From ca67376ccefee2a1d587c99b90114793013047b6 Mon Sep 17 00:00:00 2001 From: eric80116 Date: Sat, 30 Sep 2023 15:37:19 +0800 Subject: [PATCH 1/2] Add the scripts of compiling and inferencing Stable Diffusion Inpaint model on Inf2 --- .DS_Store | Bin 0 -> 6148 bytes inference/.DS_Store | Bin 0 -> 6148 bytes inference/stable-diffusion/.DS_Store | Bin 0 -> 6148 bytes .../StableDiffusion2_1-inpaint.ipynb | 11896 ++++++++++++++++ .../stable-diffusion/src-inpaint/compile.py | 193 + .../stable-diffusion/src-inpaint/inference.py | 155 + .../src-inpaint/requirements.txt | 6 + .../stable-diffusion/src-inpaint/wrapper.py | 76 + 8 files changed, 12326 insertions(+) create mode 100644 .DS_Store create mode 100644 inference/.DS_Store create mode 100644 inference/stable-diffusion/.DS_Store create mode 100644 inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb create mode 100644 inference/stable-diffusion/src-inpaint/compile.py create mode 100644 inference/stable-diffusion/src-inpaint/inference.py create mode 100644 inference/stable-diffusion/src-inpaint/requirements.txt create mode 100644 inference/stable-diffusion/src-inpaint/wrapper.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e36157074c90fdbacce4a946ccba21839d0c4ae7 GIT binary patch literal 6148 zcmeHKU279T6upy1v)dvKqRkxS5oJGF{C&hLXTCj()-J7pSy9f$rF*g+SOu&CS4{yvA3Qi?-QY~4Iy#W3 zCjhXB&&rVJzX#0m4Au?KG@=G3G!>|+!aOmArsKP3+SLutG-^5t^Y{>EWMN(?LXD38 zJ=L5KjpX!LaIRF3v literal 0 HcmV?d00001 diff --git a/inference/.DS_Store b/inference/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..ab93afb69477aa24cd44c3a04ec48f2f5102e4d1 GIT binary patch literal 6148 zcmeHK!EVz)5S>i}byOkc0I0{6dQE7WB1FZ-gmUP$uHZmXuyIDW+r0=_&Ap=XNhQpOf4q;PFfI z8vg9JHS{=EV)kRy$&uZq6t$1=n;@PMYW~Il@vr=3TAum)|4h#EtgL#yU#hXveDH8J zSPj;KkI@%1jjFhsl!JKufv*mX$&*DtPTpms>9GCyjVY=)D@J2W$kGu~4&P@*YNi7- zDbmt%BR3GVg4VFTKAZJl?sW8ae{0^+vz>mgqn~fTn9p0mla0++`@@s`v@qYS5~T2- zu=dE~0=}bjqfTF=agm$i7`cl&3%;NzAPR^Aw^hKeE5X`r3khEo5C!g?0=zzGIAiFs zc4(IlH1-GpxzHc_RCH>=`?LcI*rhiP1bB5Os*iL1JuPK=u>ic1~wx&(ySl zg8Pt@D3uX3?#_y3z%sDw4Dh$xpps@Zd5~^=zjx4b@J)%DL|a?YHA*O_fk9?Qi@&hxlbzJYFVT25=+=`NSUTSqI9`Q5j86U_2d=EoSBXWONKDy(lrDNV`8I={j*$NDEEvGzk6 z(|``374#N*bZQTLKp#Ku9H>@ykO8x^o%RmCn|&qAfMwv{GQj79i^S*|tTn2m1DOf| zfK5~@L0SF$16{5FdIoEasDTLW3e>K`gc!oa4nn)5-81&JM(s{Qjf{TG$ihS@!h{~e zRpul-jW)LoSOzv3XlS*=_y4`^@&Bfi)hq*+f&YpD(ewju62=KBC)Vt ntx>rkGuN>y@KxMJQi3*@3qa3ctr0a4`y-%eu$g7xuQKo*vZJ3? literal 0 HcmV?d00001 diff --git a/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb b/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb new file mode 100644 index 0000000..dc46516 --- /dev/null +++ b/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb @@ -0,0 +1,11896 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "76c7f449-c44e-4a5d-a344-e030018b5fc1", + "metadata": {}, + "source": [ + "# Deploy & Run Stable Diffusion Inpaint on SageMaker and Inferentia2\n", + "\n", + "**SageMaker Studio Kernel: Data Science 3.0 (ml.t3.medium)**\n", + "\n", + "Stable Diffusion is a transformer model that generates random images out of textual prompts (description of the scene). You can get more information of the model implementation at: https://github.com/Stability-AI/\n", + "\n", + "This sample shows how to compile & deploy a pre-trained [HF Stable Diffusion 2 Inpaint](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) to [Inferentia2](https://aws.amazon.com/ec2/instance-types/inf2/) using SageMaker. You need to run two steps to complete this task: 1) A SageMaker Training Job using a Trainium instance for compiling the model and; 2) create a SageMaker real-time endpoint, hosted on an Inferentia2 instance, to deploy and invoke your model.\n", + "\n", + "**Compilation:** First you'll kick-off a SageMaker training job on a **trn1.32xlarge** instance. It requires NeuronX Compiler v2.9 to compile the model. It takes ~22mins with a trn1.32xlarge. (Estimated compilation cost on 2023 Sep 30 - us-east-1 ml.trn1.32xlarge \\\\$24.73/h ::: 22mins=$9.02). You compile the model once and deploy & run as many times as you need.\n", + "\n", + "**Inference:** After compiling the model it is time to deploy. You'll create a SageMaker real-time Endpoint hosted on an **inf2** instance. SageMaker exposes your model as a webservice and allow you to invoke it with a simple API call.\n", + "\n", + "\n", + "The compilation mechanism supports datatypes in FP32 or BF16. BF16 will give you a lower latency and it is selected by default." + ] + }, + { + "cell_type": "markdown", + "id": "7e1f0d48-059a-4170-9f00-2b3ee76cee39", + "metadata": { + "tags": [] + }, + "source": [ + "## 1) Install some dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2084775d-958a-4c6d-9f23-9b946f630008", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: sagemaker in /opt/conda/lib/python3.10/site-packages (2.184.0)\n", + "Collecting sagemaker\n", + " Downloading sagemaker-2.188.0.tar.gz (892 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m892.2/892.2 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: attrs<24,>=23.1.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (23.1.0)\n", + "Requirement already satisfied: boto3<2.0,>=1.26.131 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.28.42)\n", + "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (2.2.1)\n", + "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.2.0)\n", + "Requirement already satisfied: numpy<2.0,>=1.9.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.25.2)\n", + "Requirement already satisfied: protobuf<5.0,>=3.12 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.24.2)\n", + "Requirement already satisfied: smdebug_rulesconfig==1.0.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.0.1)\n", + "Requirement already satisfied: importlib-metadata<7.0,>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.11.3)\n", + "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (21.3)\n", + "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.4.4)\n", + "Requirement already satisfied: pathos in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.3.1)\n", + "Requirement already satisfied: schema in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.7.5)\n", + "Requirement already satisfied: PyYAML~=6.0 in /opt/conda/lib/python3.10/site-packages/PyYAML-6.0-py3.10-linux-x86_64.egg (from sagemaker) (6.0)\n", + "Requirement already satisfied: jsonschema in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.19.0)\n", + "Requirement already satisfied: platformdirs in /opt/conda/lib/python3.10/site-packages (from sagemaker) (2.5.2)\n", + "Requirement already satisfied: tblib==1.7.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.7.0)\n", + "Requirement already satisfied: botocore<1.32.0,>=1.31.42 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (1.31.42)\n", + "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (0.10.0)\n", + "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (0.6.0)\n", + "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.10/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.8.0)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->sagemaker) (3.0.9)\n", + "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from google-pasta->sagemaker) (1.16.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (0.10.2)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2022.1)\n", + "Requirement already satisfied: ppft>=1.7.6.7 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (1.7.6.7)\n", + "Requirement already satisfied: dill>=0.3.7 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.3.7)\n", + "Requirement already satisfied: pox>=0.3.3 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.3.3)\n", + "Requirement already satisfied: multiprocess>=0.70.15 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.70.15)\n", + "Requirement already satisfied: contextlib2>=0.5.5 in /opt/conda/lib/python3.10/site-packages (from schema->sagemaker) (21.6.0)\n", + "Collecting urllib3<1.27,>=1.25.4 (from botocore<1.32.0,>=1.31.42->boto3<2.0,>=1.26.131->sagemaker)\n", + " Obtaining dependency information for urllib3<1.27,>=1.25.4 from https://files.pythonhosted.org/packages/c5/05/c214b32d21c0b465506f95c4f28ccbcba15022e000b043b72b3df7728471/urllib3-1.26.16-py2.py3-none-any.whl.metadata\n", + " Using cached urllib3-1.26.16-py2.py3-none-any.whl.metadata (48 kB)\n", + "Using cached urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n", + "Building wheels for collected packages: sagemaker\n", + " Building wheel for sagemaker (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for sagemaker: filename=sagemaker-2.188.0-py2.py3-none-any.whl size=1193902 sha256=9363f36c103f4d81b0ab5eb19315d02f68706f68e5eba7587dff04ae8d20f130\n", + " Stored in directory: /root/.cache/pip/wheels/94/00/3e/af9dde735e7ee826b11724be1de1a009a4179984f236a7a928\n", + "Successfully built sagemaker\n", + "Installing collected packages: urllib3, sagemaker\n", + " Attempting uninstall: urllib3\n", + " Found existing installation: urllib3 2.0.4\n", + " Uninstalling urllib3-2.0.4:\n", + " Successfully uninstalled urllib3-2.0.4\n", + " Attempting uninstall: sagemaker\n", + " Found existing installation: sagemaker 2.184.0\n", + " Uninstalling sagemaker-2.184.0:\n", + " Successfully uninstalled sagemaker-2.184.0\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", + "distributed 2022.7.0 requires tornado<6.2,>=6.0.3, but you have tornado 6.3.3 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed sagemaker-2.188.0 urllib3-1.26.16\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -U sagemaker" + ] + }, + { + "cell_type": "markdown", + "id": "04e3b84f-87ae-4d2b-9f5d-9aaea1cda18a", + "metadata": {}, + "source": [ + "## 2) Initialize variables\n", + "Not all regions have trn1 and inf2 instances available at the time this notebook was published. us-east-1 has trn1 instances and us-east-2 has inf2 instances. That way, we need to create two sagemaker sessions: 1/ for compiling the model (us-east-1); 2/ for deploying the model (us-east-2)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9d5d9d4d-b3ed-4e7c-968b-c0c95c41f028", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", + "2.188.0\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", + "sagemaker role arn: arn:aws:iam::772327914095:role/service-role/AmazonSageMaker-ExecutionRole-20220822T202655\n", + "sagemaker bucket trn1: sagemaker-us-east-1-772327914095\n", + "sagemaker bucket trn1: sagemaker-us-east-2-772327914095\n", + "sagemaker session regions. trn1: us-east-1 inf2: us-east-2\n" + ] + } + ], + "source": [ + "import boto3\n", + "import sagemaker\n", + "import numpy as np\n", + "\n", + "print(sagemaker.__version__)\n", + "if not sagemaker.__version__ >= \"2.146.0\": print(\"You need to upgrade or restart the kernel if you already upgraded\")\n", + "\n", + "region_trn1='us-east-1'\n", + "boto3_sess_trn1 = boto3.Session(region_name=region_trn1) # trn1 session\n", + "sess_trn1 = sagemaker.Session(boto3_sess_trn1)\n", + "\n", + "region_inf2='us-east-2'\n", + "boto3_sess_inf2 = boto3.Session(region_name=region_inf2) # inf2 session\n", + "sess_inf2 = sagemaker.Session(boto3_sess_inf2)\n", + "\n", + "bucket_trn1 = sess_trn1.default_bucket()\n", + "bucket_inf2 = sess_inf2.default_bucket()\n", + "role = sagemaker.get_execution_role()\n", + "\n", + "# https://github.com/aws/deep-learning-containers/blob/master/available_images.md#neuron-containers\n", + "train_image_name=\"pytorch-training-neuronx\"\n", + "inference_image_name=\"pytorch-inference-neuronx\"\n", + "# We need SDK2.13+ to deal with SD Inpaint\n", + "image_tag=\"1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04-v1.0\"\n", + "\n", + "print(f\"sagemaker role arn: {role}\")\n", + "print(f\"sagemaker bucket trn1: {bucket_trn1}\")\n", + "print(f\"sagemaker bucket trn1: {bucket_inf2}\")\n", + "print(f\"sagemaker session regions. trn1: {region_trn1} inf2: {region_inf2}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c1bf0a88-f94a-4261-822b-25ede4f4d066", + "metadata": {}, + "source": [ + "## 3) Visualize scripts\n", + "We have 3 scripts that will do the job. \n", + " - src-inpaint/wrapper.py: Helper class created to wrap the model and expose the parts that we will compile to inf2. It is also a way to put everything back together to compose a pipeline.\n", + " - src-inpaint/compile.py: NeuronSDK compilation script that makes use of the wrapper, splits the model into 4 parts and compile each one individually.