5d3nfa9X9V71KUR7w7ClRbc2cOZNZs2aRmZlZoZn4ovoqWTDwzlV3hRCiKsgcGSFEpRUUFFBYWKhVlpSUxIEDB3RuoimEEFVB5sgIISrt/PnzdO/enSFDhuDu7s7Ro0dZtmwZbm5uOou2CSFEVZBERghRaXXq1CEwMJBPP/2UzMxMHBwc6Nu3L/PmzdNZYFAIIaqCzJERQgghhNmSOTJCCCGEMFuSyAghhBDCbMkcmSqkVqu5cOECtWrVkuWrhRBCCD0oisLNmzdxd3cv9z5mkshUoQsXLujcnVkIIYQQFffPP/+Ue2d5SWSqUMky1//888897xBdUQUFBWzevFmz5Hh1IH0yfdWtPyB9MhfSJ/NQFX3KysrCw8ND87u0LCaRyCxevJj33nuPS5cu0aZNGxYtWkT79u3LrB8bG8vSpUtJS0ujXr16PP/888TExGBnZweAp6cnZ8+e1dnulVdeYfHixZw5c0br7sx3+uqrr3jhhReA4nvy7Nixg0OHDtG8eXP279+vV79KTic5OjoaNJGpUaMGjo6O1eoDIH0ybdWtPyB9MhfSJ/NQlX2619QMo0/2Xbt2LZGRkURHR7N3717atGlDSEgIGRkZpdZfs2YNUVFRREdHc+TIEZYvX87atWt54403NHV2797NxYsXNT8ly6SXJCgeHh5az1+8eJFZs2ZRs2ZNrTs0A4wcOZKBAwdWUe+FEEIIcT+MPiKzYMECRo8ezYgRIwBYtmwZGzZsIC4ujqioKJ36O3fupGPHjoSFhQHFoy+DBg1i165dmjouLi5a28ybNw9vb2+tW7O7ublp1fn2228ZMGCA1p1IFy5cCEBmZqbOnU6FEEIIYXxGTWTy8/PZs2cP06ZN05RZWFjQvXt3kpOTS90mODiY1atXk5KSQvv27Tl9+jQbN25k6NChZe5j9erVREZGljk8tWfPHvbv38/ixYvvqz95eXnk5eVpHmdlZQHFQ24FBQX31XaJknYM1Z4pkD6ZvurWH5A+mQvpk3moij5VtC2jJjKXL1+mqKiI+vXra5XXr1+fo0ePlrpNWFgYly9fplOnTiiKQmFhIePGjdM6tXSn7777juvXrzN8+PAy41i+fDnNmzcnODi40n0BiImJYdasWTrlmzdvpkaNGmVuZ2FhUe6lZXezsrJi27ZtlYrRVEmfTF9F+qNWq1Gr1Q8oIsOojnfolj6ZB+lT+XJycipUz+inlvSVlJTE3LlzWbJkCUFBQZw8eZKJEycye/ZsZsyYoVN/+fLl9O7dG3d391Lbu337NmvWrCl1W31NmzaNyMhIzeOSGdc9e/YsdbJvQUEB6enp3L59u8L7UBSF3Nxc7Ozsqs3aNNIn06dPf+zt7alfv77JT2IsKCggISGBHj16mHysFSV9Mg/Sp4opOatxL0ZNZOrVq4elpSXp6ela5enp6TpzWErMmDGDoUOHMmrUKAD8/PzIzs5mzJgxvPnmm1ojG2fPnmXLli2sX7++zBjWrVtHTk4Ow4YNu+/+2NraYmtrq1NubW2tc2DVajWnT5/G0tKShg0bYmNjU6FfeGq1mlu3blGzZk29RnFMmfTJ9FWkP4qikJ+fT2ZmJv/88w/NmjUzi76X9vk0d9In8yB9undbFWHURMbGxobAwEASExPp378/UPyFmZiYSERERKnb5OTk6Hw5WlpaAsVfpHeKj4/H1dWVvn37lhnD8uXL6devn84E4aqWn5+PWq3Gw8Oj3NNOd1Or1eTn52NnZ2cWvyQqQvpk+iraH3t7e6ytrTl79qymvikqUivsSr3Knssq6qZepUNTVywtzH/krLqR42QejH2cjH5qKTIykvDwcNq1a0f79u2JjY0lOztbcxXTsGHDaNiwITExMQCEhoayYMECAgICNKeWZsyYQWhoqCahgeIv3vj4eMLDw7GyKr2bJ0+e5Ndff2Xjxo1lPn/r1i0uXbrE7du3NevItGjRAhsbG4P0vzr8khPiTqb+nt506CKzfjzMxRu5gCWrTvxJAyc7okNb0KtVA2OHJ/5HjpN5MIXjZPREZuDAgWRmZvLWW29x6dIl/P392bRpk2YCcFpamtYX4/Tp01GpVEyfPp3z58/j4uJCaGgoc+bM0Wp3y5YtpKWlMXLkyDL3HRcXR6NGjejZs2epz48aNYrt27drHgcEBACQmpqKp6dnZbsshDCSTYcu8vLqvSh3lV+6kcvLq/eydEhb+SVpAuQ4mQdTOU4q5e7zMcJgsrKycHJy4saNGzqTfXNzc0lNTcXLy0uv4Xe1Wk1WVhaOjo4m/5dvRUmfTJ8+/anse7uqFakVOr2z9X9/OZauTg1r5vRvhYWZnr4oLCxi7969tG3bFisry3tvYILUaoU3vjvE9ZyyL72V42R89zpOKsDNyY7f/92t0qeZyvsdeiejj8iI+1OkVkhJvUrGzVxca9nR3svZqOeQVSoV3377rWbOkzmZOXMm3333nV63ovD09GTSpElMmjQJgEuXLjF06FB27tyJlZUV165dM+vXpDpJSb1abhIDcC2ngFfW7HtAEVUVS+KOHzB2EFVKjpPpU4CLN3JJSb1KB++6VbovSWTMmPa5yWIP4tzk8OHDuX79Ot99953OcxcvXqROnTpVtm99rFixghEjRuDr68uRI0e0nvv6668ZMGAAjRs35syZMwBMmTKFV199Va997N69GwcHB83jDz74gIsXL7J3794qHYkpLTlasWIFkyZN4vr165rHJXPN7mRra0tubvF7Zvjw4axcuVKnTkhICJs2bQLKvndZTExMqatvm6qMm+UnMSW86jlQ18Ewc+AeNEVRuHrtGs516pjtZf9XsvNJvZx9z3pynIyrosepop+7+yGJjJnadOgS49fsM/q5ybuVddn8g6QoCkVFRQA4ODiQkZFBcnIyHTp00NRZvnw5jzzyiNZ2NWvW1LpFRUXcfbXbqVOnCAwMpFmzZhVeA6EqOTo6cuzYMa2yu784e/XqRXx8vFbZ3csIzJo1i4EDB1KrVi1NgnavO9KaGtdaFTvNNfdZvyr/C7KqFBQUsHHjRvr0aW+2l/Umn7rCoP/+cc96cpyMq6LHqaKfu/th/ifvqwlFUcjJL6zQz63cQmb9dFgniQE0ZTN/OMzN3IIKtWfIaVIqlUozUnPmzBlUKhXr16/nySefpEaNGrRp00bn9hO///47vXv3xsHBAQ8PDyZMmEB29v9n+p999hnt2rWjVq1auLm5ERYWpnVT0aSkJFQqFT///DOBgYHY2try+++/A8Wr0YaFhREXF6epf+7cOZKSkjT36yoxc+ZM/P39NY+HDx9O//79mT9/Pg0aNKBu3bqMHz9ea9lsT09PYmNjNf//5ptvWLVqFZaWlrzyyiulvkYHDx6kW7du2NvbU7duXcaMGcOtW7c0z+/evZsePXpQr149nJyc6NKlC3v37tXaJ8Czzz6LSqUqd+K5SqXCzc1N6+fulbRtbW116tw9qlarVi3q16+vVefOkShz0N7LmQZOZX+pqige0Wzv5fzgghI6So5TWeMUcpxMgykdJxmRMRG3C4po8dYvBmlLAS5l5eI3c3OF6h9+O4QaNlX3VnjzzTeZP38+zZo1480332TQoEGcPHkSKysrTp06RZ8+fXjzzTdZsWIFV65cISIigoiICM0oQUFBAbNnz8bHx4eMjAwiIyMZPny4zmXzUVFRzJ8/nyZNmlCnTh3NKaORI0fStWtXPvzwQ2rUqMGKFSvo1auXzi/00mzbto0GDRqwbds2Tp48ycCBA/H392f06NE6dXfv3s2wYcNwdHTkgw8+KPU+IdnZ2YSEhNChQwd2795NRkYGo0aNIiIighUrVgBw8+ZNwsPDWbRoEYqi8P7779OnTx9OnDhBrVq12L17N66ursTHx9OrVy+tZQdE2SwtVESHtmDc6r06z5V8GUeHtpB1Soys5Di9vHovKtD6g02Ok+kwpeMkIzKiyk2ZMoW+ffvy6KOPMmvWLM6ePcvJkyeB4nkWYWFhvPzyyzRr1ozg4GAWLlzIqlWrNPM4Ro4cSe/evWnSpAmPP/44Cxcu5Oeff9YaxQB4++236dGjB97e3jg7//9fAQEBATRp0oR169ahKAorVqwo97L8O9WpU4ePPvoIX19fnn76afr27UtiYmKpdV1cXLC1tcXe3h43NzecnJx06qxZs4bc3FxWrVpFq1at6NatGx999BGfffaZZoXrbt26MWTIEHx9fWnevDmffPIJOTk5mqUASk5n1a5dGzc3t3IXc7xx44bmlFnJT+/evbXq/PTTTzp15s6dq1UnKiqKRo0a4ejoqKnz22+/Veg1NCUNnOxLLXdzspNLek1Ir1YNWDqkLW53jaDJcTItpnKcZETGRNhbW3L47ZB71lOr1Wz/+xzjvz5yz7orRjxWoWE9e+uq/Yu+devWmv83aFD8xs7IyMDX15cDBw7w119/sWbNGk0dRVFQq9WkpqbSvHlz9uzZw8yZMzlw4ADXrl3T3JQwLS2NFi1aaLZr165dmTGMHDmS+Ph4HnnkEbKzs+nTpw8fffTRPWNv2bKl1ohHgwYNOHjwYMU7f5cjR47Qpk0brdMyHTt2RK1Wc+zYMerXr096ejrTp08nKSmJjIwMioqKyMnJIS0tTe/91apVS+u0FBSvvnunJ598kqVLl2qV3ZkIQnEy+txzz2ndoqBhw4Z6x2NsH20rTqCfDXDnuQB3Nv+2i55PBMmKsSaoV6sG9GjhRvLJDDlOJswUjpMkMiZCpVJV6PSOWq3mca86uDnakZ6VW+o8mZLr959o5mISH/o7J7OVTDQtSUZu3brFmDFjGDFihM59fEqSjpCQEEJCQvj8889xcXEhLS2NkJAQ8vPztfZT3pyNwYMH8/rrrzNz5kyGDh1a5mrP5cVeEn9V3905PDycK1eu8OGHH9K4cWNsbW3p0KGDTn8rwsLCgqZNm5Zbx8HB4Z516tWrR5MmTcx6XZwjF7NIOJyOSgXjn2xG4zq2XDmiEGTkJQtE2SwtVAR5OctxMnHGPk6SyJghSwsVbz3dnPFr9hn93OT9atu2LUeOHCnzl+TBgwe5cuUK8+bNw8PDA4A///xT7/04OzvTr18/vvrqK5YtW2aQ2CujefPmrFixguzsbE3itWPHDiwsLPDx8dE8XrJkCX369AHgn3/+4fLly1rtWFtba67MEhVTMhrTx68BTV1rljqHSQhhfszzTytBr1ZuRj03eePGDfbv36/1888//+jdzr///W927tzJ1KlT2b9/PydOnOD777/X3DT0kUcewcbGhkWLFnH69Gl++OEHZs+eXamYV6xYweXLl/H19a3U9oYwePBg7OzsCA8P59ChQ2zbto1XX32VoUOHaiYfN2vWjM8++4wjR46wa9cuBg8erHM6yNPTk8TERC5dusS1a9fK3J+iKFy6dEnn585Rpby8PJ3n706cbt68SXp6ulYdU7i8vKJOZtxi48GLAEQ8Wf7okxDCvMiIjBkrOTdpjJV9k5KSNPeeKvHSSy/p3U7r1q3Ztm0b06ZNo0uXLiiKgre3NwMHDgSKJ7auWLGCN954g4ULF9K2bVvmz59Pv3799N6Xvb29TkLwoNWoUYNffvmFiRMn8thjj1GjRg2ee+45FixYoKmzfPlyxowZQ9u2bfHw8GDu3LlMmTJFq53333+fyMhI/vvf/9KwYUPNFVp3y8rK0sxLutPFixc1a/5s2rRJp46Pjw9Hjx7VPI6OjiY6OlqrztixY406uqWPJUknURTo3rw+zRuUvdS5EML8yL2WqpDca6lipE+mz5zvtZR2JYcn30+iSK3w/fiOtPGoDdy5KFkfs12U7G7SJ/MgfaqYit5ryfy/YYUQohxLt5+iSK3Q+VEXTRIjhKg+JJERQlRbF67fZt2e4rlbr3aTuTFCVEeSyAghqq1Pfj1NQVHxJaGPecqS9kJUR5LICCGqpYybuXyRUryI4ISnmhk5GiFEVZFExshkrrWobkzlPb38t1TyCtUEPFKbYDO9S7IQ4t4kkTGSklndOTk5Ro5ECMMqeU8b82qMq9n5fPbHWaB4bkzJitJCiOpH1pExEktLS2rXrk1GRgZQvL5IRb5s1Wo1+fn55ObmVovLekH6ZA4q0h9FUcjJySEjI4PatWsb9a7c8TtSyckvoqW7I0/6uBotDiFE1ZNExohKFiQrSWYqQlEUbt++jb29fbX5K1P6ZPr06U/JXbmN5cbtAlbsOAPIaIwQDwNJZIxIpVLRoEEDXF1dK3zfl4KCAn799Vc6d+5crRZSkj6Ztor2x9ra2qgjMQCfJZ/hZl4hzVxr0rOF8RIqIcSDIYmMCbC0tKzwl7+lpSWFhYXY2dlVi1+QIH0yB+bSn+y8Qpb/ngpARLemWJjBjVOFEPfH/E/eCyHE/3y+6yzXcgrwrFuDvn5Ve+NUIYRpkERGCFEt5BYU8cmvxaMxrzzZFCtL+XoT4mEgn3QhRLWwdvc/XL6VR8Pa9jwb0NDY4QghHhBJZIQQZi+/UM2y7acAGNfVG2sZjRHioSGfdiGE2ftm7zku3sjFtZYtLwQ2MnY4QogHSBIZIYRZKyxSsyTpJABjOjfBztq4l38LIR4sk0hkFi9ejKenJ3Z2dgQFBZGSklJu/djYWHx8fLC3t8fDw4PJkyeTm5ured7T0xOVSqXzM378eADOnDlT6vMqlYqvv/5a005aWhp9+/alRo0auLq6MnXqVAoLC6vmRRBCVMoPBy7wz9Xb1HWwISzoEWOHI4R4wIy+jszatWuJjIxk2bJlBAUFERsbS0hICMeOHcPVVXdp8TVr1hAVFUVcXBzBwcEcP36c4cOHo1KpWLBgAQC7d++mqKhIs82hQ4fo0aMHL7zwAgAeHh5cvHhRq91PPvmE9957j969ewNQVFRE3759cXNzY+fOnVy8eJFhw4ZhbW3N3Llzq+rlEELooUitsHhb8WjMS094UcPG6F9pQogHzOgjMgsWLGD06NGMGDGCFi1asGzZMmrUqEFcXFyp9Xfu3EnHjh0JCwvD09OTnj17MmjQIK1RHBcXF9zc3DQ/P/30E97e3nTp0gUoXtzrzufd3Nz49ttvGTBgADVr1gRg8+bNHD58mNWrV+Pv70/v3r2ZPXs2ixcvJj8/v+pfGCHEPf186CKnMrNxtLNi6OONjR2OEMIIjPrnS35+Pnv27GHatGmaMgsLC7p3705ycnKp2wQHB7N69WpSUlJo3749p0+fZuPGjQwdOrTMfaxevZrIyMgy77myZ88e9u/fz+LFizVlycnJ+Pn5Ub9+fU1ZSEgIL7/8Mn///TcBAQE67eTl5ZGXl6d5nJWVBRQv717RWxDcS0k7hmrPFEifTJ8p9ketVliUeAKA8A6PYGepX3ym2Kf7JX0yD9In/dq8F6MmMpcvX6aoqEgrWQCoX78+R48eLXWbsLAwLl++TKdOnVAUhcLCQsaNG8cbb7xRav3vvvuO69evM3z48DLjWL58Oc2bNyc4OFhTdunSpVLjKnmuNDExMcyaNUunfPPmzdSoUaPM/VdGQkKCQdszBdIn02dK/Tl4VcWxdEtsLRUa3DzOxo3HK9WOKfXJUKRP5kH6VL6cnJwK1TO7E8pJSUnMnTuXJUuWEBQUxMmTJ5k4cSKzZ89mxowZOvWXL19O7969cXd3L7W927dvs2bNmlK31de0adOIjIzUPM7KysLDw4OePXvi6Oh43+1DcYaakJBAjx49TPqeN/qQPpk+U+uPoigs/3gXkMXw4Ca80LOZ3m2YWp8MQfpkHqRPFVNyVuNejJrI1KtXD0tLS9LT07XK09PTcXMr/a61M2bMYOjQoYwaNQoAPz8/srOzGTNmDG+++SYWFv8/7efs2bNs2bKF9evXlxnDunXryMnJYdiwYVrlbm5uOldPlcRZVmy2trbY2trqlFtbWxv8zVoVbRqb9Mn0mUp/fj2eyV/ns7CztmB0F+/7islU+mRI0ifzIH26d1sVYdTJvjY2NgQGBpKYmKgpU6vVJCYm0qFDh1K3ycnJ0UpWAM2doxVF0SqPj4/H1dWVvn37lhnD8uXL6devHy4uLlrlHTp04ODBg2RkZGjKEhIScHR0pEWLFhXroBDC4BRFYdHW4rkxYe0bU6+m7h8PQoiHh9FPLUVGRhIeHk67du1o3749sbGxZGdnM2LECACGDRtGw4YNiYmJASA0NJQFCxYQEBCgObU0Y8YMQkNDNQkNFCdE8fHxhIeHY2VVejdPnjzJr7/+ysaNG3We69mzJy1atGDo0KG8++67XLp0ienTpzN+/PhSR12EEA/GrtSr7D5zDRtLC8Z2aWLscIQQRmb0RGbgwIFkZmby1ltvcenSJfz9/dm0aZNmYm1aWprWCMz06dNRqVRMnz6d8+fP4+LiQmhoKHPmzNFqd8uWLaSlpTFy5Mgy9x0XF0ejRo3o2bOnznOWlpb89NNPvPzyy3To0AEHBwfCw8N5++23DdRzIURlfLS1eN2YAY81or6jnZGjEUIYm9ETGYCIiAgiIiJKfS4pKUnrsZWVFdHR0URHR5fbZs+ePXVONd1t7ty55S5u17hx41JHa4QQxrE37Rq/n7yMlYWKsZ29jR2OEMIEGH1BPCGEqKiS0ZhnAxri4WzYJQ2EEOZJEhkhhFk4dP4GW49mYKGCV55sauxwhBAmQu9EpkuXLqxatYrbt29XRTxCCFGqknsqhbZxx6ueg5GjEUKYCr0TmYCAAKZMmYKbmxujR4/mjz/+qIq4hBBC43j6TX4+VLyi9ngZjRFC3EHvRCY2NpYLFy4QHx9PRkYGnTt3pkWLFsyfP19nYTshhDCEktGYXi3deLR+LSNHI4QwJZWaI2NlZcW//vUvvv/+e86dO0dYWBgzZszAw8OD/v37s3XrVkPHKYR4SKVezubHAxcAiOgmozFCCG33Ndk3JSWF6Oho3n//fVxdXZk2bRr16tXj6aefZsqUKYaKUQjxEFuadBK1At18XWnV0MnY4QghTIze68hkZGTw2WefER8fz4kTJwgNDeWLL74gJCQElUoFwPDhw+nVqxfz5883eMBCiIfHuWs5rN97HpC5MUKI0umdyDRq1Ahvb29GjhzJ8OHDde5RBNC6dWsee+wxgwQohHh4fbz9NIVqhY5N6xLYuI6xwxFCmCC9E5nExESeeOKJcus4Ojqybdu2SgclhBDpWbms/fMfACKebGbkaIQQpkrvOTKNGjXixIkTOuUnTpzgzJkzhohJCCH45NfT5BeqecyzDo83cTZ2OEIIE6V3IjN8+HB27typU75r1y6GDx9uiJiEEA+5K7fy+HzXWQAiujXTzL8TQoi76Z3I7Nu3j44dO+qUP/744+zfv98QMQkhHnLLf08lt0BN60ZOdG5Wz9jhCCFMmN6JjEql4ubNmzrlN27coKioyCBBCSEeXtdz8lmV/L/RmCebymiMEKJceicynTt3JiYmRitpKSoqIiYmhk6dOhk0OCHEw2fFzjPcyivE160W3ZvXN3Y4QggTp/dVS++88w6dO3fGx8dHc/XSb7/9RlZWlqzoK4S4LzdzC4jfcQYoXsXXwkJGY4QQ5dN7RKZFixb89ddfDBgwgIyMDG7evMmwYcM4evQorVq1qooYhRAPidV/pHHjdgFNXBzo3aqBscMRQpgBvUdkANzd3Zk7d66hYxFCPMRu5xfx6W+nARjftSmWMhojhKiASiUyADk5OaSlpZGfn69V3rp16/sOSgjx8FmTksaV7Hw8nO3p5+9u7HCEEGZC70QmMzOTESNG8PPPP5f6vFy5JITQV25BEZ/8egqAV7o2xdryvu5nK4R4iOj9bTFp0iSuX7/Orl27sLe3Z9OmTaxcuZJmzZrxww8/VEWMQohqbt2ec6Rn5dHAyY5/tW1o7HCEEGZE7xGZrVu38v3339OuXTssLCxo3LgxPXr0wNHRkZiYGPr27VsVcQohqqmCIjVLk4pHY8Z2boKtlaWRIxJCmBO9R2Sys7NxdXUFoE6dOmRmZgLg5+fH3r17DRudEKLa+3bfec5fv029mra82P4RY4cjhDAzeicyPj4+HDt2DIA2bdrw8ccfc/78eZYtW0aDBnK5pBCi4orUCku2nQRgTGcv7KxlNEYIoR+9Ty1NnDiRixcvAhAdHU2vXr34/PPPsbGxYcWKFYaOTwhRjf301wXOXMmhdg1rBgc1NnY4QggzpHciM2TIEM3/AwMDOXv2LEePHuWRRx6hXj25uZsQomLUaoXF/xuNeamjFw62lV4NQgjxENPr1FJBQQHe3t4cOXJEU1ajRg3atm0rSYwQQi+bD1/iePotatlaMSzY09jhCCHMlF6JjLW1Nbm5uVUVixDiIaEoCou2Fo/GDO/oiZO9tZEjEkKYK70n+44fP5533nmHwsJCgwSwePFiPD09sbOzIygoiJSUlHLrx8bG4uPjg729PR4eHkyePFkrufL09ESlUun8jB8/Xqud5ORkunXrhoODA46OjnTu3Jnbt29rnt+7dy89evSgdu3a1K1blzFjxnDr1i2D9FmIh13SsUz+vpBFDRtLRnT0MnY4QggzpvdJ6d27d5OYmMjmzZvx8/PDwcFB6/n169dXuK21a9cSGRnJsmXLCAoKIjY2lpCQEI4dO6a5xPtOa9asISoqiri4OIKDgzl+/DjDhw9HpVKxYMECTXx3ri586NAhevTowQsvvKApS05OplevXkybNo1FixZhZWXFgQMHsLAozusuXLhA9+7dGThwIB999BFZWVlMmjSJ4cOHs27dOr1eLyGENkVRWLj1BABDHm+Ms4ONkSMSQpgzvROZ2rVr89xzzxlk5wsWLGD06NGMGDECgGXLlrFhwwbi4uKIiorSqb9z5046duxIWFgYUDz6MmjQIHbt2qWp4+LiorXNvHnz8Pb2pkuXLpqyyZMnM2HCBK19+Pj4aP7/008/YW1tzeLFizXJzbJly2jdujUnT56kadOmBui9EA+n5FNX2Jd2HVsrC0Y9IaMxQoj7o3ciEx8fb5Ad5+fns2fPHqZNm6Yps7CwoHv37iQnJ5e6TXBwMKtXryYlJYX27dtz+vRpNm7cyNChQ8vcx+rVq4mMjESlKr6TbkZGBrt27WLw4MEEBwdz6tQpfH19mTNnDp06dQIgLy8PGxsbTRIDYG9vD8Dvv/9eZiKTl5dHXl6e5nFWVhZQPEm6oKCgoi9NuUraMVR7pkD6ZPoM2Z8PE48DMKBdI+rYWRrtNapuxwikT+ZC+qRfm/ditOsdL1++TFFREfXr19cqr1+/PkePHi11m7CwMC5fvkynTp1QFIXCwkLGjRvHG2+8UWr97777juvXrzN8+HBN2enTpwGYOXMm8+fPx9/fn1WrVvHUU09x6NAhmjVrRrdu3YiMjOS9995j4sSJZGdna0ZvStbQKU1MTAyzZs3SKd+8eTM1atQo9/XQV0JCgkHbMwXSJ9N3v/05lQW7Uq2wVCl4559m48bTBoqs8qrbMQLpk7mQPpUvJyenQvX0TmS8vLw0oxulKUkUqkJSUhJz585lyZIlBAUFcfLkSSZOnMjs2bOZMWOGTv3ly5fTu3dv3N3dNWVqtRqAsWPHak5pBQQEkJiYSFxcHDExMbRs2ZKVK1cSGRnJtGnTsLS0ZMKECdSvX19rlOZu06ZNIzIyUvM4KysLDw8PevbsiaOjo0Feg4KCAhISEujRowfW1tXjSg/pk+kzVH9eWrUHuMLzgY0Y/ExLwwVYCdXtGIH0yVxInyqm5KzGveidyEyaNEnrcUFBAfv27WPTpk1MnTq1wu3Uq1cPS0tL0tPTtcrT09Nxc3MrdZsZM2YwdOhQRo0aBRTf3yk7O5sxY8bw5ptvaiUZZ8+eZcuWLTqTj0tuo9CiRQut8ubNm5OWlqZ5HBYWRlhYGOnp6Tg4OGgmFDdp0qTMPtna2mJra6tTbm1tbfA3a1W0aWzSJ9N3P/3569x1fj1xBUsLFeOffNRkXpfqdoxA+mQupE/3bqsiKnWLgtIsXryYP//8s8Lt2NjYEBgYSGJiIv379weKR0sSExOJiIgodZucnBydERFLy+J7syiKolUeHx+Pq6urzt24PT09cXd319wvqsTx48fp3bu3zj5LTn3FxcVhZ2dHjx49KtxHIcT/K1k35pk27jxS17CnWoUQDy+DzZHp3bs306ZN02sycGRkJOHh4bRr14727dsTGxtLdna25pTPsGHDaNiwITExMQCEhoayYMECAgICNKeWZsyYQWhoqCahgeKEKD4+nvDwcKystLuoUqmYOnUq0dHRtGnTBn9/f1auXMnRo0e1Lq3+6KOPCA4OpmbNmiQkJDB16lTmzZtH7dq17+NVEuLhdORiFgmH01Gp4JUn5ao/IYThGCyRWbduHc7OznptM3DgQDIzM3nrrbe4dOkS/v7+bNq0STMKkpaWpjUCM336dFQqFdOnT+f8+fO4uLgQGhrKnDlztNrdsmULaWlpjBw5stT9Tpo0idzcXCZPnszVq1dp06YNCQkJeHt7a+qkpKQQHR3NrVu38PX15eOPPy7z6ighRPlK7qnUx68BTV1rGjkaIUR1onciExAQoDXZV1EULl26RGZmJkuWLNE7gIiIiDJPJSUlJWk9trKyIjo6mujo6HLb7Nmzp86pprtFRUWVulZNiVWrVpW7vRCiYk5l3mLDweKr/SJkNEYIYWB6JzIl81lKWFhY4OLiQteuXfH19TVUXEKIamLJtlMoCnRvXp/mDQxz9Z4QQpTQO5G512iIEEKUSLuSw3f7zwPwajcZjRFCGJ7eN43cuHEjv/zyi075L7/8ws8//2yQoIQQ1cPS7acoUit0ftSFNh61jR2OEKIa0juRiYqK0ropYwlFUcqdcyKEeLhcvHGbdXv+AWQ0RghRdfROZE6cOKGzmByAr68vJ0+eNEhQQgjz9/H20xQUKQR5OfOYp35XNAohREXpncg4OTmVehuCkydP4uDgYJCghBDmLeNmLl+kFK+UPeGpZkaORghRnemdyDzzzDNMmjSJU6dOacpOnjzJa6+9Rr9+/QwanBDCPC3/LZW8QjUBj9Qm2LuuscMRQlRjeicy7777Lg4ODvj6+uLl5YWXlxfNmzenbt26zJ8/vypiFEKYkWvZ+Xz2x1mgeG5MeTeZFUKI+6X35ddOTk7s3LmThIQEDhw4gL29Pa1bt6Zz585VEZ8QwszE70glJ7+IFg0cedLH1djhCCGquUrdokClUtGzZ0969uxp6HiEEGYsK7eA+J1nABmNEUI8GHqfWpowYQILFy7UKf/oo4+YNGmSIWISQpipVTvPcDO3kGauNQlp6WbscIQQDwG9E5lvvvmGjh076pQHBwdr3T1aCPFwyc4rZPnvqQBEdGuKhYWMxgghqp7eicyVK1dwcnLSKXd0dOTy5csGCUoIYX7W7ErjWk4BnnVr0NevgbHDEUI8JPROZJo2bcqmTZt0yn/++WeaNGlikKCEEOYlt6CIT34rXl/qla5NsbLU+6tFCCEqRe/JvpGRkURERJCZmUm3bt0ASExM5P333yc2NtbQ8QkhzMDa3f+QeTOPhrXtebZtQ2OHI4R4iOidyIwcOZK8vDzmzJnD7NmzAfD09GTp0qUMGzbM4AEKIUxbfqGaZduLF8gc19UbaxmNEUI8QJW6/Prll1/m5ZdfJjMzE3t7e2rWrAnA1atXcXaWe6oI8TBZv/ccF2/k4lrLlhcCGxk7HCHEQ+a+/nRycXGhZs2abN68mQEDBtCwoQwpC/EwKSxSsySpeDRmTOcm2FlbGjkiIcTDptKJzNmzZ4mOjsbT05MXXngBCwsLVq1aZcjYhBAm7ocDF0i7mkNdBxvCgh4xdjhCiIeQXqeW8vPzWb9+PZ9++ik7duyge/funDt3jn379uHn51dVMQohTFCRWmHxtpMAvPSEFzVsKnWmWggh7kuFR2ReffVV3N3d+fDDD3n22Wc5d+4cP/74IyqVCktLGU4W4mGz6dAlTmVm42hnxdDHGxs7HCHEQ6rCf0ItXbqUf//730RFRVGrVq2qjEkIYeIURWHR1hMAjOjoRS07ayNHJIR4WFV4ROazzz4jJSWFBg0aMHDgQH766SeKioqqMjYhhIlKPJLB0Us3qWlrxYiOnsYORwjxEKtwIjNo0CASEhI4ePAgvr6+jB8/Hjc3N9RqNYcPH67KGIUQJuTO0ZihHRpTu4aNkSMSQjzM9L5qycvLi1mzZnHmzBlWr17Nc889x5AhQ2jUqBETJkyoihiFECbktxOXOXDuBnbWFrzUycvY4QghHnKVvsxApVIREhJCSEgIV69eZdWqVcTHxxsyNiGECfpoa/GVSmHtG1Ovpq2RoxFCPOwMspa4s7MzkyZN4sCBA4ZoTghholLOXCXlzFVsLC0Y01luEiuEMD65KYoQosIWJxXf4XrAY41wc7IzcjRCCGECiczixYvx9PTEzs6OoKAgUlJSyq0fGxuLj48P9vb2eHh4MHnyZHJzczXPe3p6olKpdH7Gjx+v1U5ycjLdunXDwcEBR0dHOnfuzO3btzXPHz9+nGeeeYZ69erh6OhIp06d2LZtm2E7L4QZOXMTdp66ipWFirGdvY0djhBCAEZOZNauXUtkZCTR0dHs3buXNm3aEBISQkZGRqn116xZQ1RUFNHR0Rw5coTly5ezdu1a3njjDU2d3bt3c/HiRc1PQkICAC+88IKmTnJyMr169aJnz56kpKSwe/duIiIisLD4/5fj6aefprCwkK1bt7Jnzx7atGnD008/zaVLl6ro1RDCtG0+X/z5eDagIR7ONYwcjRBCFDPqmuILFixg9OjRjBgxAoBly5axYcMG4uLiiIqK0qm/c+dOOnbsSFhYGFA8+jJo0CB27dqlqePi4qK1zbx58/D29qZLly6assmTJzNhwgStffj4+Gj+f/nyZU6cOMHy5ctp3bq1pp0lS5Zw6NAh3NzcDNB7IczH3xey+PuaBRYqeOXJpsYORwghNCqUyPz1118VbrDkF/+95Ofns2fPHqZNm6Yps7CwoHv37iQnJ5e6TXBwMKtXryYlJYX27dtz+vRpNm7cyNChQ8vcx+rVq4mMjESlUgGQkZHBrl27GDx4MMHBwZw6dQpfX1/mzJlDp06dAKhbty4+Pj6sWrWKtm3bYmtry8cff4yrqyuBgYFl9ikvL4+8vDzN46ysLAAKCgooKCio0OtyLyXtGKo9UyB9Mn0ld7ju3bI+jZxsqkW/qtsxAumTuZA+6dfmvagURVHuVcnCwgKVSoWiKJqEoCwVXe33woULNGzYkJ07d9KhQwdN+euvv8727du1RlnutHDhQqZMmYKiKBQWFjJu3DiWLl1aat2vvvqKsLAw0tLScHd3B+CPP/6gQ4cOODs7M3/+fPz9/Vm1apVmtKVZs2YAnDt3jv79+7N3714sLCxwdXVlw4YNBAQElNmnmTNnMmvWLJ3yNWvWUKOGDMUL83QxB+YdKP6bJ6pNIQ3krSyEeABycnIICwvjxo0bODo6llmvQiMyqampmv/v27ePKVOmMHXqVE0CkpyczPvvv8+77757n2GXLykpiblz57JkyRKCgoI4efIkEydOZPbs2cyYMUOn/vLly+ndu7cmiQFQq9UAjB07VnNKKyAggMTEROLi4oiJiUFRFMaPH4+rqyu//fYb9vb2fPrpp4SGhrJ7924aNGhQanzTpk0jMjJS8zgrKwsPDw969uxZ7kHQR0FBAQkJCfTo0QNr6+pxfxvpk2l77euDwEVaO6sZ9oz596dEdTpGJaRP5kH6VDElZzXupUKJTOPG/39n2xdeeIGFCxfSp08fTVnr1q3x8PBgxowZ9O/fv0I7rlevHpaWlqSnp2uVp6enlzkHZcaMGQwdOpRRo0YB4OfnR3Z2NmPGjOHNN9/Umqx79uxZtmzZwvr167XaKElCWrRooVXevHlz0tLSANi6dSs//fQT165d0yQgS5YsISEhgZUrV5Y6fwfA1tYWW1vdBcKsra0N/matijaNTfpkes5czuangxcB6NlQbfb9KY30yTxIn8yDIftU0Xb0vmrp4MGDeHnpLkvu5eWl1z2XbGxsCAwMJDExUVOmVqtJTEzUOtV0p5ycHK1kBcDS0hIovv/LneLj43F1daVv375a5Z6enri7u3Ps2DGt8uPHj2sStpycHACdfVlYWGhGdIR4GCxNOoVaga6P1sOjprGjEUIIXXonMs2bNycmJob8/HxNWX5+PjExMTRv3lyvtiIjI/nvf//LypUrOXLkCC+//DLZ2dmaUz7Dhg3TmgwcGhrK0qVL+fLLL0lNTSUhIYEZM2YQGhqqSWigOCGKj48nPDwcKyvtQSeVSsXUqVNZuHAh69at4+TJk8yYMYOjR4/y0ksvAdChQwfq1KlDeHg4Bw4c4Pjx40ydOpXU1FSdxEiI6urctRy+2XsOgFe6yCq+QgjTpPfl18uWLSM0NJRGjRpprlD666+/UKlU/Pjjj3q1NXDgQDIzM3nrrbe4dOkS/v7+bNq0ifr16wOQlpamNSoyffp0VCoV06dP5/z587i4uBAaGsqcOXO02t2yZQtpaWmMHDmy1P1OmjSJ3NxcJk+ezNWrV2nTpg0JCQl4excv8lWvXj02bdrEm2++Sbdu3SgoKKBly5Z8//33tGnTRq8+CmGuPt5+mkK1QsemdQl4pDYXDxk7IiGE0KV3IlNy2fPnn3/O0aNHgeKEJCwsDAcHB70DiIiIICIiotTnkpKStIO1siI6Opro6Ohy2+zZs6fOqaa7RUVFlTnXBaBdu3b88ssv5bYhRHWVnpXL2j//ASDiyWZGjkYIIcpWqQXxHBwcGDNmjKFjEUKYiP/+epr8QjXtGtfh8SbOFBYWGjskIYQoVaVuUfDZZ5/RqVMn3N3dOXv2LAAffPAB33//vUGDE0I8eFdu5fH5ruIr+F59qtk9144SQghj0juRWbp0KZGRkfTu3Ztr165pFsCrU6cOsbGxho5PCPGALf89ldsFRbRu5ETnZvWMHY4QQpRL70Rm0aJF/Pe//+XNN9/UuiKoXbt2HDx40KDBCSEerBs5BaxKLh5ljXiyqYzGCCFMnt6JTGpqaqnL9Nva2pKdnW2QoIQQxrFi5xlu5RXi61aL7s3rGzscIYS4J70TGS8vL/bv369TvmnTJr3XkRFCmI5beYXE7Si+HUlEt6ZYWMhojBDC9Ol91VJkZCTjx48nNzcXRVFISUnhiy++ICYmhk8//bQqYhRCPACfJZ/lxu0Cmrg40LtV6fcTE0IIU6N3IjNq1Cjs7e2ZPn265s6U7u7ufPjhh7z44otVEaMQoordzi/i099OAzC+a1MsZTRGCGEmKrWOzODBgxk8eDA5OTncunULV1dXQ8clhHiAvkhJ40p2Ph7O9vTzd7/3BkIIYSL0niPzn//8h9TU4vPoNWrUkCRGCDOXV1jEx7+eAuCVrk2xtqzU8lJCCGEUen9jff311zRt2pTg4GCWLFnC5cuXqyIuIcQD8vWf50jPyqOBkx3/atvQ2OEIIYRe9E5kDhw4wF9//UXXrl2ZP38+7u7u9O3blzVr1pCTk1MVMQohqkhBkZqlScWjMWM7N8HWyvIeWwghhGmp1Bhyy5YtmTt3LqdPn2bbtm14enoyadIk3NzcDB2fEKIKfbfvPOev36ZeTVtebP+IscMRQgi93ffJcAcHB+zt7bGxsaGgoMAQMQkhHoAitcKS/43GjOnshZ21jMYIIcxPpRKZ1NRU5syZQ8uWLWnXrh379u1j1qxZXLp0ydDxCSGqyE9/XSD1cja1a1gzOKixscMRQohK0fvy68cff5zdu3fTunVrRowYwaBBg2jYUCYICmFO1GqFxdtOAvBSRy8cbCu1EoMQQhid3t9eTz31FHFxcbRo0aIq4hFCPACbD6dzPP0WtWytGBbsaexwhBCi0vROZObMmQNAfn4+qampeHt7a90FWwhh2hRF4aNtJwAID/bEyd7ayBEJIUTl6T1H5vbt27z00kvUqFGDli1bkpaWBsCrr77KvHnzDB6gEMKwko5ncuh8FjVsLBnZycvY4QghxH3RO5GJioriwIEDJCUlYWdnpynv3r07a9euNWhwQgjDUhSFRYnFozFDHm+Ms4ONkSMSQoj7o/c5oe+++461a9fy+OOPo1L9/43lWrZsyalTpwwanBDCsJJPXWFv2nVsrCwY9YSMxgghzJ/eIzKZmZml3l8pOztbK7ERQpieRVuLr1Qa9JgHrrXs7lFbCCFMn96JTLt27diwYYPmcUny8umnn9KhQwfDRSaEMKg/z1wl+fQVrC1VjO3ibexwhBDCIPQ+tTR37lx69+7N4cOHKSws5MMPP+Tw4cPs3LmT7du3V0WMQggDKBmNeT6wEe617Y0cjRBCGIbeIzKdOnVi//79FBYW4ufnx+bNm3F1dSU5OZnAwMCqiFEIcZ/+Oned7cczsbRQ8XKXpsYORwghDKZSC8B4e3vz3//+19CxCCGqyEf/G415po07j9StYeRohBDCcCqUyGRlZeHo6Kj5f3lK6gkhTMPRS1lsPpyOSgWvPCmjMUKI6qVCiUydOnW4ePEirq6u1K5du9SrkxRFQaVSUVRUZPAghRCVt3hb8bIIffwa0NS1ppGjEUIIw6rQHJmtW7fi7OwMwLZt29i6davOT0l5ZSxevBhPT0/s7OwICgoiJSWl3PqxsbH4+Phgb2+Ph4cHkydPJjc3V/O8p6cnKpVK52f8+PFa7SQnJ9OtWzccHBxwdHSkc+fO3L59G4CkpKRS21CpVOzevbtS/RTiQTuVeYuf/roAQISMxgghqqEKjch06dKl1P8bwtq1a4mMjGTZsmUEBQURGxtLSEgIx44dK3W9mjVr1hAVFUVcXBzBwcEcP36c4cOHo1KpWLBgAQC7d+/WGhk6dOgQPXr04IUXXtCUJScn06tXL6ZNm8aiRYuwsrLiwIEDWFgU53bBwcFcvHhRa98zZswgMTGRdu3aGfQ1EKKqLNl2CkWB7s3r07yBnPYVQlQ/lZrse/36dVJSUsjIyECtVms9N2zYML3aWrBgAaNHj2bEiBEALFu2jA0bNhAXF0dUVJRO/Z07d9KxY0fCwsKA4tGXQYMGsWvXLk0dFxcXrW3mzZuHt7e3VhI2efJkJkyYoLUPHx8fzf9tbGxwc3PTPC4oKOD777/n1VdflYX/hFn452oO3+0/D8Cr3WQ0RghRPemdyPz4448MHjyYW7du4ejoqPVLXaVS6ZXI5Ofns2fPHqZNm6Yps7CwoHv37iQnJ5e6TXBwMKtXryYlJYX27dtz+vRpNm7cyNChQ8vcx+rVq4mMjNTEmpGRwa5duxg8eDDBwcGcOnUKX19f5syZQ6dOnUpt54cffuDKlSuahKs0eXl55OXlaR6XTIwuKCigoKCg/BejgkraMVR7pkD6VDUWbztBkVrhiaZ1aeHmcF+xmEJ/DE36ZB6kT+ahKvpU0bZUiqIo+jT86KOP0qdPH+bOnUuNGvd3GeeFCxdo2LAhO3fu1FoV+PXXX2f79u1aoyx3WrhwIVOmTEFRFAoLCxk3bhxLly4tte5XX31FWFgYaWlpuLu7A/DHH3/QoUMHnJ2dmT9/Pv7+/qxatYolS5Zw6NAhmjVrptNOnz59ANi4cWOZ/Zk5cyazZs3SKV+zZs19v1ZC6ON6Hry9z5IiRcWEloV4y1klIYSZycnJISwsjBs3bpR7RbTeIzLnz59nwoQJRvvFnJSUxNy5c1myZAlBQUGcPHmSiRMnMnv2bGbMmKFTf/ny5fTu3VuTxACa02Fjx47VjLAEBASQmJhIXFwcMTExWm2cO3eOX375ha+++qrc2KZNm0ZkZKTmcVZWFh4eHvTs2dNgl6UXFBSQkJBAjx49sLa2NkibxiZ9MrzZG45SpKTR3rMOr7742H23Z+z+VAXpk3mQPpmHqujTvZZ7KaF3IhMSEsKff/5JkyZN9A7qbvXq1cPS0pL09HSt8vT0dK35KXeaMWMGQ4cOZdSoUQD4+fmRnZ3NmDFjePPNNzWTdQHOnj3Lli1bWL9+vVYbDRo0AKBFixZa5c2bNyctLU1nn/Hx8dStW5d+/fqV2x9bW1tsbW11yq2trQ3+Zq2KNo1N+mQYmTfzWPvnOQAmPPWoQfcvx8g8SJ/Mg/Tp3m1VRIUSmR9++EHz/759+zJ16lQOHz6Mn5+fzo7u9cv+TjY2NgQGBpKYmEj//v2B4tGSxMREIiIiSt0mJydHK1kBsLS0BIrXsrlTfHw8rq6u9O3bV6vc09MTd3d3jh07plV+/PhxevfurVWmKArx8fEMGzas2r3hRPX06e+nyStUE/BIbTo2rWvscIQQokpVKJEpSTLu9Pbbb+uUVWZBvMjISMLDw2nXrh3t27cnNjaW7OxszSmfYcOG0bBhQ83pntDQUBYsWEBAQIDm1NKMGTMIDQ3VJDRQnBDFx8cTHh6OlZV2N1UqFVOnTiU6Opo2bdrg7+/PypUrOXr0KOvWrdOqu3XrVlJTUzUjQEKYsmvZ+XyWfBYovlJJrrATQlR3FUpk7r7E2pAGDhxIZmYmb731FpcuXcLf359NmzZRv359ANLS0rRGYKZPn45KpWL69OmcP38eFxcXQkNDmTNnjla7W7ZsIS0tjZEjR5a630mTJpGbm8vkyZO5evUqbdq0ISEhAW9vb616y5cvJzg4GF9fXwP3XAjDi9+RSk5+ES0aOPKkj+46TEIIUd1Uah0ZQ4uIiCjzVFJSUpLWYysrK6Kjo4mOji63zZ49e+qcarpbVFRUqWvV3GnNmjXlPi+EqcjKLSB+5xlARmOEEA+PCt2i4E4TJkxg4cKFOuUfffQRkyZNMkRMQohK+Cz5LDdzC2nmWpOQlqVPlhdCiOpG70Tmm2++oWPHjjrlwcHBOvNLhBAPRk5+IZ/+dhqAiG5NsbCQ0RghxMNB70TmypUrODk56ZQ7Ojpy+fJlgwQlhNDP53+kcS2nAM+6Nejr18DY4QghxAOjdyLTtGlTNm3apFP+888/G2RtGSGEfnILivjkf6Mxr3RtipWl3h9rIYQwW3pP9o2MjCQiIoLMzEy6desGQGJiIu+//z6xsbGGjk8IcQ9f/fkPmTfzaFjbnmfbNjR2OEII8UDpnciMHDmSvLw85syZw+zZs4HiBeaWLl2q952vhRD3J79QzbKkUwCM6+qNtYzGCCEeMpW6/Prll1/m5ZdfJjMzE3t7e2rWrAnA1atXcXZ2NmiAQoiyrd97jgs3cnGtZcsLgY2MHY4QQjxw9/Xnm4uLCzVr1mTz5s0MGDCAhg1lWFuIB6WwSM2S/43GjOncBDtry3tsIYQQ1U+lE5mzZ88SHR2Np6cnL7zwAhYWFqxatcqQsQkhyvHjXxdIu5qDs4MNYUGPGDscIYQwCr1OLeXn57N+/Xo+/fRTduzYQffu3Tl37hz79u3Dz8+vqmIUQtxFrVb4aOtJAEY94UUNG5NYpFsIIR64Co/IvPrqq7i7u/Phhx/y7LPPcu7cOX788UdUKpXWzRqFEFVv09+XOJWZjaOdFUMfb2zscIQQwmgq/Gfc0qVL+fe//01UVBS1atWqypiEEOVQFIVF/xuNGdHRi1p21kaOSAghjKfCIzKfffYZKSkpNGjQgIEDB/LTTz9RVFRUlbEJIUqReCSDIxezcLCxZERHT2OHI4QQRlXhRGbQoEEkJCRw8OBBfH19GT9+PG5ubqjVag4fPlyVMQoh/kdRFBZtKx6NGRbsSe0aNkaOSAghjEvvq5a8vLyYNWsWZ86cYfXq1Tz33HMMGTKERo0aMWHChKqIUQjxP7+fvMyBf65jZ23BS528jB2OEEIYXaUvdVCpVISEhBASEsLVq1dZtWoV8fHxhoxNCHGXRYnFozFh7RtTr6atkaMRQgjjM8h65s7OzkyaNIkDBw4YojkhRCl2nb5Cypmr2FhaMKaz3KBVCCHAQImMEKLqffS/uTEDHmuEm5OdkaMRQgjTIImMEGZgX9o1fjtxGSsLFWM7exs7HCGEMBmSyAhhBkpW8X02oCEezjWMHI0QQpgOSWSEMHGHzt8g8WgGFip45cmmxg5HCCFMSqWuWsrNzeWvv/4iIyMDtVqt9Vy/fv0MEpgQotiSpOLRmNA27njVczByNEIIYVr0TmQ2bdrEsGHDuHz5ss5zKpVKVvsVwoBOpN/k50OXABgvozFCCKFD71NLr776Ki+88AIXL15ErVZr/UgSI4RhLUk6haJAr5ZuPFpf7nEmhBB30zuRSU9PJzIykvr161dFPEKI/zlzOZvv958HIKKbjMYIIURp9E5knn/+eZKSkqogFCHEnZYmnUKtwJM+LrRq6GTscIQQwiTpPUfmo48+4oUXXuC3337Dz88Pa2trreflfktC3L/z12/zzd5zAER0a2bkaIQQwnTpnch88cUXbN68GTs7O5KSklCpVJrnVCqVJDJCGMDH209RqFbo2LQugY3rGDscIYQwWXqfWnrzzTeZNWsWN27c4MyZM6Smpmp+Tp8+rXcAixcvxtPTEzs7O4KCgkhJSSm3fmxsLD4+Ptjb2+Ph4cHkyZPJzc3VPO/p6YlKpdL5GT9+vFY7ycnJdOvWDQcHBxwdHencuTO3b9/WqrNhwwaCgoKwt7enTp069O/fX+/+CaGvjKxcvtz9DwART8pojBBClEfvEZn8/HwGDhyIhcX9r6W3du1aIiMjWbZsGUFBQcTGxhISEsKxY8dwdXXVqb9mzRqioqKIi4sjODiY48ePM3z4cFQqFQsWLABg9+7dWldPHTp0iB49evDCCy9oypKTk+nVqxfTpk1j0aJFWFlZceDAAa0+ffPNN4wePZq5c+fSrVs3CgsLOXTo0H33WYh7+eTX0+QXqmnXuA6PN3E2djhCCGHS9E5kwsPDWbt2LW+88cZ973zBggWMHj2aESNGALBs2TI2bNhAXFwcUVFROvV37txJx44dCQsLA4pHXwYNGsSuXbs0dVxcXLS2mTdvHt7e3nTp0kVTNnnyZCZMmKC1Dx8fH83/CwsLmThxIu+99x4vvfSSprxFixbl9icvL4+8vDzN46ysLAAKCgooKCgod9uKKmnHUO2ZAunT/7uSnc/nu84C8EoXLwoLCw0eW2XIMTIP0ifzIH3Sr8170TuRKSoq4t133+WXX36hdevWOpN9S0ZG7iU/P589e/Ywbdo0TZmFhQXdu3cnOTm51G2Cg4NZvXo1KSkptG/fntOnT7Nx40aGDh1a5j5Wr15NZGSkZi5PRkYGu3btYvDgwQQHB3Pq1Cl8fX2ZM2cOnTp1AmDv3r2cP38eCwsLAgICuHTpEv7+/rz33nu0atWqzD7FxMQwa9YsnfLNmzdTo4Zh74+TkJBg0PZMgfQJfkqz4HaBBR4OClnHU9h4oooCqyQ5RuZB+mQepE/ly8nJqVA9vROZgwcPEhAQAKBzquXOib/3cvnyZYqKinTWo6lfvz5Hjx4tdZuwsDAuX75Mp06dUBSFwsJCxo0bV+bo0Hfffcf169cZPny4pqxkHs/MmTOZP38+/v7+rFq1iqeeeopDhw7RrFkzrToLFizA09OT999/n65du3L8+HGcnUsf7p82bRqRkZGax1lZWXh4eNCzZ08cHR0r/NqUp6CggISEBHr06KGTRJor6VOxG7cLeOP9X4Ei3ngmgO7NdU+vGoscI/MgfTIP0qeKKTmrcS96JzLbtm3TOxhDSUpKYu7cuSxZsoSgoCBOnjzJxIkTmT17NjNmzNCpv3z5cnr37o27u7umrOTeUGPHjtWc0goICCAxMZG4uDhiYmI0dd58802ee+45AOLj42nUqBFff/01Y8eOLTU+W1tbbG1tdcqtra0N/matijaN7WHv0+fbz5CdV4SvWy1CWrljYVHxPwwelIf9GJkL6ZN5kD7du62KqNRNIw2hXr16WFpakp6erlWenp6Om5tbqdvMmDGDoUOHMmrUKAD8/PzIzs5mzJgxvPnmm1qTdc+ePcuWLVtYv369VhsNGjQAdOe7NG/enLS0tDLr2Nra0qRJE00dIQzpVl4hcTtSgeJVfE0xiRFCCFOkdyLz5JNPlnsKaevWrRVqx8bGhsDAQBITEzWXNavVahITE4mIiCh1m5ycHJ2rpSwtLQFQFEWrPD4+HldXV/r27atV7unpibu7O8eOHdMqP378OL179wYgMDAQW1tbjh07ppk3U1BQwJkzZ2jcuHGF+ieEPlb/cZYbtwto4uJA71YNjB2OEEKYDb0TGX9/f63HBQUF7N+/n0OHDhEeHq5XW5GRkYSHh9OuXTvat29PbGws2dnZmlM+w4YNo2HDhsTExAAQGhrKggULCAgI0JxamjFjBqGhoZqEBooTovj4eMLDw7Gy0u6iSqVi6tSpREdH06ZNG/z9/Vm5ciVHjx5l3bp1ADg6OjJu3Diio6Px8PCgcePGvPfeewBal3ELYQi384v49LfieVnjuzbFUkZjhBCiwvROZD744INSy2fOnMmtW7f0amvgwIFkZmby1ltvaa4M2rRpk2YCcFpamtYIzPTp01GpVEyfPp3z58/j4uJCaGgoc+bM0Wp3y5YtpKWlMXLkyFL3O2nSJHJzc5k8eTJXr16lTZs2JCQk4O3tranz3nvvYWVlxdChQ7l9+zZBQUFs3bqVOnVklVVhWF+kpHH5Vj4ezvb083e/9wZCCCE0DDZHZsiQIbRv35758+frtV1ERESZp5LuvjmllZUV0dHRREdHl9tmz549dU413S0qKqrUtWpKWFtbM3/+fL37I4Q+8gqL+PjXUwC83KUp1pb3v9CkEEI8TAz2rZmcnIydnZ2hmhPiobBuzznSs/Jo4GTHc4ENjR2OEEKYHb1HZP71r39pPVYUhYsXL/Lnn3+Wegm0EKJ0BUVqliYVj8aM7dwEWyvLe2whhBDibnonMk5OTlqPLSws8PHx4e2336Znz54GC0yI6u67fec5d+029Wra8GL7R4wdjhBCmCW9E5n4+PiqiEOIh0qRWmHJ/0ZjRj/RBDtrGY0RQojKuK/Jvrdu3dKsglvCUEvxC1GdbTh4kdTL2dSuYc2Qx2VtIiGEqCy9J/umpqbSt29fHBwccHJyok6dOtSpU4fatWvLpclCVIBarbB460kAXurohYOt0RbYFkIIs6f3N+iQIUNQFIW4uDjq16+v140ihRCQcCSdY+k3qWVrxbBgT2OHI4QQZk3vRObAgQPs2bMHHx+fqohHiGpNURQWbT0BQHiwJ0721euGcUII8aDpfWrpscce459//qmKWISo9pKOZ3LofBY1bCwZ2cnL2OEIIYTZ03tE5tNPP2XcuHGcP3+eVq1a6dxmu3Xr1gYLTojqRFEUFiUWj8YMebwxzg42Ro5ICCHMn96JTGZmJqdOndLc2BGKb8SoKAoqlYqioiKDBihEdZF8+gp7065jY2XBqCdkNEYIIQxB70Rm5MiRBAQE8MUXX8hkXyH0sCix+EqlQY954FpLbuchhBCGoHcic/bsWX744QeaNm1aFfEIUS39eeYqyaevYG2pYmwX73tvIIQQokL0nuzbrVs3Dhw4UBWxCFFtfbSteDTm+cBGuNe2N3I0QghRfeg9IhMaGsrkyZM5ePAgfn5+OpN9+/XrZ7DghKgODp67QdKxTCwtVLzcRUYyhRDCkPROZMaNGwfA22+/rfOcTPYVQlfJujHPtHHnkbo1jByNEEJUL3onMnffW0kIUbZjl26y+XA6KhW88qSMxgghhKHpPUdGCFFxS39NBaCPXwOautY0cjRCCFH9VGhEZuHChYwZMwY7OzsWLlxYbt0JEyYYJDAhzF36bdh46BIAETIaI4QQVaJCicwHH3zA4MGDsbOz44MPPiiznkqlkkRGPPSK1Aq7Uq+y9pQFigJP+brSvIGjscMSQohqqUKJTGpqaqn/F0Jo23ToIrN+PMzFG7mUnLnd/891Nh26SK9WDYwbnBBCVEN6z5HJzc0t87mLFy/eVzBCmLNNhy7y8uq9/0ti/t/V7HxeXr2XTYfk8yGEEIamdyLTtm1b9u/fr1P+zTffyA0jxUOrSK0w68fDKKU8V1I268fDFKlLqyGEEKKy9E5kunbtyuOPP84777wDQHZ2NsOHD2fo0KG88cYbBg9QCHOQknpVZyTmTgpw8UYuKalXH1xQQgjxENB7HZklS5bQt29fRo0axU8//cTFixepWbMmKSkptGrVqipiFMKkKYrC1qPpFaqbcbPsZEcIIYT+9E5kAHr37s2//vUvli5dipWVFT/++KMkMeKhdDrzFrN+PMz245kVqi93vRZCCMPSO5E5deoUYWFhXLp0iV9++YXt27fTr18/Jk6cyJw5c3TuvSREdXQrr5BFW08Q93sqBUUKVhZga2VJTn5RqfNkVICbkx3tvZwfdKhCCFGt6T1Hxt/fHy8vLw4cOECPHj34z3/+w7Zt21i/fj3t27evVBCLFy/G09MTOzs7goKCSElJKbd+bGwsPj4+2Nvb4+HhweTJk7WupvL09ESlUun8jB8/Xqud5ORkunXrhoODA46OjnTu3Jnbt2+X2868efMq1UdRPSiKwnf7ztNtfhIfbz9NQZFCVx8XNk/uwvsD2gDFScudSh5Hh7bA0uLuZ4UQQtyPSs2RGTp0qFZZcHAw+/btY9KkSXoHsHbtWiIjI1m2bBlBQUHExsYSEhLCsWPHcHV11am/Zs0aoqKiiIuLIzg4mOPHjzN8+HBUKhULFiwAYPfu3Vo3rzx06BA9evTghRde0JQlJyfTq1cvpk2bxqJFi7CysuLAgQNYWGjndm+//TajR4/WPK5Vq5befRTVw6HzN5j5w9/8efYaAI3r1uCtp1vQzdcVlUpFE5eaLB3S9o51ZIq5OdkRHdpC1pERQogqoHcic3cSU6JWrVosX75c7wAWLFjA6NGjGTFiBADLli1jw4YNxMXFERUVpVN/586ddOzYkbCwMKB41GTQoEHs2rVLU8fFxUVrm3nz5uHt7U2XLl00ZZMnT2bChAla+/Dx8Sm1X25ubnr3S1Qf17Lzmb/5GGtS0lAUsLe2JKJbU17q5IWdtaVW3V6tGtCjhRvJJzPY/Nsuej4RRIemrjISI4QQVaRSk30BDh8+TFpaGvn5+ZoylUpFaGhohdvIz89nz549TJs2TVNmYWFB9+7dSU5OLnWb4OBgVq9eTUpKCu3bt+f06dNs3LixzAQrPz+f1atXExkZiUpV/MskIyODXbt2MXjwYIKDgzl16hS+vr7MmTOHTp06aW0/b948Zs+ezSOPPEJYWBiTJ0/Gyqr0ly0vL4+8vDzN46ysLAAKCgooKCio8OtSnpJ2DNWeKTDVPhUWqVn75zk+SDzJjduFAPT1c+PfIY/SwMkOUFNQUPrd4Ns2qsWVegptG9VCXVSIuqjUambDVI/R/ZA+mQfpk3moij5VtC2Voih6rdB1+vRpnn32WQ4ePIhKpaJk85Ik4c5TOvdy4cIFGjZsyM6dO+nQoYOm/PXXX2f79u1aoyx3WrhwIVOmTEFRFAoLCxk3bhxLly4tte5XX31FWFgYaWlpuLu7A/DHH3/QoUMHnJ2dmT9/Pv7+/qxatYolS5Zw6NAhmjVrBhSPFrVt2xZnZ2d27tzJtGnTGDFihOYU1t1mzpzJrFmzdMrXrFlDjRo1Kvy6COM7lQXfpFpyPqf4fd2ghsLznkU0dTJyYEII8ZDIyckhLCyMGzdu4OhY9v3q9B6RmThxIl5eXiQmJuLl5UVKSgpXrlzhtddeY/78+fcVdEUkJSUxd+5clixZQlBQECdPnmTixInMnj2bGTNm6NRfvnw5vXv31iQxAGp18V/RY8eO1ZzSCggIIDExkbi4OGJiYgCIjIzUbNO6dWtsbGwYO3YsMTEx2Nra6uxr2rRpWttkZWXh4eFBz549yz0I+igoKCAhIYEePXpUmyvETKlPl7JyeWfTcX76u/iu1U72Vkx6qikvtmuElWXF58abUp8Mobr1B6RP5kL6ZB6qok8lZzXuRe9EJjk5ma1bt1KvXj0sLCywsLCgU6dOxMTEMGHCBPbt21fhturVq4elpSXp6dqLiaWnp5c5L2XGjBkMHTqUUaNGAeDn50d2djZjxozhzTff1Jqse/bsWbZs2cL69eu12mjQoHjSZYsWLbTKmzdvTlpaWpnxBgUFUVhYyJkzZ0qdT2Nra1tqgmNtbW3wN2tVtGlsxuxTXmERy39P5aOtJ8nJL0Klghcfe4SpIT44O9hUut3qdpyqW39A+mQupE/mwZB9qmg7el9+XVRUpLlyp169ely4cAGAxo0bc+zYMb3asrGxITAwkMTERE2ZWq0mMTFR61TTnXJycnSuLLK0LJ5wefdZsvj4eFxdXenbt69WuaenJ+7u7jrxHj9+nMaNG5cZ7/79+7GwsCj1aiphvrYeTSfkg195d9MxcvKLCGxchx8jOhHzL7/7SmKEEEJUPb1HZFq1asWBAwfw8vIiKCiId999FxsbGz755BOaNGmidwCRkZGEh4fTrl072rdvT2xsLNnZ2ZpTPsOGDaNhw4aa0z2hoaEsWLCAgIAAzamlGTNmEBoaqklooDghio+PJzw8XGdyrkqlYurUqURHR9OmTRv8/f1ZuXIlR48eZd26dUDxyNOuXbt48sknqVWrFsnJyUyePJkhQ4ZQp04dvfspTE/q5Wxm/3SYrUczAHCpZcu03r48G9BQM+dLCCGEadM7kZk+fTrZ2dlA8RorTz/9NE888QR169Zl7dq1egcwcOBAMjMzeeutt7h06RL+/v5s2rSJ+vXrA5CWlqY1AjN9+nRUKhXTp0/n/PnzuLi4EBoaypw5c7Ta3bJlC2lpaYwcObLU/U6aNInc3FwmT57M1atXadOmDQkJCXh7ewPFp4m+/PJLZs6cSV5eHl5eXkyePFlrDowwT9l5hXy07STLf0slv0iNtaWKkR29ePWpZtS0rfSFfEIIIYxA72/tkJAQzf+bNm3K0aNHuXr1KnXq1Kn0X7ERERFERESU+lxSUpLWYysrK6Kjo4mOji63zZ49e+qcarpbVFRUqWvVALRt25Y//vij3O2FeVEUhR8OXCBm41EuZRUvWNf5UReiQ1vg7VLTyNEJIYSoDIP8+ensLPePEabt8IUsZv7wNylnrgLg4WzPW0+3pHtzVzmNJIQQZqzCiUxZp2juFhcXV+lghDC06zn5vL/5OJ/vOotaATtrC8Z3bcrozk10VuUVQghhfiqcyKxYsYLGjRsTEBBwz1M2QhhbkVrhi5Q05m8+xvWc4tUh+7ZuwBt9mtOwtr2RoxNCCGEoFU5kXn75Zb744gtSU1MZMWIEQ4YMkVNKwiTtPnOV6O//5vDF4sWUfOrXIrpfC4K96xk5MiGEEIZW4XVkFi9ezMWLF3n99df58ccf8fDwYMCAAfzyyy8yQiNMQnpWLpO+3McLy5I5fDELRzsrZoa2YMOETpLECCFENaXXZF9bW1sGDRrEoEGDOHv2LCtWrOCVV16hsLCQv//+m5o15coP8eDlF6qJ25HKosQTZP9vVd6B7TyYGuJD3Zq6Ky0LIYSoPip91ZKFhYXmppH63ChSCEPadiyD2T8e5vTl4rWN/D1qM6tfS9p41DZuYEIIIR4IvRKZvLw81q9fT1xcHL///jtPP/00H330Eb169dK5bYAQVensleJVebccKV6Vt15NW6J6+/KvgIZYWMjl1EII8bCocCLzyiuv8OWXX+Lh4cHIkSP54osvqFdP5h2IBysnv5DF207y31+LV+W1slAxoqMnrz7VDEe76nXzNSGEEPdW4URm2bJlPPLIIzRp0oTt27ezffv2UuvdfadpIQxBURR++usiczce4eKN4lV5n2hWj+jQFjR1rWXk6IQQQhhLhROZYcOGyQqowiiOXipelfeP08Wr8jaqY8/0vi0IaVlf3pNCCPGQ02tBPCEepBs5BSxIOMZnfxSvymtrZcErXZsytousyiuEEKKY3OpXmJwitcJXf/7De78c42p2PgC9W7nxZt/mNKpTw8jRCSGEMCWSyAiTsufsNWb+8DcHz98AoJlrTWb2a0nHpjKxXAghhC5JZIRJuJEPr39zkG/3XwSglq0Vk3o8yrAOjbG2lEv7hRBClE4SGWFU+YVqlv9+htj9luQVFScxA9o14vVevtSTVXmFEELcgyQywmh+PZ7JzB//5nRmNqCidUNH3u7vh7+syiuEEKKCJJERD1zalRxmbzhMwuF0AJwdrAlxy2XmsCBsbW2MHJ0QQghzIomMeGBu5xexNOkky349TX6hGksLFeEdPBnfxZPftyXIrQWEEELoTRIZUeUURWHjwUvM2XCYC/9blbdj07rMDG1Js/q1KCgoMHKEQgghzJUkMqJKHU+/SfT3f5N8+goADWvbM71vc3q1cpNVeYUQQtw3SWRElbhxu4DYLcdZlXyWIrWCjZUF47p483IXb+xtZFVeIYQQhiGJjDAotVrh6z3/8O6mY1z536q8IS3rM71vCzycZVVeIYQQhiWJjDCYfWnXiP7hb/46V7wqr7eLAzP7teSJZi5GjkwIIUR1JYmMuG+ZN/N4Z9NR1u05B0BNWysmdW9GeLCnrMorhBCiSkkiIyqtoEjNyp1n+HDLCW7mFQLwXNtG/Lu3D6617IwcnRBCiIeBJDKiUn4/cZmZP/7NyYxbAPg1dGJmv5YENq5j5MiEEEI8TCSREXr552oOczYcYdPflwBwdrDh9RAfBrTzkAXthBBCPHAmMYFh8eLFeHp6YmdnR1BQECkpKeXWj42NxcfHB3t7ezw8PJg8eTK5ubma5z09PVGpVDo/48eP12onOTmZbt264eDggKOjI507d+b27ds6+8vLy8Pf3x+VSsX+/fsN0mdzk1tQxAcJx+m+YDub/r6EpYWK4cGebHutKy+2f0SSGCGEEEZh9BGZtWvXEhkZybJlywgKCiI2NpaQkBCOHTuGq6urTv01a9YQFRVFXFwcwcHBHD9+nOHDh6NSqViwYAEAu3fvpqioSLPNoUOH6NGjBy+88IKmLDk5mV69ejFt2jQWLVqElZUVBw4cwMJCN7d7/fXXcXd358CBA1XwCpg2RVHYdOgS/9lwhPPXi5O8x5s4M7NfS3zdHI0cnRBCiIed0ROZBQsWMHr0aEaMGAHAsmXL2LBhA3FxcURFRenU37lzJx07diQsLAwoHn0ZNGgQu3bt0tRxcdG+3HfevHl4e3vTpUsXTdnkyZOZMGGC1j58fHx09vfzzz+zefNmvvnmG37++ef766yZOZF+k5k//s2Ok8Wr8jZwsuPNvs3p69dAVuUVQghhEoyayOTn57Nnzx6mTZumKbOwsKB79+4kJyeXuk1wcDCrV68mJSWF9u3bc/r0aTZu3MjQoUPL3Mfq1auJjIzU/PLNyMhg165dDB48mODgYE6dOoWvry9z5syhU6dOmm3T09MZPXo03333HTVq3Hsxt7y8PPLy8jSPs7KyACgoKDDY/YRK2qnK+xPdzC1g4dZTfLbrH82qvKM6ejK2syc1bKwoLCw06P4eRJ8etOrWp+rWH5A+mQvpk3moij5VtC2VoiiKwfaqpwsXLtCwYUN27txJhw4dNOWvv/4627dv1xpludPChQuZMmUKiqJQWFjIuHHjWLp0aal1v/rqK8LCwkhLS8Pd3R2AP/74gw4dOuDs7Mz8+fPx9/dn1apVLFmyhEOHDtGsWTMURaFPnz507NiR6dOnc+bMGby8vNi3bx/+/v6l7mvmzJnMmjVLp3zNmjUVSoSMTa1ASqaKH9MsuFVQnPT51VHT31NNPbmaWgghxAOUk5NDWFgYN27cwNGx7KkMRj+1pK+kpCTmzp3LkiVLCAoK4uTJk0ycOJHZs2czY8YMnfrLly+nd+/emiQGQK1WAzB27FjNKa2AgAASExOJi4sjJiaGRYsWcfPmTa3RonuZNm0akZGRmsdZWVl4eHjQs2fPcg+CPgoKCkhISKBHjx5YW1sbpE2Av87d4O0NRznwv1V5verWYHpfXzo3q2ewfZSlqvpkTNWtT9WtPyB9MhfSJ/NQFX0qOatxL0ZNZOrVq4elpSXp6ela5enp6bi5uZW6zYwZMxg6dCijRo0CwM/Pj+zsbMaMGcObb76pNVn37NmzbNmyhfXr12u10aBBAwBatGihVd68eXPS0tIA2Lp1K8nJydja2mrVadeuHYMHD2blypU6sdna2urUB7C2tjb4m9VQbV6+lce7m47y1Z/Fq/I62FgysXszhgd7YWP1YC9qq4rXydiqW5+qW39A+mQupE/mwZB9qmg7Rk1kbGxsCAwMJDExkf79+wPFoyWJiYlERESUuk1OTo7OlUWWlsV3U777LFl8fDyurq707dtXq9zT0xN3d3eOHTumVX78+HF69+4NFJ+++s9//qN57sKFC4SEhLB27VqCgoL076yJKShS81nyWT7YcpybucVzXv4V0JCo3r64Osp5JCGEEObB6KeWIiMjCQ8Pp127drRv357Y2Fiys7M1p3yGDRtGw4YNiYmJASA0NJQFCxYQEBCgObU0Y8YMQkNDNQkNFCdE8fHxhIeHY2Wl3U2VSsXUqVOJjo6mTZs2+Pv7s3LlSo4ePcq6desAeOSRR7S2qVmzJgDe3t40atSoyl6PB2HnyeJVeY+nF6/K29LdkVn9WtLO09nIkQkhhBD6MXoiM3DgQDIzM3nrrbe4dOkS/v7+bNq0ifr16wOQlpamNQIzffp0VCoV06dP5/z587i4uBAaGsqcOXO02t2yZQtpaWmMHDmy1P1OmjSJ3NxcJk+ezNWrV2nTpg0JCQl4e3tXXWeN7Pz128zZcJiNB4tX5a1Tw5qpIb4MfMwDS1nQTgghhBkyeiIDEBERUeappKSkJK3HVlZWREdHEx0dXW6bPXv21DnVdLeoqKhS16opjaen5z3bM1W5BUV8vP00S7efJLdAjYUKhjzemMgej1K7ho2xwxNCCCEqzSQSGVE1FEVh8+F0Zv90mHPXilflbe/lzKx+LWneQFblFUIIYf4kkammTmbcYtaPf/PbicsAuDna8Ubf5oS2llV5hRBCVB+SyFQzN3MLWJh4gvgdZyhUK9hYWjC6sxfjn2xKDRs53EIIIaoX+c1WTajVCuv3nWfez0e5fKv4NglP+boy4+kWeNZzMHJ0QgghRNWQRMaMFKkVdqVeZc9lFXVTr9KhqSuWFioOnrtB9A+H2Jt2HQCveg689XQLnvTVvXu4EEIIUZ1IImMmNh26yKwfD3PxRi5gyaoTf+Jay5ZH69dkx6krKArUsLHk1W7NGNnJE1sry3u2KYQQQpg7SWTMwKZDF3l59V7uvvg742YeGTeLTyP193dnWp/m1JdVeYUQQjxEJJExcUVqhVk/HtZJYu5U18GG9wf4y6J2QgghHjoP9q6AQm8pqVf/dzqpbFey80lJvfqAIhJCCCFMhyQyJi7jZvlJjL71hBBCiOpEEhkT51qrYnNeKlpPCCGEqE4kkTFx7b2caeBkR1mzX1RAAyc72nvJnauFEEI8fCSRMXGWFiqiQ1sA6CQzJY+jQ1vIRF8hhBAPJUlkzECvVg1YOqQtbk7ap4/cnOxYOqQtvVo1MFJkQgghhHHJ5ddmolerBvRo4UbyyQw2/7aLnk8EaVb2FUIIIR5WksiYEUsLFUFezlw5ohDk5SxJjBBCiIeenFoSQgghhNmSREYIIYQQZksSGSGEEEKYLUlkhBBCCGG2JJERQgghhNmSREYIIYQQZksuv65CiqIAkJWVZbA2CwoKyMnJISsrC2tra4O1a0zSJ9NX3foD0idzIX0yD1XRp5LfnSW/S8siiUwVunnzJgAeHh5GjkQIIYQwTzdv3sTJyanM51XKvVIdUWlqtZoLFy5Qq1YtVCrDLF6XlZWFh4cH//zzD46OjgZp09ikT6avuvUHpE/mQvpkHqqiT4qicPPmTdzd3bGwKHsmjIzIVCELCwsaNWpUJW07OjpWmw9ACemT6atu/QHpk7mQPpkHQ/epvJGYEjLZVwghhBBmSxIZIYQQQpgtSWTMjK2tLdHR0dja2ho7FIORPpm+6tYfkD6ZC+mTeTBmn2SyrxBCCCHMlozICCGEEMJsSSIjhBBCCLMliYwQQgghzJYkMkIIIYQwW5LImKDFixfj6emJnZ0dQUFBpKSklFv/66+/xtfXFzs7O/z8/Ni4ceMDirTi9OnTihUrUKlUWj92dnYPMNry/frrr4SGhuLu7o5KpeK777675zZJSUm0bdsWW1tbmjZtyooVK6o8Tn3o26ekpCSdY6RSqbh06dKDCfgeYmJieOyxx6hVqxaurq7079+fY8eO3XM7U/4sVaZPpv5ZWrp0Ka1bt9YsotahQwd+/vnncrcx5WME+vfJ1I/R3ebNm4dKpWLSpEnl1nuQx0kSGROzdu1aIiMjiY6OZu/evbRp04aQkBAyMjJKrb9z504GDRrESy+9xL59++jfvz/9+/fn0KFDDzjysunbJyheHfLixYuan7Nnzz7AiMuXnZ1NmzZtWLx4cYXqp6am0rdvX5588kn279/PpEmTGDVqFL/88ksVR1px+vapxLFjx7SOk6uraxVFqJ/t27czfvx4/vjjDxISEigoKKBnz55kZ2eXuY2pf5Yq0ycw7c9So0aNmDdvHnv27OHPP/+kW7duPPPMM/z999+l1jf1YwT69wlM+xjdaffu3Xz88ce0bt263HoP/DgpwqS0b99eGT9+vOZxUVGR4u7ursTExJRaf8CAAUrfvn21yoKCgpSxY8dWaZz60LdP8fHxipOT0wOK7v4Ayrfffltunddff11p2bKlVtnAgQOVkJCQKoys8irSp23btimAcu3atQcS0/3KyMhQAGX79u1l1jGHz9KdKtInc/oslahTp47y6aeflvqcuR2jEuX1yVyO0c2bN5VmzZopCQkJSpcuXZSJEyeWWfdBHycZkTEh+fn57Nmzh+7du2vKLCws6N69O8nJyaVuk5ycrFUfICQkpMz6D1pl+gRw69YtGjdujIeHxz3/mjF1pn6M7oe/vz8NGjSgR48e7Nixw9jhlOnGjRsAODs7l1nH3I5TRfoE5vNZKioq4ssvvyQ7O5sOHTqUWsfcjlFF+gTmcYzGjx9P3759dV7/0jzo4ySJjAm5fPkyRUVF1K9fX6u8fv36Zc49uHTpkl71H7TK9MnHx4e4uDi+//57Vq9ejVqtJjg4mHPnzj2IkA2urGOUlZXF7du3jRTV/WnQoAHLli3jm2++4ZtvvsHDw4OuXbuyd+9eY4emQ61WM2nSJDp27EirVq3KrGfqn6U7VbRP5vBZOnjwIDVr1sTW1pZx48bx7bff0qJFi1Lrmssx0qdP5nCMvvzyS/bu3UtMTEyF6j/o4yR3vxYmp0OHDlp/vQQHB9O8eXM+/vhjZs+ebcTIRAkfHx98fHw0j4ODgzl16hQffPABn332mREj0zV+/HgOHTrE77//buxQDKaifTKHz5KPjw/79+/nxo0brFu3jvDwcLZv317mL35zoE+fTP0Y/fPPP0ycOJGEhASTnYQsiYwJqVevHpaWlqSnp2uVp6en4+bmVuo2bm5uetV/0CrTp7tZW1sTEBDAyZMnqyLEKlfWMXJ0dMTe3t5IURle+/btTS5ZiIiI4KeffuLXX3+lUaNG5dY19c9SCX36dDdT/CzZ2NjQtGlTAAIDA9m9ezcffvghH3/8sU5dczlG+vTpbqZ2jPbs2UNGRgZt27bVlBUVFfHrr7/y0UcfkZeXh6WlpdY2D/o4yaklE2JjY0NgYCCJiYmaMrVaTWJiYpnnVzt06KBVHyAhIaHc87EPUmX6dLeioiIOHjxIgwYNqirMKmXqx8hQ9u/fbzLHSFEUIiIi+Pbbb9m6dSteXl733MbUj1Nl+nQ3c/gsqdVq8vLySn3O1I9RWcrr091M7Rg99dRTHDx4kP3792t+2rVrx+DBg9m/f79OEgNGOE5VMoVYVNqXX36p2NraKitWrFAOHz6sjBkzRqldu7Zy6dIlRVEUZejQoUpUVJSm/o4dOxQrKytl/vz5ypEjR5To6GjF2tpaOXjwoLG6oEPfPs2aNUv55ZdflFOnTil79uxRXnzxRcXOzk75+++/jdUFLTdv3lT27dun7Nu3TwGUBQsWKPv27VPOnj2rKIqiREVFKUOHDtXUP336tFKjRg1l6tSpypEjR5TFixcrlpaWyqZNm4zVBR369umDDz5QvvvuO+XEiRPKwYMHlYkTJyoWFhbKli1bjNUFLS+//LLi5OSkJCUlKRcvXtT85OTkaOqY22epMn0y9c9SVFSUsn37diU1NVX566+/lKioKEWlUimbN29WFMX8jpGi6N8nUz9Gpbn7qiVjHydJZEzQokWLlEceeUSxsbFR2rdvr/zxxx+a57p06aKEh4dr1f/qq6+URx99VLGxsVFatmypbNiw4QFHfG/69GnSpEmauvXr11f69Omj7N271whRl67k0uO7f0r6EB4ernTp0kVnG39/f8XGxkZp0qSJEh8f/8DjLo++fXrnnXcUb29vxc7OTnF2dla6du2qbN261TjBl6K0vgBar7u5fZYq0ydT/yyNHDlSady4sWJjY6O4uLgoTz31lOYXvqKY3zFSFP37ZOrHqDR3JzLGPk4qRVGUqhnrEUIIIYSoWjJHRgghhBBmSxIZIYQQQpgtSWSEEEIIYbYkkRFCCCGE2ZJERgghhBBmSxIZIYQQQpgtSWSEEEIIYbYkkRFCCCGE2ZJERghhVDNnzsTf31+vbVQqFd99912VxGMIZ86cQaVSsX//fmOHIkS1J4mMEMJgVCpVuT8zZ87U2WbKlCk6N5i7X8OHD0elUjFv3jyt8u+++w6VSmXQfQkhjMvK2AEIIaqPixcvav6/du1a3nrrLY4dO6Ypq1mzpub/iqJQVFREzZo1tcoNxc7OjnfeeYexY8dSp04dg7dvDPn5+djY2Bg7DCFMiozICCEMxs3NTfPj5OSESqXSPD569Ci1atXi559/JjAwEFtbW37//XedU0u7d++mR48e1KtXDycnJ7p06cLevXv1jqV79+64ubkRExNTZp3STmvFxsbi6empeTx8+HD69+/P3LlzqV+/PrVr1+btt9+msLCQqVOn4uzsTKNGjYiPj9dp/+jRowQHB2NnZ0erVq3Yvn271vOHDh2id+/e1KxZk/r16zN06FAuX76seb5r165EREQwadIk6tWrR0hIiN6vgxDVnSQyQogHKioqinnz5nHkyBFat26t8/zNmzcJDw/n999/548//qBZs2b06dOHmzdv6rUfS0tL5s6dy6JFizh37tx9xbx161YuXLjAr7/+yoIFC4iOjubpp5+mTp067Nq1i3HjxjF27Fid/UydOpXXXnuNffv20aFDB0JDQ7ly5QoA169fp1u3bgQEBPDnn3+yadMm0tPTGTBggFYbK1euxMbGhh07drBs2bL76ocQ1ZEkMkKIB+rtt9+mR48eeHt74+zsrPN8t27dGDJkCL6+vjRv3pxPPvmEnJwcndGMinj22Wfx9/cnOjr6vmJ2dnZm4cKF+Pj4MHLkSHx8fMjJyeGNN96gWbNmTJs2DRsbG37//Xet7SIiInjuuedo3rw5S5cuxcnJieXLlwPw0UcfERAQwNy5c/H19SUgIIC4uDi2bdvG8ePHNW00a9aMd999Fx8fH3x8fO6rH0JUR5LICCEeqHbt2pX7fHp6OqNHj6ZZs2Y4OTnh6OjIrVu3SEtLq9T+3nnnHVauXMmRI0cqtT1Ay5YtsbD4/6/L+vXr4+fnp3lsaWlJ3bp1ycjI0NquQ4cOmv9bWVnRrl07TRwHDhxg27ZtmjlCNWvWxNfXF4BTp05ptgsMDKx03EI8DGSyrxDigXJwcCj3+fDwcK5cucKHH35I48aNsbW1pUOHDuTn51dqf507dyYkJIRp06YxfPhwrecsLCxQFEWrrKCgQKcNa2trrccqlarUMrVaXeG4bt26RWhoKO+8847Ocw0aNND8/16vlxAPO0lkhBAmZceOHSxZsoQ+ffoA8M8//2hNgK2MefPm4e/vr3NqxsXFhUuXLqEoiuaybEOu/fLHH3/QuXNnAAoLC9mzZw8REREAtG3blm+++QZPT0+srOSrWIjKklNLQgiT0qxZMz777DOOHDnCrl27GDx4MPb29vfVpp+fH4MHD2bhwoVa5V27diUzM5N3332XU6dOsXjxYn7++ef72tedFi9ezLfffsvRo0cZP348165dY+TIkQCMHz+eq1evMmjQIHbv3s2pU6f45ZdfGDFiBEVFRQaLQYjqThIZIYRJWb58OdeuXaNt27YMHTqUCRMm4Orqet/tvv322zqnfpo3b86SJUtYvHgxbdq0ISUlhSlTptz3vkrMmzePefPm0aZNG37//Xd++OEH6tWrB4C7uzs7duygqKiInj174ufnx6RJk6hdu7bWfBwhRPlUyt0niIUQQgghzISk/UIIIYQwW5LICCGEEMJsSSIjhBBCCLMliYwQQgghzJYkMkIIIYQwW5LICCGEEMJsSSIjhBBCCLMliYwQQgghzJYkMkIIIYQwW5LICCGEEMJsSSIjhBBCCLP1fxJk+xbdhOC9AAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:30:17,779] A new study created in memory with name: study_LinearLog\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearLog\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.693100
30000.693100
35000.693100
40000.693100
45000.693100
50000.693100
55000.693100
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:36:01,018] Trial 0 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 16, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.693100
30000.693100
35000.693100
40000.693100
45000.693100
50000.693100
55000.693100
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:41:44,852] Trial 1 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 32, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 16, 'classifier_data_in_width': 8, 'classifier_bias_width': 16}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.693100
30000.693100
35000.693100
40000.693100
45000.693100
50000.693100
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:47:28,469] Trial 2 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 16, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 8, 'classifier_weight_width': 16, 'classifier_data_in_width': 8, 'classifier_bias_width': 8}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.693100
30000.693100
35000.693100
40000.693100
45000.693100
50000.693100
55000.693100
60000.693100

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:09]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:53:11,431] Trial 3 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 16, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 8, 'classifier_data_in_width': 16, 'classifier_bias_width': 8}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
5000.693100
10000.693100
15000.693100
20000.693100
25000.693100
30000.693100
35000.693100
40000.693100
45000.693100
50000.693100
55000.693100
60000.693100

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:09]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:58:54,707] Trial 4 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 32, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 16, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 0 with value: 0.5.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearLog saved to optuna_results_LinearLog.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 00:58:55,137] A new study created in memory with name: study_LinearBlockFP\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearBlockFP\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 16:43, Epoch 2/2]\n","
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StepTraining Loss
5000.328600
10000.317100
15000.340200
20000.343500
25000.322300
30000.366400
35000.275300
40000.244000
45000.226000
50000.275300
55000.276500
60000.240200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 07:07]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 01:22:47,786] Trial 0 finished with value: 0.8768 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 8, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 16, 'classifier_data_in_width': 8, 'classifier_bias_width': 8}. Best is trial 0 with value: 0.8768.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 16:41, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.335800
10000.310900
15000.343900
20000.355200
25000.317900
30000.372100
35000.284000
40000.244600
45000.223800
50000.275700
55000.275200
60000.241200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 07:07]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 01:46:38,300] Trial 1 finished with value: 0.87592 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 32, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 8, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_width': 16, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 32, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 0 with value: 0.8768.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 16:41, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.337200
10000.322900
15000.337600
20000.336200
25000.332100
30000.381100
35000.286900
40000.241900
45000.222800
50000.272700
55000.275300
60000.246500

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 07:07]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 02:10:28,989] Trial 2 finished with value: 0.87508 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 32, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 8, 'classifier_data_in_width': 16, 'classifier_bias_width': 8}. Best is trial 0 with value: 0.8768.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 16:40, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.335300
10000.312300
15000.332200
20000.336300
25000.323900
30000.366200
35000.280100
40000.244900
45000.221400
50000.270100
55000.272100
60000.240800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 07:08]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 02:34:19,649] Trial 3 finished with value: 0.87736 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 8, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 16, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 16, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 16, 'classifier_data_in_width': 16, 'classifier_bias_width': 8}. Best is trial 3 with value: 0.87736.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 16:42, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.327200
10000.314600
15000.339700
20000.331600
25000.317500
30000.368700
35000.279200
40000.245500
45000.231700
50000.269900
55000.271400
60000.242200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 07:08]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 02:58:11,587] Trial 4 finished with value: 0.8768 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 16, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 8, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 32, 'bert.encoder.layer.1.attention.output.dense_weight_width': 16, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 8, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 8, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 3 with value: 0.87736.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearBlockFP saved to optuna_results_LinearBlockFP.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 02:58:12,039] A new study created in memory with name: study_LinearBlockLog\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearBlockLog\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 03:19:58,228] Trial 0 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 16, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 8, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 32, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 32, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 16, 'bert.encoder.layer.0.intermediate.dense_weight_width': 8, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.0.intermediate.dense_bias_width': 32, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 8, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_width': 8, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 32, 'bert.encoder.layer.1.attention.self.value_weight_width': 8, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 8, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 16, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 32, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 16, 'classifier_data_in_width': 16, 'classifier_bias_width': 32}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 03:41:42,824] Trial 1 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 32, 'bert.encoder.layer.0.attention.self.key_bias_width': 8, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 32, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 8, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 16, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 16, 'bert.encoder.layer.1.attention.self.query_bias_width': 16, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 32, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 16, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.1.attention.output.dense_bias_width': 32, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.1.intermediate.dense_bias_width': 32, 'bert.encoder.layer.1.output.dense_weight_width': 32, 'bert.encoder.layer.1.output.dense_data_in_width': 32, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 8, 'classifier_data_in_width': 8, 'classifier_bias_width': 8}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 04:03:28,630] Trial 2 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 8, 'bert.encoder.layer.0.attention.self.query_data_in_width': 8, 'bert.encoder.layer.0.attention.self.query_bias_width': 32, 'bert.encoder.layer.0.attention.self.key_weight_width': 32, 'bert.encoder.layer.0.attention.self.key_data_in_width': 8, 'bert.encoder.layer.0.attention.self.key_bias_width': 16, 'bert.encoder.layer.0.attention.self.value_weight_width': 16, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 16, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 16, 'bert.encoder.layer.0.attention.output.dense_bias_width': 8, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 32, 'bert.encoder.layer.0.intermediate.dense_bias_width': 8, 'bert.encoder.layer.0.output.dense_weight_width': 16, 'bert.encoder.layer.0.output.dense_data_in_width': 16, 'bert.encoder.layer.0.output.dense_bias_width': 8, 'bert.encoder.layer.1.attention.self.query_weight_width': 16, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 32, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 32, 'bert.encoder.layer.1.attention.self.value_data_in_width': 8, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 32, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 32, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.1.intermediate.dense_bias_width': 16, 'bert.encoder.layer.1.output.dense_weight_width': 8, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 32, 'bert.pooler.dense_weight_width': 32, 'bert.pooler.dense_data_in_width': 8, 'bert.pooler.dense_bias_width': 32, 'classifier_weight_width': 16, 'classifier_data_in_width': 32, 'classifier_bias_width': 32}. Best is trial 0 with value: 0.5.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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StepTraining Loss
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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 06:29]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 04:46:59,021] Trial 4 finished with value: 0.5 and parameters: {'bert.encoder.layer.0.attention.self.query_weight_width': 32, 'bert.encoder.layer.0.attention.self.query_data_in_width': 16, 'bert.encoder.layer.0.attention.self.query_bias_width': 16, 'bert.encoder.layer.0.attention.self.key_weight_width': 16, 'bert.encoder.layer.0.attention.self.key_data_in_width': 16, 'bert.encoder.layer.0.attention.self.key_bias_width': 32, 'bert.encoder.layer.0.attention.self.value_weight_width': 8, 'bert.encoder.layer.0.attention.self.value_data_in_width': 8, 'bert.encoder.layer.0.attention.self.value_bias_width': 8, 'bert.encoder.layer.0.attention.output.dense_weight_width': 32, 'bert.encoder.layer.0.attention.output.dense_data_in_width': 32, 'bert.encoder.layer.0.attention.output.dense_bias_width': 32, 'bert.encoder.layer.0.intermediate.dense_weight_width': 32, 'bert.encoder.layer.0.intermediate.dense_data_in_width': 16, 'bert.encoder.layer.0.intermediate.dense_bias_width': 16, 'bert.encoder.layer.0.output.dense_weight_width': 8, 'bert.encoder.layer.0.output.dense_data_in_width': 8, 'bert.encoder.layer.0.output.dense_bias_width': 32, 'bert.encoder.layer.1.attention.self.query_weight_width': 32, 'bert.encoder.layer.1.attention.self.query_data_in_width': 32, 'bert.encoder.layer.1.attention.self.query_bias_width': 8, 'bert.encoder.layer.1.attention.self.key_weight_width': 16, 'bert.encoder.layer.1.attention.self.key_data_in_width': 8, 'bert.encoder.layer.1.attention.self.key_bias_width': 16, 'bert.encoder.layer.1.attention.self.value_weight_width': 16, 'bert.encoder.layer.1.attention.self.value_data_in_width': 32, 'bert.encoder.layer.1.attention.self.value_bias_width': 8, 'bert.encoder.layer.1.attention.output.dense_weight_width': 8, 'bert.encoder.layer.1.attention.output.dense_data_in_width': 8, 'bert.encoder.layer.1.attention.output.dense_bias_width': 8, 'bert.encoder.layer.1.intermediate.dense_weight_width': 16, 'bert.encoder.layer.1.intermediate.dense_data_in_width': 8, 'bert.encoder.layer.1.intermediate.dense_bias_width': 8, 'bert.encoder.layer.1.output.dense_weight_width': 16, 'bert.encoder.layer.1.output.dense_data_in_width': 16, 'bert.encoder.layer.1.output.dense_bias_width': 8, 'bert.pooler.dense_weight_width': 16, 'bert.pooler.dense_data_in_width': 16, 'bert.pooler.dense_bias_width': 16, 'classifier_weight_width': 32, 'classifier_data_in_width': 8, 'classifier_bias_width': 32}. Best is trial 0 with value: 0.5.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearBlockLog saved to optuna_results_LinearBlockLog.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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119/LdcEcVXjKu1zUKtWrVJPAZIyFiEkZ2ZmBjs7O5w/f77MfufPn4eDgwNMTU3L7KeOG3aVdsVMae2CILxxzIULF2LTpk3YsGEDfH19FZZt2LABISEh6Nu3LyZNmoS6detCV1cX0dHR8v8Zlpeqr0dFcgNeXjKpyuWmYs6RV9Tr25LJZACAiRMnIigoqMR1iv/n3q5dO9y4cQPbt2/Hvn37sGrVKsyfPx8rVqzAyJEjAQBhYWHo1asX/vzzT+zduxfffPMNoqOjceDAATRp0qQSM3tZBL2Nq4pUuXpMJpOhbt262LhxY4nLi4uYJ0+eoH379jA1NUVUVBRcXV1hYGCAhIQETJkyRb5/iol9rxRP7D58+LCo9SpKKpWie/fu2L59O2JjY9GzZ8+3un0Sj6djSEHPnj2RkpKi9NdesSNHjuDWrVslfrhfP1x+/fp1yGQyhb8KNH0n0SNHjmDixIkICwsr8SqG33//HfXr18e2bdswZMgQBAUFITAwUOEqH0BcHk5OTpDJZEqvT3p6Op48eQInJ6fyJaNmxXFcvXpVadnVq1fly4v/e/36daV+JbWVpPjybj09PQQGBpb4ePVIm6WlJYYNG4ZNmzbh7t278PHxkV+NVMzV1RVffPEF9u3bhwsXLqCgoKDUv8LLourrUBW5urri4cOHaN26dYmvaePGjQG8vELq4cOHiImJwYQJE9CzZ08EBgaq9TRCUVFRmVeklPY6FxQUICUlRb68eLLwhQsX3rhNiUSCjRs3olOnTvjoo4/KdZM+VeMq7XPw4sWLSrsrrDZiEUIKJk2aBENDQ4wePRoPHz5UWPbo0SN89tlnMDIywqRJk5TWXbp0qcLzxYsXA4DCvSmMjY01dlvs1NRU9O/fH23atFGa/V+s+K/NV486nDx5EidOnFDoV3wFhCq5dO/eHQCwYMEChfZ58+YBeDnXoCrw9/dH3bp1sWLFCoVLh/fs2YPLly/L47S3t4eXlxfWrVun8CVz6NAhJCcnq7StunXrokOHDli5ciVSU1OVlr86l+D192Ht2rXRoEEDeYx5eXlKRaKrqytMTEyULoFWhaqvQ1XUv39/FBUVYebMmUrLXrx4IX+/lvQ+LygowLJly9QSx8GDB/H06VN50VOSwMBA6OvrY9GiRQpxrF69GllZWfLXuWnTpnBxccGCBQuUPm8lHR3U19fHtm3b0KxZM/Tq1QunTp0SFbuqcfn7+6NOnTr4+eef8eLFC3m/jRs3qmVuUk3B0zGkoGHDhvjll18waNAgeHt7K90x9cGDB9i0aVOJl9ampKSgd+/e6Nq1K06cOIENGzbgk08+UfgfkZ+fH/7++2/MmzcP9vb2cHFxkU8qrWzjx49HZmYmJk+ejM2bNyss8/HxgY+PD3r27Ilt27ahX79+6NGjB1JSUrBixQp4eHgofOEaGhrCw8MDW7ZswbvvvgtLS0t4eXmVeN66cePGCA4Oxk8//SQ/DH7q1Cn88ssv6Nu3L957771Kz10Venp6mDNnDoYNG4b27dtj4MCB8ktTnZ2d8Z///Efed9asWejTpw9at26NYcOG4fHjx1iyZAm8vLxUvh/D0qVL0aZNG3h7e2PUqFGoX78+0tPTceLECdy7dw/nzp0D8HKibocOHeDn5wdLS0ucOXMGv//+O0JDQwEA//3vf9GpUyf0798fHh4eqFWrFv744w+kp6fj448/rtTXobzWrFmD2NhYpfbSLjdVVfv27TF69GhER0cjKSkJXbp0gZ6eHq5du4bffvsNCxcuxIcffohWrVrBwsICwcHBGD9+PCQSCdavX6/yKb9XZWVlYcOGDQBeFjrFl7MaGhqWeedga2trTJs2DZGRkejatSt69+6Nq1evYtmyZWjWrJn8Bmg6OjpYvnw5evXqBV9fXwwbNgx2dna4cuUKLl68iL179yqNbWhoiF27dqFjx47o1q0bDh06pPDZzMzMxLfffqu0nouLCwYNGqRSXPr6+pgxYwbGjRuHjh07on///rh16xZiYmLg6uqq8aO+1YbGrsuhKu38+fPCwIEDBTs7O0FPT0+wtbUVBg4cKCQnJyv1Lb4k9dKlS8KHH34omJiYCBYWFkJoaKjCJY6CIAhXrlwR2rVrJxgaGgoA5JfrlnaJbkmXZgIQxo4dq9BWfFnjq5dpvn6pbGmXE+KVS21lMpkwa9YswcnJSZBKpUKTJk2EXbt2CcHBwQqXngqCIBw/flzw8/MT9PX1FcYo6dLgwsJCITIyUnBxcRH09PQER0dHYdq0aUqXD5aWc/v27VW65K+k1+Z1xZfo/vbbbyUu37Jli9CkSRNBKpUKlpaWwqBBg4R79+4p9du8ebPg7u4uSKVSwcvLS9ixY4fwwQcfCO7u7vI+Je2XV924cUMYOnSoYGtrK+jp6QkODg5Cz549hd9//13e59tvvxUCAgIEc3NzwdDQUHB3dxe+++47oaCgQBAEQXjw4IEwduxYwd3dXTA2NhbMzMyE5s2bC7/++usbX6+yLplV5XV49bJQVRRvr7TH3bt3S71Et6TtlHYZ+k8//ST4+fkJhoaGgomJieDt7S1MnjxZuH//vrzPsWPHhBYtWgiGhoaCvb29MHnyZPkl8K9e6l7WJa2vf6YkEolgaWkp9O7dWzh79myJub/6GReEl5e+uru7C3p6eoKNjY0wZswYpUtxBeHlZbedO3cWTExMBGNjY8HHx0dYvHhxma/RgwcPBA8PD8HW1la4du1aiTG/+ujUqZPouBYtWiT//0VAQIBw7Ngxwc/PT+jatWuJrxkpkghCOUpfolcU39gnMzMTVlZWmg6HNKj4Zm6v3s2UqCaRyWSwtrbG+++/j59//lnT4VR5nBNCRKIVFhYqnAcHXk52PHfuHG9XTTXG8+fPlU5hrVu3Do8ePeLnQEWcE0JEov37778IDAzE4MGDYW9vjytXrmDFihWwtbVVuqEWkbb6559/8J///AcfffQR6tSpg4SEBKxevRpeXl746KOPNB1etcAihIhEs7CwgJ+fH1atWoXMzEwYGxujR48emD17ttLN34i0lbOzMxwdHbFo0SI8evQIlpaWGDp0KGbPnl0lfiW6OuCcECIiItIIzgkhIiIijWARQkRERBrBOSElkMlkuH//PkxMTHjDGSIiIhEEQUBOTg7s7e3f+LtKLEJKcP/+faVfaSUiIiLV3b17942/MM0ipATFP5x19+7dN/5SrKoKCwuxb98++W2UtQFzqh6YU9WnbfkAzKm6qIycsrOz4ejoqPAjlKVhEVKC4lMwpqamai1CjIyMYGpqqlVvXuZU9TGnqk/b8gGYU3VRmTmpMp2BE1OJiIhII1iEEBERkUawCCEiIiKN4JwQIiItIggCXrx4gaKiIo3GUVhYiFq1auH58+caj0VdmNNLurq6qFWrllpuYcEihIhISxQUFCA1NRV5eXmaDgWCIMDW1hZ3797VmvstMaf/MTIygp2dXYV/I4dFCBGRFpDJZEhJSYGuri7s7e2hr6+v0S9KmUyGp0+fonbt2m+8YVV1wZxeFi0FBQXIzMxESkoKGjZsWKHXgkUIEZEWKCgogEwmg6OjI4yMjDQdDmQyGQoKCmBgYKBVX9jMCTA0NISenh5u374tX7e8tONVJCIiANCaL0eq2tT1PuO7lYiIiDSCRQgRERFpBIsQIiJSUCQTcOLGQ2xP+hcnbjxEkUzQWCwSiQR//vmnxrZfHs7OzliwYEGVHa8q4cRUIiKSi72Qisidl5Ca9VzeZmdmgIheHujqZVcp2wwJCcGTJ09KLDZSU1NhYWFRKdsVKyYmBiNGjJA/NzY2hpubG7766iu8//77GourrNevquORECIiAvCyABmzIUGhAAGAtKznGLMhAbEXUt96TLa2tpBKpW99u68qvgEc8PJX1v/991+kpqYiMTERQUFB6N+/P65evarRGKsrFiFERFpKEATkFbxQ6ZHzvBAROy6ipBMvxW0zdlxCzvNClcYTBPWcwnn1dMytW7cgkUiwbds2vPfeezAyMkLjxo1x4sQJhXWOHj2Ktm3bwtDQEI6Ojhg/fjxyc3Ply9evXw9/f3+YmJjA1tYWn3zyCTIyMuTL4+PjIZFIsGfPHvj5+UEqleLo0aPyeGxtbWFra4uGDRvi22+/hY6ODs6fP19qDnfu3EGfPn1Qu3ZtmJqaon///khPT1fos3PnTjRr1gwGBgawsrJCv379Sh1v1apVMDc3x/79+1V6DQ8dOoSAgABIpVLY2dlh6tSp8qIKAHJycjB48GAYGxvDzs4O8+fPR4cOHRAWFqbS+BXB0zFERFrqWWERPKbvVctYAoC07OfwnrFPpf4XZnRWy3ZL8tVXX2Hu3Llo2LAhvvrqKwwcOBDXr19HrVq1cOPGDXTt2hXffvst1qxZg8zMTISGhiI0NBRr164F8PJW5TNnzoSbmxsyMjIQHh6OkJAQ/PXXXwrbmTp1KubOnYv69evDwsICN2/eVFheVFSEdevWAQCaNm1aYqwymUxegBw6dAgvXrzA2LFjMWDAAMTHxwMAdu/ejX79+uGrr77CunXrUFBQoBRLse+//x7ff/899u3bh4CAgDe+Vv/++y+6d++OkJAQrFu3DleuXMGoUaNgYGCAGTNmAAC+/vprHD9+HDt27ICNjQ2mT5+OhIQE+Pr6vnH8imIRQkRE1crEiRPRo0cPAEBkZCQ8PT1x/fp1uLu7Izo6GoMGDZL/Fd+wYUMsWrQI7du3x/Lly2FgYIDhw4fLx6pfvz4WLVqEZs2aye8cWiwqKgqdOysWU9nZ2TA1NQUAPHv2DHp6evjpp5/g6upaYqz79+9HcnIyUlJS4OjoCABYt24dPD09cfr0aTRr1gzfffcdPv74Y0RGRsrXa9y4sdJYU6ZMwfr163Ho0CF4enqq9FotW7YMjo6OWLJkCSQSCdzd3XH//n1MmTIF06dPR05ODjZt2oQNGzagU6dOAIC1a9fC3t5epfErikUIEZGWMtTTxaWoIJX6nkp5hJC1p9/YL2ZYMwS4WL6xn1RXgpznb+xWLj4+PvJ/29m9nCybkZEBd3d3nDt3DufPn8fGjRvlfQRBkN/WvlGjRjh79ixmzJiBc+fO4fHjx5DJZABenjbx8PCQr+fv76+0bRMTE5w5cwY6OjrIy8vD33//jc8++wx16tRBr169lPpfvnwZjo6O8gIEADw8PGBubo7Lly+jWbNmSEpKwqhRo8rM+ccff0Rubi7OnDmD+vXrq/hKvdx+y5YtFW7h37p1azx9+hT37t3Dw4cPUVhYqHBUxczMDG5ubipvoyJYhBARaSmJRAIjfdX+N9+2oTXszAyQlvW8xHkhEgC2ZgZo29Aaujpv/k2a4i/2yqCnp/e/uP7/l2vx9p4+fYrRo0dj/PjxSuu98847yM3NRVBQEIKCgrBx40ZYW1vjzp07CAoKQkFBgUJ/Y2NjpTEkEgkaNGggv2Ooj48P9u3bhzlz5pRYhKjC0NDwjX3atm2L3bt349dff8XUqVPLtZ2qiBNTiYgIujoSRPR6eRTg9RKj+HlELw+VChBNatq0KS5duoQGDRooPfT19XHlyhU8fPgQs2fPRtu2beHu7q4wKbU8dHV18ezZsxKXNWrUCHfv3sXdu3flbZcuXcKTJ0/kR118fHzeOMk0ICAAe/bswaxZszB37lyVY2vUqBFOnDihMFH42LFjMDExQb169VC/fn3o6enh9On/HQXLysrCf//7X5W3URE8EkJERACArl52WD64qdJ9Qmwr+T4hwMsvvqSkJIW2OnXqiB5nypQpaNGiBUJDQzFy5EgYGxvj0qVLiIuLw5IlS/DOO+9AX18fixcvxmeffYYLFy5g5syZKo8vCALS0tKgo6ODZ8+eIS4uDnv37sX06dNL7B8YGAhvb28MGjQICxYswIsXL/D555+jffv28tM9ERER6NSpE1xdXfHxxx/jxYsX+OuvvzBlyhSFsVq1aoW//voL3bp1Q61atRSuXint9fv888+xYMECjBs3DqGhobh69SoiIiIQHh4OHR0dmJiYYODAgZgyZQqsrKxQt25dREREQEdH5638CjOLECIikuvqZYfOHrY4lfIIGTnPUdfEAAEulpV+BCQ+Ph5NmjRRaHv1xmCq8vHxwaFDh/DVV1+hbdu2EAQBrq6uGDBgAADA2toaMTEx+PLLL7Fo0SI0bdoUc+fORe/evVUaPycnBw4ODgAAqVQKJycnREVFKRUMxSQSCbZv345x48ahXbt20NHRQdeuXbF48WJ5nw4dOuC3337DzJkzMXv2bJiamqJdu3YljtemTRvs3r0b3bt3h66uLsaNGweg9Ndv1apV+OuvvzBp0iQ0btwYlpaWGDFiBL7++mt5v2+//RZTpkxBz549YWpqismTJ+Pu3bsV+nVcVUkEdV3MrUWys7NhZmaGrKws+SzoiiosLMRff/2F7t27K5zPrM6YU/XAnKo+deTz/PlzpKSkwMXF5a18ebyJTCaTX0miLb/sW1Nyys3NhYODA3788cdSC8Gy3m9ivkN5JISIiKgGO3/+PO7evYsWLVogKysLUVFRAIA+ffpU+rZZhBAREdVw8+bNw9WrV6Gvrw8/Pz8cOXIEVlZWlb5dFiFEREQ1mI+PD06fPq2RU0zacVKLiIiIqh0WIUREWoTXGtDboK73GYsQIiItUHxVTV5enoYjoZqg+H1W0avTOCeEiEgL6OrqwtzcXH73TyMjo7dys6nSyGQyFBQU4Pnz51p1OWtNz0kQBOTl5SEjIwPm5ubQ1dWt0PZZhBARaQlbW1sAqPBtyNVBEAQ8e/YMhoaGGi2G1Ik5/Y+5ubn8/VYRLEKIiLSERCKBnZ0d6tati8LCQo3GUlhYiMOHD6Ndu3ZacUM5gDkV09PTq/ARkGIsQoiItIyurq7aviQqEsOLFy9gYGCgNV/YzEn9tOOkFhEREVU7LEKIiIhII1iEEBERkUZUiSJk6dKlcHZ2hoGBAZo3b45Tp06V2jcmJgYSiUTh8eov+BUWFmLKlCnw9vaGsbEx7O3tMXToUNy/f/9tpEJEREQq0ngRsmXLFoSHhyMiIgIJCQlo3LgxgoKCyrzEzNTUFKmpqfLH7du35cvy8vKQkJCAb775BgkJCdi2bRuuXr2K3r17v410iIiISEUavzpm3rx5GDVqFIYNGwYAWLFiBXbv3o01a9Zg6tSpJa4jkUhKvT7ZzMwMcXFxCm1LlixBQEAA7ty5g3feeUe9CRAREVG5aLQIKSgowNmzZzFt2jR5m46ODgIDA3HixIlS13v69CmcnJwgk8nQtGlTzJo1C56enqX2z8rKgkQigbm5eYnL8/PzkZ+fL3+enZ0N4OWpHXVda188jqav3Vcn5lQ9MKeqT9vyAZhTdVEZOYkZSyJo8NeO7t+/DwcHBxw/fhwtW7aUt0+ePBmHDh3CyZMnldY5ceIErl27Bh8fH2RlZWHu3Lk4fPgwLl68iHr16in1f/78OVq3bg13d3ds3LixxDhmzJiByMhIpfb/+7//g5GRUQUyJCIiqlny8vLwySefICsrC6ampmX2rXZFyOsKCwvRqFEjDBw4EDNnzlRa9sEHH+DevXuIj48v9cUo6UiIo6MjHjx48MYXUFWFhYWIi4tD586dteYmN8ypemBOVZ+25QMwp+qiMnLKzs6GlZWVSkWIRk/HWFlZQVdXF+np6Qrt6enpKt+TXk9PD02aNMH169cV2gsLC9G/f3/cvn0bBw4cKPOFkEqlkEqlJY6t7jdaZYypacypemBOVZ+25QMwp+pCnTmJGUejV8fo6+vDz88P+/fvl7fJZDLs379f4chIWYqKipCcnAw7Ozt5W3EBcu3aNfz999+oU6eO2mMnIiKiitH41THh4eEIDg6Gv78/AgICsGDBAuTm5sqvlhk6dCgcHBwQHR0NAIiKikKLFi3QoEEDPHnyBD/88ANu376NkSNHAnhZgHz44YdISEjArl27UFRUhLS0NACApaUl9PX1NZMoERERKdB4ETJgwABkZmZi+vTpSEtLg6+vL2JjY2FjYwMAuHPnDnR0/nfA5vHjxxg1ahTS0tJgYWEBPz8/HD9+HB4eHgCAf//9Fzt27AAA+Pr6Kmzr4MGD6NChw1vJi4iIiMqm8SIEAEJDQxEaGlrisvj4eIXn8+fPx/z580sdy9nZGRqca0tEREQq0vgdU4mIiKhmYhFCREREGsEihIiIiDSCRQgRERFpBIsQIiIi0gjRRUj79u2xbt06PHv2rDLiISIiohpCdBHSpEkTTJw4Eba2thg1ahT++eefyoiLiIiItJzoImTBggW4f/8+1q5di4yMDLRr1w4eHh6YO3eu0m/AEBEREZWmXHNCatWqhffffx/bt2/HvXv38Mknn+Cbb76Bo6Mj+vbtiwMHDqg7TiIiItIyFZqYeurUKURERODHH39E3bp1MW3aNFhZWaFnz56YOHGiumIkIiIiLST6tu0ZGRlYv3491q5di2vXrqFXr17YtGkTgoKCIJFIAAAhISHo2rUr5s6dq/aAiYiISDuILkLq1asHV1dXDB8+HCEhIbC2tlbq4+Pjg2bNmqklQCIiItJOoouQ/fv3o23btmX2MTU1xcGDB8sdFBEREWk/0XNC6tWrh2vXrim1X7t2Dbdu3VJHTERERFQDiC5CQkJCcPz4caX2kydPIiQkRB0xERERUQ0gughJTExE69atldpbtGiBpKQkdcRERERENYDoIkQikSAnJ0epPSsrC0VFRWoJioiIiLSf6CKkXbt2iI6OVig4ioqKEB0djTZt2qg1OCIiItJeoq+OmTNnDtq1awc3Nzf5VTJHjhxBdnY275RKREREKhN9JMTDwwPnz59H//79kZGRgZycHAwdOhRXrlyBl5dXZcRIREREWkj0kRAAsLe3x6xZs9QdCxEREdUg5SpCACAvLw937txBQUGBQruPj0+FgyIiIiLtJ7oIyczMxLBhw7Bnz54Sl/MKGSIiIlKF6DkhYWFhePLkCU6ePAlDQ0PExsbil19+QcOGDbFjx47KiJGIiIi0kOgjIQcOHMD27dvh7+8PHR0dODk5oXPnzjA1NUV0dDR69OhRGXESERGRlhF9JCQ3Nxd169YFAFhYWCAzMxMA4O3tjYSEBPVGR0RERFpLdBHi5uaGq1evAgAaN26MlStX4t9//8WKFStgZ2en9gCJiIhIO4k+HTNhwgSkpqYCACIiItC1a1ds3LgR+vr6iImJUXd8REREpKVEFyGDBw+W/9vPzw+3b9/GlStX8M4778DKykqtwREREZH2EnU6prCwEK6urrh8+bK8zcjICE2bNmUBQkRERKKIKkL09PTw/PnzyoqFiIiIahDRE1PHjh2LOXPm4MWLF5URDxEREdUQoueEnD59Gvv378e+ffvg7e0NY2NjheXbtm1TW3BERESkvUQXIebm5vjggw8qIxYiIiKqQUQXIWvXrq2MOIiIiKiGET0nhIiIiEgdRB8JcXFxgUQiKXX5zZs3KxQQERER1Qyii5CwsDCF54WFhUhMTERsbCwmTZqkrriIiIhIy5Xrtu0lWbp0Kc6cOVPhgIiIiKhmUNuckG7dumHr1q3qGo6IiIi0nNqKkN9//x2WlpbqGo6IiIi0nOjTMU2aNFGYmCoIAtLS0pCZmYlly5apNTgiIiLSXqKLkL59+yo819HRgbW1NTp06AB3d3d1xUVERERaTnQREhERURlxEBERUQ0jek7IX3/9hb179yq17927F3v27FFLUERERKT9RBchU6dORVFRkVK7IAiYOnWq6ACWLl0KZ2dnGBgYoHnz5jh16lSpfWNiYiCRSBQeBgYGCn22bduGLl26oE6dOpBIJEhKShIdExEREVU+0UXItWvX4OHhodTu7u6O69evixpry5YtCA8PR0REBBISEtC4cWMEBQUhIyOj1HVMTU2Rmpoqf9y+fVtheW5uLtq0aYM5c+aIioWIiIjeLtFzQszMzHDz5k04OzsrtF+/fh3Gxsaixpo3bx5GjRqFYcOGAQBWrFiB3bt3Y82aNaUeVZFIJLC1tS11zCFDhgAAbt26JSoWIiIiertEFyF9+vRBWFgY/vjjD7i6ugJ4WYB88cUX6N27t8rjFBQU4OzZs5g2bZq8TUdHB4GBgThx4kSp6z19+hROTk6QyWRo2rQpZs2aBU9PT7FpKMjPz0d+fr78eXZ2NoCXt6QvLCys0NjFisdR13hVAXOqHphT1adt+QDMqbqojJzEjCURBEEQM3hWVha6du2KM2fOoF69egCAe/fuoW3btti2bRvMzc1VGuf+/ftwcHDA8ePH0bJlS3n75MmTcejQIZw8eVJpnRMnTuDatWvw8fFBVlYW5s6di8OHD+PixYvyWIrdunULLi4uSExMhK+vb5mxzJgxA5GRkUrt//d//wcjIyOV8iEiIiIgLy8Pn3zyCbKysmBqalpm33Kdjjl+/Dji4uJw7tw5GBoawsfHB+3atSt3wKpq2bKlQsHSqlUrNGrUCCtXrsTMmTPLPe60adMQHh4uf56dnQ1HR0d06dLljS+gqgoLCxEXF4fOnTtDT09PLWNqGnOqHphT1adt+QDMqbqojJyKzyaoQnQRArycl9GlSxd06dKlPKsDAKysrKCrq4v09HSF9vT09DLnfLxKT08PTZo0ET0h9nVSqRRSqbTE8dX9RquMMTWNOVUPzKnq07Z8AOZUXagzJzHjiL46Zvz48Vi0aJFS+5IlSxAWFqbyOPr6+vDz88P+/fvlbTKZDPv371c42lGWoqIiJCcnw87OTuXtEhERUdUgugjZunUrWrdurdTeqlUr/P7776LGCg8Px88//4xffvkFly9fxpgxY5Cbmyu/Wmbo0KEKE1ejoqKwb98+3Lx5EwkJCRg8eDBu376NkSNHyvs8evQISUlJuHTpEgDg6tWrSEpKQlpamthUiYiIqBKJPh3z8OFDmJmZKbWbmpriwYMHosYaMGAAMjMzMX36dKSlpcHX1xexsbGwsbEBANy5cwc6Ov+rkx4/foxRo0YhLS0NFhYW8PPzw/HjxxXuW7Jjxw55EQMAH3/8MYCXt5ufMWOGqPiIiIio8oguQho0aIDY2FiEhoYqtO/Zswf169cXHUBoaKjSWMXi4+MVns+fPx/z588vc7yQkBCEhISIjoOIiIjeLtFFSHh4OEJDQ5GZmYmOHTsCAPbv348ff/wRCxYsUHd8REREpKVEFyHDhw9Hfn4+vvvuO/llsc7Ozli+fDmGDh2q9gCJiIhIO5XrEt0xY8ZgzJgxyMzMhKGhIWrXrg3g5aRQS0tLtQZIRERE2kn01TGvsra2Ru3atbFv3z70798fDg4O6oqLiIiItFy5i5Dbt28jIiICzs7O+Oijj6Cjo4N169apMzYiIiLSYqJOxxQUFGDbtm1YtWoVjh07hsDAQNy7dw+JiYnw9vaurBiJiIhIC6l8JGTcuHGwt7fHwoUL0a9fP9y7dw87d+6ERCKBrq5uZcZIREREWkjlIyHLly/HlClTMHXqVJiYmFRmTERERFQDqHwkZP369Th16hTs7OwwYMAA7Nq1C0VFRZUZGxEREWkxlYuQgQMHIi4uDsnJyXB3d8fYsWNha2sLmUwm/50WIiIiIlWJvjrGxcUFkZGRuHXrFjZs2IAPPvgAgwcPRr169TB+/PjKiJGIiIi0ULluVgYAEokEQUFBCAoKwqNHj7Bu3TqsXbtWnbERERGRFqvQzcqKWVpaIiwsDOfOnVPHcERERFQDqKUIISIiIhKLRQgRERFpBIsQIiIi0ggWIURERKQRKl0dc/78eZUH9PHxKXcwREREVHOoVIT4+vpCIpFAEARIJJIy+/IuqkRERKQKlU7HpKSk4ObNm0hJScHWrVvh4uKCZcuWITExEYmJiVi2bBlcXV2xdevWyo6XiIiItIRKR0KcnJzk//7oo4+waNEidO/eXd7m4+MDR0dHfPPNN+jbt6/agyQiIiLtI3pianJyMlxcXJTaXVxc+BsyREREpDLRRUijRo0QHR2NgoICeVtBQQGio6PRqFEjtQZHRERE2kv0b8esWLECvXr1Qr169eRXwpw/fx4SiQQ7d+5Ue4BERESknUQXIQEBAbh58yY2btyIK1euAAAGDBiATz75BMbGxmoPkIiIiLRTuX5F19jYGJ9++qm6YyEiIqIapFx3TF2/fj3atGkDe3t73L59GwAwf/58bN++Xa3BERERkfYSXYQsX74c4eHh6NatGx4/fiy/OZmFhQUWLFig7viIiIhIS4kuQhYvXoyff/4ZX331FWrV+t/ZHH9/fyQnJ6s1OCIiItJeoouQlJQUNGnSRKldKpUiNzdXLUERERGR9hNdhLi4uCApKUmpPTY2lvcJISIiIpWJvjomPDwcY8eOxfPnzyEIAk6dOoVNmzYhOjoaq1atqowYiYiISAuJLkJGjhwJQ0NDfP3118jLy8Mnn3wCe3t7LFy4EB9//HFlxEhERERaqFz3CRk0aBAGDRqEvLw8PH36FHXr1lV3XERERKTlRM8J+fbbb5GSkgIAMDIyYgFCRERE5SK6CPntt9/QoEEDtGrVCsuWLcODBw8qIy4iIiLScqKLkHPnzuH8+fPo0KED5s6dC3t7e/To0QP/93//h7y8vMqIkYiIiLRQuW7b7unpiVmzZuHmzZs4ePAgnJ2dERYWBltbW3XHR0RERFqqXEXIq4yNjWFoaAh9fX0UFhaqIyYiIiKqAcpVhKSkpOC7776Dp6cn/P39kZiYiMjISKSlpak7PiIiItJSoi/RbdGiBU6fPg0fHx8MGzYMAwcOhIODQ2XERkRERFpMdBHSqVMnrFmzBh4eHpURDxEREdUQoouQ7777DgBQUFCAlJQUuLq6KvyaLhEREZEqRM8JefbsGUaMGAEjIyN4enrizp07AIBx48Zh9uzZag+QiIiItJPoImTq1Kk4d+4c4uPjYWBgIG8PDAzEli1b1BqctiiSCTiZ8ghnH0hwMuURimSCpkOiEnA/VQ/cT1Uf91H1UBX2k+jzKH/++Se2bNmCFi1aQCKRyNs9PT1x48aNcgWxdOlS/PDDD0hLS0Pjxo2xePFiBAQElNg3JiYGw4YNU2iTSqV4/vy5/LkgCIiIiMDPP/+MJ0+eoHXr1li+fDkaNmxYrvgqIvZCKiJ3XkJq1nMAulh37QzszAwQ0csDXb3s3no8VDLup+qB+6nq4z6qHqrKfhJ9JCQzM7PE34vJzc1VKEpUtWXLFoSHhyMiIgIJCQlo3LgxgoKCkJGRUeo6pqamSE1NlT9u376tsPz777/HokWLsGLFCpw8eRLGxsYICgpSKFTehtgLqRizIeH/7+T/Sct6jjEbEhB7IfWtxkMl436qHrifqj7uo+qhKu0n0UdC/P39sXv3bowbNw4A5IXHqlWr0LJlS9EBzJs3D6NGjZIf3VixYgV2796NNWvWYOrUqSWuI5FISr07qyAIWLBgAb7++mv06dMHALBu3TrY2Njgzz//xMcffyw6xvIokgmI3HkJJR3cEgBIAMzYcQmtG1hBV0d88VYVFBa+QH4RkFfwAnpC9cyhSCYgYsdF7qcqTtv3E/dR9VBT9lPkzkvo7GH7VvaTRBAEUSeBjh49im7dumHw4MGIiYnB6NGjcenSJRw/fhyHDh2Cn5+fymMVFBTAyMgIv//+O/r27StvDw4OxpMnT7B9+3aldWJiYjBy5Eg4ODhAJpOhadOmmDVrFjw9PQEAN2/ehKurKxITE+Hr6ytfr3379vD19cXChQuVxszPz0d+fr78eXZ2NhwdHfHgwQOYmpqqnM+rTqY8wuA1Z8q1LhERkSZtGO6P5i6W5Vo3OzsbVlZWyMrKeuN3qOgjIW3atEFSUhJmz54Nb29v7Nu3D02bNsWJEyfg7e0taqwHDx6gqKgINjY2Cu02Nja4cuVKieu4ublhzZo18PHxQVZWFubOnYtWrVrh4sWLqFevnvyurSWNWdodXaOjoxEZGanUvm/fPhgZGYnKqdjZBxIAuuVal4iISJP2HTmJh5fLN1FVzI/ZlusGH66urvj555/Ls2qFtWzZUuG0T6tWrdCoUSOsXLkSM2fOLNeY06ZNQ3h4uPx58ZGQLl26lPtISJ2UR1h37c1HQlYNaYJmzhbl2oamFRa+wIEDB9CxY0fo6VXPe8WcvvUYI9cnvrEf95Nmaft+4j6qHmrSfurStnmFjoSoSqVXMTs7W/5l/KbBxXxpW1lZQVdXF+np6Qrt6enpKv8ir56eHpo0aYLr168DgHy99PR02Nn9b4Zvenq6wumZV0mlUkil0hLH1tPTUymO17VsUBd2ZgZIy3pe4rk3CQBbMwO818iuGp8fLYRUFzAzNij366Rp7zUygJ3ZZe6nKk7b9xP3UfVQk/ZTywZ1y72fxLw2Kl0dY2FhIb9axdzcHBYWFkqP4nYx9PX14efnh/3798vbZDIZ9u/fr/Ik16KiIiQnJ8sLDhcXF9ja2iqMmZ2djZMnT5Zr4mx56epIENHr5a3tX9+Nxc8jenlU2w+jtuB+qh64n6o+7qPqoartJ5WOhBw4cACWli8Pyxw8eFCtAYSHhyM4OBj+/v4ICAjAggULkJubK79aZujQoXBwcEB0dDQAICoqCi1atECDBg3w5MkT/PDDD7h9+zZGjhwJ4OWVM2FhYfj222/RsGFDuLi44JtvvoG9vb3C5Ne3oauXHZYPbvrKtdgv2fKa+SqF+6l64H6q+riPqoeqtJ9UKkLat29f4r/VYcCAAcjMzMT06dORlpYGX19fxMbGyieW3rlzBzo6/ztg8/jxY4waNQppaWmwsLCAn58fjh8/rvCDepMnT0Zubi4+/fRTPHnyBG3atEFsbKzCHV7flq5edujsYYsT1zOw78hJdGnbvEKHuahycD9VD9xPVR/3UfVQVfZTuWbWPHnyBKdOnUJGRgZkMpnCsqFDh4oeLzQ0FKGhoSUui4+PV3g+f/58zJ8/v8zxJBIJoqKiEBUVJTqWyqCrI0FzF0s8vCyguYslP4xVFPdT9cD9VPVxH1UPVWE/iS5Cdu7ciUGDBuHp06cwNTVVuEuqRCIpVxFCRERENY/o27Z/8cUXGD58OJ4+fYonT57g8ePH8sejR48qI0YiIiLSQqKLkH///Rfjx48v9028iIiIiIByFCFBQUE4c4a3IyciIqKKUWlOyI4dO+T/7tGjByZNmoRLly7B29tb6aYkvXv3Vm+EREREpJVUKkJKur9GSVeeSCQSFBUVVTgoIiIi0n4qFSGvX4ZLREREVFGi54QQERERqYPoImT8+PFYtGiRUvuSJUsQFhamjpiIiIioBhBdhGzduhWtW7dWam/VqhV+//13tQRFRERE2k90EfLw4UOYmZkptZuamuLBgwdqCYqIiIi0n+gipEGDBoiNjVVq37NnD+rXr6+WoIiIiEj7if7tmPDwcISGhiIzMxMdO3YEAOzfvx8//vgjFixYoO74iIiISEuJLkKGDx+O/Px8fPfdd5g5cyYAwNnZGcuXL+eP1xEREZHKRBchADBmzBiMGTMGmZmZMDQ0RO3atQEAjx49gqWlpVoDJCIiIu1UofuEWFtbo3bt2ti3bx/69+8PBwcHdcVFREREWq7cRcjt27cREREBZ2dnfPTRR9DR0cG6devUGRsRERFpMVGnYwoKCrBt2zasWrUKx44dQ2BgIO7du4fExER4e3tXVoxERESkhVQ+EjJu3DjY29tj4cKF6NevH+7du4edO3dCIpFAV1e3MmMkIiIiLaTykZDly5djypQpmDp1KkxMTCozJiIiIqoBVD4Ssn79epw6dQp2dnYYMGAAdu3ahaKiosqMjYiIiLSYykXIwIEDERcXh+TkZLi7u2Ps2LGwtbWFTCbDpUuXKjNGIiIi0kKir45xcXFBZGQkbt26hQ0bNuCDDz7A4MGDUa9ePYwfP74yYiQiIiItVK6blQGARCJBUFAQgoKC8OjRI6xbtw5r165VZ2xERESkxSp0s7JilpaWCAsLw7lz59QxHBEREdUAailCiIiIiMRiEUJEREQawSKEiIiINIJFCBEREWlEua6Oef78Oc6fP4+MjAzIZDKFZb1791ZLYERERKTdRBchsbGxGDp0KB48eKC0TCKR8C6qREREpBLRp2PGjRuHjz76CKmpqZDJZAoPFiBERESkKtFFSHp6OsLDw2FjY1MZ8RAREVENIboI+fDDDxEfH18JoRAREVFNInpOyJIlS/DRRx/hyJEj8Pb2hp6ensJy/n4MERERqUJ0EbJp0ybs27cPBgYGiI+Ph0QikS+TSCQsQoiIiEgloouQr776CpGRkZg6dSp0dHibESIiIiof0VVEQUEBBgwYwAKEiIiIKkR0JREcHIwtW7ZURixERERUg4g+HVNUVITvv/8ee/fuhY+Pj9LE1Hnz5qktOCIiItJeoouQ5ORkNGnSBABw4cIFhWWvTlIlIiIiKovoIuTgwYOVEQcRERHVMJxdSkRERBoh+kjIe++9V+ZplwMHDlQoICIiIqoZRBchvr6+Cs8LCwuRlJSECxcuIDg4WF1xERERkZYTfTpm/vz5Co8lS5bg6NGjCAsLU7pSRhVLly6Fs7MzDAwM0Lx5c5w6dUql9TZv3gyJRIK+ffsqtKenpyMkJAT29vYwMjJC165dce3aNdFxERERUeVS25yQwYMHY82aNaLW2bJlC8LDwxEREYGEhAQ0btwYQUFByMjIKHO9W7duYeLEiWjbtq1CuyAI6Nu3L27evInt27cjMTERTk5OCAwMRG5uruiciIiIqPKorQg5ceIEDAwMRK0zb948jBo1CsOGDYOHhwdWrFgBIyOjMouZoqIiDBo0CJGRkahfv77CsmvXruGff/7B8uXL0axZM7i5uWH58uV49uwZNm3aVK68iIiIqHKInhPy/vvvKzwXBAGpqak4c+YMvvnmG5XHKSgowNmzZzFt2jR5m46ODgIDA3HixIlS14uKikLdunUxYsQIHDlyRGFZfn4+ACgUQzo6OpBKpTh69ChGjhxZ4pj5+fnydQEgOzsbwMv5LoWFhSrnVJbicdQ1XlXAnKoH5lT1aVs+AHOqLiojJzFjiS5CzMzMFJ7r6OjAzc0NUVFR6NKli8rjPHjwAEVFRbCxsVFot7GxwZUrV0pc5+jRo1i9ejWSkpJKXO7u7o533nkH06ZNw8qVK2FsbIz58+fj3r17SE1NLTWW6OhoREZGKrXv27cPRkZGKuekiri4OLWOVxUwp+qBOVV92pYPwJyqC3XmlJeXp3Jf0UXI2rVrxa6iFjk5ORgyZAh+/vlnWFlZldhHT08P27Ztw4gRI2BpaQldXV0EBgaiW7duEASh1LGnTZuG8PBw+fPs7Gw4OjqiS5cuMDU1VUv8hYWFiIuLQ+fOncs1gbcqYk7VA3Oq+rQtH4A5VReVkVPx2QRViC5CXvX06VPIZDKFNlW/tK2srKCrq4v09HSF9vT0dNja2ir1v3HjBm7duoVevXrJ24q3XatWLVy9ehWurq7w8/NDUlISsrKyUFBQAGtrazRv3hz+/v6lxiKVSiGVSpXa9fT01P5Gq4wxNY05VQ/MqerTtnwA5lRdqDMnMeOInpiakpKCHj16wNjYGGZmZrCwsICFhQXMzc1hYWGh8jj6+vrw8/PD/v375W0ymQz79+9Hy5Ytlfq7u7sjOTkZSUlJ8kfv3r3x3nvvISkpCY6Ojgr9zczMYG1tjWvXruHMmTPo06eP2FSJiIioEok+EjJ48GAIgoA1a9bAxsamQj9aFx4ejuDgYPj7+yMgIAALFixAbm4uhg0bBgAYOnQoHBwcEB0dDQMDA3h5eSmsb25uDgAK7b/99husra3xzjvvIDk5GRMmTEDfvn1FzVchIiKiyie6CDl37hzOnj0LNze3Cm98wIAByMzMxPTp05GWlgZfX1/ExsbKJ6veuXMHOjriDtakpqYiPDwc6enpsLOzw9ChQ0VdtUNERERvh+gipFmzZrh7965aihAACA0NRWhoaInL4uPjy1w3JiZGqW38+PEYP368GiIjIiKiyiS6CFm1ahU+++wz/Pvvv/Dy8lKagOLj46O24IiIiEh7iS5CMjMzcePGDfm8DQCQSCQQBAESiQRFRUVqDZCIiIi0k+giZPjw4WjSpAk2bdpU4YmpREREVHOJLkJu376NHTt2oEGDBpURDxEREdUQou8T0rFjR5w7d64yYiEiIqIaRPSRkF69euE///kPkpOT4e3trTQxtXfv3moLjoiIiLSX6CLks88+A/Dy12xfx4mpREREpCrRRcjrvxVDREREVB6i54QQERERqYNKR0IWLVqETz/9FAYGBli0aFGZfXm3UiIiIlKFSkXI/PnzMWjQIBgYGGD+/Pml9pNIJCxCiIiISCUqFSEpKSkl/puIiIiovETPCXn+/Hmpy1JTUysUDBEREdUcoouQpk2bIikpSal969at/PE6IiIiUpnoIqRDhw5o0aIF5syZAwDIzc1FSEgIhgwZgi+//FLtARIREZF2En2fkGXLlqFHjx4YOXIkdu3ahdTUVNSuXRunTp2Cl5dXZcRIREREWkh0EQIA3bp1w/vvv4/ly5ejVq1a2LlzJwsQIiIiEkX06ZgbN26gZcuW2LVrF/bu3YvJkyejd+/emDx5MgoLCysjRiIiItJCoosQX19fuLi44Ny5c+jcuTO+/fZbHDx4ENu2bUNAQEBlxEhERERaSHQRsmzZMmzevBnm5ubytlatWiExMRFNmzZVZ2xERESkxUQXIUOGDCmx3cTEBKtXr65wQERERFQzlGtiKgBcunQJd+7cQUFBgbxNIpGgV69eagmMiIiItJvoIuTmzZvo168fkpOTIZFIIAgCgJcFCAAUFRWpN0IiIiLSSqJPx0yYMAEuLi7IyMiAkZERLl68iMOHD8Pf3x/x8fGVECIRERFpI9FHQk6cOIEDBw7AysoKOjo60NHRQZs2bRAdHY3x48cjMTGxMuIkIiIiLSP6SEhRURFMTEwAAFZWVrh//z4AwMnJCVevXlVvdERERKS1RB8J8fLywrlz5+Di4oLmzZvj+++/h76+Pn766SfUr1+/MmIkIiIiLSS6CPn666+Rm5sLAIiKikLPnj3Rtm1b1KlTB1u2bFF7gERERKSdRBchQUFB8n83aNAAV65cwaNHj2BhYSG/QoaIiIjoTcp9n5BXWVpaqmMYIiIiqkFULkKGDx+uUr81a9aUOxgiIiKqOVQuQmJiYuDk5IQmTZrIb1BGREREVF4qFyFjxozBpk2bkJKSgmHDhmHw4ME8DUNERETlpvJ9QpYuXYrU1FRMnjwZO3fuhKOjI/r374+9e/fyyAgRERGJJupmZVKpFAMHDkRcXBwuXboET09PfP7553B2dsbTp08rK0YiIiLSQqLvmCpfUUdH/gN2/NE6IiIiEktUEZKfn49Nmzahc+fOePfdd5GcnIwlS5bgzp07qF27dmXFSERERFpI5Ympn3/+OTZv3gxHR0cMHz4cmzZtgpWVVWXGRkRERFpM5SJkxYoVeOedd1C/fn0cOnQIhw4dKrHftm3b1BYcERERaS+Vi5ChQ4fytuxERESkNqJuVkZERESkLuW+OoaIiIioIliEEBERkUawCCEiIiKNYBFCREREGqHxImTp0qVwdnaGgYEBmjdvjlOnTqm03ubNmyGRSNC3b1+F9qdPnyI0NBT16tWDoaEhPDw8sGLFikqInIiIiCpCo0XIli1bEB4ejoiICCQkJKBx48YICgpCRkZGmevdunULEydORNu2bZWWhYeHIzY2Fhs2bMDly5cRFhaG0NBQ7Nixo7LSICIionLQaBEyb948jBo1CsOGDZMfsTAyMsKaNWtKXaeoqAiDBg1CZGQk6tevr7T8+PHjCA4ORocOHeDs7IxPP/0UjRs3VvkICxEREb0dKt8nRN0KCgpw9uxZTJs2Td6mo6ODwMBAnDhxotT1oqKiULduXYwYMQJHjhxRWt6qVSvs2LEDw4cPh729PeLj4/Hf//4X8+fPL3XM/Px85Ofny59nZ2cDAAoLC1FYWFie9JQUj6Ou8aoC5lQ9MKeqT9vyAZhTdVEZOYkZS2NFyIMHD1BUVAQbGxuFdhsbG1y5cqXEdY4ePYrVq1cjKSmp1HEXL16MTz/9FPXq1UOtWrWgo6ODn3/+Ge3atSt1nejoaERGRiq179u3D0ZGRqolpKK4uDi1jlcVMKfqgTlVfdqWD8Ccqgt15pSXl6dyX40VIWLl5ORgyJAh+Pnnn8v84bzFixfjn3/+wY4dO+Dk5ITDhw9j7NixsLe3R2BgYInrTJs2DeHh4fLn2dnZcHR0RJcuXWBqaqqW+AsLCxEXF4fOnTtDT09PLWNqGnOqHphT1adt+QDMqbqojJyKzyaoQmNFiJWVFXR1dZGenq7Qnp6eDltbW6X+N27cwK1bt9CrVy95m0wmAwDUqlULV69ehb29Pb788kv88ccf6NGjBwDAx8cHSUlJmDt3bqlFiFQqhVQqVWrX09NT+xutMsbUNOZUPTCnqk/b8gGYU3WhzpzEjKOxian6+vrw8/PD/v375W0ymQz79+9Hy5Ytlfq7u7sjOTkZSUlJ8kfv3r3x3nvvISkpCY6OjvI5HDo6imnp6urKCxYiIiKqGjR6OiY8PBzBwcHw9/dHQEAAFixYgNzcXAwbNgzAy1/udXBwQHR0NAwMDODl5aWwvrm5OQDI2/X19dG+fXtMmjQJhoaGcHJywqFDh7Bu3TrMmzfvreZGREREZdNoETJgwABkZmZi+vTpSEtLg6+vL2JjY+WTVe/cuaN0VONNNm/ejGnTpmHQoEF49OgRnJyc8N133+Gzzz6rjBSIiIionDQ+MTU0NBShoaElLouPjy9z3ZiYGKU2W1tbrF27Vg2RERERUWXS+G3biYiIqGZiEUJEREQawSKEiIiINIJFCBEREWkEixAiIiLSCBYhREREpBEsQoiIiEgjWIQQERGRRrAIISIiIo1gEUJEREQawSKEiIiINIJFCBEREWkEixAiIiLSCBYhREREpBEsQoiIiEgjWIQQERGRRrAIISIiIo1gEUJEREQawSKEiIiINIJFCBEREWkEixAiIiLSCBYhREREpBEsQoiIiEgjWIQQERGRRrAIISIiIo1gEUJEREQawSKEiIiINIJFCBEREWkEixAiIiLSCBYhREREpBEsQoiIiEgjWIQQERGRRrAIISIiIo1gEUJEREQaUUvTAVRFgiAAALKzs9U2ZmFhIfLy8pCdnQ09PT21jatJzKl6YE5Vn7blAzCn6qIycir+7iz+Li0Li5AS5OTkAAAcHR01HAkREVH1lJOTAzMzszL7SARVSpUaRiaT4f79+zAxMYFEIlHLmNnZ2XB0dMTdu3dhamqqljE1jTlVD8yp6tO2fADmVF1URk6CICAnJwf29vbQ0Sl71gePhJRAR0cH9erVq5SxTU1NtebNW4w5VQ/MqerTtnwA5lRdqDunNx0BKcaJqURERKQRLEKIiIhII1iEvCVSqRQRERGQSqWaDkVtmFP1wJyqPm3LB2BO1YWmc+LEVCIiItIIHgkhIiIijWARQkRERBrBIoSIiIg0gkUIERERaQSLEDVaunQpnJ2dYWBggObNm+PUqVNl9v/tt9/g7u4OAwMDeHt746+//npLkapOTE4xMTGQSCQKDwMDg7cYbdkOHz6MXr16wd7eHhKJBH/++ecb14mPj0fTpk0hlUrRoEEDxMTEVHqcYojNKT4+XmkfSSQSpKWlvZ2AVRAdHY1mzZrBxMQEdevWRd++fXH16tU3rldVP0/lyaeqf5aWL18OHx8f+Q2uWrZsiT179pS5TlXdP8XE5lTV91FJZs+eDYlEgrCwsDL7vc19xSJETbZs2YLw8HBEREQgISEBjRs3RlBQEDIyMkrsf/z4cQwcOBAjRoxAYmIi+vbti759++LChQtvOfLSic0JeHnXvdTUVPnj9u3bbzHisuXm5qJx48ZYunSpSv1TUlLQo0cPvPfee0hKSkJYWBhGjhyJvXv3VnKkqhObU7GrV68q7Ke6detWUoTiHTp0CGPHjsU///yDuLg4FBYWokuXLsjNzS11nar8eSpPPkDV/izVq1cPs2fPxtmzZ3HmzBl07NgRffr0wcWLF0vsX5X3TzGxOQFVex+97vTp01i5ciV8fHzK7PfW95VAahEQECCMHTtW/ryoqEiwt7cXoqOjS+zfv39/oUePHgptzZs3F0aPHl2pcYohNqe1a9cKZmZmbym6igEg/PHHH2X2mTx5suDp6anQNmDAACEoKKgSIys/VXI6ePCgAEB4/PjxW4lJHTIyMgQAwqFDh0rtUx0+T8VUyac6fZaKWVhYCKtWrSpxWXXaP68qK6fqtI9ycnKEhg0bCnFxcUL79u2FCRMmlNr3be8rHglRg4KCApw9exaBgYHyNh0dHQQGBuLEiRMlrnPixAmF/gAQFBRUav+3rTw5AcDTp0/h5OQER0fHN/4VUdVV9X1UEb6+vrCzs0Pnzp1x7NgxTYdTpqysLACApaVlqX2q075SJR+g+nyWioqKsHnzZuTm5qJly5Yl9qlO+wdQLSeg+uyjsWPHokePHkr7oCRve1+xCFGDBw8eoKioCDY2NgrtNjY2pZ5rT0tLE9X/bStPTm5ublizZg22b9+ODRs2QCaToVWrVrh3797bCFntSttH2dnZePbsmYaiqhg7OzusWLECW7duxdatW+Ho6IgOHTogISFB06GVSCaTISwsDK1bt4aXl1ep/ar656mYqvlUh89ScnIyateuDalUis8++wx//PEHPDw8SuxbXfaPmJyqwz4CgM2bNyMhIQHR0dEq9X/b+4q/oktq07JlS4W/Glq1aoVGjRph5cqVmDlzpgYjo2Jubm5wc3OTP2/VqhVu3LiB+fPnY/369RqMrGRjx47FhQsXcPToUU2Hohaq5lMdPktubm5ISkpCVlYWfv/9dwQHB+PQoUOlfmlXB2Jyqg776O7du5gwYQLi4uKq7KRZFiFqYGVlBV1dXaSnpyu0p6enw9bWtsR1bG1tRfV/28qT0+v09PTQpEkTXL9+vTJCrHSl7SNTU1MYGhpqKCr1CwgIqJJf8qGhodi1axcOHz6MevXqldm3qn+eAHH5vK4qfpb09fXRoEEDAICfnx9Onz6NhQsXYuXKlUp9q8P+AcTl9LqquI/Onj2LjIwMNG3aVN5WVFSEw4cPY8mSJcjPz4eurq7COm97X/F0jBro6+vDz88P+/fvl7fJZDLs37+/1POJLVu2VOgPAHFxcWWef3ybypPT64qKipCcnAw7O7vKCrNSVfV9pC5JSUlVah8JgoDQ0FD88ccfOHDgAFxcXN64TlXeV+XJ53XV4bMkk8mQn59f4rKqvH/KUlZOr6uK+6hTp05ITk5GUlKS/OHv749BgwYhKSlJqQABNLCvKmW6aw20efNmQSqVCjExMcKlS5eETz/9VDA3NxfS0tIEQRCEIUOGCFOnTpX3P3bsmFCrVi1h7ty5wuXLl4WIiAhBT09PSE5O1lQKSsTmFBkZKezdu1e4ceOGcPbsWeHjjz8WDAwMhIsXL2oqBQU5OTlCYmKikJiYKAAQ5s2bJyQmJgq3b98WBEEQpk6dKgwZMkTe/+bNm4KRkZEwadIk4fLly8LSpUsFXV1dITY2VlMpKBGb0/z584U///xTuHbtmpCcnCxMmDBB0NHREf7++29NpaBkzJgxgpmZmRAfHy+kpqbKH3l5efI+1enzVJ58qvpnaerUqcKhQ4eElJQU4fz588LUqVMFiUQi7Nu3TxCE6rV/ionNqarvo9K8fnWMpvcVixA1Wrx4sfDOO+8I+vr6QkBAgPDPP//Il7Vv314IDg5W6P/rr78K7777rqCvry94enoKu3fvfssRv5mYnMLCwuR9bWxshO7duwsJCQkaiLpkxZenvv4oziE4OFho37690jq+vr6Cvr6+UL9+fWHt2rVvPe6yiM1pzpw5gqurq2BgYCBYWloKHTp0EA4cOKCZ4EtRUj4AFF776vR5Kk8+Vf2zNHz4cMHJyUnQ19cXrK2thU6dOsm/rAWheu2fYmJzqur7qDSvFyGa3lcSQRCEyjnGQkRERFQ6zgkhIiIijWARQkRERBrBIoSIiIg0gkUIERERaQSLECIiItIIFiFERESkESxCiIiISCNYhBAREZFGsAghonKZMWMGfH19Ra0jkUjw559/Vko86nDr1i1IJBIkJSVpOhSiGoFFCBFBIpGU+ZgxY4bSOhMnTlT6oauKCgkJgUQiwezZsxXa//zzT0gkErVui4g0r5amAyAizUtNTZX/e8uWLZg+fTquXr0qb6tdu7b834IgoKioCLVr11ZoVxcDAwPMmTMHo0ePhoWFhdrH14SCggLo6+trOgyiKodHQogItra28oeZmRkkEon8+ZUrV2BiYoI9e/bAz88PUqkUR48eVTodc/r0aXTu3BlWVlYwMzND+/btkZCQIDqWwMBA2NraIjo6utQ+JZ0KWrBgAZydneXPQ0JC0LdvX8yaNQs2NjYwNzdHVFQUXrx4gUmTJsHS0hL16tXD2rVrlca/cuUKWrVqBQMDA3h5eeHQoUMKyy9cuIBu3bqhdu3asLGxwZAhQ/DgwQP58g4dOiA0NBRhYWGwsrJCUFCQ6NeBqCZgEUJEKpk6dSpmz56Ny5cvw8fHR2l5Tk4OgoODcfToUfzzzz9o2LAhunfvjpycHFHb0dXVxaxZs7B48WLcu3evQjEfOHAA9+/fx+HDhzFv3jxERESgZ8+esLCwwMmTJ/HZZ59h9OjRStuZNGkSvvjiCyQmJqJly5bo1asXHj58CAB48uQJOnbsiCZNmuDMmTOIjY1Feno6+vfvrzDGL7/8An19fRw7dgwrVqyoUB5E2opFCBGpJCoqCp07d4arqyssLS2Vlnfs2BGDBw+Gu7s7GjVqhJ9++gl5eXlKRxFU0a9fP/j6+iIiIqJCMVtaWmLRokVwc3PD8OHD4ebmhry8PHz55Zdo2LAhpk2bBn19fRw9elRhvdDQUHzwwQdo1KgRli9fDjMzM6xevRoAsGTJEjRp0gSzZs2Cu7s7mjRpgjVr1uDgwYP473//Kx+jYcOG+P777+Hm5gY3N7cK5UGkrViEEJFK/P39y1yenp6OUaNGoWHDhjAzM4OpqSmePn2KO3fulGt7c+bMwS+//ILLly+Xa30A8PT0hI7O//43Z2NjA29vb/lzXV1d1KlTBxkZGQrrtWzZUv7vWrVqwd/fXx7HuXPncPDgQfmcmNq1a8Pd3R0AcOPGDfl6fn5+5Y6bqKbgxFQiUomxsXGZy4ODg/Hw4UMsXLgQTk5OkEqlaNmyJQoKCsq1vXbt2iEoKAjTpk1DSEiIwjIdHR0IgqDQVlhYqDSGnp6ewnOJRFJim0wmUzmup0+folevXpgzZ47SMjs7O/m/3/R6ERGLECJSk2PHjmHZsmXo3r07AODu3bsKkzXLY/bs2fD19VU6nWFtbY20tDQIgiC/dFed9/b4559/0K5dOwDAixcvcPbsWYSGhgIAmjZtiq1bt8LZ2Rm1avF/oUQVwdMxRKQWDRs2xPr163H58mWcPHkSgwYNgqGhYYXG9Pb2xqBBg7Bo0SKF9g4dOiAzMxPff/89bty4gaVLl2LPnj0V2tarli5dij/++ANXrlzB2LFj8fjxYwwfPhwAMHbsWDx69AgDBw7E6dOncePGDezduxfDhg1DUVGR2mIgqglYhBCRWqxevRqPHz9G06ZNMWTIEIwfPx5169at8LhRUVFKp0saNWqEZcuWYenSpWjcuDFOnTqFiRMnVnhbxWbPno3Zs2ejcePGOHr0KHbs2AErKysAgL29PY4dO4aioiJ06dIF3t7eCAsLg7m5ucL8EyJ6M4nw+olVIiIioreAZTsRERFpBIsQIiIi0ggWIURERKQRLEKIiIhII1iEEBERkUawCCEiIiKNYBFCREREGsEihIiIiDSCRQgRERFpBIsQIiIi0ggWIURERKQR/w9MU7sGXphAQQAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 04:46:59,441] A new study created in memory with name: study_LinearBinary\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearBinary\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:45, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50020.625900