\n", + " - src-inpaint/inference.py: SageMaker inference script that also makes use of wrapper to reload the compiled parts and re-build the pipeline responsible for getting the predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "73dd42ba-7ec5-4404-a144-c5b1ccb96bb1", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36munet_2d_condition\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m UNet2DConditionOutput\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m#from diffusers.models.attention_processor import Attention\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mUNetWrap\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unet):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.unet = unet\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " out_tuple = \u001b[36mself\u001b[39;49;00m.unet(sample, timestep, encoder_hidden_states, return_dict=\u001b[34mFalse\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m out_tuple\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronUNet\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unetwrap):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.unetwrap = unetwrap\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.config = unetwrap.unet.config\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.in_channels = unetwrap.unet.in_channels\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.device = unetwrap.unet.device\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m, return_dict=\u001b[34mFalse\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " sample = \u001b[36mself\u001b[39;49;00m.unetwrap(sample, timestep.float().expand((sample.shape[\u001b[34m0\u001b[39;49;00m],)), encoder_hidden_states)[\u001b[34m0\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m UNet2DConditionOutput(sample=sample)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronTextEncoder\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, text_encoder):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.neuron_text_encoder = text_encoder\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.config = text_encoder.config\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.dtype = text_encoder.dtype\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.device = text_encoder.device\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, emb, attention_mask = \u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m [\u001b[36mself\u001b[39;49;00m.neuron_text_encoder(emb)[\u001b[33m'\u001b[39;49;00m\u001b[33mlast_hidden_state\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]]\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m# Optimized attention\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_attention_scores\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, query, key, attn_mask): \u001b[37m\u001b[39;49;00m\n", + " dtype = query.dtype\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_attention:\u001b[37m\u001b[39;49;00m\n", + " query = query.float()\u001b[37m\u001b[39;49;00m\n", + " key = key.float()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Check for square matmuls\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m(query.size() == key.size()):\u001b[37m\u001b[39;49;00m\n", + " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", + " key,\u001b[37m\u001b[39;49;00m\n", + " query.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", + " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " attention_probs = torch.nn.functional.softmax(attention_scores, dim=\u001b[34m1\u001b[39;49;00m).permute(\u001b[34m0\u001b[39;49;00m,\u001b[34m2\u001b[39;49;00m,\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", + " query,\u001b[37m\u001b[39;49;00m\n", + " key.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", + " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " attention_probs = torch.nn.functional.softmax(attention_scores, dim=-\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m attention_probs\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mcustom_badbmm\u001b[39;49;00m(a, b):\u001b[37m\u001b[39;49;00m\n", + " bmm = torch.bmm(a, b)\u001b[37m\u001b[39;49;00m\n", + " scaled = bmm * \u001b[34m0.125\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m scaled\u001b[37m\u001b[39;49;00m\n" + ] + } + ], + "source": [ + "!pygmentize src-inpaint/wrapper.py" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e063c730-5b62-4600-950d-51f1646c3686", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[37m# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m# SPDX-License-Identifier: MIT-0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mNEURON_FUSE_SOFTMAX\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m] = \u001b[33m\"\u001b[39;49;00m\u001b[33m1\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtime\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcopy\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mshutil\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch_neuronx\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mwrapper\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m NeuronTextEncoder, UNetWrap, NeuronUNet, get_attention_scores\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36munet_2d_condition\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m UNet2DConditionOutput\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m StableDiffusionPipeline, StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mattention_processor\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Attention\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "height = \u001b[34m512\u001b[39;49;00m // \u001b[34m8\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "width = \u001b[34m512\u001b[39;49;00m // \u001b[34m8\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_text_encoder\u001b[39;49;00m(text_encoder, args):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling text encoder...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33mtext_encoder\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " t = time.time()\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Apply the wrapper to deal with custom return type\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " text_encoder = NeuronTextEncoder(text_encoder)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Compile text encoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# This is used for indexing a lookup table in torch.nn.Embedding,\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# so using random numbers may give errors (out of range).\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " emb = torch.tensor([[\u001b[34m49406\u001b[39;49;00m, \u001b[34m18376\u001b[39;49;00m, \u001b[34m525\u001b[39;49;00m, \u001b[34m7496\u001b[39;49;00m, \u001b[34m49407\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", + " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m]])\u001b[37m\u001b[39;49;00m\n", + " text_encoder_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", + " text_encoder.neuron_text_encoder, emb,\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Save the compiled text encoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " text_encoder_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " torch.jit.save(text_encoder_neuron, text_encoder_filename)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m text_encoder\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m text_encoder_neuron\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_vae\u001b[39;49;00m(decoder, args, dtype):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling VAE...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33mvae_decoder\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " t = time.time()\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Compile vae decoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " decoder_in = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m4\u001b[39;49;00m, height, width]).type(dtype)\u001b[37m\u001b[39;49;00m\n", + " decoder_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", + " decoder,\u001b[37m\u001b[39;49;00m\n", + " decoder_in,\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " compiler_args=[\u001b[33m\"\u001b[39;49;00m\u001b[33m--verbose\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33minfo\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Save the compiled vae decoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " decoder_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " torch.jit.save(decoder_neuron, decoder_filename)\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m decoder\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m decoder_neuron\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_unet\u001b[39;49;00m(unet, args, dtype):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling U-Net...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33munet\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " t = time.time()\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Compile unet - BF16\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " sample_1b = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m9\u001b[39;49;00m, height, width]).type(dtype)\u001b[37m\u001b[39;49;00m\n", + " timestep_1b = torch.tensor(\u001b[34m999\u001b[39;49;00m).type(dtype).expand((\u001b[34m1\u001b[39;49;00m,))\u001b[37m\u001b[39;49;00m\n", + " encoder_hidden_states_1b = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m77\u001b[39;49;00m, \u001b[34m1024\u001b[39;49;00m]).type(dtype)\u001b[37m\u001b[39;49;00m\n", + " example_inputs = sample_1b, timestep_1b, encoder_hidden_states_1b\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " unet_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", + " unet,\u001b[37m\u001b[39;49;00m\n", + " example_inputs,\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " compiler_args=[\u001b[33m\"\u001b[39;49;00m\u001b[33m--model-type=unet-inference\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33m--verbose=info\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# save compiled unet\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " unet_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " torch.jit.save(unet_neuron, unet_filename)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m unet\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m unet_neuron\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_vae_post_quant_conv\u001b[39;49;00m(post_quant_conv, args, dtype):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling Post Quant Conv...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33mvae_post_quant_conv\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " t = time.time()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# # Compile vae post_quant_conv\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv_in = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m4\u001b[39;49;00m, height, width]).type(dtype)\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv,\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv_in,\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " compiler_args=[\u001b[33m\"\u001b[39;49;00m\u001b[33m--verbose\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33minfo\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# # Save the compiled vae post_quant_conv\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " torch.jit.save(post_quant_conv_neuron, post_quant_conv_filename)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m post_quant_conv\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m post_quant_conv_neuron\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mif\u001b[39;49;00m \u001b[31m__name__\u001b[39;49;00m==\u001b[33m'\u001b[39;49;00m\u001b[33m__main__\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " parser = argparse.ArgumentParser(description=\u001b[33m'\u001b[39;49;00m\u001b[33mTrain the UNet on images and target masks\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--model-path\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mPath where we\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mll save the model\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_MODEL_DIR\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n", + " parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--checkpoints-path\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mPath where we\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mll save the best model and cache\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, default=\u001b[33m'\u001b[39;49;00m\u001b[33m/opt/ml/checkpoints\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--dtype\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mDatatype of the weights\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, default=\u001b[33m'\u001b[39;49;00m\u001b[33mfp32\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " args = parser.parse_args()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# make sure the checkpoint path exists\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(args.checkpoints_path, exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Model ID for SD version pipeline\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " model_id = \u001b[33m\"\u001b[39;49;00m\u001b[33mstabilityai/stable-diffusion-2-inpainting\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# --- Compile CLIP text encoder and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " dtype = torch.float32\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", + " text_encoder = copy.deepcopy(pipe.text_encoder)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", + " compile_text_encoder(text_encoder, args)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# --- Compile VAE decoder and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", + " decoder = copy.deepcopy(pipe.vae.decoder)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", + " compile_vae(decoder, args, dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# --- Compile UNet and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Replace original cross-attention module with custom cross-attention module for better performance\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " Attention.get_attention_scores = get_attention_scores\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Apply double wrapper to deal with custom return type\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " pipe.unet = NeuronUNet(UNetWrap(pipe.unet))\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " unet = copy.deepcopy(pipe.unet.unetwrap)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", + " compile_unet(unet, args, dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# --- Compile VAE post_quant_conv and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv = copy.deepcopy(pipe.vae.post_quant_conv)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", + " compile_vae_post_quant_conv(post_quant_conv, args, dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " code_path = os.path.join(args.model_path, \u001b[33m'\u001b[39;49;00m\u001b[33mcode\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " os.makedirs(code_path, exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " shutil.copyfile(\u001b[33m'\u001b[39;49;00m\u001b[33minference.py\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, os.path.join(code_path, \u001b[33m'\u001b[39;49;00m\u001b[33minference.py\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m#shutil.copyfile('wrapper.py', os.path.join(code_path, 'wrapper.py'))\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " shutil.copyfile(\u001b[33m'\u001b[39;49;00m\u001b[33mrequirements.txt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, os.path.join(code_path, \u001b[33m'\u001b[39;49;00m\u001b[33mrequirements.txt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n" + ] + } + ], + "source": [ + "!pygmentize src-inpaint/compile.py" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "85d0251e-4d73-4986-8b71-10faf2bef429", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "os.environ[\u001b[33m'\u001b[39;49;00m\u001b[33mNEURON_RT_NUM_CORES\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m] = \u001b[33m'\u001b[39;49;00m\u001b[33m2\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch_neuronx\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtime\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36munet_2d_condition\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m UNet2DConditionOutput\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mattention_processor\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Attention\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mthreading\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msys\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcopy\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mPIL\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mmath\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mjson\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mrequests\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mio\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mio\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m BytesIO\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mbase64\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mPIL\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Image\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "model_id = \u001b[33m\"\u001b[39;49;00m\u001b[33mstabilityai/stable-diffusion-2-inpainting\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "dtype = torch.float32\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mUNetWrap\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unet):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.unet = unet\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " out_tuple = \u001b[36mself\u001b[39;49;00m.unet(sample, timestep, encoder_hidden_states, return_dict=\u001b[34mFalse\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m out_tuple\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronUNet\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unetwrap):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.unetwrap = unetwrap\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.config = unetwrap.unet.config\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.in_channels = unetwrap.unet.in_channels\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.device = unetwrap.unet.device\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m, return_dict=\u001b[34mFalse\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " sample = \u001b[36mself\u001b[39;49;00m.unetwrap(sample, timestep.float().expand((sample.shape[\u001b[34m0\u001b[39;49;00m],)), encoder_hidden_states)[\u001b[34m0\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m UNet2DConditionOutput(sample=sample)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronTextEncoder\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, text_encoder):\u001b[37m\u001b[39;49;00m\n", + " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.neuron_text_encoder = text_encoder\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.config = text_encoder.config\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.dtype = text_encoder.dtype\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mself\u001b[39;49;00m.device = text_encoder.device\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, emb, attention_mask = \u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m [\u001b[36mself\u001b[39;49;00m.neuron_text_encoder(emb)[\u001b[33m'\u001b[39;49;00m\u001b[33mlast_hidden_state\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]]\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m# Optimized attention\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_attention_scores\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, query, key, attn_mask): \u001b[37m\u001b[39;49;00m\n", + " dtype = query.dtype\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_attention:\u001b[37m\u001b[39;49;00m\n", + " query = query.float()\u001b[37m\u001b[39;49;00m\n", + " key = key.float()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Check for square matmuls\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m(query.size() == key.size()):\u001b[37m\u001b[39;49;00m\n", + " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", + " key,\u001b[37m\u001b[39;49;00m\n", + " query.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", + " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " attention_probs = torch.nn.functional.softmax(attention_scores, dim=\u001b[34m1\u001b[39;49;00m).permute(\u001b[34m0\u001b[39;49;00m,\u001b[34m2\u001b[39;49;00m,\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", + " query,\u001b[37m\u001b[39;49;00m\n", + " key.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " )\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", + " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " attention_probs = torch.nn.functional.softmax(attention_scores, dim=-\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m attention_probs\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mcustom_badbmm\u001b[39;49;00m(a, b):\u001b[37m\u001b[39;49;00m\n", + " bmm = torch.bmm(a, b)\u001b[37m\u001b[39;49;00m\n", + " scaled = bmm * \u001b[34m0.125\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m scaled\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mmodel_fn\u001b[39;49;00m(model_dir, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mglobal\u001b[39;49;00m model_id, dtype\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mLoading model parts...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " t=time.time()\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " text_encoder_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mtext_encoder/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " decoder_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mvae_decoder/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " unet_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33munet/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " post_quant_conv_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mvae_post_quant_conv/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Load the compiled UNet onto two neuron cores.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " pipe.unet = NeuronUNet(UNetWrap(pipe.unet))\u001b[37m\u001b[39;49;00m\n", + " device_ids = [\u001b[34m0\u001b[39;49;00m,\u001b[34m1\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", + " pipe.unet.unetwrap = torch_neuronx.DataParallel(torch.jit.load(unet_filename), device_ids, set_dynamic_batching=\u001b[34mFalse\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[37m# Load other compiled models onto a single neuron core.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " pipe.text_encoder = NeuronTextEncoder(pipe.text_encoder)\u001b[37m\u001b[39;49;00m\n", + " pipe.text_encoder.neuron_text_encoder = torch.jit.load(text_encoder_filename)\u001b[37m\u001b[39;49;00m\n", + " pipe.vae.decoder = torch.jit.load(decoder_filename)\u001b[37m\u001b[39;49;00m\n", + " pipe.vae.post_quant_conv = torch.jit.load(post_quant_conv_filename)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32minput_fn\u001b[39;49;00m(request_body, request_content_type, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m request_content_type == \u001b[33m'\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " req = json.loads(request_body)\u001b[37m\u001b[39;49;00m\n", + " prompt = req.get(\u001b[33m'\u001b[39;49;00m\u001b[33mprompt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " init_image = req.get(\u001b[33m'\u001b[39;49;00m\u001b[33minit_image\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " mask_image = req.get(\u001b[33m'\u001b[39;49;00m\u001b[33mmask_image\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " height = \u001b[34m512\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + " width = \u001b[34m512\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m prompt \u001b[35mis\u001b[39;49;00m \u001b[34mNone\u001b[39;49;00m \u001b[35mor\u001b[39;49;00m \u001b[36mtype\u001b[39;49;00m(prompt) != \u001b[36mstr\u001b[39;49;00m \u001b[35mor\u001b[39;49;00m \u001b[36mlen\u001b[39;49;00m(prompt) < \u001b[34m5\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mraise\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mInvalid prompt. It needs to be a string > 5\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m prompt,init_image,mask_image,height,width\u001b[37m\u001b[39;49;00m\n", + " \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mraise\u001b[39;49;00m \u001b[36mException\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mUnsupported mime type: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mrequest_content_type\u001b[33m}\u001b[39;49;00m\u001b[33m. Supported: application/json\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32mpredict_fn\u001b[39;49;00m(input_req, model, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " prompt,init_image,mask_image,height,width = input_req\u001b[37m\u001b[39;49;00m\n", + " init_image_input = Image.open(io.BytesIO(base64.b64decode((init_image)))).convert(\u001b[33m\"\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m).resize((width, height))\u001b[37m\u001b[39;49;00m\n", + " mask_image_input = Image.open(io.BytesIO(base64.b64decode((mask_image)))).convert(\u001b[33m\"\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m).resize((width, height))\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m model(prompt,image=init_image_input, mask_image=mask_image_input, height=height, width=width).images[\u001b[34m0\u001b[39;49;00m] \u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + "\u001b[34mdef\u001b[39;49;00m \u001b[32moutput_fn\u001b[39;49;00m(image, accept, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mif\u001b[39;49;00m accept!=\u001b[33m'\u001b[39;49;00m\u001b[33mimage/jpeg\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mraise\u001b[39;49;00m \u001b[36mException\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mInvalid data type. Expected image/jpeg, got \u001b[39;49;00m\u001b[33m{\u001b[39;49;00maccept\u001b[33m}\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + "\u001b[37m\u001b[39;49;00m\n", + " buffer = io.BytesIO()\u001b[37m\u001b[39;49;00m\n", + " image.save(buffer, \u001b[33m'\u001b[39;49;00m\u001b[33mjpeg\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, icc_profile=image.info.get(\u001b[33m'\u001b[39;49;00m\u001b[33micc_profile\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n", + " buffer.seek(\u001b[34m0\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", + " \u001b[34mreturn\u001b[39;49;00m buffer.read()\u001b[37m\u001b[39;49;00m\n" + ] + } + ], + "source": [ + "!pygmentize src-inpaint/inference.py" + ] + }, + { + "cell_type": "markdown", + "id": "ca09112a-bb10-4719-b8f6-d823fcb72ade", + "metadata": {}, + "source": [ + "## 4) Compile the model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "03717dd9-53de-4b0b-b892-53ccb89c0680", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.pytorch import PyTorch\n", + "\n", + "estimator = PyTorch(\n", + " entry_point=\"compile.py\", # Specify your train script\n", + " source_dir=\"src-inpaint\",\n", + " image_uri=f\"763104351884.dkr.ecr.