10002.693900
15002.793600
20002.728900
25002.803300
30002.786500
35002.761000
40002.701200
45002.686900
50002.540700
55002.297700
60002.233000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:47]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 04:51:32,955] Trial 0 finished with value: 0.50424 and parameters: {}. Best is trial 0 with value: 0.50424.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:45, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50020.625900
10002.693900
15002.793600
20002.728900
25002.803300
30002.786500
35002.761000
40002.701200
45002.686900
50002.540700
55002.297700
60002.233000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:47]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 04:56:06,735] Trial 1 finished with value: 0.50424 and parameters: {}. Best is trial 0 with value: 0.50424.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:45, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50020.625900
10002.693900
15002.793600
20002.728900
25002.803300
30002.786500
35002.761000
40002.701200
45002.686900
50002.540700
55002.297700
60002.233000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:46]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:00:40,169] Trial 2 finished with value: 0.50424 and parameters: {}. Best is trial 0 with value: 0.50424.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:46, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50020.625900
10002.693900
15002.793600
20002.728900
25002.803300
30002.786500
35002.761000
40002.701200
45002.686900
50002.540700
55002.297700
60002.233000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:46]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:05:14,883] Trial 3 finished with value: 0.50424 and parameters: {}. Best is trial 0 with value: 0.50424.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 03:45, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50020.625900
10002.693900
15002.793600
20002.728900
25002.803300
30002.786500
35002.761000
40002.701200
45002.686900
50002.540700
55002.297700
60002.233000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:46]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:09:48,193] Trial 4 finished with value: 0.50424 and parameters: {}. Best is trial 0 with value: 0.50424.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearBinary saved to optuna_results_LinearBinary.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:09:48,623] A new study created in memory with name: study_LinearBinaryScaling\n"]},{"output_type":"stream","name":"stdout","text":["\n","Running study for precision type: LinearBinaryScaling\n"]},{"output_type":"stream","name":"stderr","text":["/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 04:15, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50014.275700
100013.318000
150014.536000
200015.108200
250014.419800
300014.840800
350013.196800
400013.216700
450013.769600
500012.910300
550012.410900
600012.834800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 00:59]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:15:05,318] Trial 0 finished with value: 0.59436 and parameters: {}. Best is trial 0 with value: 0.59436.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 04:14, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50014.275700
100013.318000
150014.536000
200015.108200
250014.419800
300014.840800
350013.196800
400013.216700
450013.769600
500012.910300
550012.410900
600012.834800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:00]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:20:22,234] Trial 1 finished with value: 0.59436 and parameters: {}. Best is trial 0 with value: 0.59436.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 04:14, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50014.275700
100013.318000
150014.536000
200015.108200
250014.419800
300014.840800
350013.196800
400013.216700
450013.769600
500012.910300
550012.410900
600012.834800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:01]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:25:38,729] Trial 2 finished with value: 0.59436 and parameters: {}. Best is trial 0 with value: 0.59436.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 04:14, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50014.275700
100013.318000
150014.536000
200015.108200
250014.419800
300014.840800
350013.196800
400013.216700
450013.769600
500012.910300
550012.410900
600012.834800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:00]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:30:55,143] Trial 3 finished with value: 0.59436 and parameters: {}. Best is trial 0 with value: 0.59436.\n","/content/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
\n"," \n"," \n"," [6250/6250 04:14, Epoch 2/2]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
50014.275700
100013.318000
150014.536000
200015.108200
250014.419800
300014.840800
350013.196800
400013.216700
450013.769600
500012.910300
550012.410900
600012.834800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3125/3125 01:00]\n","
\n"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["[I 2025-02-04 05:36:11,735] Trial 4 finished with value: 0.59436 and parameters: {}. Best is trial 0 with value: 0.59436.\n"]},{"output_type":"stream","name":"stdout","text":["Results for precision LinearBinaryScaling saved to optuna_results_LinearBinaryScaling.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"error","ename":"TypeError","evalue":"NDFrame.to_csv() got an unexpected keyword argument 'dpi'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;31m# Save the combined results to CSV.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m \u001b[0mcombined_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"optuna_combined_results.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 280\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Combined results saved to optuna_combined_results.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m )\n\u001b[0;32m--> 333\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: NDFrame.to_csv() got an unexpected keyword argument 'dpi'"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import matplotlib.ticker as mticker\n","import pandas as pd\n","\n","def plot_extended_optuna_search_results(csv_filepath):\n"," \"\"\"\n"," Plots both the running max accuracy and the accuracy reached at each iteration.\n"," \"\"\"\n"," # Read the CSV file containing the trial results\n"," results_df = pd.read_csv(csv_filepath)\n","\n"," # Ensure the trials are sorted by trial_number\n"," results_df = results_df.sort_values(by=\"trial_number\")\n","\n"," # Compute the running maximum accuracy up to each trial\n"," running_max = []\n"," current_max = float(\"-inf\")\n"," for acc in results_df[\"accuracy\"]:\n"," current_max = max(current_max, acc)\n"," running_max.append(current_max)\n"," results_df[\"running_max\"] = running_max\n","\n"," # Create the plot\n"," plt.figure(figsize=(10, 6))\n","\n"," # Plot accuracy reached at each iteration\n"," plt.plot(results_df[\"trial_number\"], results_df[\"accuracy\"],\n"," marker=\"o\", linestyle=\"-\", linewidth=2, label=\"Accuracy per Trial\", color=\"tab:blue\")\n","\n"," # Plot running max accuracy\n"," plt.plot(results_df[\"trial_number\"], results_df[\"running_max\"],\n"," marker=\"s\", linestyle=\"--\", linewidth=2, label=\"Maximum Achieved Accuracy\", color=\"tab:red\")\n","\n"," # Label the axes and the plot\n"," plt.xlabel(\"Number of Trials\", fontsize=12)\n"," plt.ylabel(\"Accuracy\", fontsize=12)\n"," plt.title(\"Mixed Precision Search\", fontsize=14, pad=15)\n"," plt.grid(True, linestyle=\"--\", alpha=0.5)\n","\n"," # Format the y-axis to display percentages\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.1f}%\"))\n","\n"," # Annotate the running max points with their percentage values\n"," for trial, acc in zip(results_df[\"trial_number\"], results_df[\"running_max\"]):\n"," plt.text(trial, acc, f\"{acc*100:.1f}%\", fontsize=8, ha=\"center\", va=\"bottom\")\n","\n"," # Add legend\n"," plt.legend()\n","\n"," plt.tight_layout()\n"," plt.savefig(\"extended_optuna_search_results_2_curves.png\", dpi=300)\n"," plt.show()\n","\n","def plot_extended_optuna_search_results_v2(csv_filepath):\n"," \"\"\"\n"," Plots the running max accuracy for the second method,\n"," where each precision type is tested separately.\n"," Creates multiple curves, one per precision type.\n"," \"\"\"\n"," # Read the CSV file\n"," results_df = pd.read_csv(csv_filepath)\n","\n"," # Ensure trials are sorted correctly\n"," results_df = results_df.sort_values(by=\"trial_number\")\n","\n"," # Prepare figure\n"," plt.figure(figsize=(10, 6))\n","\n"," # Process each precision type separately\n"," for precision, group in results_df.groupby(\"precision_type\"):\n"," group = group.sort_values(\"trial_number\")\n"," trial_nums = group[\"trial_number\"].tolist()\n"," accuracies = group[\"trial_accuracy\"].tolist()\n","\n"," # Compute running maximum accuracy\n"," running_max = []\n"," current_max = float(\"-inf\")\n"," for acc in accuracies:\n"," current_max = max(current_max, acc)\n"," running_max.append(current_max)\n","\n"," # Plot the running maximum for this precision type\n"," plt.plot(\n"," trial_nums,\n"," running_max,\n"," marker=\"o\",\n"," linestyle=\"--\",\n"," linewidth=2,\n"," label=precision, # Use precision type as the label\n"," )\n","\n"," # Grid and labels\n"," plt.grid(visible=True, linestyle=\"--\", alpha=0.5)\n"," plt.xlabel(\"Number of Trials\", fontsize=12)\n"," plt.ylabel(\"Maximum Achieved Accuracy\", fontsize=12)\n"," plt.title(\"Optuna Search Results - Per Precision Type\", fontsize=14, pad=15)\n","\n"," # Format y-axis as percentage\n"," plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{x*100:.1f}%\"))\n"," plt.legend(fontsize=10, loc=\"lower right\", title=\"Precision Type\")\n"," plt.tight_layout()\n"," plt.savefig(\"optuna_combined_precision_progress.png\", dpi=300)\n"," plt.show()\n","\n","plot_extended_optuna_search_results(\"extended_optuna_results.csv\")\n","plot_extended_optuna_search_results_v2(\"optuna_combined_results.csv\")\n"],"metadata":{"id":"QPOgcm1lxUGT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import glob\n","from google.colab import files\n","\n","def download_all_csvs():\n"," \"\"\"\n"," Find all CSV files in the directory structure and download them to the local machine.\n"," Handles errors gracefully and provides feedback for each file.\n"," \"\"\"\n"," try:\n"," # Find all CSV files (searches recursively)\n"," csv_files = glob.glob(\"**/*.csv\", recursive=True)\n","\n"," if not csv_files:\n"," print(\"No CSV files found.\")\n"," return\n","\n"," print(f\"Found {len(csv_files)} CSV file(s):\")\n"," for file in 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b/Working_Data/optuna_combined_precision_progress_zoomed.png differ diff --git a/Working_Data/optuna_search_results.png b/Working_Data/optuna_search_results.png new file mode 100644 index 000000000..33bb80763 Binary files /dev/null and b/Working_Data/optuna_search_results.png differ diff --git a/Working_Data/plotting_test_3.py b/Working_Data/plotting_test_3.py new file mode 100644 index 000000000..ce27d462e --- /dev/null +++ b/Working_Data/plotting_test_3.py @@ -0,0 +1,180 @@ +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker + +# (Full-Precision) First test is full precision model, +# (PTQ) Second test is quantised model accuracy evaluation (no retraining), +# (QAT) Third test is quantised model accuracy evaluation (with retraining) + +results = [ + { + "test_id": 1, + "data_in_width": 4, + "data_in_frac_width": 2, + "weight_width": 4, + "weight_frac_width": 2, + "bias_width": 4, + "bias_frac_width": 2, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.5, + "qat_accuracy": 0.5, + }, + { + "test_id": 2, + "data_in_width": 8, + "data_in_frac_width": 4, + "weight_width": 8, + "weight_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.78388, + "qat_accuracy": 0.84076, + }, + { + "test_id": 5, + "data_in_width": 12, + "data_in_frac_width": 8, + "weight_width": 12, + "weight_frac_width": 8, + "bias_width": 12, + "bias_frac_width": 8, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.83704, + "qat_accuracy": 0.84412, + }, + { + "test_id": 6, + "data_in_width": 16, + "data_in_frac_width": 8, + "weight_width": 16, + "weight_frac_width": 8, + "bias_width": 16, + "bias_frac_width": 8, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.83796, + "qat_accuracy": 0.84468, + }, + { + "test_id": 6, + "data_in_width": 20, + "data_in_frac_width": 10, + "weight_width": 20, + "weight_frac_width": 10, + "bias_width": 20, + "bias_frac_width": 10, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.83776, + "qat_accuracy": 0.845, + }, + { + "test_id": 6, + "data_in_width": 24, + "data_in_frac_width": 12, + "weight_width": 24, + "weight_frac_width": 12, + "bias_width": 24, + "bias_frac_width": 12, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.83732, + "qat_accuracy": 0.84488, + }, + { + "test_id": 7, + "data_in_width": 28, + "data_in_frac_width": 14, + "weight_width": 28, + "weight_frac_width": 14, + "bias_width": 28, + "bias_frac_width": 14, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.83736, + "qat_accuracy": 0.84504, + }, + { + "test_id": 7, + "data_in_width": 32, + "data_in_frac_width": 16, + "weight_width": 32, + "weight_frac_width": 16, + "bias_width": 32, + "bias_frac_width": 16, + "full_precision_accuracy": 0.83732, + "ptq_accuracy": 0.83736, + "qat_accuracy": 0.84492, + }] + +def plot_results_separated(results): + fixed_point_widths = [4, 8, 12, 16, 20, 24, 28, 32] + ptq_accuracies = [res["ptq_accuracy"] for res in results if res["ptq_accuracy"] is not None] + qat_accuracies = [res["qat_accuracy"] for res in results if res["qat_accuracy"] is not None] + highest_accuracies = [max(ptq_accuracies[i], qat_accuracies[i]) for i in range(len(ptq_accuracies))] + + # Plot 1: PTQ vs QAT + plt.figure(figsize=(10, 6)) + plt.plot( + fixed_point_widths[:len(ptq_accuracies)], + ptq_accuracies, + marker="s", + label="PTQ Accuracy", + linestyle="-.", + linewidth=2, + alpha=0.8, + ) + plt.plot( + fixed_point_widths[:len(qat_accuracies)], + qat_accuracies, + marker="d", + label="QAT Accuracy", + linestyle=":", + linewidth=2, + alpha=0.8, + ) + # Add labels, grid, and legend + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Fixed Point Width", fontsize=12) + plt.ylabel("Accuracy", fontsize=12) + plt.title("PTQ vs QAT Accuracy by Fixed Point Width", fontsize=14, pad=15) + plt.legend(fontsize=10, loc="lower right") + + # Format y-axis to show percentages + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.0f}%")) + + # Add value labels on the points + for x, y in zip(fixed_point_widths[:len(ptq_accuracies)], ptq_accuracies): + plt.text(x, y + 0.002, f"{y*100:.2f}%", fontsize=8, ha="center") + for x, y in zip(fixed_point_widths[:len(qat_accuracies)], qat_accuracies): + plt.text(x, y + 0.002, f"{y*100:.2f}%", fontsize=8, ha="center") + + plt.tight_layout() + plt.savefig("ptq_vs_qat_accuracy.png", dpi=300) + plt.show() + + # Plot 2: Highest Accuracy vs Fixed Width + plt.figure(figsize=(10, 6)) + plt.plot( + fixed_point_widths[:len(highest_accuracies)], + highest_accuracies, + marker="o", + label="Highest Accuracy", + linestyle="--", + linewidth=2, + ) + + # Add labels, grid, and legend + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Fixed Point Width", fontsize=12) + plt.ylabel("Accuracy", fontsize=12) + plt.title("Highest Accuracy by Fixed Point Width", fontsize=14, pad=15) + + # Format y-axis to show percentages + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.0f}%")) + + # Add value labels on the points + for x, y in zip(fixed_point_widths[:len(highest_accuracies)], highest_accuracies): + plt.text(x, y + 0.002, f"{y*100:.2f}%", fontsize=8, ha="center") + + plt.tight_layout() + plt.savefig("fixed_point_width_vs_accuracy.png", dpi=300) + plt.show() + +plot_results_separated(results) \ No newline at end of file diff --git a/Working_Data/plotting_test_4.py b/Working_Data/plotting_test_4.py new file mode 100644 index 000000000..a4a1af736 --- /dev/null +++ b/Working_Data/plotting_test_4.py @@ -0,0 +1,150 @@ +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker + +results = [ + { + "sparsity": 0.1, + "l1_initial_pruned_accuracy": 0.84368, + "l1_fine_tuned_accuracy": 0.8604, + "rand_initial_pruned_accuracy": 0.73676, + "rand_fine_tuned_accuracy": 0.83952 + }, + { + "sparsity": 0.2, + "l1_initial_pruned_accuracy": 0.83992, + "l1_fine_tuned_accuracy": 0.85832, + "rand_initial_pruned_accuracy": 0.6308, + "rand_fine_tuned_accuracy": 0.8232 + }, + { + "sparsity": 0.3, + "l1_initial_pruned_accuracy": 0.83004, + "l1_fine_tuned_accuracy": 0.85504, + "rand_initial_pruned_accuracy": 0.54652, + "rand_fine_tuned_accuracy": 0.80812 + }, + { + "sparsity": 0.4, + "l1_initial_pruned_accuracy": 0.80796, + "l1_fine_tuned_accuracy": 0.8468, + "rand_initial_pruned_accuracy": 0.50972, + "rand_fine_tuned_accuracy": 0.78696 + }, + { + "sparsity": 0.5, + "l1_initial_pruned_accuracy": 0.76728, + "l1_fine_tuned_accuracy": 0.84148, + "rand_initial_pruned_accuracy": 0.5066, + "rand_fine_tuned_accuracy": 0.74436 + }, + { + "sparsity": 0.6, + "l1_initial_pruned_accuracy": 0.563, + "l1_fine_tuned_accuracy": 0.82284, + "rand_initial_pruned_accuracy": 0.5046, + "rand_fine_tuned_accuracy": 0.52732 + }, + { + "sparsity": 0.7, + "l1_initial_pruned_accuracy": 0.54052, + "l1_fine_tuned_accuracy": 0.8098, + "rand_initial_pruned_accuracy": 0.50292, + "rand_fine_tuned_accuracy": 0.50436 + }, + { + "sparsity": 0.8, + "l1_initial_pruned_accuracy": 0.50076, + "l1_fine_tuned_accuracy": 0.76204, + "rand_initial_pruned_accuracy": 0.49896, + "rand_fine_tuned_accuracy": 0.504 + }, + { + "sparsity": 0.9, + "l1_initial_pruned_accuracy": 0.50312, + "l1_fine_tuned_accuracy": 0.56104, + "rand_initial_pruned_accuracy": 0.50224, + "rand_fine_tuned_accuracy": 0.5034 + } +] + +import matplotlib.pyplot as plt + +def plot_pruning_results_separated(results): + # Extract sparsity levels + sparsities = [res["sparsity"] for res in results] + + # Extract accuracies + l1_initial = [res["l1_initial_pruned_accuracy"] for res in results] + l1_fine_tuned = [res["l1_fine_tuned_accuracy"] for res in results] + rand_initial = [res["rand_initial_pruned_accuracy"] for res in results] + rand_fine_tuned = [res["rand_fine_tuned_accuracy"] for res in results] + + # Calculate highest achieved accuracy per sparsity + highest_accuracies = [ + max(filter(None, [l1_init, l1_fine, rand_init, rand_fine])) + for l1_init, l1_fine, rand_init, rand_fine in zip(l1_initial, l1_fine_tuned, rand_initial, rand_fine_tuned) + ] + + # Plot 1: Highest achieved accuracy per sparsity + plt.figure(figsize=(10, 6)) + plt.plot( + sparsities, + highest_accuracies, + marker="o", + linestyle="--", + linewidth=2, + label="Highest Accuracy", + ) + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Sparsity", fontsize=12) + plt.ylabel("Accuracy", fontsize=12) + plt.title("Highest Achieved Accuracy by Sparsity", fontsize=14, pad=15) + plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{x*100:.0f}%")) + for x, y in zip(sparsities, highest_accuracies): + plt.text(x, y + 0.005, f"{y*100:.1f}%", fontsize=8, ha="center") + plt.tight_layout() + plt.savefig("highest_accuracy_by_sparsity.png", dpi=300) + plt.show() + + # Plot 2: Random and L1-Norm accuracy curves + plt.figure(figsize=(10, 6)) + rand_accuracies = [ + max(filter(None, [rand_init, rand_fine])) + for rand_init, rand_fine in zip(rand_initial, rand_fine_tuned) + ] + l1_accuracies = [ + max(filter(None, [l1_init, l1_fine])) + for l1_init, l1_fine in zip(l1_initial, l1_fine_tuned) + ] + plt.plot( + sparsities, + rand_accuracies, + marker="s", + linestyle="-.", + linewidth=2, + label="Random Pruning", + ) + plt.plot( + sparsities, + l1_accuracies, + marker="d", + linestyle=":", + linewidth=2, + label="L1-Norm Pruning", + ) + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Sparsity", fontsize=12) + plt.ylabel("Accuracy", fontsize=12) + plt.title("Effect of Sparsity on Pruning Accuracy", fontsize=14, pad=15) + plt.legend(fontsize=10, loc="lower left") + plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{x*100:.0f}%")) + for x, y in zip(sparsities, rand_accuracies): + plt.text(x, y + 0.005, f"{y*100:.1f}%", fontsize=8, ha="center") + for x, y in zip(sparsities, l1_accuracies): + plt.text(x, y + 0.005, f"{y*100:.1f}%", fontsize=8, ha="center") + plt.tight_layout() + plt.savefig("pruning_accuracy_by_sparsity.png", dpi=300) + plt.show() + +# Call the function +plot_pruning_results_separated(results) diff --git a/Working_Data/plotting_test_5a.py b/Working_Data/plotting_test_5a.py new file mode 100644 index 000000000..c271b067d --- /dev/null +++ b/Working_Data/plotting_test_5a.py @@ -0,0 +1,59 @@ +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import pandas as pd + +def plot_combined_optuna_results(csv_paths): + plt.figure(figsize=(10, 6)) + + for csv_path in csv_paths: + data = pd.read_csv(csv_path) + accuracies = data["accuracy"].tolist() + + running_max = [] + current_max = float('-inf') + for acc in accuracies: + current_max = max(current_max, acc) + running_max.append(current_max) + + # Determine the label based on the file name + if 'random' in csv_path.lower(): + label = 'Random Search' + elif 'grid' in csv_path.lower(): + label = 'Grid Search' + elif 'tpe' in csv_path.lower(): + label = 'TPE Search' + else: + label = 'Unknown Search Method' + + # Plot the running maximum for the current search method + plt.plot( + range(len(running_max)), + running_max, + marker="o", + linestyle="--", + linewidth=2, + label=label, + ) + + # Grid and labels + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Number of Trials", fontsize=12) + plt.ylabel("Maximum Achieved Accuracy", fontsize=12) + plt.title("Comparison of Maximum Achieved Accuracy by Search Method", fontsize=14, pad=15) + + # Format y-axis as percentage + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + + # Legend and layout + plt.legend(fontsize=10, loc="lower right") + plt.tight_layout() + plt.savefig('combined_optuna_results.png', dpi=300) + plt.show() + +csv_paths = [ + 'random_study_trials.csv', + 'grid_study_trials.csv', + 'tpe_study_trials.csv', +] + +plot_combined_optuna_results(csv_paths) diff --git a/Working_Data/plotting_test_5b.py b/Working_Data/plotting_test_5b.py new file mode 100644 index 000000000..3b6480932 --- /dev/null +++ b/Working_Data/plotting_test_5b.py @@ -0,0 +1,184 @@ +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import pandas as pd + +# # Function to load results from CSV +# def load_results_from_csv(filepath): +# return pd.read_csv(filepath) + +# # Load the results +# csv_filepath = "formatted_training_results.csv" +# results_df = load_results_from_csv(csv_filepath) + + +# def plot_compression_aware_results(results): +# import matplotlib.ticker as mticker + +# # Separate results by complexity type +# no_compression = [res for res in results if res["complexity"] == "No Compression"] +# compression_no_post = [res for res in results if res["complexity"] == "Compression-aware (No Post-Training)"] +# compression_with_post = [res for res in results if res["complexity"] == "Compression-aware (Post-Training)"] + +# # Helper to compute running max +# def get_running_max(data): +# running_max = [] +# current_max = float('-inf') +# for res in data: +# current_max = max(current_max, res["value"]) +# running_max.append(current_max) +# return running_max + +# # Compute running max for each scenario +# no_compression_max = get_running_max(no_compression) +# compression_no_post_max = get_running_max(compression_no_post) +# compression_with_post_max = get_running_max(compression_with_post) + +# # Trials count +# trials_no_compression = list(range(len(no_compression_max))) +# trials_compression_no_post = list(range(len(compression_no_post_max))) +# trials_compression_with_post = list(range(len(compression_with_post_max))) + +# # Plot the results +# plt.figure(figsize=(10, 6)) + +# # No Compression +# plt.plot( +# trials_no_compression, +# no_compression_max, +# marker="o", +# linestyle="--", +# linewidth=2, +# label="No Compression", +# ) + +# # Compression-aware (No Post-Training) +# plt.plot( +# trials_compression_no_post, +# compression_no_post_max, +# marker="s", +# linestyle="-.", +# linewidth=2, +# label="Compression-aware (No Post-Training)", +# ) + +# # Compression-aware (Post-Training) +# plt.plot( +# trials_compression_with_post, +# compression_with_post_max, +# marker="d", +# linestyle=":", +# linewidth=2, +# label="Compression-aware (Post-Training)", +# ) + +# # Format the plot +# plt.grid(visible=True, linestyle="--", alpha=0.5) +# plt.xlabel("Number of Trials", fontsize=12) +# plt.ylabel("Maximum Achieved Accuracy", fontsize=12) +# plt.title("Compression-Aware Search Results", fontsize=14, pad=15) + +# # Format y-axis as percentage +# plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + +# # Add annotations for each point +# for i, val in enumerate(no_compression_max): +# plt.text(i, val, f"{val*100:.1f}%", fontsize=8, ha="center", va="bottom") +# for i, val in enumerate(compression_no_post_max): +# plt.text(i, val, f"{val*100:.1f}%", fontsize=8, ha="center", va="bottom") + + + +# plot_compression_aware_results(results_p2) + +# Function to load results from CSV +def load_results_from_csv(filepath): + return pd.read_csv(filepath) + +# Load the results +csv_filepath = "formatted_training_results.csv" +results_df = load_results_from_csv(csv_filepath) + +# Function to plot the compression-aware results +def plot_compression_aware_results(results_df): + # Separate results by complexity type + no_compression = results_df[results_df["complexity"] == "No Compression"] + compression_no_post = results_df[results_df["complexity"] == "Compression-aware (No Post-Training)"] + compression_with_post = results_df[results_df["complexity"] == "Compression-aware (Post-Training)"] + + # Helper to compute running max + def get_running_max(values): + running_max = [] + current_max = float('-inf') + for val in values: + current_max = max(current_max, val) + running_max.append(current_max) + return running_max + + # Compute running max for each scenario + no_compression_max = get_running_max(no_compression["value"].tolist()) + compression_no_post_max = get_running_max(compression_no_post["value"].tolist()) + compression_with_post_max = get_running_max(compression_with_post["value"].tolist()) + + # Trials count + trials_no_compression = no_compression["trial"].tolist() + trials_compression_no_post = compression_no_post["trial"].tolist() + trials_compression_with_post = compression_with_post["trial"].tolist() + + # Plot the results + plt.figure(figsize=(10, 6)) + + # No Compression + plt.plot( + trials_no_compression, + no_compression_max, + marker="o", + linestyle="--", + linewidth=2, + label="No Compression", + ) + + # Compression-aware (No Post-Training) + plt.plot( + trials_compression_no_post, + compression_no_post_max, + marker="s", + linestyle="-.", + linewidth=2, + label="Compression-aware (No Post-Training)", + ) + + # Compression-aware (Post-Training) + plt.plot( + trials_compression_with_post, + compression_with_post_max, + marker="d", + linestyle=":", + linewidth=2, + label="Compression-aware (Post-Training)", + ) + + # Format the plot + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Number of Trials", fontsize=12) + plt.ylabel("Maximum Achieved Accuracy", fontsize=12) + plt.title("Compression-Aware Search Results", fontsize=14, pad=15) + + # Format y-axis as percentage + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + + # Add annotations for each point + for i, val in zip(trials_no_compression, no_compression_max): + plt.text(i, val, f"{val*100:.1f}%", fontsize=8, ha="center", va="bottom") + for i, val in zip(trials_compression_no_post, compression_no_post_max): + plt.text(i, val, f"{val*100:.1f}%", fontsize=8, ha="center", va="bottom") + for i, val in zip(trials_compression_with_post, compression_with_post_max): + plt.text(i, val, f"{val*100:.1f}%", fontsize=8, ha="center", va="bottom") + + # Show legend + plt.legend() + plt.tight_layout() + plt.savefig("compression_aware_results.png", dpi=300) + plt.show() + +# Plot the results using the updated function +plot_compression_aware_results(results_df) diff --git a/Working_Data/plotting_test_6a.py b/Working_Data/plotting_test_6a.py new file mode 100644 index 000000000..ec60afd55 --- /dev/null +++ b/Working_Data/plotting_test_6a.py @@ -0,0 +1,43 @@ +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import pandas as pd + +def plot_optuna_search_results(csv_filepath): + # Read the CSV file containing the trial results + results_df = pd.read_csv(csv_filepath) + + # Ensure the trials are in order (by trial_number) + results_df = results_df.sort_values(by='trial_number') + + # Compute the running maximum accuracy up to each trial + running_max = [] + current_max = float('-inf') + for acc in results_df['accuracy']: + current_max = max(current_max, acc) + running_max.append(current_max) + results_df['running_max'] = running_max + + # Create the plot + plt.figure(figsize=(10, 6)) + plt.plot(results_df['trial_number'], results_df['running_max'], + marker='o', linestyle='-', linewidth=2) + + # Label the axes and the plot + plt.xlabel("Number of Trials", fontsize=12) + plt.ylabel("Maximum Achieved Accuracy", fontsize=12) + plt.title("Optuna Search Results", fontsize=14, pad=15) + plt.grid(True, linestyle="--", alpha=0.5) + + # Format the y-axis to display percentages + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + + # Annotate each point with its percentage value + for trial, acc in zip(results_df['trial_number'], results_df['running_max']): + plt.text(trial, acc, f"{acc*100:.1f}%", fontsize=8, ha='center', va='bottom') + + plt.tight_layout() + plt.savefig("optuna_search_results.png", dpi=300) + plt.show() + +plot_optuna_search_results("optuna_results.csv") + diff --git a/Working_Data/plotting_test_6b.py b/Working_Data/plotting_test_6b.py new file mode 100644 index 000000000..6efc47648 --- /dev/null +++ b/Working_Data/plotting_test_6b.py @@ -0,0 +1,151 @@ +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import pandas as pd + +def plot_extended_optuna_search_results(csv_filepath): + """ + Plots both the running max accuracy and the accuracy reached at each iteration. + """ + # Read the CSV file containing the trial results + results_df = pd.read_csv(csv_filepath) + + # Ensure the trials are sorted by trial_number + results_df = results_df.sort_values(by="trial_number") + + # Compute the running maximum accuracy up to each trial + running_max = [] + current_max = float("-inf") + for acc in results_df["accuracy"]: + current_max = max(current_max, acc) + running_max.append(current_max) + results_df["running_max"] = running_max + + # Create the plot + plt.figure(figsize=(10, 6)) + + # Plot accuracy reached at each iteration + plt.plot(results_df["trial_number"], results_df["accuracy"], + marker="o", linestyle="-", linewidth=2, label="Accuracy per Trial", color="tab:blue") + + # Plot running max accuracy + plt.plot(results_df["trial_number"], results_df["running_max"], + marker="s", linestyle="--", linewidth=2, label="Maximum Achieved Accuracy", color="tab:red") + + # Label the axes and the plot + plt.xlabel("Number of Trials", fontsize=12) + plt.ylabel("Accuracy", fontsize=12) + plt.title("Mixed Precision Search", fontsize=14, pad=15) + plt.grid(True, linestyle="--", alpha=0.5) + + # Format the y-axis to display percentages + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + + # Annotate the running max points with their percentage values + for trial, acc in zip(results_df["trial_number"], results_df["running_max"]): + plt.text(trial, acc, f"{acc*100:.1f}%", fontsize=8, ha="center", va="bottom") + + # Add legend + plt.legend() + + plt.tight_layout() + plt.savefig("extended_optuna_search_results_2_curves.png", dpi=300) + plt.show() + +# def plot_extended_optuna_search_results(csv_filepath): +# """ +# Plots the running max accuracy for the first method, +# where multiple layer types are tested in a single search. +# """ +# # Read the CSV file containing the trial results +# results_df = pd.read_csv(csv_filepath) + +# # Ensure the trials are sorted by trial_number +# results_df = results_df.sort_values(by="trial_number") + +# # Compute the running maximum accuracy up to each trial +# running_max = [] +# current_max = float("-inf") +# for acc in results_df["accuracy"]: +# current_max = max(current_max, acc) +# running_max.append(current_max) +# results_df["running_max"] = running_max + +# # Create the plot +# plt.figure(figsize=(10, 6)) +# plt.plot(results_df["trial_number"], results_df["running_max"], +# marker="o", linestyle="-", linewidth=2) + +# # Label the axes and the plot +# plt.xlabel("Number of Trials", fontsize=12) +# plt.ylabel("Maximum Achieved Accuracy", fontsize=12) +# plt.title("Optuna Search Results - Mixed Layer Search", fontsize=14, pad=15) +# plt.grid(True, linestyle="--", alpha=0.5) + +# # Format the y-axis to display percentages +# plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + +# # Annotate each point with its percentage value +# for trial, acc in zip(results_df["trial_number"], results_df["running_max"]): +# plt.text(trial, acc, f"{acc*100:.1f}%", fontsize=8, ha="center", va="bottom") + +# plt.tight_layout() +# plt.savefig("extended_optuna_search_results.png", dpi=300) +# plt.show() + +def plot_extended_optuna_search_results_v2(csv_filepath): + """ + Plots the running max accuracy for the second method, + where each precision type is tested separately. + Creates multiple curves, one per precision type. + """ + # Read the CSV file + results_df = pd.read_csv(csv_filepath) + + # Ensure trials are sorted correctly + results_df = results_df.sort_values(by="trial_number") + + # Prepare figure + plt.figure(figsize=(10, 6)) + + # Process each precision type separately + for precision, group in results_df.groupby("precision_type"): + group = group.sort_values("trial_number") + trial_nums = group["trial_number"].tolist() + accuracies = group["trial_accuracy"].tolist() + + # Compute running maximum accuracy + running_max = [] + current_max = float("-inf") + for acc in accuracies: + current_max = max(current_max, acc) + running_max.append(current_max) + + # Plot the running maximum for this precision type + plt.plot( + trial_nums, + running_max, + marker="o", + linestyle="--", + linewidth=2, + label=precision, # Use precision type as the label + ) + + # Grid and labels + plt.grid(visible=True, linestyle="--", alpha=0.5) + plt.xlabel("Number of Trials", fontsize=12) + plt.ylabel("Maximum Achieved Accuracy", fontsize=12) + plt.title("Optuna Search Results - Per Precision Type", fontsize=14, pad=15) + + # Format y-axis as percentage + plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x*100:.1f}%")) + # plt.ylim(0.85, 0.9) + # Legend and layout + plt.legend(fontsize=10, loc="lower right", title="Precision Type") + plt.tight_layout() + plt.savefig("optuna_combined_precision_progress.png", dpi=300) + plt.show() + + +# Run both plots +plot_extended_optuna_search_results("extended_optuna_results.csv") +plot_extended_optuna_search_results_v2("optuna_combined_results.csv") diff --git a/Working_Data/practicle_1.md b/Working_Data/practicle_1.md new file mode 100644 index 000000000..4cef3bf0b --- /dev/null +++ b/Working_Data/practicle_1.md @@ -0,0 +1,21 @@ +## Practicle 1: Introduction + +### Run-Time Error +Here is the run-time error encountered during the session: + +```plaintext +RuntimeError: Tried to erase Node bert_embeddings_dropout but it still had 6 users in the graph: +{getattr_2: None, size_4: None, bert_encoder_layer_0_attention_self_query: None, +bert_encoder_layer_0_attention_self_key: None, bert_encoder_layer_0_attention_self_value: None, +add_7: None}! +``` + +## Tutorial 2: Introduction + +### Removing attention_mask, labels from hf_input_names and its effect on the graphs: +The graph was created with and without the extra information and compared. + +Firstly, having no labels meant that at the end of the process, there is no cross-entropy calcualted and viewed (so 4 blocks are removed). This is becuase the ground truth labels are required for loss calcualtions and not having them therefore means no losses can be calculated. Secondly, when there is no attention_mask specified, the model calls an extra block called getattr_1 after the input, instead of having a seperate input attention_mask block. When no mask is specified, more information from the model is used as an input to the masking process, implying that the mask is created based on the input information, whilst the external mask would be used for manually choosing which information to mask or not. + + + diff --git a/Working_Data/pruning_accuracy_by_sparsity.png b/Working_Data/pruning_accuracy_by_sparsity.png new file mode 100644 index 000000000..b67b425ec Binary files /dev/null and b/Working_Data/pruning_accuracy_by_sparsity.png differ diff --git a/Working_Data/ptq_vs_qat_accuracy.png b/Working_Data/ptq_vs_qat_accuracy.png new file mode 100644 index 000000000..7b9aba324 Binary files /dev/null and b/Working_Data/ptq_vs_qat_accuracy.png differ diff --git a/Working_Data/temp.md b/Working_Data/temp.md new file mode 100644 index 000000000..6072fe2aa --- /dev/null +++ b/Working_Data/temp.md @@ -0,0 +1,12 @@ + +1. Tutorial 5 shows how to use random search to find the optimal configuration of hyperparameters and layer choices for the Bert model. + + a. Now, explore using the GridSampler and TPESampler in Optuna. + + b. Plot a figure that has the number of trials on the x axis, and the maximum achieved accuracy up to that point on the y axis. Plot one curve for each sampler to compare their performance. + +2. In Tutorial 5, NAS is used to find an optimal configuration of hyperparameters, then we use the CompressionPipeline in Mase to quantize and prune the model after search is finished. However, the final compressed model may not be optimal, since different model architectures may have different sensitivities to quantization and pruning. Ideally, we want to run a compression-aware search flow, where the quantization and pruning is considered in each trial. + + a. In the objective function, after the model is constructed and trained for some iterations, call the CompressionPipeline to quantize and prune the model, then continue training for a few more epochs. Use the sampler that yielded the best results in Task 1 to run the compression-aware search. The objective function should return the final accuracy of the model after compression. Consider also the case where final training is performed after quantization/pruning. + + b. Plot a new figure that has the number of trials on the x axis, and the maximum achieved accuracy up to that point on the y axis. There should be three curves: 1. the best performance from Task 1 (without compression), compression-aware search without post-compression training, and compression-aware search with post-compression training. \ No newline at end of file diff --git a/Working_Data/tutorial_1.mz b/Working_Data/tutorial_1.mz new file mode 100644 index 000000000..4b2545e95 Binary files /dev/null and b/Working_Data/tutorial_1.mz differ diff --git a/Working_Data/tutorial_1.pt b/Working_Data/tutorial_1.pt new file mode 100644 index 000000000..8c023df24 Binary files /dev/null and b/Working_Data/tutorial_1.pt differ diff --git a/Working_Data/tutorial_2_lora.mz b/Working_Data/tutorial_2_lora.mz new file mode 100644 index 000000000..f227e5034 Binary files /dev/null and b/Working_Data/tutorial_2_lora.mz differ diff --git a/Working_Data/tutorial_2_lora.pt b/Working_Data/tutorial_2_lora.pt new file mode 100644 index 000000000..a6aaf58b7 Binary files /dev/null and b/Working_Data/tutorial_2_lora.pt differ diff --git a/Working_Data/tutorial_2_sft.mz b/Working_Data/tutorial_2_sft.mz new file mode 100644 index 000000000..59bc7253c Binary files /dev/null and b/Working_Data/tutorial_2_sft.mz differ diff --git a/Working_Data/tutorial_2_sft.pt b/Working_Data/tutorial_2_sft.pt new file mode 100644 index 000000000..7004137a5 Binary files /dev/null and b/Working_Data/tutorial_2_sft.pt differ diff --git a/Working_Data/tutorial_3_ptq.mz b/Working_Data/tutorial_3_ptq.mz new file mode 100644 index 000000000..dcf9efd4f Binary files /dev/null and b/Working_Data/tutorial_3_ptq.mz differ diff --git a/Working_Data/tutorial_3_ptq.pt b/Working_Data/tutorial_3_ptq.pt new file mode 100644 index 000000000..a5f73391e Binary files /dev/null and b/Working_Data/tutorial_3_ptq.pt differ diff --git a/Working_Data/tutorial_3_qat.mz b/Working_Data/tutorial_3_qat.mz new file mode 100644 index 000000000..dcf9efd4f Binary files /dev/null and b/Working_Data/tutorial_3_qat.mz differ diff --git a/Working_Data/tutorial_3_qat.pt b/Working_Data/tutorial_3_qat.pt new file mode 100644 index 000000000..4fb2d77de Binary files /dev/null and b/Working_Data/tutorial_3_qat.pt differ diff --git a/Working_Data/tutorial_answers.md b/Working_Data/tutorial_answers.md new file mode 100644 index 000000000..7c9e4b257 --- /dev/null +++ b/Working_Data/tutorial_answers.md @@ -0,0 +1,185 @@ +## Tutorial 1 (Lab 0): Introduction + +### Run-Time Error +Here is the run-time error encountered during the session: + +```plaintext +RuntimeError: Tried to erase Node bert_embeddings_dropout but it still had 6 users in the graph: +{getattr_2: None, size_4: None, bert_encoder_layer_0_attention_self_query: None, +bert_encoder_layer_0_attention_self_key: None, bert_encoder_layer_0_attention_self_value: None, +add_7: None}! +``` + +## Tutorial 2 (Lab 0): Lora Finetune + +### Removing attention_mask, labels from hf_input_names and its effect on the graphs: +The graph was created with and without the extra information and compared. + +Firstly, having no labels meant that at the end of the process, there is no cross-entropy calculated and viewed (so 4 blocks are removed). This is becuase the ground truth labels are required for loss calculations and not having them therefore means no losses can be calculated. Secondly, when there is no attention_mask specified, the model calls an extra block called getattr_1 after the input, instead of having a seperate input attention_mask block. When no mask is specified, more information from the model is used as an input to the masking process, implying that the mask is created based on the input information, whilst the external mask would be used for manually choosing which information to mask or not. + + +## Tutorial 3 (Lab 1): QAT +The following combinations of widths were tested: (4,2) (8,4) (12,8) (16,8) (20,10) (24,12) (28,14) (32,16). + +![Fixed point width vs highest achieved accuracy](fixed_point_width_vs_accuracy.png) +![PTQ and QAT comparison vs highest achieved accuracy](ptq_vs_qat_accuracy.png) + +It is clear that quantisation-aware training is much more effective in improving accuracy than just quantising after training, as it allows the model to adapt to the lower precision during training, reducing the loss that would otherwise be encoutered when quantisation is applied at the end of the processs. +The model that offered a strong balance between maximising accuracy and amount of quantisation was with fixed width 16 (16,8) + +## Tutorial 4 (Lab 1): Pruning +A range of sparisty levels were then applied, ranging from 0.1 to 0.9. Each sparsity level was tested with both the random and the l1-norm method. For each combination, 5 epochs of training were ran to allow the accuracies reached to closely match their potential if many more epochs of training were done. + +![Sparsity vs highest achieved accuracy](highest_accuracy_by_sparsity.png) +![Random vs L1-Norm comparison](pruning_accuracy_by_sparsity.png) + +L1-norm performed better than random in all cases and allowed for more drastic pruning. + +## Tutorial 5 (Lab 2): Nas Optuna +Each sampler was given the same search space, and 10 iterations to find the optimal setup. Each search space setup combination was trained for 3 epochs to allow the accuracies reached to stablise and represent the effectiveness of the search-space input more truly. +![Random vs Grid vs TPE search method comparison](combined_optuna_results.png) + +TPE found the highest accuracy combination the fastest and reached the highest accuracy, therefore was the best search method. +TPE was then used in part b, and compression-aware search ran and tested. + +![Effects of compression and post-compression fine-tuning](compression_aware_results.png) + +No compression eventually performed the best, mainly due to the compression being quite severe, but the compression aware training method reach similar accuracy levels to the non-compressed model with a much smaller model size. + +## Tutorial 6 (Lab 3): Mixed Precision Search +![Number of trails vs maximum achieved accuracy](optuna_search_results.png) + +TPE Sampler was used to search for the optimal configuration, which was found on the 3rd iteration. +The search was then extended to contain the following quantised configuartions for each layer: + +- torch.nn.Linear (no quantisation) +- LinearInteger, +- LinearMinifloatDenorm +- LinearMinifloatIEEE +- LinearLog +- LinearBlockFP +- LinearBlockLog +- LinearBinary +- LinearBinaryScaling + +(I wasn't able to use LinearBlockMinifloat without errors and LinearBinaryResidualSign had not been implemented yet so these were ommited.) I initially used the Optuna sampler (TPE in this case) to search for the optimal layer types which yielded the following results. + +![Number of trails vs maximum achieved accuracy](extended_optuna_search_results_2_curves.png) + +Iteration 2 (the highest accuracy iteration) used LinearMinifloatIEEE. After realising seperate curves for each precision were wanted, the code was rewritten to do 5 iterations of 3 epochs for each layer type. Here are these results. + +![Maximum achieved accuracy after 5 iterations for each precision layer type](optuna_combined_precision_progress.png) +The LinearLog and LinearBlockLog were both found to be completely inneffective (maybe implemented incorrectly), with LinearBinary and LinearBinaryScaling found not much higher in accuracy achieved. + +To see the trends in the rest of the results, here is a zoomed in view of these, showing that eventually LinearInteger performed best and even surpassed the full precision version. +![Best performing precision layer types](optuna_combined_precision_progress_zoomed.png) + +## Lab 4: ADLS Software Stream +### Part 1 +The pre-compiled model showed a longer processing time of 24.5413s compared to the JIT-compiled model with 15.3287s. This could be down to a few reasons that were tested by modifying the code. + +- Variations in time could be introduced by different backend compilation methods, which are optimised for different purposes. The compilers already available to test were inductor, cudagraphs and eager, with the default compiler being inductor offering a trade-off between efficiency and memory overhead. +- The timing method seemed to change its results quite a lot depending on if the script run was the first time or had been previously done already. This indicates some degree of warming-up was required for the precompiled method, potentially due to memory bottlenecks on the first runthrough. A likely cause of this is that the first run-through includes tracing and graph transformations that later ones do not. +- In addition, the lower number of iterations chosen in the code (n=5) meant that the results were more susceptible to initial memory overhead changes than in consequent iterations, reliability of the timing tests could be improved with more iterations. + +To test how much the timings were affected by number of itertaions, CPU/GPU, compiler methods, the following combinations were ran (the Colab environment was reset between every 5 or 20 runs to reset the environment, accomodating for potential warm-up effects). Cudagraphs was ommited from CPU runs since it requires GPU. + +5 runs on "cpu": +- Original model: 15.3287 s +- Optimised model: 24.5413 s +- Optimised model (inductor): 10.8946 s +- Optimised model (eager): 14.0508 s + +20 runs on "cpu": +- Original model: 15.0116 s +- Optimised model: 14.1991 s +- Optimised model (inductor): 11.3416 s +- Optimised model (eager): 13.3609 s + +5 runs on "cuda": +- Original model: 0.2519 s +- Optimised model: 5.0008 s +- Optimised model (inductor): 0.1103 s +- Optimised model (eager): 0.2530 s +- Optimised model (cudagraphs): 0.9695 s + +20 runs on "cuda": +- Original model: 0.1099 s +- Optimised model: 0.4991 s +- Optimised model (inductor): 0.1129 s +- Optimised model (eager): 0.1394 s +- Optimised model (cudagraphs): 0.3148 s + +20 runs but ignoring first 10 on "cuda": +- Original model: 0.0950 s +- Optimised model: 0.1106 s +- Optimised model (inductor): 0.1106 s +- Optimised model (eager): 0.0952 s +- Optimised model (cudagraphs): 0.1577 s + +100 runs but ignoring first 10 on "cuda": +- Original model: 0.0950 s +- Optimised model: 0.1120 s +- Optimised model (inductor): 0.1128 s +- Optimised model (eager): 0.0968 s +- Optimised model (cudagraphs): 0.0990 s + +The main takeaways from this is that: +- significant initial overhead in all cases due to tracing and graph transformations +- low number of iterations (n=5) skew results due to this, therefore more needed to stabilise. +- chaning the device from cpu to cuda allowed the GPU to be used much more effectively, showing the biggest improvement in performance. +- overall, the pre-compiled method did not show improvements indicating that the optimisation under the hood was well purposed for the already optimised ResNet18. Potentially for more custom models, this would not be the case. + +### Part 2 +For 5 iterations: +- Naive SDPA time (cpu): 0.033738 s +- Fused SDPA time (cpu): 0.020238 s +- Naive SDPA time (cuda): 0.000379 s +- Fused SDPA time (cuda): 0.000047 s + +For 20 iterations: +- Naive SDPA time (cpu): 0.026931 s +- Fused SDPA time (cpu): 0.020990 s +- Naive SDPA time (cuda): 0.000140 s +- Fused SDPA time (cuda): 0.000022 s + +For 100 iterations: +- Naive SDPA time (cpu): 0.025309 s +- Fused SDPA time (cpu): 0.020964 s +- Naive SDPA time (cuda): 0.000202 s +- Fused SDPA time (cuda): 0.000044 s + +The fused SDPA kernel outperformed the naive implementation in every case and device type, most significantly on CUDA. On CPU, the fused version showed around 20-25% increase in speed, whilst on GPU, the fused version was 500-10000% faster than the naive version. This shows that the kernel fusion on CPUs was still limited by CPU memory bandwidth and its lack of parallelism, whilst the GPU case was able to take advantage of fused kernels, thanks to the reduced memory overhead removing the memory bottleneck. + +### Part 3: +Answer 3a: MXINT8 benefits custom hardware if both activaton and weights are quantised in the same method for a few reasons: +- Consistent data types means that fusing mutliple operations, e.g. matrix multiplication and then an activation layer, can be done more easily without extra data type conversions that woudl otherwise require additional memory reads/writes. If all the data types are consistent, many layers can be fused into one highly optimised kernel which is beneficial for larger GPU based models. +- Using MXINT8 for both also means that there are no intermediate tensors being stored, reducing memory requirements. This is beneficial since memory bandwith is often a limiting factor in custom hardware methods. +- There are also dedicated MXINT8 hardware units that are optimised for certain data types, such as INT8 multiply-accumulate (MAC) units. Consistency then allows these hardwares to be better used and optimised. +- Lastly having more efficient processes means less power consumption overall, reducing costs, hardware weardown and maintencance, increases working life etc. + +Answer 3b: The rold of the dont_need_abs variable and bias_variable is to correctly scale the 'value' part of the number, so that when the exponent is then applied to it, it is scaled correctly to represent the desired number. This methodology is required since MXINT has no implicit leading bit for the mantissa. For example, if a 6-bit mantissa number (bit count starts from 0) is SXXX_XXXX, so the left-most X is the 6th bit, this 6th bit determines whether the mantissa is in the range of 0-63 or 64-127. Similar to scientific number format, if the value is smaller than 64, it also contains some scaling information which should be contained already by the exponent. Therefore the value has to be normalised to within the correct range, so that its job is just to represent the value, and then to be scaled by the exponent. If the scaling information (due to too small a value) was retained and then the exponent applied, the resulting number would be some 2^integer out in order of magnitude. To implement this, the 6th bit is checked if its 1 or 0. If its 1, nothing is changed (dont_need_abs = True), if it is 0, this means the values of mantissa need to shifted in position to the left until the 6th bit is 1. This is done by adding a 2^num to the value, depending on the number of position shifts required. The number of shifts required to scale correctly can be calculated by the equation: bias_variable = 2^(exp - 127) * C, where exp is the original MXINT8 exponent value for that group. + +Answer 3c: The cta_tiler partitions the data in a memory-efficient way. It allows a large input tensor or data structure to have smaller sub-tensors or components to be extracted and processed in seperate units (such as threads, warps and thread blocks) and then reformed into a new large tensor correctly, retaining the indexing structure. This allows for more efficient parrallelism and more efficient/higher accuracy memory/cache access. + +The cta_tiler dynamically adjusts the block sizes being sent for processing based on the incoming group size. The larger the group size, the larger BLK_M, but the smaller BLK_K is - changing the sub-tensor shape. This helps to maintain a consitent computational workload per tile since roughly the same amount of data is being sent to each thread in the Cooperative Thread Arrays (CTAs). This is how it allows memory requirements stay consistent and stable during processing. + +layout_sX partitions the threads in a threadblock by applying the make_layout command from CuTe. It takes in a shape (e.g. a matrix of 8 by 8) and then creates a layout object, which is essential for mapping from coordinate space(s) to an index space. This is what ensures that the larger tensor can be reconstructed correctly after tiling. The layout object, just like cta_tiler, dynamically changes the shape of the sub-tensor based on the input group size (since the shape input to make_layout is also BLK_M and BLK_K), mainting consistent amounts of data per each tile and therefore processing thread. + +Answer 3d: +Theres a few reasons why the saved GPU memory is not exactly (32 - (4+8/32))/32 = 86.7% The first is that the code actually uses MXINT8 not MXINT4, meaning the above equation is slightly incorrect. The correct theroetical savings should be (32 - (8 + 8/32))/32 = 74.2%, since the mantissa should be 8 bits not 4. Even with this change, the theoretical savings are not reached. This is due to the quantisation process only being applied to certain layer types. The following code block shows that only linear layers are quantised, and any others (such as activation layers) are left in full precision. + +```plaintext +for layer_name, layer in model.named_modules(): + if not isinstance(layer, torch.nn.Linear): + continue + if "classifier" in layer_name: + continue + layer.cuda() + layer_q = QLinearPacked.build_from_linear(layer, group_size=mxint8_group_size) + set_layer_by_name(model, layer_name, layer_q) + del layer + torch.cuda.empty_cache() +``` + +Therefore, even though the quantised weights take up far less space, the overall memory saving is reduced to about 66.4% instead of the upped theoretical bound 74.2% due to non-quantised layers also being used by the GPU. diff --git a/Working_Data/~$estion_5b_results.docx b/Working_Data/~$estion_5b_results.docx new file mode 100644 index 000000000..7c1de0368 Binary files /dev/null and b/Working_Data/~$estion_5b_results.docx differ diff --git a/docs/custom_scripts_matt/flexround_example.py b/docs/custom_scripts_matt/flexround_example.py new file mode 100644 index 000000000..b0a784f8b --- /dev/null +++ b/docs/custom_scripts_matt/flexround_example.py @@ -0,0 +1,217 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes +from chop.tools import get_logger +from chop.passes.module import report_trainable_parameters_analysis_pass +# Import the FlexRound utilities: +from chop.passes.graph.transforms.quantize.flexround import ( + FlexRoundQuantizer, + apply_flexround_transform +) +# Import the modular quantization pass – ensure this is the correct path +from chop.passes.graph.transforms.quantize.quantize import quantize_transform_pass + + +# Ensure that FlexRound keys are in the quantized module map +from chop.nn.quantized.modules import quantized_module_map +from chop.nn.quantized.modules.flexround_modules import LinearFlexRound, Conv2dFlexRound, Conv1dFlexRound + +quantized_module_map["linear_flexround"] = LinearFlexRound +quantized_module_map["conv2d_flexround"] = Conv2dFlexRound +quantized_module_map["conv1d_flexround"] = Conv1dFlexRound + +from chop.passes.module import report_trainable_parameters_analysis_pass + + + + + +# Set up logger +logger = get_logger(__name__) +logger.setLevel("INFO") + +def count_nonzero_parameters(model): + """Count the actual non-zero parameters in the model.""" + total_params = 0 + nonzero_params = 0 + for name, param in model.named_parameters(): + if 'weight' in name and 'parametrizations' not in name: + total_params += param.numel() + nonzero_params += (param != 0).sum().item() + return total_params, nonzero_params + +def print_parameter_count(model, description): + """Helper function to count and print parameters.""" + total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + total, nonzero = countnonzero = count_nonzero_parameters(model) + sparsity = 1.0 - (nonzero / total) if total > 0 else 0 + print(f"\n===== {description} =====") + print(f"Total trainable parameters: {total_params:,}") + print(f"Total weight parameters: {total:,}") + print(f"Non-zero weight parameters: {nonzero:,}") + print(f"Sparsity: {sparsity:.2%}") + return total_params, nonzero, sparsity + +def main(): + print("\n===== FlexRound Quantization Example =====") + + # Load a pretrained model + checkpoint = "facebook/wav2vec2-base-960h" + model = AutoModelForCTC.from_pretrained(checkpoint) + encoder = model.wav2vec2 + + # Create a MASE graph from the encoder + mg = MaseGraph(encoder, hf_input_names=["input_values", "attention_mask"]) + mg, _ = passes.init_metadata_analysis_pass(mg) + + # Define dummy input for analysis + dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), + } + mg, _ = passes.add_common_metadata_analysis_pass(mg, pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + }) + + + + + # Print initial parameter count + before_params, before_nonzero, _ = print_parameter_count(mg.model, "BEFORE QUANTIZATION") + + # ------------------------- + # Method 1: Direct Module Quantization using FlexRoundQuantizer + # ------------------------- + print("\n===== METHOD 1: DIRECT MODULE QUANTIZATION =====") + _, _ = report_trainable_parameters_analysis_pass(mg.model) + + found_module = False + for name, module in mg.model.named_modules(): + if isinstance(module, nn.Linear) and hasattr(module, 'weight'): + found_module = True + print(f"Found sample module: {name}") + + # Create a quantizer instance + quantizer = FlexRoundQuantizer(bit_width=8, frac_width=4) + + # Get weight statistics before quantization + weight_mean_before = module.weight.abs().mean().item() + unique_values_before = torch.unique(module.weight).numel() + print("Module weight stats before quantization:") + print(f" Mean abs value: {weight_mean_before:.6f}") + print(f" Number of unique values: {unique_values_before}") + + with torch.no_grad(): + # Save original weights + original_weight = module.weight.clone() + # Apply quantization directly + module.weight.data = quantizer(module.weight.data) + weight_mean_after = module.weight.abs().mean().item() + unique_values_after = torch.unique(module.weight).numel() + print("Module weight stats after quantization:") + print(f" Mean abs value: {weight_mean_after:.6f}") + print(f" Number of unique values: {unique_values_after}") + print(f" Reduction in unique values: {unique_values_before/unique_values_after:.2f}x") + # Restore original weights + module.weight.data = original_weight + break + if not found_module: + print("Could not find a Linear module with weights for direct quantization") + + # ------------------------- + # Method 2: Using the FlexRound API for the entire model + # ------------------------- + print("\n===== METHOD 2: USING FlexRound QUANTIZATION API =====") + flexround_config = { + "default": { + "weight_bit_width": 8, + "weight_frac_width": 4 + } + } + quantized_graph, _ = apply_flexround_transform(mg, flexround_config) + after_params, after_nonzero, _ = print_parameter_count(quantized_graph.model, "AFTER QUANTIZATION (Method 2)") + print("\n===== QUANTIZATION SUMMARY (Method 2) =====") + print(f"Parameters before quantization: {before_params:,}") + print(f"Non-zero params before: {before_nonzero:,}") + print(f"Non-zero params after: {after_nonzero:,}") + print(f"Change in effective parameters: {before_nonzero - after_nonzero:,}") + quantized_modules = sum(1 for _, m in quantized_graph.model.named_modules() + if hasattr(m, '_forward_hooks') and len(m._forward_hooks) > 0) + print(f"Total quantized modules: {quantized_modules}") + + # ------------------------- + # Method 3: Using quantize_transform_pass for full-model quantization with FlexRound + # ------------------------- + print("\n===== METHOD 3: USING quantize_transform_pass =====") + + # from chop.passes.graph.transforms.quantize.quant_parsers.update_node_meta import quant_arith_to_list_fn + # quant_arith_to_list_fn["flexround"] = [ + # "weight_bit_width", "weight_frac_width", + # "act_bit_width", "act_frac_width", + # "bias_bit_width", "bias_frac_width", + # ] + + + quant_config = { + "by": "type", + "default": {"config": {"name": None}}, # leave unlisted ops unchanged + "linear": { + "config": { + "name": "flexround", + "weight_width": 8, + "weight_frac_width": 4, + "data_in_width": 8, + "data_in_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "weight_only": False, + } + }, + "conv2d": { + "config": { + "name": "flexround", + "weight_width": 8, + "weight_frac_width": 4, + "data_in_width": 8, + "data_in_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "weight_only": False, + } + }, + "conv1d": { + "config": { + "name": "flexround", + "weight_width": 8, + "weight_frac_width": 4, + "data_in_width": 8, + "data_in_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "weight_only": False, + } + }, + # Add other op types if needed... + } + + + quantized_graph2, _ = quantize_transform_pass(mg, quant_config) + final_params, final_nonzero, _ = print_parameter_count(quantized_graph2.model, "AFTER quantize_transform_pass (Method 3)") + print("\n===== QUANTIZATION SUMMARY (Method 3) =====") + print(f"Parameters before quantization: {before_params:,}") + print(f"Non-zero params before: {before_nonzero:,}") + print(f"Non-zero params after: {final_nonzero:,}") + print(f"Change in effective parameters: {before_nonzero - final_nonzero:,}") + final_quantized_modules = sum(1 for _, m in quantized_graph2.model.named_modules() + if hasattr(m, '_forward_hooks') and len(m._forward_hooks) > 0) + print(f"Total quantized modules (Method 3): {final_quantized_modules}") + + return quantized_graph2.model + +if __name__ == "__main__": + main() diff --git a/docs/custom_scripts_matt/flexround_new_example.py b/docs/custom_scripts_matt/flexround_new_example.py new file mode 100644 index 000000000..06a3792c0 --- /dev/null +++ b/docs/custom_scripts_matt/flexround_new_example.py @@ -0,0 +1,278 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import AutoModelForCTC, Wav2Vec2Processor +from datasets import load_dataset, Dataset, DatasetDict +from pathlib import Path +from pyctcdecode import build_ctcdecoder + +# CHOP imports +from chop import MaseGraph +import chop.passes as passes +from chop.tools import get_logger, get_trainer +from chop.passes.module import report_trainable_parameters_analysis_pass +from chop.passes.graph.transforms.quantize.flexround import FlexRoundQuantizer, apply_flexround_transform +from chop.passes.graph.transforms.quantize.quantize import quantize_transform_pass + +# Ensure FlexRound keys are in the quantized module map +from chop.nn.quantized.modules import quantized_module_map +from chop.nn.quantized.modules.flexround_modules import LinearFlexRound, Conv2dFlexRound, Conv1dFlexRound +quantized_module_map["linear_flexround"] = LinearFlexRound +quantized_module_map["conv2d_flexround"] = Conv2dFlexRound +quantized_module_map["conv1d_flexround"] = Conv1dFlexRound + + +from chop.passes.graph.analysis.quantization.cal_flexround import calibrate_flexround + + + + + +logger = get_logger(__name__) +logger.setLevel("INFO") + + +def count_nonzero_parameters(model): + """Count the actual non-zero parameters in the model.""" + total_params = 0 + nonzero_params = 0 + for name, param in model.named_parameters(): + if 'weight' in name and 'parametrizations' not in name: + total_params += param.numel() + nonzero_params += (param != 0).sum().item() + return total_params, nonzero_params + + +def print_parameter_count(model, description): + """Helper function to count and print parameters.""" + total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + total, nonzero = count_nonzero_parameters(model) + sparsity = 1.0 - (nonzero / total) if total > 0 else 0 + print(f"\n===== {description} =====") + print(f"Total trainable parameters: {total_params:,}") + print(f"Total weight parameters: {total:,}") + print(f"Non-zero weight parameters: {nonzero:,}") + print(f"Sparsity: {sparsity:.2%}") + return total_params, nonzero, sparsity + + +# Define a preprocessing function for the nyal dataset. +def preprocess_function(example): + # example["audio"] is expected to be a dict with keys "array" and "sampling_rate" + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + # Create an attention mask of ones (same shape as input_values) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + # Process the transcript text to get labels + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + + +# ------------------------------- +# Main script +# ------------------------------- +def main(): + print("\n===== FlexRound Quantization Example =====") + + # Set checkpoint and dataset name + checkpoint = "facebook/wav2vec2-base-960h" + dataset_name = "nyalpatel/condensed_librispeech_asr" # nyal dataset identifier + + # Load the processor and model + global processor + processor = Wav2Vec2Processor.from_pretrained(checkpoint) + model = AutoModelForCTC.from_pretrained(checkpoint) + + # Split the model: use only the encoder for quantization; keep CTC head unquantized. + encoder = model.wav2vec2 # static FX-friendly encoder + ctc_head = model.lm_head # dynamic CTC head (left in full precision) + + # Build a small evaluation sample from the nyal dataset. + # Here we use the "validation.clean" split. + sample_list = list(load_dataset(dataset_name, split="validation.clean").take(50)) + small_dataset = Dataset.from_list(sample_list) + small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] + ) + tokenized_dataset = DatasetDict({"train": small_dataset, "test": small_dataset}) + + # Build a vocabulary and decoder for CTC (for decoding during inference) + vocab = processor.tokenizer.convert_ids_to_tokens(range(processor.tokenizer.vocab_size)) + decoder = build_ctcdecoder(vocab) + + # ------------------------------- + # 1. Build MASE graph from the encoder only. + # ------------------------------- + mg = MaseGraph(encoder, hf_input_names=["input_values", "attention_mask"]) + mg, _ = passes.init_metadata_analysis_pass(mg) + dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), + } + mg, _ = passes.add_common_metadata_analysis_pass(mg, pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + }) + + before_params, before_nonzero, _ = print_parameter_count(mg.model, "Encoder BEFORE QUANTIZATION") + + # ------------------------------- + # 2. Apply FlexRound quantization using quantize_transform_pass on the encoder. + # ------------------------------- + flexround_config = { + "by": "type", + "default": {"config": {"name": None}}, # leave unlisted ops unchanged + "linear": { + "config": { + "name": "flexround", + "weight_width": 8, + "weight_frac_width": 4, + "data_in_width": 8, + "data_in_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "weight_only": False, + } + }, + "conv2d": { + "config": { + "name": "flexround", + "weight_width": 8, + "weight_frac_width": 4, + "data_in_width": 8, + "data_in_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "weight_only": False, + } + }, + "conv1d": { + "config": { + "name": "flexround", + "weight_width": 8, + "weight_frac_width": 4, + "data_in_width": 8, + "data_in_frac_width": 4, + "bias_width": 8, + "bias_frac_width": 4, + "weight_only": False, + } + }, + } + + mg_quant, _ = quantize_transform_pass(mg, flexround_config) + after_params, after_nonzero, _ = print_parameter_count(mg_quant.model, "Encoder AFTER QUANTIZATION") + + + calibrate_flexround(mg_quant.model, calibration_loader) #need to make + + # After applying quantization: + for n, p in mg_quant.model.named_parameters(): + if "weight" in n and "parametrizations" not in n: + p.requires_grad = False # Freeze original weights + + # Ensure only quantization params are trainable: + trainable_params = [ + p for p in mg_quant.model.parameters() + if p.requires_grad and ("log_s1" in n or "log_S2" in n or "log_s3" in n) + ] + optimizer = torch.optim.Adam(trainable_params, lr=1e-4) + + + # ------------------------------- + # 3. Combine the quantized encoder with the original CTC head. + # ------------------------------- + class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + """ + Combines a quantized encoder with an unquantized CTC head. + Args: + encoder: Quantized encoder (result from mg_quant.model) + ctc_head: Original full‑precision CTC head + blank_id: Token ID for the blank symbol (typically 0) + beam_width: Beam width for decoding (if using a decoder) + decoder: (Optional) Beam search decoder (e.g., from pyctcdecode) + """ + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) + output = {"logits": logits, "labels": labels} + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + loss = F.ctc_loss( + log_probs, + labels, + input_lengths, + target_lengths, + blank=self.blank_id, + reduction="mean", + zero_infinity=True, + ) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + + combined_model = CombinedWav2Vec2CTC(encoder=mg_quant.model, ctc_head=ctc_head, decoder=decoder, beam_width=10) + print("\n===== Combined Model Summary =====") + print(combined_model) + + # ------------------------------- + # 4. Set up trainer using the nyal (Condensed LibriSpeech ASR) dataset. + # ------------------------------- + # Use the preprocessed small_dataset from the "validation.clean" split. + tokenized_dataset = DatasetDict({"train": small_dataset, "test": small_dataset}) + + # Create a data collator for CTC padding. + from chop.models import DataCollatorCTCWithPadding + data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) + + # Set up trainer with WER as the evaluation metric. + trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=processor.tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, + ) + + print("\n===== Starting Training =====") + trainer.train() + + print("\n===== Evaluating Combined Model =====") + eval_results = trainer.evaluate() + print("Quantisation Pass Evaluation:") + print(f"Evaluation WER: {eval_results.get('eval_wer', 'N/A')}") + print(f"Evaluation Loss: {eval_results.get('eval_loss', 'N/A')}") + print(f"Evaluation Runtime: {eval_results.get('eval_runtime', 'N/A')}") + print(f"Evaluation Samples/s: {eval_results.get('eval_samples_per_second', 'N/A')}") + print(f"Evaluation Steps/s: {eval_results.get('eval_steps_per_second', 'N/A')}") + + return combined_model + +if __name__ == "__main__": + combined_model = main() diff --git a/docs/custom_scripts_nyal/final_pipeline.py b/docs/custom_scripts_nyal/final_pipeline.py new file mode 100644 index 000000000..b58e81884 --- /dev/null +++ b/docs/custom_scripts_nyal/final_pipeline.py @@ -0,0 +1,1051 @@ +import torch +import torch.nn as nn +import pandas as pd +import matplotlib.pyplot as plt +import optuna +from optuna.samplers import TPESampler +import dill +from copy import deepcopy +from pathlib import Path +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes +from chop.tools import get_trainer, get_tokenized_dataset +from chop.tools.utils import deepsetattr +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC +from pyctcdecode import build_ctcdecoder +from chop.passes.graph import ( + summarize_quantization_analysis_pass, + add_common_metadata_analysis_pass, + init_metadata_analysis_pass, + runtime_analysis_pass, + onnx_runtime_interface_pass, + quantize_transform_pass, + prune_transform_pass, + bit_width_analysis_pass, +) +from chop.dataset.nlp.speech_recognition import CondensedLibrispeechASRDataset +from chop.dataset import MaseDataModule +from chop.nn.quantized.modules.linear import ( + LinearInteger, + LinearMinifloatDenorm, + LinearMinifloatIEEE, + LinearLog, + LinearBlockFP, +) + +# Set up logging +import logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# ------------------------------- +# 1. Model Importing and Dataset Setup +# ------------------------------- + +def import_model_and_dataset(): + """Import model, tokenizer, and dataset""" + logger.info("Importing model and dataset...") + + checkpoint = "facebook/wav2vec2-base-960h" + tokenizer_checkpoint = "facebook/wav2vec2-base-960h" + dataset_name = "nyalpatel/condensed_librispeech_asr" + + # Get tokenized dataset, tokenizer, and processor + tokenized_dataset, tokenizer, processor = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=tokenizer_checkpoint, + return_tokenizer=True, + return_processor=True, + ) + + data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) + vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) + decoder = build_ctcdecoder(vocab) + + # Load model + model = AutoModelForCTC.from_pretrained(checkpoint) + model.config.gradient_checkpointing = True + encoder = model.wav2vec2 # static, FX-friendly + ctc_head = model.lm_head # dynamic CTC head, separate this + + # Setup data module + dataset_path = Path("./preprocessed_data") + condensed_dataset = CondensedLibrispeechASRDataset(dataset_path=dataset_path, split="train") + condensed_dataset.prepare_data() + condensed_dataset.setup() + + data_module = MaseDataModule( + name="nyalpatel/condensed_librispeech_asr", + batch_size=1, + model_name=checkpoint, + num_workers=0, + ) + data_module.prepare_data() + data_module.setup() + + logger.info("Model and dataset imported successfully") + + return { + "encoder": encoder, + "ctc_head": ctc_head, + "tokenized_dataset": tokenized_dataset, + "tokenizer": tokenizer, + "processor": processor, + "data_collator": data_collator, + "vocab": vocab, + "decoder": decoder, + "data_module": data_module, + "checkpoint": checkpoint, + "dataset_name": dataset_name, + } + +# ------------------------------- +# 2. Initial MASE Graph Setup +# ------------------------------- + +class ONNXWrapper(nn.Module): + def __init__(self, encoder): + super().