{region_trn1}.amazonaws.com/{train_image_name}:{image_tag}\",\n", + " role=role,\n", + " sagemaker_session=sess_trn1,\n", + " instance_count=1,\n", + " instance_type='ml.trn1.32xlarge',\n", + " disable_profiler=True,\n", + " output_path=f\"s3://{bucket_trn1}/output\", \n", + " volume_size = 384,\n", + " \n", + " # Parameters required to enable checkpointing\n", + " # This is necessary for caching XLA HLO files and reduce training time next time \n", + " checkpoint_s3_uri=f\"s3://{bucket_trn1}/checkpoints\",\n", + " hyperparameters={\n", + " \"dtype\": \"fp32\"\n", + " }\n", + ")\n", + "estimator.framework_version = '1.13.1' # workround when using image_uri\n", + "estimator._is_compiled_model = True" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f401d597-ed80-4fbc-ab1a-8c63d263976d", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating training-job with name: pytorch-training-neuronx-2023-09-30-04-54-35-074\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using provided s3_resource\n", + "2023-09-30 04:54:35 Starting - Starting the training job.........\n", + "2023-09-30 04:55:57 Starting - Preparing the instances for training.........\n", + "2023-09-30 04:57:32 Downloading - Downloading input data\n", + "2023-09-30 04:57:32 Training - Downloading the training image.....................\n", + "2023-09-30 05:00:43 Training - Training image download completed. Training in progress....\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n", + "\u001b[34mbash: no job control in this shell\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:22,877 sagemaker-training-toolkit INFO Imported framework sagemaker_pytorch_container.training\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:22,878 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:23,307 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:23,316 sagemaker_pytorch_container.training INFO Block until all host DNS lookups succeed.\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:23,317 sagemaker_pytorch_container.training INFO Invoking user training script.\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:24,280 sagemaker-training-toolkit INFO Installing dependencies from requirements.txt:\u001b[0m\n", + "\u001b[34m/usr/local/bin/python3.10 -m pip install -r requirements.txt\u001b[0m\n", + "\u001b[34mLooking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\u001b[0m\n", + "\u001b[34mCollecting diffusers==0.20.2 (from -r requirements.txt (line 1))\u001b[0m\n", + "\u001b[34mDownloading diffusers-0.20.2.tar.gz (989 kB)\u001b[0m\n", + "\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 989.1/989.1 kB 71.4 MB/s eta 0:00:00\u001b[0m\n", + "\u001b[34mInstalling build dependencies: started\u001b[0m\n", + "\u001b[34mInstalling build dependencies: finished with status 'done'\u001b[0m\n", + "\u001b[34mGetting requirements to build wheel: started\u001b[0m\n", + "\u001b[34mGetting requirements to build wheel: finished with status 'done'\u001b[0m\n", + "\u001b[34mPreparing metadata (pyproject.toml): started\u001b[0m\n", + "\u001b[34mPreparing metadata (pyproject.toml): finished with status 'done'\u001b[0m\n", + "\u001b[34mRequirement already satisfied: transformers==4.33.1 in /usr/local/lib/python3.10/site-packages 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"\u001b[34mBuilding wheels for collected packages: diffusers\u001b[0m\n", + "\u001b[34mBuilding wheel for diffusers (pyproject.toml): started\u001b[0m\n", + "\u001b[34mBuilding wheel for diffusers (pyproject.toml): finished with status 'done'\u001b[0m\n", + "\u001b[34mCreated wheel for diffusers: filename=diffusers-0.20.2-py3-none-any.whl size=1342633 sha256=e88357d4078229ab564d5d0ef6a2a61686052e27e2a507b39b33a01131c1861a\u001b[0m\n", + "\u001b[34mStored in directory: /root/.cache/pip/wheels/dc/8b/d9/34f7a1936109e05e9bba0cc2241a6f8cd89e25959dc7aae942\u001b[0m\n", + "\u001b[34mSuccessfully built diffusers\u001b[0m\n", + "\u001b[34mInstalling collected packages: safetensors, diffusers\u001b[0m\n", + "\u001b[34mAttempting uninstall: safetensors\u001b[0m\n", + "\u001b[34mFound existing installation: safetensors 0.3.3\u001b[0m\n", + "\u001b[34mUninstalling safetensors-0.3.3:\u001b[0m\n", + "\u001b[34mSuccessfully uninstalled safetensors-0.3.3\u001b[0m\n", + "\u001b[34mSuccessfully installed diffusers-0.20.2 safetensors-0.3.1\u001b[0m\n", + "\u001b[34mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:31,830 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code.\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:31,830 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:31,831 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:32,284 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:32,295 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:32,756 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:32,766 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:33,224 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:33,234 sagemaker-training-toolkit INFO Invoking user script\u001b[0m\n", + "\u001b[34mTraining Env:\u001b[0m\n", + "\u001b[34m{\n", + " \"additional_framework_parameters\": {},\n", + " \"channel_input_dirs\": {},\n", + " \"current_host\": \"algo-1\",\n", + " \"current_instance_group\": \"homogeneousCluster\",\n", + " \"current_instance_group_hosts\": [\n", + " \"algo-1\"\n", + " ],\n", + " \"current_instance_type\": \"ml.trn1.32xlarge\",\n", + " \"distribution_hosts\": [],\n", + " \"distribution_instance_groups\": [],\n", + " \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n", + " \"hosts\": [\n", + " \"algo-1\"\n", + " ],\n", + " \"hyperparameters\": {\n", + " \"dtype\": \"fp32\"\n", + " },\n", + " \"input_config_dir\": \"/opt/ml/input/config\",\n", + " \"input_data_config\": {},\n", + " \"input_dir\": \"/opt/ml/input\",\n", + " \"instance_groups\": [\n", + " \"homogeneousCluster\"\n", + " ],\n", + " \"instance_groups_dict\": {\n", + " \"homogeneousCluster\": {\n", + " \"instance_group_name\": \"homogeneousCluster\",\n", + " \"instance_type\": \"ml.trn1.32xlarge\",\n", + " \"hosts\": [\n", + " \"algo-1\"\n", + " ]\n", + " }\n", + " },\n", + " \"is_hetero\": false,\n", + " \"is_master\": true,\n", + " \"is_modelparallel_enabled\": null,\n", + " \"is_smddpmprun_installed\": false,\n", + " \"job_name\": \"pytorch-training-neuronx-2023-09-30-04-54-35-074\",\n", + " \"log_level\": 20,\n", + " \"master_hostname\": \"algo-1\",\n", + " \"model_dir\": \"/opt/ml/model\",\n", + " \"module_dir\": \"s3://sagemaker-us-east-1-772327914095/pytorch-training-neuronx-2023-09-30-04-54-35-074/source/sourcedir.tar.gz\",\n", + " \"module_name\": \"compile\",\n", + " \"network_interface_name\": \"eth0\",\n", + " \"num_cpus\": 128,\n", + " \"num_gpus\": 0,\n", + " \"num_neurons\": 32,\n", + " \"output_data_dir\": \"/opt/ml/output/data\",\n", + " \"output_dir\": \"/opt/ml/output\",\n", + " \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n", + " \"resource_config\": {\n", + " \"current_host\": \"algo-1\",\n", + " \"current_instance_type\": \"ml.trn1.32xlarge\",\n", + " \"current_group_name\": \"homogeneousCluster\",\n", + " \"hosts\": [\n", + " \"algo-1\"\n", + " ],\n", + " \"instance_groups\": [\n", + " {\n", + " \"instance_group_name\": \"homogeneousCluster\",\n", + " \"instance_type\": \"ml.trn1.32xlarge\",\n", + " \"hosts\": [\n", + " \"algo-1\"\n", + " ]\n", + " }\n", + " ],\n", + " \"network_interface_name\": \"eth0\"\n", + " },\n", + " \"user_entry_point\": \"compile.py\"\u001b[0m\n", + "\u001b[34m}\u001b[0m\n", + "\u001b[34mEnvironment variables:\u001b[0m\n", + "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n", + "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n", + "\u001b[34mSM_HPS={\"dtype\":\"fp32\"}\u001b[0m\n", + "\u001b[34mSM_USER_ENTRY_POINT=compile.py\u001b[0m\n", + "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n", + "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.trn1.32xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n", + "\u001b[34mSM_INPUT_DATA_CONFIG={}\u001b[0m\n", + "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n", + "\u001b[34mSM_CHANNELS=[]\u001b[0m\n", + "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n", + "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.trn1.32xlarge\u001b[0m\n", + "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n", + "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n", + "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n", + "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}}\u001b[0m\n", + "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n", + "\u001b[34mSM_IS_HETERO=false\u001b[0m\n", + "\u001b[34mSM_MODULE_NAME=compile\u001b[0m\n", + "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n", + "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n", + "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n", + "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n", + "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n", + "\u001b[34mSM_NUM_CPUS=128\u001b[0m\n", + "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n", + "\u001b[34mSM_NUM_NEURONS=32\u001b[0m\n", + "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n", + "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-1-772327914095/pytorch-training-neuronx-2023-09-30-04-54-35-074/source/sourcedir.tar.gz\u001b[0m\n", + "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.trn1.32xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"dtype\":\"fp32\"},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":false,\"job_name\":\"pytorch-training-neuronx-2023-09-30-04-54-35-074\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-772327914095/pytorch-training-neuronx-2023-09-30-04-54-35-074/source/sourcedir.tar.gz\",\"module_name\":\"compile\",\"network_interface_name\":\"eth0\",\"num_cpus\":128,\"num_gpus\":0,\"num_neurons\":32,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.trn1.32xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"compile.py\"}\u001b[0m\n", + "\u001b[34mSM_USER_ARGS=[\"--dtype\",\"fp32\"]\u001b[0m\n", + "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n", + "\u001b[34mSM_HP_DTYPE=fp32\u001b[0m\n", + "\u001b[34mPYTHONPATH=/opt/ml/code:/usr/local/bin:/usr/local/lib/python310.zip:/usr/local/lib/python3.10:/usr/local/lib/python3.10/lib-dynload:/usr/local/lib/python3.10/site-packages\u001b[0m\n", + "\u001b[34mInvoking script with the following command:\u001b[0m\n", + "\u001b[34m/usr/local/bin/python3.10 compile.py --dtype fp32\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:33,235 sagemaker-training-toolkit INFO Exceptions not imported for SageMaker Debugger as it is not installed.\u001b[0m\n", + "\u001b[34m2023-09-30 05:01:33,235 sagemaker-training-toolkit INFO Exceptions not imported for SageMaker TF as Tensorflow is not installed.\u001b[0m\n", + "\u001b[34mDownloading (…)ain/model_index.json: 0%| | 0.00/544 [00:00 ACT: 22320\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [PeepholeOpts]: COPY -> ACT: 69317\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [PeepholeOpts]: RECIPROCAL -> ACT: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: peephole_opts finished after 1.269 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: curr_vmrss: 4415mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump after peephole_opts\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 18\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Done debug_dump after peephole_opts\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663600 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: Running lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Inputs to lower_kernel: modules=1 functions=1 allocs=159647 blocks=1 instructions=663600 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Started running LowerKernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Start of kernel lowering pass, number of insts: 663600, number of allocs: 159647\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Scan BKs time (s): 0.113715\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Lower BKs time (s): 0.001777\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: lower_kernel finished after 0.165 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: curr_vmrss: 4410mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 19\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663600 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: Running build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Inputs to build_fdeps: modules=1 functions=1 allocs=159647 blocks=1 instructions=663600 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [build_flow_deps]: Start build fdeps. Invocation: 2Sat Sep 30 05:06:26 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [build_flow_deps]: Allocs: 159647 instructions: 663600\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [build_flow_deps]: Build fdeps inserted 2562527 edges \u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [build_flow_deps]: Done build fdeps 2562527 Sat Sep 30 05:06:28 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z USER 7014 [WalrusDriver]: build_fdeps finished after 1.758 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: curr_vmrss: 5094mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump after build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 20\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Done debug_dump after build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663600 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z USER 7014 [WalrusDriver]: Running remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Inputs to remove_redundancies: modules=1 functions=1 allocs=159647 blocks=1 instructions=663600 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove_clobbered_writes\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove_clobbered_writes: 384\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove_useless_insts\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove Useless Instructions: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z USER 7014 [WalrusDriver]: remove_redundancies finished after 0.681 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: curr_vmrss: 5082mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump after remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 21\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Done debug_dump after remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663216 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:29Z USER 7014 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:29Z INFO 7014 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=159647 blocks=1 instructions=663216 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:29Z INFO 7014 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:29Z INFO 7014 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 70298 access patterns a mean/median 1.41111/1 intervals per access pattern and mean/median 3.40997/3.03362 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-32-128]: Finished analyzing 59139 access patterns a mean/median 1.02969/1 intervals per access pattern and mean/median 2.83166/2.42382 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-32-128]: Finished analyzing 59486 access patterns a mean/median 1.02407/1 intervals per access pattern and mean/median 2.76871/2.43956 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-32-128]: Finished analyzing 58752 access patterns a mean/median 1.02243/1 intervals per access pattern and mean/median 3.12924/2.5462 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-32-128]: Finished analyzing 58828 access patterns a mean/median 1.02121/1 intervals per access pattern and mean/median 2.83007/2.58563 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-32-128]: Finished analyzing 58486 access patterns a mean/median 1.01932/1 intervals per access pattern and mean/median 2.66362/2.47247 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 69290 access patterns a mean/median 1.41192/1 intervals per access pattern and mean/median 3.47085/3.27302 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 69572 access patterns a mean/median 1.40194/1 intervals per access pattern and mean/median 3.4515/3.08572 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 69592 access patterns a mean/median 1.41283/1 intervals per access pattern and mean/median 3.62958/3.33807 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-32-128]: Finished analyzing 58982 access patterns a mean/median 1.02628/1 intervals per access pattern and mean/median 2.66817/2.31134 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-32-128]: Finished analyzing 58758 access patterns a mean/median 1.02366/1 intervals per access pattern and mean/median 2.89662/2.40036 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 69589 access patterns a mean/median 1.41435/1 intervals per access pattern and mean/median 3.42818/3.04032 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 35937 access patterns a mean/median 1/1 intervals per access pattern and mean/median 2.05256/1.21708 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-32-128]: Finished analyzing 59023 access patterns a mean/median 1.02873/1 intervals per access pattern and mean/median 2.84405/2.39523 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 69899 access patterns a mean/median 1.41743/1 intervals per access pattern and mean/median 3.41474/3.07501 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 69702 access patterns a mean/median 1.40263/1 intervals per access pattern and mean/median 3.3655/3.08343 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 69944 access patterns a mean/median 1.41788/1 intervals per access pattern and mean/median 3.45172/3.07181 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-3-4]: Finished analyzing 1153303 access patterns a mean/median 1.91116/1 intervals per access pattern and mean/median 1.68452/1.0244 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-35-36]: Finished analyzing 1139792 access patterns a mean/median 1.91446/1 intervals per access pattern and mean/median 1.68163/1.02403 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-67-96]: Finished analyzing 1066076 access patterns a mean/median 2.06705/1 intervals per access pattern and mean/median 1.61466/1.02038 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-36-64]: Finished analyzing 1139784 access patterns a mean/median 1.91447/1 intervals per access pattern and mean/median 1.68164/1.02295 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-33-35]: Finished analyzing 1139924 access patterns a mean/median 1.91436/1 intervals per access pattern and mean/median 1.68259/1.02402 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-100-128]: Finished analyzing 1069167 access patterns a mean/median 2.06443/1 intervals per access pattern and mean/median 1.7063/1.018 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-4-32]: Finished analyzing 1150985 access patterns a mean/median 1.90601/1 intervals per access pattern and mean/median 1.68422/1.02448 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 1081126 access patterns a mean/median 2.05396/1 intervals per access pattern and mean/median 1.7056/1.0057 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 1067170 access patterns a mean/median 2.06622/1 intervals per access pattern and mean/median 1.61574/1.0171 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-65-67]: Finished analyzing 1066208 access patterns a mean/median 2.06691/1 intervals per access pattern and mean/median 1.61553/1.02037 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-97-99]: Finished analyzing 1069545 access patterns a mean/median 2.06406/1 intervals per access pattern and mean/median 1.70748/1.01831 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 1147657 access patterns a mean/median 1.90853/1 intervals per access pattern and mean/median 1.6812/1.01955 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-99-100]: Finished analyzing 1069173 access patterns a mean/median 2.06443/1 intervals per access pattern and mean/median 1.7063/1.01839 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-1-3]: Finished analyzing 1153948 access patterns a mean/median 1.91065/1 intervals per access pattern and mean/median 1.68547/1.02438 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 1208795 access patterns a mean/median 1.86993/1 intervals per access pattern and mean/median 1.68491/1.00014 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z USER 7014 [WalrusDriver]: anti_dependency_analyzer finished after 7.176 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: curr_vmrss: 7023mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 22\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663216 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z USER 7014 [WalrusDriver]: Running post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Inputs to post_sched: modules=1 functions=1 allocs=159647 blocks=1 instructions=663216 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [post_scheduler]: Start PosT ScheD 3 sunda Sat Sep 30 05:06:36 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:48Z INFO 7014 [post_scheduler]: Tensor CP elimination: 384\u001b[0m\n", + "\u001b[34m2023-09-30T05:06:48Z INFO 7014 [post_scheduler]: Time-aware hwm post-sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:01Z INFO 7014 [post_scheduler]: Time-aware simulation time: 97496788\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [post_scheduler]: Done PosT ScheD Sat Sep 30 05:07:06 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z USER 7014 [WalrusDriver]: post_sched finished after 30.064 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: curr_vmrss: 7021mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Running debug_dump after post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 23\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Done debug_dump after post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z USER 7014 [WalrusDriver]: Running address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Inputs to address_rotation_sb: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:13Z INFO 7014 [DMAOptimizationBase]: PSUM Rotation rotated 21101 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:15Z INFO 7014 [DMAOptimizationBase]: PSUM Rotation rotated 18878 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:16Z INFO 7014 [DMAOptimizationBase]: PSUM Rotation rotated 7889 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:17Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 1181 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:19Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 2868 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:22Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 7500 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:31Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 3884 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:40Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 17177 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:42Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 191 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z USER 7014 [WalrusDriver]: address_rotation_sb finished after 37.296 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: curr_vmrss: 6987mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Running debug_dump after address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 24\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Done debug_dump after address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:44Z USER 7014 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:44Z INFO 7014 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:44Z INFO 7014 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:44Z INFO 7014 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-32-128]: Finished analyzing 59344 access patterns a mean/median 1.02558/1 intervals per access pattern and mean/median 2.72231/2.44076 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-32-128]: Finished analyzing 59403 access patterns a mean/median 1.02326/1 intervals per access pattern and mean/median 3.02721/2.44987 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-32-128]: Finished analyzing 59364 access patterns a mean/median 1.02675/1 intervals per access pattern and mean/median 2.99854/2.59105 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 70236 access patterns a mean/median 1.41469/1 intervals per access pattern and mean/median 3.49664/3.06486 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-32-128]: Finished analyzing 58640 access patterns a mean/median 1.025/1 intervals per access pattern and mean/median 3.00107/2.78138 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 70282 access patterns a mean/median 1.40611/1 intervals per access pattern and mean/median 3.46408/3.04038 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-32-128]: Finished analyzing 58567 access patterns a mean/median 1.02257/1 intervals per access pattern and mean/median 2.78026/2.49181 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 69510 access patterns a mean/median 1.41384/1 intervals per access pattern and mean/median 3.49008/3.03643 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 70491 access patterns a mean/median 1.42797/1 intervals per access pattern and mean/median 3.51453/3.07235 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-32-128]: Finished analyzing 58632 access patterns a mean/median 1.02442/1 intervals per access pattern and mean/median 3.12939/2.54852 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 69519 access patterns a mean/median 1.42049/1 intervals per access pattern and mean/median 3.49695/3.06175 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-32-128]: Finished analyzing 58380 access patterns a mean/median 1.02203/1 intervals per access pattern and mean/median 2.89497/2.62033 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-32-128]: Finished analyzing 59124 access patterns a mean/median 1.02581/1 intervals per access pattern and mean/median 2.88403/2.6009 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 69043 access patterns