__init__() + self.encoder = encoder + + def forward(self, inputs): + if isinstance(inputs, dict): + input_values = inputs["input_values"] + attention_mask = inputs["attention_mask"] + else: + input_values = inputs + attention_mask = torch.ones_like(inputs, dtype=torch.long) + return self.encoder(input_values, attention_mask=attention_mask) + + @property + def graph(self): + # Expose the underlying FX graph for later passes + return self.encoder.graph + +def setup_mase_graph(encoder): + """Create and initialize MASE graph with metadata""" + logger.info("Setting up MASE graph...") + + # Create MASE graph + mg = MaseGraph( + encoder, + hf_input_names=["input_values", "attention_mask"], + ) + + # Initialize metadata + mg, _ = passes.init_metadata_analysis_pass(mg) + + # Create dummy input for analysis + dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), + } + + # Add common metadata + mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } + ) + + logger.info("MASE graph setup complete") + + return mg, dummy_in + +# ------------------------------- +# 3. Create Combined Model +# ------------------------------- + +def create_combined_model(mg, ctc_head, decoder): + """Create a combined model with encoder, CTC head, and decoder""" + logger.info("Creating combined model...") + + combined_model = CombinedWav2Vec2CTC( + encoder=mg.model, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 + ) + + logger.info("Combined model created successfully") + + return combined_model + +# ------------------------------- +# 4. Run Baseline Metrics +# ------------------------------- + +def run_baseline_metrics(mg, data_module, checkpoint, dataset_name, decoder, tokenizer): + """Run baseline metrics to establish reference performance""" + logger.info("Running baseline metrics...") + + # Wrap the model for ONNX compatibility + mg.model = ONNXWrapper(mg.model) + + # Configure SmoothQuant with alpha=0 (no smoothing) + smoothquant_config = { + "smoothquant": True, + "alpha": 0.0, + "model": checkpoint, + "task": "ctc", + "dataset": dataset_name, + "accelerator": "cuda", + "data_module": data_module, + "batch_size": 1, + } + + # Configure runtime analysis + runtime_analysis_config = { + "num_batches": 100, + "num_GPU_warmup_batches": 5, + "test": True, + "data_module": data_module, + "model": checkpoint, + "accelerator": "cuda", + "task": "ctc", + "decoder": decoder, + "beam_width": 10, + "tokenizer": tokenizer, + "batch_size": 2, + "sample_rate": 16000, + } + + # Run ONNX interface pass and runtime analysis + mg, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) + _, baseline_results = runtime_analysis_pass(mg, pass_args=runtime_analysis_config) + + # Run bit width analysis for baseline + _, bitwidth_results = bit_width_analysis_pass(mg) + baseline_results.update({"avg_bitwidth": bitwidth_results.get("average_bitwidth", 32)}) + + logger.info("Baseline metrics complete") + logger.info("Baseline Results:") + for k, v in baseline_results.items(): + logger.info(f" {k}: {v}") + + return baseline_results, mg + +# ------------------------------- +# 5. Define Search Space +# ------------------------------- + +def define_search_space(): + """Define the search space for optimization""" + logger.info("Defining search space...") + + # Quantization methods + quantization_methods = [ + ("full_precision", nn.Linear), + ("integer", LinearInteger), + ("minifloat_denorm", LinearMinifloatDenorm), + ("minifloat_ieee", LinearMinifloatIEEE), + ("log", LinearLog), + ("block_fp", LinearBlockFP), + ] + + # Pruning methods + pruning_methods = [ + "none", + "hwpq", + "movement", + "random", + ] + + # Width and fractional width choices + width_choices = [8, 16, 32] + frac_width_choices = [4, 8, 16] + + # SmoothQuant alpha choices + alpha_choices = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] + + # Sparsity choices + sparsity_choices = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9] + + # Structured sparsity choices + structured_sparsity_choices = [True, False] + + search_space = { + "quantization_methods": quantization_methods, + "pruning_methods": pruning_methods, + "width_choices": width_choices, + "frac_width_choices": frac_width_choices, + "alpha_choices": alpha_choices, + "sparsity_choices": sparsity_choices, + "structured_sparsity_choices": structured_sparsity_choices, + } + + logger.info("Search space defined") + + return search_space + +# ------------------------------- +# 6. Pruning and Sparsity Metrics +# ------------------------------- + +def calculate_pruning_metrics(model): + """Calculate pruning metrics (sparsity, parameter counts)""" + total_params = 0 + nonzero_params = 0 + pruned_params = 0 + + # Count parameters using parametrizations (for masked models) + for name, module in model.named_modules(): + if hasattr(module, 'parametrizations') and hasattr(module.parametrizations, 'weight'): + for p in module.parametrizations.weight: + if hasattr(p, 'mask'): + total_in_layer = module.weight.numel() + nonzero_in_layer = p.mask.sum().item() + pruned_in_layer = total_in_layer - nonzero_in_layer + + pruned_params += pruned_in_layer + total_params += total_in_layer + + # If no pruning parametrizations found, count zeros in weights + if total_params == 0: + for name, param in model.named_parameters(): + if 'weight' in name and 'parametrizations' not in name: + total_params += param.numel() + nonzero_params += (param != 0).sum().item() + + pruned_params = total_params - nonzero_params + else: + nonzero_params = total_params - pruned_params + + # Calculate overall sparsity + overall_sparsity = pruned_params / total_params if total_params > 0 else 0 + + return { + "total_weight_params": total_params, + "nonzero_weight_params": nonzero_params, + "pruned_weight_params": pruned_params, + "overall_sparsity": overall_sparsity + } + +# ------------------------------- +# 7. Model Transformation Functions +# ------------------------------- + +def apply_pruning(model, trial, pruning_method): + """Apply pruning to the model based on trial parameters""" + if pruning_method == "none": + return model + + # Make a copy of the model + pruned_model = deepcopy(model) + + # Select sparsity level + sparsity = trial.suggest_categorical("pruning_sparsity", [0.0, 0.1, 0.3, 0.5, 0.7, 0.9]) + + # Use unstructured sparsity by default, but allow structured for hwpq + structured_sparsity = False + if pruning_method == "hwpq": + structured_sparsity = trial.suggest_categorical("structured_sparsity", [True, False]) + + # Create pruning config + pruning_config = { + "weight": { + "sparsity": sparsity, + "method": pruning_method, + "scope": "local", + "structured_sparsity": structured_sparsity + }, + "activation": { + "sparsity": 0.0, + "method": "random", + "scope": "local", + }, + } + + # Create temporary MaseGraph for this model instance + temp_mg = MaseGraph( + pruned_model, + hf_input_names=["input_values", "attention_mask"], + ) + + # Initialize metadata + temp_mg, _ = passes.init_metadata_analysis_pass(temp_mg) + + # Create dummy input + dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), + } + + # Add common metadata + temp_mg, _ = passes.add_common_metadata_analysis_pass( + temp_mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } + ) + + # Apply pruning transform pass + temp_mg, _ = passes.prune_transform_pass(temp_mg, pass_args=pruning_config) + + return temp_mg.model + +def apply_quantization(model, trial, quant_method, quant_class): + """Apply quantization to the model based on trial parameters""" + if quant_method == "full_precision": + return model + + # Make a copy of the model + quant_model = deepcopy(model) + + # Apply quantization to each linear layer + for name, layer in quant_model.named_modules(): + if isinstance(layer, nn.Linear): + kwargs = {"in_features": layer.in_features, "out_features": layer.out_features} + + if quant_method == "integer": + config = { + "weight_width": trial.suggest_categorical(f"{name[:10]}_weight_width", [8, 16, 32]), + "weight_frac_width": trial.suggest_categorical(f"{name[:10]}_weight_frac_width", [4, 8, 16]), + "data_in_width": trial.suggest_categorical(f"{name[:10]}_data_in_width", [8, 16, 32]), + "data_in_frac_width": trial.suggest_categorical(f"{name[:10]}_data_in_frac_width", [4, 8, 16]), + "bias_width": trial.suggest_categorical(f"{name[:10]}_bias_width", [8, 16, 32]), + "bias_frac_width": trial.suggest_categorical(f"{name[:10]}_bias_frac_width", [4, 8, 16]), + "floor": False, + } + elif quant_method in ["minifloat_denorm", "minifloat_ieee"]: + config = { + "weight_width": trial.suggest_categorical(f"{name[:10]}_weight_width", [8, 16, 32]), + "weight_exponent_width": 5, + "weight_exponent_bias": 15, + "data_in_width": trial.suggest_categorical(f"{name[:10]}_data_in_width", [8, 16, 32]), + "data_in_exponent_width": 5, + "data_in_exponent_bias": 15, + "bias_width": trial.suggest_categorical(f"{name[:10]}_bias_width", [8, 16, 32]), + "bias_exponent_width": 5, + "bias_exponent_bias": 15, + } + else: + # Default config for other quantization methods + config = { + "weight_width": trial.suggest_categorical(f"{name[:10]}_weight_width", [8, 16, 32]), + "data_in_width": trial.suggest_categorical(f"{name[:10]}_data_in_width", [8, 16, 32]), + } + + # Create new quantized layer + new_layer = quant_class(**kwargs, config=config) + new_layer.weight.data = layer.weight.data.clone() + if layer.bias is not None: + new_layer.bias.data = layer.bias.data.clone() + + # Replace the original layer with the quantized one + deepsetattr(quant_model, name, new_layer) + + return quant_model + +def construct_optimized_model(trial, mg_model, ctc_head, decoder, baseline_model_data): + """ + Construct a model with both quantization and pruning applied + based on trial parameters + """ + logger.info("Constructing optimized model for trial...") + + # Select quantization method + quant_methods = baseline_model_data["search_space"]["quantization_methods"] + quant_method_idx = trial.suggest_categorical("quantization_method_idx", list(range(len(quant_methods)))) + quant_method_name, quant_class = quant_methods[quant_method_idx] + + # Select pruning method + pruning_methods = baseline_model_data["search_space"]["pruning_methods"] + pruning_method = trial.suggest_categorical("pruning_method", pruning_methods) + + # Make a copy of the base encoder + encoder_copy = deepcopy(mg_model) + + # Apply pruning first (if enabled) + if pruning_method != "none": + encoder_copy = apply_pruning(encoder_copy, trial, pruning_method) + + # Apply quantization (if not full precision) + if quant_method_name != "full_precision": + encoder_copy = apply_quantization(encoder_copy, trial, quant_method_name, quant_class) + + # Create the combined model + optimized_model = CombinedWav2Vec2CTC( + encoder=encoder_copy, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 + ) + + # Calculate and store pruning metrics if pruning was applied + if pruning_method != "none": + pruning_metrics = calculate_pruning_metrics(optimized_model.encoder) + for k, v in pruning_metrics.items(): + trial.set_user_attr(k, v) + + trial.set_user_attr("quantization_method", quant_method_name) + trial.set_user_attr("pruning_method", pruning_method) + + logger.info(f"Optimized model constructed with quantization={quant_method_name}, pruning={pruning_method}") + + return optimized_model + +# ------------------------------- +# 8. Objective Function +# ------------------------------- + +def objective(trial, baseline_model_data): + """ + Objective function for optimization that: + 1) Creates optimized model (quantized + pruned) + 2) Trains the optimized model + 3) Performs runtime analysis + 4) Calculates composite score based on metrics + """ + # Unpack baseline data + mg = baseline_model_data["mg"] + ctc_head = baseline_model_data["ctc_head"] + decoder = baseline_model_data["decoder"] + tokenized_dataset = baseline_model_data["tokenized_dataset"] + tokenizer = baseline_model_data["tokenizer"] + data_collator = baseline_model_data["data_collator"] + data_module = baseline_model_data["data_module"] + checkpoint = baseline_model_data["checkpoint"] + dataset_name = baseline_model_data["dataset_name"] + baseline_metrics = baseline_model_data["baseline_metrics"] + + # 1. Construct optimized model + optimized_model = construct_optimized_model( + trial, + mg.model, + ctc_head, + decoder, + baseline_model_data + ) + + # 2. Train the optimized model + logger.info("Training optimized model...") + trainer = get_trainer( + model=optimized_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + data_collator=data_collator, + num_train_epochs=1, # Reduced for faster trials + gradient_accumulation_steps=4, + per_device_train_batch_size=2, + per_device_eval_batch_size=2, + ) + trainer.train() + + # Evaluate model + eval_results = trainer.evaluate() + wer = eval_results.get("eval_wer", None) + loss = eval_results.get("eval_loss", None) + trial.set_user_attr("eval_wer_pt", wer) + trial.set_user_attr("eval_loss_pt", loss) + + # 3. Prepare for runtime analysis + logger.info("Running performance analysis...") + + # Select alpha parameter for SmoothQuant + alpha_val = trial.suggest_categorical("smoothquant_alpha", baseline_model_data["search_space"]["alpha_choices"]) + + # Set up model for ONNX + mg.model = trainer.model.encoder + mg.model = ONNXWrapper(mg.model) + + # SmoothQuant configuration + smoothquant_config = { + "smoothquant": True, + "alpha": alpha_val, + "model": checkpoint, + "task": "ctc", + "dataset": dataset_name, + "accelerator": "cuda", + "data_module": data_module, + "batch_size": 1, + } + + # Runtime analysis configuration + runtime_analysis_config = { + "num_batches": 100, + "num_GPU_warmup_batches": 5, + "test": True, + "data_module": data_module, + "model": checkpoint, + "accelerator": "cuda", + "task": "ctc", + "decoder": decoder, + "beam_width": 10, + "tokenizer": tokenizer, + "batch_size": 2, + "sample_rate": 16000, + } + + # Run ONNX interface pass + mg, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) + + # Run runtime analysis + _, runtime_results = runtime_analysis_pass(mg, pass_args=runtime_analysis_config) + trial.set_user_attr("runtime_metrics", runtime_results) + + # Run bit width analysis + _, bitwidth_results = bit_width_analysis_pass(mg) + avg_bitwidth = bitwidth_results.get("average_bitwidth", 32) + trial.set_user_attr("avg_bitwidth", avg_bitwidth) + + # 4. Calculate metrics and composite score + relevant_keys = [ + "Average WER", + "Average Latency", + "Average RTF", + "Average GPU Power Usage", + "Inference Energy Consumption", + ] + + # Store individual metrics + for k in relevant_keys: + val = runtime_results.get(k, float("inf")) + trial.set_user_attr(f"runtime_{k.lower().replace(' ', '_')}", val) + + # Calculate percentage changes from baseline + pct_changes = {} + for k in relevant_keys: + trial_val = runtime_results.get(k, None) + base_val = baseline_metrics.get(k, None) + if trial_val is not None and base_val is not None and base_val != 0: + pct_change = (trial_val - base_val) / base_val * 100.0 + pct_changes[k] = pct_change + trial.set_user_attr(f"pct_change_{k.lower().replace(' ', '_')}", pct_change) + + # Calculate bit width reduction + base_bitwidth = baseline_metrics.get("avg_bitwidth", 32) + bitwidth_reduction = (base_bitwidth - avg_bitwidth) / base_bitwidth + trial.set_user_attr("bitwidth_reduction", bitwidth_reduction) + + # Calculate sparsity if pruning was applied + sparsity = trial.user_attrs.get("overall_sparsity", 0.0) + + # Calculate composite score based on WER, latency, energy, bit width, and sparsity + # Lower WER, latency, energy, bit width are better + # Higher sparsity is better + + # Get the percentage changes (negative means improvement) + wer_change = pct_changes.get("Average WER", 0.0) + latency_change = pct_changes.get("Average Latency", 0.0) + energy_change = pct_changes.get("Inference Energy Consumption", 0.0) + + # Weight the different metrics (these can be tuned) + wer_weight = 0.4 # Accuracy is important + latency_weight = 0.2 + energy_weight = 0.1 + bitwidth_weight = 0.2 + sparsity_weight = 0.1 + + # Combine into composite score (negative is better as it means reduction) + composite_score = ( + wer_weight * wer_change + + latency_weight * latency_change + + energy_weight * energy_change - + bitwidth_weight * (bitwidth_reduction * 100) - # Convert to percentage + sparsity_weight * (sparsity * 100) # Convert to percentage + ) + + # Invert for Optuna (which maximizes) + composite_metric = -composite_score + + trial.set_user_attr("composite_score", composite_score) + trial.set_user_attr("composite_metric", composite_metric) + + logger.info(f"Trial complete with composite score: {composite_score}") + + return composite_metric + +# ------------------------------- +# 9. Optimization Study +# ------------------------------- + +def run_optimization_study(baseline_model_data, n_trials=30): + """Run the Optuna optimization study""" + logger.info(f"Starting optimization study with {n_trials} trials...") + + # Create study with TPE sampler + sampler = TPESampler() + study = optuna.create_study( + direction="maximize", + study_name="wav2vec2_optimization", + sampler=sampler + ) + + # Run optimization + study.optimize( + lambda trial: objective(trial, baseline_model_data), + n_trials=n_trials, + timeout=60 * 60 * 12 # 12 hour timeout + ) + + # Get best trial + best_trial = study.best_trial + logger.info("\nBest trial:") + logger.info(f" Trial Number: {best_trial.number}") + logger.info(f" Composite Metric: {best_trial.value}") + logger.info(f" Quantization Method: {best_trial.user_attrs.get('quantization_method', 'N/A')}") + logger.info(f" Pruning Method: {best_trial.user_attrs.get('pruning_method', 'N/A')}") + + if "overall_sparsity" in best_trial.user_attrs: + logger.info(f" Overall Sparsity: {best_trial.user_attrs['overall_sparsity']:.2%}") + + logger.info(f" Average Bit Width: {best_trial.user_attrs.get('avg_bitwidth', 'N/A')}") + logger.info(f" WER: {best_trial.user_attrs.get('runtime_average_wer', 'N/A')}") + logger.info(f" Latency: {best_trial.user_attrs.get('runtime_average_latency', 'N/A')}") + + # Save best trial model + save_best_model(best_trial, baseline_model_data) + + # Process and analyze study results + results_df = process_study_results(study) + + # Create visualizations + create_visualizations(study, results_df, baseline_model_data["baseline_metrics"]) + + return study, results_df + +# ------------------------------- +# 10. Results Processing and Visualization +# ------------------------------- +def process_study_results(study): + """Process study results into a DataFrame""" + logger.info("Processing study results...") + + results = [] + for t in sorted(study.trials, key=lambda t: t.number): + # Basic trial info + row = { + "trial_number": t.number, + "composite_metric": t.value, + "composite_score": t.user_attrs.get("composite_score", None), + } + + # Trial parameters + row.update({ + "quantization_method": t.user_attrs.get("quantization_method", "N/A"), + "pruning_method": t.user_attrs.get("pruning_method", "N/A"), + "smoothquant_alpha": t.params.get("smoothquant_alpha", None), + }) + + # If pruning was used, add pruning parameters + if t.user_attrs.get("pruning_method", "none") != "none": + row.update({ + "pruning_sparsity": t.params.get("pruning_sparsity", None), + "structured_sparsity": t.params.get("structured_sparsity", None), + "overall_sparsity": t.user_attrs.get("overall_sparsity", None), + }) + + # Performance metrics + runtime_metrics = ["average_wer", "average_latency", "average_rtf", + "average_gpu_power_usage", "inference_energy_consumption"] + + for metric in runtime_metrics: + row[f"runtime_{metric}"] = t.user_attrs.get(f"runtime_{metric}", None) + row[f"pct_change_{metric}"] = t.user_attrs.get(f"pct_change_{metric}", None) + + # Bit width metrics + row["avg_bitwidth"] = t.user_attrs.get("avg_bitwidth", None) + row["bitwidth_reduction"] = t.user_attrs.get("bitwidth_reduction", None) + + # Add pytorch evaluation metrics + row["eval_wer_pt"] = t.user_attrs.get("eval_wer_pt", None) + row["eval_loss_pt"] = t.user_attrs.get("eval_loss_pt", None) + + results.append(row) + + # Create DataFrame + df = pd.DataFrame(results) + + # Save results to CSV + csv_name = "optuna_study_results.csv" + df.to_csv(csv_name, index=False) + logger.info(f"Study results saved to {csv_name}") + + return df + +def save_best_model(best_trial, baseline_model_data): + """Save the best model from the study""" + logger.info("Saving best model...") + + # Reconstruct the best model + best_model = construct_optimized_model( + best_trial, + baseline_model_data["mg"].model, + baseline_model_data["ctc_head"], + baseline_model_data["decoder"], + baseline_model_data + ) + + # Save model using dill + model_filename = "best_optimized_model.pkl" + with open(model_filename, "wb") as f: + dill.dump(best_model, f) + + logger.info(f"Best model saved to {model_filename}") + + return best_model + +def create_visualizations(study, results_df, baseline_metrics): + """Create visualizations for the optimization study""" + logger.info("Creating visualizations...") + + # 1. Optimization history plot + plt.figure(figsize=(12, 6)) + plt.title("Optimization History") + plt.plot(results_df["trial_number"], results_df["composite_metric"], "o-") + plt.xlabel("Trial Number") + plt.ylabel("Composite Metric (higher is better)") + plt.grid(True) + plt.savefig("optimization_history.png") + plt.close() + + # 2. Parallel coordinate plot for hyperparameters + try: + optuna.visualization.plot_parallel_coordinate( + study, params=["quantization_method_idx", "pruning_method", "smoothquant_alpha"] + ) + plt.savefig("parallel_coordinate.png") + plt.close() + except Exception as e: + logger.warning(f"Could not create parallel coordinate plot: {e}") + + # 3. Parameter importance + try: + optuna.visualization.plot_param_importances(study) + plt.savefig("param_importances.png") + plt.close() + except Exception as e: + logger.warning(f"Could not create parameter importance plot: {e}") + + # 4. WER vs Latency scatter plot + if "runtime_average_wer" in results_df.columns and "runtime_average_latency" in results_df.columns: + plt.figure(figsize=(10, 8)) + + # Color points by quantization method + quant_methods = results_df["quantization_method"].unique() + cm = plt.cm.get_cmap("tab10", len(quant_methods)) + + for i, method in enumerate(quant_methods): + method_df = results_df[results_df["quantization_method"] == method] + plt.scatter( + method_df["runtime_average_latency"], + method_df["runtime_average_wer"], + label=method, + alpha=0.7, + c=[cm(i)], + s=50 + ) + + # Add baseline + if "Average WER" in baseline_metrics and "Average Latency" in baseline_metrics: + plt.scatter( + baseline_metrics["Average Latency"], + baseline_metrics["Average WER"], + marker="*", + s=200, + c="red", + label="Baseline" + ) + + plt.xlabel("Latency (ms)") + plt.ylabel("WER (%)") + plt.title("WER vs Latency Trade-off") + plt.legend(title="Quantization Method") + plt.grid(True) + plt.savefig("wer_vs_latency.png") + plt.close() + + # 5. Sparsity vs performance metrics + if "overall_sparsity" in results_df.columns: + sparsity_df = results_df[results_df["overall_sparsity"].notnull()] + + if not sparsity_df.empty: + fig, axs = plt.subplots(1, 3, figsize=(15, 5)) + + # WER vs Sparsity + if "runtime_average_wer" in sparsity_df.columns: + axs[0].scatter(sparsity_df["overall_sparsity"], sparsity_df["runtime_average_wer"]) + axs[0].set_xlabel("Sparsity") + axs[0].set_ylabel("WER (%)") + axs[0].set_title("WER vs Sparsity") + axs[0].grid(True) + + # Latency vs Sparsity + if "runtime_average_latency" in sparsity_df.columns: + axs[1].scatter(sparsity_df["overall_sparsity"], sparsity_df["runtime_average_latency"]) + axs[1].set_xlabel("Sparsity") + axs[1].set_ylabel("Latency (ms)") + axs[1].set_title("Latency vs Sparsity") + axs[1].grid(True) + + # Energy vs Sparsity + if "runtime_inference_energy_consumption" in sparsity_df.columns: + axs[2].scatter(sparsity_df["overall_sparsity"], sparsity_df["runtime_inference_energy_consumption"]) + axs[2].set_xlabel("Sparsity") + axs[2].set_ylabel("Energy Consumption") + axs[2].set_title("Energy vs Sparsity") + axs[2].grid(True) + + plt.tight_layout() + plt.savefig("sparsity_performance.png") + plt.close() + + # 6. Bit width vs performance metrics + if "avg_bitwidth" in results_df.columns: + bitwidth_df = results_df[results_df["avg_bitwidth"].notnull()] + + if not bitwidth_df.empty: + fig, axs = plt.subplots(1, 3, figsize=(15, 5)) + + # WER vs Bit Width + if "runtime_average_wer" in bitwidth_df.columns: + axs[0].scatter(bitwidth_df["avg_bitwidth"], bitwidth_df["runtime_average_wer"]) + axs[0].set_xlabel("Average Bit Width") + axs[0].set_ylabel("WER (%)") + axs[0].set_title("WER vs Bit Width") + axs[0].grid(True) + + # Latency vs Bit Width + if "runtime_average_latency" in bitwidth_df.columns: + axs[1].scatter(bitwidth_df["avg_bitwidth"], bitwidth_df["runtime_average_latency"]) + axs[1].set_xlabel("Average Bit Width") + axs[1].set_ylabel("Latency (ms)") + axs[1].set_title("Latency vs Bit Width") + axs[1].grid(True) + + # Energy vs Bit Width + if "runtime_inference_energy_consumption" in bitwidth_df.columns: + axs[2].scatter(bitwidth_df["avg_bitwidth"], bitwidth_df["runtime_inference_energy_consumption"]) + axs[2].set_xlabel("Average Bit Width") + axs[2].set_ylabel("Energy Consumption") + axs[2].set_title("Energy vs Bit Width") + axs[2].grid(True) + + plt.tight_layout() + plt.savefig("bitwidth_performance.png") + plt.close() + + # 7. Method comparison box plots + # Create plots comparing different quantization methods + plt.figure(figsize=(12, 8)) + + # Compare WER across methods + plt.subplot(2, 2, 1) + if "quantization_method" in results_df.columns and "runtime_average_wer" in results_df.columns: + results_df.boxplot(column="runtime_average_wer", by="quantization_method", ax=plt.gca()) + plt.ylabel("WER (%)") + plt.title("WER by Quantization Method") + plt.suptitle("") # Remove pandas-generated suptitle + plt.xticks(rotation=45) + + # Compare Latency across methods + plt.subplot(2, 2, 2) + if "quantization_method" in results_df.columns and "runtime_average_latency" in results_df.columns: + results_df.boxplot(column="runtime_average_latency", by="quantization_method", ax=plt.gca()) + plt.ylabel("Latency (ms)") + plt.title("Latency by Quantization Method") + plt.suptitle("") + plt.xticks(rotation=45) + + # Compare Energy across methods + plt.subplot(2, 2, 3) + if "quantization_method" in results_df.columns and "runtime_inference_energy_consumption" in results_df.columns: + results_df.boxplot(column="runtime_inference_energy_consumption", by="quantization_method", ax=plt.gca()) + plt.ylabel("Energy Consumption") + plt.title("Energy by Quantization Method") + plt.suptitle("") + plt.xticks(rotation=45) + + # Compare bit width across methods + plt.subplot(2, 2, 4) + if "quantization_method" in results_df.columns and "avg_bitwidth" in results_df.columns: + results_df.boxplot(column="avg_bitwidth", by="quantization_method", ax=plt.gca()) + plt.ylabel("Average Bit Width") + plt.title("Bit Width by Quantization Method") + plt.suptitle("") + plt.xticks(rotation=45) + + plt.tight_layout() + plt.savefig("method_comparison.png") + plt.close() + + logger.info("Visualizations created") + +# ------------------------------- +# 11. Main Function +# ------------------------------- + +def main(): + """Main function to run the optimization pipeline""" + logger.info("Starting optimization pipeline") + + # 1. Import model and dataset + model_data = import_model_and_dataset() + + # 2. Setup MASE graph + mg, dummy_in = setup_mase_graph(model_data["encoder"]) + model_data["mg"] = mg + + # 3. Create combined model + combined_model = create_combined_model(mg, model_data["ctc_head"], model_data["decoder"]) + + # 4. Run baseline metrics + baseline_metrics, updated_mg = run_baseline_metrics( + mg, + model_data["data_module"], + model_data["checkpoint"], + model_data["dataset_name"], + model_data["decoder"], + model_data["tokenizer"] + ) + model_data["mg"] = updated_mg + + # 5. Define search space + search_space = define_search_space() + + # Prepare baseline_model_data dictionary for optimization + baseline_model_data = { + **model_data, + "baseline_metrics": baseline_metrics, + "search_space": search_space, + } + + # 6. Run optimization study + study, results_df = run_optimization_study(baseline_model_data, n_trials=30) + + # 7. Save final results + logger.info("Optimization pipeline complete") + logger.info(f"Best trial: {study.best_trial.number}") + logger.info(f"Best composite metric: {study.best_trial.value}") + + # Create a summary of the best configuration + best_config = { + "quantization_method": study.best_trial.user_attrs.get("quantization_method", "N/A"), + "pruning_method": study.best_trial.user_attrs.get("pruning_method", "N/A"), + "smoothquant_alpha": study.best_trial.params.get("smoothquant_alpha", None), + "overall_sparsity": study.best_trial.user_attrs.get("overall_sparsity", None), + "avg_bitwidth": study.best_trial.user_attrs.get("avg_bitwidth", None), + "runtime_average_wer": study.best_trial.user_attrs.get("runtime_average_wer", None), + "runtime_average_latency": study.best_trial.user_attrs.get("runtime_average_latency", None), + "runtime_inference_energy_consumption": study.best_trial.user_attrs.get("runtime_inference_energy_consumption", None), + } + + # Save best configuration to JSON + import json + with open("best_configuration.json", "w") as f: + json.dump(best_config, f, indent=2) + + logger.info("Best configuration saved to best_configuration.json") + + return study, results_df, baseline_model_data + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/docs/custom_scripts_nyal/get_tokenized_dataset_test.py b/docs/custom_scripts_nyal/get_tokenized_dataset_test.py new file mode 100644 index 000000000..e48e72dbc --- /dev/null +++ b/docs/custom_scripts_nyal/get_tokenized_dataset_test.py @@ -0,0 +1,10 @@ +from chop.tools import get_tokenized_dataset +checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" + + +dataset, tokenizer = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=checkpoint, + return_tokenizer=True, +) \ No newline at end of file diff --git a/docs/custom_scripts_nyal/hwpq_example.py b/docs/custom_scripts_nyal/hwpq_example.py new file mode 100644 index 000000000..c0a06d847 --- /dev/null +++ b/docs/custom_scripts_nyal/hwpq_example.py @@ -0,0 +1,127 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes +from chop.tools import get_logger +from chop.passes.module import report_trainable_parameters_analysis_pass + +# Set up logger +logger = get_logger(__name__) +logger.setLevel("INFO") + +def count_nonzero_parameters(model): + """Count the actual non-zero parameters in the model""" + total_params = 0 + nonzero_params = 0 + + for name, param in model.named_parameters(): + if 'weight' in name and 'parametrizations' not in name: + # Count total parameters + total_params += param.numel() + + # Count non-zero parameters + nonzero_params += (param != 0).sum().item() + + return total_params, nonzero_params + +def print_parameter_count(model, description): + """Helper function to count and print parameters""" + total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + # Also count non-zero parameters + total, nonzero = count_nonzero_parameters(model) + sparsity = 1.0 - (nonzero / total) if total > 0 else 0 + + print(f"\n===== {description} =====") + print(f"Total trainable parameters: {total_params:,}") + print(f"Total weight parameters: {total:,}") + print(f"Non-zero weight parameters: {nonzero:,}") + print(f"Sparsity: {sparsity:.2%}") + + return total_params, nonzero, sparsity + +def main(): + print("\n===== HWPQ Pruning Example =====") + + # Load a pretrained model + checkpoint = "facebook/wav2vec2-base-960h" + model = AutoModelForCTC.from_pretrained(checkpoint) + encoder = model.wav2vec2 + + # Create a MASE graph + mg = MaseGraph(encoder, hf_input_names=["input_values", "attention_mask"]) + mg, _ = passes.init_metadata_analysis_pass(mg) + + # Define dummy input for analysis pass + dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), + } + mg, _ = passes.add_common_metadata_analysis_pass(mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + }) + + # Print initial parameter count + before_params, before_nonzero, _ = print_parameter_count(mg.model, "BEFORE PRUNING") + + # Configure HWPQ pruning + hwpq_config = { + "weight": { + "sparsity": 0.1, # 50% sparsity + "method": "hwpq", # Use our HWPQ method + "scope": "local", # Apply locally per layer + "structured_sparsity": False # Use 2:4 structured sparsity + }, + "activation": { + "sparsity": 0.0, # No activation pruning for HWPQ + "method": "random", + "scope": "local", + }, + } + + print("\n===== APPLYING HWPQ PRUNING =====") + mg, _ = passes.prune_transform_pass(mg, pass_args=hwpq_config) + + # Count pruned parameters by examining the parametrizations + pruned_params = 0 + total_weight_params = 0 + + for name, module in mg.model.named_modules(): + if hasattr(module, 'parametrizations') and hasattr(module.parametrizations, 'weight'): + for p in module.parametrizations.weight: + if hasattr(p, 'mask'): + # This is our pruning parametrization + weight_shape = module.weight.shape + total_in_layer = module.weight.numel() + nonzero_in_layer = p.mask.sum().item() + pruned_in_layer = total_in_layer - nonzero_in_layer + + print(f"Layer {name}: pruned {pruned_in_layer}/{total_in_layer} params ({pruned_in_layer/total_in_layer:.2%})") + + pruned_params += pruned_in_layer + total_weight_params += total_in_layer + + if total_weight_params > 0: + overall_sparsity = pruned_params / total_weight_params + print(f"\nOverall from HWPQ masks: {pruned_params}/{total_weight_params} params pruned ({overall_sparsity:.2%} sparsity)") + + # Print parameter stats after pruning + after_params, after_nonzero, after_sparsity = print_parameter_count(mg.model, "AFTER PRUNING") + + # Calculate and print change in parameters + print(f"\n===== PRUNING SUMMARY =====") + print(f"Parameters before pruning: {before_params:,}") + print(f"Non-zero params before: {before_nonzero:,}") + print(f"Non-zero params after: {after_nonzero:,}") + print(f"Reduction in parameters: {before_nonzero - after_nonzero:,}") + print(f"Overall sparsity achieved: {after_sparsity:.2%}") + + return mg.model + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/docs/custom_scripts_nyal/test_dataset.py b/docs/custom_scripts_nyal/test_dataset.py new file mode 100644 index 000000000..d122aff0a --- /dev/null +++ b/docs/custom_scripts_nyal/test_dataset.py @@ -0,0 +1,215 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore +from datasets import DatasetDict, Dataset, load_dataset +from chop.models import DataCollatorCTCWithPadding +from pyctcdecode import build_ctcdecoder +from chop.passes.graph import ( + summarize_quantization_analysis_pass, + add_common_metadata_analysis_pass, + init_metadata_analysis_pass, + onnx_runtime_interface_pass, + quantize_transform_pass, +) +from pathlib import Path + +# ----------------------------------------------------------------------------- +# 1. Define the model and dataset +# ----------------------------------------------------------------------------- +checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained(checkpoint) +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +tokenizer = processor.tokenizer + +# Build vocabulary and decoder for CTC decoding +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +# Use the CondensedLibrispeechASRDataset class to preprocess and load the dataset +from chop.dataset.nlp.speech_recognition import CondensedLibrispeechASRDataset + +dataset_path = Path("./preprocessed_data") +# NOTE: Choose a valid split. Here we use "train.clean.100" for pre-processing. +condensed_dataset = CondensedLibrispeechASRDataset(dataset_path=dataset_path, split="train") +condensed_dataset.prepare_data() +condensed_dataset.setup() + +# Create a data module instance for calibration and ONNX conversion +from chop.dataset import MaseDataModule + +data_module = MaseDataModule( + name=dataset_name, + batch_size=1, + model_name=checkpoint, + num_workers=0, +) +data_module.prepare_data() +data_module.setup() + +# Define a preprocessing function for sample data +def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +# Load and preprocess a small sample dataset using a valid split (e.g., "validation.clean") +sample_list = list(load_dataset(dataset_name, split="validation.clean").take(50)) +small_dataset = Dataset.from_list(sample_list) +small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] +) + +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +model = AutoModelForCTC.from_pretrained(checkpoint) +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head + +# ----------------------------------------------------------------------------- +# 2. Define the MASE graph and run metadata passes +# ----------------------------------------------------------------------------- +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], +) + +mg, _ = init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +# ----------------------------------------------------------------------------- +# 3. ONNX Export with SmoothQuant Enabled +# ----------------------------------------------------------------------------- +# Define ONNX configuration with SmoothQuant enabled. +smoothquant_config = { + "smoothquant": True, # Enable SmoothQuant in the ONNX pipeline + "alpha": 0.75, # Smoothing parameter + "model": checkpoint, # Model identifier + "task": "ctc", # Task name + "dataset": dataset_name, # Dataset name + "accelerator": "cuda", # Device for export + "data_module": data_module, # Data module for calibration + "batch_size": 1, # Batch size for calibration +} + +# Import the ONNX Runtime Interface Pass from the interface submodule +from chop.passes.interface.onnxrt import onnx_runtime_interface_pass + +# Run the ONNX export pass; this converts the PyTorch model (mg.model) into an ONNX model +# and applies SmoothQuant optimizations during pre-processing. +mg, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) + +# onnx_meta is a dictionary containing keys like 'onnx_path' (the path to the ONNX model) + +# ----------------------------------------------------------------------------- +# 4. Further Quantization (Optional) +# ----------------------------------------------------------------------------- +# If additional PTQ is desired, run the standard quantization pass. +quantization_config = { + "by": "type", + "default": {"config": {"name": None}}, + "linear": { + "config": { + "name": "integer", + "data_in_width": 16, + "data_in_frac_width": 8, + "weight_width": 16, + "weight_frac_width": 8, + "bias_width": 16, + "bias_frac_width": 8, + } + }, +} + +mg, _ = passes.quantize_transform_pass(mg, pass_args=quantization_config) + +# ----------------------------------------------------------------------------- +# 5. Combine the Optimized Encoder with the CTC Head and Train/Evaluate +# ----------------------------------------------------------------------------- +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) + output = {"logits": logits, "labels": labels} + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + loss = F.ctc_loss(log_probs, labels, input_lengths, target_lengths, blank=self.blank_id, reduction="mean", zero_infinity=True) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + +combined_model = CombinedWav2Vec2CTC(encoder=mg.model, ctc_head=ctc_head, decoder=decoder, beam_width=10) + +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, +) +trainer.train() + +eval_results = trainer.evaluate() + +print("Quantising Pass") +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") \ No newline at end of file diff --git a/docs/custom_scripts_tony/01_wave2vec_graph.py b/docs/custom_scripts_tony/01_wave2vec_graph.py new file mode 100644 index 000000000..a12133399 --- /dev/null +++ b/docs/custom_scripts_tony/01_wave2vec_graph.py @@ -0,0 +1,55 @@ +import torch +from transformers import Wav2Vec2Processor, Wav2Vec2Model +from chop.models import get_model, ModelFactory +from chop import MaseGraph +import chop.passes as passes +from datasets import load_dataset +import string + +def normalize_text(text): + return text.lower().translate(str.maketrans("", "", string.punctuation)).strip() + +# Set checkpoint - use the Wav2Vec2 checkpoint +checkpoint = "wav2vec2-base" + # Load processor and model +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base") +model = get_model(checkpoint, pretrained=True) + +dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = list(dataset.take(50)) +for sample in sample_list: + sample["text"] = normalize_text(sample["text"]) + +sample_audio = { + "array": sample_list[0]["audio"]["array"], + "sampling_rate": sample_list[0]["audio"]["sampling_rate"] +} + +# Process the audio input +input_values = processor( + audio=sample_audio["array"], # remove batch dimension and convert to numpy array #.squeeze(0).numpy(), + sampling_rate=sample_audio["sampling_rate"], + return_tensors="pt" +).input_values + +# Prepare dummy inputs matching Wav2Vec2's expected signature +dummy_inputs = { + "input_values": input_values, + "attention_mask": torch.ones_like(input_values[:, :1], dtype=torch.long), +} + +# Create MASE graph +mg = MaseGraph( + model, + # Use the keys from dummy_inputs + hf_input_names=list(dummy_inputs.keys()) +) + + +mg.draw("wave2vec_graph.svg") +# Initialize metadata analysis passes +mg, _ = passes.init_metadata_analysis_pass(mg) +#mg, _ = passes.add_common_metadata_analysis_pass(mg) + +# Print confirmation +print("MASE graph created successfully.") \ No newline at end of file diff --git a/docs/custom_scripts_tony/02_wave2vec_inference.py b/docs/custom_scripts_tony/02_wave2vec_inference.py new file mode 100644 index 000000000..dbc0dfc49 --- /dev/null +++ b/docs/custom_scripts_tony/02_wave2vec_inference.py @@ -0,0 +1,128 @@ +import torch +import torch.nn.functional as F +import jiwer +from transformers import Wav2Vec2Processor, AutoModelForCTC +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC + +from chop.models import get_model, ModelFactory +from chop import MaseGraph +import chop.passes as passes +from datasets import load_dataset +import string +from pyctcdecode import build_ctcdecoder + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base") +tokenizer = processor.tokenizer +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +model = AutoModelForCTC.from_pretrained(checkpoint) +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Import dataset +# ------------------------------- + +dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = list(dataset.take(50)) +for sample in sample_list: + sample["text"] = sample["text"].lower() + +sample_audio = { + "array": sample_list[0]["audio"]["array"], + "sampling_rate": sample_list[0]["audio"]["sampling_rate"] +} +input_values = processor( + audio=sample_audio["array"], + sampling_rate=sample_audio["sampling_rate"], + return_tensors="pt" +).input_values + +# ------------------------------- +# 3. Prepare dummy inputs +# ------------------------------- + +dummy_inputs = { + "input_values": input_values, +} + +mg = MaseGraph( + encoder, + hf_input_names=list(dummy_inputs.keys()) +) + +# mg.draw("wave2vec_inference_graph.svg") +mg, _ = passes.init_metadata_analysis_pass(mg) +print("MASE graph created successfully.") + +# ------------------------------------------------------------------- +# 4. Define a helper function for full inference (encoder + CTC head) +# ------------------------------------------------------------------- + +def full_inference_beam(encoder, ctc_head, input_values, beam_width=10): + """ + Run the encoder (tracked by MASE) and then apply the CTC head. + Instead of greedy decoding, use beam search (via pyctcdecode) to generate the transcription. + """ + with torch.no_grad(): + # Run the encoder to get hidden states. + encoder_outputs = encoder(input_values) + + # MASE-traced models return dicts, extract the tensor + if isinstance(encoder_outputs, dict): + hidden_states = encoder_outputs["last_hidden_state"] + else: + hidden_states = encoder_outputs.last_hidden_state # Original model case + + # Apply the CTC head to obtain logits. + logits = ctc_head(hidden_states) # shape: [batch, seq_len, vocab_size] + # Compute log probabilities for beam search. + log_probs = F.log_softmax(logits, dim=-1) + # Assume batch size 1; extract the sequence of log probabilities. + log_probs_np = log_probs.cpu().numpy()[0] # shape: [seq_len, vocab_size] + # Use the pyctcdecode beam search decoder. + transcription = decoder.decode(log_probs_np, beam_width=beam_width).lower() + return transcription + +# ------------------------------------------------------------------- +# 5. Evaluate the model on the sample subset using the full inference +# ------------------------------------------------------------------- + +def evaluate_model(encoder, ctc_head, processor, dataset): + total_wer = 0.0 + for idx, sample in enumerate(dataset): + audio_input = sample["audio"]["array"] + sampling_rate = sample["audio"]["sampling_rate"] + reference = sample["text"] + + inputs = processor( + audio=audio_input, + sampling_rate=sampling_rate, + return_tensors="pt" + ).input_values + + transcription = full_inference_beam(encoder, ctc_head, inputs) + wer = jiwer.wer(reference, transcription) + total_wer += wer + + print(f"Sample {idx}:") + print(" Reference :", reference) + print(" Transcription:", transcription) + print(" WER :", wer) + + avg_wer = total_wer / len(dataset) + print("\nAverage WER:", avg_wer) + return avg_wer + +print("\nEvaluating the original model on the sample subset:") +evaluate_model(mg.model, ctc_head, processor, sample_list) diff --git a/docs/custom_scripts_tony/03_OLD_wave2vec_analysis_pass.py b/docs/custom_scripts_tony/03_OLD_wave2vec_analysis_pass.py new file mode 100644 index 000000000..8f33ea606 --- /dev/null +++ b/docs/custom_scripts_tony/03_OLD_wave2vec_analysis_pass.py @@ -0,0 +1,439 @@ +import torch +import torch.fx as fx +import torch.nn.functional as F +import jiwer +from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC +from chop.models import get_model, ModelFactory, DataCollatorCTCWithPadding +from chop.tools import get_trainer +from chop import MaseGraph +import chop.passes as passes +from datasets import load_dataset +import string +from pyctcdecode import build_ctcdecoder +from datasets import Dataset, DatasetDict + +checkpoint = "DeepWokLab/bert-tiny" +tokenizer_checkpoint = "DeepWokLab/bert-tiny" +dataset_name = "imdb" + +dataset, tokenizer = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=tokenizer_checkpoint, + return_tokenizer=True, +) + + + + + + + +checkpoint = "wav2vec2-base-960h" +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") + +full_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") +encoder = full_model.wav2vec2 # The encoder component (tracked by MASE) +ctc_head = full_model.lm_head # The CTC head (kept untracked) + +vocab = processor.tokenizer.convert_ids_to_tokens(range(processor.tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = dataset.take(50) # ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'] +sample_list = list(sample_list.remove_columns(["speaker_id", "chapter_id", "id"])) +for sample in sample_list: + sample["text"] = sample["text"].lower() + +# print("sample", sample_list.column_names) + + +# Use the first sample for building dummy inputs +sample_audio = { + "array": sample_list[0]["audio"]["array"], + "sampling_rate": sample_list[0]["audio"]["sampling_rate"] +} +input_values = processor( + audio=sample_audio["array"], + sampling_rate=sample_audio["sampling_rate"], + return_tensors="pt" +).input_values + +dummy_inputs = { + "input_values": input_values, + "attention_mask": torch.ones_like(input_values[:, :1], dtype=torch.long), +} + +mg = MaseGraph( + encoder, + hf_input_names=list(dummy_inputs.keys()) +) + +mg, _ = passes.init_metadata_analysis_pass(mg) +# mg, _ = passses.add_ +mg.draw("wave2vec_analysis_pass_graph.svg") +print("MASE graph created successfully.") + +# --------------------------- +# Dropout Analysis and Removal Passes +# --------------------------- +from chop.tools import get_logger +logger = get_logger("mase_logger") +logger.setLevel("INFO") + +def count_dropout_analysis_pass(mg, pass_args={}): + dropout_modules = 0 + dropout_functions = 0 # In this example, we focus on modules. + + for node in mg.fx_graph.nodes: + if node.op == "call_module" and "dropout" in str(node.target).lower(): + logger.info(f"Found dropout module: {node.target}") + dropout_modules += 1 + else: + logger.debug(f"Skipping node: {node.target}") + return mg, {"dropout_count": dropout_modules + dropout_functions} + +def remove_dropout_transform_pass(mg, pass_args={}): + for node in list(mg.fx_graph.nodes): # Convert to list to safely modify graph + if node.op == "call_module" and "dropout" in str(node.target).lower(): + logger.info(f"Removing dropout module: {node.target}") + + # Replace all users of the dropout node with its input + parent_node = node.args[0] + logger.debug(f"Parent node: {parent_node}") + node.replace_all_uses_with(parent_node) + + # Erase the dropout node + mg.fx_graph.erase_node(node) + else: + logger.debug(f"Skipping node: {node.target}") + return mg, {} + +def count_feature_projection_analysis_pass(mg, pass_args={}): + feature_projection_modules = 0 + feature_projection_functions = 0 # In this example, we focus on modules. + + for node in mg.fx_graph.nodes: + if node.op == "call_module" and "feature_projection" in str(node.target).lower(): + logger.info(f"Found feature projection module: {node.target}") + feature_projection_modules += 1 + else: + logger.debug(f"Skipping node: {node.target}") + return mg, {"feature_projection_count": feature_projection_modules + feature_projection_functions} + +def remove_feature_projection_transform_pass(mg, pass_args={}): + for node in list(mg.fx_graph.nodes): # Convert to list to safely modify graph + if node.op == "call_module" and "feature_projection" in str(node.target).lower(): + logger.info(f"Removing feature projection module: {node.target}") + + # Replace all users of the feature projection node with its input + parent_node = node.args[0] + logger.debug(f"Parent node: {parent_node}") + node.replace_all_uses_with(parent_node) + + # Erase the feature projection node + mg.fx_graph.erase_node(node) + else: + logger.debug(f"Skipping node: {node.target}") + return mg, {} + + +# Run dropout analysis before removal +mg, analysis_out = count_dropout_analysis_pass(mg) +logger.info(f"Dropout count before removal: {analysis_out['dropout_count']}") + +# Optionally remove dropout layers (if desired for optimization) +mg, _ = remove_dropout_transform_pass(mg) +mg, analysis_out = count_dropout_analysis_pass(mg) +logger.info(f"Dropout count after removal: {analysis_out['dropout_count']}") + +# Optionally remove feature projection layers (if desired for optimization) +mg, _ = remove_feature_projection_transform_pass(mg) +mg, analysis_out = count_feature_projection_analysis_pass(mg) +logger.info(f"Feature projection count after removal: {analysis_out['feature_projection_count']}") + +model = mg.model +mg.draw("wave2vec_transformed_analysis_pass_graph.svg") + +# --------------------------- +# Evaluate the modified model +# --------------------------- +# data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) + + +def preprocess_function(example): + """ + Prepares audio samples for Wav2Vec2 by extracting input values, + creating attention masks, and tokenizing transcriptions. + """ + + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] # ✅ Ensure this is an integer + + # Convert audio into model-compatible inputs + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) # ✅ Ensures correct batch size + + # Tokenize text transcription + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), # ✅ Ensure correct shape + "attention_mask": attention_mask.squeeze(0), # ✅ Ensure correct attention mask + "labels": labels.squeeze(0) # ✅ Ensure correct label shape + } + + +# Convert sample list to Dataset +small_dataset = Dataset.from_list(sample_list) + +# Apply preprocessing +small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] +) + +# Convert to DatasetDict (as required by MASE) +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +print("Tokenized dataset:", tokenized_dataset.keys()) +print("Tokenized dataset :", tokenized_dataset["train"].keys()) +print("Tokenized dataset :", tokenized_dataset["test"].keys()) + +# ✅ Create Trainer +trainer = get_trainer( + model=mg.model, + tokenized_dataset=tokenized_dataset, + tokenizer=processor.tokenizer, + evaluate_metric="wer", +) + +# Evaluate Model +eval_results = trainer.evaluate() +print(f"Evaluation WER: {eval_results['wer']}") # ✅ Correct final print output + + + +# def preprocess_function(batch): +# audio = batch["audio"] +# inputs = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt", padding=True) +# batch["input_values"] = inputs.input_values[0] +# batch["attention_mask"] = inputs.attention_mask[0] +# batch["labels"] = processor.tokenizer(batch["text"]).input_ids +# return batch + +# dataset = dataset.map(preprocess_function, remove_columns=["audio", "text"]) +# data_collator = DataCollatorForCTC(processor=processor, padding=True) + +# training_args = TrainingArguments( +# output_dir="mase-trainer", +# evaluation_strategy="steps", +# per_device_train_batch_size=8, +# gradient_accumulation_steps=2, +# num_train_epochs=1, +# save_steps=500, +# eval_steps=500, +# logging_steps=100, +# learning_rate=1e-4, +# warmup_steps=500, +# save_total_limit=2, +# fp16=True, +# push_to_hub=False, +# ) + +# trainer = get_trainer( +# model=mg.model, +# tokenized_dataset=dataset["train"], +# tokenizer=processor.feature_extractor, +# evaluate_metric="wer", +# data_collator=data_collator, +# ) + +# eval_results = trainer.evaluate() +# print(f"Evaluation WER: {eval_results['wer']}") + +# def preprocess_function(example): +# """ +# Converts raw audio samples into model inputs (input_values) and labels (text tokens). +# """ +# audio_array = example["audio"]["array"] +# sampling_rate = example["audio"]["sampling_rate"] + +# # Convert audio into model-compatible inputs +# inputs = processor(audio=audio_array, sampling_rate=sampling_rate, return_tensors="pt") + +# # Tokenize text transcription +# with processor.as_target_processor(): +# labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + +# return { +# "input_values": inputs.input_values.squeeze(0), # ✅ Ensure correct shape +# "attention_mask": torch.ones(inputs.input_values.shape, dtype=torch.long), # ✅ Required for transformers +# "labels": labels.squeeze(0) # ✅ Ensure correct label shape +# } + + +# small_dataset = Dataset.from_list(sample_list).map(preprocess_function, remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"]) +# # small_dataset = small_dataset.shuffle(seed=42) + +# tokenized_dataset = DatasetDict({ +# "train": small_dataset, +# "test": small_dataset +# }) + +# training_args = TrainingArguments( +# output_dir="mase-trainer", +# use_mps_device=False, +# report_to="none", +# num_train_epochs=1, +# remove_unused_columns=False, +# ) + +# data_collator = DataCollatorWithPadding(tokenizer=processor, padding=True) + +# trainer = get_trainer( +# model=mg.model, +# tokenized_dataset=tokenized_dataset, +# tokenizer=processor, +# evaluate_metric="wer", +# data_collator=data_collator, +# ) + +# # Evaluate accuracy +# eval_results = trainer.evaluate() +# print(f"Evaluation WER: {eval_results}") + + + + + +# processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") + +# vocab = processor.tokenizer.convert_ids_to_tokens(range(processor.tokenizer.vocab_size)) +# decoder = build_ctcdecoder(vocab) + +# dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True) +# sample_list = list(dataset.take(50)) +# for sample in sample_list: +# sample["text"] = sample["text"].lower() + +# # Use the first sample for building dummy inputs +# sample_audio = { +# "array": sample_list[0]["audio"]["array"], +# "sampling_rate": sample_list[0]["audio"]["sampling_rate"] +# } +# input_values = processor( +# audio=sample_audio["array"], +# sampling_rate=sample_audio["sampling_rate"], +# return_tensors="pt" +# ).input_values + + + +# trainer = get_trainer( +# model=mg.model, +# tokenized_dataset=dataset, +# tokenizer=tokenizer, +# evaluate_metric="accuracy", +# ) + +# # Evaluate accuracy +# eval_results = trainer.evaluate() + + + + + + + + + + + + + + +# def full_inference_beam(model, ctc_head, processor, input_values, beam_width=10): +# """ +# Run the encoder (tracked by MASE) and then apply the CTC head. +# Instead of greedy decoding, use beam search (via pyctcdecode) to generate the transcription. +# """ +# with torch.no_grad(): +# attn_mask = torch.ones_like(input_values, dtype=torch.long) +# # Run the encoder to get hidden states. +# encoder_outputs = model(input_values, attention_mask=attn_mask) +# # hidden_states = encoder_outputs.last_hidden_state # shape: [batch, seq_len, hidden_dim] +# hidden_states = encoder_outputs["last_hidden_state"] + +# # Apply the CTC head to obtain logits. +# logits = ctc_head(hidden_states) # shape: [batch, seq_len, vocab_size] +# # Compute log probabilities for beam search. +# log_probs = F.log_softmax(logits, dim=-1) +# # Assume batch size 1; extract the sequence of log probabilities. +# log_probs_np = log_probs.cpu().numpy()[0] # shape: [seq_len, vocab_size] +# # Use the pyctcdecode beam search decoder. +# transcription = decoder.decode(log_probs_np, beam_width=beam_width).lower() +# return transcription + +# # --------------------------- +# # Evaluate the model on the sample subset using full inference +# # --------------------------- +# def evaluate_model(model, ctc_head, processor, dataset): +# total_wer = 0.0 +# for idx, sample in enumerate(dataset): +# audio_input = sample["audio"]["array"] +# sampling_rate = sample["audio"]["sampling_rate"] +# reference = sample["text"] + +# inputs = processor( +# audio=audio_input, +# sampling_rate=sampling_rate, +# return_tensors="pt" +# ).input_values + +# transcription = full_inference_beam(model, ctc_head, processor, inputs) +# wer = jiwer.wer(reference, transcription) +# total_wer += wer + +# print(f"Sample {idx}:") +# print(" Reference :", reference) +# print(" Transcription:", transcription) +# print(" WER :", wer) + +# avg_wer = total_wer / len(dataset) +# print("\nAverage WER:", avg_wer) +# return avg_wer + +# print("\nEvaluating the original model on the sample subset:") +# evaluate_model(model, ctc_head, processor, sample_list) + + + + + + + + + + +# --------------------------- +# Define full inference function (encoder + CTC head) +# --------------------------- + +# class DebugInterpreter(fx.Interpreter): +# def run_node(self, n): +# result = super().run_node(n) +# if isinstance(result, torch.Tensor): +# print(f"Node {n.name} output shape: {result.shape}") +# else: +# print(f"Node {n.name} output: {result}") +# return result + +# attn_mask = torch.ones_like(input_values, dtype=torch.long) +# print("Debugging intermediate outputs:") +# debug_interpreter = DebugInterpreter(model) +# # Pass inputs as positional arguments: +# debug_interpreter.run(input_values, attn_mask) \ No newline at end of file diff --git a/docs/custom_scripts_tony/04_OLD_wave2vec_train.py b/docs/custom_scripts_tony/04_OLD_wave2vec_train.py new file mode 100644 index 000000000..6923da58e --- /dev/null +++ b/docs/custom_scripts_tony/04_OLD_wave2vec_train.py @@ -0,0 +1,206 @@ +import torch +import torch.nn.functional as F +import jiwer +from transformers import Wav2Vec2Processor, Wav2Vec2Model, Wav2Vec2ForCTC +from chop.models import get_model, ModelFactory +from chop import MaseGraph +import chop.passes as passes +from datasets import load_dataset +import string +from pyctcdecode import build_ctcdecoder + + +full_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") +encoder = full_model.wav2vec2 +ctc_head = full_model.lm_head + +vocab = processor.tokenizer.convert_ids_to_tokens(range(processor.tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = list(dataset.take(50)) +for sample in sample_list: + sample["text"] = sample["text"].lower() + +# Use the first sample for building dummy inputs +sample_audio = { + "array": sample_list[0]["audio"]["array"], + "sampling_rate": sample_list[0]["audio"]["sampling_rate"] +} +input_values = processor( + audio=sample_audio["array"], + sampling_rate=sample_audio["sampling_rate"], + return_tensors="pt" +).input_values + +dummy_inputs = { + "input_values": input_values, + #"attention_mask": torch.ones_like(input_values[:, :1], dtype=torch.long), +} + +# Run original encoder before MASE modifications +encoder_outputs = encoder(input_values) +hidden_states_original = encoder_outputs["last_hidden_state"] +print("🔹 Original Encoder hidden states shape:", hidden_states_original.shape) +print("🔹 Original Encoder first hidden state values:", hidden_states_original[0, :5]) + +mg = MaseGraph( + encoder, + hf_input_names=list(dummy_inputs.keys()) +) +mg.draw("wave2vec_inference_graph.svg") +mg, _ = passes.init_metadata_analysis_pass(mg) +print("MASE graph created successfully.") + +# --------------------------- +# Dropout Analysis and Removal Passes +# --------------------------- +from chop.tools import get_logger +logger = get_logger("mase_logger") +logger.setLevel("INFO") + +def count_dropout_analysis_pass(mg, pass_args={}): + dropout_modules = 0 + dropout_functions = 0 # In this example, we focus on modules. + + for node in mg.fx_graph.nodes: + if node.op == "call_module" and "dropout" in str(node.target).lower(): + logger.info(f"Found dropout module: {node.target}") + dropout_modules += 1 + else: + logger.debug(f"Skipping node: {node.target}") + return mg, {"dropout_count": dropout_modules + dropout_functions} + +def remove_dropout_transform_pass(mg, pass_args={}): + for node in list(mg.fx_graph.nodes): # Convert to list to safely modify graph + if node.op == "call_module" and "dropout" in str(node.target).lower(): + logger.info(f"Removing dropout module: {node.target}") + + # Replace all users of the dropout node with its input + parent_node = node.args[0] + logger.debug(f"Parent node: {parent_node}") + node.replace_all_uses_with(parent_node) + + # Erase the dropout node + mg.fx_graph.erase_node(node) + else: + logger.debug(f"Skipping node: {node.target}") + return mg, {} + +def count_transformer_layer_analysis_pass(mg, pass_args={}): + transformer_layers = 0 + + for node in mg.fx_graph.nodes: + if node.op == "call_module" and "encoder.layers" in str(node.target).lower(): + logger.info(f"Found transformer encoder layer: {node.target}") + transformer_layers += 1 + else: + logger.debug(f"Skipping node: {node.target}") + + return mg, {"transformer_layer_count": transformer_layers} + + +def remove_transformer_layer_transform_pass(mg, pass_args={}): + for node in list(mg.fx_graph.nodes): # Convert to list to safely modify graph + if node.op == "call_module" and "encoder.layers" in str(node.target).lower(): + logger.info(f"Removing transformer encoder layer: {node.target}") + + # Replace all users of the transformer layer with its input + parent_node = node.args[0] + logger.debug(f"Parent node: {parent_node}") + node.replace_all_uses_with(parent_node) + + # Erase the transformer layer node + mg.fx_graph.erase_node(node) + else: + logger.debug(f"Skipping node: {node.target}") + + return mg, {} + +# Run dropout analysis before removal +mg, analysis_out = count_dropout_analysis_pass(mg) +logger.info(f"Dropout count before removal: {analysis_out['dropout_count']}") + +# Optionally remove dropout layers (if desired for optimization) +mg, _ = remove_dropout_transform_pass(mg) +mg, analysis_out = count_dropout_analysis_pass(mg) +logger.info(f"Dropout count after removal: {analysis_out['dropout_count']}") + +# Optionally remove feature projection layers (if desired for optimization) +mg, _ = remove_transformer_layer_transform_pass(mg) +mg, analysis_out = count_transformer_layer_analysis_pass(mg) +logger.info(f"Transformer layer count after removal: {analysis_out['transformer_layer_count']}") + +model = mg.model +mg.draw("wave2vec_transformed_analysis_pass_graph.svg") + +# Run MASE-modified model after removing feature projection +encoder_outputs_mase = model(input_values) +hidden_states_mase = encoder_outputs_mase["last_hidden_state"] +print("🔹 MASE Model hidden states shape:", hidden_states_mase.shape) +print("🔹 MASE Model first hidden state values:", hidden_states_mase[0, :5]) + + +# ------------------------------------------------------------------- +# 5. Define a helper function for full inference (encoder + CTC head) +# ------------------------------------------------------------------- +def full_inference_beam(model, ctc_head, processor, input_values, beam_width=10): + """ + Run the encoder (tracked by MASE) and then apply the CTC head. + Instead of greedy decoding, use beam search (via pyctcdecode) to generate the transcription. + """ + with torch.no_grad(): + # Run the encoder to get hidden states. + encoder_outputs = model(input_values) + + # MASE-traced models return dicts, extract the tensor + if isinstance(encoder_outputs, dict): + hidden_states = encoder_outputs["last_hidden_state"] + else: + hidden_states = encoder_outputs.last_hidden_state # Original model case + + # Apply the CTC head to obtain logits. + logits = ctc_head(hidden_states) # shape: [batch, seq_len, vocab_size] + # Compute log probabilities for beam search. + log_probs = F.log_softmax(logits, dim=-1) + # Assume batch size 1; extract the sequence of log probabilities. + log_probs_np = log_probs.cpu().numpy()[0] # shape: [seq_len, vocab_size] + # Use the pyctcdecode beam search decoder. + transcription = decoder.decode(log_probs_np, beam_width=beam_width).lower() + return transcription + + +# ------------------------------------------------------------------- +# 6. Evaluate the model on the sample subset using the full inference +# (Note: Only the encoder is tracked; the CTC head is applied separately.) +# ------------------------------------------------------------------- +def evaluate_model(model, ctc_head, processor, dataset): + total_wer = 0.0 + for idx, sample in enumerate(dataset): + audio_input = sample["audio"]["array"] + sampling_rate = sample["audio"]["sampling_rate"] + reference = sample["text"] + + inputs = processor( + audio=audio_input, + sampling_rate=sampling_rate, + return_tensors="pt" + ).input_values + + transcription = full_inference_beam(model, ctc_head, processor, inputs) + wer = jiwer.wer(reference, transcription) + total_wer += wer + + print(f"Sample {idx}:") + print(" Reference :", reference) + print(" Transcription:", transcription) + print(" WER :", wer) + + avg_wer = total_wer / len(dataset) + print("\nAverage WER:", avg_wer) + return avg_wer + +print("\nEvaluating the original model on the sample subset:") +print("Using model:", type(model)) +evaluate_model(model, ctc_head, processor, sample_list) diff --git a/docs/custom_scripts_tony/05_bert_test.py b/docs/custom_scripts_tony/05_bert_test.py new file mode 100644 index 000000000..95f61f423 --- /dev/null +++ b/docs/custom_scripts_tony/05_bert_test.py @@ -0,0 +1,67 @@ + +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForSequenceClassification # type: ignore +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore + +checkpoint = "DeepWokLab/bert-tiny" +tokenizer_checkpoint = "DeepWokLab/bert-tiny" +dataset_name = "imdb" + +dataset, tokenizer = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=tokenizer_checkpoint, + return_tokenizer=True, +) + +model = AutoModelForSequenceClassification.from_pretrained(checkpoint) +model.config.problem_type = "single_label_classification" + +mg = MaseGraph( + model, + hf_input_names=[ + "input_ids", + "attention_mask", + "labels", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) +mg, _ = passes.add_common_metadata_analysis_pass(mg) + +mg, _ = passes.insert_lora_adapter_transform_pass( + mg, + pass_args={ + "rank": 6, + "alpha": 1.0, + "dropout": 0.5, + }, +) + +mg.draw() + +_, _ = report_trainable_parameters_analysis_pass(mg.model) + +for param in mg.model.bert.embeddings.parameters(): + param.requires_grad = False + +trainer = get_trainer( + model=mg.model, + tokenized_dataset=dataset, + tokenizer=tokenizer, + evaluate_metric="accuracy", + num_train_epochs=1, +) +trainer.train() + +# Evaluate accuracy +eval_results = trainer.evaluate() +print(f"Evaluation accuracy: {eval_results['eval_accuracy']}") + +mg, _ = passes.fuse_lora_weights_transform_pass(mg) +eval_results = trainer.evaluate() +print(f"Evaluation accuracy: {eval_results['eval_accuracy']}") + +mg.export("tutorial_2_sft") \ No newline at end of file diff --git a/docs/custom_scripts_tony/06_wave2vec_graph.py b/docs/custom_scripts_tony/06_wave2vec_graph.py new file mode 100644 index 000000000..ee811a354 --- /dev/null +++ b/docs/custom_scripts_tony/06_wave2vec_graph.py @@ -0,0 +1,56 @@ +import torch +import torch.nn as nn +import torch.fx as fx +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "librispeech_asr" + +dataset, tokenizer = get_tokenized_dataset( # Didn't have enough memory for this + dataset=dataset_name, + checkpoint=tokenizer_checkpoint, + return_tokenizer=True, +) + +model = AutoModelForCTC.from_pretrained(checkpoint) +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# print(model.config) +# model.config.problem_type = "" Not needed for CTC + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", # Or "input_ids", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +mg.draw() + +_, _ = report_trainable_parameters_analysis_pass(mg.model) diff --git a/docs/custom_scripts_tony/07_wave2vec_evaluate.py b/docs/custom_scripts_tony/07_wave2vec_evaluate.py new file mode 100644 index 000000000..7de3d8cf6 --- /dev/null +++ b/docs/custom_scripts_tony/07_wave2vec_evaluate.py @@ -0,0 +1,166 @@ + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC +from pyctcdecode import build_ctcdecoder + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" # "librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +tokenizer = processor.tokenizer + +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +# dataset = load_dataset(dataset_name, "clean", split="validation", streaming=True, trust_remote_code=True) +dataset = load_dataset(dataset_name, split="test.clean") +sample_list = list(dataset.take(50)) + +def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +small_dataset = Dataset.from_list(sample_list) +small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] +) + +# Convert to DatasetDict (as required by MASE) +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +model = AutoModelForCTC.from_pretrained(checkpoint) +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Define the MASE graph +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", # Or "input_ids", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +# ------------------------------- +# 3. Combine the models +# ------------------------------- + +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + """ + Args: + encoder: The traced encoder (e.g., mg.model) + ctc_head: The CTC head (usually a linear layer) + blank_id: The token ID for the blank symbol (typically 0) + beam_width: Width for beam search decoding (if using a decoder) + decoder: (Optional) A beam search decoder (e.g., from pyctcdecode) + """ + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder # Only used during inference + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) # outputs tensor as expected + + output = {"logits": logits, "labels": labels} + + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + + loss = F.ctc_loss(log_probs, labels, input_lengths, target_lengths, blank=self.blank_id, reduction="mean", zero_infinity=True) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + +combined_model = CombinedWav2Vec2CTC(encoder=mg.model, ctc_head=ctc_head, decoder=decoder, beam_width=10) + +# ------------------------------- +# 4. Train & Evaluate the model +# ------------------------------- + +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, + gradient_accumulation_steps=4, + per_device_train_batch_size=2, + per_device_eval_batch_size=2, + decoder=decoder, + beam_width=10, +) +# trainer.train() + +# Evaluate accuracy +eval_results = trainer.evaluate() +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") diff --git a/docs/custom_scripts_tony/08_wave2vec_quantise.py b/docs/custom_scripts_tony/08_wave2vec_quantise.py new file mode 100644 index 000000000..d40714799 --- /dev/null +++ b/docs/custom_scripts_tony/08_wave2vec_quantise.py @@ -0,0 +1,194 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding +from pyctcdecode import build_ctcdecoder + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +tokenizer = processor.tokenizer + +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +dataset = load_dataset(dataset_name, "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = list(dataset.take(50)) + +def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +small_dataset = Dataset.from_list(sample_list) +small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] +) + +# Convert to DatasetDict (as required by MASE) +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +model = AutoModelForCTC.from_pretrained(checkpoint) +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Define the MASE graph +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", # Or "input_ids", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +# ------------------------------- +# 2.1 Implement Quantisation Pass +# ------------------------------- +quantization_config = { + "by": "type", + "default": { + "config": { + "name": None, + } + }, + "linear": { + "config": { + "name": "integer", + # data + "data_in_width": 16, + "data_in_frac_width": 8, + # weight + "weight_width": 16, + "weight_frac_width": 8, + # bias + "bias_width": 16, + "bias_frac_width": 8, + } + }, +} + +mg, _ = passes.quantize_transform_pass( + mg, + pass_args=quantization_config, +) + +# ------------------------------- +# 3. Combine the models +# ------------------------------- + +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + """ + Args: + encoder: The traced encoder (e.g., mg.model) + ctc_head: The CTC head (usually a linear layer) + blank_id: The token ID for the blank symbol (typically 0) + beam_width: Width for beam search decoding (if using a decoder) + decoder: (Optional) A beam search decoder (e.g., from pyctcdecode) + """ + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder # Only used during inference + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) # outputs tensor as expected + + output = {"logits": logits, "labels": labels} + + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + + loss = F.ctc_loss(log_probs, labels, input_lengths, target_lengths, blank=self.blank_id, reduction="mean", zero_infinity=True) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + +combined_model = CombinedWav2Vec2CTC(encoder=mg.model, ctc_head=ctc_head, decoder=decoder, beam_width=10) + +# ------------------------------- +# 4. Train the model +# ------------------------------- + +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, +) +trainer.train() + +# mg, _ = passes.fuse_lora_weights_transform_pass(mg) + +# Evaluate accuracy +eval_results = trainer.evaluate() + +print("Quantising Pass") +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") \ No newline at end of file diff --git a/docs/custom_scripts_tony/09_wave2vec_NAS.py b/docs/custom_scripts_tony/09_wave2vec_NAS.py new file mode 100644 index 000000000..7afade371 --- /dev/null +++ b/docs/custom_scripts_tony/09_wave2vec_NAS.py @@ -0,0 +1,254 @@ +import logging +logging.getLogger("transformers").setLevel(logging.ERROR) + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx +from chop.tools import get_tokenized_dataset # type: ignore +from chop.nn.modules import Identity +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding +from pyctcdecode import build_ctcdecoder +import dill +import optuna +from optuna.samplers import GridSampler, TPESampler, RandomSampler +from chop.tools.utils import deepsetattr +import pandas as pd + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +tokenizer = processor.tokenizer + +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +dataset = load_dataset(dataset_name, "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = list(dataset.take(50)) + +def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +small_dataset = Dataset.from_list(sample_list) +small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] +) + +# Convert to DatasetDict (as required by MASE) +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +# ------------------------------- +# 2 Construct Model +# ------------------------------- + +def construct_model(trial): + config = AutoModelForCTC.from_pretrained(checkpoint).config + + config.num_hidden_layers = trial.suggest_categorical("num_layers", search_space["num_layers"]) + config.num_attention_heads = trial.suggest_categorical("num_heads", search_space["num_heads"]) + config.hidden_size = trial.suggest_categorical("hidden_size", search_space["hidden_size"]) + config.intermediate_size = trial.suggest_categorical("intermediate_size", search_space["intermediate_size"]) + + model = AutoModelForCTC.from_pretrained(checkpoint, config=config, ignore_mismatched_sizes=True) + encoder = model.wav2vec2 + ctc_head = model.lm_head + + # print(model.config) + + # print(f"Encoder hidden size: {config.hidden_size}") # Debugging + # print(f"CTC head input size: {model.lm_head.in_features}, output size: {model.lm_head.out_features}") + + # model.lm_head = nn.Linear(config.hidden_size, tokenizer.vocab_size) + + linear_choice = trial.suggest_categorical("linear_layer_choices", search_space["linear_layer_choices"]) + if linear_choice == "identity": + for name, layer in encoder.named_modules(): + if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features: + deepsetattr(encoder, name, Identity()) + + model = CombinedWav2Vec2CTC(encoder=encoder, ctc_head=ctc_head, decoder=decoder, beam_width=10) + + return model + + +def objective(trial): + trial_model = construct_model(trial) + + trainer = get_trainer( + model=trial_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, + ) + + # Train, evaluate and return + trainer.train() + eval_results = trainer.evaluate() + trial.set_user_attr("model", trial_model) + return eval_results['eval_wer'] + +# ------------------------------- +# 2.1 Implement NAS +# ------------------------------- + +# Search space definition +search_space = { + "num_layers": [2, 4, 8], + "num_heads": [2, 4, 8, 16], + "hidden_size": [128, 192, 256, 384, 512], + "intermediate_size": [512, 768, 1024, 1536, 2048], + "linear_layer_choices": ["linear", "identity"], + "beam_width": [5, 10, 20], +} + +# ------------------------------- +# 3. Combine the models +# ------------------------------- + +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + """ + Args: + encoder: The traced encoder (e.g., mg.model) + ctc_head: The CTC head (usually a linear layer) + blank_id: The token ID for the blank symbol (typically 0) + beam_width: Width for beam search decoding (if using a decoder) + decoder: (Optional) A beam search decoder (e.g., from pyctcdecode) + """ + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder # Only used during inference + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) # outputs tensor as expected + + output = {"logits": logits, "labels": labels} + + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + + loss = F.ctc_loss(log_probs, labels, input_lengths, target_lengths, blank=self.blank_id, reduction="mean", zero_infinity=True) + output["loss"] = loss + else: # During evaluation/inference, decode instead of computing loss + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs.cpu().detach().numpy() + + # Decode each sample in batch + pred_texts = [self.decoder.decode(lp, beam_width=self.beam_width).lower() for lp in log_probs_np] + output["transcriptions"] = pred_texts + + return output + +def run_study_and_get_curve(sampler, n_trials=None, study_name="study"): + """ + Runs an Optuna study with the provided sampler and returns: + - the study object + - a list of best accuracies up to each trial (running max) + """ + study = optuna.create_study( + direction="minimize", + study_name=study_name, + sampler=sampler, + ) + + study.optimize( + objective, + n_trials=n_trials, + timeout=60 * 60 * 24, + show_progress_bar=True, + ) + + # Retains the minimum WER reached + running_min_wer = [] + current_min = 1.0 + for t in study.trials: + if t.value is not None and t.value < current_min: + current_min = t.value + running_min_wer.append(current_min) + + return study, running_min_wer + +def save_study_results_to_csv(study, filename): + """ + Saves each trial's results into a CSV, including: + - trial number + - objective value (accuracy) + - parameters + - model config parameters + """ + rows = [] + for t in study.trials: + row = { + "trial_number": t.number, + "wer": t.value, + } + # Merge in parameter key-value pairs directly + row.update(t.params) + + # Add model config if it exists in user attributes + if "model" in t.user_attrs: + model_config = t.user_attrs["model"].config.to_dict() + for key, value in model_config.items(): + row[f"config_{key}"] = value + + rows.append(row) + + df = pd.DataFrame(rows) + df.to_csv(filename, index=False) + print(f"Saved {len(rows)} trials with model configs to {filename}.") + +if __name__ == "__main__": + tpe_sampler = TPESampler() + + tpe_study, tpe_max_curve = run_study_and_get_curve( + sampler=tpe_sampler, + n_trials=10, + study_name="wave2vec-tpe-study", + ) + + best_tpe_model = tpe_study.best_trial.user_attrs["model"].cpu() + torch.save(best_tpe_model.state_dict(), "best_tpe_model.pth") + print("✅ Best model saved successfully!") + + save_study_results_to_csv(tpe_study, "tpe_study_trials.csv") \ No newline at end of file diff --git a/docs/custom_scripts_tony/10_wave2vec_mov_pruning.py b/docs/custom_scripts_tony/10_wave2vec_mov_pruning.py new file mode 100644 index 000000000..f62829284 --- /dev/null +++ b/docs/custom_scripts_tony/10_wave2vec_mov_pruning.py @@ -0,0 +1,228 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor, TrainerCallback +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding +from pyctcdecode import build_ctcdecoder + +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + """ + Args: + encoder: The traced encoder (e.g., mg.model) + ctc_head: The CTC head (usually a linear layer) + blank_id: The token ID for the blank symbol (typically 0) + beam_width: Width for beam search decoding (if using a decoder) + decoder: (Optional) A beam search decoder (e.g., from pyctcdecode) + """ + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder # Only used during inference + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) # outputs tensor as expected + + output = {"logits": logits, "labels": labels} + + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + + loss = F.ctc_loss(log_probs, labels, input_lengths, target_lengths, blank=self.blank_id, reduction="mean", zero_infinity=True) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + +class MovementTrackingCallback(TrainerCallback): + def on_train_begin(self, args, state, control, **kwargs): + self.prev_params = {} + self.movement_stats = {} + model = kwargs["model"] + for name, module in model.encoder.