a mean/median 1.3957/1 intervals per access pattern and mean/median 3.50033/3.03206 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 35937 access patterns a mean/median 1/1 intervals per access pattern and mean/median 2.05256/1.21684 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 68950 access patterns a mean/median 1.39643/1 intervals per access pattern and mean/median 3.38656/3.02634 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 69855 access patterns a mean/median 1.41446/1 intervals per access pattern and mean/median 3.46563/3.07305 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-67-96]: Finished analyzing 1066072 access patterns a mean/median 2.06717/1 intervals per access pattern and mean/median 1.6311/1.02054 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-4-32]: Finished analyzing 1150831 access patterns a mean/median 1.90602/1 intervals per access pattern and mean/median 1.70241/1.02583 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-3-4]: Finished analyzing 1153149 access patterns a mean/median 1.91117/1 intervals per access pattern and mean/median 1.70267/1.02563 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-100-128]: Finished analyzing 1069531 access patterns a mean/median 2.06407/1 intervals per access pattern and mean/median 1.72272/1.01864 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 1083416 access patterns a mean/median 2.05184/1 intervals per access pattern and mean/median 1.72182/1.00459 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-36-64]: Finished analyzing 1139578 access patterns a mean/median 1.91463/1 intervals per access pattern and mean/median 1.69961/1.02557 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-33-35]: Finished analyzing 1139724 access patterns a mean/median 1.91452/1 intervals per access pattern and mean/median 1.7005/1.02556 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-35-36]: Finished analyzing 1139586 access patterns a mean/median 1.91463/1 intervals per access pattern and mean/median 1.6996/1.02557 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 1145317 access patterns a mean/median 1.91043/1 intervals per access pattern and mean/median 1.69986/1.02027 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 1067660 access patterns a mean/median 2.06586/1 intervals per access pattern and mean/median 1.63198/1.0186 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 1207587 access patterns a mean/median 1.87056/1 intervals per access pattern and mean/median 1.70274/1.00016 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-65-67]: Finished analyzing 1066183 access patterns a mean/median 2.06706/1 intervals per access pattern and mean/median 1.63191/1.02053 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-97-99]: Finished analyzing 1069942 access patterns a mean/median 2.06366/1 intervals per access pattern and mean/median 1.72422/1.01764 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-1-3]: Finished analyzing 1153776 access patterns a mean/median 1.91067/1 intervals per access pattern and mean/median 1.70354/1.02562 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-99-100]: Finished analyzing 1069537 access patterns a mean/median 2.06406/1 intervals per access pattern and mean/median 1.72272/1.01774 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z USER 7014 [WalrusDriver]: anti_dependency_analyzer finished after 7.545 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: curr_vmrss: 6871mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 25\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z USER 7014 [WalrusDriver]: Running dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Inputs to dep_opt: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:55Z INFO 7014 [build_flow_deps]: Start build fdeps. Invocation: 3Sat Sep 30 05:07:55 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:55Z INFO 7014 [build_flow_deps]: Allocs: 159263 instructions: 662832\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:57Z INFO 7014 [build_flow_deps]: Build fdeps inserted 2268858 edges \u001b[0m\n", + "\u001b[34m2023-09-30T05:07:57Z INFO 7014 [build_flow_deps]: Done build fdeps 2268858 Sat Sep 30 05:07:57 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z USER 7014 [WalrusDriver]: dep_opt finished after 6.447 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: curr_vmrss: 5804mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Running debug_dump after dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 26\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Done debug_dump after dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z USER 7014 [WalrusDriver]: Running report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Inputs to report_stats: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [ReportStats]: Data Movement Statistics: sg0000\u001b[0m\n", + "\u001b[34m┌──────────────┬────────────────────────────┬───────┬────────────┐\u001b[0m\n", + "\u001b[34m│ Instruction │ Kind │ Count │ Bytes │\u001b[0m\n", + "\u001b[34m├──────────────┼────────────────────────────┼───────┼────────────┤\u001b[0m\n", + "\u001b[34m│ DMACopy │ Internal │ 512 │ 268435456 │\u001b[0m\n", + "\u001b[34m│ Load │ Const -> Internal │ 409 │ 99678476 │\u001b[0m\n", + "\u001b[34m│ Load │ ExternalInput -> Internal │ 8 │ 79872 │\u001b[0m\n", + "\u001b[34m│ Load │ Internal │ 20794 │ 6617424128 │\u001b[0m\n", + "\u001b[34m│ Save │ Internal │ 4498 │ 1198778368 │\u001b[0m\n", + "\u001b[34m│ Save │ Internal -> ExternalOutput │ 256 │ 3145728 │\u001b[0m\n", + "\u001b[34m│ Save (Spill) │ Internal │ 10133 │ 4068137984 │\u001b[0m\n", + "\u001b[34m└──────────────┴────────────────────────────┴───────┴────────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [ReportStats]: \u001b[0m\n", + "\u001b[34m┌─────────────────────┬───────┐\u001b[0m\n", + "\u001b[34m│ Bytes per partition │ Count │\u001b[0m\n", + "\u001b[34m├─────────────────────┼───────┤\u001b[0m\n", + "\u001b[34m│ 4 │ 1 │\u001b[0m\n", + "\u001b[34m│ 28 │ 1 │\u001b[0m\n", + "\u001b[34m│ 54 │ 1 │\u001b[0m\n", + "\u001b[34m│ 64 │ 14 │\u001b[0m\n", + "\u001b[34m│ 240 │ 1 │\u001b[0m\n", + "\u001b[34m│ 256 │ 1 │\u001b[0m\n", + "\u001b[34m│ 260 │ 3917 │\u001b[0m\n", + "\u001b[34m│ 320 │ 3814 │\u001b[0m\n", + "\u001b[34m│ 512 │ 14 │\u001b[0m\n", + "\u001b[34m│ 516 │ 549 │\u001b[0m\n", + "\u001b[34m│ 528 │ 741 │\u001b[0m\n", + "\u001b[34m│ 1024 │ 139 │\u001b[0m\n", + "\u001b[34m│ 1028 │ 3948 │\u001b[0m\n", + "\u001b[34m│ 1040 │ 1139 │\u001b[0m\n", + "\u001b[34m│ 1056 │ 118 │\u001b[0m\n", + "\u001b[34m│ 2048 │ 9321 │\u001b[0m\n", + "\u001b[34m│ 2064 │ 2556 │\u001b[0m\n", + "\u001b[34m│ 2080 │ 2509 │\u001b[0m\n", + "\u001b[34m│ 2112 │ 513 │\u001b[0m\n", + "\u001b[34m│ 2304 │ 193 │\u001b[0m\n", + "\u001b[34m│ 2560 │ 6 │\u001b[0m\n", + "\u001b[34m│ 3072 │ 450 │\u001b[0m\n", + "\u001b[34m│ 3080 │ 48 │\u001b[0m\n", + "\u001b[34m│ 3584 │ 192 │\u001b[0m\n", + "\u001b[34m│ 4096 │ 2754 │\u001b[0m\n", + "\u001b[34m│ 5136 │ 13 │\u001b[0m\n", + "\u001b[34m│ 6656 │ 448 │\u001b[0m\n", + "\u001b[34m│ 7192 │ 10 │\u001b[0m\n", + "\u001b[34m│ 7680 │ 4 │\u001b[0m\n", + "\u001b[34m│ 8192 │ 1261 │\u001b[0m\n", + "\u001b[34m│ 9216 │ 224 │\u001b[0m\n", + "\u001b[34m│ 9248 │ 11 │\u001b[0m\n", + "\u001b[34m│ 10240 │ 8 │\u001b[0m\n", + "\u001b[34m│ 11304 │ 11 │\u001b[0m\n", + "\u001b[34m│ 12288 │ 4 │\u001b[0m\n", + "\u001b[34m│ 13360 │ 36 │\u001b[0m\n", + "\u001b[34m│ 14336 │ 2 │\u001b[0m\n", + "\u001b[34m│ 15416 │ 191 │\u001b[0m\n", + "\u001b[34m│ 16384 │ 377 │\u001b[0m\n", + "\u001b[34m│ 24576 │ 2 │\u001b[0m\n", + "\u001b[34m│ 25600 │ 498 │\u001b[0m\n", + "\u001b[34m│ 26624 │ 16 │\u001b[0m\n", + "\u001b[34m│ 27648 │ 6 │\u001b[0m\n", + "\u001b[34m│ 30720 │ 2 │\u001b[0m\n", + "\u001b[34m│ 31744 │ 2 │\u001b[0m\n", + "\u001b[34m│ 32768 │ 544 │\u001b[0m\n", + "\u001b[34m└─────────────────────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [ReportStats]: MM Stats: #MatMults 449876 #MatMult-Transposes 71552\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: report_stats finished after 0.318 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: curr_vmrss: 5722mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump after report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 27\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Done debug_dump after report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: Running assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Inputs to assign_trigger_engine: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [AssignTriggerEngine]: Assigned trigger engine for 14631 DMA instructions\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: assign_trigger_engine finished after 0.340 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: curr_vmrss: 5747mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump after assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 28\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Done debug_dump after assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: Running alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Inputs to alloc_queues: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: alloc_queues finished after 0.177 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: curr_vmrss: 5742mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump after alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 29\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Done debug_dump after alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: Running dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Inputs to dep_reduction: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [DepReduction]: Start Dependency Reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:00Z INFO 7014 [DepReduction]: Processing async instrs...\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:00Z INFO 7014 [DepReduction]: Processing secondary edges per engine...\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:01Z INFO 7014 [DepReduction]: Processing secondary edges per engine, Done. Num edges removed 827616\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:01Z INFO 7014 [DepReduction]: Processing redundant descendants...\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:03Z INFO 7014 [DepReduction]: Processing redundant descendants, Done. Num edges removed 51154\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:03Z INFO 7014 [DepReduction]: Processing async instrs, Done. Num edges removed 878770\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [DepReduction]: Num Async removed: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [DepReduction]: Finished dependency reduction: 5334354 removed, new total 196341\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [DepReduction]: Finished Dependency Reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z USER 7014 [WalrusDriver]: dep_reduction finished after 12.489 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: curr_vmrss: 5897mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Running debug_dump after dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 30\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Done debug_dump after dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:13Z USER 7014 [WalrusDriver]: Running bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:13Z INFO 7014 [WalrusDriver]: Inputs to bir_racecheck: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z USER 7014 [WalrusDriver]: bir_racecheck finished after 8.019 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: curr_vmrss: 6998mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Running debug_dump after bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 31\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Done debug_dump after bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z USER 7014 [WalrusDriver]: Running lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Inputs to lower_dma: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z USER 7014 [WalrusDriver]: lower_dma finished after 1.298 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: curr_vmrss: 6072mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 32\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662861 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:22Z USER 7014 [WalrusDriver]: Running alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:23Z INFO 7014 [WalrusDriver]: Inputs to alloc_semaphores: modules=1 functions=1 allocs=159263 blocks=1 instructions=662861 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: alloc_semaphores finished after 1.056 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: curr_vmrss: 6047mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump after alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 33\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Done debug_dump after alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662861 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: Running expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Inputs to expand_inst_late: modules=1 functions=1 allocs=159263 blocks=1 instructions=662861 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: expand_inst_late finished after 0.152 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: curr_vmrss: 6047mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump after expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 34\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Done debug_dump after expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662861 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: Running lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Inputs to lower_sync: modules=1 functions=1 allocs=159263 blocks=1 instructions=662861 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: lower_sync finished after 0.445 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: curr_vmrss: 6139mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 35\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 701954 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z USER 7014 [WalrusDriver]: Running lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Inputs to lower_act: modules=1 functions=1 allocs=159263 blocks=1 instructions=701954 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z USER 7014 [WalrusDriver]: lower_act finished after 0.193 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: curr_vmrss: 6151mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 36\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z USER 7014 [WalrusDriver]: Running lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Inputs to lower_dve: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z USER 7014 [WalrusDriver]: lower_dve finished after 1.093 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: curr_vmrss: 6266mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 37\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z USER 7014 [WalrusDriver]: Running lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Inputs to lower_ap: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: lower_ap finished after 0.285 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: curr_vmrss: 6151mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 38\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: Running alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Inputs to alloc_regs: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [AllocRegs]: allocating REG\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [AllocRegs]: main loop iteration 1\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: alloc_regs finished after 0.039 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: curr_vmrss: 6151mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump after alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 39\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Done debug_dump after alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: Running birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Inputs to birverifier: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z USER 7014 [WalrusDriver]: birverifier finished after 1.950 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: curr_vmrss: 6165mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Running debug_dump after birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 40\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Done debug_dump after birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z USER 7014 [WalrusDriver]: Running codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Inputs to codegen: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: Total compiler allocated DRAM tensors: 0.492149 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: Total un-allocated DRAM tensors by kind:\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────────┬─────────────┐\u001b[0m\n", + "\u001b[34m│ TensorKind │ Size (GB) │\u001b[0m\n", + "\u001b[34m├────────────────┼─────────────┤\u001b[0m\n", + "\u001b[34m│ ExternalInput │ 6.10352e-05 │\u001b[0m\n", + "\u001b[34m│ ExternalOutput │ 0.00292969 │\u001b[0m\n", + "\u001b[34m│ Const │ 0.0923445 │\u001b[0m\n", + "\u001b[34m└────────────────┴─────────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: Total runtime managed DRAM tensors: 0.0953353 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: Instruction Stats:\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: \u001b[0m\n", + "\u001b[34m┌──────────────────────┬────────┐\u001b[0m\n", + "\u001b[34m│ Opcode │ Count │\u001b[0m\n", + "\u001b[34m├──────────────────────┼────────┤\u001b[0m\n", + "\u001b[34m│ MATMUL │ 513016 │\u001b[0m\n", + "\u001b[34m│ LDWEIGHTS │ 513016 │\u001b[0m\n", + "\u001b[34m│ ACTIVATE │ 104649 │\u001b[0m\n", + "\u001b[34m│ TENSOR_TENSOR │ 55507 │\u001b[0m\n", + "\u001b[34m│ EVENT_SEMAPHORE │ 39093 │\u001b[0m\n", + "\u001b[34m│ PSEUDO_DMA_TRIGGER │ 36610 │\u001b[0m\n", + "\u001b[34m│ DRAIN │ 8244 │\u001b[0m\n", + "\u001b[34m│ COPY │ 6136 │\u001b[0m\n", + "\u001b[34m│ TENSOR_REDUCE │ 5952 │\u001b[0m\n", + "\u001b[34m│ ACT_TABLE_LOAD │ 4122 │\u001b[0m\n", + "\u001b[34m│ LOAD_MASK_SELECT │ 2248 │\u001b[0m\n", + "\u001b[34m│ STREAM_SHUFFLE │ 2248 │\u001b[0m\n", + "\u001b[34m│ BATCH_NORM_STATS2 │ 1408 │\u001b[0m\n", + "\u001b[34m│ MEMSET │ 319 │\u001b[0m\n", + "\u001b[34m│ TENSOR_SCALAR │ 64 │\u001b[0m\n", + "\u001b[34m│ RECIPROCAL │ 32 │\u001b[0m\n", + "\u001b[34m│ NOP │ 29 │\u001b[0m\n", + "\u001b[34m│ STREAM_TRANSPOSE │ 20 │\u001b[0m\n", + "\u001b[34m│ BATCH_NORM_AGGREGATE │ 11 │\u001b[0m\n", + "\u001b[34m└──────────────────────┴────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────┬─────────┐\u001b[0m\n", + "\u001b[34m│ Engine │ Count │\u001b[0m\n", + "\u001b[34m├────────────┼─────────┤\u001b[0m\n", + "\u001b[34m│ Pool │ 552 │\u001b[0m\n", + "\u001b[34m│ Activation │ 146952 │\u001b[0m\n", + "\u001b[34m│ PE │ 1028395 │\u001b[0m\n", + "\u001b[34m│ DVE │ 83737 │\u001b[0m\n", + "\u001b[34m│ SP │ 33088 │\u001b[0m\n", + "\u001b[34m└────────────┴─────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: Total instructions: 1292724 (0.0770524 GB)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:33Z INFO 7014 [Codegen]: Number of DMA descriptors on each queue:\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:33Z INFO 7014 [Codegen]: \u001b[0m\n", + "\u001b[34m┌───────────────────┬─────────┐\u001b[0m\n", + "\u001b[34m│ Queue │ Count │\u001b[0m\n", + "\u001b[34m├───────────────────┼─────────┤\u001b[0m\n", + "\u001b[34m│ qActSpillReload0 │ 2585854 │\u001b[0m\n", + "\u001b[34m│ qDVESpillReload0 │ 426688 │\u001b[0m\n", + "\u001b[34m│ qPoolPIO0 │ 1536 │\u001b[0m\n", + "\u001b[34m│ qPoolSpillReload0 │ 4864 │\u001b[0m\n", + "\u001b[34m│ qSPPIO0 │ 64 │\u001b[0m\n", + "\u001b[34m│ qSPSpillReload0 │ 6706518 │\u001b[0m\n", + "\u001b[34m└───────────────────┴─────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:33Z INFO 7014 [Codegen]: Total descriptors: 9725524 (0.144922 GB)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:37Z INFO 7014 [Codegen]: Estimated peak DRAM usage: 0.809459 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:38Z USER 7014 [WalrusDriver]: codegen finished after 9.312 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: curr_vmrss: 7387mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Running debug_dump after codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 41\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Done debug_dump after codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z USER 7014 [WalrusDriver]: Running neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Inputs to neff_packager: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z USER 7014 [WalrusDriver]: neff_packager finished after 0.201 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: curr_vmrss: 6586mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Running debug_dump after neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 42\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Done debug_dump after neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.WalrusDriver.0]: Job #0 finished\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Finished job job.WalrusDriver.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Starting job job.BIRLinker.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.BIRLinker.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp_qegp74y/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-ta6seopo/sg00\", \"state_id\": \"sg00\"}' --pipeline BIRLinker\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.BIRLinker.0]: BIRLinker cwd: /opt/ml/code/neuronxcc-ta6seopo\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.BIRLinker.0]: Linking not needed. Netlist doesnt exist\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Finished job job.BIRLinker.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Starting job job.Kelper.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.Kelper.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp_qegp74y/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-ta6seopo/sg00\", \"state_id\": \"sg00\"}' --pipeline Kelper\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:57Z INFO 7014 [job.Kelper.0]: IR signature: 17eb18d15504fbbdaee28a808265cd54 for neff artifacts\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [job.Kelper.0]: neuronxcc version is 2.9.0.40+07376825f, neff version is 1.0 (features 0)\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z USER 7014 [job.Kelper.0]: Wrote /tmp/tmp_qegp74y/graph.neff\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [pipeline.Pipeline.0]: Finished job job.Kelper.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [pipeline.Pipeline.0]: Finished pipeline Pipeline\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [pipeline.Pipeline.0]: Job #0 finished\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z USER 7014 [root]: Compiler status PASS\u001b[0m\n", + "\u001b[34m2023-09-30T05:08:58Z INFO 6950 [root]: Subcommand returned with exitcode=0\u001b[0m\n", + "\u001b[34mDone. Elapsed time: 355390.53106307983ms\u001b[0m\n", + "\u001b[34mLoading pipeline components...: 0%| | 0/6 [00:00 ACT: 19969\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [PeepholeOpts]: COPY -> ACT: 17590\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [PeepholeOpts]: RECIPROCAL -> ACT: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: peephole_opts finished after 0.312 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: curr_vmrss: 10544mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump after peephole_opts\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 18\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Done debug_dump after peephole_opts\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: Running lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Inputs to lower_kernel: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Started running LowerKernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Start of kernel lowering pass, number of insts: 243631, number of allocs: 88876\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Scan BKs time (s): 0.039997\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Lower BKs time (s): 5e-06\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: lower_kernel finished after 0.056 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: curr_vmrss: 10544mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 19\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: Running build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Inputs to build_fdeps: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [build_flow_deps]: Start build fdeps. Invocation: 2Sat Sep 30 05:13:38 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [build_flow_deps]: Allocs: 88876 instructions: 243631\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [build_flow_deps]: Build fdeps inserted 672517 edges \u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [build_flow_deps]: Done build fdeps 672517 Sat Sep 30 05:13:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: build_fdeps finished after 0.644 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: curr_vmrss: 10752mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump after build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 20\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Done debug_dump after build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: Running remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Inputs to remove_redundancies: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove_clobbered_writes\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove_clobbered_writes: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove_useless_insts\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove Useless Instructions: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: remove_redundancies finished after 0.185 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: curr_vmrss: 10744mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump after remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 21\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Done debug_dump after remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-64-128]: Finished analyzing 21295 access patterns a mean/median 2.76877/1 