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + self.prev_params[name] = module.weight.detach().clone() + self.movement_stats[name] = torch.zeros_like(module.weight) + if not hasattr(module.weight, "metadata"): + module.metadata["weight"] = {} + module.metadata["weight"]["stats"] = {"movement": self.movement_stats[name]} + return control + + def on_step_end(self, args, state, control, **kwargs): + model = kwargs["model"] + for name, module in model.encoder.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + movement = (module.weight.detach() - self.prev_params[name]).abs() + self.movement_stats[name] += movement + self.prev_params[name].copy_(module.weight.detach()) + if not hasattr(module, "metadata"): + module.metadata = {} + if "weight" not in module.metadata: + module.metadata["weight"] = {} + module.metadata["weight"]["stats"] = {"movement": self.movement_stats[name]} + + return control + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" # "librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained(tokenizer_checkpoint) +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +tokenizer = processor.tokenizer + +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +# dataset = load_dataset(dataset_name, "clean", split="validation", streaming=True, trust_remote_code=True) +dataset = load_dataset(dataset_name, split="validation.clean", trust_remote_code=True) +sample_list = list(dataset.take(50)) + +def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +small_dataset = Dataset.from_list(sample_list) +small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] +) + +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +model = AutoModelForCTC.from_pretrained(checkpoint) +model.config.gradient_checkpointing = True +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Define the MASE graph & movement metadata +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +for module in mg.model.modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + if not hasattr(module, "metadata"): + module.metadata = {} + module.metadata["weight"] = {"stats": {"movement": torch.zeros_like(module.weight)}} + +combined_model = CombinedWav2Vec2CTC( + encoder=mg.model, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 +) + +# ------------------------------- +# 4. Warm-Up train the model +# ------------------------------- + +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, + gradient_accumulation_steps = 4, + per_device_train_batch_size = 2, + per_device_eval_batch_size = 2, +) +trainer.add_callback(MovementTrackingCallback()) + +print("Starting warm-up training to accumulate movement data...") +# trainer.train() +print("Warm-up training complete.") + +# ------------------------------- +# 5. Prune the model +# ------------------------------- + +pruning_config = { + "weight": { + "sparsity": 0.0, + "method": "l1-norm", + "scope": "local", + "granularity": "elementwise", + }, + "activation": { + "sparsity": 0.2, + "method": "l1-norm", + "scope": "local", + "granularity": "elementwise", + }, +} + +mg, _ = passes.prune_transform_pass(mg, pass_args=pruning_config) + +print("Starting fine-tuning of the pruned model...") +trainer.train() + +# Start evaluation +eval_results = trainer.evaluate() +print("Movement Pruning Pass") +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") \ No newline at end of file diff --git a/docs/custom_scripts_tony/11_wave2vec_organise.py b/docs/custom_scripts_tony/11_wave2vec_organise.py new file mode 100644 index 000000000..934577c5b --- /dev/null +++ b/docs/custom_scripts_tony/11_wave2vec_organise.py @@ -0,0 +1,139 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor, TrainerCallback +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes import add_movement_metadata_analysis_pass +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.passes.graph.transforms.pruning import MovementTrackingCallback +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC +from pyctcdecode import build_ctcdecoder + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "librispeech_asr" + +# Logic inside get_tockenized_dataset needs to be improved using nyal's changes +dataset, tokenizer, processor = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=tokenizer_checkpoint, + return_tokenizer=True, + return_processor=True, +) + +# Logic needs to be improved for seperated train and test split +tokenized_dataset = DatasetDict({ + "train": dataset, + "test": dataset +}) + +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +model = AutoModelForCTC.from_pretrained(checkpoint) +model.config.gradient_checkpointing = True +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Define the MASE graph & movement metadata +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +mg, _ = passes.add_movement_metadata_analysis_pass(mg) + +combined_model = CombinedWav2Vec2CTC( + encoder=mg.model, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 +) + +# ------------------------------- +# 4. Warm-Up train the model +# ------------------------------- + +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, + gradient_accumulation_steps = 4, + per_device_train_batch_size = 2, + per_device_eval_batch_size = 2, + decoder=decoder, + beam_width=10, +) +trainer.add_callback(MovementTrackingCallback()) + +print("Starting warm-up training to accumulate movement data...") +trainer.train() +print("Warm-up training complete.") + +# ------------------------------- +# 5. Prune the model +# ------------------------------- + +pruning_config = { + "weight": { + "sparsity": 0.0, + "method": "l1-norm", + "scope": "local", + "granularity": "elementwise", + }, + "activation": { + "sparsity": 0.2, + "method": "l1-norm", + "scope": "local", + "granularity": "elementwise", + }, +} + +mg, _ = passes.prune_transform_pass(mg, pass_args=pruning_config) + +print("Starting fine-tuning of the pruned model...") +trainer.train() + +# Start evaluation +eval_results = trainer.evaluate() +print("Movement Pruning Pass") +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") \ No newline at end of file diff --git a/docs/custom_scripts_tony/12_wave2vec_onnx.py b/docs/custom_scripts_tony/12_wave2vec_onnx.py new file mode 100644 index 000000000..ab4b1073d --- /dev/null +++ b/docs/custom_scripts_tony/12_wave2vec_onnx.py @@ -0,0 +1,194 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx + +from pathlib import Path +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor, TrainerCallback +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes import add_movement_metadata_analysis_pass +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.passes.graph.transforms.pruning import MovementTrackingCallback +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC +from pyctcdecode import build_ctcdecoder +from chop.dataset.nlp.speech_recognition import CondensedLibrispeechASRDataset +from chop.dataset import MaseDataModule +from chop.passes.graph import ( + summarize_quantization_analysis_pass, + add_common_metadata_analysis_pass, + init_metadata_analysis_pass, + runtime_analysis_pass, + onnx_runtime_interface_pass, + quantize_transform_pass, +) + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" + +# Logic inside get_tockenized_dataset needs to be improved using nyal's changes +tokenized_dataset, tokenizer, processor = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=checkpoint, + tokenizer_checkpoint=tokenizer_checkpoint, + return_tokenizer=True, + return_processor=True, +) + +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +model = AutoModelForCTC.from_pretrained(checkpoint) +model.config.gradient_checkpointing = True +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Import ONNX dataset & Wrapper +# ------------------------------- + +dataset_path = Path("./preprocessed_data") +condensed_dataset = CondensedLibrispeechASRDataset(dataset_path=dataset_path, split="train") # Choose valid split +condensed_dataset.prepare_data() +condensed_dataset.setup() + +data_module = MaseDataModule( + name=dataset_name, + batch_size=2, + model_name=checkpoint, + num_workers=0, +) +data_module.prepare_data() +data_module.setup() + +class ONNXWrapper(nn.Module): + def __init__(self, encoder): + super().__init__() + self.encoder = encoder + + def forward(self, inputs): + if isinstance(inputs, dict): + input_values = inputs["input_values"] + attention_mask = inputs["attention_mask"] + else: + input_values = inputs + attention_mask = torch.ones_like(inputs, dtype=torch.long) + return self.encoder(input_values, attention_mask=attention_mask) + + @property + def graph(self): + # Expose the underlying FX graph for later passes + return self.encoder.graph + +# ------------------------------- +# 3. Define the MASE graph & metadata +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +combined_model = CombinedWav2Vec2CTC( + encoder=mg.model, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 + ) + +# ------------------------------- +# 4. Initial Train & Evaluate +# ------------------------------- + +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=0.1, + data_collator=data_collator, + gradient_accumulation_steps = 4, + per_device_train_batch_size = 2, + per_device_eval_batch_size = 2, +) + +trainer.train() +eval_results = trainer.evaluate() + +print("No ONNX Pass") +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") + +# ------------------------------- +# 5. Add ONNX pass +# ------------------------------- + +smoothquant_config = { + "smoothquant": True, # Enable SmoothQuant in the ONNX pipeline + "alpha": 0, # Smoothing parameter + "model": checkpoint, # Model identifier + "task": "ctc", # Task name + "dataset": dataset_name, # Dataset name + "accelerator": "cuda", # Device for export + "data_module": data_module, # Data module for calibration + "batch_size": 2, # Batch size for calibration +} + +runtime_analysis_config = { + "num_batches": 10, + "num_GPU_warmup_batches": 5, + "test": True, + "data_module": data_module, + "model": checkpoint, + "accelerator": "cuda", + "task": "ctc", + "decoder": decoder, + "beam_width": 10, + "tokenizer": tokenizer, + "ctc_head": ctc_head, + "sample_rate": 16000, + "batch_size": 2, +} + +mg.model = ONNXWrapper(mg.model) + +mg, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) +print("ONNX Pass") +_, results = runtime_analysis_pass(mg, pass_args=runtime_analysis_config) + +print(f"Average WER", f"{results['Average WER']:.5g}") +print(f"Average Latency", f"{results['Average Latency']:.5g} ms") +print(f"Average RTF", f"{results['Average RTF']:.5g}") +print(f"Average GPU Power Usage", f"{results['Average GPU Power Usage']:.5g} W") +print(f"Inference Energy Consumption", f"{results['Inference Energy Consumption']:.5g} mWh") diff --git a/docs/custom_scripts_tony/13_wave2vec_mps.py b/docs/custom_scripts_tony/13_wave2vec_mps.py new file mode 100644 index 000000000..d745923e9 --- /dev/null +++ b/docs/custom_scripts_tony/13_wave2vec_mps.py @@ -0,0 +1,518 @@ +# import random +import dill +from copy import deepcopy +from pathlib import Path +import torch +import torch.nn as nn +import pandas as pd +import matplotlib.pyplot as plt +import optuna +from optuna.samplers import TPESampler +from transformers import AutoModelForCTC, Wav2Vec2Processor +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.tools import get_trainer, get_tokenized_dataset # type: ignore +from chop.tools.utils import deepsetattr # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC +from pyctcdecode import build_ctcdecoder +from chop.passes.graph import ( + summarize_quantization_analysis_pass, + add_common_metadata_analysis_pass, + init_metadata_analysis_pass, + runtime_analysis_pass, + onnx_runtime_interface_pass, + quantize_transform_pass, +) +from chop.dataset.nlp.speech_recognition import CondensedLibrispeechASRDataset +from chop.dataset import MaseDataModule +from chop.nn.quantized.modules.linear import ( + LinearInteger, + LinearMinifloatDenorm, + LinearMinifloatIEEE, + LinearLog, + LinearBlockFP, + LinearBlockMinifloat, + LinearBlockLog, + LinearBinary, + LinearBinaryScaling, +) + +baseline_metrics = None + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "librispeech_asr" + +# Logic inside get_tockenized_dataset needs to be improved using nyal's changes +dataset, tokenizer, processor = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=tokenizer_checkpoint, + return_tokenizer=True, + return_processor=True, +) + +# Logic needs to be improved for seperated train and test split +tokenized_dataset = DatasetDict({ + "train": dataset, + "test": dataset +}) + +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +model = AutoModelForCTC.from_pretrained(checkpoint) +model.config.gradient_checkpointing = True +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Import ONNX dataset & Wrapper +# ------------------------------- + +dataset_path = Path("./preprocessed_data") +condensed_dataset = CondensedLibrispeechASRDataset(dataset_path=dataset_path, split="train") # Choose valid split +condensed_dataset.prepare_data() +condensed_dataset.setup() + +data_module = MaseDataModule( + name="nyalpatel/condensed_librispeech_asr", + batch_size=1, + model_name=checkpoint, + num_workers=0, +) +data_module.prepare_data() +data_module.setup() + +class ONNXWrapper(nn.Module): + def __init__(self, encoder): + super().__init__() + self.encoder = encoder + + def forward(self, inputs): + if isinstance(inputs, dict): + input_values = inputs["input_values"] + attention_mask = inputs["attention_mask"] + else: + input_values = inputs + attention_mask = torch.ones_like(inputs, dtype=torch.long) + return self.encoder(input_values, attention_mask=attention_mask) + + @property + def graph(self): + # Expose the underlying FX graph for later passes + return self.encoder.graph + +# ------------------------------- +# 3. Define the MASE graph & metadata +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +combined_model = CombinedWav2Vec2CTC( + encoder=mg.model, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 + ) + +# ------------------------------- +# 4. Define Search Space & Baseline +# ------------------------------- + +def run_onnx_baseline(mg, data_module, checkpoint, dataset_name, decoder, tokenizer): + """ + Performs an ONNX pass without quantization or SmoothQuant, + then runs runtime analysis to get baseline metrics. + """ + mg.model = ONNXWrapper(mg.model) + + # alpha=0 => no smoothing + smoothquant_config = { + "smoothquant": True, + "alpha": 0.0, + "model": checkpoint, + "task": "ctc", + "dataset": dataset_name, + "accelerator": "cuda", + "data_module": data_module, + "batch_size": 1, + } + + runtime_analysis_config = { + "num_batches": 100, + "num_GPU_warmup_batches": 5, + "test": True, + "data_module": data_module, + "model": checkpoint, + "accelerator": "cuda", + "task": "ctc", + "decoder": decoder, + "beam_width": 10, + "tokenizer": tokenizer, + "batch_size": 2, + "sample_rate": 16000, + } + + mg, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) + _, baseline_results = runtime_analysis_pass(mg, pass_args=runtime_analysis_config) + + print("\nBaseline (no quant, alpha=0) ONNX Metrics:") + for k, v in baseline_results.items(): + print(f" {k}: {v}") + return baseline_results + +precision_choices = [ + # nn.Linear, + LinearInteger, + # LinearMinifloatDenorm, + # LinearMinifloatIEEE, + # LinearLog, + # LinearBlockFP, + # LinearBlockLog, + # LinearBinary, + # LinearBinaryScaling, +] + +width_choices = [16, 32] +frac_width_choices = [8, 16] + +# ------------------------------- +# 5. Construct Quantized Model +# ------------------------------- + +def construct_quantized_model(trial, chosen_precision): + """ + Constructs a quantized model where nn.Linear layers in the encoder + are replaced with the chosen quantization precision type. + """ + # Copy the base encoder to avoid modifying the original + encoder_quant = deepcopy(mg.model) + + for name, layer in encoder_quant.named_modules(): + if isinstance(layer, nn.Linear): + if chosen_precision == nn.Linear: + continue + + kwargs = {"in_features": layer.in_features, "out_features": layer.out_features} + + # [!!! FIX] If chosen_precision is LinearInteger or others: + if chosen_precision == LinearInteger: + config = { + "weight_width": trial.suggest_categorical(f"{name}_weight_width", width_choices), + "weight_frac_width": trial.suggest_categorical(f"{name}_weight_frac_width", frac_width_choices), + "data_in_width": trial.suggest_categorical(f"{name}_data_in_width", width_choices), + "data_in_frac_width": trial.suggest_categorical(f"{name}_data_in_frac_width", frac_width_choices), + "bias_width": trial.suggest_categorical(f"{name}_bias_width", width_choices), + "bias_frac_width": trial.suggest_categorical(f"{name}_bias_frac_width", frac_width_choices), + "floor": False, + } + elif chosen_precision in [LinearMinifloatDenorm, LinearMinifloatIEEE]: + config = { + "weight_width": trial.suggest_categorical(f"{name}_weight_width", width_choices), + "weight_exponent_width": 5, + "weight_exponent_bias": 15, + "data_in_width": trial.suggest_categorical(f"{name}_data_in_width", width_choices), + "data_in_exponent_width": 5, + "data_in_exponent_bias": 15, + "bias_width": trial.suggest_categorical(f"{name}_bias_width", width_choices), + "bias_exponent_width": 5, + "bias_exponent_bias": 15, + } + else: + config = {} + + new_layer = chosen_precision(**kwargs, config=config) + new_layer.weight.data = layer.weight.data.clone() + if layer.bias is not None: + new_layer.bias.data = layer.bias.data.clone() + + deepsetattr(encoder_quant, name, new_layer) + + # Create the combined model with the quantized encoder + quantized_model = CombinedWav2Vec2CTC( + encoder=encoder_quant, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 + ) + return quantized_model + +# ------------------------------- +# 6. Objective Function for Optimization +# ------------------------------- + +def objective(trial, chosen_precision, baseline_metrics): + """ + Mixed-precision search objective. + 1) Quantize + 2) (Optional) train quantized model + 3) ONNX pass (with alpha in [0.0,1.0]) + 4) Compare final ONNX metrics to baseline + """ + # 1) Build the quantized model + quant_model = construct_quantized_model(trial, chosen_precision) + + # 2) Train the quantized model + trainer = get_trainer( + model=quant_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + data_collator=data_collator, + num_train_epochs=1, + gradient_accumulation_steps=4, + per_device_train_batch_size=2, + per_device_eval_batch_size=2, + ) + trainer.train() + + eval_results = trainer.evaluate() + wer = eval_results.get("eval_wer", None) + loss = eval_results.get("eval_loss", None) + trial.set_user_attr("eval_wer_pt", wer) + trial.set_user_attr("eval_loss_pt", loss) + + # 3) SmoothQuant alpha from search space + alpha_val = trial.suggest_float("smoothquant_alpha", 0.0, 1.0, step=0.1) + + mg.model = trainer.model.encoder + mg.model = ONNXWrapper(mg.model) + + smoothquant_config = { + "smoothquant": True, + "alpha": alpha_val, + "model": checkpoint, + "task": "ctc", + "dataset": dataset_name, + "accelerator": "cuda", + "data_module": data_module, + "batch_size": 1, + } + + runtime_analysis_config = { + "num_batches": 100, + "num_GPU_warmup_batches": 5, + "test": True, + "data_module": data_module, + "model": checkpoint, + "accelerator": "cuda", + "task": "ctc", + "decoder": decoder, + "beam_width": 10, + "tokenizer": tokenizer, + "batch_size": 2, + "sample_rate": 16000, + } + + mg, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) + _, onnx_results = runtime_analysis_pass(mg, pass_args=runtime_analysis_config) + trial.set_user_attr("onnx_metrics", onnx_results) + + # 4) Compare final ONNX metrics to baseline + relevant_keys = [ + "Average WER", + "Average Latency", + "Average RTF", + "Average GPU Power Usage", + "Inference Energy Consumption", + ] + + for k in relevant_keys: + val = onnx_results.get(k, float("inf")) + trial.set_user_attr(f"onnx_{k}", val) + + # For WER, latency, RTF, etc. smaller=better => ratio = trial_val / baseline_val + ratios = [] + for k in relevant_keys: + trial_val = onnx_results.get(k, None) + base_val = baseline_metrics.get(k, None) + if trial_val is not None and base_val is not None and base_val != 0: + ratio = trial_val / base_val + ratios.append(ratio) + + composite_ratio = sum(ratios) / len(ratios) if ratios else 1.0 + composite_metric = 1.0 - composite_ratio + trial.set_user_attr("composite_ratio", composite_ratio) + trial.set_user_attr("composite_metric", composite_metric) + + return composite_metric + +# ------------------------------- +# 7. Run Optimization Studies +# ------------------------------- + +def run_study_for_precision(chosen_precision, n_trials=5): + """ + Runs an Optuna study for a fixed precision type, comparing results to the baseline. + """ + global baseline_metrics + if baseline_metrics is None: + print("Running baseline ONNX pass (no quant, alpha=0) for reference...") + baseline_metrics = run_onnx_baseline(mg, data_module, checkpoint, dataset_name, decoder, tokenizer) + + sampler = TPESampler() + study_name = f"study_{chosen_precision.__name__}" if chosen_precision != nn.Linear else "study_FullPrecision" + study = optuna.create_study(direction="maximize", study_name=study_name, sampler=sampler) + + study.optimize(lambda trial: objective(trial, chosen_precision, baseline_metrics), + n_trials=n_trials, + timeout=60 * 60 * 24) + + best_trial = study.best_trial + print(f"\nBest trial for {chosen_precision.__name__ if chosen_precision != nn.Linear else 'FullPrecision'}:") + print(" Trial Number:", best_trial.number) + print(" Best Composite Metric:", best_trial.value) + print(" PT-based WER:", best_trial.user_attrs.get("eval_wer_pt", "N/A")) + print(" ONNX-based metrics:", best_trial.user_attrs.get("onnx_metrics", {})) + + results = [] + for t in sorted(study.trials, key=lambda t: t.number): + composite = t.value + onnx_m = t.user_attrs.get("onnx_metrics", {}) + alpha_val = t.params.get("smoothquant_alpha", None) + + row = { + "trial_number": t.number, + "trial_composite_metric": composite, + "smoothquant_alpha": alpha_val, + "onnx_wer": onnx_m.get("Average WER", None), + "onnx_latency": onnx_m.get("Average Latency", None), + "onnx_rtf": onnx_m.get("Average RTF", None), + "onnx_power": onnx_m.get("Average GPU Power Usage", None), + "onnx_energy": onnx_m.get("Inference Energy Consumption", None), + } + results.append(row) + + df = pd.DataFrame(results) + csv_name = f"optuna_results_{chosen_precision.__name__ if chosen_precision != nn.Linear else 'FullPrecision'}.csv" + df.to_csv(csv_name, index=False) + print(f"Results for precision {chosen_precision.__name__ if chosen_precision != nn.Linear else 'FullPrecision'} saved to {csv_name}") + + return df, best_trial + +# ------------------------------- +# 8. Get Best Model and Save Results +# ------------------------------- + +def save_best_model(best_trial, chosen_precision): + best_model = construct_quantized_model(best_trial, chosen_precision) + with open(f"best_model_{chosen_precision.__name__}.pkl", "wb") as f: + dill.dump(best_model, f) + print(f"Best model saved to best_model_{chosen_precision.__name__}.pkl") + return best_model + +# ------------------------------- +# 9. Main execution +# ------------------------------- + +all_results = [] +for prec in precision_choices: + print(f"\nRunning study for precision type: {prec.__name__ if prec != nn.Linear else 'FullPrecision'}") + df_prec, best_trial = run_study_for_precision(prec, n_trials=10) + + # Save the best model for this precision type + best_model = save_best_model(best_trial, prec) + + # Append results + df_prec["precision_type"] = prec.__name__ if prec != nn.Linear else "FullPrecision" + all_results.append(df_prec) + + # If you want a WER progress plot, you must track PT-based WER or ONNX-based WER over trials. + # For example, let's track "PT-based WER" from user_attrs: + + if "trial_wer" not in df_prec.columns: + # Let's add a column for trial_wer from user attrs "eval_wer_pt" + df_prec["trial_wer"] = df_prec.apply( + lambda row: [t.user_attrs.get("eval_wer_pt", None) for t in best_trial.study.trials + if t.number == row["trial_number"]][0], + axis=1 + ) + + df_temp = df_prec.sort_values("trial_number") + trial_nums = df_temp["trial_number"].tolist() + wer_values = df_temp["trial_wer"].tolist() + cum_best_wer = [] + current_best_wer = float("inf") + for val in wer_values: + if val is not None: + current_best_wer = min(current_best_wer, val) + cum_best_wer.append(current_best_wer) + + plt.figure(figsize=(6,4)) + plt.plot(trial_nums, cum_best_wer, marker="o", + label=prec.__name__ if prec != nn.Linear else "FullPrecision") + plt.xlabel("Trial Number") + plt.ylabel("Cumulative Best PT-based WER") + plt.title(f"PT WER Progress for {prec.__name__ if prec != nn.Linear else 'FullPrecision'}") + plt.legend() + plt.grid(True) + plt.savefig(f"optuna_wer_progress_{prec.__name__ if prec != nn.Linear else 'FullPrecision'}.png") + plt.close() + +combined_df = pd.concat(all_results, ignore_index=True) + +def compute_percentage_change(current, baseline): + if baseline == 0: + return 0.0 + return (current - baseline) / baseline * 100.0 + +for metric_col, metric_key in [ + ("onnx_wer", "Average WER"), + ("onnx_latency", "Average Latency"), + ("onnx_rtf", "Average RTF"), + ("onnx_power", "Average GPU Power Usage"), + ("onnx_energy", "Inference Energy Consumption"), +]: + baseline_val = baseline_metrics.get(metric_key, None) + combined_df[f"{metric_col}_pct_change"] = combined_df[metric_col].apply( + lambda x: compute_percentage_change(x, baseline_val) if x is not None and baseline_val else None + ) + +# Example plot: WER % change vs baseline +plt.figure(figsize=(10,6)) +for precision, group in combined_df.groupby("precision_type"): + group = group.sort_values("trial_number") + wer_pct_change = group["onnx_wer_pct_change"].tolist() + trial_nums = group["trial_number"].tolist() + + plt.plot(trial_nums, wer_pct_change, marker="o", label=precision) + +plt.xlabel("Trial Number") +plt.ylabel("WER % Change vs Baseline") +plt.title("WER Improvement Over Baseline by Precision Type") +plt.legend() +plt.grid(True) +plt.savefig("optuna_combined_wer_pct_change.png") +plt.close() + +# Save combined results +combined_df.to_csv("optuna_combined_results.csv", index=False) +print("Combined results saved to optuna_combined_results.csv") diff --git a/docs/custom_scripts_tony/14_wave2vec_quantise_onnx.py b/docs/custom_scripts_tony/14_wave2vec_quantise_onnx.py new file mode 100644 index 000000000..4201c7338 --- /dev/null +++ b/docs/custom_scripts_tony/14_wave2vec_quantise_onnx.py @@ -0,0 +1,169 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx + +import logging +logging.getLogger("pyctcdecode").setLevel(logging.ERROR) +from pyctcdecode import build_ctcdecoder + +from pathlib import Path +from chop.tools import get_tokenized_dataset # type: ignore +from transformers import AutoModelForCTC, Wav2Vec2Processor, TrainerCallback +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes import add_movement_metadata_analysis_pass +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.passes.graph.transforms.pruning import MovementTrackingCallback +from chop.tools import get_trainer # type: ignore +from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset +from chop.models import DataCollatorCTCWithPadding, CombinedWav2Vec2CTC +from chop.dataset.nlp.speech_recognition import CondensedLibrispeechASRDataset +from chop.dataset import MaseDataModule +from chop.passes.graph import ( + summarize_quantization_analysis_pass, + add_common_metadata_analysis_pass, + init_metadata_analysis_pass, + runtime_analysis_pass, + onnx_runtime_interface_pass, + quantize_transform_pass, +) + +# ------------------------------- +# 1. Define the model and dataset +# ------------------------------- + +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "nyalpatel/condensed_librispeech_asr" + +# Logic inside get_tockenized_dataset needs to be improved using nyal's changes +tokenized_dataset, tokenizer, processor = get_tokenized_dataset( + dataset=dataset_name, + checkpoint=checkpoint, + tokenizer_checkpoint=tokenizer_checkpoint, + return_tokenizer=True, + return_processor=True, +) + +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +model = AutoModelForCTC.from_pretrained(checkpoint) +# model.config.gradient_checkpointing = True +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head, separate this + +# ------------------------------- +# 2. Import ONNX dataset & Wrapper +# ------------------------------- + +dataset_path = Path("./preprocessed_data") +condensed_dataset = CondensedLibrispeechASRDataset(dataset_path=dataset_path, split="train") # Choose valid split +condensed_dataset.prepare_data() +condensed_dataset.setup() +batch_size = 4 + +data_module = MaseDataModule( + name=dataset_name, + batch_size=batch_size, + model_name=checkpoint, + num_workers=0, +) +data_module.prepare_data() +data_module.setup() + +# ------------------------------- +# 3. Define the MASE graph & metadata +# ------------------------------- + +mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], +) + +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} + +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } +) + +smoothquant_config = { + "smoothquant": True, + "alpha": 0.5, + "model": checkpoint, + "task": "ctc", + "dataset": dataset_name, + "accelerator": "cuda", + "data_module": data_module, + "batch_size": batch_size, +} + + +quantization_config = { + "by": "type", + "default": { + "config": { + "name": None, + } + }, + "linear": { + "config": { + "name": "integer", + # data + "data_in_width": 16, + "data_in_frac_width": 8, + # weight + "weight_width": 16, + "weight_frac_width": 8, + # bias + "bias_width": 16, + "bias_frac_width": 8, + } + }, +} + + +runtime_analysis_config = { + "num_batches": 100, + "num_GPU_warmup_batches": 5, + "test": True, + "data_module": data_module, + "model": checkpoint, + "accelerator": "cuda", + "task": "ctc", + "decoder": decoder, + "beam_width": 10, + "tokenizer": tokenizer, + "batch_size": batch_size, + "sample_rate": 16000, +} + +mg_onnx, onnx_meta = onnx_runtime_interface_pass(mg, pass_args=smoothquant_config) + +mg_onnx, _ = passes.quantize_transform_pass( + mg_onnx, + pass_args=quantization_config, +) + +_, results_onnx = runtime_analysis_pass(mg, pass_args=runtime_analysis_config) + +# print(f"Average WER", f"{results_onnx['Average WER']:.5g}") +# print(f"Average Latency", f"{results_onnx['Average Latency']:.5g} ms") +# print(f"Average RTF", f"{results_onnx['Average RTF']:.5g}") +# print(f"Average GPU Power Usage", f"{results_onnx['Average GPU Power Usage']:.5g} W") +# print(f"Inference Energy Consumption", f"{results_onnx['Inference Energy Consumption']:.5g} mWh") diff --git a/docs/custom_scripts_tony/tony_results.md b/docs/custom_scripts_tony/tony_results.md new file mode 100644 index 000000000..1b8a78e66 --- /dev/null +++ b/docs/custom_scripts_tony/tony_results.md @@ -0,0 +1,27 @@ +Wav2Vec (Base): Train = 1, Results: + Evaluation WER: 0.008871989860583017 + Evaluation loss: 0.11790326982736588 + Evaluation runtime: 38.2567 + Evaluation samples per second: 1.307 + Evaluation steps per second: 0.183 + +Wav2Vec (LoRA): Train = 1, Results: + Evaluation WER: 0.050697084917617236 + Evaluation loss: 0.3851337432861328 + Evaluation runtime: 31.9776 + Evaluation samples per second: 1.564 + Evaluation steps per second: 0.219 + +Wav2Vec (Fused LoRA): Train = 1, Results: + Evaluation WER: 0.06844106463878327 + Evaluation loss: 0.356110155582428 + Evaluation runtime: 30.1212 + Evaluation samples per second: 1.66 + Evaluation steps per second: 0.232 + +Wav2Vec (Quantising Pass): Train = 1, Results: + Evaluation WER: 0.010139416983523447 + Evaluation loss: 0.10648082941770554 + Evaluation runtime: 39.4345 + Evaluation samples per second: 1.268 + Evaluation steps per second: 0.178 diff --git a/docs/custom_scripts_tony/tony_to_do.md b/docs/custom_scripts_tony/tony_to_do.md new file mode 100644 index 000000000..124edf605 --- /dev/null +++ b/docs/custom_scripts_tony/tony_to_do.md @@ -0,0 +1,24 @@ + +Implemented List: +- Wav2ec model, including: + - new speech model type, + - 4 new packages in setup ("librosa", "soundfile", "jiwer", "pyctcdecode"), + - 3 new MASE_IMPLICIT_FUNCTIONS ("cumsum", "flip", "repeat"), + - 3 new MASE_MODULE_RELATED_FUNCS ("zeros", "setitem", "invert"), + - fixed common_metadata_layers "div" config parameters in func_data + - added common_metadata_layers ("zeros", "setitem", "invert") w configs to func_data + - added common_metadata_layers ("cumsum", "flip", "repeat") w configs to module_data + +- DataCollatorCTCWithPadding, including: + - additional logic for defining a custom data collator in get_trainer() + +- Added WER (Word Error Rate) as a new evaluate_metric within chop/tools/huggingface + +- Added weight movement pruning, local and global, including: + - added logic to get_weight_hook to pass correct info for global vs local cases + - fixed l1-norm global pruning, also gave it a fall back low memory method in case of memory issues + - also added fall back low memory method for global movement pruning + +- Added optional parameters to get_trainer() for gradient accumalation and mini batch sizes + +- Added librspeech_asr dataset to get_tokenized_dataset() function including preprocessing, tokenizer and processor \ No newline at end of file diff --git a/docs/source/modules/documentation/tutorials/bert-base-uncased.svg b/docs/source/modules/documentation/tutorials/bert-base-uncased.svg new file mode 100644 index 000000000..4d71e65d7 --- /dev/null +++ b/docs/source/modules/documentation/tutorials/bert-base-uncased.svg @@ -0,0 +1,3003 @@ + + + + + + +masegraph + + + +input_ids + +name=%input_ids + +op_code=placeholder + +target=input_ids + +num_users=4 + + + +size + +name=%size + +op_code=call_method + +target=size + +num_users=2 + + + +input_ids->size + + + + + +size_1 + +name=%size_1 + +op_code=call_method + +target=size + +num_users=1 + + + +input_ids->size_1 + + + + + +bert_embeddings_word_embeddings + +name=%bert_embeddings_word_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 30522 +embedding_dim: 128 +padding_idx: 0 +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +input_ids->bert_embeddings_word_embeddings + + + + + +getattr_1 + +name=%getattr_1 + +op_code=call_function + +target=getattr + +args=(device,) + +num_users=1 + + + +input_ids->getattr_1 + + + + + +getitem + +name=%getitem + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=2 + + + +size->getitem + + + + + +getitem_1 + +name=%getitem_1 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=4 + + + +size->getitem_1 + + + + + +expand + +name=%expand + +op_code=call_method + +target=expand + +num_users=1 + + + +getitem->expand + + + + + +ones + +name=%ones + +op_code=call_function + +target=torch.ones + +num_users=4 + + + +getitem->ones + + + + + +getitem_2 + +name=%getitem_2 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), slice(None, getitem_1, None)),) + +num_users=1 + + + +getitem_1->getitem_2 + + + + + +getitem_1->expand + + + + + +add_3 + +name=%add_3 + +op_code=call_function + +target=_operator.add + +args=(0,) + +num_users=1 + + + +getitem_1->add_3 + + + + + +expand_1 + +name=%expand_1 + +op_code=call_method + +target=expand + +args=(1,) + +num_users=1 + + + +getitem_1->expand_1 + + + + + +bert_embeddings_token_type_ids + +name=%bert_embeddings_token_type_ids + +op_code=get_attr + +target=bert.embeddings.token_type_ids + +num_users=1 + + + +bert_embeddings_token_type_ids->getitem_2 + + + + + +getitem_2->expand + + + + + +bert_embeddings_token_type_embeddings + +name=%bert_embeddings_token_type_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 2 +embedding_dim: 128 +padding_idx: None +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +expand->bert_embeddings_token_type_embeddings + + + + + +getitem_3 + +name=%getitem_3 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_1->getitem_3 + + + + + +add + +name=%add + +op_code=call_function + +target=_operator.add + +args=(0,) + +num_users=1 + + + +getitem_3->add + + + + + +bert_embeddings_position_ids + +name=%bert_embeddings_position_ids + +op_code=get_attr + +target=bert.embeddings.position_ids + +num_users=1 + + + +getitem_4 + +name=%getitem_4 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), slice(0, add, None)),) + +num_users=1 + + + +bert_embeddings_position_ids->getitem_4 + + + + + +add->getitem_4 + + + + + +bert_embeddings_position_embeddings + +name=%bert_embeddings_position_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 512 +embedding_dim: 128 +padding_idx: None +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +getitem_4->bert_embeddings_position_embeddings + + + + + +add_1 + +name=%add_1 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_embeddings_word_embeddings->add_1 + + + + + +bert_embeddings_word_embeddings_weight + +bert_embeddings_word_embeddings_weight + +op_code=get_parametertorch.float32[30522, 128] + + + +bert_embeddings_word_embeddings_weight->bert_embeddings_word_embeddings + + + + + +bert_embeddings_token_type_embeddings->add_1 + + + + + +bert_embeddings_token_type_embeddings_weight + +bert_embeddings_token_type_embeddings_weight + +op_code=get_parametertorch.float32[2, 128] + + + +bert_embeddings_token_type_embeddings_weight->bert_embeddings_token_type_embeddings + + + + + +add_2 + +name=%add_2 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +add_1->add_2 + + + + + +bert_embeddings_position_embeddings->add_2 + + + + + +bert_embeddings_position_embeddings_weight + +bert_embeddings_position_embeddings_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_embeddings_position_embeddings_weight->bert_embeddings_position_embeddings + + + + + +bert_embeddings_layer_norm + +name=%bert_embeddings_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_2->bert_embeddings_layer_norm + + + + + +bert_embeddings_dropout + +name=%bert_embeddings_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_embeddings_layer_norm->bert_embeddings_dropout + + + + + +bert_embeddings_layer_norm_weight + +bert_embeddings_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_embeddings_layer_norm_weight->bert_embeddings_layer_norm + + + + + +bert_embeddings_layer_norm_bias + +bert_embeddings_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_embeddings_layer_norm_bias->bert_embeddings_layer_norm + + + + + +getattr_2 + +name=%getattr_2 + +op_code=call_function + +target=getattr + +args=(dtype,) + +num_users=2 + + + +bert_embeddings_dropout->getattr_2 + + + + + +size_4 + +name=%size_4 + +op_code=call_method + +target=size + +num_users=3 + + + +bert_embeddings_dropout->size_4 + + + + + +bert_encoder_layer_0_attention_self_query + +name=%bert_encoder_layer_0_attention_self_query + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_embeddings_dropout->bert_encoder_layer_0_attention_self_query + + + + + +bert_encoder_layer_0_attention_self_key + +name=%bert_encoder_layer_0_attention_self_key + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_embeddings_dropout->bert_encoder_layer_0_attention_self_key + + + + + +bert_encoder_layer_0_attention_self_value + +name=%bert_encoder_layer_0_attention_self_value + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_embeddings_dropout->bert_encoder_layer_0_attention_self_value + + + + + +add_7 + +name=%add_7 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_embeddings_dropout->add_7 + + + + + +add_3->ones + + + + + +getattr_1->ones + + + + + +dim + +name=%dim + +op_code=call_method + +target=dim + +num_users=1 + + + +ones->dim + + + + + +size_2 + +name=%size_2 + +op_code=call_method + +target=size + +num_users=2 + + + +ones->size_2 + + + + + +size_3 + +name=%size_3 + +op_code=call_method + +target=size + +num_users=2 + + + +ones->size_3 + + + + + +getitem_9 + +name=%getitem_9 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), None, None, slice(None, None, None)),) + +num_users=1 + + + +ones->getitem_9 + + + + + +eq + +name=%eq + +op_code=call_function + +target=_operator.eq + +args=(2,) + +num_users=0 + + + +dim->eq + + + + + +getitem_5 + +name=%getitem_5 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=0 + + + +size_2->getitem_5 + + + + + +getitem_6 + +name=%getitem_6 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=0 + + + +size_2->getitem_6 + + + + + +getitem_7 + +name=%getitem_7 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_3->getitem_7 + + + + + +getitem_8 + +name=%getitem_8 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_3->getitem_8 + + + + + +getitem_7->expand_1 + + + + + +getitem_8->expand_1 + + + + + +getitem_9->expand_1 + + + + + +to + +name=%to + +op_code=call_method + +target=to + +num_users=1 + + + +expand_1->to + + + + + +getattr_2->to + + + + + +finfo + +name=%finfo + +op_code=call_function + +target=torch.finfo + +num_users=1 + + + +getattr_2->finfo + + + + + +sub + +name=%sub + +op_code=call_function + +target=_operator.sub + +args=(1.0,) + +num_users=2 + + + +to->sub + + + + + +to_1 + +name=%to_1 + +op_code=call_method + +target=to + +args=(torch.bool,) + +num_users=1 + + + +sub->to_1 + + + + + +masked_fill + +name=%masked_fill + +op_code=call_method + +target=masked_fill + +num_users=2 + + + +sub->masked_fill + + + + + +to_1->masked_fill + + + + + +getattr_3 + +name=%getattr_3 + +op_code=call_function + +target=getattr + +args=(min,) + +num_users=1 + + + +finfo->getattr_3 + + + + + +getattr_3->masked_fill + + + + + +scaled_dot_product_attention + +name=%scaled_dot_product_attention + +op_code=call_function + +target=torch._C._nn.scaled_dot_product_attention + +kwargs={ +dropout_p: 0.0, +is_causal: False, +} + +num_users=1 + + + +masked_fill->scaled_dot_product_attention + + + + + +scaled_dot_product_attention_1 + +name=%scaled_dot_product_attention_1 + +op_code=call_function + +target=torch._C._nn.scaled_dot_product_attention + +kwargs={ +dropout_p: 0.0, +is_causal: False, +} + +num_users=1 + + + +masked_fill->scaled_dot_product_attention_1 + + + + + +getitem_10 + +name=%getitem_10 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_4->getitem_10 + + + + + +getitem_11 + +name=%getitem_11 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_4->getitem_11 + + + + + +getitem_12 + +name=%getitem_12 + +op_code=call_function + +target=_operator.getitem + +args=(2,) + +num_users=0 + + + +size_4->getitem_12 + + + + + +reshape + +name=%reshape + +op_code=call_method + +target=reshape + +args=(128,) + +num_users=1 + + + +getitem_10->reshape + + + + + +getitem_11->reshape + + + + + +size_5 + +name=%size_5 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_query->size_5 + + + + + +view + +name=%view + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_query->view + + + + + +bert_encoder_layer_0_attention_self_query_weight + +bert_encoder_layer_0_attention_self_query_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_query_weight->bert_encoder_layer_0_attention_self_query + + + + + +bert_encoder_layer_0_attention_self_query_bias + +bert_encoder_layer_0_attention_self_query_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_query_bias->bert_encoder_layer_0_attention_self_query + + + + + +getitem_13 + +name=%getitem_13 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_5->getitem_13 + + + + + +add_4 + +name=%add_4 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_13->add_4 + + + + + +add_4->view + + + + + +permute + +name=%permute + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view->permute + + + + + +permute->scaled_dot_product_attention + + + + + +size_6 + +name=%size_6 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_key->size_6 + + + + + +view_1 + +name=%view_1 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_key->view_1 + + + + + +bert_encoder_layer_0_attention_self_key_weight + +bert_encoder_layer_0_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_key_weight->bert_encoder_layer_0_attention_self_key + + + + + +bert_encoder_layer_0_attention_self_key_bias + +bert_encoder_layer_0_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_key_bias->bert_encoder_layer_0_attention_self_key + + + + + +getitem_14 + +name=%getitem_14 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_6->getitem_14 + + + + + +add_5 + +name=%add_5 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_14->add_5 + + + + + +add_5->view_1 + + + + + +permute_1 + +name=%permute_1 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_1->permute_1 + + + + + +permute_1->scaled_dot_product_attention + + + + + +size_7 + +name=%size_7 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_value->size_7 + + + + + +view_2 + +name=%view_2 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_value->view_2 + + + + + +bert_encoder_layer_0_attention_self_value_weight + +bert_encoder_layer_0_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_value_weight->bert_encoder_layer_0_attention_self_value + + + + + +bert_encoder_layer_0_attention_self_value_bias + +bert_encoder_layer_0_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_value_bias->bert_encoder_layer_0_attention_self_value + + + + + +getitem_15 + +name=%getitem_15 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_7->getitem_15 + + + + + +add_6 + +name=%add_6 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_15->add_6 + + + + + +add_6->view_2 + + + + + +permute_2 + +name=%permute_2 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_2->permute_2 + + + + + +permute_2->scaled_dot_product_attention + + + + + +transpose + +name=%transpose + +op_code=call_method + +target=transpose + +args=( +1, +2, +) + +num_users=1 + + + +scaled_dot_product_attention->transpose + + + + + +transpose->reshape + + + + + +bert_encoder_layer_0_attention_output_dense + +name=%bert_encoder_layer_0_attention_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +reshape->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dropout + +name=%bert_encoder_layer_0_attention_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_attention_output_dense->bert_encoder_layer_0_attention_output_dropout + + + + + +bert_encoder_layer_0_attention_output_dense_weight + +bert_encoder_layer_0_attention_output_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_output_dense_weight->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dense_bias + +bert_encoder_layer_0_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_dense_bias->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dropout->add_7 + + + + + +bert_encoder_layer_0_attention_output_layer_norm + +name=%bert_encoder_layer_0_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_7->bert_encoder_layer_0_attention_output_layer_norm + + + + + +bert_encoder_layer_0_intermediate_dense + +name=%bert_encoder_layer_0_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + +bert_encoder_layer_0_attention_output_layer_norm->bert_encoder_layer_0_intermediate_dense + + + + + +add_8 + +name=%add_8 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_attention_output_layer_norm->add_8 + + + + + +bert_encoder_layer_0_attention_output_layer_norm_weight + +bert_encoder_layer_0_attention_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_layer_norm_weight->bert_encoder_layer_0_attention_output_layer_norm + + + + + +bert_encoder_layer_0_attention_output_layer_norm_bias + +bert_encoder_layer_0_attention_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_layer_norm_bias->bert_encoder_layer_0_attention_output_layer_norm + + + + + +gelu + +name=%gelu + +op_code=call_function + +target=torch._C._nn.gelu + +num_users=1 + + + +bert_encoder_layer_0_intermediate_dense->gelu + + + + + +bert_encoder_layer_0_intermediate_dense_weight + +bert_encoder_layer_0_intermediate_dense_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_encoder_layer_0_intermediate_dense_weight->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_intermediate_dense_bias + +bert_encoder_layer_0_intermediate_dense_bias + +op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_0_intermediate_dense_bias->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_output_dense + +name=%bert_encoder_layer_0_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout + +name=%bert_encoder_layer_0_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_output_dense->bert_encoder_layer_0_output_dropout + + + + + +bert_encoder_layer_0_output_dense_weight + +bert_encoder_layer_0_output_dense_weight + +op_code=get_parametertorch.float32[128, 512] + + + +bert_encoder_layer_0_output_dense_weight->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dense_bias + +bert_encoder_layer_0_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_dense_bias->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout->add_8 + + + + + +bert_encoder_layer_0_output_layer_norm + +name=%bert_encoder_layer_0_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_8->bert_encoder_layer_0_output_layer_norm + + + + + +size_8 + +name=%size_8 + +op_code=call_method + +target=size + +num_users=3 + + + +bert_encoder_layer_0_output_layer_norm->size_8 + + + + + +bert_encoder_layer_1_attention_self_query + +name=%bert_encoder_layer_1_attention_self_query + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_key + +name=%bert_encoder_layer_1_attention_self_key + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_value + +name=%bert_encoder_layer_1_attention_self_value + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_value + + + + + +add_12 + +name=%add_12 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_output_layer_norm->add_12 + + + + + +bert_encoder_layer_0_output_layer_norm_weight + +bert_encoder_layer_0_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_weight->bert_encoder_layer_0_output_layer_norm + + + + + +bert_encoder_layer_0_output_layer_norm_bias + +bert_encoder_layer_0_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_bias->bert_encoder_layer_0_output_layer_norm + + + + + +getitem_16 + +name=%getitem_16 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_8->getitem_16 + + + + + +getitem_17 + +name=%getitem_17 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_8->getitem_17 + + + + + +getitem_18 + +name=%getitem_18 + +op_code=call_function + +target=_operator.getitem + +args=(2,) + +num_users=0 + + + +size_8->getitem_18 + + + + + +reshape_1 + +name=%reshape_1 + +op_code=call_method + +target=reshape + +args=(128,) + +num_users=1 + + + +getitem_16->reshape_1 + + + + + +getitem_17->reshape_1 + + + + + +size_9 + +name=%size_9 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->size_9 + + + + + +view_3 + +name=%view_3 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->view_3 + + + + + +bert_encoder_layer_1_attention_self_query_weight + +bert_encoder_layer_1_attention_self_query_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_query_weight->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_query_bias + +bert_encoder_layer_1_attention_self_query_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_query_bias->bert_encoder_layer_1_attention_self_query + + + + + +getitem_19 + +name=%getitem_19 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_9->getitem_19 + + + + + +add_9 + +name=%add_9 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_19->add_9 + + + + + +add_9->view_3 + + + + + +permute_3 + +name=%permute_3 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_3->permute_3 + + + + + +permute_3->scaled_dot_product_attention_1 + + + + + +size_10 + +name=%size_10 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->size_10 + + + + + +view_4 + +name=%view_4 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->view_4 + + + + + +bert_encoder_layer_1_attention_self_key_weight + +bert_encoder_layer_1_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_key_weight->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_key_bias + +bert_encoder_layer_1_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + 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+bert_encoder_layer_1_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_value_weight->bert_encoder_layer_1_attention_self_value + + + + + +bert_encoder_layer_1_attention_self_value_bias + +bert_encoder_layer_1_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_value_bias->bert_encoder_layer_1_attention_self_value + + + + + +getitem_21 + +name=%getitem_21 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_11->getitem_21 + + + + + +add_11 + +name=%add_11 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_21->add_11 + + + + + +add_11->view_5 + + + + + +permute_5 + +name=%permute_5 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_5->permute_5 + + + + + +permute_5->scaled_dot_product_attention_1 + + + + + +transpose_1 + +name=%transpose_1 + +op_code=call_method + +target=transpose + +args=( +1, +2, +) + +num_users=1 + + + +scaled_dot_product_attention_1->transpose_1 + + + + + +transpose_1->reshape_1 + + + + + +bert_encoder_layer_1_attention_output_dense + +name=%bert_encoder_layer_1_attention_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +reshape_1->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout + +name=%bert_encoder_layer_1_attention_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_1_attention_output_dense->bert_encoder_layer_1_attention_output_dropout + + + + + +bert_encoder_layer_1_attention_output_dense_weight + +bert_encoder_layer_1_attention_output_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_output_dense_weight->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dense_bias + +bert_encoder_layer_1_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_dense_bias->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout->add_12 + + + + + +bert_encoder_layer_1_attention_output_layer_norm + +name=%bert_encoder_layer_1_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_12->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_intermediate_dense + +name=%bert_encoder_layer_1_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + +bert_encoder_layer_1_attention_output_layer_norm->bert_encoder_layer_1_intermediate_dense + + + + + +add_13 + +name=%add_13 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_1_attention_output_layer_norm->add_13 + + + + + +bert_encoder_layer_1_attention_output_layer_norm_weight + +bert_encoder_layer_1_attention_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_layer_norm_weight->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_attention_output_layer_norm_bias + +bert_encoder_layer_1_attention_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_layer_norm_bias->bert_encoder_layer_1_attention_output_layer_norm + + + + + +gelu_1 + +name=%gelu_1 + +op_code=call_function + +target=torch._C._nn.gelu + +num_users=1 + + + +bert_encoder_layer_1_intermediate_dense->gelu_1 + + + + + +bert_encoder_layer_1_intermediate_dense_weight + +bert_encoder_layer_1_intermediate_dense_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_encoder_layer_1_intermediate_dense_weight->bert_encoder_layer_1_intermediate_dense + + + + + +bert_encoder_layer_1_intermediate_dense_bias + +bert_encoder_layer_1_intermediate_dense_bias + +op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_1_intermediate_dense_bias->bert_encoder_layer_1_intermediate_dense + + + + + +bert_encoder_layer_1_output_dense + +name=%bert_encoder_layer_1_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu_1->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dropout + +name=%bert_encoder_layer_1_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_1_output_dense->bert_encoder_layer_1_output_dropout + + + + + 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000000000..76c642fd8 --- /dev/null +++ b/docs/source/modules/documentation/tutorials/mase_graph.svg @@ -0,0 +1,3031 @@ + + + + + + +masegraph + + + +input_ids + +name=%input_ids + +op_code=placeholder + +target=input_ids + +num_users=3 + + + +size + +name=%size + +op_code=call_method + +target=size + +num_users=2 + + + +input_ids->size + + + + + +size_1 + +name=%size_1 + +op_code=call_method + +target=size + +num_users=1 + + + +input_ids->size_1 + + + + + +bert_embeddings_word_embeddings + +name=%bert_embeddings_word_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 30522 +embedding_dim: 128 +padding_idx: 0 +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +input_ids->bert_embeddings_word_embeddings + + + + + +attention_mask + +name=%attention_mask + +op_code=placeholder + +target=attention_mask + +num_users=4 + + + +dim + +name=%dim + +op_code=call_method + +target=dim + +num_users=1 + + + +attention_mask->dim + + + + + +size_2 + +name=%size_2 + +op_code=call_method + +target=size + +num_users=2 + + + +attention_mask->size_2 + + + + + +size_3 + +name=%size_3 + +op_code=call_method + +target=size + +num_users=2 + + + +attention_mask->size_3 + + + + + +getitem_9 + +name=%getitem_9 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), None, None, slice(None, None, None)),) + +num_users=1 + + + +attention_mask->getitem_9 + + + + + +labels + +name=%labels + +op_code=placeholder + +target=labels + +num_users=1 + + + +view_7 + +name=%view_7 + +op_code=call_method + +target=view + +args=(-1,) + +num_users=1 + + + +labels->view_7 + + + + + +getitem + +name=%getitem + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size->getitem + + + + + +getitem_1 + +name=%getitem_1 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=3 + + + +size->getitem_1 + + + + + +expand + +name=%expand + +op_code=call_method + +target=expand + +num_users=1 + + + +getitem->expand + + + + + +getitem_2 + +name=%getitem_2 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), slice(None, getitem_1, None)),) + +num_users=1 + + + +getitem_1->getitem_2 + + + + + +getitem_1->expand + + + + + +expand_1 + +name=%expand_1 + +op_code=call_method + +target=expand + +args=(1,) + +num_users=1 + + + +getitem_1->expand_1 + + + + + +bert_embeddings_token_type_ids + +name=%bert_embeddings_token_type_ids + +op_code=get_attr + +target=bert.embeddings.token_type_ids + +num_users=1 + + + +bert_embeddings_token_type_ids->getitem_2 + + + + + +getitem_2->expand + + + + + +bert_embeddings_token_type_embeddings + +name=%bert_embeddings_token_type_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 2 +embedding_dim: 128 +padding_idx: None +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +expand->bert_embeddings_token_type_embeddings + + + + + +getitem_3 + +name=%getitem_3 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_1->getitem_3 + + + + + +add + +name=%add + +op_code=call_function + +target=_operator.add + +args=(0,) + +num_users=1 + + + +getitem_3->add + + + + + +bert_embeddings_position_ids + +name=%bert_embeddings_position_ids + +op_code=get_attr + +target=bert.embeddings.position_ids + +num_users=1 + + + +getitem_4 + +name=%getitem_4 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), slice(0, add, None)),) + +num_users=1 + + + +bert_embeddings_position_ids->getitem_4 + + + + + +add->getitem_4 + + + + + +bert_embeddings_position_embeddings + +name=%bert_embeddings_position_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 512 +embedding_dim: 128 +padding_idx: None +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +getitem_4->bert_embeddings_position_embeddings + + + + + +add_1 + +name=%add_1 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_embeddings_word_embeddings->add_1 + + + + + +bert_embeddings_word_embeddings_weight + +bert_embeddings_word_embeddings_weight + +op_code=get_parametertorch.float32[30522, 128] + + + +bert_embeddings_word_embeddings_weight->bert_embeddings_word_embeddings + + + + + +bert_embeddings_token_type_embeddings->add_1 + + + + + +bert_embeddings_token_type_embeddings_weight + +bert_embeddings_token_type_embeddings_weight + +op_code=get_parametertorch.float32[2, 128] + + + +bert_embeddings_token_type_embeddings_weight->bert_embeddings_token_type_embeddings + + + + + +add_2 + +name=%add_2 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +add_1->add_2 + + + + + +bert_embeddings_position_embeddings->add_2 + + + + + +bert_embeddings_position_embeddings_weight + +bert_embeddings_position_embeddings_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_embeddings_position_embeddings_weight->bert_embeddings_position_embeddings + + + + + +bert_embeddings_layer_norm + +name=%bert_embeddings_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_2->bert_embeddings_layer_norm + + + + + +bert_embeddings_dropout + +name=%bert_embeddings_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_embeddings_layer_norm->bert_embeddings_dropout + + + + + +bert_embeddings_layer_norm_weight + +bert_embeddings_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_embeddings_layer_norm_weight->bert_embeddings_layer_norm + + + + + +bert_embeddings_layer_norm_bias + +bert_embeddings_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_embeddings_layer_norm_bias->bert_embeddings_layer_norm + + + + + +getattr_1 + +name=%getattr_1 + +op_code=call_function + +target=getattr + +args=(dtype,) + +num_users=2 + + + +bert_embeddings_dropout->getattr_1 + + + + + +size_4 + +name=%size_4 + +op_code=call_method + +target=size + +num_users=3 + + + +bert_embeddings_dropout->size_4 + + + + + +bert_encoder_layer_0_attention_self_query + +name=%bert_encoder_layer_0_attention_self_query + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_embeddings_dropout->bert_encoder_layer_0_attention_self_query + + + + + +bert_encoder_layer_0_attention_self_key + +name=%bert_encoder_layer_0_attention_self_key + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_embeddings_dropout->bert_encoder_layer_0_attention_self_key + + + + + +bert_encoder_layer_0_attention_self_value + +name=%bert_encoder_layer_0_attention_self_value + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_embeddings_dropout->bert_encoder_layer_0_attention_self_value + + + + + +add_6 + +name=%add_6 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_embeddings_dropout->add_6 + + + + + +eq + +name=%eq + +op_code=call_function + +target=_operator.eq + +args=(2,) + +num_users=0 + + + +dim->eq + + + + + +getitem_5 + +name=%getitem_5 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=0 + + + +size_2->getitem_5 + + + + + +getitem_6 + +name=%getitem_6 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=0 + + + +size_2->getitem_6 + + + + + +getitem_7 + +name=%getitem_7 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_3->getitem_7 + + + + + +getitem_8 + +name=%getitem_8 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_3->getitem_8 + + + + + +getitem_7->expand_1 + + + + + +getitem_8->expand_1 + + + + + +getitem_9->expand_1 + + + + + +to + +name=%to + +op_code=call_method + +target=to + +num_users=1 + + + +expand_1->to + + + + + +getattr_1->to + + + + + +finfo + +name=%finfo + +op_code=call_function + +target=torch.finfo + +num_users=1 + + + +getattr_1->finfo + + + + + +sub + +name=%sub + +op_code=call_function + +target=_operator.sub + +args=(1.0,) + +num_users=2 + + + +to->sub + + + + + +to_1 + +name=%to_1 + +op_code=call_method + +target=to + +args=(torch.bool,) + +num_users=1 + + + +sub->to_1 + + + + + +masked_fill + +name=%masked_fill + +op_code=call_method + +target=masked_fill + +num_users=2 + + + +sub->masked_fill + + + + + +to_1->masked_fill + + + + + +getattr_2 + +name=%getattr_2 + +op_code=call_function + +target=getattr + +args=(min,) + +num_users=1 + + + +finfo->getattr_2 + + + + + +getattr_2->masked_fill + + + + + +scaled_dot_product_attention + +name=%scaled_dot_product_attention + +op_code=call_function + +target=torch._C._nn.scaled_dot_product_attention + +kwargs={ +dropout_p: 0.0, +is_causal: False, +} + +num_users=1 + + + +masked_fill->scaled_dot_product_attention + + + + + +scaled_dot_product_attention_1 + +name=%scaled_dot_product_attention_1 + +op_code=call_function + +target=torch._C._nn.scaled_dot_product_attention + +kwargs={ +dropout_p: 0.0, +is_causal: False, +} + +num_users=1 + + + +masked_fill->scaled_dot_product_attention_1 + + + + + +getitem_10 + +name=%getitem_10 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_4->getitem_10 + + + + + +getitem_11 + +name=%getitem_11 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_4->getitem_11 + + + + + +getitem_12 + +name=%getitem_12 + +op_code=call_function + +target=_operator.getitem + +args=(2,) + +num_users=0 + + + +size_4->getitem_12 + + + + + +reshape + +name=%reshape + +op_code=call_method + +target=reshape + +args=(128,) + +num_users=1 + + + +getitem_10->reshape + + + + + +getitem_11->reshape + + + + + +size_5 + +name=%size_5 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_query->size_5 + + + + + +view + +name=%view + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_query->view + + + + + +bert_encoder_layer_0_attention_self_query_weight + +bert_encoder_layer_0_attention_self_query_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_query_weight->bert_encoder_layer_0_attention_self_query + + + + + +bert_encoder_layer_0_attention_self_query_bias + +bert_encoder_layer_0_attention_self_query_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_query_bias->bert_encoder_layer_0_attention_self_query + + + + + +getitem_13 + +name=%getitem_13 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_5->getitem_13 + + + + + +add_3 + +name=%add_3 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_13->add_3 + + + + + +add_3->view + + + + + +permute + +name=%permute + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view->permute + + + + + +permute->scaled_dot_product_attention + + + + + +size_6 + +name=%size_6 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_key->size_6 + + + + + +view_1 + +name=%view_1 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_key->view_1 + + + + + +bert_encoder_layer_0_attention_self_key_weight + +bert_encoder_layer_0_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_key_weight->bert_encoder_layer_0_attention_self_key + + + + + +bert_encoder_layer_0_attention_self_key_bias + +bert_encoder_layer_0_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_key_bias->bert_encoder_layer_0_attention_self_key + + + + + +getitem_14 + +name=%getitem_14 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_6->getitem_14 + + + + + +add_4 + +name=%add_4 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_14->add_4 + + + + + +add_4->view_1 + + + + + +permute_1 + +name=%permute_1 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_1->permute_1 + + + + + +permute_1->scaled_dot_product_attention + + + + + +size_7 + +name=%size_7 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_value->size_7 + + + + + +view_2 + +name=%view_2 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_value->view_2 + + + + + +bert_encoder_layer_0_attention_self_value_weight + +bert_encoder_layer_0_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_value_weight->bert_encoder_layer_0_attention_self_value + + + + + +bert_encoder_layer_0_attention_self_value_bias + +bert_encoder_layer_0_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_value_bias->bert_encoder_layer_0_attention_self_value + + + + + +getitem_15 + +name=%getitem_15 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_7->getitem_15 + + + + + +add_5 + +name=%add_5 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_15->add_5 + + + + + +add_5->view_2 + + + + + +permute_2 + +name=%permute_2 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_2->permute_2 + + + + + +permute_2->scaled_dot_product_attention + + + + + +transpose + +name=%transpose + +op_code=call_method + +target=transpose + +args=( +1, +2, +) + +num_users=1 + + + +scaled_dot_product_attention->transpose + + + + + +transpose->reshape + + + + + +bert_encoder_layer_0_attention_output_dense + +name=%bert_encoder_layer_0_attention_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +reshape->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dropout + +name=%bert_encoder_layer_0_attention_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_attention_output_dense->bert_encoder_layer_0_attention_output_dropout + + + + + +bert_encoder_layer_0_attention_output_dense_weight + +bert_encoder_layer_0_attention_output_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_output_dense_weight->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dense_bias + +bert_encoder_layer_0_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_dense_bias->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dropout->add_6 + + + + + +bert_encoder_layer_0_attention_output_layer_norm + +name=%bert_encoder_layer_0_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_6->bert_encoder_layer_0_attention_output_layer_norm + + + + + +bert_encoder_layer_0_intermediate_dense + +name=%bert_encoder_layer_0_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + +bert_encoder_layer_0_attention_output_layer_norm->bert_encoder_layer_0_intermediate_dense + + + + + +add_7 + +name=%add_7 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_attention_output_layer_norm->add_7 + + + + + +bert_encoder_layer_0_attention_output_layer_norm_weight + +bert_encoder_layer_0_attention_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_layer_norm_weight->bert_encoder_layer_0_attention_output_layer_norm + + + + + +bert_encoder_layer_0_attention_output_layer_norm_bias + +bert_encoder_layer_0_attention_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_layer_norm_bias->bert_encoder_layer_0_attention_output_layer_norm + + + + + +gelu + +name=%gelu + +op_code=call_function + +target=torch._C._nn.gelu + +num_users=1 + + + +bert_encoder_layer_0_intermediate_dense->gelu + + + + + +bert_encoder_layer_0_intermediate_dense_weight + +bert_encoder_layer_0_intermediate_dense_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_encoder_layer_0_intermediate_dense_weight->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_intermediate_dense_bias + +bert_encoder_layer_0_intermediate_dense_bias + +op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_0_intermediate_dense_bias->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_output_dense + +name=%bert_encoder_layer_0_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout + +name=%bert_encoder_layer_0_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_output_dense->bert_encoder_layer_0_output_dropout + + + + + +bert_encoder_layer_0_output_dense_weight + +bert_encoder_layer_0_output_dense_weight + +op_code=get_parametertorch.float32[128, 512] + + + +bert_encoder_layer_0_output_dense_weight->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dense_bias + +bert_encoder_layer_0_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_dense_bias->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout->add_7 + + + + + +bert_encoder_layer_0_output_layer_norm + +name=%bert_encoder_layer_0_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_7->bert_encoder_layer_0_output_layer_norm + + + + + +size_8 + +name=%size_8 + +op_code=call_method + +target=size + +num_users=3 + + + +bert_encoder_layer_0_output_layer_norm->size_8 + + + + + +bert_encoder_layer_1_attention_self_query + +name=%bert_encoder_layer_1_attention_self_query + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_key + +name=%bert_encoder_layer_1_attention_self_key + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_value + +name=%bert_encoder_layer_1_attention_self_value + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_value + + + + + +add_11 + +name=%add_11 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_output_layer_norm->add_11 + + + + + +bert_encoder_layer_0_output_layer_norm_weight + +bert_encoder_layer_0_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_weight->bert_encoder_layer_0_output_layer_norm + + + + + +bert_encoder_layer_0_output_layer_norm_bias + +bert_encoder_layer_0_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_bias->bert_encoder_layer_0_output_layer_norm + + + + + +getitem_16 + +name=%getitem_16 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_8->getitem_16 + + + + + +getitem_17 + +name=%getitem_17 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_8->getitem_17 + + + + + +getitem_18 + +name=%getitem_18 + +op_code=call_function + +target=_operator.getitem + +args=(2,) + +num_users=0 + + + +size_8->getitem_18 + + + + + +reshape_1 + +name=%reshape_1 + +op_code=call_method + +target=reshape + +args=(128,) + +num_users=1 + + + +getitem_16->reshape_1 + + + + + +getitem_17->reshape_1 + + + + + +size_9 + +name=%size_9 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->size_9 + + + + + +view_3 + +name=%view_3 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->view_3 + + + + + +bert_encoder_layer_1_attention_self_query_weight + +bert_encoder_layer_1_attention_self_query_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_query_weight->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_query_bias + +bert_encoder_layer_1_attention_self_query_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_query_bias->bert_encoder_layer_1_attention_self_query + + + + + +getitem_19 + +name=%getitem_19 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_9->getitem_19 + + + + + +add_8 + +name=%add_8 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_19->add_8 + + + + + +add_8->view_3 + + + + + +permute_3 + +name=%permute_3 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_3->permute_3 + + + + + +permute_3->scaled_dot_product_attention_1 + + + + + +size_10 + +name=%size_10 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->size_10 + + + + + +view_4 + +name=%view_4 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->view_4 + + + + + +bert_encoder_layer_1_attention_self_key_weight + +bert_encoder_layer_1_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_key_weight->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_key_bias + +bert_encoder_layer_1_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_key_bias->bert_encoder_layer_1_attention_self_key + + + + + +getitem_20 + +name=%getitem_20 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_10->getitem_20 + + + + + +add_9 + +name=%add_9 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_20->add_9 + + + + + +add_9->view_4 + + + + + +permute_4 + +name=%permute_4 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_4->permute_4 + + + + + +permute_4->scaled_dot_product_attention_1 + + + + + +size_11 + +name=%size_11 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_value->size_11 + + + + + +view_5 + +name=%view_5 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_value->view_5 + + + + + +bert_encoder_layer_1_attention_self_value_weight + +bert_encoder_layer_1_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_value_weight->bert_encoder_layer_1_attention_self_value + + + + + +bert_encoder_layer_1_attention_self_value_bias + +bert_encoder_layer_1_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_value_bias->bert_encoder_layer_1_attention_self_value + + + + + +getitem_21 + +name=%getitem_21 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_11->getitem_21 + + + + + +add_10 + +name=%add_10 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_21->add_10 + + + + + +add_10->view_5 + + + + + +permute_5 + +name=%permute_5 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_5->permute_5 + + + + + +permute_5->scaled_dot_product_attention_1 + + + + + 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+bert_encoder_layer_1_attention_output_dense_weight->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dense_bias + +bert_encoder_layer_1_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_dense_bias->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout->add_11 + + + + + +bert_encoder_layer_1_attention_output_layer_norm + +name=%bert_encoder_layer_1_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_11->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_intermediate_dense + +name=%bert_encoder_layer_1_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + 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+name=%crossentropyloss_0 + +op_code=call_module +torch.nn.modules.loss.CrossEntropyLoss + +ignore_index: -100 +reduction: mean +label_smoothing: 0.0 + + + +view_6->crossentropyloss_0 + + + + + +view_7->crossentropyloss_0 + + + + + +crossentropyloss_0->output + + + + + diff --git a/docs/source/modules/documentation/tutorials/mase_graph_not_removed.svg b/docs/source/modules/documentation/tutorials/mase_graph_not_removed.svg new file mode 100644 index 000000000..76c642fd8 --- /dev/null +++ b/docs/source/modules/documentation/tutorials/mase_graph_not_removed.svg @@ -0,0 +1,3031 @@ + + + + + + +masegraph + + + +input_ids + +name=%input_ids + +op_code=placeholder + +target=input_ids + +num_users=3 + + + +size + +name=%size + +op_code=call_method + +target=size + +num_users=2 + + + +input_ids->size + + + + + +size_1 + +name=%size_1 + +op_code=call_method + +target=size + +num_users=1 + + + +input_ids->size_1 + + + + + +bert_embeddings_word_embeddings + +name=%bert_embeddings_word_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 30522 +embedding_dim: 128 +padding_idx: 0 +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +input_ids->bert_embeddings_word_embeddings + + + + + +attention_mask + +name=%attention_mask + +op_code=placeholder + +target=attention_mask + +num_users=4 + + + +dim + +name=%dim + +op_code=call_method + +target=dim + +num_users=1 + + + +attention_mask->dim + + + + + +size_2 + +name=%size_2 + +op_code=call_method + +target=size + +num_users=2 + + + +attention_mask->size_2 + + + + + +size_3 + +name=%size_3 + +op_code=call_method + +target=size + +num_users=2 + + + +attention_mask->size_3 + + + + + +getitem_9 + +name=%getitem_9 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), None, None, slice(None, None, None)),) + +num_users=1 + + + +attention_mask->getitem_9 + + + + + +labels + +name=%labels + +op_code=placeholder + +target=labels + +num_users=1 + + + +view_7 + +name=%view_7 + +op_code=call_method + +target=view + +args=(-1,) + +num_users=1 + + + +labels->view_7 + + + + + +getitem + +name=%getitem + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size->getitem + + + + + +getitem_1 + +name=%getitem_1 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=3 + + + +size->getitem_1 + + + + + +expand + +name=%expand + +op_code=call_method + +target=expand + +num_users=1 + + + +getitem->expand + + + + + +getitem_2 + +name=%getitem_2 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), slice(None, getitem_1, None)),) + +num_users=1 + + + +getitem_1->getitem_2 + + + + + +getitem_1->expand + + + + + +expand_1 + +name=%expand_1 + +op_code=call_method + +target=expand + +args=(1,) + +num_users=1 + + + +getitem_1->expand_1 + + + + + +bert_embeddings_token_type_ids + +name=%bert_embeddings_token_type_ids + +op_code=get_attr + +target=bert.embeddings.token_type_ids + +num_users=1 + + + +bert_embeddings_token_type_ids->getitem_2 + + + + + +getitem_2->expand + + + + + +bert_embeddings_token_type_embeddings + +name=%bert_embeddings_token_type_embeddings + +op_code=call_module +torch.nn.modules.sparse.Embedding + +num_embeddings: 2 +embedding_dim: 128 +padding_idx: None +max_norm: None +norm_type: 2.0 +scale_grad_by_freq: False +sparse: False + + + +expand->bert_embeddings_token_type_embeddings + + + + + +getitem_3 + +name=%getitem_3 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_1->getitem_3 + + + + + +add + +name=%add + +op_code=call_function + +target=_operator.add + +args=(0,) + +num_users=1 + + + +getitem_3->add + + + + + +bert_embeddings_position_ids + +name=%bert_embeddings_position_ids + +op_code=get_attr + +target=bert.embeddings.position_ids + +num_users=1 + + + +getitem_4 + +name=%getitem_4 + +op_code=call_function + +target=_operator.getitem + 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+bert_encoder_layer_0_attention_self_key->view_1 + + + + + +bert_encoder_layer_0_attention_self_key_weight + +bert_encoder_layer_0_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_key_weight->bert_encoder_layer_0_attention_self_key + + + + + +bert_encoder_layer_0_attention_self_key_bias + +bert_encoder_layer_0_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_key_bias->bert_encoder_layer_0_attention_self_key + + + + + +getitem_14 + +name=%getitem_14 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_6->getitem_14 + + + + + +add_4 + +name=%add_4 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_14->add_4 + + + + + +add_4->view_1 + + + + + +permute_1 + +name=%permute_1 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_1->permute_1 + + + + + +permute_1->scaled_dot_product_attention + + + + + +size_7 + +name=%size_7 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_0_attention_self_value->size_7 + + + + + +view_2 + +name=%view_2 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_0_attention_self_value->view_2 + + + + + +bert_encoder_layer_0_attention_self_value_weight + +bert_encoder_layer_0_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_self_value_weight->bert_encoder_layer_0_attention_self_value + + + + + +bert_encoder_layer_0_attention_self_value_bias + +bert_encoder_layer_0_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_self_value_bias->bert_encoder_layer_0_attention_self_value + + + + + +getitem_15 + +name=%getitem_15 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_7->getitem_15 + + + + + +add_5 + +name=%add_5 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_15->add_5 + + + + + +add_5->view_2 + + + + + +permute_2 + +name=%permute_2 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_2->permute_2 + + + + + +permute_2->scaled_dot_product_attention + + + + + +transpose + +name=%transpose + +op_code=call_method + +target=transpose + +args=( +1, +2, +) + +num_users=1 + + + +scaled_dot_product_attention->transpose + + + + + +transpose->reshape + + + + + +bert_encoder_layer_0_attention_output_dense + +name=%bert_encoder_layer_0_attention_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +reshape->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dropout + +name=%bert_encoder_layer_0_attention_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_attention_output_dense->bert_encoder_layer_0_attention_output_dropout + + + + + +bert_encoder_layer_0_attention_output_dense_weight + +bert_encoder_layer_0_attention_output_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_0_attention_output_dense_weight->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dense_bias + +bert_encoder_layer_0_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_dense_bias->bert_encoder_layer_0_attention_output_dense + + + + + +bert_encoder_layer_0_attention_output_dropout->add_6 + + + + + +bert_encoder_layer_0_attention_output_layer_norm + +name=%bert_encoder_layer_0_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_6->bert_encoder_layer_0_attention_output_layer_norm + + + + + +bert_encoder_layer_0_intermediate_dense + +name=%bert_encoder_layer_0_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + +bert_encoder_layer_0_attention_output_layer_norm->bert_encoder_layer_0_intermediate_dense + + + + + +add_7 + +name=%add_7 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_attention_output_layer_norm->add_7 + + + + + +bert_encoder_layer_0_attention_output_layer_norm_weight + +bert_encoder_layer_0_attention_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_layer_norm_weight->bert_encoder_layer_0_attention_output_layer_norm + + + + + +bert_encoder_layer_0_attention_output_layer_norm_bias + +bert_encoder_layer_0_attention_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_attention_output_layer_norm_bias->bert_encoder_layer_0_attention_output_layer_norm + + + + + +gelu + +name=%gelu + +op_code=call_function + +target=torch._C._nn.gelu + +num_users=1 + + + +bert_encoder_layer_0_intermediate_dense->gelu + + + + + +bert_encoder_layer_0_intermediate_dense_weight + +bert_encoder_layer_0_intermediate_dense_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_encoder_layer_0_intermediate_dense_weight->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_intermediate_dense_bias + +bert_encoder_layer_0_intermediate_dense_bias + +op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_0_intermediate_dense_bias->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_output_dense + +name=%bert_encoder_layer_0_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout + +name=%bert_encoder_layer_0_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_output_dense->bert_encoder_layer_0_output_dropout + + + + + +bert_encoder_layer_0_output_dense_weight + +bert_encoder_layer_0_output_dense_weight + +op_code=get_parametertorch.float32[128, 512] + + + +bert_encoder_layer_0_output_dense_weight->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dense_bias + +bert_encoder_layer_0_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_dense_bias->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout->add_7 + + + + + +bert_encoder_layer_0_output_layer_norm + +name=%bert_encoder_layer_0_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_7->bert_encoder_layer_0_output_layer_norm + + + + + +size_8 + +name=%size_8 + +op_code=call_method + +target=size + +num_users=3 + + + +bert_encoder_layer_0_output_layer_norm->size_8 + + + + + +bert_encoder_layer_1_attention_self_query + +name=%bert_encoder_layer_1_attention_self_query + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_key + +name=%bert_encoder_layer_1_attention_self_key + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_value + +name=%bert_encoder_layer_1_attention_self_value + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_value + + + + + +add_11 + +name=%add_11 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_output_layer_norm->add_11 + + + + + +bert_encoder_layer_0_output_layer_norm_weight + +bert_encoder_layer_0_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_weight->bert_encoder_layer_0_output_layer_norm + + + + + +bert_encoder_layer_0_output_layer_norm_bias + +bert_encoder_layer_0_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_bias->bert_encoder_layer_0_output_layer_norm + + + + + +getitem_16 + +name=%getitem_16 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_8->getitem_16 + + + + + +getitem_17 + +name=%getitem_17 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_8->getitem_17 + + + + + +getitem_18 + +name=%getitem_18 + +op_code=call_function + +target=_operator.getitem + +args=(2,) + +num_users=0 + + + +size_8->getitem_18 + + + + + +reshape_1 + +name=%reshape_1 + +op_code=call_method + +target=reshape + +args=(128,) + +num_users=1 + + + +getitem_16->reshape_1 + + + + + +getitem_17->reshape_1 + + + + + +size_9 + +name=%size_9 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->size_9 + + + + + +view_3 + +name=%view_3 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->view_3 + + + + + +bert_encoder_layer_1_attention_self_query_weight + +bert_encoder_layer_1_attention_self_query_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_query_weight->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_query_bias + +bert_encoder_layer_1_attention_self_query_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_query_bias->bert_encoder_layer_1_attention_self_query + + + + + +getitem_19 + +name=%getitem_19 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_9->getitem_19 + + + + + +add_8 + +name=%add_8 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_19->add_8 + + + + + +add_8->view_3 + + + + + +permute_3 + +name=%permute_3 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_3->permute_3 + + + + + +permute_3->scaled_dot_product_attention_1 + + + + + +size_10 + +name=%size_10 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->size_10 + + + + + +view_4 + +name=%view_4 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->view_4 + + + + + +bert_encoder_layer_1_attention_self_key_weight + +bert_encoder_layer_1_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_key_weight->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_key_bias + +bert_encoder_layer_1_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_key_bias->bert_encoder_layer_1_attention_self_key + + + + + +getitem_20 + +name=%getitem_20 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_10->getitem_20 + + + + + +add_9 + +name=%add_9 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_20->add_9 + + + + + +add_9->view_4 + + + + + +permute_4 + +name=%permute_4 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_4->permute_4 + + + + + +permute_4->scaled_dot_product_attention_1 + + + + + +size_11 + +name=%size_11 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_value->size_11 + + + + + +view_5 + +name=%view_5 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_value->view_5 + + + + + +bert_encoder_layer_1_attention_self_value_weight + +bert_encoder_layer_1_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_value_weight->bert_encoder_layer_1_attention_self_value + + + + + +bert_encoder_layer_1_attention_self_value_bias + +bert_encoder_layer_1_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_value_bias->bert_encoder_layer_1_attention_self_value + + + + + +getitem_21 + +name=%getitem_21 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_11->getitem_21 + + + + + +add_10 + +name=%add_10 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_21->add_10 + + + + + +add_10->view_5 + + + + + +permute_5 + +name=%permute_5 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_5->permute_5 + + + + + +permute_5->scaled_dot_product_attention_1 + + + + + +transpose_1 + +name=%transpose_1 + +op_code=call_method + +target=transpose + +args=( +1, +2, +) + +num_users=1 + + + +scaled_dot_product_attention_1->transpose_1 + + + + + +transpose_1->reshape_1 + + + + + +bert_encoder_layer_1_attention_output_dense + +name=%bert_encoder_layer_1_attention_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +reshape_1->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout + +name=%bert_encoder_layer_1_attention_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_1_attention_output_dense->bert_encoder_layer_1_attention_output_dropout + + + + + +bert_encoder_layer_1_attention_output_dense_weight + +bert_encoder_layer_1_attention_output_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_output_dense_weight->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dense_bias + +bert_encoder_layer_1_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_dense_bias->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout->add_11 + + + + + +bert_encoder_layer_1_attention_output_layer_norm + +name=%bert_encoder_layer_1_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_11->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_intermediate_dense + +name=%bert_encoder_layer_1_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + +bert_encoder_layer_1_attention_output_layer_norm->bert_encoder_layer_1_intermediate_dense + + + + + +add_12 + +name=%add_12 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_1_attention_output_layer_norm->add_12 + + + + + +bert_encoder_layer_1_attention_output_layer_norm_weight + +bert_encoder_layer_1_attention_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_layer_norm_weight->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_attention_output_layer_norm_bias + +bert_encoder_layer_1_attention_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_layer_norm_bias->bert_encoder_layer_1_attention_output_layer_norm + + + + + +gelu_1 + +name=%gelu_1 + +op_code=call_function + +target=torch._C._nn.gelu + +num_users=1 + + + +bert_encoder_layer_1_intermediate_dense->gelu_1 + + + + + +bert_encoder_layer_1_intermediate_dense_weight + +bert_encoder_layer_1_intermediate_dense_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_encoder_layer_1_intermediate_dense_weight->bert_encoder_layer_1_intermediate_dense + + + + + +bert_encoder_layer_1_intermediate_dense_bias + +bert_encoder_layer_1_intermediate_dense_bias + +op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_1_intermediate_dense_bias->bert_encoder_layer_1_intermediate_dense + + + + + +bert_encoder_layer_1_output_dense + +name=%bert_encoder_layer_1_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu_1->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dropout + +name=%bert_encoder_layer_1_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_1_output_dense->bert_encoder_layer_1_output_dropout + + + + + +bert_encoder_layer_1_output_dense_weight + +bert_encoder_layer_1_output_dense_weight + +op_code=get_parametertorch.float32[128, 512] + + + +bert_encoder_layer_1_output_dense_weight->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dense_bias + +bert_encoder_layer_1_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_output_dense_bias->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dropout->add_12 + + + + + +bert_encoder_layer_1_output_layer_norm + +name=%bert_encoder_layer_1_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_12->bert_encoder_layer_1_output_layer_norm + + + + + +getitem_22 + +name=%getitem_22 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), 0),) + +num_users=1 + + + +bert_encoder_layer_1_output_layer_norm->getitem_22 + + + + + +bert_encoder_layer_1_output_layer_norm_weight + +bert_encoder_layer_1_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_output_layer_norm_weight->bert_encoder_layer_1_output_layer_norm + + + + + +bert_encoder_layer_1_output_layer_norm_bias + +bert_encoder_layer_1_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_output_layer_norm_bias->bert_encoder_layer_1_output_layer_norm + + + + + +bert_pooler_dense + +name=%bert_pooler_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +getitem_22->bert_pooler_dense + + + + + +bert_pooler_activation + +name=%bert_pooler_activation + 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+op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_0_intermediate_dense_bias->bert_encoder_layer_0_intermediate_dense + + + + + +bert_encoder_layer_0_output_dense + +name=%bert_encoder_layer_0_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout + +name=%bert_encoder_layer_0_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_0_output_dense->bert_encoder_layer_0_output_dropout + + + + + +bert_encoder_layer_0_output_dense_weight + +bert_encoder_layer_0_output_dense_weight + +op_code=get_parametertorch.float32[128, 512] + + + +bert_encoder_layer_0_output_dense_weight->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dense_bias + +bert_encoder_layer_0_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_dense_bias->bert_encoder_layer_0_output_dense + + + + + +bert_encoder_layer_0_output_dropout->add_8 + + + + + +bert_encoder_layer_0_output_layer_norm + +name=%bert_encoder_layer_0_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_8->bert_encoder_layer_0_output_layer_norm + + + + + +size_8 + +name=%size_8 + +op_code=call_method + +target=size + +num_users=3 + + + +bert_encoder_layer_0_output_layer_norm->size_8 + + + + + +bert_encoder_layer_1_attention_self_query + +name=%bert_encoder_layer_1_attention_self_query + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_key + +name=%bert_encoder_layer_1_attention_self_key + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_value + +name=%bert_encoder_layer_1_attention_self_value + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +bert_encoder_layer_0_output_layer_norm->bert_encoder_layer_1_attention_self_value + + + + + +add_12 + +name=%add_12 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_0_output_layer_norm->add_12 + + + + + +bert_encoder_layer_0_output_layer_norm_weight + +bert_encoder_layer_0_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_weight->bert_encoder_layer_0_output_layer_norm + + + + + +bert_encoder_layer_0_output_layer_norm_bias + +bert_encoder_layer_0_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_0_output_layer_norm_bias->bert_encoder_layer_0_output_layer_norm + + + + + +getitem_16 + +name=%getitem_16 + +op_code=call_function + +target=_operator.getitem + +args=(0,) + +num_users=1 + + + +size_8->getitem_16 + + + + + +getitem_17 + +name=%getitem_17 + +op_code=call_function + +target=_operator.getitem + +args=(1,) + +num_users=1 + + + +size_8->getitem_17 + + + + + +getitem_18 + +name=%getitem_18 + +op_code=call_function + +target=_operator.getitem + +args=(2,) + +num_users=0 + + + +size_8->getitem_18 + + + + + +reshape_1 + +name=%reshape_1 + +op_code=call_method + +target=reshape + +args=(128,) + +num_users=1 + + + +getitem_16->reshape_1 + + + + + +getitem_17->reshape_1 + + + + + +size_9 + +name=%size_9 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->size_9 + + + + + +view_3 + +name=%view_3 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_query->view_3 + + + + + +bert_encoder_layer_1_attention_self_query_weight + +bert_encoder_layer_1_attention_self_query_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_query_weight->bert_encoder_layer_1_attention_self_query + + + + + +bert_encoder_layer_1_attention_self_query_bias + +bert_encoder_layer_1_attention_self_query_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_query_bias->bert_encoder_layer_1_attention_self_query + + + + + +getitem_19 + +name=%getitem_19 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_9->getitem_19 + + + + + +add_9 + +name=%add_9 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_19->add_9 + + + + + +add_9->view_3 + + + + + +permute_3 + +name=%permute_3 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_3->permute_3 + + + + + +permute_3->scaled_dot_product_attention_1 + + + + + +size_10 + +name=%size_10 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->size_10 + + + + + +view_4 + +name=%view_4 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_key->view_4 + + + + + +bert_encoder_layer_1_attention_self_key_weight + +bert_encoder_layer_1_attention_self_key_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_key_weight->bert_encoder_layer_1_attention_self_key + + + + + +bert_encoder_layer_1_attention_self_key_bias + +bert_encoder_layer_1_attention_self_key_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_key_bias->bert_encoder_layer_1_attention_self_key + + + + + +getitem_20 + +name=%getitem_20 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_10->getitem_20 + + + + + +add_10 + +name=%add_10 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_20->add_10 + + + + + +add_10->view_4 + + + + + +permute_4 + +name=%permute_4 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_4->permute_4 + + + + + +permute_4->scaled_dot_product_attention_1 + + + + + +size_11 + +name=%size_11 + +op_code=call_method + +target=size + +num_users=1 + + + +bert_encoder_layer_1_attention_self_value->size_11 + + + + + +view_5 + +name=%view_5 + +op_code=call_method + +target=view + +num_users=1 + + + +bert_encoder_layer_1_attention_self_value->view_5 + + + + + +bert_encoder_layer_1_attention_self_value_weight + +bert_encoder_layer_1_attention_self_value_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_self_value_weight->bert_encoder_layer_1_attention_self_value + + + + + +bert_encoder_layer_1_attention_self_value_bias + +bert_encoder_layer_1_attention_self_value_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_self_value_bias->bert_encoder_layer_1_attention_self_value + + + + + +getitem_21 + +name=%getitem_21 + +op_code=call_function + +target=_operator.getitem + +args=(slice(None, -1, None),) + +num_users=1 + + + +size_11->getitem_21 + + + + + +add_11 + +name=%add_11 + +op_code=call_function + +target=_operator.add + +args=((2, 64),) + +num_users=1 + + + +getitem_21->add_11 + + + + + +add_11->view_5 + + + + + +permute_5 + +name=%permute_5 + +op_code=call_method + +target=permute + +args=( +0, +2, +1, +3, +) + +num_users=1 + + + +view_5->permute_5 + + + + + +permute_5->scaled_dot_product_attention_1 + + + + + +transpose_1 + +name=%transpose_1 + +op_code=call_method + +target=transpose + +args=( +1, +2, +) + +num_users=1 + + + +scaled_dot_product_attention_1->transpose_1 + + + + + +transpose_1->reshape_1 + + + + + +bert_encoder_layer_1_attention_output_dense + +name=%bert_encoder_layer_1_attention_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +reshape_1->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout + +name=%bert_encoder_layer_1_attention_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_1_attention_output_dense->bert_encoder_layer_1_attention_output_dropout + + + + + +bert_encoder_layer_1_attention_output_dense_weight + +bert_encoder_layer_1_attention_output_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_encoder_layer_1_attention_output_dense_weight->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dense_bias + +bert_encoder_layer_1_attention_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_dense_bias->bert_encoder_layer_1_attention_output_dense + + + + + +bert_encoder_layer_1_attention_output_dropout->add_12 + + + + + +bert_encoder_layer_1_attention_output_layer_norm + +name=%bert_encoder_layer_1_attention_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_12->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_intermediate_dense + +name=%bert_encoder_layer_1_intermediate_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 512 + + + +bert_encoder_layer_1_attention_output_layer_norm->bert_encoder_layer_1_intermediate_dense + + + + + +add_13 + +name=%add_13 + +op_code=call_function + +target=_operator.add + +num_users=1 + + + +bert_encoder_layer_1_attention_output_layer_norm->add_13 + + + + + +bert_encoder_layer_1_attention_output_layer_norm_weight + +bert_encoder_layer_1_attention_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_layer_norm_weight->bert_encoder_layer_1_attention_output_layer_norm + + + + + +bert_encoder_layer_1_attention_output_layer_norm_bias + +bert_encoder_layer_1_attention_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_attention_output_layer_norm_bias->bert_encoder_layer_1_attention_output_layer_norm + + + + + +gelu_1 + +name=%gelu_1 + +op_code=call_function + +target=torch._C._nn.gelu + +num_users=1 + + + +bert_encoder_layer_1_intermediate_dense->gelu_1 + + + + + +bert_encoder_layer_1_intermediate_dense_weight + +bert_encoder_layer_1_intermediate_dense_weight + +op_code=get_parametertorch.float32[512, 128] + + + +bert_encoder_layer_1_intermediate_dense_weight->bert_encoder_layer_1_intermediate_dense + + + + + +bert_encoder_layer_1_intermediate_dense_bias + +bert_encoder_layer_1_intermediate_dense_bias + +op_code=get_parametertorch.float32[512] + + + +bert_encoder_layer_1_intermediate_dense_bias->bert_encoder_layer_1_intermediate_dense + + + + + +bert_encoder_layer_1_output_dense + +name=%bert_encoder_layer_1_output_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 512 +out_features: 128 + + + +gelu_1->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dropout + +name=%bert_encoder_layer_1_output_dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_encoder_layer_1_output_dense->bert_encoder_layer_1_output_dropout + + + + + +bert_encoder_layer_1_output_dense_weight + +bert_encoder_layer_1_output_dense_weight + +op_code=get_parametertorch.float32[128, 512] + + + +bert_encoder_layer_1_output_dense_weight->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dense_bias + +bert_encoder_layer_1_output_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_output_dense_bias->bert_encoder_layer_1_output_dense + + + + + +bert_encoder_layer_1_output_dropout->add_13 + + + + + +bert_encoder_layer_1_output_layer_norm + +name=%bert_encoder_layer_1_output_layer_norm + +op_code=call_module +torch.nn.modules.normalization.LayerNorm + +normalized_shape: (128,) +eps: 1e-12 +elementwise_affine: True + + + +add_13->bert_encoder_layer_1_output_layer_norm + + + + + +getitem_22 + +name=%getitem_22 + +op_code=call_function + +target=_operator.getitem + +args=((slice(None, None, None), 0),) + +num_users=1 + + + +bert_encoder_layer_1_output_layer_norm->getitem_22 + + + + + +bert_encoder_layer_1_output_layer_norm_weight + +bert_encoder_layer_1_output_layer_norm_weight + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_output_layer_norm_weight->bert_encoder_layer_1_output_layer_norm + + + + + +bert_encoder_layer_1_output_layer_norm_bias + +bert_encoder_layer_1_output_layer_norm_bias + +op_code=get_parametertorch.float32[128] + + + +bert_encoder_layer_1_output_layer_norm_bias->bert_encoder_layer_1_output_layer_norm + + + + + +bert_pooler_dense + +name=%bert_pooler_dense + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 128 + + + +getitem_22->bert_pooler_dense + + + + + +bert_pooler_activation + +name=%bert_pooler_activation + +op_code=call_module +torch.nn.modules.activation.Tanh + + + + +bert_pooler_dense->bert_pooler_activation + + + + + +bert_pooler_dense_weight + +bert_pooler_dense_weight + +op_code=get_parametertorch.float32[128, 128] + + + +bert_pooler_dense_weight->bert_pooler_dense + + + + + +bert_pooler_dense_bias + +bert_pooler_dense_bias + +op_code=get_parametertorch.float32[128] + + + +bert_pooler_dense_bias->bert_pooler_dense + + + + + +dropout + +name=%dropout + +op_code=call_module +torch.nn.modules.dropout.Dropout + +p: 0.1 +inplace: False + + + +bert_pooler_activation->dropout + + + + + +classifier + +name=%classifier + +op_code=call_module +torch.nn.modules.linear.Linear + +in_features: 128 +out_features: 2 + + + +dropout->classifier + + + + + +output + +name=%output + +op_code=output + +target=output + +num_users=0 + + + +classifier->output + + + + + +classifier_weight + +classifier_weight + +op_code=get_parametertorch.float32[2, 128] + + + +classifier_weight->classifier + + + + + +classifier_bias + +classifier_bias + +op_code=get_parametertorch.float32[2] + + + +classifier_bias->classifier + + + + + diff --git a/docs/source/modules/documentation/tutorials/tutorial_1_introduction_to_mase.ipynb b/docs/source/modules/documentation/tutorials/tutorial_1_introduction_to_mase.ipynb index 6bffe37d9..5f21967ce 100644 --- a/docs/source/modules/documentation/tutorials/tutorial_1_introduction_to_mase.ipynb +++ b/docs/source/modules/documentation/tutorials/tutorial_1_introduction_to_mase.ipynb @@ -25,7 +25,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/yz10513/anaconda3/envs/mase/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/opt/anaconda3/envs/mase/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" @@ -279,10 +279,853 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n", + " [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n", + "tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n", + "tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n", + " [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n", + "tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 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-8.2075e-01, -1.1496e+00],\n", + " [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n", + " -5.5070e-01, -1.8440e+00],\n", + " [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n", + " -4.6589e-01, -2.0454e+00],\n", + " ...,\n", + " [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n", + " 3.1643e-01, -2.0784e+00],\n", + " [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n", + " -1.0588e+00, -7.6781e-01],\n", + " [-1.6402e+00, 3.1250e-01, -6.6098e-04, ..., 6.6493e-01,\n", + " -9.1110e-01, -1.0382e+00]]]],\n", + " grad_fn=)\n", + "tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n", + " -6.5765e-01, -1.4711e-01],\n", + " [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3790e-03,\n", + " -5.2249e-01, -4.2095e-01]],\n", + "\n", + " [[ 3.9396e-01, 4.7657e-02, -1.0277e+00, ..., -8.4926e-01,\n", + " -2.7815e-01, -2.6628e-01],\n", + " [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n", + " -4.1385e-01, -1.3744e+00]],\n", + "\n", + " [[ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n", + " -4.3860e-01, -2.1582e-01],\n", + " [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n", + " -1.6626e-01, -7.0940e-01]],\n", + "\n", + " ...,\n", + "\n", + " [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n", + " 6.3807e-01, -8.3788e-02],\n", + " [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n", + " -6.9561e-01, -8.5675e-01]],\n", + "\n", + " [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n", + " 4.1589e-01, -1.8024e-01],\n", + " [-9.1553e-01, 1.4648e-01, -5.5622e-02, ..., 1.8643e-01,\n", + " -1.0965e+00, -4.8097e-01]],\n", + "\n", + " [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n", + " 8.4396e-02, -6.9034e-02],\n", + " [-1.0678e+00, 9.0884e-02, -4.5400e-02, ..., 1.5424e-01,\n", + " -1.1762e+00, -2.9385e-01]]],\n", + "\n", + "\n", + " [[[ 4.9445e-01, 9.6662e-03, -5.7118e-01, ..., -9.7640e-01,\n", + " -9.0948e-01, -1.3761e-01],\n", + " [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n", + " -8.2075e-01, -1.1496e+00]],\n", + "\n", + " [[ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n", + " -3.9088e-01, -3.1635e-01],\n", + " [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n", + " -5.5070e-01, -1.8440e+00]],\n", + "\n", + " [[ 4.5120e-01, -5.8034e-02, -6.8736e-01, ..., -1.0352e+00,\n", + " -4.7996e-01, -4.5624e-01],\n", + " [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n", + " -4.6589e-01, -2.0454e+00]],\n", + "\n", + " ...,\n", + "\n", + " [[ 3.3362e-01, 1.5913e-03, -1.0108e+00, ..., -1.5704e+00,\n", + " -3.9079e-01, -2.1742e-01],\n", + " [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n", + " 3.1643e-01, -2.0784e+00]],\n", + "\n", + " [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n", + " 1.3298e-01, -1.5481e+00],\n", + " [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n", + " -1.0588e+00, -7.6781e-01]],\n", + "\n", + " [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n", + " -4.1355e-01, -5.8558e-01],\n", + " [-1.6402e+00, 3.1250e-01, -6.6098e-04, ..., 6.6493e-01,\n", + " -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n" + ] + } + ], "source": [ "import torch\n", - "import chop.passes as passes\n", + "import chop.passes as passes # type: ignore\n", "from transformers import AutoTokenizer\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", @@ -321,11 +1164,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.embeddings.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.0.attention.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.0.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.1.attention.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: bert.encoder.layer.1.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mFound dropout module: dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mDropout count is: 6\u001b[0m\n" + ] + } + ], "source": [ - "from chop.tools import get_logger\n", + "from chop.tools import get_logger # type: ignore\n", "\n", "logger = get_logger(\"mase_logger\")\n", "logger.setLevel(\"INFO\")\n", @@ -371,9 +1228,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.embeddings.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.0.attention.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.0.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.1.attention.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: bert.encoder.layer.1.output.dropout\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mRemoving dropout module: dropout\u001b[0m\n" + ] + } + ], "source": [ "import torch.fx as fx\n", "\n", @@ -419,9 +1289,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /Users/antoniotarizzo/tutorial_1.pt, /Users/antoniotarizzo/tutorial_1.mz\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /Users/antoniotarizzo/tutorial_1.pt\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /Users/antoniotarizzo/tutorial_1.mz\u001b[0m\n" + ] + } + ], "source": [ "from pathlib import Path\n", "\n", @@ -437,9 +1317,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/antoniotarizzo/iCloud-Drive-Offline/Documents/ Imperial/0109 Advanced Deep Learning/adl-mase/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " loaded_model = torch.load(f)\n" + ] + } + ], "source": [ "new_mg = MaseGraph.from_checkpoint(f\"{Path.home()}/tutorial_1\")" ] @@ -461,7 +1350,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/docs/source/modules/documentation/tutorials/tutorial_2_lora_finetune.ipynb b/docs/source/modules/documentation/tutorials/tutorial_2_lora_finetune.ipynb index eb6894e00..1dbca8041 100644 --- a/docs/source/modules/documentation/tutorials/tutorial_2_lora_finetune.ipynb +++ b/docs/source/modules/documentation/tutorials/tutorial_2_lora_finetune.ipynb @@ -56,15 +56,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/mnt/data/zz7522/miniconda/envs/mase/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/opt/anaconda3/envs/mase/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for DeepWokLab/bert-tiny.\u001b[0m\n", - "Map: 100%|██████████| 25000/25000 [00:03<00:00, 6535.11 examples/s]\n" + "Map: 100%|██████████| 25000/25000 [00:03<00:00, 6402.87 examples/s]\n" ] } ], "source": [ - "from chop.tools import get_tokenized_dataset\n", + "from chop.tools import get_tokenized_dataset # type: ignore\n", "\n", "dataset, tokenizer = get_tokenized_dataset(\n", " dataset=dataset_name,\n", @@ -230,7 +230,7 @@ " [ 2.4334, -0.3068, 0.8250, ..., 0.1475, 0.1790, 2.2907],\n", " [-1.3637, 0.7055, -0.2177, ..., 0.3557, -0.3971, -0.3107]]],\n", " grad_fn=)\n", - "tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n", + "tensor([[[ 9.7740e-01, 2.5481e-03, -5.2921e-01, ..., 1.4757e-01,\n", " 1.8900e-01, 2.8282e-01],\n", " [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n", " 1.1978e+00, 2.1808e+00],\n", @@ -239,25 +239,25 @@ " ...,\n", " [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n", " -1.2971e-01, 1.1265e+00],\n", - " [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n", + " [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3173e+00,\n", " 2.2258e-01, 8.8157e-01],\n", " [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n", " 7.2677e-02, 1.1724e-01]],\n", "\n", - " [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n", + " [[ 9.7740e-01, 2.5481e-03, -5.2921e-01, ..., 1.4757e-01,\n", " 1.8900e-01, 2.8282e-01],\n", " [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n", " 1.4373e+00, 2.4267e+00],\n", " [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n", " -3.0482e-01, 1.4038e+00],\n", " ...,\n", - " [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n", + " [ 5.1574e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n", " 7.5490e-01, 8.2911e-01],\n", " [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n", " -6.0733e-01, 3.3097e-01],\n", " [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n", " 7.2677e-02, 1.1724e-01]]], grad_fn=)\n", - "tensor([[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n", + "tensor([[[ 9.7740e-01, 2.5481e-03, -5.2921e-01, ..., 1.4757e-01,\n", " 1.8900e-01, 2.8282e-01],\n", " [-3.5020e-02, -6.1047e-02, -1.0465e-01, ..., -8.1892e-01,\n", " 1.1978e+00, 2.1808e+00],\n", @@ -266,25 +266,25 @@ " ...,\n", " [ 1.4783e+00, 1.0907e-01, -1.5222e+00, ..., -3.0983e-01,\n", " -1.2971e-01, 1.1265e+00],\n", - " [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3174e+00,\n", + " [ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., -1.3173e+00,\n", " 2.2258e-01, 8.8157e-01],\n", " [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n", " 7.2677e-02, 1.1724e-01]],\n", "\n", - " [[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., 1.4757e-01,\n", + " [[ 9.7740e-01, 2.5481e-03, -5.2921e-01, ..., 1.4757e-01,\n", " 1.8900e-01, 2.8282e-01],\n", " [-4.4781e-01, -7.9224e-01, -2.1741e+00, ..., -5.9181e-01,\n", " 1.4373e+00, 2.4267e+00],\n", " [-2.5942e-01, 9.7163e-01, -3.2928e+00, ..., -5.9773e-01,\n", " -3.0482e-01, 1.4038e+00],\n", " ...,\n", - " [ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n", + " [ 5.1574e-02, 3.5218e-01, -3.8926e-01, ..., -1.1508e+00,\n", " 7.5490e-01, 8.2911e-01],\n", " [ 1.6107e+00, 6.8170e-02, 9.2537e-01, ..., -1.5233e+00,\n", " -6.0733e-01, 3.3097e-01],\n", " [-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., -1.6159e+00,\n", " 7.2677e-02, 1.1724e-01]]], grad_fn=)\n", - "tensor([[[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n", + "tensor([[[[ 9.7740e-01, 2.5481e-03, -5.2921e-01, ..., -5.2614e-01,\n", " -3.5687e-01, -2.6793e-01],\n", " [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n", " 1.8900e-01, 2.8282e-01]],\n", @@ -296,7 +296,7 @@ "\n", " [[ 4.3982e-01, -1.9602e+00, -6.8830e-01, ..., -2.2501e-01,\n", " 7.2290e-02, -1.8290e+00],\n", - " [ 8.9952e-01, 1.0029e+00, 7.4536e-04, ..., -6.3025e-01,\n", + " [ 8.9952e-01, 1.0029e+00, 7.4520e-04, ..., -6.3025e-01,\n", " -1.5967e-01, 1.3284e+00]],\n", "\n", " ...,\n", @@ -308,7 +308,7 @@ "\n", " [[ 1.5890e+00, -1.6859e+00, 7.8703e-01, ..., 6.5467e-01,\n", " -6.8451e-01, 6.5081e-01],\n", - " [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3174e+00,\n", + " [ 7.0729e-01, 1.4499e+00, -1.5089e-01, ..., -1.3173e+00,\n", " 2.2258e-01, 8.8157e-01]],\n", "\n", " [[-3.7517e-01, 1.5191e+00, -2.6796e-01, ..., 3.3130e-01,\n", @@ -317,7 +317,7 @@ " 7.2677e-02, 1.1724e-01]]],\n", "\n", "\n", - " [[[ 9.7740e-01, 2.5482e-03, -5.2921e-01, ..., -5.2614e-01,\n", + " [[[ 9.7740e-01, 2.5481e-03, -5.2921e-01, ..., -5.2614e-01,\n", " -3.5687e-01, -2.6793e-01],\n", " [ 2.0335e-01, -5.4534e-01, 3.0686e-01, ..., 1.4757e-01,\n", " 1.8900e-01, 2.8282e-01]],\n", @@ -334,7 +334,7 @@ "\n", " ...,\n", "\n", - " [[ 5.1575e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n", + " [[ 5.1574e-02, 3.5218e-01, -3.8926e-01, ..., -1.2252e-02,\n", " 1.0394e+00, 4.2402e-01],\n", " [-4.7386e-01, 2.6401e+00, 1.7024e+00, ..., -1.1508e+00,\n", " 7.5490e-01, 8.2911e-01]],\n", @@ -730,25 +730,25 @@ " [[ 1.3511, 1.3329, -0.0782, ..., -0.5836, 0.6491, 2.6554],\n", " [-1.3686, -0.2348, -1.7438, ..., -0.8454, -0.7400, 1.5966]]]],\n", " grad_fn=)\n", - "tensor([[[ 3.8307e-01, 1.8837e-02, -1.0314e+00, ..., 6.4335e-01,\n", + "tensor([[[ 3.8307e-01, 1.8836e-02, -1.0314e+00, ..., 6.4335e-01,\n", " -3.1830e-01, -1.7296e+00],\n", " [ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., 1.5973e+00,\n", " 1.1907e+00, -9.0454e-01],\n", " [-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -5.3627e-01,\n", " -1.0456e+00, -1.6301e+00],\n", " ...,\n", - " [-1.7466e-01, 3.0707e-02, 7.5225e-01, ..., -1.3217e+00,\n", + " [-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.3217e+00,\n", " -1.3415e+00, -3.8328e-01],\n", " [ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -1.6600e-01,\n", " -6.5011e-01, 2.1798e-02],\n", - " [-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., 1.3441e-03,\n", + " [-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., 1.3448e-03,\n", " -1.6754e+00, 3.1771e-01]],\n", "\n", " [[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., 6.4928e-01,\n", " -3.2944e-01, -2.4185e+00],\n", " [ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.8623e+00,\n", " -1.1230e+00, 3.5013e-01],\n", - " [ 1.1642e-01, 1.2460e+00, 7.7941e-02, ..., 6.4975e-01,\n", + " [ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., 6.4975e-01,\n", " -7.3862e-01, -2.1510e+00],\n", " ...,\n", " [ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 1.7637e+00,\n", @@ -757,25 +757,25 @@ " 8.6736e-01, -2.3894e+00],\n", " [ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., 6.2708e-01,\n", " -1.3601e+00, -3.9984e-01]]], grad_fn=)\n", - "tensor([[[ 3.8307e-01, 1.8837e-02, -1.0314e+00, ..., 6.4335e-01,\n", + "tensor([[[ 3.8307e-01, 1.8836e-02, -1.0314e+00, ..., 6.4335e-01,\n", " -3.1830e-01, -1.7296e+00],\n", " [ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., 1.5973e+00,\n", " 1.1907e+00, -9.0454e-01],\n", " [-5.8138e-01, 6.7943e-01, -1.3203e+00, ..., -5.3627e-01,\n", " -1.0456e+00, -1.6301e+00],\n", " ...,\n", - " [-1.7466e-01, 3.0707e-02, 7.5225e-01, ..., -1.3217e+00,\n", + " [-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.3217e+00,\n", " -1.3415e+00, -3.8328e-01],\n", " [ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -1.6600e-01,\n", " -6.5011e-01, 2.1798e-02],\n", - " [-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., 1.3441e-03,\n", + " [-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., 1.3448e-03,\n", " -1.6754e+00, 3.1771e-01]],\n", "\n", " [[ 4.7844e-01, -3.1772e-01, -1.0617e+00, ..., 6.4928e-01,\n", " -3.2944e-01, -2.4185e+00],\n", " [ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.8623e+00,\n", " -1.1230e+00, 3.5013e-01],\n", - " [ 1.1642e-01, 1.2460e+00, 7.7941e-02, ..., 6.4975e-01,\n", + " [ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., 6.4975e-01,\n", " -7.3862e-01, -2.1510e+00],\n", " ...,\n", " [ 1.1291e+00, -1.3637e+00, -1.5779e+00, ..., 1.7637e+00,\n", @@ -784,9 +784,9 @@ " 8.6736e-01, -2.3894e+00],\n", " [ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., 6.2708e-01,\n", " -1.3601e+00, -3.9984e-01]]], grad_fn=)\n", - "tensor([[[[ 3.8307e-01, 1.8837e-02, -1.0314e+00, ..., -8.0955e-01,\n", + "tensor([[[[ 3.8307e-01, 1.8836e-02, -1.0314e+00, ..., -8.0955e-01,\n", " -2.8557e-01, -2.3318e-01],\n", - " [-9.9661e-02, 3.1722e-01, -3.0517e-01, ..., 6.4335e-01,\n", + " [-9.9660e-02, 3.1722e-01, -3.0517e-01, ..., 6.4335e-01,\n", " -3.1830e-01, -1.7296e+00]],\n", "\n", " [[ 1.5897e+00, 1.3689e-01, -6.8915e-01, ..., -7.9549e-01,\n", @@ -801,9 +801,9 @@ "\n", " ...,\n", "\n", - " [[-1.7466e-01, 3.0707e-02, 7.5225e-01, ..., -1.9281e+00,\n", + " [[-1.7466e-01, 3.0706e-02, 7.5225e-01, ..., -1.9281e+00,\n", " 1.1489e+00, -2.4530e-01],\n", - " [-7.6225e-02, 8.5814e-01, -1.5467e+00, ..., -1.3217e+00,\n", + " [-7.6226e-02, 8.5814e-01, -1.5467e+00, ..., -1.3217e+00,\n", " -1.3415e+00, -3.8328e-01]],\n", "\n", " [[ 1.5170e-01, 5.1089e-01, 1.3993e-01, ..., -2.4168e+00,\n", @@ -813,7 +813,7 @@ "\n", " [[-2.4311e-01, 1.6726e+00, 1.6682e-01, ..., -1.0481e+00,\n", " -2.7634e+00, 2.2741e-01],\n", - " [-1.4603e+00, 3.1239e-02, 3.8892e-01, ..., 1.3441e-03,\n", + " [-1.4603e+00, 3.1239e-02, 3.8892e-01, ..., 1.3448e-03,\n", " -1.6754e+00, 3.1771e-01]]],\n", "\n", "\n", @@ -823,11 +823,11 @@ " -3.2944e-01, -2.4185e+00]],\n", "\n", " [[ 5.9122e-01, -2.2648e-01, 1.7474e-01, ..., -1.6488e+00,\n", - " -8.6854e-01, -8.0783e-01],\n", + " -8.6853e-01, -8.0782e-01],\n", " [-2.1516e+00, -2.4247e-01, -8.1713e-01, ..., -1.8623e+00,\n", " -1.1230e+00, 3.5013e-01]],\n", "\n", - " [[ 1.1642e-01, 1.2460e+00, 7.7941e-02, ..., -1.3121e+00,\n", + " [[ 1.1642e-01, 1.2460e+00, 7.7942e-02, ..., -1.3121e+00,\n", " -6.7044e-01, -1.1324e+00],\n", " [ 4.3930e-01, 3.4082e-01, -1.2243e+00, ..., 6.4975e-01,\n", " -7.3862e-01, -2.1510e+00]],\n", @@ -845,13 +845,13 @@ " 8.6736e-01, -2.3894e+00]],\n", "\n", " [[ 1.8451e-01, 1.2740e+00, 4.2857e-01, ..., -1.1366e+00,\n", - " -2.8409e+00, 4.6711e-01],\n", - " [-1.9576e+00, 2.0176e-01, -4.1035e-02, ..., 6.2708e-01,\n", + " -2.8409e+00, 4.6710e-01],\n", + " [-1.9576e+00, 2.0176e-01, -4.1034e-02, ..., 6.2708e-01,\n", " -1.3601e+00, -3.9984e-01]]]], grad_fn=)\n", "tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n", " -6.5765e-01, -1.4711e-01],\n", - " [ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n", - " -2.7815e-01, -2.6627e-01],\n", + " [ 3.9396e-01, 4.7657e-02, -1.0277e+00, ..., -8.4926e-01,\n", + " -2.7815e-01, -2.6628e-01],\n", " [ 9.9586e-01, 2.4414e-01, -8.3459e-01, ..., -8.5795e-01,\n", " -4.3860e-01, -2.1582e-01],\n", " ...,\n", @@ -860,31 +860,31 @@ " [ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n", " 4.1589e-01, -1.8024e-01],\n", " [ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n", - " 8.4395e-02, -6.9034e-02]],\n", + " 8.4396e-02, -6.9034e-02]],\n", "\n", - " [[-7.7230e-01, -1.1852e-01, -8.0274e-02, ..., -1.3794e-03,\n", + " [[-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3790e-03,\n", " -5.2249e-01, -4.2095e-01],\n", " [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n", " -4.1385e-01, -1.3744e+00],\n", " [-1.7985e-01, -2.6037e-01, 3.5678e-01, ..., 2.4736e-01,\n", " -1.6626e-01, -7.0940e-01],\n", " ...,\n", - " [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6187e-02,\n", + " [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n", " -6.9561e-01, -8.5675e-01],\n", - " [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n", + " [-9.1553e-01, 1.4648e-01, -5.5622e-02, ..., 1.8643e-01,\n", " -1.0965e+00, -4.8097e-01],\n", - " [-1.0678e+00, 9.0885e-02, -4.5400e-02, ..., 1.5424e-01,\n", + " [-1.0678e+00, 9.0884e-02, -4.5400e-02, ..., 1.5424e-01,\n", " -1.1762e+00, -2.9385e-01]]],\n", "\n", "\n", - " [[[ 4.9445e-01, 9.6665e-03, -5.7117e-01, ..., -9.7640e-01,\n", + " [[[ 4.9445e-01, 9.6662e-03, -5.7118e-01, ..., -9.7640e-01,\n", " -9.0948e-01, -1.3761e-01],\n", " [ 4.7672e-01, -2.3672e-01, -8.9246e-01, ..., -8.9927e-01,\n", " -3.9088e-01, -3.1635e-01],\n", - " [ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n", + " [ 4.5120e-01, -5.8034e-02, -6.8736e-01, ..., -1.0352e+00,\n", " -4.7996e-01, -4.5624e-01],\n", " ...,\n", - " [ 3.3361e-01, 1.5918e-03, -1.0108e+00, ..., -1.5704e+00,\n", + " [ 3.3362e-01, 1.5913e-03, -1.0108e+00, ..., -1.5704e+00,\n", " -3.9079e-01, -2.1742e-01],\n", " [ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n", " 1.3298e-01, -1.5481e+00],\n", @@ -900,18 +900,18 @@ " ...,\n", " [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n", " 3.1643e-01, -2.0784e+00],\n", - " [-1.8375e+00, 2.6226e-01, 3.7726e-02, ..., 6.6286e-01,\n", + " [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n", " -1.0588e+00, -7.6781e-01],\n", - " [-1.6402e+00, 3.1250e-01, -6.6194e-04, ..., 6.6493e-01,\n", + " [-1.6402e+00, 3.1250e-01, -6.6098e-04, ..., 6.6493e-01,\n", " -9.1110e-01, -1.0382e+00]]]],\n", " grad_fn=)\n", "tensor([[[[ 3.7393e-01, 3.7078e-01, -7.5553e-01, ..., -9.4280e-01,\n", " -6.5765e-01, -1.4711e-01],\n", - " [-7.7230e-01, -1.1852e-01, -8.0274e-02, ..., -1.3794e-03,\n", + " [-7.7230e-01, -1.1852e-01, -8.0275e-02, ..., -1.3790e-03,\n", " -5.2249e-01, -4.2095e-01]],\n", "\n", - " [[ 3.9396e-01, 4.7658e-02, -1.0277e+00, ..., -8.4926e-01,\n", - " -2.7815e-01, -2.6627e-01],\n", + " [[ 3.9396e-01, 4.7657e-02, -1.0277e+00, ..., -8.4926e-01,\n", + " -2.7815e-01, -2.6628e-01],\n", " [-4.6209e-01, -1.2680e-01, -2.5711e-01, ..., 1.3235e-01,\n", " -4.1385e-01, -1.3744e+00]],\n", "\n", @@ -924,21 +924,21 @@ "\n", " [[ 1.4504e+00, -1.2077e-01, -4.8160e-01, ..., -1.6701e+00,\n", " 6.3807e-01, -8.3788e-02],\n", - " [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6187e-02,\n", + " [-6.2069e-01, 2.5298e-01, -7.8416e-01, ..., 8.6186e-02,\n", " -6.9561e-01, -8.5675e-01]],\n", "\n", " [[ 3.8821e-01, 1.4150e-01, -1.5051e-01, ..., -1.5785e+00,\n", " 4.1589e-01, -1.8024e-01],\n", - " [-9.1553e-01, 1.4648e-01, -5.5621e-02, ..., 1.8643e-01,\n", + " [-9.1553e-01, 1.4648e-01, -5.5622e-02, ..., 1.8643e-01,\n", " -1.0965e+00, -4.8097e-01]],\n", "\n", " [[ 1.0467e-01, 5.5196e-01, 1.0818e-01, ..., -2.0373e+00,\n", - " 8.4395e-02, -6.9034e-02],\n", - " [-1.0678e+00, 9.0885e-02, -4.5400e-02, ..., 1.5424e-01,\n", + " 8.4396e-02, -6.9034e-02],\n", + " [-1.0678e+00, 9.0884e-02, -4.5400e-02, ..., 1.5424e-01,\n", " -1.1762e+00, -2.9385e-01]]],\n", "\n", "\n", - " [[[ 4.9445e-01, 9.6665e-03, -5.7117e-01, ..., -9.7640e-01,\n", + " [[[ 4.9445e-01, 9.6662e-03, -5.7118e-01, ..., -9.7640e-01,\n", " -9.0948e-01, -1.3761e-01],\n", " [-1.0884e+00, 2.0838e-01, -5.0948e-01, ..., 2.2845e-01,\n", " -8.2075e-01, -1.1496e+00]],\n", @@ -948,38 +948,48 @@ " [-3.4296e-01, 5.0833e-01, -8.6644e-01, ..., 3.3008e-01,\n", " -5.5070e-01, -1.8440e+00]],\n", "\n", - " [[ 4.5120e-01, -5.8035e-02, -6.8736e-01, ..., -1.0352e+00,\n", + " [[ 4.5120e-01, -5.8034e-02, -6.8736e-01, ..., -1.0352e+00,\n", " -4.7996e-01, -4.5624e-01],\n", " [-3.2602e-01, 6.0671e-01, -7.5711e-01, ..., 4.7493e-01,\n", " -4.6589e-01, -2.0454e+00]],\n", "\n", " ...,\n", "\n", - " [[ 3.3361e-01, 1.5918e-03, -1.0108e+00, ..., -1.5704e+00,\n", + " [[ 3.3362e-01, 1.5913e-03, -1.0108e+00, ..., -1.5704e+00,\n", " -3.9079e-01, -2.1742e-01],\n", " [-1.7399e+00, 4.6359e-01, 6.5213e-01, ..., 9.0025e-01,\n", " 3.1643e-01, -2.0784e+00]],\n", "\n", " [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n", " 1.3298e-01, -1.5481e+00],\n", - " [-1.8375e+00, 2.6226e-01, 3.7726e-02, ..., 6.6286e-01,\n", + " [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n", " -1.0588e+00, -7.6781e-01]],\n", "\n", " [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n", " -4.1355e-01, -5.8558e-01],\n", - " [-1.6402e+00, 3.1250e-01, -6.6194e-04, ..., 6.6493e-01,\n", + " [-1.6402e+00, 3.1250e-01, -6.6098e-04, ..., 6.6493e-01,\n", " -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n", - "tensor([[-0.3776, -0.2373],\n", - " [-0.3180, -0.2760]], grad_fn=)\n", + "tensor([[ 0.4684, -0.0993],\n", + " [ 0.3700, -0.0888]], grad_fn=)\n", "tensor([1, 0])\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] } ], "source": [ - "from transformers import AutoModelForSequenceClassification\n", + "from transformers import AutoModelForSequenceClassification # type: ignore\n", "\n", "from chop import MaseGraph\n", - "import chop.passes as passes\n", + "import chop.passes as passes # type: ignore\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n", "model.config.problem_type = \"single_label_classification\"\n", @@ -994,7 +1004,9 @@ ")\n", "\n", "mg, _ = passes.init_metadata_analysis_pass(mg)\n", - "mg, _ = passes.add_common_metadata_analysis_pass(mg)" + "mg, _ = passes.add_common_metadata_analysis_pass(mg)\n", + "\n", + "mg.draw()" ] }, { @@ -1123,7 +1135,7 @@ } ], "source": [ - "from chop.passes.module import report_trainable_parameters_analysis_pass\n", + "from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore\n", "\n", "_, _ = report_trainable_parameters_analysis_pass(mg.model)" ] @@ -1161,13 +1173,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/zz7522/Projects/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", + "/Users/antoniotarizzo/iCloud-Drive-Offline/Documents/ Imperial/0109 Advanced Deep Learning/adl-mase/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n" ] } ], "source": [ - "from chop.tools import get_trainer\n", + "from chop.tools import get_trainer # type: ignore\n", "\n", "trainer = get_trainer(\n", " model=mg.model,\n", @@ -1189,22 +1201,14 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/data/zz7522/miniconda/envs/mase/lib/python3.11/site-packages/torch/nn/parallel/_functions.py:71: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", - " warnings.warn(\n" - ] - }, { "data": { "text/html": [ "\n", "
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" @@ -1280,18 +1296,10 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/data/zz7522/miniconda/envs/mase/lib/python3.11/site-packages/torch/nn/parallel/_functions.py:71: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ - "TrainOutput(global_step=782, training_loss=0.5414889596612252, metrics={'train_runtime': 34.4035, 'train_samples_per_second': 726.671, 'train_steps_per_second': 22.73, 'total_flos': 0.0, 'train_loss': 0.5414889596612252, 'epoch': 1.0})" + "TrainOutput(global_step=3125, training_loss=0.4784295935058594, metrics={'train_runtime': 420.0203, 'train_samples_per_second': 59.521, 'train_steps_per_second': 7.44, 'total_flos': 0.0, 'train_loss': 0.4784295935058594, 'epoch': 1.0})" ] }, "execution_count": 8, @@ -1315,19 +1323,11 @@ "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/data/zz7522/miniconda/envs/mase/lib/python3.11/site-packages/torch/nn/parallel/_functions.py:71: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Evaluation accuracy: 0.788\n" + "Evaluation accuracy: 0.8202\n" ] } ], @@ -1352,9 +1352,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /home/zz7522/tutorial_2_sft.pt, /home/zz7522/tutorial_2_sft.mz\u001b[0m\n", - "\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /home/zz7522/tutorial_2_sft.pt\u001b[0m\n", - "\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /home/zz7522/tutorial_2_sft.mz\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseGraph to /Users/antoniotarizzo/tutorial_2_sft.pt, /Users/antoniotarizzo/tutorial_2_sft.mz\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mExporting GraphModule to /Users/antoniotarizzo/tutorial_2_sft.pt\u001b[0m\n", + "\u001b[32mINFO \u001b[0m \u001b[34mExporting MaseMetadata to /Users/antoniotarizzo/tutorial_2_sft.mz\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34mFailed to pickle call_function node: finfo\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34mcannot pickle 'torch.finfo' object\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34mFailed to pickle call_function node: getattr_2\u001b[0m\n", @@ -1687,10 +1687,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/zz7522/Projects/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", - " trainer = Trainer(\n", - "/mnt/data/zz7522/miniconda/envs/mase/lib/python3.11/site-packages/torch/nn/parallel/_functions.py:71: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", - " warnings.warn(\n" + "/Users/antoniotarizzo/iCloud-Drive-Offline/Documents/ Imperial/0109 Advanced Deep Learning/adl-mase/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", + " trainer = Trainer(\n" ] }, { @@ -1699,8 +1697,8 @@ "\n", "

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Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/opt/anaconda3/envs/mase/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", "`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting model.config.use_cache = False.\n", - "\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n", - "/Users/yz10513/anaconda3/envs/mase/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", - " warnings.warn(\n" + "\u001b[32mINFO \u001b[0m \u001b[34mGetting dummy input for prajjwal1/bert-tiny.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n", + " [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n", + "tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n", + "tensor([[ 101, 9932, 2089, 2202, 2058, 1996, 2088, 2028, 2154, 102],\n", + " [ 101, 2023, 2003, 2339, 2017, 2323, 4553, 4748, 4877, 102]])\n", + "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n", + "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n", + "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n", + "tensor([[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],\n", + "\n", + "\n", + " [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]])\n", + "tensor([[[[1, 1, 1, 1, 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-2.0784e+00]],\n", + "\n", + " [[ 1.0123e+00, -1.1147e+00, -1.3032e+00, ..., 3.4082e-01,\n", + " 1.3298e-01, -1.5481e+00],\n", + " [-1.8375e+00, 2.6226e-01, 3.7727e-02, ..., 6.6286e-01,\n", + " -1.0588e+00, -7.6781e-01]],\n", + "\n", + " [[ 1.4273e+00, -1.2951e+00, 5.7719e-01, ..., -1.2490e+00,\n", + " -4.1355e-01, -5.8558e-01],\n", + " [-1.6402e+00, 3.1250e-01, -6.6098e-04, ..., 6.6493e-01,\n", + " -9.1110e-01, -1.0382e+00]]]], grad_fn=)\n", + "tensor([[ 0.1732, -0.0116],\n", + " [ 0.0985, -0.0118]], grad_fn=)\n", + "tensor([1, 0])\n" ] } ], @@ -90,13 +933,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "/Users/antoniotarizzo/iCloud-Drive-Offline/Documents/ Imperial/0109 Advanced Deep Learning/adl-mase/mase/src/chop/ir/graph/mase_graph.py:380: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " loaded_model = torch.load(f)\n", "\u001b[33mWARNING \u001b[0m \u001b[34mNode finfo not found in loaded metadata.\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34mNode getattr_2 not found in loaded metadata.\u001b[0m\n" ] @@ -106,7 +951,9 @@ "from pathlib import Path\n", "from chop import MaseGraph\n", "\n", - "mg = MaseGraph.from_checkpoint(f\"{Path.home()}/tutorial_2_lora\")" + "custom_path = Path.home() / \"iCloud-Drive-Offline/Documents/ Imperial/0109 Advanced Deep Learning/adl-mase\"\n", + "\n", + "mg = MaseGraph.from_checkpoint(f\"{custom_path}/tutorial_2_lora\")" ] }, { @@ -125,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -133,43 +980,36 @@ "output_type": "stream", "text": [ "\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + "Map: 100%|██████████| 25000/25000 [00:04<00:00, 5215.16 examples/s]\n", + "Map: 100%|██████████| 25000/25000 [00:03<00:00, 6326.09 examples/s]\n", + "Map: 100%|██████████| 50000/50000 [00:08<00:00, 5857.29 examples/s]\n", + "/Users/antoniotarizzo/iCloud-Drive-Offline/Documents/ Imperial/0109 Advanced Deep Learning/adl-mase/mase/src/chop/tools/huggingface.py:157: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", + " trainer = Trainer(\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-12-01 13:51:21,992] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to mps (auto detect)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "W1201 13:51:23.472000 8580182592 torch/distributed/elastic/multiprocessing/redirects.py:27] NOTE: Redirects are currently not supported in Windows or MacOs.\n", - "100%|██████████| 3125/3125 [05:14<00:00, 9.95it/s]" - ] + "data": { + "text/html": [ + "\n", + "
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Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "\u001b[32mINFO \u001b[0m \u001b[34mTokenizing dataset imdb with AutoTokenizer for bert-base-uncased.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from chop.tools import get_tokenized_dataset\n", "\n", @@ -244,137 +234,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2024-12-01 22:51:50,104] A new study created in memory with name: bert-tiny-nas-study\n", - "/Users/yz10513/anaconda3/envs/mase/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", - " warnings.warn(\n", - "/Users/yz10513/anaconda3/envs/mase/lib/python3.11/site-packages/optuna/distributions.py:524: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n", - " warnings.warn(message)\n", - "/Users/yz10513/anaconda3/envs/mase/lib/python3.11/site-packages/optuna/distributions.py:524: UserWarning: Choices for a categorical distribution should be a tuple of None, bool, int, float and str for persistent storage but contains which is of type type.\n", - " warnings.warn(message)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-12-01 22:51:52,032] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to mps (auto detect)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "W1201 22:51:52.945000 8580182592 torch/distributed/elastic/multiprocessing/redirects.py:27] NOTE: Redirects are currently not supported in Windows or MacOs.\n", - " 16%|█▌ | 500/3125 [00:44<03:42, 11.81it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'loss': 0.668, 'grad_norm': 4.309191703796387, 'learning_rate': 4.2e-05, 'epoch': 0.16}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 32%|███▏ | 1000/3125 [01:28<05:01, 7.06it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'loss': 0.5047, 'grad_norm': 23.95120620727539, 'learning_rate': 3.4000000000000007e-05, 'epoch': 0.32}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 48%|████▊ | 1500/3125 [02:13<01:41, 16.07it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'loss': 0.4092, 'grad_norm': 12.666841506958008, 'learning_rate': 2.6000000000000002e-05, 'epoch': 0.48}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 64%|██████▍ | 2000/3125 [02:52<01:49, 10.28it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'loss': 0.3682, 'grad_norm': 21.01616859436035, 'learning_rate': 1.8e-05, 'epoch': 0.64}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 80%|████████ | 2500/3125 [03:25<00:38, 16.24it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'loss': 0.3396, 'grad_norm': 19.34454917907715, 'learning_rate': 1e-05, 'epoch': 0.8}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 96%|█████████▌| 3000/3125 [03:59<00:08, 14.06it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'loss': 0.3538, 'grad_norm': 21.48626708984375, 'learning_rate': 2.0000000000000003e-06, 'epoch': 0.96}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3125/3125 [04:07<00:00, 12.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'train_runtime': 247.935, 'train_samples_per_second': 100.833, 'train_steps_per_second': 12.604, 'train_loss': 0.4358914270019531, 'epoch': 1.0}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3125/3125 [03:58<00:00, 13.08it/s]\n", - "[I 2024-12-01 23:00:00,199] Trial 0 finished with value: 0.86256 and parameters: {'num_layers': 2, 'num_heads': 1, 'hidden_size': 3, 'intermediate_size': 3, 'bert.encoder.layer.0.attention.self.query_type': , 'bert.encoder.layer.0.attention.self.key_type': , 'bert.encoder.layer.0.attention.self.value_type': , 'bert.encoder.layer.0.attention.output.dense_type': , 'bert.encoder.layer.1.attention.self.query_type': , 'bert.encoder.layer.1.attention.self.key_type': , 'bert.encoder.layer.1.attention.self.value_type': , 'bert.encoder.layer.1.attention.output.dense_type': , 'bert.pooler.dense_type': }. Best is trial 0 with value: 0.86256.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import optuna\n", "\n", @@ -499,6 +361,1756 @@ "source": [ "mg.export(f\"{Path.home()}/tutorial_5_nas_compressed\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Part 5a, full code for random method ###\n", + "\n", + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_tokenized_dataset, get_trainer\n", + "import optuna\n", + "from optuna.samplers import GridSampler, TPESampler, RandomSampler\n", + "import matplotlib.pyplot as plt\n", + "import dill\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "# -----------------------------\n", + "# 1. Define your checkpoints & dataset\n", + "# -----------------------------\n", + "checkpoint = \"prajjwal1/bert-tiny\"\n", + "tokenizer_checkpoint = \"bert-base-uncased\"\n", + "dataset_name = \"imdb\"\n", + "\n", + "# Load tokenized dataset and tokenizer\n", + "dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + ")\n", + "\n", + "# -----------------------------\n", + "# 2. Define search space\n", + "# -----------------------------\n", + "grid_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "# Same discrete sets for TPE/Random:\n", + "tpe_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "\n", + "# -----------------------------\n", + "# 3. Construct the model given a trial\n", + "# -----------------------------\n", + "def construct_model(trial):\n", + " \"\"\"\n", + " Creates a BERT model with the hyperparameters suggested by the trial.\n", + " We manually map each parameter to the correct config attribute or layer override.\n", + " \"\"\"\n", + " # Load the default config from a small BERT checkpoint\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Map parameters to config\n", + " config.num_hidden_layers = trial.suggest_categorical(\n", + " \"num_layers\", tpe_search_space[\"num_layers\"]\n", + " )\n", + " config.num_attention_heads = trial.suggest_categorical(\n", + " \"num_heads\", tpe_search_space[\"num_heads\"]\n", + " )\n", + " config.hidden_size = trial.suggest_categorical(\n", + " \"hidden_size\", tpe_search_space[\"hidden_size\"]\n", + " )\n", + " config.intermediate_size = trial.suggest_categorical(\n", + " \"intermediate_size\", tpe_search_space[\"intermediate_size\"]\n", + " )\n", + "\n", + " model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " # Handle linear layer choice\n", + " linear_choice = trial.suggest_categorical(\n", + " \"linear_layer_choices\", tpe_search_space[\"linear_layer_choices\"]\n", + " )\n", + " if linear_choice == \"identity\":\n", + " # For each linear layer that is square (in_features == out_features),\n", + " # replace it with Identity\n", + " for name, layer in model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " deepsetattr(model, name, Identity())\n", + "\n", + " return model\n", + "\n", + "\n", + "# -----------------------------\n", + "# 4. Define the objective function\n", + "# -----------------------------\n", + "def objective(trial):\n", + " # Build the model given the trial\n", + " trial_model = construct_model(trial)\n", + "\n", + " # Create a Trainer (from chop) that handles fine-tuning\n", + " trainer = get_trainer(\n", + " model=trial_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=3,\n", + " )\n", + "\n", + " # Train and evaluate\n", + " trainer.train()\n", + " eval_results = trainer.evaluate()\n", + "\n", + " # Save the model to the trial user attributes for later retrieval if needed\n", + " trial.set_user_attr(\"model\", trial_model)\n", + "\n", + " # Return the metric you want to optimize\n", + " return eval_results[\"eval_accuracy\"]\n", + "\n", + "\n", + "# -----------------------------\n", + "# 5. Helper to run a study and collect \"running best accuracies\"\n", + "# -----------------------------\n", + "def run_study_and_get_curve(sampler, n_trials=None, study_name=\"study\"):\n", + " \"\"\"\n", + " Runs an Optuna study with the provided sampler and returns:\n", + " - the study object\n", + " - a list of best accuracies up to each trial (running max)\n", + " \"\"\"\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=study_name,\n", + " sampler=sampler,\n", + " )\n", + "\n", + " study.optimize(\n", + " objective,\n", + " n_trials=n_trials,\n", + " timeout=60 * 60 * 24, # or specify a time limit\n", + " show_progress_bar=False,\n", + " )\n", + "\n", + " # Build a list of max accuracies up to each trial\n", + " running_max_accuracies = []\n", + " current_max = 0.0\n", + " for t in study.trials:\n", + " if t.value is not None and t.value > current_max:\n", + " current_max = t.value\n", + " running_max_accuracies.append(current_max)\n", + "\n", + " return study, running_max_accuracies\n", + "\n", + "# -----------------------------\n", + "# 6. Function to save trials data to CSV\n", + "# -----------------------------\n", + "def save_study_results_to_csv(study, filename):\n", + " \"\"\"\n", + " Saves each trial's results into a CSV, including:\n", + " - trial number\n", + " - objective value (accuracy)\n", + " - parameters\n", + " - model config parameters\n", + " \"\"\"\n", + " rows = []\n", + " for t in study.trials:\n", + " row = {\n", + " \"trial_number\": t.number,\n", + " \"accuracy\": t.value,\n", + " }\n", + " # Merge in parameter key-value pairs directly\n", + " row.update(t.params)\n", + "\n", + " # Add model config if it exists in user attributes\n", + " if \"model\" in t.user_attrs:\n", + " model_config = t.user_attrs[\"model\"].config.to_dict()\n", + " for key, value in model_config.items():\n", + " row[f\"config_{key}\"] = value\n", + "\n", + " rows.append(row)\n", + "\n", + " df = pd.DataFrame(rows)\n", + " df.to_csv(filename, index=False)\n", + " print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n", + "\n", + "if __name__ == \"__main__\":\n", + " random_sampler = RandomSampler()\n", + " random_study, random_max_curve = run_study_and_get_curve(\n", + " sampler=random_sampler,\n", + " n_trials=10,\n", + " study_name=\"bert-random-study\",\n", + " )\n", + " print(f\"[RandomSampler] Number of trials: {len(random_study.trials)}\")\n", + " print(f\"[RandomSampler] Best accuracy: {random_study.best_value:.4f}\")\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(range(1, len(random_max_curve)+1), random_max_curve, label=\"RandomSampler\")\n", + " plt.xlabel(\"Number of Trials\")\n", + " plt.ylabel(\"Max Accuracy So Far\")\n", + " plt.title(\"Comparison of GridSampler vs TPESampler vs RandomSampler\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " best_random_model = random_study.best_trial.user_attrs[\"model\"].cpu()\n", + " with open(\"best_random_model.pkl\", \"wb\") as f:\n", + " dill.dump(best_random_model, f)\n", + "\n", + " save_study_results_to_csv(random_study, \"random_study_trials.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Part a, full code for grid method ###\n", + "\n", + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_tokenized_dataset, get_trainer\n", + "import optuna\n", + "from optuna.samplers import GridSampler, TPESampler, RandomSampler\n", + "import matplotlib.pyplot as plt\n", + "import dill\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "# -----------------------------\n", + "# 1. Define your checkpoints & dataset\n", + "# -----------------------------\n", + "checkpoint = \"prajjwal1/bert-tiny\"\n", + "tokenizer_checkpoint = \"bert-base-uncased\"\n", + "dataset_name = \"imdb\"\n", + "\n", + "# Load tokenized dataset and tokenizer\n", + "dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + ")\n", + "\n", + "# -----------------------------\n", + "# 2. Define search space\n", + "# -----------------------------\n", + "grid_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "# Same discrete sets for TPE/Random:\n", + "tpe_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "\n", + "# -----------------------------\n", + "# 3. Construct the model given a trial\n", + "# -----------------------------\n", + "def construct_model(trial):\n", + " \"\"\"\n", + " Creates a BERT model with the hyperparameters suggested by the trial.\n", + " We manually map each parameter to the correct config attribute or layer override.\n", + " \"\"\"\n", + " # Load the default config from a small BERT checkpoint\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Map parameters to config\n", + " config.num_hidden_layers = trial.suggest_categorical(\n", + " \"num_layers\", grid_search_space[\"num_layers\"]\n", + " )\n", + " config.num_attention_heads = trial.suggest_categorical(\n", + " \"num_heads\", grid_search_space[\"num_heads\"]\n", + " )\n", + " config.hidden_size = trial.suggest_categorical(\n", + " \"hidden_size\", grid_search_space[\"hidden_size\"]\n", + " )\n", + " config.intermediate_size = trial.suggest_categorical(\n", + " \"intermediate_size\", grid_search_space[\"intermediate_size\"]\n", + " )\n", + "\n", + " model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " # Handle linear layer choice\n", + " linear_choice = trial.suggest_categorical(\n", + " \"linear_layer_choices\", grid_search_space[\"linear_layer_choices\"]\n", + " )\n", + " if linear_choice == \"identity\":\n", + " # For each linear layer that is square (in_features == out_features),\n", + " # replace it with Identity\n", + " for name, layer in model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " deepsetattr(model, name, Identity())\n", + "\n", + " return model\n", + "\n", + "\n", + "# -----------------------------\n", + "# 4. Define the objective function\n", + "# -----------------------------\n", + "def objective(trial):\n", + " # Build the model given the trial\n", + " trial_model = construct_model(trial)\n", + "\n", + " # Create a Trainer (from chop) that handles fine-tuning\n", + " trainer = get_trainer(\n", + " model=trial_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=3,\n", + " )\n", + "\n", + " # Train and evaluate\n", + " trainer.train()\n", + " eval_results = trainer.evaluate()\n", + "\n", + " # Save the model to the trial user attributes for later retrieval if needed\n", + " trial.set_user_attr(\"model\", trial_model)\n", + "\n", + " # Return the metric you want to optimize\n", + " return eval_results[\"eval_accuracy\"]\n", + "\n", + "\n", + "# -----------------------------\n", + "# 5. Helper to run a study and collect \"running best accuracies\"\n", + "# -----------------------------\n", + "def run_study_and_get_curve(sampler, n_trials=None, study_name=\"study\"):\n", + " \"\"\"\n", + " Runs an Optuna study with the provided sampler and returns:\n", + " - the study object\n", + " - a list of best accuracies up to each trial (running max)\n", + " \"\"\"\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=study_name,\n", + " sampler=sampler,\n", + " )\n", + "\n", + " study.optimize(\n", + " objective,\n", + " n_trials=n_trials,\n", + " timeout=60 * 60 * 24, # or specify a time limit\n", + " show_progress_bar=False,\n", + " )\n", + "\n", + " # Build a list of max accuracies up to each trial\n", + " running_max_accuracies = []\n", + " current_max = 0.0\n", + " for t in study.trials:\n", + " if t.value is not None and t.value > current_max:\n", + " current_max = t.value\n", + " running_max_accuracies.append(current_max)\n", + "\n", + " return study, running_max_accuracies\n", + "\n", + "# -----------------------------\n", + "# 6. Function to save trials data to CSV\n", + "# -----------------------------\n", + "def save_study_results_to_csv(study, filename):\n", + " \"\"\"\n", + " Saves each trial's results into a CSV, including:\n", + " - trial number\n", + " - objective value (accuracy)\n", + " - parameters\n", + " - model config parameters\n", + " \"\"\"\n", + " rows = []\n", + " for t in study.trials:\n", + " row = {\n", + " \"trial_number\": t.number,\n", + " \"accuracy\": t.value,\n", + " }\n", + " # Merge in parameter key-value pairs directly\n", + " row.update(t.params)\n", + "\n", + " # Add model config if it exists in user attributes\n", + " if \"model\" in t.user_attrs:\n", + " model_config = t.user_attrs[\"model\"].config.to_dict()\n", + " for key, value in model_config.items():\n", + " row[f\"config_{key}\"] = value\n", + "\n", + " rows.append(row)\n", + "\n", + " df = pd.DataFrame(rows)\n", + " df.to_csv(filename, index=False)\n", + " print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n", + "\n", + "\n", + "\n", + "# -----------------------------\n", + "# 7. Compare GridSampler vs TPESampler vs RandomSampler\n", + "# -----------------------------\n", + "if __name__ == \"__main__\":\n", + " # For GridSampler, define discrete search space\n", + " grid_sampler = GridSampler(\n", + " search_space={\n", + " \"num_layers\": grid_search_space[\"num_layers\"],\n", + " \"num_heads\": grid_search_space[\"num_heads\"],\n", + " \"hidden_size\": grid_search_space[\"hidden_size\"],\n", + " \"intermediate_size\": grid_search_space[\"intermediate_size\"],\n", + " \"linear_layer_choices\": grid_search_space[\"linear_layer_choices\"],\n", + " }\n", + " )\n", + "\n", + " #tpe_sampler = TPESampler()\n", + " # random_sampler = RandomSampler()\n", + "\n", + " # ---------------------------------------\n", + " # 7a. Run with GridSampler\n", + " # ---------------------------------------\n", + " grid_study, grid_max_curve = run_study_and_get_curve(\n", + " sampler=grid_sampler,\n", + " n_trials=10, # product of all combos if you set this None or a bigger number\n", + " study_name=\"bert-grid-study\",\n", + " )\n", + " print(f\"[GridSampler] Number of trials: {len(grid_study.trials)}\")\n", + " print(f\"[GridSampler] Best accuracy: {grid_study.best_value:.4f}\")\n", + "\n", + " # ---------------------------------------\n", + " # 7b. Run with TPESampler\n", + " # ---------------------------------------\n", + " #tpe_study, tpe_max_curve = run_study_and_get_curve(\n", + " # sampler=tpe_sampler,\n", + " # n_trials=10, # pick more if desired\n", + " # study_name=\"bert-tpe-study\",\n", + " #)\n", + " #print(f\"[TPESampler] Number of trials: {len(tpe_study.trials)}\")\n", + " #print(f\"[TPESampler] Best accuracy: {tpe_study.best_value:.4f}\")\n", + "\n", + " # ---------------------------------------\n", + " # 7c. Run with RandomSampler\n", + " # ---------------------------------------\n", + " # random_study, random_max_curve = run_study_and_get_curve(\n", + " # sampler=random_sampler,\n", + " # n_trials=10, # pick more if desired\n", + " # study_name=\"bert-random-study\",\n", + " # )\n", + " # print(f\"[RandomSampler] Number of trials: {len(random_study.trials)}\")\n", + " # print(f\"[RandomSampler] Best accuracy: {random_study.best_value:.4f}\")\n", + "\n", + " # -----------------------------\n", + " # 8. Plot the results\n", + " # -----------------------------\n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(range(1, len(grid_max_curve)+1), grid_max_curve, label=\"GridSampler\")\n", + " #plt.plot(range(1, len(tpe_max_curve)+1), tpe_max_curve, label=\"TPESampler\")\n", + " # plt.plot(range(1, len(random_max_curve)+1), random_max_curve, label=\"RandomSampler\")\n", + " plt.xlabel(\"Number of Trials\")\n", + " plt.ylabel(\"Max Accuracy So Far\")\n", + " plt.title(\"Comparison of GridSampler vs TPESampler vs RandomSampler\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # -----------------------------\n", + " # 9. (Optional) Save best models\n", + " # -----------------------------\n", + " best_grid_model = grid_study.best_trial.user_attrs[\"model\"].cpu()\n", + " with open(\"best_grid_model.pkl\", \"wb\") as f:\n", + " dill.dump(best_grid_model, f)\n", + "\n", + " #best_tpe_model = tpe_study.best_trial.user_attrs[\"model\"].cpu()\n", + " #with open(\"best_tpe_model.pkl\", \"wb\") as f:\n", + " #dill.dump(best_tpe_model, f)\n", + "\n", + " # best_random_model = random_study.best_trial.user_attrs[\"model\"].cpu()\n", + " # with open(\"best_random_model.pkl\", \"wb\") as f:\n", + " # dill.dump(best_random_model, f)\n", + "\n", + " # -----------------------------\n", + " # 10. Save all trials to CSV\n", + " # -----------------------------\n", + " save_study_results_to_csv(grid_study, \"grid_study_trials.csv\")\n", + " # save_study_results_to_csv(tpe_study, \"tpe_study_trials.csv\")\n", + " # save_study_results_to_csv(random_study, \"random_study_trials.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Part a, full code for TPE method ###\n", + "\n", + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_tokenized_dataset, get_trainer\n", + "import optuna\n", + "from optuna.samplers import GridSampler, TPESampler, RandomSampler\n", + "import matplotlib.pyplot as plt\n", + "import dill\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "# -----------------------------\n", + "# 1. Define your checkpoints & dataset\n", + "# -----------------------------\n", + "checkpoint = \"prajjwal1/bert-tiny\"\n", + "tokenizer_checkpoint = \"bert-base-uncased\"\n", + "dataset_name = \"imdb\"\n", + "\n", + "# Load tokenized dataset and tokenizer\n", + "dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + ")\n", + "\n", + "# -----------------------------\n", + "# 2. Define search space\n", + "# -----------------------------\n", + "grid_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "# Same discrete sets for TPE/Random:\n", + "tpe_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "\n", + "# -----------------------------\n", + "# 3. Construct the model given a trial\n", + "# -----------------------------\n", + "def construct_model(trial):\n", + " \"\"\"\n", + " Creates a BERT model with the hyperparameters suggested by the trial.\n", + " We manually map each parameter to the correct config attribute or layer override.\n", + " \"\"\"\n", + " # Load the default config from a small BERT checkpoint\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Map parameters to config\n", + " config.num_hidden_layers = trial.suggest_categorical(\n", + " \"num_layers\", tpe_search_space[\"num_layers\"]\n", + " )\n", + " config.num_attention_heads = trial.suggest_categorical(\n", + " \"num_heads\", tpe_search_space[\"num_heads\"]\n", + " )\n", + " config.hidden_size = trial.suggest_categorical(\n", + " \"hidden_size\", tpe_search_space[\"hidden_size\"]\n", + " )\n", + " config.intermediate_size = trial.suggest_categorical(\n", + " \"intermediate_size\", tpe_search_space[\"intermediate_size\"]\n", + " )\n", + "\n", + " model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " # Handle linear layer choice\n", + " linear_choice = trial.suggest_categorical(\n", + " \"linear_layer_choices\", tpe_search_space[\"linear_layer_choices\"]\n", + " )\n", + " if linear_choice == \"identity\":\n", + " # For each linear layer that is square (in_features == out_features),\n", + " # replace it with Identity\n", + " for name, layer in model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " deepsetattr(model, name, Identity())\n", + "\n", + " return model\n", + "\n", + "\n", + "# -----------------------------\n", + "# 4. Define the objective function\n", + "# -----------------------------\n", + "def objective(trial):\n", + " # Build the model given the trial\n", + " trial_model = construct_model(trial)\n", + "\n", + " # Create a Trainer (from chop) that handles fine-tuning\n", + " trainer = get_trainer(\n", + " model=trial_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=3,\n", + " )\n", + "\n", + " # Train and evaluate\n", + " trainer.train()\n", + " eval_results = trainer.evaluate()\n", + "\n", + " # Save the model to the trial user attributes for later retrieval if needed\n", + " trial.set_user_attr(\"model\", trial_model)\n", + "\n", + " # Return the metric you want to optimize\n", + " return eval_results[\"eval_accuracy\"]\n", + "\n", + "\n", + "# -----------------------------\n", + "# 5. Helper to run a study and collect \"running best accuracies\"\n", + "# -----------------------------\n", + "def run_study_and_get_curve(sampler, n_trials=None, study_name=\"study\"):\n", + " \"\"\"\n", + " Runs an Optuna study with the provided sampler and returns:\n", + " - the study object\n", + " - a list of best accuracies up to each trial (running max)\n", + " \"\"\"\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=study_name,\n", + " sampler=sampler,\n", + " )\n", + "\n", + " study.optimize(\n", + " objective,\n", + " n_trials=n_trials,\n", + " timeout=60 * 60 * 24, # or specify a time limit\n", + " show_progress_bar=False,\n", + " )\n", + "\n", + " # Build a list of max accuracies up to each trial\n", + " running_max_accuracies = []\n", + " current_max = 0.0\n", + " for t in study.trials:\n", + " if t.value is not None and t.value > current_max:\n", + " current_max = t.value\n", + " running_max_accuracies.append(current_max)\n", + "\n", + " return study, running_max_accuracies\n", + "\n", + "# -----------------------------\n", + "# 6. Function to save trials data to CSV\n", + "# -----------------------------\n", + "def save_study_results_to_csv(study, filename):\n", + " \"\"\"\n", + " Saves each trial's results into a CSV, including:\n", + " - trial number\n", + " - objective value (accuracy)\n", + " - parameters\n", + " - model config parameters\n", + " \"\"\"\n", + " rows = []\n", + " for t in study.trials:\n", + " row = {\n", + " \"trial_number\": t.number,\n", + " \"accuracy\": t.value,\n", + " }\n", + " # Merge in parameter key-value pairs directly\n", + " row.update(t.params)\n", + "\n", + " # Add model config if it exists in user attributes\n", + " if \"model\" in t.user_attrs:\n", + " model_config = t.user_attrs[\"model\"].config.to_dict()\n", + " for key, value in model_config.items():\n", + " row[f\"config_{key}\"] = value\n", + "\n", + " rows.append(row)\n", + "\n", + " df = pd.DataFrame(rows)\n", + " df.to_csv(filename, index=False)\n", + " print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n", + "\n", + "\n", + "\n", + "# -----------------------------\n", + "# 7. Compare GridSampler vs TPESampler vs RandomSampler\n", + "# -----------------------------\n", + "if __name__ == \"__main__\":\n", + " # For GridSampler, define discrete search space\n", + " # grid_sampler = GridSampler(\n", + " # search_space={\n", + " # \"num_layers\": grid_search_space[\"num_layers\"],\n", + " # \"num_heads\": grid_search_space[\"num_heads\"],\n", + " # \"hidden_size\": grid_search_space[\"hidden_size\"],\n", + " # \"intermediate_size\": grid_search_space[\"intermediate_size\"],\n", + " # \"linear_layer_choices\": grid_search_space[\"linear_layer_choices\"],\n", + " # }\n", + " # )\n", + "\n", + " tpe_sampler = TPESampler()\n", + " # random_sampler = RandomSampler()\n", + "\n", + " # ---------------------------------------\n", + " # 7a. Run with GridSampler\n", + " # ---------------------------------------\n", + " # grid_study, grid_max_curve = run_study_and_get_curve(\n", + " # sampler=grid_sampler,\n", + " # n_trials=10, # product of all combos if you set this None or a bigger number\n", + " # study_name=\"bert-grid-study\",\n", + " # )\n", + " # print(f\"[GridSampler] Number of trials: {len(grid_study.trials)}\")\n", + " # print(f\"[GridSampler] Best accuracy: {grid_study.best_value:.4f}\")\n", + "\n", + " # ---------------------------------------\n", + " # 7b. Run with TPESampler\n", + " # ---------------------------------------\n", + " tpe_study, tpe_max_curve = run_study_and_get_curve(\n", + " sampler=tpe_sampler,\n", + " n_trials=10, # pick more if desired\n", + " study_name=\"bert-tpe-study\",\n", + " )\n", + " print(f\"[TPESampler] Number of trials: {len(tpe_study.trials)}\")\n", + " print(f\"[TPESampler] Best accuracy: {tpe_study.best_value:.4f}\")\n", + "\n", + " # ---------------------------------------\n", + " # 7c. Run with RandomSampler\n", + " # ---------------------------------------\n", + " # random_study, random_max_curve = run_study_and_get_curve(\n", + " # sampler=random_sampler,\n", + " # n_trials=10, # pick more if desired\n", + " # study_name=\"bert-random-study\",\n", + " # )\n", + " # print(f\"[RandomSampler] Number of trials: {len(random_study.trials)}\")\n", + " # print(f\"[RandomSampler] Best accuracy: {random_study.best_value:.4f}\")\n", + "\n", + " # -----------------------------\n", + " # 8. Plot the results\n", + " # -----------------------------\n", + " plt.figure(figsize=(8, 6))\n", + " # plt.plot(range(1, len(grid_max_curve)+1), grid_max_curve, label=\"GridSampler\")\n", + " plt.plot(range(1, len(tpe_max_curve)+1), tpe_max_curve, label=\"TPESampler\")\n", + " # plt.plot(range(1, len(random_max_curve)+1), random_max_curve, label=\"RandomSampler\")\n", + " plt.xlabel(\"Number of Trials\")\n", + " plt.ylabel(\"Max Accuracy So Far\")\n", + " plt.title(\"Comparison of GridSampler vs TPESampler vs RandomSampler\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # -----------------------------\n", + " # 9. (Optional) Save best models\n", + " # -----------------------------\n", + " # best_grid_model = grid_study.best_trial.user_attrs[\"model\"].cpu()\n", + " # with open(\"best_grid_model.pkl\", \"wb\") as f:\n", + " # dill.dump(best_grid_model, f)\n", + "\n", + " best_tpe_model = tpe_study.best_trial.user_attrs[\"model\"].cpu()\n", + " with open(\"best_tpe_model.pkl\", \"wb\") as f:\n", + " dill.dump(best_tpe_model, f)\n", + "\n", + " # best_random_model = random_study.best_trial.user_attrs[\"model\"].cpu()\n", + " # with open(\"best_random_model.pkl\", \"wb\") as f:\n", + " # dill.dump(best_random_model, f)\n", + "\n", + " # -----------------------------\n", + " # 10. Save all trials to CSV\n", + " # -----------------------------\n", + " # save_study_results_to_csv(grid_study, \"grid_study_trials.csv\")\n", + " save_study_results_to_csv(tpe_study, \"tpe_study_trials.csv\")\n", + " # save_study_results_to_csv(random_study, \"random_study_trials.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Part 5b Combined ###\n", + "\n", + "from chop.tools import get_tokenized_dataset\n", + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_trainer\n", + "import optuna\n", + "from optuna.samplers import GridSampler, RandomSampler, TPESampler\n", + "from pathlib import Path\n", + "import dill\n", + "from chop.pipelines import CompressionPipeline\n", + "from chop import MaseGraph\n", + "\n", + "def construct_model(trial):\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Update the paramaters in the config\n", + " for param in [\n", + " \"num_layers\",\n", + " \"num_heads\",\n", + " \"hidden_size\",\n", + " \"intermediate_size\",\n", + " ]:\n", + " chosen_idx = trial.suggest_int(param, 0, len(search_space[param]) - 1)\n", + " setattr(config, param, search_space[param][chosen_idx])\n", + "\n", + " trial_model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " for name, layer in trial_model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " new_layer_cls = trial.suggest_categorical(\n", + " f\"{name}_type\",\n", + " search_space[\"linear_layer_choices\"],\n", + " )\n", + "\n", + " if new_layer_cls == nn.Linear:\n", + " continue\n", + " elif new_layer_cls == Identity:\n", + " new_layer = Identity()\n", + " deepsetattr(trial_model, name, new_layer)\n", + " else:\n", + " raise ValueError(f\"Unknown layer type: {new_layer_cls}\")\n", + "\n", + " return trial_model\n", + "\n", + "def objective(trial):\n", + "\n", + " # Define the model\n", + " model = construct_model(trial)\n", + "\n", + " trainer = get_trainer(\n", + " model=model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=1,\n", + " )\n", + "\n", + " trainer.train()\n", + "\n", + " # Quantize and prune the model\n", + " mg = MaseGraph(model)\n", + " pipe = CompressionPipeline()\n", + "\n", + " quantization_config = {\n", + " \"by\": \"type\",\n", + " \"default\": {\n", + " \"config\": {\n", + " \"name\": None,\n", + " }\n", + " },\n", + " \"linear\": {\n", + " \"config\": {\n", + " \"name\": \"integer\",\n", + " \"data_in_width\": 8,\n", + " \"data_in_frac_width\": 4,\n", + " \"weight_width\": 8,\n", + " \"weight_frac_width\": 4,\n", + " \"bias_width\": 8,\n", + " \"bias_frac_width\": 4,\n", + " }\n", + " },\n", + " }\n", + "\n", + " pruning_config = {\n", + " \"weight\": {\n", + " \"sparsity\": 0.5,\n", + " \"method\": \"l1-norm\",\n", + " \"scope\": \"local\",\n", + " },\n", + " \"activation\": {\n", + " \"sparsity\": 0.5,\n", + " \"method\": \"l1-norm\",\n", + " \"scope\": \"local\",\n", + " },\n", + " }\n", + "\n", + " mg, _ = pipe(\n", + " mg,\n", + " pass_args={\n", + " \"quantize_transform_pass\": quantization_config,\n", + " \"prune_transform_pass\": pruning_config,\n", + " },\n", + " )\n", + "\n", + " # Replace the original model with the compressed model\n", + " model = mg.model\n", + "\n", + " trainer = get_trainer(\n", + " model=model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=1, # Post-compression training phase\n", + " )\n", + "\n", + " trainer.train()\n", + " eval_results = trainer.evaluate()\n", + "\n", + " # Set the model as an attribute so we can fetch it later\n", + " trial.set_user_attr(\"model\", model)\n", + "\n", + " return eval_results[\"eval_accuracy\"]\n", + "\n", + "def main_run():\n", + " global checkpoint, tokenizer, dataset, search_space\n", + "\n", + " checkpoint = \"prajjwal1/bert-tiny\"\n", + " tokenizer_checkpoint = \"bert-base-uncased\"\n", + " dataset_name = \"imdb\"\n", + "\n", + " dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + " )\n", + "\n", + " search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\n", + " nn.Linear,\n", + " Identity,\n", + " ],\n", + " }\n", + "\n", + " sampler = TPESampler()\n", + "\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=\"bert-tiny-compression-aware-study\",\n", + " sampler=sampler,\n", + " )\n", + "\n", + " study.optimize(\n", + " objective,\n", + " n_trials=3,\n", + " timeout=60 * 60 * 24,\n", + " )\n", + "\n", + " # Save the best model\n", + " model = study.best_trial.user_attrs[\"model\"].cpu()\n", + "\n", + " with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"wb\") as f:\n", + " dill.dump(model, f)\n", + "\n", + "main_run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Download all csvs ###\n", + "\n", + "import glob\n", + "# from google.colab import files\n", + "\n", + "def download_all_csvs():\n", + " \"\"\"\n", + " Find all CSV files in the directory structure and download them to the local machine.\n", + " Handles errors gracefully and provides feedback for each file.\n", + " \"\"\"\n", + " try:\n", + " # Find all CSV files (searches recursively)\n", + " csv_files = glob.glob(\"**/*.csv\", recursive=True)\n", + "\n", + " if not csv_files:\n", + " print(\"No CSV files found.\")\n", + " return\n", + "\n", + " print(f\"Found {len(csv_files)} CSV file(s):\")\n", + " for file in csv_files:\n", + " print(f\"- {file}\")\n", + "\n", + " # Download each CSV file\n", + " for file in csv_files:\n", + " try:\n", + " print(f\"Downloading {file}...\")\n", + " files.download(file)\n", + " except Exception as e:\n", + " print(f\"Error downloading {file}: {e}\")\n", + "\n", + " print(\"All CSV files processed.\")\n", + "\n", + " except Exception as e:\n", + " print(f\"An error occurred: {e}\")\n", + "\n", + "# Call the function\n", + "download_all_csvs()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Print Forward Info ###\n", + "\n", + "import inspect\n", + "\n", + "def print_model_forward_info(model, label=\"Model\"):\n", + " print(f\"[INFO] === {label} ===\")\n", + " print(f\"[INFO] Class: {model.__class__}\")\n", + " # Attempt to show the forward signature\n", + " try:\n", + " sig = inspect.signature(model.forward)\n", + " print(f\"[INFO] forward signature: {sig}\")\n", + " except ValueError:\n", + " print(\"[INFO] Could not retrieve a signature (might be a built-in / GraphModule).\")\n", + "\n", + " # Try printing the docstring if available\n", + " doc = inspect.getdoc(model.forward)\n", + " print(f\"[INFO] forward docstring:\\n{doc}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Complete pass of compression-aware training ###\n", + "\n", + "### Part 5b Combined ###\n", + "import torch\n", + "from chop.tools import get_tokenized_dataset\n", + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_trainer\n", + "import optuna\n", + "from optuna.samplers import GridSampler, RandomSampler, TPESampler\n", + "from pathlib import Path\n", + "import dill\n", + "from chop.pipelines import CompressionPipeline\n", + "from chop import MaseGraph\n", + "import chop.passes as passes\n", + "import inspect\n", + "\n", + "\n", + "def construct_model(trial):\n", + " \"\"\"\n", + " Creates a BERT model with the hyperparameters suggested by the trial.\n", + " We manually map each parameter to the correct config attribute or layer override.\n", + " \"\"\"\n", + " # Load the default config from a small BERT checkpoint\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Map parameters to config\n", + " config.num_hidden_layers = trial.suggest_categorical(\n", + " \"num_layers\", search_space[\"num_layers\"]\n", + " )\n", + " config.num_attention_heads = trial.suggest_categorical(\n", + " \"num_heads\", search_space[\"num_heads\"]\n", + " )\n", + " config.hidden_size = trial.suggest_categorical(\n", + " \"hidden_size\", search_space[\"hidden_size\"]\n", + " )\n", + " config.intermediate_size = trial.suggest_categorical(\n", + " \"intermediate_size\", search_space[\"intermediate_size\"]\n", + " )\n", + "\n", + " model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " # Handle linear layer choice\n", + " linear_choice = trial.suggest_categorical(\n", + " \"linear_layer_choices\", search_space[\"linear_layer_choices\"]\n", + " )\n", + " if linear_choice == \"identity\":\n", + " # For each linear layer that is square (in_features == out_features),\n", + " # replace it with Identity\n", + " for name, layer in model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " deepsetattr(model, name, Identity())\n", + "\n", + " return model\n", + "\n", + "def objective(trial):\n", + " # Define the model and ensure it is on the correct device\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + " print(f\"[INFO] Model is being initialized on: {device}\")\n", + "\n", + " model = construct_model(trial).to(device)\n", + " print(f\"[INFO] Model moved to: {next(model.parameters()).device}\")\n", + "\n", + " original_forward = model.forward\n", + " model.config.use_cache = False\n", + "\n", + " # Set up the trainer\n", + " trainer = get_trainer(\n", + " model=model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=1,\n", + " )\n", + "\n", + " print(\"[INFO] Starting initial training...\")\n", + " trainer.train()\n", + "\n", + " # Move model to CPU after training\n", + " model.cpu()\n", + " print(f\"[INFO] Model moved to: {next(model.parameters()).device} after training\")\n", + "\n", + " # print_model_forward_info(model, label=\"Model BEFORE compression\")\n", + "\n", + " # MaseGraph processing\n", + " # mg = MaseGraph(model) # Create MaseGraph on the same device\n", + " mg = MaseGraph(\n", + " model,\n", + " hf_input_names=[\n", + " \"input_ids\",\n", + " \"attention_mask\",\n", + " \"labels\",\n", + " ],\n", + " )\n", + "\n", + " mg, _ = passes.init_metadata_analysis_pass(mg)\n", + " mg, _ = passes.add_common_metadata_analysis_pass(mg)\n", + " pipe = CompressionPipeline()\n", + "\n", + " quantization_config = {\n", + " \"by\": \"type\",\n", + " \"default\": {\"config\": {\"name\": None}},\n", + " \"linear\": {\n", + " \"config\": {\n", + " \"name\": \"integer\",\n", + " \"data_in_width\": 8,\n", + " \"data_in_frac_width\": 4,\n", + " \"weight_width\": 8,\n", + " \"weight_frac_width\": 4,\n", + " \"bias_width\": 8,\n", + " \"bias_frac_width\": 4,\n", + " }\n", + " },\n", + " }\n", + "\n", + " pruning_config = {\n", + " \"weight\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n", + " \"activation\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n", + " }\n", + "\n", + " print(\"[INFO] Applying compression pipeline (quantization & pruning)...\")\n", + " mg, _ = pipe(\n", + " mg,\n", + " pass_args={\n", + " \"quantize_transform_pass\": quantization_config,\n", + " \"prune_transform_pass\": pruning_config,\n", + " },\n", + " )\n", + "\n", + " compressed_model = mg.model.to(device)\n", + " print(f\"[INFO] Compressed model class: {compressed_model.__class__}\")\n", + " print(f\"[INFO] Compressed model moved to: {next(compressed_model.parameters()).device}\")\n", + "\n", + " print(\"[INFO] Reassigning original forward to the compressed model...\")\n", + " # compressed_model.forward = original_forward\n", + "\n", + " print_model_forward_info(compressed_model, label=\"Model AFTER compression\")\n", + "\n", + " # Continue training the compressed model\n", + " trainer = get_trainer(\n", + " model=compressed_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=1, # Post-compression training phase\n", + " )\n", + "\n", + " print(\"[INFO] Starting post-compression training...\")\n", + " trainer.train()\n", + "\n", + " # Evaluate the final model\n", + " eval_results = trainer.evaluate()\n", + " print(f\"[INFO] Final evaluation accuracy: {eval_results['eval_accuracy']:.4f}\")\n", + "\n", + " # Set the model as an attribute for later use\n", + " trial.set_user_attr(\"model\", model)\n", + "\n", + " return eval_results[\"eval_accuracy\"]\n", + "\n", + "\n", + "def main_run():\n", + " global checkpoint, tokenizer, dataset, search_space\n", + "\n", + " checkpoint = \"prajjwal1/bert-tiny\"\n", + " tokenizer_checkpoint = \"bert-base-uncased\"\n", + " dataset_name = \"imdb\"\n", + "\n", + " dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + " )\n", + "\n", + " sampler = TPESampler()\n", + "\n", + " # search_space = {\n", + " # \"num_layers\": [2, 4, 8],\n", + " # \"num_heads\": [2, 4, 8, 16],\n", + " # \"hidden_size\": [128, 192, 256, 384, 512],\n", + " # \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " # \"linear_layer_choices\": [\n", + " # nn.Linear,\n", + " # Identity,\n", + " # ],\n", + " # }\n", + "\n", + " search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + " }\n", + "\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=\"bert-tiny-compression-aware-study\",\n", + " sampler=sampler,\n", + " )\n", + "\n", + " print('Reached!')\n", + " study.optimize(\n", + " objective,\n", + " n_trials=3,\n", + " timeout=60 * 60 * 24,\n", + " )\n", + "\n", + " # Save the best model\n", + " try:\n", + " model = study.best_trial.user_attrs[\"model\"].cpu()\n", + " except AttributeError: # If `.cpu()` fails, fetch without it\n", + " model = study.best_trial.user_attrs[\"model\"]\n", + "\n", + "\n", + " with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"wb\") as f:\n", + " dill.dump(model, f)\n", + "\n", + "main_run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Part 5b Combined ###\n", + "\n", + "import torch\n", + "from chop.tools import get_tokenized_dataset\n", + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_trainer\n", + "import optuna\n", + "from optuna.samplers import TPESampler\n", + "from pathlib import Path\n", + "import dill\n", + "from chop.pipelines import CompressionPipeline\n", + "from chop import MaseGraph\n", + "import chop.passes as passes\n", + "import inspect\n", + "import csv\n", + "\n", + "def construct_model(trial):\n", + " \"\"\"\n", + " Creates a BERT model with the hyperparameters suggested by the trial.\n", + " We manually map each parameter to the correct config attribute or layer override.\n", + " \"\"\"\n", + " # Load the default config from a small BERT checkpoint\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Map parameters to config\n", + " config.num_hidden_layers = trial.suggest_categorical(\n", + " \"num_layers\", search_space[\"num_layers\"]\n", + " )\n", + " config.num_attention_heads = trial.suggest_categorical(\n", + " \"num_heads\", search_space[\"num_heads\"]\n", + " )\n", + " config.hidden_size = trial.suggest_categorical(\n", + " \"hidden_size\", search_space[\"hidden_size\"]\n", + " )\n", + " config.intermediate_size = trial.suggest_categorical(\n", + " \"intermediate_size\", search_space[\"intermediate_size\"]\n", + " )\n", + "\n", + " model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " # Handle linear layer choice\n", + " linear_choice = trial.suggest_categorical(\n", + " \"linear_layer_choices\", search_space[\"linear_layer_choices\"]\n", + " )\n", + " if linear_choice == \"identity\":\n", + " # For each linear layer that is square (in_features == out_features),\n", + " # replace it with Identity\n", + " for name, layer in model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " deepsetattr(model, name, Identity())\n", + "\n", + " return model\n", + "\n", + "def objective(trial):\n", + " # Define the model and ensure it is on the correct device\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + " print(f\"[INFO] Model is being initialized on: {device}\")\n", + "\n", + " model = construct_model(trial).to(device)\n", + " print(f\"[INFO] Model moved to: {next(model.parameters()).device}\")\n", + "\n", + " # Keep a copy of the original forward if needed later\n", + " original_forward = model.forward\n", + " model.config.use_cache = False\n", + "\n", + " # === 1) Training Only (No Compression) ===\n", + " print(\"[INFO] Starting initial training (no compression)...\")\n", + " trainer_no_comp = get_trainer(\n", + " model=model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=3,\n", + " )\n", + " trainer_no_comp.train()\n", + " eval_results_no_comp = trainer_no_comp.evaluate()\n", + " no_comp_acc = eval_results_no_comp[\"eval_accuracy\"]\n", + " print(f\"[INFO] Accuracy (no compression): {no_comp_acc:.4f}\")\n", + "\n", + " # Move model to CPU after initial training if you prefer\n", + " model.cpu()\n", + " print(f\"[INFO] Model moved to: {next(model.parameters()).device} after initial training\")\n", + "\n", + " # === 2) Apply Compression, Evaluate with No Additional Training ===\n", + " print(\"[INFO] Building MaseGraph and applying compression pipeline...\")\n", + " mg = MaseGraph(\n", + " model,\n", + " hf_input_names=[\"input_ids\", \"attention_mask\", \"labels\"],\n", + " )\n", + " mg, _ = passes.init_metadata_analysis_pass(mg)\n", + " mg, _ = passes.add_common_metadata_analysis_pass(mg)\n", + "\n", + " pipe = CompressionPipeline()\n", + " quantization_config = {\n", + " \"by\": \"type\",\n", + " \"default\": {\"config\": {\"name\": None}},\n", + " \"linear\": {\n", + " \"config\": {\n", + " \"name\": \"integer\",\n", + " \"data_in_width\": 8,\n", + " \"data_in_frac_width\": 4,\n", + " \"weight_width\": 8,\n", + " \"weight_frac_width\": 4,\n", + " \"bias_width\": 8,\n", + " \"bias_frac_width\": 4,\n", + " }\n", + " },\n", + " }\n", + " pruning_config = {\n", + " \"weight\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n", + " \"activation\": {\"sparsity\": 0.5, \"method\": \"l1-norm\", \"scope\": \"local\"},\n", + " }\n", + "\n", + " mg, _ = pipe(\n", + " mg,\n", + " pass_args={\n", + " \"quantize_transform_pass\": quantization_config,\n", + " \"prune_transform_pass\": pruning_config,\n", + " },\n", + " )\n", + "\n", + " compressed_model = mg.model.to(device)\n", + " print(f\"[INFO] Compressed model moved to: {next(compressed_model.parameters()).device}\")\n", + "\n", + " print(\"[INFO] Evaluating compression result (no post-training)...\")\n", + " trainer_comp_no_post = get_trainer(\n", + " model=compressed_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=1, # We'll just create a trainer; not calling train() means no extra training\n", + " )\n", + " # Just evaluate directly (no further training call):\n", + " eval_results_no_post = trainer_comp_no_post.evaluate()\n", + " compression_no_post_acc = eval_results_no_post[\"eval_accuracy\"]\n", + " print(f\"[INFO] Accuracy (compression, no post-training): {compression_no_post_acc:.4f}\")\n", + "\n", + " # === 3) Continue Training the Compressed Model (Post-Compression Training) ===\n", + " print(\"[INFO] Starting post-compression training...\")\n", + " trainer_comp_post = get_trainer(\n", + " model=compressed_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=3, # Post-compression training phase\n", + " )\n", + " trainer_comp_post.train()\n", + " eval_results_post_train = trainer_comp_post.evaluate()\n", + " compression_post_acc = eval_results_post_train[\"eval_accuracy\"]\n", + " print(f\"[INFO] Accuracy (compression + post-training): {compression_post_acc:.4f}\")\n", + "\n", + " # Set the model as an attribute for later use\n", + " # (the final compressed & post-trained model)\n", + " trial.set_user_attr(\"model\", compressed_model)\n", + "\n", + " # Store scenario accuracies for CSV logging\n", + " trial.set_user_attr(\"no_compression_acc\", no_comp_acc)\n", + " trial.set_user_attr(\"compression_no_post_acc\", compression_no_post_acc)\n", + " trial.set_user_attr(\"compression_post_acc\", compression_post_acc)\n", + "\n", + " # Return the final accuracy after compression + post-training\n", + " return compression_post_acc\n", + "\n", + "def main_run():\n", + " global checkpoint, tokenizer, dataset, search_space\n", + "\n", + " checkpoint = \"prajjwal1/bert-tiny\"\n", + " tokenizer_checkpoint = \"bert-base-uncased\"\n", + " dataset_name = \"imdb\"\n", + "\n", + " dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + " )\n", + "\n", + " sampler = TPESampler()\n", + "\n", + " search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + " }\n", + "\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=\"bert-tiny-compression-aware-study\",\n", + " sampler=sampler,\n", + " )\n", + "\n", + " print(\"[INFO] Optimisation started...\")\n", + " study.optimize(\n", + " objective,\n", + " n_trials=10, # Adjust as needed\n", + " timeout=60 * 60 * 24, # One hour; adjust as needed\n", + " )\n", + "\n", + " # Save the best model\n", + " try:\n", + " best_model = study.best_trial.user_attrs[\"model\"].cpu()\n", + " except AttributeError:\n", + " best_model = study.best_trial.user_attrs[\"model\"]\n", + "\n", + " with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"wb\") as f:\n", + " dill.dump(best_model, f)\n", + "\n", + " # === Write the results of all trials into a CSV file ===\n", + " results_path = \"results.csv\" # Will be saved in your Colab working directory\n", + " with open(results_path, mode=\"w\", newline=\"\") as f:\n", + " writer = csv.writer(f)\n", + " writer.writerow([\"trial_number\", \"no_compression_acc\", \"compression_no_post_acc\", \"compression_post_acc\"])\n", + " for t in study.trials:\n", + " writer.writerow([\n", + " t.number,\n", + " t.user_attrs.get(\"no_compression_acc\", None),\n", + " t.user_attrs.get(\"compression_no_post_acc\", None),\n", + " t.user_attrs.get(\"compression_post_acc\", None),\n", + " ])\n", + "\n", + " print(f\"[INFO] Results written to {results_path}\")\n", + "\n", + "main_run()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "from chop.nn.modules import Identity\n", + "from transformers import AutoConfig, AutoModelForSequenceClassification\n", + "from chop.tools.utils import deepsetattr\n", + "from chop.tools import get_tokenized_dataset, get_trainer\n", + "import optuna\n", + "from optuna.samplers import GridSampler, TPESampler, RandomSampler\n", + "import matplotlib.pyplot as plt\n", + "import dill\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "# -----------------------------\n", + "# 1. Define your checkpoints & dataset\n", + "# -----------------------------\n", + "checkpoint = \"prajjwal1/bert-tiny\"\n", + "tokenizer_checkpoint = \"bert-base-uncased\"\n", + "dataset_name = \"imdb\"\n", + "\n", + "# Load tokenized dataset and tokenizer\n", + "dataset, tokenizer = get_tokenized_dataset(\n", + " dataset=dataset_name,\n", + " checkpoint=tokenizer_checkpoint,\n", + " return_tokenizer=True,\n", + ")\n", + "\n", + "# -----------------------------\n", + "# 2. Define search space\n", + "# -----------------------------\n", + "grid_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "# Same discrete sets for TPE/Random:\n", + "tpe_search_space = {\n", + " \"num_layers\": [2, 4, 8],\n", + " \"num_heads\": [2, 4, 8, 16],\n", + " \"hidden_size\": [128, 192, 256, 384, 512],\n", + " \"intermediate_size\": [512, 768, 1024, 1536, 2048],\n", + " \"linear_layer_choices\": [\"linear\", \"identity\"],\n", + "}\n", + "\n", + "\n", + "# -----------------------------\n", + "# 3. Construct the model given a trial\n", + "# -----------------------------\n", + "def construct_model(trial):\n", + " \"\"\"\n", + " Creates a BERT model with the hyperparameters suggested by the trial.\n", + " We manually map each parameter to the correct config attribute or layer override.\n", + " \"\"\"\n", + " # Load the default config from a small BERT checkpoint\n", + " config = AutoConfig.from_pretrained(checkpoint)\n", + "\n", + " # Map parameters to config\n", + " config.num_hidden_layers = trial.suggest_categorical(\n", + " \"num_layers\", tpe_search_space[\"num_layers\"]\n", + " )\n", + " config.num_attention_heads = trial.suggest_categorical(\n", + " \"num_heads\", tpe_search_space[\"num_heads\"]\n", + " )\n", + " config.hidden_size = trial.suggest_categorical(\n", + " \"hidden_size\", tpe_search_space[\"hidden_size\"]\n", + " )\n", + " config.intermediate_size = trial.suggest_categorical(\n", + " \"intermediate_size\", tpe_search_space[\"intermediate_size\"]\n", + " )\n", + "\n", + " model = AutoModelForSequenceClassification.from_config(config)\n", + "\n", + " # Handle linear layer choice\n", + " linear_choice = trial.suggest_categorical(\n", + " \"linear_layer_choices\", tpe_search_space[\"linear_layer_choices\"]\n", + " )\n", + " if linear_choice == \"identity\":\n", + " # For each linear layer that is square (in_features == out_features),\n", + " # replace it with Identity\n", + " for name, layer in model.named_modules():\n", + " if isinstance(layer, nn.Linear) and layer.in_features == layer.out_features:\n", + " deepsetattr(model, name, Identity())\n", + "\n", + " return model\n", + "\n", + "\n", + "# -----------------------------\n", + "# 4. Define the objective function\n", + "# -----------------------------\n", + "def objective(trial):\n", + " # Build the model given the trial\n", + " trial_model = construct_model(trial)\n", + "\n", + " # Create a Trainer (from chop) that handles fine-tuning\n", + " trainer = get_trainer(\n", + " model=trial_model,\n", + " tokenized_dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " evaluate_metric=\"accuracy\",\n", + " num_train_epochs=1, # For demonstration only\n", + " )\n", + "\n", + " # Train and evaluate\n", + " trainer.train()\n", + " eval_results = trainer.evaluate()\n", + "\n", + " # Save the model to the trial user attributes for later retrieval if needed\n", + " trial.set_user_attr(\"model\", trial_model)\n", + "\n", + " # Return the metric you want to optimize\n", + " return eval_results[\"eval_accuracy\"]\n", + "\n", + "\n", + "# -----------------------------\n", + "# 5. Helper to run a study and collect \"running best accuracies\"\n", + "# -----------------------------\n", + "def run_study_and_get_curve(sampler, n_trials=None, study_name=\"study\"):\n", + " \"\"\"\n", + " Runs an Optuna study with the provided sampler and returns:\n", + " - the study object\n", + " - a list of best accuracies up to each trial (running max)\n", + " \"\"\"\n", + " study = optuna.create_study(\n", + " direction=\"maximize\",\n", + " study_name=study_name,\n", + " sampler=sampler,\n", + " )\n", + "\n", + " study.optimize(\n", + " objective,\n", + " n_trials=n_trials,\n", + " timeout=60 * 60 * 24, # or specify a time limit\n", + " show_progress_bar=False,\n", + " )\n", + "\n", + " # Build a list of max accuracies up to each trial\n", + " running_max_accuracies = []\n", + " current_max = 0.0\n", + " for t in study.trials:\n", + " if t.value is not None and t.value > current_max:\n", + " current_max = t.value\n", + " running_max_accuracies.append(current_max)\n", + "\n", + " return study, running_max_accuracies\n", + "\n", + "# -----------------------------\n", + "# 6. Function to save trials data to CSV\n", + "# -----------------------------\n", + "def save_study_results_to_csv(study, filename):\n", + " \"\"\"\n", + " Saves each trial's results into a CSV, including:\n", + " - trial number\n", + " - objective value (accuracy)\n", + " - parameters\n", + " - model config parameters\n", + " \"\"\"\n", + " rows = []\n", + " for t in study.trials:\n", + " row = {\n", + " \"trial_number\": t.number,\n", + " \"accuracy\": t.value,\n", + " }\n", + " # Merge in parameter key-value pairs directly\n", + " row.update(t.params)\n", + "\n", + " # Add model config if it exists in user attributes\n", + " if \"model\" in t.user_attrs:\n", + " model_config = t.user_attrs[\"model\"].config.to_dict()\n", + " for key, value in model_config.items():\n", + " row[f\"config_{key}\"] = value\n", + "\n", + " rows.append(row)\n", + "\n", + " df = pd.DataFrame(rows)\n", + " df.to_csv(filename, index=False)\n", + " print(f\"Saved {len(rows)} trials with model configs to {filename}.\")\n", + "\n", + "\n", + "\n", + "# -----------------------------\n", + "# 7. Compare GridSampler vs TPESampler vs RandomSampler\n", + "# -----------------------------\n", + "if __name__ == \"__main__\":\n", + " # For GridSampler, define discrete search space\n", + " #grid_sampler = GridSampler(\n", + " # search_space={\n", + " # \"num_layers\": grid_search_space[\"num_layers\"],\n", + " # \"num_heads\": grid_search_space[\"num_heads\"],\n", + " # \"hidden_size\": grid_search_space[\"hidden_size\"],\n", + " # \"intermediate_size\": grid_search_space[\"intermediate_size\"],\n", + " # \"linear_layer_choices\": grid_search_space[\"linear_layer_choices\"],\n", + " # }\n", + " #)\n", + "\n", + " #tpe_sampler = TPESampler()\n", + " random_sampler = RandomSampler()\n", + "\n", + " # ---------------------------------------\n", + " # 7a. Run with GridSampler\n", + " # ---------------------------------------\n", + " #grid_study, grid_max_curve = run_study_and_get_curve(\n", + " # sampler=grid_sampler,\n", + " # n_trials=10, # product of all combos if you set this None or a bigger number\n", + " # study_name=\"bert-grid-study\",\n", + " #)\n", + " #print(f\"[GridSampler] Number of trials: {len(grid_study.trials)}\")\n", + " #print(f\"[GridSampler] Best accuracy: {grid_study.best_value:.4f}\")\n", + "\n", + " # ---------------------------------------\n", + " # 7b. Run with TPESampler\n", + " # ---------------------------------------\n", + " #tpe_study, tpe_max_curve = run_study_and_get_curve(\n", + " # sampler=tpe_sampler,\n", + " # n_trials=10, # pick more if desired\n", + " # study_name=\"bert-tpe-study\",\n", + " #)\n", + " #print(f\"[TPESampler] Number of trials: {len(tpe_study.trials)}\")\n", + " #print(f\"[TPESampler] Best accuracy: {tpe_study.best_value:.4f}\")\n", + "\n", + " # ---------------------------------------\n", + " # 7c. Run with RandomSampler\n", + " # ---------------------------------------\n", + " random_study, random_max_curve = run_study_and_get_curve(\n", + " sampler=random_sampler,\n", + " n_trials=10, # pick more if desired\n", + " study_name=\"bert-random-study\",\n", + " )\n", + " print(f\"[RandomSampler] Number of trials: {len(random_study.trials)}\")\n", + " print(f\"[RandomSampler] Best accuracy: {random_study.best_value:.4f}\")\n", + "\n", + " # -----------------------------\n", + " # 8. Plot the results\n", + " # -----------------------------\n", + " plt.figure(figsize=(8, 6))\n", + " #plt.plot(range(1, len(grid_max_curve)+1), grid_max_curve, label=\"GridSampler\")\n", + " #plt.plot(range(1, len(tpe_max_curve)+1), tpe_max_curve, label=\"TPESampler\")\n", + " plt.plot(range(1, len(random_max_curve)+1), random_max_curve, label=\"RandomSampler\")\n", + " plt.xlabel(\"Number of Trials\")\n", + " plt.ylabel(\"Max Accuracy So Far\")\n", + " plt.title(\"Comparison of GridSampler vs TPESampler vs RandomSampler\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # -----------------------------\n", + " # 9. (Optional) Save best models\n", + " # -----------------------------\n", + " #best_grid_model = grid_study.best_trial.user_attrs[\"model\"].cpu()\n", + " #with open(\"best_grid_model.pkl\", \"wb\") as f:\n", + " #dill.dump(best_grid_model, f)\n", + "\n", + " #best_tpe_model = tpe_study.best_trial.user_attrs[\"model\"].cpu()\n", + " #with open(\"best_tpe_model.pkl\", \"wb\") as f:\n", + " #dill.dump(best_tpe_model, f)\n", + "\n", + " best_random_model = random_study.best_trial.user_attrs[\"model\"].cpu()\n", + " with open(\"best_random_model.pkl\", \"wb\") as f:\n", + " dill.dump(best_random_model, f)\n", + "\n", + " # -----------------------------\n", + " # 10. Save all trials to CSV\n", + " # -----------------------------\n", + " #save_study_results_to_csv(grid_study, \"grid_study_trials.csv\")\n", + " #save_study_results_to_csv(tpe_study, \"tpe_study_trials.csv\")\n", + " save_study_results_to_csv(random_study, \"random_study_trials.csv\")" + ] } ], "metadata": { @@ -517,7 +2129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/docs/source/modules/documentation/tutorials/tutorial_6_mixed_precision_search.ipynb b/docs/source/modules/documentation/tutorials/tutorial_6_mixed_precision_search.ipynb index cfff717e4..f449a1426 100644 --- a/docs/source/modules/documentation/tutorials/tutorial_6_mixed_precision_search.ipynb +++ b/docs/source/modules/documentation/tutorials/tutorial_6_mixed_precision_search.ipynb @@ -55,7 +55,7 @@ "metadata": {}, "outputs": [], "source": [ - "from transformers import AutoModel\n", + "from transformers import AutoModel # type: ignore\n", "\n", "model = AutoModel.from_pretrained(checkpoint)" ] @@ -74,7 +74,7 @@ "outputs": [], "source": [ "from pathlib import Path\n", - "import dill\n", + "import dill # type: ignore\n", "\n", "with open(f\"{Path.home()}/tutorial_5_best_model.pkl\", \"rb\") as f:\n", " base_model = dill.load(f)" @@ -93,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "from chop.tools import get_tokenized_dataset\n", + "from chop.tools import get_tokenized_dataset # type: ignore\n", "\n", "dataset, tokenizer = get_tokenized_dataset(\n", " dataset=dataset_name,\n", @@ -122,8 +122,8 @@ "metadata": {}, "outputs": [], "source": [ - "import torch\n", - "from chop.nn.quantized.modules.linear import (\n", + "import torch # type: ignore\n", + "from chop.nn.quantized.modules.linear import ( # type: ignore\n", " LinearInteger,\n", " LinearMinifloatDenorm,\n", " LinearMinifloatIEEE,\n", @@ -164,7 +164,7 @@ "metadata": {}, "outputs": [], "source": [ - "from chop.tools.utils import deepsetattr\n", + "from chop.tools.utils import deepsetattr # type: ignore\n", "from copy import deepcopy\n", "\n", "\n", @@ -231,7 +231,7 @@ "metadata": {}, "outputs": [], "source": [ - "from chop.tools import get_trainer\n", + "from chop.tools import get_trainer # type: ignore\n", "import random\n", "\n", "\n", @@ -282,7 +282,7 @@ "metadata": {}, "outputs": [], "source": [ - "from optuna.samplers import GridSampler, RandomSampler, TPESampler\n", + "from optuna.samplers import GridSampler, RandomSampler, TPESampler # type: ignore\n", "\n", "sampler = RandomSampler()" ] @@ -300,7 +300,7 @@ "metadata": {}, "outputs": [], "source": [ - "import optuna\n", + "import optuna # type: ignore\n", "\n", "study = optuna.create_study(\n", " direction=\"maximize\",\n", diff --git a/docs/source/modules/labs_2024/lab_3_mixed_precision_search.rst b/docs/source/modules/labs_2024/lab_3_mixed_precision_search.rst index df1639dcb..7dd8bfb3c 100644 --- a/docs/source/modules/labs_2024/lab_3_mixed_precision_search.rst +++ b/docs/source/modules/labs_2024/lab_3_mixed_precision_search.rst @@ -44,4 +44,4 @@ Implementation tasks a. Now, extend the search to consider all supported precisions for the Linear layer in Mase, including Minifloat, BlockFP, BlockLog, Binary, etc. This may also require changing the model constructor so the required arguments are passed when instantiating each layer. - b. Run the search again, and plot a figure that has the number of trials on the x axis, and the maximum achieved accuracy up to that point on the y axis. Plot one curve for each precision to compare their performance. \ No newline at end of file + b. Run the search again, and plot a figure that has the number of trials on the x axis, and the maximum achieved accuracy up to that point on the y axis. Plot one curve for each precision to compare their performance. \ No newline at end of file diff --git a/setup.py b/setup.py index 27d865de1..2147eeee3 100644 --- a/setup.py +++ b/setup.py @@ -26,7 +26,7 @@ def get_system(): "toml", "GitPython", "colorlog", - "cocotb[bus]==1.8.0", + "cocotb[bus]", "pytest", "pytorch-lightning", "transformers", @@ -95,7 +95,15 @@ def get_system(): "sphinx-book-theme", "pydot", "attr-dot-dict", -] + "librosa", # Added for Wave2Vec2 audio dataset loading + "soundfile", # Added for Wave2Vec2 audio dataset loading + "jiwer", # Added for Wave2Vec2's WER (Word Error Rate) evaluation + "pyctcdecode", # Added for Wave2Vec2's CTC decoding + "torchaudio", # Added for audio processing support + "tensorrt", # Added TensorRT for ONNX runtime analysis + "pycuda", # Added CUDA support for ONNX runtime analysis + # "pytorch_quantization", # Added PyTorch quantization for ONNX runtime analysis + ] setup( name="mase-tools", diff --git a/src/chop/dataset/nlp/__init__.py b/src/chop/dataset/nlp/__init__.py index 9bacb3238..9a82656f1 100644 --- a/src/chop/dataset/nlp/__init__.py +++ b/src/chop/dataset/nlp/__init__.py @@ -31,6 +31,7 @@ TranslationDatasetWMT19_ZH_EN, ) +from .speech_recognition import CondensedLibrispeechASRDataset def get_nlp_dataset( name: str, @@ -48,6 +49,7 @@ def get_nlp_dataset( "test", "pred", ], f"Unknown split {split}, should be one of ['train', 'validation', 'test', 'pred']" + match name: case "sst2": @@ -98,6 +100,10 @@ def get_nlp_dataset( dataset_cls = TranslationDatasetIWSLT2017_EN_CH case "wmt16_ro_en": dataset_cls = TranslationDatasetWMT16_RO_EN + case "librispeech_asr": + dataset_cls = CondensedLibrispeechASRDataset + case "nyalpatel/condensed_librispeech_asr": + dataset_cls = CondensedLibrispeechASRDataset case _: raise ValueError(f"Unknown dataset {name}") @@ -154,6 +160,7 @@ def get_nlp_dataset( "alpaca": LanguageModelingDatasetAlpaca, "mmlu-0-shot": LanguageModelingDatasetMMLUZeroShot, # "scienceqa": LanguageModelingDatasetScienceQA, + "nyalpatel/condensed_librispeech_asr": CondensedLibrispeechASRDataset, } diff --git a/src/chop/dataset/nlp/speech_recognition.py b/src/chop/dataset/nlp/speech_recognition.py new file mode 100644 index 000000000..ce2b4e0a4 --- /dev/null +++ b/src/chop/dataset/nlp/speech_recognition.py @@ -0,0 +1,160 @@ +import logging +from pathlib import Path +import torch +from datasets import load_dataset +from sklearn.model_selection import train_test_split +from transformers import Wav2Vec2Processor +from torch.utils.data import Dataset +import torchaudio +from ..utils import add_dataset_info + +logger = logging.getLogger(__name__) + +LIBRISPEECH_CONFIG = { + "sample_rate": 16000, + "normalize_waveform": True, + "tokenizer_checkpoint": "facebook/wav2vec2-base-960h", + "train_size": 0.8, + "validation_size": 0.1, + "test_size": 0.1, + "max_audio_length": 16 * 16000, +} + +processor = Wav2Vec2Processor.from_pretrained(LIBRISPEECH_CONFIG["tokenizer_checkpoint"]) + +@add_dataset_info( + name="nyalpatel/condensed_librispeech_asr", + dataset_source="hf_datasets", + # Change this to use standard split names that are already in the enum + available_splits=("train", "validation", "test"), + seq2seqLM=True, + num_features=LIBRISPEECH_CONFIG["sample_rate"] * 16, +) + +class CondensedLibrispeechASRDataset(Dataset): + def __init__( + self, + split="train", + tokenizer=None, + max_token_len=None, + num_workers=0, + load_from_cache_file=True, + auto_setup=False, + dataset_path=Path("./preprocessed_data"), + config=LIBRISPEECH_CONFIG + ): + super().__init__() + self.split = split + self.dataset_path = dataset_path + self.config = config + self.X = None + self.Y = None + + # Store the additional parameters that the framework passes + self.tokenizer = tokenizer + self.max_token_len = max_token_len + self.num_workers = num_workers + self.load_from_cache_file = load_from_cache_file + + # Automatically call setup if requested + if auto_setup: + self.setup() + + def __len__(self): + return len(self.X) + + def __getitem__(self, idx): + return self.X[idx], self.Y[idx] + + def prepare_data(self) -> None: + # Map the standard split name to a Librispeech-specific split for dataset loading + librispeech_split = "train.clean.100" # default + + if self.split == "train": + librispeech_split = "train.clean.100" # or other training split as needed + elif self.split == "validation": + librispeech_split = "validation.clean" + elif self.split == "test": + librispeech_split = "test.clean" + + _preprocess_librispeech_dataset(self.dataset_path, self.config, split=librispeech_split) + + def setup(self) -> None: + # Map the standard split name to the corresponding file paths + if self.split == "train": + x_path, y_path = "X_train.pt", "Y_train.pt" + elif self.split == "validation": + x_path, y_path = "X_val.pt", "Y_val.pt" + elif self.split == "test": + x_path, y_path = "X_test.pt", "Y_test.pt" + else: + raise ValueError(f"Split {self.split} is not supported.") + + assert (self.dataset_path / x_path).exists(), "Dataset is missing, run prepare_data() first." + + self.X = torch.load(self.dataset_path / x_path) + self.Y = torch.load(self.dataset_path / y_path) + + +def _preprocess_librispeech_dataset(save_path: Path, config: dict = LIBRISPEECH_CONFIG, split="validation.clean"): + dataset = load_dataset("nyalpatel/condensed_librispeech_asr", split=split) + + input_values, labels = [], [] + + for example in dataset: + waveform = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + text = example["text"] + + # Convert waveform to tensor if it's not already + if isinstance(waveform, list): + waveform = torch.tensor(waveform, dtype=torch.float32) + + if sampling_rate != config["sample_rate"]: + waveform = torchaudio.transforms.Resample( + orig_freq=sampling_rate, new_freq=config["sample_rate"] + )(waveform) + + if config["normalize_waveform"]: + # Check if waveform has enough elements to calculate std + if waveform.numel() > 0 and waveform.std() > 0: + waveform = (waveform - waveform.mean()) / waveform.std() + else: + # Handle edge case with zero std + waveform = waveform - waveform.mean() if waveform.numel() > 0 else waveform + + with processor.as_target_processor(): + label = processor.tokenizer(text, return_tensors="pt").input_ids.squeeze(0) + + max_length = config["max_audio_length"] + if waveform.shape[0] > max_length: + waveform = waveform[:max_length] + else: + pad = torch.zeros(max_length) + pad[: waveform.shape[0]] = waveform + waveform = pad + + input_values.append(waveform) + labels.append(label) + + input_values = torch.stack(input_values) + labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=-100) + + X_train, X_temp, Y_train, Y_temp = train_test_split( + input_values, labels, test_size=config["test_size"] + config["validation_size"], random_state=42 + ) + X_val, X_test, Y_val, Y_test = train_test_split( + X_temp, Y_temp, test_size=config["test_size"] / (config["test_size"] + config["validation_size"]), random_state=42 + ) + + # Create directory if it doesn't exist + save_path.mkdir(parents=True, exist_ok=True) + + torch.save(X_train, save_path / "X_train.pt") + torch.save(Y_train, save_path / "Y_train.pt") + torch.save(X_val, save_path / "X_val.pt") + torch.save(Y_val, save_path / "Y_val.pt") + torch.save(X_test, save_path / "X_test.pt") + torch.save(Y_test, save_path / "Y_test.pt") + + print("✅ Condensed Librispeech dataset preprocessed and saved!") \ No newline at end of file diff --git a/src/chop/ir/common.py b/src/chop/ir/common.py index c5dbb4b09..1da39effe 100644 --- a/src/chop/ir/common.py +++ b/src/chop/ir/common.py @@ -52,6 +52,9 @@ "index_select", "detach", "tensor", + "cumsum", # Added for Wave2Vec + "flip", # Added for Wave2Vec + "repeat", # Added for Wave2Vec ] MASE_MODULE_RELATED_FUNCS = [ @@ -128,6 +131,9 @@ "embedding", "df_split", "getitem", + "zeros", # Added for Wave2Vec + "setitem", # Added for Wave2Vec + "invert", # Added for Wave2Vec ] diff --git a/src/chop/models/__init__.py b/src/chop/models/__init__.py index ff73c16f9..2a38539c6 100644 --- a/src/chop/models/__init__.py +++ b/src/chop/models/__init__.py @@ -22,6 +22,8 @@ from .toy import * from .vgg_cifar.vgg_cifar import * from .wideresnet.wideresnet import * +from .wav2vec.modeling_wav2vec import * +from .wav2vec.data_collator_ctc_with_padding import * def get_model_info(name: str) -> MaseModelInfo: diff --git a/src/chop/models/utils.py b/src/chop/models/utils.py index 67fee5a95..df5b071b8 100644 --- a/src/chop/models/utils.py +++ b/src/chop/models/utils.py @@ -30,12 +30,14 @@ class ModelTaskType(Enum): - VISION: computer vision - PHYSICAL: categorize data points into predefined classes based on their features or attributes - NERF: estimate neural radiance field (NeRF) of a 3D scene + - SPEECH: speech processing tasks """ NLP = "nlp" VISION = "vision" PHYSICAL = "physical" NERF = "nerf" + SPEECH = "speech" @dataclass diff --git a/src/chop/models/wav2vec/data_collator_ctc_with_padding.py b/src/chop/models/wav2vec/data_collator_ctc_with_padding.py new file mode 100644 index 000000000..25b9739db --- /dev/null +++ b/src/chop/models/wav2vec/data_collator_ctc_with_padding.py @@ -0,0 +1,74 @@ +import torch + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Union +from transformers import Wav2Vec2Processor + +@dataclass +class DataCollatorCTCWithPadding: + """ + Data collator that will dynamically pad the inputs received. + Args: + processor (:class:`~transformers.Wav2Vec2Processor`) + The processor used for proccessing the data. + padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding index) + among: + * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the + maximum acceptable input length for the model if that argument is not provided. + * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of + different lengths). + max_length (:obj:`int`, `optional`): + Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). + max_length_labels (:obj:`int`, `optional`): + Maximum length of the ``labels`` returned list and optionally padding length (see above). + pad_to_multiple_of (:obj:`int`, `optional`): + If set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= + 7.5 (Volta). + """ + + padding: Union[bool, str] = True + max_length: Optional[int] = None + max_length_labels: Optional[int] = None + pad_to_multiple_of: Optional[int] = None + pad_to_multiple_of_labels: Optional[int] = None + + def __init__(self, processor: Wav2Vec2Processor, **kwargs): + self.processor = processor + for key, value in kwargs.items(): + setattr(self, key, value) + + def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: + input_features = [{"input_values": feature["input_values"]} for feature in features] + label_features = [{"input_ids": feature["labels"]} for feature in features] + + batch = self.processor.pad( + input_features, + padding=self.padding, + max_length=self.max_length, + pad_to_multiple_of=self.pad_to_multiple_of, + return_tensors="pt", + ) + with self.processor.as_target_processor(): + labels_batch = self.processor.pad( + label_features, + padding=self.padding, + max_length=self.max_length_labels, + pad_to_multiple_of=self.pad_to_multiple_of_labels, + return_tensors="pt", + ) + + # replace padding with -100 to ignore loss correctly + labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) + batch["labels"] = labels + + pad_value = self.processor.feature_extractor.padding_value if hasattr(self.processor, "feature_extractor") else 0 + batch["attention_mask"] = (batch["input_values"] != pad_value).long() + + # if "attention_mask" not in batch or batch["attention_mask"] is None: + # batch["attention_mask"] = torch.ones_like(batch["input_values"]) + + return batch diff --git a/src/chop/models/wav2vec/modeling_wav2vec.py b/src/chop/models/wav2vec/modeling_wav2vec.py new file mode 100644 index 000000000..f359c68c2 --- /dev/null +++ b/src/chop/models/wav2vec/modeling_wav2vec.py @@ -0,0 +1,112 @@ +import logging +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Optional +from transformers import Wav2Vec2Model, Wav2Vec2Config +from chop.models.utils import register_mase_model, register_mase_checkpoint + +logger = logging.getLogger(__name__) + +@register_mase_model( + name="wav2vec", + checkpoints=[ + "wav2vec2-base", + "wav2vec2-base-960h", + "wav2vec2-large", + "wav2vec2-large-960h", + "wav2vec2-large-xlsr" + ], + model_source="hf_transformers", + task_type="speech", + is_fx_traceable=True, +) + +class Wav2VecModelWrapper(nn.Module): + def __init__(self, config, **kwargs): + super().__init__() + self.model = Wav2Vec2Model(config) + + def forward( + self, + input_values, + attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + **kwargs + ): + + return self.model(input_values, **kwargs) + +def _get_wav2vec_model(model_size: str, pretrained: bool = False, **kwargs): + """Helper function to get Wav2Vec models""" + if pretrained: + model_id = f"facebook/wav2vec2-{model_size}" + model = Wav2Vec2Model.from_pretrained(model_id) + logger.info(f"Loaded pretrained Wav2Vec2 {model_size} model") + else: + config = Wav2Vec2Config.from_pretrained(f"facebook/wav2vec2-{model_size}") + model = Wav2VecModelWrapper(config) + logger.info(f"Initialized Wav2Vec2 {model_size} model from config") + + return model + +@register_mase_checkpoint("wav2vec2-base") +def get_wav2vec_base(pretrained: bool = False, **kwargs): + return _get_wav2vec_model("base", pretrained, **kwargs) + +@register_mase_checkpoint("wav2vec2-base-960h") +def get_wav2vec_base_960h(pretrained: bool = False, **kwargs): + return _get_wav2vec_model("base", pretrained, **kwargs) + +@register_mase_checkpoint("wav2vec2-large") +def get_wav2vec_large(pretrained: bool = False, **kwargs): + return _get_wav2vec_model("large", pretrained, **kwargs) + +@register_mase_checkpoint("wav2vec2-large-960h") +def get_wav2vec_large_960h(pretrained: bool = False, **kwargs): + return _get_wav2vec_model("large-960h", pretrained, **kwargs) + +@register_mase_checkpoint("wav2vec2-large-xlsr") +def get_wav2vec_large_xlsr(pretrained: bool = False, **kwargs): + return _get_wav2vec_model("large-xlsr-53", pretrained, **kwargs) + + +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + """ + Args: + encoder: The traced encoder (e.g., mg.model) + ctc_head: The CTC head (usually a linear layer) + blank_id: The token ID for the blank symbol (typically 0) + beam_width: Width for beam search decoding (if using a decoder) + decoder: (Optional) A beam search decoder (e.g., from pyctcdecode) + """ + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder # Only used during inference + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) # outputs tensor as expected + + output = {"logits": logits, "labels": labels} + + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + + loss = F.ctc_loss(log_probs, labels, input_lengths, target_lengths, blank=self.blank_id, reduction="mean", zero_infinity=True) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output \ No newline at end of file diff --git a/src/chop/nn/quantized/modules/__init__.py b/src/chop/nn/quantized/modules/__init__.py index 4219d6da9..85158f137 100644 --- a/src/chop/nn/quantized/modules/__init__.py +++ b/src/chop/nn/quantized/modules/__init__.py @@ -273,3 +273,15 @@ "bert_self_attention_integer": BertSelfAttentionInteger, "grouped_query_attention_integer": GroupedQueryAttentionInteger, } + +def get_quantized_module_map(): + """ + Lazily load the FlexRound modules and return the complete module map. + """ + # Import here (local import) to avoid circular dependencies. + from .flexround_modules import LinearFlexRound, Conv2dFlexRound, Conv1dFlexRound + # Insert the FlexRound keys into the existing map. + quantized_module_map["linear_flexround"] = LinearFlexRound + quantized_module_map["conv2d_flexround"] = Conv2dFlexRound + quantized_module_map["conv1d_flexround"] = Conv1dFlexRound + return quantized_module_map diff --git a/src/chop/nn/quantized/modules/flexround_modules.py b/src/chop/nn/quantized/modules/flexround_modules.py new file mode 100644 index 000000000..741f55ce0 --- /dev/null +++ b/src/chop/nn/quantized/modules/flexround_modules.py @@ -0,0 +1,139 @@ +# File: src/chop/nn/quantized/modules/flexround_modules.py +import torch +import torch.nn as nn +import torch.nn.functional as F +from chop.passes.graph.transforms.quantize.flexround import FlexRoundQuantizer + +############################################# +# Linear FlexRound wrapper +############################################# +class LinearFlexRound(nn.Linear): + def __init__(self, in_features, out_features, bias=True, config=None): + """ + A linear layer wrapped with FlexRound quantization. + Quantized weights are computed as: + Ŵ = s1 * floor( W / (s1 * S2 * s3) + 0.5 ) + where: + - s1 is a learnable scalar (common grid size) + - S2 is a learnable tensor (same shape as weight) + - s3 is a learnable vector of shape (out_features, 1) + The config dict should contain keys: + "weight_width", "weight_frac_width", + "data_in_width", "data_in_frac_width", + "bias_width", "bias_frac_width", and optionally "weight_only". + It may also contain "s1_init" (default=1.0). + """ + super().__init__(in_features, out_features, bias=bias) + config = config or {} + s1_init = config.get("s1_init", 1.0) + # Initialize learnable parameters for FlexRound: + self.s1 = nn.Parameter(torch.tensor(s1_init, dtype=self.weight.dtype)) + self.S2 = nn.Parameter(torch.ones_like(self.weight)) + # s3 is a per–output–channel scaling factor (shape: [out_features, 1]) + self.s3 = nn.Parameter(torch.ones(self.out_features, 1, dtype=self.weight.dtype)) + + # Set up activation quantizer if needed. + if not config.get("weight_only", False): + act_width = config.get("data_in_width", 8) + act_frac = config.get("data_in_frac_width", 4) + self.act_quant = FlexRoundQuantizer(bit_width=act_width, frac_width=act_frac) + else: + self.act_quant = None + + def forward(self, input): + # Compute the effective division factor + div_factor = self.s1 * self.S2 * self.s3 + # Apply FlexRound quantization to weights + quant_w = self.s1 * torch.floor(self.weight / div_factor + 0.5) + out = F.linear(input, quant_w, self.bias) + if self.act_quant is not None: + out = self.act_quant(out) + return out + +############################################# +# Conv2d FlexRound wrapper +############################################# +class Conv2dFlexRound(nn.Conv2d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros", + config=None): + """ + A Conv2d layer wrapped with FlexRound quantization. + Quantized weights are computed as: + Ŵ = s1 * floor( W / (s1 * S2 * s3 * s4) + 0.5 ) + where: + - s1 is a learnable scalar, + - S2 is a learnable tensor (same shape as weight), + - s3 is a learnable per–output–channel factor (shape: [out_channels, 1, 1, 1]), + - s4 is a learnable per–input–channel factor (shape: [1, in_channels, 1, 1]). + """ + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode) + config = config or {} + s1_init = config.get("s1_init", 1.0) + self.s1 = nn.Parameter(torch.tensor(s1_init, dtype=self.weight.dtype)) + self.S2 = nn.Parameter(torch.ones_like(self.weight)) + self.s3 = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1, dtype=self.weight.dtype)) + self.s4 = nn.Parameter(torch.ones(1, self.in_channels, 1, 1, dtype=self.weight.dtype)) + + if not config.get("weight_only", False): + act_width = config.get("data_in_width", 8) + act_frac = config.get("data_in_frac_width", 4) + self.act_quant = FlexRoundQuantizer(bit_width=act_width, frac_width=act_frac) + else: + self.act_quant = None + + def forward(self, x): + div_factor = self.s1 * self.S2 * self.s3 * self.s4 + quant_w = self.s1 * torch.floor(self.weight / div_factor + 0.5) + out = self._conv_forward(x, quant_w, self.bias) + if self.act_quant is not None: + out = self.act_quant(out) + return out + +############################################# +# Conv1d FlexRound wrapper +############################################# +class Conv1dFlexRound(nn.Conv1d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, config=None, **kwargs): + """ + A Conv1d layer wrapped with FlexRound quantization. + Quantized weights are computed as: + Ŵ = s1 * floor( W / (s1 * S2 * s3) + 0.5 ) + where: + - s1 is a learnable scalar, + - S2 is a learnable tensor (same shape as weight), + - s3 is a learnable per–output–channel factor (shape: [out_channels, 1, 1]). + The **kwargs argument allows ignoring unsupported parameters such as padding_mode. + """ + # nn.Conv1d does not support padding_mode, so we pop it if present. + kwargs.pop("padding_mode", None) + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, **kwargs) + config = config or {} + s1_init = config.get("s1_init", 1.0) + self.s1 = nn.Parameter(torch.tensor(s1_init, dtype=self.weight.dtype)) + self.S2 = nn.Parameter(torch.ones_like(self.weight)) + self.s3 = nn.Parameter(torch.ones(self.out_channels, 1, 1, dtype=self.weight.dtype)) + + if not config.get("weight_only", False): + act_width = config.get("data_in_width", 8) + act_frac = config.get("data_in_frac_width", 4) + self.act_quant = FlexRoundQuantizer(bit_width=act_width, frac_width=act_frac) + else: + self.act_quant = None + + def forward(self, x): + div_factor = self.s1 * self.S2 * self.s3 + quant_w = self.s1 * torch.floor(self.weight / div_factor + 0.5) + out = F.conv1d(x, quant_w, self.bias, self.stride, self.padding, self.dilation, self.groups) + if self.act_quant is not None: + out = self.act_quant(out) + return out + +############################################# +# (Optional) Activation FlexRound quantizer +############################################# +# If needed, you can also include a simple FlexRoundQuantizer definition here, +# but it’s already available in the quantize/flexround.py module. diff --git a/src/chop/nn/quantized/modules/flexround_modules_goodbefore.py b/src/chop/nn/quantized/modules/flexround_modules_goodbefore.py new file mode 100644 index 000000000..0553731ae --- /dev/null +++ b/src/chop/nn/quantized/modules/flexround_modules_goodbefore.py @@ -0,0 +1,220 @@ +# import torch +# import torch.nn as nn +# from chop.passes.graph.transforms.quantize.flexround import FlexRoundQuantizer + +# class LinearFlexRound(nn.Linear): +# """ +# A linear layer wrapped with FlexRound quantization. +# """ +# def __init__(self, in_features, out_features, bias=True, config=None): +# super().__init__(in_features, out_features, bias=bias) +# # Use the config to extract the quantization parameters. +# # Here we assume the config uses keys "weight_width" and "weight_frac_width", etc. +# weight_width = config.get("weight_width", 8) +# weight_frac = config.get("weight_frac_width", 4) +# data_in_width = config.get("data_in_width", 8) +# data_in_frac = config.get("data_in_frac_width", 4) +# bias_width = config.get("bias_width", 8) +# bias_frac = config.get("bias_frac_width", 4) +# weight_only = config.get("weight_only", False) + +# self.weight_quant = FlexRoundQuantizer(bit_width=weight_width, frac_width=weight_frac) +# # Optionally quantize activations if not weight-only. +# if not weight_only: +# self.act_quant = FlexRoundQuantizer(bit_width=data_in_width, frac_width=data_in_frac) +# else: +# self.act_quant = None + +# def forward(self, input): +# # Quantize weight (note: in practice you might want to wrap weight or use a hook) +# quant_w = self.weight_quant(self.weight) +# out = nn.functional.linear(input, quant_w, self.bias) +# # Optionally quantize activations +# if self.act_quant is not None: +# out = self.act_quant(out) +# return out + +# # Similarly for conv2d: +# class Conv2dFlexRound(nn.Conv2d): +# """ +# A Conv2d layer wrapped with FlexRound quantization. +# """ +# def __init__(self, in_channels, out_channels, kernel_size, stride=1, +# padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', config=None): +# super().__init__(in_channels, out_channels, kernel_size, stride, +# padding, dilation, groups, bias, padding_mode) +# weight_width = config.get("weight_width", 8) +# weight_frac = config.get("weight_frac_width", 4) +# data_in_width = config.get("data_in_width", 8) +# data_in_frac = config.get("data_in_frac_width", 4) +# bias_width = config.get("bias_width", 8) +# bias_frac = config.get("bias_frac_width", 4) +# weight_only = config.get("weight_only", False) + +# self.weight_quant = FlexRoundQuantizer(bit_width=weight_width, frac_width=weight_frac) +# if not weight_only: +# self.act_quant = FlexRoundQuantizer(bit_width=data_in_width, frac_width=data_in_frac) +# else: +# self.act_quant = None + +# def forward(self, x): +# quant_w = self.weight_quant(self.weight) +# out = self._conv_forward(x, quant_w, self.bias) +# if self.act_quant is not None: +# out = self.act_quant(out) +# return out + + +# class Conv1dFlexRound(nn.Conv1d): +# """ +# A Conv1d layer wrapped with FlexRound quantization. +# """ +# def __init__(self, in_channels, out_channels, kernel_size, stride=1, +# padding=0, dilation=1, groups=1, bias=True, config=None): +# super().__init__(in_channels, out_channels, kernel_size, stride, +# padding, dilation, groups, bias) +# # Use the config to set quantization parameters. +# weight_width = config.get("weight_width", 8) +# weight_frac = config.get("weight_frac_width", 4) +# data_in_width = config.get("data_in_width", 8) +# data_in_frac = config.get("data_in_frac_width", 4) +# bias_width = config.get("bias_width", 8) +# bias_frac = config.get("bias_frac_width", 4) +# weight_only = config.get("weight_only", False) + +# self.weight_quant = FlexRoundQuantizer(bit_width=weight_width, frac_width=weight_frac) +# if not weight_only: +# self.act_quant = FlexRoundQuantizer(bit_width=data_in_width, frac_width=data_in_frac) +# else: +# self.act_quant = None + +# def forward(self, x): +# quant_w = self.weight_quant(self.weight) +# out = nn.functional.conv1d(x, quant_w, self.bias, self.stride, +# self.padding, self.dilation, self.groups) +# if self.act_quant is not None: +# out = self.act_quant(out) +# return out + + + +""" +File: src/chop/nn/quantized/modules/flexround_modules.py + +This file defines operator‐specific FlexRound wrappers for quantization. +Each class subclasses the corresponding PyTorch module (e.g. nn.Linear, nn.Conv2d, nn.Conv1d) +and applies FlexRound quantization to the weights (and optionally activations) during forward. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from chop.passes.graph.transforms.quantize.flexround import FlexRoundQuantizer + +class LinearFlexRound(nn.Linear): + def __init__(self, in_features, out_features, bias=True, config=None): + """ + A linear layer wrapped with FlexRound quantization. + + The config should contain: + - "weight_width", "weight_frac_width" + - "data_in_width", "data_in_frac_width" + - "bias_width", "bias_frac_width" + - "weight_only" (bool) + """ + super().__init__(in_features, out_features, bias=bias) + config = config or {} + # Use the standard key names expected by the parser. + weight_width = config.get("weight_width", 8) + weight_frac = config.get("weight_frac_width", 4) + data_in_width = config.get("data_in_width", 8) + data_in_frac = config.get("data_in_frac_width", 4) + bias_width = config.get("bias_width", 8) + bias_frac = config.get("bias_frac_width", 4) + weight_only = config.get("weight_only", False) + self.weight_quantizer = FlexRoundQuantizer(bit_width=weight_width, frac_width=weight_frac) + # If not weight-only, quantize activations as well. + if not weight_only: + self.act_quantizer = FlexRoundQuantizer(bit_width=data_in_width, frac_width=data_in_frac) + else: + self.act_quantizer = None + + def forward(self, input): + # Quantize the weight + quant_w = self.weight_quantizer(self.weight) + out = F.linear(input, quant_w, self.bias) + # Optionally quantize the activation + if self.act_quantizer is not None: + out = self.act_quantizer(out) + return out + +class Conv2dFlexRound(nn.Conv2d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros", + config=None): + """ + A Conv2d layer wrapped with FlexRound quantization. + + Expects config with keys: + - "weight_width", "weight_frac_width" + - "data_in_width", "data_in_frac_width" + - "bias_width", "bias_frac_width" + - "weight_only" (bool) + """ + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode) + config = config or {} + weight_width = config.get("weight_width", 8) + weight_frac = config.get("weight_frac_width", 4) + data_in_width = config.get("data_in_width", 8) + data_in_frac = config.get("data_in_frac_width", 4) + bias_width = config.get("bias_width", 8) + bias_frac = config.get("bias_frac_width", 4) + weight_only = config.get("weight_only", False) + self.weight_quantizer = FlexRoundQuantizer(bit_width=weight_width, frac_width=weight_frac) + if not weight_only: + self.act_quantizer = FlexRoundQuantizer(bit_width=data_in_width, frac_width=data_in_frac) + else: + self.act_quantizer = None + + def forward(self, x): + quant_w = self.weight_quantizer(self.weight) + out = self._conv_forward(x, quant_w, self.bias) + if self.act_quantizer is not None: + out = self.act_quantizer(out) + return out + +class Conv1dFlexRound(nn.Conv1d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, config=None): + """ + A Conv1d layer wrapped with FlexRound quantization. + + Expects config with keys: + - "weight_width", "weight_frac_width" + - "data_in_width", "data_in_frac_width" + - "bias_width", "bias_frac_width" + - "weight_only" (bool) + """ + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias) + config = config or {} + weight_width = config.get("weight_width", 8) + weight_frac = config.get("weight_frac_width", 4) + data_in_width = config.get("data_in_width", 8) + data_in_frac = config.get("data_in_frac_width", 4) + bias_width = config.get("bias_width", 8) + bias_frac = config.get("bias_frac_width", 4) + weight_only = config.get("weight_only", False) + self.weight_quantizer = FlexRoundQuantizer(bit_width=weight_width, frac_width=weight_frac) + if not weight_only: + self.act_quantizer = FlexRoundQuantizer(bit_width=data_in_width, frac_width=data_in_frac) + else: + self.act_quantizer = None + + def forward(self, x): + quant_w = self.weight_quantizer(self.weight) + out = F.conv1d(x, quant_w, self.bias, self.stride, self.padding, self.dilation, self.groups) + if self.act_quantizer is not None: + out = self.act_quantizer(out) + return out diff --git a/src/chop/passes/__init__.py b/src/chop/passes/__init__.py index bfc69b47e..5e0669bfa 100644 --- a/src/chop/passes/__init__.py +++ b/src/chop/passes/__init__.py @@ -16,6 +16,7 @@ verify_common_metadata_analysis_pass, run_cosim_analysis_pass, get_synthesis_results, + add_movement_metadata_analysis_pass, ) from .graph.transforms import ( prune_transform_pass, diff --git a/src/chop/passes/graph/__init__.py b/src/chop/passes/graph/__init__.py index 188038a14..9ef27c0cb 100644 --- a/src/chop/passes/graph/__init__.py +++ b/src/chop/passes/graph/__init__.py @@ -15,6 +15,7 @@ get_synthesis_results, calculate_avg_bits_mg_analysis_pass, add_pruning_metadata_analysis_pass, + add_movement_metadata_analysis_pass, add_natural_sparsity_metadata_analysis_pass, hook_inspection_analysis_pass, verify_common_metadata_analysis_pass, diff --git a/src/chop/passes/graph/analysis/__init__.py b/src/chop/passes/graph/analysis/__init__.py index 1150c1ccf..2591f44ce 100644 --- a/src/chop/passes/graph/analysis/__init__.py +++ b/src/chop/passes/graph/analysis/__init__.py @@ -30,6 +30,7 @@ add_pruning_metadata_analysis_pass, add_natural_sparsity_metadata_analysis_pass, hook_inspection_analysis_pass, + add_movement_metadata_analysis_pass, ) from .plot import plot_graph_analysis_pass diff --git a/src/chop/passes/graph/analysis/add_metadata/add_common_metadata.py b/src/chop/passes/graph/analysis/add_metadata/add_common_metadata.py index 66fe9cc28..b41a2e2a5 100644 --- a/src/chop/passes/graph/analysis/add_metadata/add_common_metadata.py +++ b/src/chop/passes/graph/analysis/add_metadata/add_common_metadata.py @@ -2,6 +2,7 @@ import torch.fx as fx from torch import nn +import torch from chop.passes.graph.analysis.utils import ( is_tensor_constant, @@ -135,6 +136,7 @@ def graph_iterator_for_mase_ops(graph): node.meta["mase"].parameters["common"]["mase_op"] = mase_op elif node.op == "call_function": + # we might have things like mult_1, add_2, so we need to match the pattern matching, matched_name = match_and_filter( node.target.__name__, @@ -235,7 +237,6 @@ def graph_iterator_for_metadata( analyse_fn = analyse_common_parameters_function elif node.op == "call_method": self_obj, *args = load_arg(node.args, env) - print(self_obj) kwargs = load_arg(node.kwargs, env) result = getattr(self_obj, node.target)(*args, **kwargs) analyse_fn = analyse_common_parameters_method diff --git a/src/chop/passes/graph/analysis/add_metadata/common_metadata_layers.py b/src/chop/passes/graph/analysis/add_metadata/common_metadata_layers.py index 15680a1e7..854f4298e 100644 --- a/src/chop/passes/graph/analysis/add_metadata/common_metadata_layers.py +++ b/src/chop/passes/graph/analysis/add_metadata/common_metadata_layers.py @@ -98,7 +98,7 @@ # https://pytorch.org/docs/stable/generated/torch.sqrt.html "sqrt": {"input": "data_in"}, # https://pytorch.org/docs/stable/generated/torch.div.html - "div": {"input": "data_in", "other": "data_in"}, + "div": {"rounding_mode": "config", "input": "data_in", "other": "data_in"}, # https://pytorch.org/docs/stable/generated/torch.cat.html "cat": {"tensors": "data_in", "dim": "config"}, # onnx_slice (custom implementation in onnx config) @@ -302,6 +302,23 @@ "adaptive_avg_pool2d": {"input": "data_in", "output_size": "config"}, "adaptive_max_pool1d": {"input": "data_in", "output_size": "config"}, "adaptive_max_pool2d": {"input": "data_in", "output_size": "config"}, + "zeros": { # Added for Wave2Vec + "size": "config", + "dtype": "config", + "layout": "config", + "device": "config", + "requires_grad": "config", + "out": "config" + }, + "setitem": { # Added for Wave2Vec + "container": "data_in", + "key": "config", + "value": "data_in" + }, + "invert": { # Added for Wave2Vec + "input": "data_in", + # "out": "config" + }, } module_data = { @@ -415,7 +432,7 @@ "keepdim": "config", }, # https://pytorch.org/docs/stable/generated/torch.Tensor.round.html - "round": {}, + # "round": {}, # Removed for Wave2Vec # https://pytorch.org/docs/stable/generated/torch.Tensor.floor.html "floor": {}, # https://pytorch.org/docs/stable/generated/torch.Tensor.clamp.html @@ -458,6 +475,17 @@ }, # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html "detach": {"input": "data_in"}, + "cumsum": {"dim": "config"}, # Added for Wave2Vec + "flip": { # Added for Wave2Vec + "input": "data_in", + "dims": "config" + }, + "repeat": { + "size_0": "config", + "size_1": "config", + "size_2": "config", + "size_3": "config", + }, # Added for Wave2Vec } # ---------------------------------------------------------- @@ -712,10 +740,7 @@ def analyse_common_parameters_placeholder(meta, result, args, kwargs, add_value= def analyse_common_parameters_function(meta, result, args, kwargs, add_value=True): # fetch mase info mase_op = meta["common"]["mase_op"] - - # deal with result meta = _annotate_result_metadata(meta, result, add_value) - # deal with args and kwargs meta = _annotate_arg_metadata(meta, args, kwargs, func_data[mase_op], add_value) return meta diff --git a/src/chop/passes/graph/analysis/pruning/__init__.py b/src/chop/passes/graph/analysis/pruning/__init__.py index ac6d95933..ca07a2504 100644 --- a/src/chop/passes/graph/analysis/pruning/__init__.py +++ b/src/chop/passes/graph/analysis/pruning/__init__.py @@ -1,3 +1,3 @@ -from .calculate_sparsity import add_pruning_metadata_analysis_pass +from .calculate_sparsity import add_pruning_metadata_analysis_pass, add_movement_metadata_analysis_pass from .calculate_natural_sparsity import add_natural_sparsity_metadata_analysis_pass from .hook_inspector import hook_inspection_analysis_pass diff --git a/src/chop/passes/graph/analysis/pruning/calculate_sparsity.py b/src/chop/passes/graph/analysis/pruning/calculate_sparsity.py index 94c146c4b..ef1cf847e 100644 --- a/src/chop/passes/graph/analysis/pruning/calculate_sparsity.py +++ b/src/chop/passes/graph/analysis/pruning/calculate_sparsity.py @@ -1,4 +1,5 @@ import torch +from torch import nn from chop.passes.graph.analysis.utils import fetch_attr, load_arg @@ -87,3 +88,24 @@ def add_pruning_metadata_analysis_pass(graph, pass_args: dict = {}): graph, pass_args["dummy_in"], pass_args["add_value"] ) return graph, sparsity_info + + +def add_movement_metadata_analysis_pass(graph, pass_args=None): + """ + Adds movement metadata to all Conv2d and Linear layers with a weight attribute + in the given MaseGraph's model. + + Args: + mg: The MaseGraph instance. + pass_args: Optional dictionary for future expansion. + + Returns: + A tuple of the updated MaseGraph and an empty dictionary. + """ + for module in graph.model.modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + if not hasattr(module, "metadata"): + module.metadata = {} + module.metadata["weight"] = {"stats": {"movement": torch.zeros_like(module.weight)}} + + return graph, {} diff --git a/src/chop/passes/graph/analysis/quantization/cal_flexround.py b/src/chop/passes/graph/analysis/quantization/cal_flexround.py new file mode 100644 index 000000000..0fefaefa0 --- /dev/null +++ b/src/chop/passes/graph/analysis/quantization/cal_flexround.py @@ -0,0 +1,21 @@ +# After quantize_transform_pass: + + +def calibrate_flexround(model, calibration_data, num_iters=1000): + model.eval() + optimizer = torch.optim.Adam([ + p for n, p in model.named_parameters() + if "log_s1" in n or "log_S2" in n or "log_s3" in n # Only train quantization params + ]) + + for _ in range(num_iters): + batch = calibration_data.next() # Use a small calibration dataset + output_quant = model(batch["input_values"]) + with torch.no_grad(): + output_fp = original_float_model(batch["input_values"]) # Original FP model + + loss = F.mse_loss(output_quant["last_hidden_state"], output_fp["last_hidden_state"]) + loss.backward() + optimizer.step() + optimizer.zero_grad() + diff --git a/src/chop/passes/graph/analysis/runtime/runtime_analysis.py b/src/chop/passes/graph/analysis/runtime/runtime_analysis.py index 4f55dd5da..71a53f121 100644 --- a/src/chop/passes/graph/analysis/runtime/runtime_analysis.py +++ b/src/chop/passes/graph/analysis/runtime/runtime_analysis.py @@ -4,6 +4,7 @@ from chop.ir import MaseGraph from .utils import PowerMonitor, get_execution_provider import os +import inspect from tabulate import tabulate import torchmetrics import numpy as np @@ -13,7 +14,6 @@ from pathlib import Path import time - from chop.passes.utils import register_mase_pass @@ -61,8 +61,17 @@ def runtime_analysis_pass(model, pass_args=None): These metrics provide valuable insights into the model's efficiency, effectiveness, and operational cost, crucial for informed decision-making regarding model deployment in production environments. """ - import tensorrt as trt - from cuda import cudart + try: + import tensorrt as trt # type: ignore + globals()['trt'] = trt + except ImportError: + raise ImportError("tensorrt is required for this functionality. Please install it from NVIDIA's repositories.") + + try: + from cuda import cudart # type: ignore + globals()['cudart'] = cudart + except ImportError: + raise ImportError("pycuda's cudart is required for this functionality. Please install pycuda.") analysis = RuntimeAnalysis(model, pass_args) results = analysis.evaluate() @@ -84,6 +93,8 @@ def __init__(self, model, config): self.logger = logging.getLogger(__name__) self.num_of_classes = self.config["data_module"].dataset_info.num_classes + # print(f"[DEBUG] Available Attributes in data_module: {dir(self.config['data_module'])}") + match model: case MaseGraph(): # Check if model is mase graph @@ -160,7 +171,7 @@ def _prepare_save_path(self, method: str, suffix: str): """Creates and returns a save path for the model.""" root = Path(__file__).resolve().parents[7] current_date = datetime.now().strftime("%Y-%m-%d") - model_dir = f'{self.config["model"]}_{self.config["task"]}_{self.config["dataset"]}_{current_date}' + model_dir = f'{self.config["model"]}_{self.config["task"]}_{self.config["data_module"].name}_{current_date}' save_dir = root / f"mase_output/tensorrt/quantization/{model_dir}/{method}" save_dir.mkdir(parents=True, exist_ok=True) @@ -204,16 +215,22 @@ def _summarize(self): io_info_str = "\n".join(io_info_lines) self.logger.info(f"\nTensorRT Engine Input/Output Information:\n{io_info_str}") - def infer_mg_cpu(self, model, input_data): + def infer_mg_cpu(self, model, input_values, attention_mask=None): # Ensure model and input data are on CPU - input_data = input_data.cpu() + input_values = input_values.cpu() model = model.cpu() + if attention_mask is not None: + attention_mask = attention_mask.cpu() + inputs = (input_values, attention_mask) + else: + inputs = (input_values,) + # Start timing CPU operations start_time = time.time() # INFERENCE! - preds = model(input_data) + preds = model(*inputs) # End timing CPU operations end_time = time.time() @@ -223,21 +240,37 @@ def infer_mg_cpu(self, model, input_data): end_time - start_time ) * 1000.0 # Convert from seconds to milliseconds - # Return the predictions - return preds.detach(), latency + if isinstance(preds, dict): + detached_dict = {} + for k, v in preds.items(): + if torch.is_tensor(v): + detached_dict[k] = v.detach() + else: + detached_dict[k] = v + preds = detached_dict + elif torch.is_tensor(preds): + preds = preds.detach() + + return preds, latency - def infer_mg_cuda(self, model, input_data): + def infer_mg_cuda(self, model, input_values, attention_mask=None): # send model and input data to GPU for inference - input_data = input_data.cuda() + input_values = input_values.cuda() model = model.cuda() + if attention_mask is not None: + attention_mask = attention_mask.cuda() + inputs = (input_values, attention_mask) + else: + inputs = (input_values,) + # Create CUDA events for timing GPU operations start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) # INFERENCE! start.record() - preds = model(input_data) + preds = model(*inputs) end.record() # Synchronize to ensure all GPU operations are finished @@ -246,12 +279,28 @@ def infer_mg_cuda(self, model, input_data): # Calculate latency between start and end events latency = start.elapsed_time(end) - # return the prediction back to the CPU - return preds.detach().cpu(), latency - - def infer_trt_cuda(self, trt_context, input_data): + if isinstance(preds, dict): + detached_dict = {} + for k, v in preds.items(): + detached_dict[k] = v.detach().cpu() if torch.is_tensor(v) else v + preds = detached_dict + elif torch.is_tensor(preds): + preds = preds.detach().cpu() + + return preds, latency + + def infer_trt_cuda(self, trt_context, input_values, attention_mask=None): + input_values_np = input_values.cpu().numpy() # ensure on CPU before converting + if attention_mask is not None: + attention_mask_np = attention_mask.cpu().numpy() + input_arrays = [input_values_np, attention_mask_np] + else: + input_arrays = [input_values_np] + bufferH = [] - bufferH.append(np.ascontiguousarray(input_data)) + for arr in input_arrays: + bufferH.append(np.ascontiguousarray(arr)) + for i in range(self.n_Input, self.num_io): bufferH.append( np.empty( @@ -290,18 +339,15 @@ def infer_trt_cuda(self, trt_context, input_data): latency = start.elapsed_time(end) # Copying data from device to host and collecting output tensors - output_data = [ - bufferH[i] - for i in range(self.n_Input, self.num_io) - for _ in [ - cudart.cudaMemcpy( - bufferH[i].ctypes.data, - bufferD[i], - bufferH[i].nbytes, - cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, - ) - ] - ] + output_data = [] + for i in range(self.n_Input, self.num_io): + cudart.cudaMemcpy( + bufferH[i].ctypes.data, + bufferD[i], + bufferH[i].nbytes, + cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, + ) + output_data.append(bufferH[i]) # Flatten output if it consists of only one item output_data = output_data[0] if len(output_data) == 1 else output_data @@ -309,20 +355,25 @@ def infer_trt_cuda(self, trt_context, input_data): for b in bufferD: cudart.cudaFree(b) + if len(output_data) == 1: + output_data = output_data[0] # Convert the raw scores from numpy array to PyTorch tensor preds_tensor = torch.tensor(output_data, device="cpu", dtype=torch.float32) return preds_tensor, latency - def infer_onnx_cpu(self, ort_inference_session, input_data): + def infer_onnx_cpu(self, ort_inference_session, input_values, attention_mask=None): # Convert PyTorch tensor to numpy array for ONNX Runtime - input_data_np = input_data.numpy() - - # Start timing CPU operations - start_time = time.time() + input_values_np = input_values.cpu().numpy() + if attention_mask is not None: + attention_mask_np = attention_mask.cpu().numpy() + inputs = {"input": {"input_values": input_values_np, "attention_mask": attention_mask_np}} + else: + inputs = {"input": {"input_values": input_values_np}} # Run inference using ONNX Runtime - output_data = ort_inference_session.run(None, {"input": input_data_np}) + start_time = time.time() + output_data = ort_inference_session.run(None, inputs) # End timing CPU operations end_time = time.time() @@ -342,15 +393,21 @@ def infer_onnx_cpu(self, ort_inference_session, input_data): return preds_tensor, latency - def infer_onnx_cuda(self, ort_inference_session, input_data): - input_data_np = input_data.numpy() + def infer_onnx_cuda(self, ort_inference_session, input_values, attention_mask=None): + input_values_np = input_values.cpu().numpy() + if attention_mask is not None: + attention_mask_np = attention_mask.cpu().numpy() + inputs = {"input": {"input_values": input_values_np, "attention_mask": attention_mask_np}} + else: + inputs = {"input": {"input_values": input_values_np}} + # Create CUDA events for timing GPU operations start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record() - output_data = ort_inference_session.run(None, {"input": input_data_np}) + output_data = ort_inference_session.run(None, inputs) end.record() # Synchronize to ensure all GPU operations are finished @@ -371,38 +428,54 @@ def evaluate(self): self.logger.info(f"Starting transformation analysis on {self.model_name}") num_GPU_warmup_batches = self.config["num_GPU_warmup_batches"] + task = self.config["task"] - match self.config["task"]: - case "cls": - metric = torchmetrics.classification.MulticlassAccuracy( - num_classes=self.num_of_classes - ) - precision_metric = torchmetrics.Precision( - num_classes=self.num_of_classes, - average="weighted", - task="multiclass", - ) - recall_metric = torchmetrics.Recall( - num_classes=self.num_of_classes, - average="weighted", - task="multiclass", - ) - f1_metric = torchmetrics.F1Score( - num_classes=self.num_of_classes, - average="weighted", - task="multiclass", - ) - case _: - raise Exception( - f"Unsupported task type {self.config['task']}. Please set a supported task type in the config file." - ) + requires_attention_mask = self.config.get( + "requires_attention_mask", + "attention_mask" in inspect.signature(self.model.forward).parameters + ) - # Initialize lists to store metrics for each configuration - recorded_accs = [] - latencies = [] - gpu_power_usages = [] - accs, losses = [], [] + # ---------- 1) SET UP METRICS BASED ON TASK ---------- + if task == "cls": + metric = torchmetrics.classification.MulticlassAccuracy( + num_classes=self.num_of_classes + ) + precision_metric = torchmetrics.Precision( + num_classes=self.num_of_classes, + average="weighted", + task="multiclass", + ) + recall_metric = torchmetrics.Recall( + num_classes=self.num_of_classes, + average="weighted", + task="multiclass", + ) + f1_metric = torchmetrics.F1Score( + num_classes=self.num_of_classes, + average="weighted", + task="multiclass", + ) + + # Arrays to store classification metrics + accs, losses = [], [] + + elif task == "ctc": + wer_metric = torchmetrics.text.WordErrorRate() + decoder = self.config.get("decoder", None) + beam_width = self.config.get("beam_width", 10) + tokenizer = self.config["tokenizer"] + ctc_head = self.config.get("ctc_head", None) + padding_value = self.config.get("padding_value", -100) + # We'll collect WER from each batch + batch_wers = [] + + else: + raise Exception( + f"Unsupported task type {task}. Please set a supported task type in the config file." + ) + + # ---------- 2) PREPARE DATA LOADER (TEST OR VALIDATION) ---------- if "test" in self.config and self.config["test"]: dataloader = self.config["data_module"].test_dataloader() dataset = "Test" @@ -410,139 +483,235 @@ def evaluate(self): dataloader = self.config["data_module"].val_dataloader() dataset = "Validation" - # Iterate over batches in the validation/train dataset + # ---------- 3) ARRAYS FOR LATENCIES & POWER FOR ALL TASKS ---------- + latencies = [] + gpu_power_usages = [] + rtfs = [] + + # ---------- 4) MAIN EVALUATION LOOP ---------- for j, (xs, ys) in enumerate(dataloader): - # Break the loop after processing the specified number of batches or to drop the last incomplete batch - if ( - j >= self.config["num_batches"] - or xs.shape[0] != self.config["batch_size"] - ): + # Stop if we've exceeded our number of evaluation batches, or if the batch is incomplete + if j >= self.config["num_batches"] or xs.shape[0] != self.config["batch_size"]: + print(f"[DEBUG] Batch {j+1} - xs shape: {xs.shape}, Expected batch size: {self.config['batch_size']}") break - # Instantiate and start the power monitor + # Power monitoring (start) power_monitor = PowerMonitor(self.config) power_monitor.start() - # Synchronize to ensure all GPU operations are finished + # Clear GPU caches & sync torch.cuda.synchronize() torch.cuda.empty_cache() - # TRT Inference + if requires_attention_mask: + attention_mask = torch.ones_like(xs, dtype=torch.long).to(self.config["accelerator"]) + model_input = (xs, attention_mask) + else: + model_input = (xs,) + + # ---------------- (A) RUN INFERENCE (TRT, ONNX, or MaseGraph) ---------------- if isinstance(self.model, trt.IExecutionContext): + # TensorRT if self.config["accelerator"] != "cuda": - raise Exception( - "TensorRT inference is only supported on CUDA devices." - ) - preds, latency = self.infer_trt_cuda(self.model, xs) + raise Exception("TensorRT inference is only supported on CUDA devices.") + preds, latency = self.infer_trt_cuda(self.model, *model_input) - # ONNX Inference elif isinstance(self.model, ort.InferenceSession): + # ONNX Runtime if self.config["accelerator"] == "cpu": - preds, latency = self.infer_onnx_cpu(self.model, xs) + preds, latency = self.infer_onnx_cpu(self.model, *model_input) elif self.config["accelerator"] == "cuda": - preds, latency = self.infer_onnx_cuda(self.model, xs) + preds, latency = self.infer_onnx_cuda(self.model, *model_input) else: raise Exception( f"ONNX inference is not support by device {self.config['accelerator']}." ) - # MaseGraph Inference else: + # MaseGraph or raw PyTorch if self.config["accelerator"] == "cpu": - preds, latency = self.infer_mg_cpu(self.model, xs) + preds, latency = self.infer_mg_cpu(self.model, *model_input) elif self.config["accelerator"] == "cuda": - preds, latency = self.infer_mg_cuda(self.model, xs) + print(f"[DEBUG] Running MaseGraph inference on batch {j+1}") + preds, latency = self.infer_mg_cuda(self.model, *model_input) else: raise Exception( f"MaseGraph inference is not support by device {self.config['accelerator']}." ) - # Stop the power monitor and calculate average power + # Power monitoring (stop) power_monitor.stop() - power_monitor.join() # Ensure monitoring thread has finished + power_monitor.join() - # Skip the first number of iterations to allow the model to warm up + # Skip warmup batches if j < num_GPU_warmup_batches: continue + # Record latency latencies.append(latency) + # Compute average power usage for this batch avg_power = ( sum(power_monitor.power_readings) / len(power_monitor.power_readings) if power_monitor.power_readings else 0 ) - # Store the calculated average power gpu_power_usages.append(avg_power) - # Calculate accuracy and loss for the batch - loss = torch.nn.functional.cross_entropy(preds, ys) - acc = metric(preds, ys) - accs.append(acc.item()) - losses.append(loss.item()) - - # Update torchmetrics metrics - preds_labels = torch.argmax(preds, dim=1) - precision_metric(preds_labels, ys) - recall_metric(preds_labels, ys) - f1_metric(preds_labels, ys) - - # Compute final precision, recall, and F1 for this configuration - avg_precision = precision_metric.compute() - avg_recall = recall_metric.compute() - avg_f1 = f1_metric.compute() - - # Convert metrics to float if they are tensors - avg_precision = ( - avg_precision.item() if torch.is_tensor(avg_precision) else avg_precision - ) - avg_recall = avg_recall.item() if torch.is_tensor(avg_recall) else avg_recall - avg_f1 = avg_f1.item() if torch.is_tensor(avg_f1) else avg_f1 - - # Reset metrics for the next configuration - precision_metric.reset() - recall_metric.reset() - f1_metric.reset() - - # Calculate and record average metrics for the current configuration - acc_avg = sum(accs) / len(accs) - loss_avg = sum(losses) / len(losses) - recorded_accs.append(acc_avg) - avg_latency = sum(latencies) / len(latencies) - avg_gpu_power_usage = sum(gpu_power_usages) / len(gpu_power_usages) + print(f"[DEBUG] Batch {j+1} - Latency: {latency:.3f} ms") + print(f"[DEBUG] Batch {j+1} - GPU Power Usage: {avg_power:.3f} W") + + # ---------- (B) METRICS DEPENDING ON TASK ---------- + if task == "cls": + # Classification logic + # preds: [batch_size, num_classes] + # ys: [batch_size] + + # Cross-entropy (classification) loss + loss = torch.nn.functional.cross_entropy(preds, ys) + losses.append(loss.item()) + + # Accuracy (MulticlassAccuracy) + acc = metric(preds, ys) + accs.append(acc.item()) + + # Update precision, recall, F1 + preds_labels = torch.argmax(preds, dim=1) + precision_metric(preds_labels, ys) + recall_metric(preds_labels, ys) + f1_metric(preds_labels, ys) + + elif task == "ctc": + # Real-Time Factor (RTF) = Latency / (1 / sample_rate) + max_length = xs.shape[1] + audio_duration = max_length / self.config["sample_rate"] + rtf = (latency / 1000.0) / audio_duration + rtfs.append(rtf) + + # WER CTC logic + # preds: [batch_size, time_steps, vocab_size] or [batch_size, *] + # ys: [batch_size, time_steps] + + # Apply CTC decoding to get predicted text + if ctc_head is None: + raise Exception("CTC head must be provided in config for full model evaluation") + + ctc_head = ctc_head.cpu() + encoder_output = preds["last_hidden_state"].cpu() + + predictions = ctc_head(encoder_output) + + if torch.is_tensor(predictions): + predictions = predictions.detach().cpu() + else: + predictions = torch.tensor(predictions).cpu() + + preds_np = predictions.numpy() + + pred_texts = [] + label_texts = [] + + for i in range(preds_np.shape[0]): + sample_logits = torch.from_numpy(preds_np[i]) + sample_log_probs = sample_logits.log_softmax(dim=-1).cpu().numpy() + if decoder is not None: + transcription = decoder.decode(sample_log_probs, beam_width=beam_width) + else: + raise Exception( + "Decoder must be provided for CTC runtime analysis. Pass 'decoder' in config.") + pred_texts.append(transcription.lower()) + + for label_seq in ys: + label_filtered = [token for token in label_seq if token != padding_value] + label_text = tokenizer.decode(label_filtered, skip_special_tokens=True) + label_texts.append(label_text.lower()) + + # Now compute batch WER + batch_wer = wer_metric(pred_texts, label_texts) + batch_wers.append(batch_wer.item() if torch.is_tensor(batch_wer) else batch_wer) + + # ---------- 5) AFTER LOOP, COMPUTE FINAL METRICS BASED ON TASK ---------- + avg_latency = sum(latencies) / len(latencies) if latencies else 0 + avg_gpu_power_usage = sum(gpu_power_usages) / len(gpu_power_usages) if gpu_power_usages else 0 avg_gpu_energy_usage = (avg_gpu_power_usage * 1000) * (avg_latency / 3600000) - metrics = [ - ["Average " + dataset + " Accuracy", f"{acc_avg:.5g}"], - ["Average Precision", f"{avg_precision:.5g}"], - ["Average Recall", f"{avg_recall:.5g}"], - ["Average F1 Score", f"{avg_f1:.5g}"], - ["Average Loss", f"{loss_avg:.5g}"], - ["Average Latency", f"{avg_latency:.5g} ms"], - ["Average GPU Power Usage", f"{avg_gpu_power_usage:.5g} W"], - ["Inference Energy Consumption", f"{avg_gpu_energy_usage:.5g} mWh"], - ] + if task == "cls": + # Classification final metrics + avg_acc = sum(accs) / len(accs) if accs else 0 + avg_loss = sum(losses) / len(losses) if losses else 0 + + avg_precision = precision_metric.compute() + avg_recall = recall_metric.compute() + avg_f1 = f1_metric.compute() + + # Convert to float + avg_precision = avg_precision.item() if torch.is_tensor(avg_precision) else avg_precision + avg_recall = avg_recall.item() if torch.is_tensor(avg_recall) else avg_recall + avg_f1 = avg_f1.item() if torch.is_tensor(avg_f1) else avg_f1 + + # Reset metrics + precision_metric.reset() + recall_metric.reset() + f1_metric.reset() + + metrics_table = [ + ["Average " + dataset + " Accuracy", f"{avg_acc:.5g}"], + ["Average Precision", f"{avg_precision:.5g}"], + ["Average Recall", f"{avg_recall:.5g}"], + ["Average F1 Score", f"{avg_f1:.5g}"], + ["Average Loss", f"{avg_loss:.5g}"], + ["Average Latency", f"{avg_latency:.5g} ms"], + ["Average GPU Power Usage", f"{avg_gpu_power_usage:.5g} W"], + ["Inference Energy Consumption", f"{avg_gpu_energy_usage:.5g} mWh"], + ] + + results = { + "Average Accuracy": avg_acc, + "Average Precision": avg_precision, + "Average Recall": avg_recall, + "Average F1 Score": avg_f1, + "Average Loss": avg_loss, + "Average Latency": avg_latency, + "Average GPU Power Usage": avg_gpu_power_usage, + "Inference Energy Consumption": avg_gpu_energy_usage, + } + + elif task == "ctc": + # Real-Time Factor metric + avg_rtf = sum(rtfs) / len(rtfs) if rtfs else 0 + + # CTC final metrics + avg_wer = sum(batch_wers) / len(batch_wers) if batch_wers else 0 + + metrics_table = [ + ["Average " + dataset + " WER", f"{avg_wer:.5g}"], + ["Average Latency", f"{avg_latency:.5g} ms"], + ["Average RTF", f"{avg_rtf:.5g}"], + ["Average GPU Power Usage", f"{avg_gpu_power_usage:.5g} W"], + ["Inference Energy Consumption", f"{avg_gpu_energy_usage:.5g} mWh"], + ] - # Formatting the table with tabulate + print(f"[DEBUG] Average Latency: {avg_latency:.3f} ms") + print(f"[DEBUG] Average GPU Power Usage: {avg_gpu_power_usage:.3f} W") + print(f"[DEBUG] Average RTF: {avg_rtf:.3f}") + print(f"[DEBUG] Average WER: {avg_wer:.3f}") + print(f"[DEBUG] Inference Energy Consumption: {avg_gpu_energy_usage:.3f} mWh") + + results = { + "Average WER": avg_wer, + "Average Latency": avg_latency, + "Average RTF": avg_rtf, + "Average GPU Power Usage": avg_gpu_power_usage, + "Inference Energy Consumption": avg_gpu_energy_usage, + } + + # ---------- 6) LOG RESULTS ---------- formatted_metrics = tabulate( - metrics, + metrics_table, headers=["Metric (Per Batch)", "Value"], tablefmt="pretty", floatfmt=".5g", ) - - # Print result summary self.logger.info(f"\nResults {self.model_name}:\n" + formatted_metrics) - # Store the results in a dictionary and return it - results = { - "Average Accuracy": acc_avg, - "Average Precision": avg_precision, - "Average Recall": avg_recall, - "Average F1 Score": avg_f1, - "Average Loss": loss_avg, - "Average Latency": avg_latency, - "Average GPU Power Usage": avg_gpu_power_usage, - "Inference Energy Consumption": avg_gpu_energy_usage, - } return results diff --git a/src/chop/passes/graph/interface/onnxrt/onnx_runtime.py b/src/chop/passes/graph/interface/onnxrt/onnx_runtime.py index 3128e6ae1..359ffdca0 100644 --- a/src/chop/passes/graph/interface/onnxrt/onnx_runtime.py +++ b/src/chop/passes/graph/interface/onnxrt/onnx_runtime.py @@ -1,4 +1,5 @@ import logging +import inspect import torch import os from datetime import datetime @@ -80,14 +81,27 @@ def pytorch_to_onnx(self, model): train_sample = next(iter(dataloader))[0] train_sample = train_sample.to(self.config["accelerator"]) + # Dynamically checks if attention_mask is needed first, then optional explicit override + requires_attention_mask = self.config.get( + "requires_attention_mask", + "attention_mask" in inspect.signature(model.forward).parameters + ) + + if requires_attention_mask: + attention_mask = torch.ones_like(train_sample, dtype=torch.long).to(self.config["accelerator"]) + onnx_inputs = (train_sample, attention_mask) + input_names = ["input_values", "attention_mask"] + else: + onnx_inputs = (train_sample,) + input_names = ["input_values"] + torch.onnx.export( model, - train_sample, + onnx_inputs, save_path, export_params=True, - opset_version=11, - do_constant_folding=True, - input_names=["input"], + input_names=input_names, + opset_version=14, ) self.logger.info(f"ONNX Conversion Complete. Stored ONNX model to {save_path}") diff --git a/src/chop/passes/graph/transforms/pruning/DELETE_movement_test.py b/src/chop/passes/graph/transforms/pruning/DELETE_movement_test.py new file mode 100644 index 000000000..f758953a0 --- /dev/null +++ b/src/chop/passes/graph/transforms/pruning/DELETE_movement_test.py @@ -0,0 +1,237 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import AutoModelForCTC, Wav2Vec2Processor, TrainerCallback +from datasets import DatasetDict, load_dataset, Dataset +from pyctcdecode import build_ctcdecoder +import numpy as np + +# Import MASE graph and passes (ensure PYTHONPATH is set correctly) +from chop import MaseGraph +import chop.passes as passes # type: ignore +from chop.passes.module import report_trainable_parameters_analysis_pass # type: ignore +from chop.tools import get_trainer # type: ignore + +# ------------------------------------------------------------------------------ +# Helper function: convert tensor to list if needed. +# ------------------------------------------------------------------------------ +def to_list(x): + if isinstance(x, torch.Tensor): + return x.tolist() + return x + +# ------------------------------------------------------------------------------ +# Revised DataCollatorCTCWithPadding: manually pad sequences +# ------------------------------------------------------------------------------ +class DataCollatorCTCWithPadding: + def __init__(self, processor, padding=True): + self.processor = processor + self.padding = padding + + def __call__(self, features): + # Convert fields to lists + input_values_list = [to_list(f["input_values"]) for f in features] + attention_mask_list = [to_list(f["attention_mask"]) for f in features] + label_list = [to_list(f["labels"]) for f in features] + + # Compute max lengths + max_input_length = max(len(seq) for seq in input_values_list) + max_label_length = max(len(seq) for seq in label_list) + + # Manually pad sequences + padded_input_values = [ + seq + [0.0] * (max_input_length - len(seq)) for seq in input_values_list + ] + padded_attention_mask = [ + seq + [0] * (max_input_length - len(seq)) for seq in attention_mask_list + ] + padded_labels = [ + seq + [-100] * (max_label_length - len(seq)) for seq in label_list + ] + + batch = { + "input_values": torch.tensor(padded_input_values, dtype=torch.float), + "attention_mask": torch.tensor(padded_attention_mask, dtype=torch.long), + "labels": torch.tensor(padded_labels, dtype=torch.long), + } + return batch + +# ------------------------------------------------------------------------------ +# 1. Define the model and dataset +# ------------------------------------------------------------------------------ +checkpoint = "facebook/wav2vec2-base-960h" +tokenizer_checkpoint = "facebook/wav2vec2-base-960h" +dataset_name = "librispeech_asr" + +processor = Wav2Vec2Processor.from_pretrained(tokenizer_checkpoint) +data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) +tokenizer = processor.tokenizer + +vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) +decoder = build_ctcdecoder(vocab) + +# Load a small sample for quick experimentation +dataset = load_dataset(dataset_name, "clean", split="validation", streaming=True, trust_remote_code=True) +sample_list = list(dataset.take(50)) + +def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), + return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +small_dataset = Dataset.from_list(sample_list) +small_dataset = small_dataset.map(preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"]) +tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset +}) + +model = AutoModelForCTC.from_pretrained(checkpoint) +encoder = model.wav2vec2 # static, FX-friendly +ctc_head = model.lm_head # dynamic CTC head + +# ------------------------------------------------------------------------------ +# 2. Build the MASE graph for the encoder and initialize metadata +# ------------------------------------------------------------------------------ +mg = MaseGraph(encoder, hf_input_names=["input_values", "attention_mask"]) +mg, _ = passes.init_metadata_analysis_pass(mg) + +dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), +} +mg, _ = passes.add_common_metadata_analysis_pass(mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + }) + +if not hasattr(mg.model, "metadata"): + mg.model.metadata = {} +for name, param in mg.model.named_parameters(): + mg.model.metadata[name] = {"stats": {"movement": torch.zeros_like(param)}} + +# ------------------------------------------------------------------------------ +# 3. Build the combined model (without pruning applied yet) +# ------------------------------------------------------------------------------ +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) + output = {"logits": logits, "labels": labels} + if labels is not None: + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + loss = F.ctc_loss( + log_probs, labels, input_lengths, target_lengths, + blank=self.blank_id, reduction="mean", zero_infinity=True + ) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + +combined_model = CombinedWav2Vec2CTC(encoder=mg.model, ctc_head=ctc_head, decoder=decoder, beam_width=10) + +# ------------------------------------------------------------------------------ +# 4. Define a Movement Tracking Callback to accumulate real movement data during training +# ------------------------------------------------------------------------------ +class MovementTrackingCallback(TrainerCallback): + def on_train_begin(self, args, state, control, **kwargs): + self.prev_params = {} + self.movement_stats = {} + model = kwargs["model"] + for name, param in model.encoder.named_parameters(): + self.prev_params[name] = param.detach().clone() + self.movement_stats[name] = torch.zeros_like(param) + if not hasattr(model.encoder, "metadata"): + model.encoder.metadata = {} + if name not in model.encoder.metadata: + model.encoder.metadata[name] = {"stats": {}} + model.encoder.metadata[name]["stats"]["movement"] = self.movement_stats[name] + return control + + def on_step_end(self, args, state, control, **kwargs): + model = kwargs["model"] + for name, param in model.encoder.named_parameters(): + movement = (param.detach() - self.prev_params[name]).abs() + self.movement_stats[name] += movement + self.prev_params[name].copy_(param.detach()) + model.encoder.metadata[name]["stats"]["movement"] = self.movement_stats[name] + return control + +# ------------------------------------------------------------------------------ +# 5. Setup Trainer and run warm-up training to accumulate movement data +# ------------------------------------------------------------------------------ +trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, # Warm-up training epoch + data_collator=data_collator, +) +trainer.add_callback(MovementTrackingCallback()) + +print("Starting warm-up training to accumulate movement data...") +trainer.train() +print("Warm-up training complete.") + +# ------------------------------------------------------------------------------ +# 6. Apply movement-based pruning pass using the accumulated movement data +# ------------------------------------------------------------------------------ +pruning_config = { + "weight": { + "sparsity": 0.2, + "method": "movement", + "scope": "local", + }, + "activation": { + "sparsity": 0.2, + "method": "movement", + "scope": "local", + }, +} + +mg, _ = passes.prune_transform_pass(mg, pass_args=pruning_config) +_, _ = report_trainable_parameters_analysis_pass(mg.model) +combined_model.encoder = mg.model + +# ------------------------------------------------------------------------------ +# 7. Optionally, fine-tune the pruned model and evaluate +# ------------------------------------------------------------------------------ +print("Starting fine-tuning of the pruned model...") +trainer.train() +eval_results = trainer.evaluate() +print(f"Evaluation WER: {eval_results['eval_wer']}") +print(f"Evaluation loss: {eval_results['eval_loss']}") +print(f"Evaluation runtime: {eval_results['eval_runtime']}") +print(f"Evaluation samples per second: {eval_results['eval_samples_per_second']}") +print(f"Evaluation steps per second: {eval_results['eval_steps_per_second']}") + diff --git a/src/chop/passes/graph/transforms/pruning/__init__.py b/src/chop/passes/graph/transforms/pruning/__init__.py index 333ed925f..e96dd3014 100644 --- a/src/chop/passes/graph/transforms/pruning/__init__.py +++ b/src/chop/passes/graph/transforms/pruning/__init__.py @@ -1,2 +1,5 @@ from .prune import prune_transform_pass from .prune_detach_hook import prune_detach_hook_transform_pass +from .prune_movment_helper import MovementTrackingCallback +from .hwpq import HWPQ_PruningOnly, hwpq_pruning_only, HWPQParameterization +from .snip_helper import SNIPTrackingCallback, SNIPCallback \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/pruning/docs/snip_pruning.md b/src/chop/passes/graph/transforms/pruning/docs/snip_pruning.md new file mode 100644 index 000000000..ca9428d6e --- /dev/null +++ b/src/chop/passes/graph/transforms/pruning/docs/snip_pruning.md @@ -0,0 +1,163 @@ +# SNIP Pruning + +## Overview + +SNIP (Single-shot Network Pruning) is a pruning technique that identifies important connections in a neural network before training. Unlike traditional pruning methods that require a fully trained model, SNIP evaluates the importance of weights at initialization time, making it more efficient. + +The key idea behind SNIP is to measure the sensitivity of the loss function with respect to each weight by calculating gradients through a binary mask applied to the weights. Weights with higher sensitivity (i.e., larger gradients) are considered more important. + +## Reference + +Lee, N., Ajanthan, T., & Torr, P. H. (2018). SNIP: Single-shot network pruning based on connection sensitivity. International Conference on Learning Representations (ICLR 2019). [https://arxiv.org/abs/1810.02340](https://arxiv.org/abs/1810.02340) + +## How SNIP Works + +1. **Connection Sensitivity**: SNIP defines the "connection sensitivity" as the magnitude of the gradient of the loss with respect to a binary mask multiplied by the corresponding weight. + +2. **Single Forward-Backward Pass**: The method requires only a single mini-batch of data to compute these sensitivities with a forward and backward pass. + +3. **One-shot Pruning**: Once the sensitivities are calculated, weights with the lowest sensitivities are pruned in a single step. + +4. **Train the Pruned Network**: The pruned network is then trained normally from scratch. + +## Implementation Details + +In our implementation, SNIP pruning involves: + +1. **SNIPTrackingCallback**: A callback that uses monkey-patching to calculate SNIP saliency scores for network weights. It adds temporary mask parameters to weights, performs a forward-backward pass, and captures gradients. + +2. **SNIPCallback**: A class that implements the HuggingFace TrainerCallback interface, allowing SNIP sensitivity computation to be integrated with the training workflow. + +3. **Pruning Functions**: + - `snip`: Performs local pruning within each layer based on sensitivity scores + - `global_weight_snip`: Performs global pruning across all layers, with memory-efficient fallback for large models + +## Usage + +### Basic Usage + +To use SNIP pruning in your code: + +```python +import torch +from chop.ir.graph.mase_graph import MaseGraph +from chop.passes.graph.transforms.pruning.snip_helper import SNIPTrackingCallback +from chop.passes.graph.transforms.pruning.prune import prune_transform_pass + +# 1. Prepare your model and a batch of data +model = your_model +dummy_data = torch.randn(batch_size, *input_shape) + +# 2. Create a MaseGraph from your model +mg = MaseGraph(model) +mg, _ = passes.init_metadata_analysis_pass(mg) +mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_data, + "add_value": True, + "force_device_meta": False, + } +) + +# 3. Compute SNIP saliency scores +callback = SNIPTrackingCallback() +callback(mg.model, dummy_data) + +# 4. Apply pruning with SNIP method +pruning_config = { + "weight": { + "method": "snip", + "granularity": "elementwise", + "scope": "local", # or "global" for global pruning + "sparsity": 0.5 # Prune 50% of weights + }, + "activation": { + "method": "random", + "granularity": "elementwise", + "scope": "local", + "sparsity": 0.0 # No activation pruning + } +} + +mg, _ = prune_transform_pass(mg, pass_args=pruning_config) + +# 5. Train the pruned model from scratch +# ... +``` + +### Using with HuggingFace Trainer + +For integration with HuggingFace's Trainer: + +```python +from transformers import Trainer, TrainingArguments +from chop.passes.graph.transforms.pruning.snip_helper import SNIPCallback + +# Setup your model, tokenizer, dataset, etc. +# ... + +# Create a representative batch for SNIP computation +dummy_batch = data_collator([dataset[i] for i in range(batch_size)]) + +# Initialize the Trainer with SNIPCallback +trainer = Trainer( + model=model, + args=training_args, + train_dataset=dataset, + # Other trainer parameters... +) + +# Add the SNIP callback +trainer.add_callback(SNIPCallback(representative_batch=dummy_batch)) + +# Train (the first step will compute SNIP scores) +trainer.train() + +# Apply pruning with the scores +# ... +``` + +### Configuration Options + +- **method**: Use "snip" for SNIP pruning. +- **granularity**: Currently only "elementwise" is supported for SNIP. +- **scope**: + - "local": Apply pruning independently to each layer + - "global": Apply pruning globally across all layers +- **sparsity**: The fraction of weights to prune (between 0 and 1). + +## SNIP Workflow + +1. **Sensitivity Score Calculation** + - Replace each weight W with W⊙M (element-wise multiply with a mask) + - Initialize mask M to all ones and make it learnable + - Perform forward pass with a representative batch + - Calculate loss and backpropagate + - Store absolute gradient values |∂L/∂M| as sensitivity scores + +2. **Pruning Based on Scores** + - For local pruning: Calculate threshold for each layer separately + - For global pruning: Calculate a single threshold across all layers + - Create binary masks (0 or 1) based on thresholds + - Apply masks to weights using PyTorch parametrization + +3. **Train the Pruned Network** + - Train the network from scratch with pruned weights + +## Benefits of SNIP + +- **Efficiency**: Pruning at initialization avoids the computational cost of training before pruning. +- **Performance**: SNIP typically maintains better performance compared to random pruning. +- **One-shot**: No iterative pruning process is needed. +- **Flexibility**: Can be used for both local (layer-wise) and global pruning. + +## Limitations + +- Requires a representative batch of data to compute saliency scores. +- May not be optimal for very deep networks or certain architectures. +- Does not consider the dynamics of training or the interaction between pruned connections. + +## See Also + +Check out the example in `examples/snip_example.py` for a complete demonstration of SNIP pruning on a speech recognition model. The example shows both the direct calculation approach and integration with the HuggingFace Trainer. \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/pruning/examples/snip_example.py b/src/chop/passes/graph/transforms/pruning/examples/snip_example.py new file mode 100644 index 000000000..302d002ce --- /dev/null +++ b/src/chop/passes/graph/transforms/pruning/examples/snip_example.py @@ -0,0 +1,425 @@ +import os +import sys + +# Add the src directory to the Python path so that the chop module can be found +src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../../../../")) +if src_dir not in sys.path: + sys.path.insert(0, src_dir) + +# Check for required packages and install them if needed +required_packages = ['dill', 'toml', 'torch', 'torchvision', 'transformers', 'datasets', 'pyctcdecode'] +missing_packages = [] + +for package in required_packages: + try: + __import__(package) + except ImportError: + missing_packages.append(package) + +if missing_packages: + print(f"Missing required packages: {', '.join(missing_packages)}") + print("Please install the missing packages with:") + print(f"pip install {' '.join(missing_packages)}") + print("\nAlternatively, you can run this script with PYTHONPATH set to include the src directory:") + print(f"PYTHONPATH={src_dir} python {__file__}") + sys.exit(1) + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fx as fx +import types +import importlib +from torch.utils.data import DataLoader + +from chop.ir.graph.mase_graph import MaseGraph +from chop.passes.graph.transforms.pruning.prune import prune_transform_pass +from chop.passes.graph.transforms.pruning.snip_helper import SNIPCallback +import chop.passes as passes +from chop.passes.module import report_trainable_parameters_analysis_pass + +# Check if transformers is available, otherwise give installation instructions +try: + from transformers import AutoModelForCTC, Wav2Vec2Processor, TrainerCallback + from datasets import concatenate_datasets, DatasetDict, load_dataset, Dataset + from pyctcdecode import build_ctcdecoder + transformers_available = True +except ImportError: + transformers_available = False + print("This example requires transformers, datasets, and pyctcdecode.") + print("Please install them with: pip install transformers datasets pyctcdecode") + +# Check if we have the necessary modules for training +try: + from chop.tools import get_tokenized_dataset, get_trainer + from chop.models import DataCollatorCTCWithPadding + trainer_available = True +except ImportError: + trainer_available = False + print("Some tools for training are not available. Basic pruning will work, but not training.") + +# Print available pruning methods +import chop.passes.graph.transforms.pruning.prune as prune_mod +importlib.reload(prune_mod) +print("Available pruning methods:", list(prune_mod.weight_criteria_map["local"]["elementwise"].keys())) + +# ------------------------------- +# Model and combined module definitions +# ------------------------------- +class CombinedWav2Vec2CTC(nn.Module): + def __init__(self, encoder, ctc_head, blank_id=0, beam_width=10, decoder=None): + super().__init__() + self.encoder = encoder + self.ctc_head = ctc_head + self.blank_id = blank_id + self.beam_width = beam_width + self.decoder = decoder # Only used during inference + + def forward(self, input_values, attention_mask=None, labels=None): + encoder_out = self.encoder(input_values, attention_mask=attention_mask) + hidden_states = encoder_out["last_hidden_state"] + logits = self.ctc_head(hidden_states) + output = {"logits": logits, "labels": labels} + + if labels is not None: + # Ensure labels has a batch dimension + if labels.dim() == 1: + labels = labels.unsqueeze(0) + + log_probs = logits.log_softmax(dim=-1).transpose(0, 1) + batch_size, time_steps, _ = logits.shape + input_lengths = torch.full((batch_size,), time_steps, dtype=torch.long, device=logits.device) + target_lengths = (labels != -100).sum(dim=1) + loss = F.ctc_loss( + log_probs, labels, input_lengths, target_lengths, + blank=self.blank_id, reduction="mean", zero_infinity=True + ) + output["loss"] = loss + else: + if self.decoder is not None: + log_probs = logits.log_softmax(dim=-1) + log_probs_np = log_probs[0].cpu().detach().numpy() + transcription = self.decoder.decode(log_probs_np, beam_width=self.beam_width).lower() + output["transcription"] = transcription + return output + + +def main(): + if not transformers_available: + print("Required libraries not available. Exiting.") + return + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + # ------------------------------- + # 1. Define the model and dataset + # ------------------------------- + print("Loading model and dataset...") + checkpoint = "facebook/wav2vec2-base-960h" + tokenizer_checkpoint = "facebook/wav2vec2-base-960h" + dataset_name = "nyalpatel/condensed_librispeech_asr" + + # Load processor + processor = Wav2Vec2Processor.from_pretrained(tokenizer_checkpoint) + data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) + tokenizer = processor.tokenizer + vocab = tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size)) + decoder = build_ctcdecoder(vocab) + + # Load a small subset of data + dataset = load_dataset(dataset_name, split="validation.clean", trust_remote_code=True) + sample_list = list(dataset.take(50)) + + def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + + small_dataset = Dataset.from_list(sample_list) + small_dataset = small_dataset.map( + preprocess_function, + remove_columns=["speaker_id", "file", "id", "chapter_id", "audio"] + ) + tokenized_dataset = DatasetDict({ + "train": small_dataset, + "test": small_dataset + }) + + # Load model + model = AutoModelForCTC.from_pretrained(checkpoint) + model.config.gradient_checkpointing = False + encoder = model.wav2vec2 # static, FX-friendly + ctc_head = model.lm_head # dynamic CTC head, separate this + + # ------------------------------- + # 2. Define the MASE graph & metadata + # ------------------------------- + print("Creating MASE graph and computing metadata...") + mg = MaseGraph( + encoder, + hf_input_names=[ + "input_values", + "attention_mask", + ], + ) + mg, _ = passes.init_metadata_analysis_pass(mg) + dummy_in = { + "input_values": torch.zeros((1, 16000), dtype=torch.float32), + "attention_mask": torch.ones((1, 16000), dtype=torch.long), + } + mg, _ = passes.add_common_metadata_analysis_pass( + mg, + pass_args={ + "dummy_in": dummy_in, + "add_value": True, + "force_device_meta": False, + } + ) + + # Create combined model for training + combined_model = CombinedWav2Vec2CTC( + encoder=mg.model, + ctc_head=ctc_head, + decoder=decoder, + beam_width=10 + ) + + # ------------------------------- + # 3. Print parameter count before pruning + # ------------------------------- + print("\nParameter count before pruning:") + try: + if hasattr(passes.module, "report_trainable_parameters_analysis_pass"): + _, report_before = passes.module.report_trainable_parameters_analysis_pass(mg.model) + for key, value in report_before.items(): + print(f" {key}: {value}") + else: + raise AttributeError("report_trainable_parameters_analysis_pass not available") + except (AttributeError, TypeError) as e: + print(f"Could not use detailed parameter report: {e}") + total_params = sum(p.numel() for p in mg.model.parameters() if p.requires_grad) + print(f" Total trainable parameters: {total_params}") + + # ------------------------------- + # 4. Apply SNIP callback for pruning preparation + # ------------------------------- + print("\nPreparing for SNIP pruning...") + + if trainer_available: + # Use the trainer approach with SNIPCallback + trainer = get_trainer( + model=combined_model, + tokenized_dataset=tokenized_dataset, + tokenizer=tokenizer, + evaluate_metric="wer", + num_train_epochs=1, + data_collator=data_collator, + gradient_accumulation_steps=4, + per_device_train_batch_size=2, + per_device_eval_batch_size=2, + ) + + # Get a representative batch for SNIP computation + try: + # Try to get a batch directly from the trainer's dataloader + print("Getting a batch from trainer's dataloader...") + train_dataloader = trainer.get_train_dataloader() + dummy_batch = next(iter(train_dataloader)) + except (AttributeError, StopIteration) as e: + # Fall back to manually creating a batch with the proper collator + print(f"Falling back to manual batch creation: {e}") + # Use the data_collator to properly handle variable-sized inputs + dummy_batch = data_collator([tokenized_dataset["train"][i] for i in range(min(2, len(tokenized_dataset["train"])))]) + + # Move tensors to the right device + for k, v in dummy_batch.items(): + if isinstance(v, torch.Tensor): + dummy_batch[k] = v.to(device) + + # Create the callback with the representative batch + snip_callback = SNIPCallback(representative_batch=dummy_batch) + trainer.add_callback(snip_callback) + + print("Running warm-up training step to accumulate SNIP gradient data...") + trainer.train() + print("Warm-up training complete.") + else: + # Use direct monkey-patching without trainer + print("Training tools not available, using direct SNIP computation...") + + # Monkey-patch the model modules directly + original_forwards = {} + for name, module in mg.model.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + original_forwards[name] = module.forward # Save original forward + + # Create weight_mask parameter if not present + if not hasattr(module, "weight_mask"): + module.weight_mask = nn.Parameter(torch.ones_like(module.weight)) + + # Override forward method + if isinstance(module, nn.Conv2d): + def new_forward(self, x): + return F.conv2d( + x, + self.weight.detach() * self.weight_mask, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + module.forward = types.MethodType(new_forward, module) + elif isinstance(module, nn.Linear): + def new_forward(self, x): + return F.linear(x, self.weight.detach() * self.weight_mask, self.bias) + module.forward = types.MethodType(new_forward, module) + + # Get a dummy input batch + dummy_batch = next(iter(DataLoader(tokenized_dataset["train"], batch_size=2))) + + # Forward-backward pass + mg.model.zero_grad() + output = mg.model( + input_values=dummy_batch["input_values"].to(device), + attention_mask=dummy_batch["attention_mask"].to(device) + ) + + # Use mean of hidden states as a simple loss + loss = output["last_hidden_state"].abs().mean() + loss.backward() + + # Store gradients + for name, module in mg.model.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight_mask"): + grad = module.weight_mask.grad + if grad is not None: + if not hasattr(module, "metadata"): + module.metadata = {} + if "weight" not in module.metadata: + module.metadata["weight"] = {} + if "stats" not in module.metadata["weight"]: + module.metadata["weight"]["stats"] = {} + + module.metadata["weight"]["stats"]["snip_scores"] = grad.abs().detach().clone() + print(f"Module {name}: SNIP score norm = {grad.abs().norm().item()}") + + # Restore original forwards + for name, module in mg.model.named_modules(): + if name in original_forwards: + module.forward = original_forwards[name] + + # ------------------------------- + # 5. Apply pruning pass using SNIP method + # ------------------------------- + print("\nApplying SNIP pruning...") + for name, module in mg.model.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "metadata"): + stats = module.metadata.get("weight", {}).get("stats", {}) + if "snip_scores" in stats: + print(f"{name}: snip_scores present, norm = {stats['snip_scores'].norm().item()}") + else: + print(f"{name}: snip_scores MISSING") + + pruning_config = { + "weight": { + "sparsity": 0.5, # Adjust target sparsity as desired + "method": "snip", # Use SNIP ranking function + "scope": "local", + "granularity": "elementwise", + }, + "activation": { + "sparsity": 0.0, + "method": "l1-norm", + "scope": "local", + "granularity": "elementwise", + }, + } + + mg, _ = passes.prune_transform_pass(mg, pass_args=pruning_config) + + # ------------------------------- + # 6. Print parameter count after pruning + # ------------------------------- + print("\n" + "="*80) + print("PARAMETER COUNTS AFTER PRUNING") + print("="*80) + + # Get the full model's parameter count (encoder + CTC head) + full_model_params = sum(p.numel() for p in combined_model.parameters() if p.requires_grad) + encoder_params = sum(p.numel() for p in mg.model.parameters() if p.requires_grad) + ctc_head_params = sum(p.numel() for p in ctc_head.parameters() if p.requires_grad) + + print(f"\n1. FULL MODEL (Encoder + CTC Head):") + print(f" Total parameters: {full_model_params:,}") + print(f" - Encoder parameters: {encoder_params:,}") + print(f" - CTC Head parameters: {ctc_head_params:,}") + + # Optionally show the report from the analysis pass + try: + if hasattr(passes.module, "report_trainable_parameters_analysis_pass"): + _, report_after = passes.module.report_trainable_parameters_analysis_pass(mg.model) + if "Total Trainable Parameters" in report_after: + print(f"\n Note: Analysis report shows: {report_after['Total Trainable Parameters']:,}") + except (AttributeError, TypeError) as e: + pass + + # Count remaining parameters (non-pruned) for the encoder only + print(f"\n2. PRUNING DETAILS (Encoder only - this is what's being pruned):") + + remaining_params = 0 + prunable_params_total = 0 + + for module in mg.model.modules(): + if hasattr(module, 'parametrizations'): + for param_name, param_list in module.parametrizations.items(): + for param in param_list: + if hasattr(param, 'mask'): + mask_size = param.mask.numel() + prunable_params_total += mask_size + remaining_params += param.mask.sum().item() + + non_prunable_params = encoder_params - prunable_params_total + + print(f" Total encoder parameters: {encoder_params:,}") + print(f" - Prunable parameters: {prunable_params_total:,} ({prunable_params_total/encoder_params:.1%} of encoder)") + print(f" - Non-prunable parameters: {non_prunable_params:,} ({non_prunable_params/encoder_params:.1%} of encoder)") + print(f" After pruning:") + print(f" - Kept parameters: {remaining_params:,} (of prunable)") + print(f" - Pruned parameters: {prunable_params_total - remaining_params:,} (of prunable)") + print(f" - Total remaining params: {remaining_params + non_prunable_params:,}") + print(f" - Sparsity within prunable: {1.0 - remaining_params / prunable_params_total:.2%}") + print(f" - Overall encoder sparsity: {1.0 - (remaining_params + non_prunable_params) / encoder_params:.2%}") + print(f" - Target sparsity (config): {pruning_config['weight']['sparsity']:.2%}") + print("="*80) + + # ------------------------------- + # 7. Optional fine-tuning and evaluation + # ------------------------------- + if trainer_available: + print("\nStarting fine-tuning of the pruned model...") + trainer.train() + + eval_results = trainer.evaluate() + print("\nEvaluation Results:") + print(f" WER: {eval_results.get('eval_wer', 'N/A')}") + print(f" Loss: {eval_results.get('eval_loss', 'N/A')}") + print(f" Runtime: {eval_results.get('eval_runtime', 'N/A')}") + print(f" Samples per second: {eval_results.get('eval_samples_per_second', 'N/A')}") + print(f" Steps per second: {eval_results.get('eval_steps_per_second', 'N/A')}") + else: + print("\nTraining tools not available. Skipping fine-tuning and evaluation.") + + print("\nSNIP pruning example complete!") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/pruning/hwpq.py b/src/chop/passes/graph/transforms/pruning/hwpq.py new file mode 100644 index 000000000..ec7c54fc9 --- /dev/null +++ b/src/chop/passes/graph/transforms/pruning/hwpq.py @@ -0,0 +1,154 @@ +import torch + +class HWPQ_PruningOnly: + def __init__(self, structured_sparsity=False): + """ + Initialize pruning + + Args: + structured_sparsity: Whether to use 2:4 structured sparsity + """ + self.structured_sparsity = structured_sparsity + + def compute_contribution(self, weights): + """ + Compute contribution metric L = w_i^2 / (1 - x_i^2/S) for each weight + + Args: + weights: Tensor of weights in a single layer + + Returns: + Tensor of contribution metrics for each weight + """ + # Compute S (sum of squared weights) + S = torch.sum(weights**2) + + # Compute the contribution metric for each weight + x_squared = weights**2 + denominator = 1 - x_squared / (S + 1e-10) # Add epsilon to avoid division by zero + # Avoid division by zero or negative values + denominator = torch.clamp(denominator, min=1e-10) + + contributions = x_squared / denominator + return contributions, S + + def prune_weights(self, weights, sparsity_level=0.5): + """ + Apply pruning to weights without quantization + + Args: + weights: The weight tensor to be pruned + sparsity_level: Target sparsity level (0.0 to 1.0) + + Returns: + Pruned tensor and mask + """ + # Make a copy of the weights to modify + pruned_weights = weights.clone() + mask = torch.ones_like(weights, dtype=torch.bool) + + # Track statistics + total_weights = weights.numel() + total_kept = 0 + + print(f"\nPruning details:") + print(f" Input tensor shape: {weights.shape}") + print(f" Target sparsity: {sparsity_level:.2%}") + + # Process each row independently + for i in range(weights.shape[0]): + row_weights = weights[i].flatten() + + # Get contribution metrics + contributions, S = self.compute_contribution(row_weights) + + # Create a mask for this row + row_mask = torch.ones_like(row_weights, dtype=torch.bool) + + # Count for achieving target sparsity + n_weights = row_weights.numel() + target_prune = int(n_weights * sparsity_level) + + if target_prune >= n_weights: + # Avoid pruning all weights + target_prune = max(0, n_weights - 1) + + # For unstructured pruning + # Sort contributions to ensure we prune exactly the target number + sorted_indices = torch.argsort(contributions) + + # Prune the weights with lowest contributions up to target sparsity + prune_indices = sorted_indices[:target_prune] + row_mask[prune_indices] = 0 + + # Ensure we're not pruning everything + if (row_mask == 0).all(): + # Keep at least one weight (the one with highest contribution) + max_idx = torch.argmax(contributions) + row_mask[max_idx] = 1 + + # Apply pruning (just set to zero) + result = torch.zeros_like(row_weights) + result[row_mask] = row_weights[row_mask] + + # Count non-zeros after pruning + kept_weights = row_mask.sum().item() + + # Print row statistics + if i < 3 or i == weights.shape[0] - 1: # Print first 3 rows and last row + print(f" Row {i}: kept {kept_weights}/{n_weights} weights " + f"({kept_weights/n_weights:.2%})") + elif i == 3: + print(" ...") + + # Update total statistics + total_kept += kept_weights + + # Save the result and reshape the mask + pruned_weights[i] = result.reshape(weights[i].shape) + mask[i] = row_mask.reshape(weights[i].shape) + + # Overall statistics + actual_sparsity = 1 - (total_kept / total_weights) + print(f" Overall: kept {total_kept}/{total_weights} weights, " + f"sparsity = {actual_sparsity:.4f}") + + return pruned_weights, mask + +def hwpq_pruning_only(tensor: torch.Tensor, info: dict, sparsity: float) -> torch.Tensor: + """ + Pruning-only ranking function. + + Args: + tensor: Weight tensor to be pruned + info: Dictionary with metadata for the tensor + sparsity: Target sparsity level + + Returns: + Boolean mask indicating which weights to keep (True) or prune (False) + """ + pruner = HWPQ_PruningOnly() + + # Apply pruning to get pruned weights and mask + _, mask = pruner.prune_weights(tensor, sparsity) + + return mask + +# For use with pruned modules +class HWPQParameterization(torch.nn.Module): + """ + Parametrization for pruning only. + """ + def __init__(self, mask): + super().__init__() + self.register_buffer("mask", mask) + + def forward(self, x): + assert self.mask.shape == x.shape + # Simply apply the mask for pruning + pruned = self.mask * x + return pruned + + def state_dict(self, *args, **kwargs): + # Avoid double saving masks + return {} \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/pruning/load.py b/src/chop/passes/graph/transforms/pruning/load.py index bc887761e..7c2f57b56 100644 --- a/src/chop/passes/graph/transforms/pruning/load.py +++ b/src/chop/passes/graph/transforms/pruning/load.py @@ -4,28 +4,32 @@ import torch import torch.nn as nn - logger = logging.getLogger(__name__) - -# Constants ---------------------------------------------------------------------------- +# ------------------------------------------------------------------------------ # Pruneable MASE operators # NOTE: We don't do activation pruning for conv1d and linear layers. -PRUNEABLE_OPS = {"conv1d": nn.Conv1d, "conv2d": nn.Conv2d, "linear": nn.Linear} - -WEIGHT_PRUNE_METHODS = ["random", "l1-norm"] -ACTIVATION_PRUNE_METHODS = ["random", "l1-norm"] - -# A registry of available pruning strategies (i.e. algorithms) -# PRUNE_METHODS = { -# # A basic one-shot pruner that prunes to a given sparsity level -# "level-pruner": LevelPruner, -# "channel-pruner": ChannelPruner, -# # Add more here... -# } - - -def load(config: dict): +# ------------------------------------------------------------------------------ +PRUNEABLE_OPS = { + "conv1d": nn.Conv1d, + "conv2d": nn.Conv2d, + "linear": nn.Linear +} + +# ------------------------------------------------------------------------------ +# Add "movement" to these lists so the pruning pass recognizes it. +# ------------------------------------------------------------------------------ +# Add "hwpq" to the valid weight pruning methods +WEIGHT_PRUNE_METHODS = ["random", "l1-norm", "movement", "hwpq", "snip"] +ACTIVATION_PRUNE_METHODS = ["random", "l1-norm", "movement"] + +# ------------------------------------------------------------------------------ +# The main load functions that parse and validate pruning configs +# ------------------------------------------------------------------------------ +def load(config: dict, graph): + """ + Common loader for the config, which also verifies scope, granularity, and sparsity. + """ sparsity = config.get("sparsity", 0.0) scope = config.get("scope", "local") granularity = config.get("granularity", "elementwise") @@ -42,12 +46,12 @@ def load(config: dict): scope, ["local", "global"] ) ) + # If the user provided a dictionary of layer-wise sparsities, validate them: if isinstance(sparsity, dict): # Make sure that the scope is local - if scope != "local": # not local + if scope != "local": raise ValueError("Layer-wise budgets only possible with a local scope!") - - # Verify that the keys are valid node names and that the values are valid + # Verify that the keys are valid node names in the FX graph names = [node.target for node in graph.fx_graph.nodes] for name in sparsity.keys(): if name not in names: @@ -55,14 +59,18 @@ def load(config: dict): _verify_sparsity(sparsity[name]) else: _verify_sparsity(sparsity) - return sparsity, scope, granularity + return sparsity, scope, granularity def load_weight_prune_config(config: dict, graph): - sparsity, scope, granularity = load(config) - + """ + Loads and validates the weight pruning config. + Expects config to have 'method', 'sparsity', 'scope', and 'granularity'. + """ + sparsity, scope, granularity = load(config, graph) method = config.get("method", "random") - # Validate the parameters + + # Validate the method if method not in WEIGHT_PRUNE_METHODS: raise ValueError( "Unsupported pruning method {}. Please choose from {}".format( @@ -77,12 +85,15 @@ def load_weight_prune_config(config: dict, graph): "sparsity": sparsity, } - def load_activation_prune_config(config: dict, graph): - sparsity, scope, granularity = load(config) - + """ + Loads and validates the activation pruning config. + Expects config to have 'method', 'sparsity', 'scope', and 'granularity'. + """ + sparsity, scope, granularity = load(config, graph) method = config.get("method", "random") - # Validate the parameters + + # Validate the method if method not in ACTIVATION_PRUNE_METHODS: raise ValueError( "Unsupported pruning method {}. Please choose from {}".format( @@ -97,15 +108,14 @@ def load_activation_prune_config(config: dict, graph): "sparsity": sparsity, } - -# Get a pre-trained vision model from Torchvision and return the layer-wise sparsity -# distribution that a globally scoped pruner would compute for it. -# NOTE: Here, we assume the dataset is ImageNet, as indicated by the number of classes -# in the dataset info. This will need fixing later to support any dataset the user -# wants to use. Also, this code is really similar to the one in the level pruner, except -# that here we do it for the full model. We should probably refactor this later. +# ------------------------------------------------------------------------------ +# Example function for distributing global sparsity (not used by MASE by default) +# ------------------------------------------------------------------------------ def get_unfused_distribution(sparsity: float, criterion: callable, name: str): - # We import here to avoid a failing test via a circular import in the CI. + """ + Example function that could compute a layer-wise sparsity distribution + for a given global sparsity target. Not used by default in MASE. + """ from chop.models.vision import VISION_MODELS, get_vision_model if name is None or name not in VISION_MODELS: @@ -113,48 +123,33 @@ def get_unfused_distribution(sparsity: float, criterion: callable, name: str): model = get_vision_model(name, "cls", {"num_classes": 1000}, pretrained=True) tensors = {} - sparsities = {} - for name, module in model.named_modules(): + for mod_name, module in model.named_modules(): if not isinstance(module, tuple(PRUNEABLE_OPS.values())): continue - - # Collect all the weight tensors in the model - tensors[name] = { + # Collect weight tensors + tensors[mod_name] = { "tensor": module.weight.data.clone().flatten(), "shape": module.weight.shape, } - # Compute the layer-wise sparsities! concatenated = torch.cat([t["tensor"] for t in tensors.values()]) - mask = ( - # Currently, there's no criterion with custom kwargs, so we don't pass it in - criterion(concatenated, sparsity) - if sparsity > 0.0 - else torch.ones_like(concatenated, dtype=torch.bool) - ) - # Split the masks into chunks and compute the sparsity of each chunk + # If sparsity=0, we keep everything + mask = criterion(concatenated, sparsity) if sparsity > 0.0 else torch.ones_like(concatenated, dtype=torch.bool) + sizes = [t["tensor"].numel() for t in tensors.values()] masks = torch.split(mask, sizes) - sparsities = [1 - m.count_nonzero() / m.numel() for m in masks] - sparsities = dict(zip(tensors.keys(), sparsities)) - - return sparsities + layer_sparsities = [1 - m.count_nonzero() / m.numel() for m in masks] + layer_sparsities = dict(zip(tensors.keys(), layer_sparsities)) + return layer_sparsities +# ------------------------------------------------------------------------------ +# Helper function to verify a numeric sparsity value +# ------------------------------------------------------------------------------ def _verify_sparsity(sparsity): if not isinstance(sparsity, float): raise ValueError("Sparsity must be a float. Got {}".format(type(sparsity))) if sparsity < 0 or sparsity > 1: raise ValueError("Sparsity must be between 0 and 1. Got {}".format(sparsity)) - -# def _log_metadata(graph): -# logger.info( -# "\n" -# f"Sparsity : {measure_sparsity(graph.model):>14.3f}\n" -# f"Params (TOT): {count_parameters(graph.model):>14,}\n" -# f"Params (NNZ): {count_parameters(graph.model, nonzero_only=True):>14,}\n" -# f"Buffers : {count_buffers(graph.model):>14,}\n" -# f"Size : {estimate_model_size(graph.model):>14.3f} MB" -# ) diff --git a/src/chop/passes/graph/transforms/pruning/prune.py b/src/chop/passes/graph/transforms/pruning/prune.py index 373249706..386c963c9 100644 --- a/src/chop/passes/graph/transforms/pruning/prune.py +++ b/src/chop/passes/graph/transforms/pruning/prune.py @@ -1,10 +1,12 @@ + import torch from chop.tools import get_logger -from .load import load_activation_prune_config, load_weight_prune_config -from .pruning_methods import weight_criteria_map, activation_criteria_map -from .sparse_parameterization import FakeSparseWeight, FakeStructuredSparseWeight +from chop.passes.graph.transforms.pruning.load import load_activation_prune_config, load_weight_prune_config +from chop.passes.graph.transforms.pruning.pruning_methods import weight_criteria_map, activation_criteria_map +from chop.passes.graph.transforms.pruning.sparse_parameterization import FakeSparseWeight, FakeStructuredSparseWeight +from chop.passes.graph.transforms.pruning.hwpq import HWPQParameterization logger = get_logger(__name__) logger.setLevel("INFO") @@ -23,12 +25,32 @@ def get_activation_rank_fn(c): def get_weight_hook(name, info, named_info, w_config: dict): - # register parameterization + """ + get_weight_hook is called for each node 'name' in the FX graph. + 'name' is the node_name (e.g. "encoder.layers.0.attention.q_proj"). + 'info' is the entire dictionary of node -> metadata. + 'named_info' is just this node's metadata. + """ w_rank_fn = get_weight_rank_fn(w_config) value = named_info["value"] w_sparsity = named_info["weight_sparsity"] register_parameter_name = "weight" - parameterization = FakeSparseWeight(w_rank_fn(value, info, w_sparsity)) + + # Add structured_sparsity flag if using HWPQ + if w_config["method"] == "hwpq": + named_info["structured_sparsity"] = w_config.get("structured_sparsity", True) + + if w_config["scope"] == "global": + param_mask = w_rank_fn(value, info, w_sparsity, node_name=name) + else: + param_mask = w_rank_fn(value, named_info, w_sparsity) + + # Use special parameterization for HWPQ to handle both pruning and quantization + if w_config["method"] == "hwpq": + parameterization = HWPQParameterization(param_mask) + else: + parameterization = FakeSparseWeight(param_mask) + return (register_parameter_name, parameterization) @@ -36,18 +58,20 @@ def get_activation_hook(name, info, named_info, a_config: dict): a_rank_fn = get_activation_rank_fn(a_config) a_sparsity = named_info["activation_sparsity"] - # register forward hook def sparsify_input(module, args): if len(args) > 1: raise ValueError( - f"{module.__class__.__name__} takes more than 1 argument at inference, the current sparsiy_input pre forward hook only allows one!" + f"{module.__class__.__name__} takes more than 1 argument at inference," + " the current 'sparsify_input' pre-forward hook only allows one!" ) x = args[0] - mask = a_rank_fn(x, info, a_sparsity) + + if a_config["scope"] == "global": # Extra logic required for eventual movement pruning + mask = a_rank_fn(x, info, a_sparsity, node_name=name) + else: + mask = a_rank_fn(x, named_info, a_sparsity) # Changed from info module.activation_mask = mask - # it seems like the output of this can be a non-tuple thing?? return x * mask - return ("register_forward_pre_hook", sparsify_input) @@ -63,6 +87,7 @@ def build_pruning_hooks(info, w_config, a_config): } if "weight_stats" in v.keys(): w_info["stats"] = v["weight_stats"] + # for activations a_info = { "module_type": v["module_type"], @@ -78,13 +103,16 @@ def build_pruning_hooks(info, w_config, a_config): } return named_hooks - def fetch_info(node, module): - # deal with conv2d + """ + Fetches metadata for the module from the FX node. + For Conv2d and Linear modules, if the node's software stats are not present, + it falls back to the module's metadata (which should contain the movement stats + updated during training). + """ if isinstance(module, torch.nn.Conv2d): a_value = node.meta["mase"].parameters["common"]["args"]["data_in_0"]["value"] a_shape = node.meta["mase"].parameters["common"]["args"]["data_in_0"]["shape"] - w_value = node.meta["mase"].parameters["common"]["args"]["weight"]["value"] w_shape = node.meta["mase"].parameters["common"]["args"]["weight"]["shape"] @@ -96,22 +124,19 @@ def fetch_info(node, module): "activation_shape": a_shape, } - # Register weight/activation statistics for pruning methods that require the profile_statistics_analysis_pass if "args" in node.meta["mase"].parameters["software"]: - out["activation_stats"] = node.meta["mase"].parameters["software"]["args"][ - "data_in_0" - ]["stat"] - out["weight_stats"] = node.meta["mase"].parameters["software"]["args"][ - "weight" - ]["stat"] - + out["activation_stats"] = node.meta["mase"].parameters["software"]["args"]["data_in_0"]["stat"] + out["weight_stats"] = node.meta["mase"].parameters["software"]["args"]["weight"]["stat"] + elif hasattr(module, "metadata"): + for pname, _ in module.named_parameters(): + if pname in module.metadata: + out["weight_stats"] = module.metadata[pname]["stats"] + break return out - # deal with linear if isinstance(module, torch.nn.Linear): a_value = node.meta["mase"].parameters["common"]["args"]["data_in_0"]["value"] a_shape = node.meta["mase"].parameters["common"]["args"]["data_in_0"]["shape"] - w_value = node.meta["mase"].parameters["common"]["args"]["weight"]["value"] w_shape = node.meta["mase"].parameters["common"]["args"]["weight"]["shape"] out = { @@ -122,18 +147,16 @@ def fetch_info(node, module): "activation_shape": a_shape, } - # Register weight/activation statistics for pruning methods that require the profile_statistics_analysis_pass if "args" in node.meta["mase"].parameters["software"]: - out["activation_stats"] = node.meta["mase"].parameters["software"]["args"][ - "data_in_0" - ]["stat"] - out["weight_stats"] = node.meta["mase"].parameters["software"]["args"][ - "weight" - ]["stat"] - + out["activation_stats"] = node.meta["mase"].parameters["software"]["args"]["data_in_0"]["stat"] + out["weight_stats"] = node.meta["mase"].parameters["software"]["args"]["weight"]["stat"] + elif hasattr(module, "metadata"): + for pname, _ in module.named_parameters(): + if pname in module.metadata: + out["weight_stats"] = module.metadata[pname]["stats"] + break return out - # otherwise we just return None, and this module would be ignore in build_pruning_hooks return None @@ -142,31 +165,26 @@ def prune_graph_iterator(graph, config: dict): w_config = load_weight_prune_config(config["weight"], graph) a_config = load_activation_prune_config(config["activation"], graph) - # we need to loop twice, the first time is to fetch all necessary information - # first loop + # First loop: fetch info from each node info = {} for node in graph.fx_graph.nodes: - # pruning only deals with modules at the moment if node.op == "call_module": module = graph.modules[node.target] meta = fetch_info(node, module) info[node.target] = meta - # hook building + # Build hooks from the info dictionary hooks = build_pruning_hooks(info, w_config, a_config) - # prune in second loop by applying hooks to relevant modules + # Second loop: apply the hooks for node in graph.fx_graph.nodes: - # pruning only deals with modules at the moment if node.op == "call_module": name = node.target if name in hooks.keys(): logger.info(f"Pruning module: {node.name}") node_hooks = hooks[name] - # check weight hook, if it exits, apply it if node_hooks["w_hook"] is not None: register_name, parameterization = node_hooks["w_hook"] - # apply weigh pruning torch.nn.utils.parametrize.register_parametrization( graph.modules[node.target], register_name, parameterization ) @@ -180,34 +198,11 @@ def prune_transform_pass(graph, pass_args: dict = {}): """ Apply pruning transformation to the given graph. This is achieved by adding a register_parametrization hook to weights - and a register_pre_forward hook to activations - + and a register_pre_forward hook to activations. + :param graph: The input graph to be pruned. - :type graph: MaseGraph - :param pass_args: Optional arguments for the pruning transformation. - :type pass_args: dict - - pass_args should take the following form: - - .. code-block:: python - - pass_config = { - "weight" : { - "scope": "local", # ["local, "global"] are available - "granularity": "element", # ["element"] are available - "method": "l1", # ["l1", "random"] are available - "sparsity": 0.5, # a float between 0.0 and 1.0 - }, - "activation" : { - "scope": "local", # ["local, "global"] are available - "granularity": "element", # ["element"] are available - "method": "l1", # ["l1", "random"] are available - "sparsity": 0.5, # a float between 0.0 and 1.0 - } - :return: The pruned graph and an empty dictionary. - :rtype: tuple """ graph = prune_graph_iterator(graph, pass_args) - return graph, {} + return graph, {} \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/pruning/prune_movment_helper.py b/src/chop/passes/graph/transforms/pruning/prune_movment_helper.py new file mode 100644 index 000000000..e5d6941de --- /dev/null +++ b/src/chop/passes/graph/transforms/pruning/prune_movment_helper.py @@ -0,0 +1,33 @@ +import torch +from torch import nn +from transformers import TrainerCallback + +class MovementTrackingCallback(TrainerCallback): + def on_train_begin(self, args, state, control, **kwargs): + self.prev_params = {} + self.movement_stats = {} + model = kwargs["model"] + for name, module in model.encoder.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + self.prev_params[name] = module.weight.detach().clone() + self.movement_stats[name] = torch.zeros_like(module.weight) + if not hasattr(module.weight, "metadata"): + module.metadata["weight"] = {} + module.metadata["weight"]["stats"] = {"movement": self.movement_stats[name]} + return control + + def on_step_end(self, args, state, control, **kwargs): + model = kwargs["model"] + for name, module in model.encoder.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + movement = (module.weight.detach() - self.prev_params[name]).abs() + self.movement_stats[name] += movement + self.prev_params[name].copy_(module.weight.detach()) + if not hasattr(module, "metadata"): + module.metadata = {} + if "weight" not in module.metadata: + module.metadata["weight"] = {} + module.metadata["weight"]["stats"] = {"movement": self.movement_stats[name]} + + return control + diff --git a/src/chop/passes/graph/transforms/pruning/pruning_methods.py b/src/chop/passes/graph/transforms/pruning/pruning_methods.py index 665abc17e..d98d09d65 100644 --- a/src/chop/passes/graph/transforms/pruning/pruning_methods.py +++ b/src/chop/passes/graph/transforms/pruning/pruning_methods.py @@ -1,3 +1,5 @@ +from chop.passes.graph.transforms.pruning.hwpq import hwpq_pruning_only, HWPQParameterization + # Ranking criteria for pruning weights # NOTE: For now, we assume that all ranking functions take in two positional arguments # (a tensor along with a sparsity target) and return an appropriately computed mask. All @@ -11,59 +13,95 @@ import torch """ -These implemntations are for the pruning functional we assume info always have the following form: +These implementations are for the pruning functional we assume info always have the following form: an info entry = { 'module_type': 'conv2d', 'value': w_value, - 'stats': w_stats, + 'stats': w_stats, # here we assume w_stats may contain a "movement" entry, among others 'shape': w_shape, ... } """ - def random(tensor: torch.Tensor, info: dict, sparsity: float) -> torch.Tensor: - """set sparsity percentage of values - in the mask to False (i.e. 0) randomly + """set sparsity percentage of values in the mask to False (i.e. 0) randomly. Pre: sparsity is not 0.0 - - :param tensor: input tensor - :type tensor: torch.Tensor - :param sparsity: sparsity level, this suppose to be a ratio in between 0.0 and 1.0 - :type sparsity: float - :return: a random sparsity mask generated based on the sparsity value - :rtype: torch.Tensor """ mask = torch.ones(tensor.size(), dtype=torch.bool, device=tensor.device) mask[torch.rand(tensor.size()) < sparsity] = False return mask - def l1(tensor: torch.Tensor, info: dict, sparsity: float) -> torch.Tensor: - """Use the L1 norm of values in the tensor - to rank them and return a mask where values + """Use the L1 norm of values in the tensor to rank them and return a mask where values lower than the threshold are set to False (i.e. 0). Pre: sparsity is not 0.0 - - :param tensor: input tensor - :type tensor: torch.Tensor - :param sparsity: sparsity level, this suppose to be a ratio between 0.0 and 1.0 - :type sparsity: float - :return: a sparsity mask - :rtype: torch.Tensor """ threshold = torch.quantile(tensor.abs().flatten(), sparsity) mask = (tensor.abs() > threshold).to(torch.bool).to(tensor.device) return mask +def global_weight_l1(tensor: torch.Tensor, info: dict, sparsity: float, node_name: str, bins: int = 2048, + _state={"did_fail": False}) -> torch.Tensor: + r""" + Attempt a "full flatten + quantile" global L1 pruning. If we hit a RuntimeError + (OOM or "tensor too large" on GPU/MPS), fall back to a histogram-based approach. -def global_weight_l1(tensor: torch.Tensor, info: dict, sparsity: float): - tensors = [v["weight_value"] for _, v in info.items() if v is not None] - flattened_tensors = [t.abs().flatten() for t in tensors] - threshold = torch.quantile(torch.cat(flattened_tensors, dim=0), sparsity) - mask = (tensor.abs() > threshold).to(torch.bool).to(tensor.device) - return mask + :param tensor: The weight tensor of the current module to prune. + :param info: The full dictionary (across all modules) of metadata. + :param sparsity: The desired global sparsity (fraction to prune). + E.g. 0.2 means keep the top 80% of weights by magnitude. + :param bins: Number of bins to use in fallback histogram approach (2048 by default). + :return: A boolean mask (same shape as `tensor`) that is True for weights to keep + and False for weights to prune. + """ + if not _state["did_fail"]: + try: + arrays = [] + for _, v in info.items(): + if v is not None: + arrays.append(v["weight_value"].abs().flatten()) + big_flat = torch.cat(arrays, dim=0) + threshold = torch.quantile(big_flat, sparsity) + mask = (tensor.abs() > threshold).bool().to(tensor.device) + return mask + + except RuntimeError as e: + print("[DEBUG] Global L1 full-quantile approach failed. Falling back to histogram.") + print("[DEBUG] Caught RuntimeError:", str(e)) + _state["did_fail"] = True + + global_max = 0.0 + total_elements = 0 + for _, v in info.items(): + if v is not None: + w = v["weight_value"] + current_max = w.abs().max().item() + if current_max > global_max: + global_max = current_max + total_elements += w.numel() + if global_max == 0.0: + threshold = 0.0 + else: + bin_edges = torch.linspace(0, global_max, steps=bins+1) # on CPU now + global_hist = torch.zeros(bins) + for _, v in info.items(): + if v is not None: + w_vals = v["weight_value"].abs().flatten().cpu() + h = torch.histc(w_vals, bins=bins, min=0, max=global_max) + global_hist += h + + target_count = sparsity * total_elements + cumulative = torch.cumsum(global_hist, dim=0) + bin_idx_tensor = (cumulative >= target_count).nonzero(as_tuple=False) + if len(bin_idx_tensor) == 0: + threshold = global_max + else: + idx = bin_idx_tensor[0].item() + threshold = (bin_edges[idx] + bin_edges[idx + 1]) / 2 + + mask = (tensor.abs() > threshold).bool().to(tensor.device) + return mask def global_activation_l1(tensor: torch.Tensor, info: dict, sparsity: float): tensors = [v["activation_value"] for _, v in info.items() if v is not None] @@ -72,26 +110,8 @@ def global_activation_l1(tensor: torch.Tensor, info: dict, sparsity: float): mask = (tensor.abs() > threshold).to(torch.bool).to(tensor.device) return mask - -"""this is from the old pruning old, leaving it here in case we need them later""" - - -def neurons_random_rank( - tensor: torch.Tensor, sparsity: float, layer_type: str -) -> torch.Tensor: - """set sparsity percentage of values - in the mask to False (i.e. 0) randomly - pre: sparsity is not 0.0 - - :param tensor: input tensor - :type tensor: torch.Tensor - :param sparsity: sparsity level, this suppose to be a ratio between 0.0 and 1.0 - :type sparsity: float - :param layer_type: layer type can be in ["Linear", "Conv2d"] - :type layer_type: str - :return: a sparsity mask - :rtype: torch.Tensor - """ +"""this is from the old pruning, leaving it here in case we need them later""" +def neurons_random_rank(tensor: torch.Tensor, sparsity: float, layer_type: str) -> torch.Tensor: mask = torch.ones(tensor.size(), dtype=torch.bool) mask = mask.reshape(tensor.shape[0], -1) if layer_type == "Linear": @@ -105,16 +125,10 @@ def neurons_random_rank( mask.reshape(*tensor.shape) return mask - -"""this is from the old pruning old, leaving it here in case we need them later""" - - -# Pruning each neurons connections by specified fan_in randomly -def neurons_random_fan_in( - tensor: torch.Tensor, sparsity: float, layer_type: str, fan_in: int -) -> torch.Tensor: - if fan_in == None: - raise ValueError("fan_in is not been specified") +"""this is from the old pruning, leaving it here in case we need them later""" +def neurons_random_fan_in(tensor: torch.Tensor, sparsity: float, layer_type: str, fan_in: int) -> torch.Tensor: + if fan_in is None: + raise ValueError("fan_in has not been specified") mask = torch.zeros(tensor.size(), dtype=torch.bool) mask = mask.reshape(tensor.shape[0], -1) if layer_type == "Linear": @@ -126,18 +140,285 @@ def neurons_random_fan_in( mask.reshape(*tensor.shape) return mask +def movement(tensor: torch.Tensor, info: dict, sparsity: float) -> torch.Tensor: + """ + Movement pruning ranking function for a given tensor. + Assumes that info["stats"] contains a "movement" entry representing accumulated movement scores. + We prune (set to False) the weights with the lowest movement (in absolute terms). + """ + if "movement" not in info.get("stats", {}): + raise ValueError("Movement information not found in info stats.") + movement_scores = info["stats"]["movement"] + threshold = torch.quantile(movement_scores.abs().flatten(), sparsity) + mask = (movement_scores.abs() > threshold).to(torch.bool).to(tensor.device) + return mask + +def global_weight_movement(tensor: torch.Tensor, info: dict, sparsity: float, node_name: str, bins: int = 2048, + _state={"did_fail": False}) -> torch.Tensor: + """ + Global movement pruning ranking function with fallback method for memory constrained applications. + Aggregates movement scores from the info dictionary over all relevant weight tensors to compute a global threshold. + """ + + def _apply_movement_threshold(tensor: torch.Tensor, info: dict, threshold: float, node_name: str) -> torch.Tensor: + if node_name not in info: + raise ValueError(f"Node {node_name} not found in info dictionary.") + + current_node_meta = info[node_name] + if "weight_stats" not in current_node_meta or "movement" not in current_node_meta["weight_stats"]: + raise ValueError( + f"Current node '{node_name}' has no movement data, " + "but is being pruned with global movement." + ) + + current_movement = current_node_meta["weight_stats"]["movement"] + mask = (current_movement.abs() > threshold).bool().to(tensor.device) + return mask + + all_movements = [] + for _, v in info.items(): + if v is not None and "weight_stats" in v: + ws = v["weight_stats"] + if "movement" in ws: + all_movements.append(ws["movement"]) + + if not all_movements: + raise ValueError("No global movement data found: no 'weight_stats'/'movement' in info.") + + if not _state["did_fail"]: + try: + big_flat = torch.cat([m.abs().flatten().to(tensor.device) for m in all_movements], dim=0) + threshold = torch.quantile(big_flat, sparsity) + + return _apply_movement_threshold(tensor, info, threshold, node_name) + + except RuntimeError as e: + print("[DEBUG] Global movement flatten+quantile approach failed. Falling back to histogram.") + print("[DEBUG] Caught RuntimeError:", str(e)) + _state["did_fail"] = True + + global_max = 0.0 + total_elements = 0 + for m in all_movements: + mx = m.abs().max().item() + if mx > global_max: + global_max = mx + total_elements += m.numel() + + if global_max == 0.0: + threshold = 0.0 + else: + bin_edges = torch.linspace(0, global_max, steps=bins + 1) # CPU by default + global_hist = torch.zeros(bins) + + for m in all_movements: + m_cpu = m.abs().flatten().cpu() + h = torch.histc(m_cpu, bins=bins, min=0, max=global_max) + global_hist += h + + target_count = sparsity * total_elements + cumulative = torch.cumsum(global_hist, dim=0) + idx_tensor = (cumulative >= target_count).nonzero(as_tuple=False) + if idx_tensor.numel() == 0: + threshold = global_max + else: + idx = idx_tensor[0].item() + threshold = (bin_edges[idx] + bin_edges[idx + 1]) / 2 + + return _apply_movement_threshold(tensor, info, threshold, node_name) + +# Not Implemented Yet, Needs Changing +def activation_movement(tensor: torch.Tensor, info: dict, sparsity: float) -> torch.Tensor: + """ + Movement-based pruning ranking function for activations. + Assumes that info["stats"] contains a "movement" entry for activations. + """ + if "movement" not in info.get("stats", {}): + raise ValueError("Movement information not found in info stats.") + movement_scores = info["stats"]["movement"] + threshold = torch.quantile(movement_scores.abs().flatten(), sparsity) + mask = (movement_scores.abs() > threshold).to(torch.bool).to(tensor.device) + return mask + +# Not Implemented Yet, Needs Changing +def global_activation_movement(tensor: torch.Tensor, info: dict, sparsity: float): + """ + Global movement-based pruning for activations. + """ + movements = [v["stats"]["movement"] for _, v in info.items() if v is not None and "movement" in v.get("stats", {})] + if len(movements) == 0: + raise ValueError("No movement information found in info stats.") + flattened_movements = [m.abs().flatten() for m in movements] + threshold = torch.quantile(torch.cat(flattened_movements, dim=0), sparsity) + current_movement = info["stats"]["movement"] + mask = (current_movement.abs() > threshold).to(torch.bool).to(tensor.device) + return mask + +def snip(tensor: torch.Tensor, info: dict, sparsity: float) -> torch.Tensor: + """ + SNIP (Single-shot Network Pruning) pruning ranking function for a given tensor. + + SNIP evaluates the importance of weights by calculating the gradients of the loss + with respect to a binary mask applied to the weights. It uses the pre-computed + saliency scores stored in the info dict to determine which weights to prune. + + Reference: https://arxiv.org/abs/1810.02340 + + Args: + tensor: The weight tensor to be pruned + info: Dictionary containing metadata including pre-computed SNIP saliency scores + sparsity: The fraction of weights to prune (between 0 and 1) + + Returns: + A boolean mask with the same shape as tensor, where True indicates weights to keep + """ + # Check for SNIP saliency scores in different possible locations + snip_scores = None + + # First check in stats dictionary + if "stats" in info: + if "snip_scores" in info["stats"]: + snip_scores = info["stats"]["snip_scores"] + elif "snip_grad" in info["stats"]: + # Also look for alternative name used in the callback + snip_scores = torch.abs(info["stats"]["snip_grad"]) + + # If not found, try in weight_stats + if snip_scores is None and "weight_stats" in info: + if "snip_scores" in info["weight_stats"]: + snip_scores = info["weight_stats"]["snip_scores"] + elif "snip_grad" in info["weight_stats"]: + snip_scores = torch.abs(info["weight_stats"]["snip_grad"]) + + if snip_scores is None: + raise ValueError("SNIP saliency scores not found in info stats. Run SNIP preprocessing first.") + + # Calculate threshold based on sparsity + threshold = torch.quantile(snip_scores.flatten(), sparsity) + + # Create mask where True means keep the weight + mask = (snip_scores > threshold).to(torch.bool).to(tensor.device) + + return mask + +def global_weight_snip(tensor: torch.Tensor, info: dict, sparsity: float, node_name: str, bins: int = 2048, + _state={"did_fail": False}) -> torch.Tensor: + """ + Global SNIP pruning ranking function with fallback method for memory constrained applications. + + Aggregates SNIP saliency scores from the info dictionary over all relevant weight tensors + to compute a global threshold. + + Args: + tensor: The weight tensor to be pruned + info: Dictionary containing metadata for all nodes including pre-computed SNIP saliency scores + sparsity: The fraction of weights to prune (between 0 and 1) + node_name: Name of the current node being processed + bins: Number of bins to use in fallback histogram approach (2048 by default) + + Returns: + A boolean mask with the same shape as tensor, where True indicates weights to keep + """ + def _get_snip_scores(node_info): + """Helper to extract SNIP scores from different possible locations in the metadata""" + if "weight_stats" in node_info: + if "snip_scores" in node_info["weight_stats"]: + return node_info["weight_stats"]["snip_scores"] + elif "snip_grad" in node_info["weight_stats"]: + return torch.abs(node_info["weight_stats"]["snip_grad"]) + if "stats" in node_info: + if "snip_scores" in node_info["stats"]: + return node_info["stats"]["snip_scores"] + elif "snip_grad" in node_info["stats"]: + return torch.abs(node_info["stats"]["snip_grad"]) + return None + + def _apply_snip_threshold(tensor: torch.Tensor, info: dict, threshold: float, node_name: str) -> torch.Tensor: + if node_name not in info: + raise ValueError(f"Node {node_name} not found in info dictionary.") + + current_node_meta = info[node_name] + snip_scores = _get_snip_scores(current_node_meta) + + if snip_scores is None: + raise ValueError( + f"Current node '{node_name}' has no SNIP saliency data, " + "but is being pruned with global SNIP." + ) + + mask = (snip_scores > threshold).bool().to(tensor.device) + return mask + + # Collect SNIP saliency scores from all nodes + all_scores = [] + for node_name, v in info.items(): + if v is not None: + snip_scores = _get_snip_scores(v) + if snip_scores is not None: + all_scores.append(snip_scores) + + if not all_scores: + raise ValueError("No global SNIP data found. Run SNIP preprocessing first.") + + # Try the direct approach first (may fail with OOM for large models) + if not _state["did_fail"]: + try: + big_flat = torch.cat([s.flatten().to(tensor.device) for s in all_scores], dim=0) + threshold = torch.quantile(big_flat, sparsity) + return _apply_snip_threshold(tensor, info, threshold, node_name) + except RuntimeError as e: + print("[DEBUG] Global SNIP flatten+quantile approach failed. Falling back to histogram.") + print("[DEBUG] Caught RuntimeError:", str(e)) + _state["did_fail"] = True + + # Fallback to histogram approach + global_max = 0.0 + total_elements = 0 + for s in all_scores: + mx = s.max().item() + if mx > global_max: + global_max = mx + total_elements += s.numel() + + if global_max == 0.0: + threshold = 0.0 + else: + bin_edges = torch.linspace(0, global_max, steps=bins + 1) # CPU by default + global_hist = torch.zeros(bins) + + for s in all_scores: + s_cpu = s.flatten().cpu() + h = torch.histc(s_cpu, bins=bins, min=0, max=global_max) + global_hist += h + + target_count = sparsity * total_elements + cumulative = torch.cumsum(global_hist, dim=0) + idx_tensor = (cumulative >= target_count).nonzero(as_tuple=False) + if idx_tensor.numel() == 0: + threshold = global_max + else: + idx = idx_tensor[0].item() + threshold = (bin_edges[idx] + bin_edges[idx + 1]) / 2 + + return _apply_snip_threshold(tensor, info, threshold, node_name) weight_criteria_map = { "local": { "elementwise": { "random": random, "l1-norm": l1, + "movement": movement, + "hwpq": hwpq_pruning_only, # Add the HWPQ method here + "snip": snip, # Add the SNIP method here } }, "global": { "elementwise": { "random": random, "l1-norm": global_weight_l1, + "movement": global_weight_movement, + "snip": global_weight_snip, # Add the global SNIP method here + # We're not implementing global HWPQ for now } }, } @@ -147,12 +428,15 @@ def neurons_random_fan_in( "elementwise": { "random": random, "l1-norm": l1, + "movement": activation_movement, # Yet to be implemented, activation movement pruning (local) } }, "global": { "elementwise": { "random": random, "l1-norm": global_activation_l1, + "movement": global_activation_movement, # Yet to be implemented, activation movement pruning (global) } }, } + diff --git a/src/chop/passes/graph/transforms/pruning/snip_helper.py b/src/chop/passes/graph/transforms/pruning/snip_helper.py new file mode 100644 index 000000000..3fe399f1a --- /dev/null +++ b/src/chop/passes/graph/transforms/pruning/snip_helper.py @@ -0,0 +1,406 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Dict, List, Optional, Tuple, Any, Callable +import types +from transformers import TrainerCallback + + +class SNIPTrackingCallback: + """ + SNIP Callback for calculating saliency scores using monkey-patching. + + For each prunable module (Conv2d/Linear) in the model: + 1. Add a learnable parameter 'weight_mask' (initialized to ones) if not present. + 2. Override the forward method so that it computes using (weight.detach() * weight_mask). + This ensures that no gradients flow into the weight. + 3. Run one forward-backward pass on one mini-batch and capture the gradient on weight_mask. + 4. Store that gradient in module.metadata["weight"]["stats"]["snip_scores"]. + 5. Restore the original forward method. + + Reference: https://arxiv.org/abs/1810.02340 + """ + + def __init__(self, training_step_fn: Optional[Callable] = None): + """ + Initialize the SNIP tracking callback. + + Args: + training_step_fn: Optional function to use for the forward-backward pass. + If None, a default cross-entropy or MSE loss will be used. + """ + self.training_step_fn = training_step_fn + + def __call__(self, graph): + """ + Apply SNIP saliency score computation to the graph's model. + + Args: + graph: The computational graph to analyze + + Returns: + Updated graph with SNIP saliency scores + """ + model = graph.model + + # Save original forward methods for later restoration + original_forwards = {} + + # Collect prunable modules + prunable_modules = [] + for name, module in model.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + prunable_modules.append((name, module)) + + # No prunable modules found + if not prunable_modules: + print("No prunable modules found in the model.") + return graph + + device = next(model.parameters()).device + + # Enable gradient computation + was_training = model.training + model.train() + + # Monkey-patch prunable modules + for name, module in prunable_modules: + original_forwards[name] = module.forward # Save original forward + + # Create weight_mask parameter if not present + if not hasattr(module, "weight_mask"): + module.weight_mask = nn.Parameter(torch.ones_like(module.weight)) + + # Override forward method to use weight.detach() * weight_mask + if isinstance(module, nn.Conv2d): + def new_forward(self, x): + return F.conv2d( + x, + self.weight.detach() * self.weight_mask, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + module.forward = types.MethodType(new_forward, module) + elif isinstance(module, nn.Linear): + def new_forward(self, x): + return F.linear(x, self.weight.detach() * self.weight_mask, self.bias) + module.forward = types.MethodType(new_forward, module) + + # Get a batch from the input metadata if available + dummy_input = None + for node in graph.fx_graph.nodes: + if node.op == "placeholder": + if "value" in node.meta.get("mase", {}).get("parameters", {}).get("common", {}).get("args", {}): + input_value = node.meta["mase"]["parameters"]["common"]["args"]["value"] + if isinstance(input_value, torch.Tensor): + if dummy_input is None: + dummy_input = {node.name: input_value.to(device)} + else: + dummy_input[node.name] = input_value.to(device) + + # If no dummy input found, use a fallback + if dummy_input is None: + print("Warning: No dummy input found in graph metadata. SNIP scores may be inaccurate.") + # Try to infer input shape from the first layer + for _, module in prunable_modules: + if isinstance(module, nn.Conv2d): + in_channels = module.in_channels + dummy_input = {"input": torch.randn(1, in_channels, 28, 28).to(device)} + break + elif isinstance(module, nn.Linear): + in_features = module.in_features + dummy_input = {"input": torch.randn(1, in_features).to(device)} + break + + # Zero gradients + model.zero_grad() + + # Forward pass + try: + if isinstance(dummy_input, dict): + output = model(**dummy_input) + else: + output = model(dummy_input) + + # Compute loss and backward + if self.training_step_fn is not None: + # Use provided training step function + loss = self.training_step_fn(model, dummy_input, output) + else: + # Default loss computation + if isinstance(output, dict) and "loss" in output: + # If model returns a dict with loss + loss = output["loss"] + elif isinstance(output, torch.Tensor): + # Default to MSE loss against zeros (arbitrary target) + if output.dim() > 1 and output.size(1) > 1: + # Probably logits, use cross-entropy + target = torch.zeros(output.size(0), dtype=torch.long, device=device) + loss = F.cross_entropy(output, target) + else: + # Use MSE loss + target = torch.zeros_like(output) + loss = F.mse_loss(output, target) + else: + raise ValueError("Could not determine how to compute loss from model output") + + # Backward pass + loss.backward() + + except Exception as e: + print(f"Error during SNIP forward-backward pass: {e}") + # Restore original forwards + for name, module in prunable_modules: + if name in original_forwards: + module.forward = original_forwards[name] + + # Restore training mode + model.train(was_training) + return graph + + # For each prunable module, store the gradient of weight_mask as snip_scores + for name, module in prunable_modules: + grad = module.weight_mask.grad + if grad is not None: + # Calculate SNIP score: |weight * grad| + snip_scores = torch.abs(grad) + + # Store in module.metadata + if not hasattr(module, "metadata"): + module.metadata = {} + if "weight" not in module.metadata: + module.metadata["weight"] = {} + if "stats" not in module.metadata["weight"]: + module.metadata["weight"]["stats"] = {} + + # Store the SNIP saliency scores + module.metadata["weight"]["stats"]["snip_scores"] = snip_scores + + print(f"Module {name}: SNIP score norm = {snip_scores.norm().item()}") + else: + print(f"Module {name}: no SNIP score computed (grad is None)") + + # Restore original forward methods + for name, module in prunable_modules: + if name in original_forwards: + module.forward = original_forwards[name] + + # Update FX graph metadata with SNIP scores + for node in graph.fx_graph.nodes: + if node.op == "call_module": + module_name = node.target + module = getattr(graph.model, module_name, None) + + if module is not None and hasattr(module, "metadata"): + module_metadata = module.metadata + if "weight" in module_metadata and "stats" in module_metadata["weight"]: + if "snip_scores" in module_metadata["weight"]["stats"]: + # Add the scores to the node's metadata + if "software" not in node.meta.get("mase", {}).get("parameters", {}): + node.meta.setdefault("mase", {}).setdefault("parameters", {})["software"] = {"args": {}} + if "args" not in node.meta["mase"]["parameters"]["software"]: + node.meta["mase"]["parameters"]["software"]["args"] = {} + if "weight" not in node.meta["mase"]["parameters"]["software"]["args"]: + node.meta["mase"]["parameters"]["software"]["args"]["weight"] = {"stat": {}} + if "stat" not in node.meta["mase"]["parameters"]["software"]["args"]["weight"]: + node.meta["mase"]["parameters"]["software"]["args"]["weight"]["stat"] = {} + + # Store the SNIP scores + node.meta["mase"]["parameters"]["software"]["args"]["weight"]["stat"]["snip_scores"] = \ + module_metadata["weight"]["stats"]["snip_scores"] + + # Restore training mode + model.train(was_training) + + return graph + + +class SNIPCallback(TrainerCallback): + """Callback for applying SNIP sensitivity analysis during training. + + This callback can be used with a Hugging Face Trainer to compute and store + SNIP sensitivity scores in the module metadata during the first training step. + """ + + def __init__(self, representative_batch=None): + """Initialize the SNIP callback. + + Args: + representative_batch: Optional batch of data to use for SNIP computation. + If not provided, will use the first batch from the training dataloader. + """ + self.representative_batch = representative_batch + self.snip_computed = False + + def on_train_begin(self, args, state, control, **kwargs): + """Compute SNIP scores at the beginning of training. + + This method is called by the Hugging Face Trainer at the start of training. + It will compute SNIP scores for all prunable layers in the model. + + Args: + args: Arguments passed to the Trainer. + state: Current training state. + control: Training control variables. + **kwargs: Additional keyword arguments. + + Returns: + control: Updated training control variables. + """ + import types + import torch.nn as nn + import torch.nn.functional as F + + model = kwargs.get("model", None) + if model is None: + raise ValueError("No model provided to SNIP callback.") + + device = next(model.parameters()).device + + # Save the original forward methods of all prunable modules. + original_forwards = {} + for name, module in model.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight"): + original_forwards[name] = module.forward + + # Create weight_mask parameter if not present + if not hasattr(module, "weight_mask"): + module.weight_mask = nn.Parameter(torch.ones_like(module.weight)) + + # Override forward methods to use weight_mask + if isinstance(module, nn.Conv2d): + def new_forward(self, x): + return F.conv2d( + x, + self.weight.detach() * self.weight_mask, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + module.forward = types.MethodType(new_forward, module) + elif isinstance(module, nn.Linear): + def new_forward(self, x): + return F.linear(x, self.weight.detach() * self.weight_mask, self.bias) + module.forward = types.MethodType(new_forward, module) + + # Get a batch of data + inputs = None + + # Option 1: Use representative batch passed during initialization + if self.representative_batch is not None: + print("Using provided representative batch for SNIP computation") + inputs = self.representative_batch + for k, v in inputs.items(): + if isinstance(v, torch.Tensor): + inputs[k] = v.to(device) + + # Option 2: Get the first batch from the trainer's dataloader + elif hasattr(kwargs.get("trainer", {}), "get_train_dataloader"): + print("Using first batch from trainer's dataloader for SNIP computation") + train_dataloader = kwargs["trainer"].get_train_dataloader() + try: + inputs = next(iter(train_dataloader)) + for k, v in inputs.items(): + if isinstance(v, torch.Tensor): + inputs[k] = v.to(device) + except StopIteration: + raise ValueError("Empty training dataloader in SNIP callback") + + # Option 3: Use the train_dataloader provided in kwargs + elif kwargs.get("train_dataloader") is not None: + print("Using provided train_dataloader for SNIP computation") + try: + inputs = next(iter(kwargs["train_dataloader"])) + for k, v in inputs.items(): + if isinstance(v, torch.Tensor): + inputs[k] = v.to(device) + except StopIteration: + raise ValueError("Empty training dataloader in SNIP callback") + + # No data found + if inputs is None: + raise ValueError( + "No data available for SNIP computation. Please provide either:" + "\n1. A representative_batch when initializing SNIPCallback" + "\n2. Ensure the trainer has a working get_train_dataloader method" + "\n3. Pass train_dataloader in kwargs" + ) + + # Run one forward-backward pass + model.zero_grad() + output = model(**inputs) + loss = output.get("loss", None) + if loss is None: + raise ValueError("Loss is None in SNIP callback; ensure your model returns a loss.") + + loss.backward() + + # For each prunable module, store the gradient of weight_mask in metadata. + for name, module in model.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)) and hasattr(module, "weight_mask"): + grad = module.weight_mask.grad + if grad is not None: + if not hasattr(module, "metadata"): + module.metadata = {} + if "weight" not in module.metadata: + module.metadata["weight"] = {} + if "stats" not in module.metadata["weight"]: + module.metadata["weight"]["stats"] = {} + + # Store the SNIP scores (absolute gradient values) + module.metadata["weight"]["stats"]["snip_scores"] = grad.abs().detach().clone() + print(f"Module {name}: SNIP score norm = {grad.abs().norm().item()}") + else: + print(f"Module {name}: no SNIP score computed (grad is None)") + + # Restore the original forward methods. + for name, module in model.named_modules(): + if name in original_forwards: + module.forward = original_forwards[name] + + # Mark SNIP as computed so we don't recompute + self.snip_computed = True + + return control + + # Implement required TrainerCallback methods + def on_epoch_begin(self, args, state, control, **kwargs): + """Called at the beginning of an epoch.""" + return control + + def on_epoch_end(self, args, state, control, **kwargs): + """Called at the end of an epoch.""" + return control + + def on_step_begin(self, args, state, control, **kwargs): + """Called at the beginning of a training step.""" + return control + + def on_step_end(self, args, state, control, **kwargs): + """Called at the end of a training step.""" + return control + + def on_evaluate(self, args, state, control, **kwargs): + """Called after evaluation.""" + return control + + def on_save(self, args, state, control, **kwargs): + """Called when saving the model.""" + return control + + def on_log(self, args, state, control, **kwargs): + """Called when logging metrics.""" + return control + + def on_prediction_step(self, args, state, control, **kwargs): + """Called during evaluation on a prediction step.""" + return control + + def on_train_end(self, args, state, control, **kwargs): + """Called at the end of training.""" + return control \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/quantize/__init__.py b/src/chop/passes/graph/transforms/quantize/__init__.py index e2e6643de..37c0efb9d 100644 --- a/src/chop/passes/graph/transforms/quantize/__init__.py +++ b/src/chop/passes/graph/transforms/quantize/__init__.py @@ -1,2 +1,3 @@ from .quantize import QUANTIZEABLE_OP, quantize_transform_pass from .summary import summarize_quantization_analysis_pass +from .flexround import FlexRoundQuantizer, FlexRoundQuantizedLayer, quantize_model_with_flexround, apply_flexround_transform diff --git a/src/chop/passes/graph/transforms/quantize/flexround.py b/src/chop/passes/graph/transforms/quantize/flexround.py new file mode 100644 index 000000000..29d4f5ea7 --- /dev/null +++ b/src/chop/passes/graph/transforms/quantize/flexround.py @@ -0,0 +1,255 @@ +import torch +import math +import torch.nn as nn + +class FlexRoundQuantizer(nn.Module): + """ + FlexRound quantization module for MASE + + This module implements the FlexRound quantization technique, which uses + a fixed-point representation with configurable bit-width and fraction width. + """ + def __init__(self, bit_width=8, frac_width=4, symmetric=True): + """ + Initialize FlexRound quantizer with configurable parameters. + + Args: + bit_width (int): Total number of bits for quantization + frac_width (int): Number of fractional bits for fixed-point representation + symmetric (bool): Whether to use symmetric quantization + """ + super().__init__() + self.bit_width = bit_width + self.frac_width = frac_width + self.symmetric = symmetric + self.scale = 2.0 ** frac_width + + # Calculate quantization bounds + if symmetric: + self.min_val = -2**(bit_width-1) / self.scale + self.max_val = (2**(bit_width-1) - 1) / self.scale + else: + self.min_val = 0 + self.max_val = (2**bit_width - 1) / self.scale + + def forward(self, x): + """ + Apply FlexRound quantization to input tensor. + + Args: + x (torch.Tensor): Input tensor to quantize + + Returns: + torch.Tensor: Quantized tensor + """ + # Skip quantization for very small tensors + if x.numel() < 10: + return x + + # Check if tensor has enough significant values to quantize + if x.abs().max() < 1e-5: + return x + + # Clamp values to representable range + x_clamped = torch.clamp(x, self.min_val, self.max_val) + + # Apply fixed-point quantization + scaled = x_clamped * self.scale + rounded = torch.round(scaled) + quantized = rounded / self.scale + + return quantized + + def get_config(self): + """Return the configuration parameters of the quantizer""" + return { + "bit_width": self.bit_width, + "frac_width": self.frac_width, + "symmetric": self.symmetric, + "scale": self.scale, + "min_val": self.min_val, + "max_val": self.max_val + } + +class FlexRoundQuantizedLayer(nn.Module): + """ + A module wrapper that applies FlexRound quantization to a layer's weights and/or activations + """ + def __init__(self, + module, + weight_width=8, + weight_frac_width=4, + data_in_width=8, + data_in_frac_width=4, + weight_only=False): + """ + Initialize a FlexRound quantized layer. + + Args: + module (nn.Module): The module to quantize + weight_bit_width (int): Bit width for weight quantization + weight_frac_width (int): Fraction width for weight quantization + act_bit_width (int): Bit width for activation quantization + act_frac_width (int): Fraction width for activation quantization + weight_only (bool): If True, only quantize weights (not activations) + """ + super().__init__() + self.module = module + self.weight_quantizer = FlexRoundQuantizer(weight_width, weight_frac_width) + + if not weight_only: + self.act_quantizer = FlexRoundQuantizer(data_in_width, data_in_frac_width) + else: + self.act_quantizer = None + + # Initialize to apply quantization + self.apply_weight_quant = True + self.apply_act_quant = not weight_only + + def forward(self, x): + """Apply quantization to weights and forward pass""" + # Backup original weights + if hasattr(self.module, 'weight'): + orig_weight = self.module.weight.data.clone() + + # Apply weight quantization + if self.apply_weight_quant: + self.module.weight.data = self.weight_quantizer(orig_weight) + + # Run forward pass + out = self.module(x) + + # Restore original weights + self.module.weight.data = orig_weight + else: + out = self.module(x) + + # Apply activation quantization if enabled + if self.apply_act_quant and self.act_quantizer is not None: + out = self.act_quantizer(out) + + return out + + def set_quant_state(self, weight_quant=None, act_quant=None): + """Enable/disable quantization for weights and/or activations""" + if weight_quant is not None: + self.apply_weight_quant = weight_quant + if act_quant is not None: + self.apply_act_quant = act_quant + +def quantize_model_with_flexround(model, config): + """ + Apply FlexRound quantization to a model based on the provided configuration. + + Args: + model (nn.Module): The model to quantize + config (dict): Configuration specifying how to quantize the model + + Returns: + nn.Module: The quantized model + """ + # Create a copy of the model to avoid modifying the original + quantized_model = type(model)(model.config) if hasattr(model, 'config') else model + + # Default configuration + default_config = config.get('default', {}) + default_weight_bit_width = default_config.get('weight_width', 8) + default_weight_frac_width = default_config.get('weight_frac_width', 4) + default_act_bit_width = default_config.get('data_in_width', 8) + default_act_frac_width = default_config.get('data_in_frac_width', 4) + default_weight_only = default_config.get('weight_only', False) + + # Track quantized modules + quantized_modules = {} + + # Replace modules with quantized versions according to configuration + for name, module in model.named_modules(): + # Skip the top level module + if name == '': + continue + + # Check if this module has a specific configuration + if name in config: + module_config = config[name] + weight_bit_width = module_config.get('weight_width', default_weight_bit_width) + weight_frac_width = module_config.get('weight_frac_width', default_weight_frac_width) + act_bit_width = module_config.get('data_in_width', default_act_bit_width) + act_frac_width = module_config.get('data_in_frac_width', default_act_frac_width) + weight_only = module_config.get('weight_only', default_weight_only) + + # Replace with quantized version if it has weights + if hasattr(module, 'weight'): + parent_name = name.rsplit('.', 1)[0] if '.' in name else '' + child_name = name.rsplit('.', 1)[1] if '.' in name else name + + parent = model if parent_name == '' else getattr(model, parent_name) + + quantized_module = FlexRoundQuantizedLayer( + module, + weight_frac_width, + act_bit_width, + act_frac_width, + weight_only + ) + + # Replace the module + setattr(parent, child_name, quantized_module) + quantized_modules[name] = quantized_module + + print(f"Applied FlexRound quantization to {len(quantized_modules)} modules") + return quantized_model + + +# For use with the MASE framework +def apply_flexround_transform(graph, config): + """ + Apply FlexRound quantization to a MASE graph. + + Args: + graph: The MASE graph to apply quantization to + config: Configuration dict with quantization parameters + + Returns: + The quantized graph + """ + # Default config + default_config = config.get('default', {}) + weight_bit_width = default_config.get('weight_bit_width', 8) + weight_frac_width = default_config.get('weight_frac_width', 4) + + # Apply quantization to each module + for node in graph.fx_graph.nodes: + if node.op == 'call_module': + module = graph.modules[node.target] + module_name = node.target + + # Get module-specific config if available + if module_name in config: + module_config = config[module_name] + this_weight_bit_width = module_config.get('weight_bit_width', weight_bit_width) + this_weight_frac_width = module_config.get('weight_frac_width', weight_frac_width) + else: + this_weight_bit_width = weight_bit_width + this_weight_frac_width = weight_frac_width + + # Check if the module has weights to quantize + if hasattr(module, 'weight'): + # Apply quantization by wrapping the module weight parameter + quantizer = FlexRoundQuantizer( + bit_width=this_weight_bit_width, + frac_width=this_weight_frac_width + ) + + # Create a forward hook to quantize weights during inference + def make_quant_hook(mod, quantizer): + def hook(module, inputs): + module.weight.data = quantizer(module.weight.data) + return None + return hook + + # Register the pre-forward hook to quantize weights + module.register_forward_pre_hook(make_quant_hook(module, quantizer)) + + print(f"Applied FlexRound quantization to {module_name} with {this_weight_bit_width} bits") + + return graph, {} \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/quantize/quant_parsers/__init__.py b/src/chop/passes/graph/transforms/quantize/quant_parsers/__init__.py index 72a5b3b68..16a3d01bb 100644 --- a/src/chop/passes/graph/transforms/quantize/quant_parsers/__init__.py +++ b/src/chop/passes/graph/transforms/quantize/quant_parsers/__init__.py @@ -1,2 +1,2 @@ from .parse_quant_config import parse_node_config -from .update_node_meta import relink_node_meta, update_quant_meta_param +from .update_node_meta import relink_node_meta, update_quant_meta_param \ No newline at end of file diff --git a/src/chop/passes/graph/transforms/quantize/quant_parsers/parse_quant_config.py b/src/chop/passes/graph/transforms/quantize/quant_parsers/parse_quant_config.py index e027b0819..2bdd67cc4 100644 --- a/src/chop/passes/graph/transforms/quantize/quant_parsers/parse_quant_config.py +++ b/src/chop/passes/graph/transforms/quantize/quant_parsers/parse_quant_config.py @@ -23,6 +23,11 @@ "data_in_entries": ("data_in_width", "data_in_frac_width"), "bias_entries": ("bias_width", "bias_frac_width"), }, + "flexround": { + "weight_entries": ("weight_width", "weight_frac_width"), + "data_in_entries": ("data_in_width", "data_in_frac_width"), + "bias_entries": ("bias_width", "bias_frac_width"), + }, "fixed": { "weight_entries": ("weight_width", "weight_frac_width"), "data_in_entries": ("data_in_width", "data_in_frac_width"), diff --git a/src/chop/passes/graph/transforms/quantize/quant_parsers/update_node_meta.py b/src/chop/passes/graph/transforms/quantize/quant_parsers/update_node_meta.py index 0c580c4f2..a7201eae3 100644 --- a/src/chop/passes/graph/transforms/quantize/quant_parsers/update_node_meta.py +++ b/src/chop/passes/graph/transforms/quantize/quant_parsers/update_node_meta.py @@ -8,6 +8,7 @@ def entry_to_list(config: dict, entry: str, suffixes: tuple[str]): QUANT_ARITH_TO_SUFFIXES = { "integer": ("width", "frac_width"), + "flexround": ("width", "frac_width"), "fixed": ("width", "frac_width"), "binary": ( "width", diff --git a/src/chop/tools/huggingface.py b/src/chop/tools/huggingface.py index 7cf675460..0318b6668 100644 --- a/src/chop/tools/huggingface.py +++ b/src/chop/tools/huggingface.py @@ -9,8 +9,9 @@ Trainer, TrainingArguments, PreTrainedTokenizer, + Wav2Vec2Processor, ) -from datasets import load_dataset, DatasetDict +from datasets import load_dataset, DatasetDict, Dataset import evaluate from chop.tools import get_logger @@ -56,39 +57,205 @@ def get_tokenized_dataset( dataset: str, checkpoint: str, return_tokenizer: bool = False, + return_processor: bool = False, + text_column: str = "text", + tokenizer_checkpoint: str = None, ): """ - Tokenizes a dataset using the AutoTokenizer from Huggingface. + Tokenizes a dataset using the appropriate tokenizer from Huggingface. Args: - dataset: str Name of the dataset to tokenize. - checkpoint: str - Name of the checkpoint to use for tokenization. + Name of the checkpoint to use for model. - return_tokenizer: bool Whether to return the tokenizer used for tokenization. + - return_processor: bool + Whether to return the processor used for tokenization (for speech models). + - text_column: str + Name of the column containing the text to tokenize (default: "text"). + - tokenizer_checkpoint: str + Optional different checkpoint to use for tokenizer. If None, uses the same as model checkpoint. Returns: - tokenized_datasets: DatasetDict Tokenized dataset. - tokenizer: PreTrainedTokenizer Tokenizer used for tokenization. Only returned if return_tokenizer is True. + - processor: PreTrainedProcessor + Returns processor, which is tokenizer combined with feature extractor, useful for Wav2Vec2 models. """ - logger.info(f"Tokenizing dataset {dataset} with AutoTokenizer for {checkpoint}.") + # Use tokenizer_checkpoint if provided, otherwise use model checkpoint + tokenizer_ckpt = tokenizer_checkpoint if tokenizer_checkpoint else checkpoint + logger.info(f"Tokenizing dataset {dataset} with tokenizer from {tokenizer_ckpt} for model {checkpoint}.") - # Load and tokenize datasets - raw_datasets = load_dataset(dataset) - tokenizer = AutoTokenizer.from_pretrained(checkpoint) + # Check if this is a wav2vec2 or speech model + is_speech_model = "wav2vec2" in checkpoint.lower() or (tokenizer_checkpoint and "wav2vec2" in tokenizer_checkpoint.lower()) + + if is_speech_model: + logger.info(f"Detected speech model. Using Wav2Vec2Processor for tokenization.") + processor = Wav2Vec2Processor.from_pretrained(tokenizer_ckpt) + tokenizer = processor.tokenizer + + # Determine if this is a LibriSpeech dataset + is_librispeech = "librispeech" in dataset.lower() or "asr" in dataset.lower() + + if is_librispeech: + logger.info(f"Processing LibriSpeech-like ASR dataset: {dataset}") + + # Load all available splits + raw_datasets_dict = {} + available_splits = ["train.clean.100", "validation.clean", "test.clean"] + + # Try specific splits for the condensed_librispeech dataset + for split in available_splits: + try: + logger.info(f"Loading split: {split}") + split_dataset = load_dataset(dataset, split=split, trust_remote_code=True) + raw_datasets_dict[split.replace('.', '_')] = split_dataset + logger.info(f"Successfully loaded split: {split}") + except Exception as e: + logger.warning(f"Could not load split {split}: {e}") + + # If no specific splits could be loaded, try to load the dataset without specifying splits + if not raw_datasets_dict: + try: + logger.info("Attempting to load dataset without specific split") + full_dataset = load_dataset(dataset, trust_remote_code=True) + + if isinstance(full_dataset, dict) or isinstance(full_dataset, DatasetDict): + for split_name, split_dataset in full_dataset.items(): + raw_datasets_dict[split_name] = split_dataset + logger.info(f"Loaded split: {split_name}") + else: + # If it's a single dataset, just use it as the training set + raw_datasets_dict["train"] = full_dataset + logger.info("Loaded dataset as single training set") + except Exception as e: + logger.error(f"Failed to load dataset {dataset}: {e}") + raise ValueError(f"Could not load dataset {dataset}") + + if not raw_datasets_dict: + logger.error(f"Failed to load any splits for dataset {dataset}") + raise ValueError(f"Could not load any splits for dataset {dataset}") + + # Create a dictionary to hold the tokenized datasets + tokenized_datasets_dict = {} + + # Process each split + for split_name, split_dataset in raw_datasets_dict.items(): + logger.info(f"Processing split: {split_name}") + + # Check which columns are available in the dataset + available_columns = split_dataset.column_names + columns_to_remove = [] + for col in ["speaker_id", "file", "id", "chapter_id", "audio"]: + if col in available_columns: + columns_to_remove.append(col) + + # Define preprocessing function for this dataset + def preprocess_function(example): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + + # Map the preprocessing function + logger.info(f"Preprocessing split {split_name}") + tokenized_dataset = split_dataset.map( + preprocess_function, + remove_columns=columns_to_remove + ) + + # Add to the dictionary with standardized split names + if "train" in split_name: + tokenized_datasets_dict["train"] = tokenized_dataset + elif "val" in split_name or "validation" in split_name: + tokenized_datasets_dict["validation"] = tokenized_dataset + elif "test" in split_name: + tokenized_datasets_dict["test"] = tokenized_dataset + else: + # Keep original name if it doesn't match standard patterns + tokenized_datasets_dict[split_name] = tokenized_dataset + + # Make sure we have at least train and test splits + if "train" not in tokenized_datasets_dict: + # Use the first available split as train + first_split = list(tokenized_datasets_dict.keys())[0] + train_data = tokenized_datasets_dict[first_split] + if "test" in tokenized_datasets_dict: + # Already have test, just add train + tokenized_datasets_dict["train"] = train_data + else: + # Split the data into train and test + train_test_split = train_data.train_test_split(test_size=0.2) + tokenized_datasets_dict["train"] = train_test_split["train"] + tokenized_datasets_dict["test"] = train_test_split["test"] + elif "test" not in tokenized_datasets_dict and "validation" not in tokenized_datasets_dict: + # Need to create a test set + train_data = tokenized_datasets_dict["train"] + train_test_split = train_data.train_test_split(test_size=0.2) + tokenized_datasets_dict["train"] = train_test_split["train"] + tokenized_datasets_dict["test"] = train_test_split["test"] + + # Create the final DatasetDict + tokenized_datasets = DatasetDict(tokenized_datasets_dict) + logger.info(f"Created dataset with splits: {list(tokenized_datasets.keys())}") + + if return_processor and return_tokenizer: + return tokenized_datasets, tokenizer, processor + elif return_tokenizer: + return tokenized_datasets, tokenizer + elif return_processor: + return tokenized_datasets, processor + else: + return tokenized_datasets + else: + logger.error(f"Dataset type not recognized for speech model: {dataset}") + raise NotImplementedError(f"Dataset {dataset} not implemented for speech models yet") + + # Standard text datasets (non-speech models) + logger.info(f"Using AutoTokenizer for text dataset tokenization") + tokenizer = AutoTokenizer.from_pretrained(tokenizer_ckpt) + + # Load and tokenize datasets + try: + raw_datasets = load_dataset(dataset) + except Exception as e: + logger.warning(f"Failed to load dataset directly: {e}") + try: + # Try with streaming + logger.info("Attempting to load with streaming") + raw_datasets = load_dataset(dataset, streaming=True) + # Convert streaming dataset to regular dataset + dataset_dict = {} + for split_name, split_dataset in raw_datasets.items(): + dataset_dict[split_name] = Dataset.from_list(list(split_dataset)) + raw_datasets = DatasetDict(dataset_dict) + except Exception as e2: + logger.error(f"Failed to load dataset: {e2}") + raise ValueError(f"Could not load dataset {dataset}") + def tokenize_function(example): return tokenizer( - example["text"], + example[text_column] if text_column in example else example["text"], truncation=True, ) - + # Tokenize tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) - + if return_tokenizer: return tokenized_datasets, tokenizer else: @@ -100,10 +267,16 @@ def get_trainer( tokenized_dataset: DatasetDict, tokenizer: PreTrainedTokenizer, evaluate_metric: str = "accuracy", + data_collator = None, output_dir: str = "mase-trainer", use_mps_device: bool = False, report_to: str = "none", num_train_epochs: int = 1, + gradient_accumulation_steps: int = 1, + per_device_train_batch_size: int = 8, + per_device_eval_batch_size: int = 8, + decoder = None, + beam_width = 10, ): """ Returns a Trainer object for a given model and tokenized dataset. @@ -143,6 +316,37 @@ def compute_accuracy(eval_pred): return metric.compute(predictions=predictions, references=labels) metric_fn = compute_accuracy + + elif evaluate_metric == "wer": + def compute_wer(eval_pred): + raw_logits = eval_pred.predictions[0] + labels = eval_pred.label_ids + + pred_texts = [] + + for i in range(raw_logits.shape[0]): + sample_logits = torch.from_numpy(raw_logits[i]) + sample_log_probs = sample_logits.log_softmax(dim=-1).cpu().numpy() + + if decoder is not None: + transcription = decoder.decode(sample_log_probs, beam_width=beam_width) + else: + greedy_ids = np.argmax(sample_log_probs, axis=-1) + transcription = tokenizer.decode(greedy_ids, skip_special_tokens=True) + + pred_texts.append(transcription.lower()) + + # Decode each label individually, filtering out the padding (-100) + label_texts = [] + for label_seq in labels: + label_filtered = [token for token in label_seq if token != -100] + label_text = tokenizer.decode(label_filtered, skip_special_tokens=True) + label_texts.append(label_text.lower()) + + return {"wer": metric.compute(predictions=pred_texts, references=label_texts)} + + metric_fn = compute_wer + else: raise NotImplementedError(f"Metric {metric} not implemented.") @@ -152,16 +356,42 @@ def compute_accuracy(eval_pred): use_mps_device=use_mps_device, report_to=report_to, num_train_epochs=num_train_epochs, + gradient_accumulation_steps=gradient_accumulation_steps, + per_device_train_batch_size=per_device_train_batch_size, + per_device_eval_batch_size=per_device_eval_batch_size, + # remove_unused_columns=False, ) + if data_collator is None: + data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + trainer = Trainer( model, training_args, train_dataset=tokenized_dataset["train"], eval_dataset=tokenized_dataset["test"], - data_collator=DataCollatorWithPadding(tokenizer=tokenizer), + data_collator=data_collator, tokenizer=tokenizer, compute_metrics=metric_fn, ) return trainer + + +def preprocess_librispeech_asr(example, processor): + audio_array = example["audio"]["array"] + sampling_rate = example["audio"]["sampling_rate"] + + inputs = processor(audio=audio_array, sampling_rate=int(sampling_rate), return_tensors="pt", padding=True) + attention_mask = torch.ones(inputs.input_values.shape, dtype=torch.long) + + with processor.as_target_processor(): + labels = processor.tokenizer(example["text"], return_tensors="pt").input_ids + + return { + "input_values": inputs.input_values.squeeze(0), + "attention_mask": attention_mask.squeeze(0), + "labels": labels.squeeze(0) + } + +