intervals per access pattern and mean/median 2.89342/1.99882 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-32-64]: Finished analyzing 24257 access patterns a mean/median 2.82121/1 intervals per access pattern and mean/median 3.17518/1.85728 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 24176 access patterns a mean/median 2.63584/1.00015 intervals per access pattern and mean/median 3.19031/2.00003 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-64-128]: Finished analyzing 21418 access patterns a mean/median 2.75931/1.00005 intervals per access pattern and mean/median 2.91006/1.98947 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-32-64]: Finished analyzing 23516 access patterns a mean/median 2.67639/1 intervals per access pattern and mean/median 3.12067/1.99893 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 24292 access patterns a mean/median 2.63021/1.00011 intervals per access pattern and mean/median 3.19249/2.00003 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 24271 access patterns a mean/median 2.49681/1 intervals per access pattern and mean/median 3.57787/1.98984 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-64-128]: Finished analyzing 21503 access patterns a mean/median 2.60103/1 intervals per access pattern and mean/median 3.26305/1.98956 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-32-64]: Finished analyzing 23723 access patterns a mean/median 2.52632/1 intervals per access pattern and mean/median 3.66492/1.99851 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-32-64]: Finished analyzing 23718 access patterns a mean/median 2.66435/1.00001 intervals per access pattern and mean/median 3.2163/1.99863 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-64-128]: Finished analyzing 21504 access patterns a mean/median 2.5885/1 intervals per access pattern and mean/median 3.37414/1.97692 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-64-128]: Finished analyzing 21490 access patterns a mean/median 2.44677/1 intervals per access pattern and mean/median 3.55284/1.99841 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 24311 access patterns a mean/median 2.47386/1 intervals per access pattern and mean/median 3.4675/2.00002 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 24326 access patterns a mean/median 2.35271/1 intervals per access pattern and mean/median 3.65296/2.00001 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-64-128]: Finished analyzing 22030 access patterns a mean/median 2.92388/1 intervals per access pattern and mean/median 2.96324/1.82179 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-32-64]: Finished analyzing 23666 access patterns a mean/median 2.38511/1 intervals per access pattern and mean/median 3.71164/1.99849 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-32-64]: Finished analyzing 23694 access patterns a mean/median 2.50692/1 intervals per access pattern and mean/median 3.6265/1.99867 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 24792 access patterns a mean/median 2.78675/1.00035 intervals per access pattern and mean/median 3.1941/1.85633 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 24464 access patterns a mean/median 2.52526/1.00009 intervals per access pattern and mean/median 3.20322/1.99997 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 24610 access patterns a mean/median 2.81353/1.00038 intervals per access pattern and mean/median 3.19392/2.00002 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-64-128]: Finished analyzing 21796 access patterns a mean/median 2.96004/1.00017 intervals per access pattern and mean/median 2.856/1.9988 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-64-128]: Finished analyzing 21850 access patterns a mean/median 2.63735/1 intervals per access pattern and mean/median 3.0524/1.9979 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-32-64]: Finished analyzing 23896 access patterns a mean/median 2.55624/1 intervals per access pattern and mean/median 3.16609/1.99845 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-32-64]: Finished analyzing 23988 access patterns a mean/median 2.8553/1 intervals per access pattern and mean/median 3.17341/1.99852 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 2113 access patterns a mean/median 1/1 intervals per access pattern and mean/median 1.78912/1.08411 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-100-105]: Finished analyzing 425319 access patterns a mean/median 2.14058/1 intervals per access pattern and mean/median 3.30196/1.00023 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-105-128]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.30183/1.00024 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-97-100]: Finished analyzing 425331 access patterns a mean/median 2.14055/1 intervals per access pattern and mean/median 3.30191/1.00023 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 427056 access patterns a mean/median 2.13594/1 intervals per access pattern and mean/median 3.28911/1.00014 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-77-96]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.30183/1.00024 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-68-73]: Finished analyzing 429751 access patterns a mean/median 2.12882/1 intervals per access pattern and mean/median 3.30517/1.00025 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-65-68]: Finished analyzing 429754 access patterns a mean/median 2.12881/1 intervals per access pattern and mean/median 3.30515/1.00028 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-73-77]: Finished analyzing 429745 access patterns a mean/median 2.12884/1 intervals per access pattern and mean/median 3.30521/1.00028 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 430525 access patterns a mean/median 2.12679/1 intervals per access pattern and mean/median 3.30045/1.00027 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-4-9]: Finished analyzing 464067 access patterns a mean/median 2.12778/1 intervals per access pattern and mean/median 3.3226/1.00009 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 464343 access patterns a mean/median 2.12465/1 intervals per access pattern and mean/median 3.321/1.00009 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-1-4]: Finished analyzing 464070 access patterns a mean/median 2.12777/1 intervals per access pattern and mean/median 3.32258/1.00009 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-9-32]: Finished analyzing 463596 access patterns a mean/median 2.12646/1 intervals per access pattern and mean/median 3.32634/1.00009 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-41-64]: Finished analyzing 463534 access patterns a mean/median 2.12661/1 intervals per access pattern and mean/median 3.32687/1.00008 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-33-36]: Finished analyzing 463547 access patterns a mean/median 2.12658/1 intervals per access pattern and mean/median 3.3268/1.00011 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-36-41]: Finished analyzing 463544 access patterns a mean/median 2.12659/1 intervals per access pattern and mean/median 3.32682/1.00009 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 467334 access patterns a mean/median 2.11989/1 intervals per access pattern and mean/median 3.312/1.00006 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z USER 13755 [WalrusDriver]: anti_dependency_analyzer finished after 2.641 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: curr_vmrss: 11269mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 22\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:42Z USER 13755 [WalrusDriver]: Running post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:42Z INFO 13755 [WalrusDriver]: Inputs to post_sched: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:42Z INFO 13755 [post_scheduler]: Start PosT ScheD 3 sunda Sat Sep 30 05:13:42 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:45Z INFO 13755 [post_scheduler]: Tensor CP elimination: 97\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:45Z INFO 13755 [post_scheduler]: Time-aware hwm post-sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:47Z INFO 13755 [post_scheduler]: Time-aware simulation time: 31404281\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [post_scheduler]: Done PosT ScheD Sat Sep 30 05:13:48 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z USER 13755 [WalrusDriver]: post_sched finished after 6.600 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: curr_vmrss: 11176mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Running debug_dump after post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 23\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Done debug_dump after post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z USER 13755 [WalrusDriver]: Running address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Inputs to address_rotation_sb: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:50Z INFO 13755 [DMAOptimizationBase]: PSUM Rotation rotated 12187 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:50Z INFO 13755 [DMAOptimizationBase]: PSUM Rotation rotated 11175 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:51Z INFO 13755 [DMAOptimizationBase]: PSUM Rotation rotated 1889 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:51Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 26 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:52Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 1619 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:53Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 172 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:55Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 496 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:56Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 9468 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 14 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 1 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z USER 13755 [WalrusDriver]: address_rotation_sb finished after 9.336 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: curr_vmrss: 11151mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Running debug_dump after address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 24\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Done debug_dump after address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:58Z USER 13755 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:58Z INFO 13755 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:58Z INFO 13755 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:58Z INFO 13755 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 2113 access patterns a mean/median 1/1 intervals per access pattern and mean/median 1.78912/1.0593 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 24186 access patterns a mean/median 2.60366/1.00016 intervals per access pattern and mean/median 3.35077/1.99998 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-64-128]: Finished analyzing 21554 access patterns a mean/median 2.71203/1 intervals per access pattern and mean/median 3.15596/1.99833 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 24361 access patterns a mean/median 2.56237/1.00002 intervals per access pattern and mean/median 3.35735/2 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 24307 access patterns a mean/median 2.59028/1.00003 intervals per access pattern and mean/median 3.19146/2 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-64-128]: Finished analyzing 21511 access patterns a mean/median 2.67472/1 intervals per access pattern and mean/median 3.22437/1.99829 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 24312 access patterns a mean/median 2.41757/1 intervals per access pattern and mean/median 3.35383/2.00001 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 24388 access patterns a mean/median 2.67238/1 intervals per access pattern and mean/median 3.35263/2.00003 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 24463 access patterns a mean/median 2.69386/1.00021 intervals per access pattern and mean/median 3.1555/1.99947 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-32-64]: Finished analyzing 23784 access patterns a mean/median 2.61995/1 intervals per access pattern and mean/median 3.43968/1.99833 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-64-128]: Finished analyzing 21526 access patterns a mean/median 2.50033/1 intervals per access pattern and mean/median 3.28288/1.99763 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-32-64]: Finished analyzing 23748 access patterns a mean/median 2.5974/1 intervals per access pattern and mean/median 3.3895/1.99897 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-64-128]: Finished analyzing 21629 access patterns a mean/median 2.7962/1.00006 intervals per access pattern and mean/median 3.30205/1.99933 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-32-64]: Finished analyzing 23765 access patterns a mean/median 2.71092/1.00001 intervals per access pattern and mean/median 3.48949/1.99846 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-64-128]: Finished analyzing 21674 access patterns a mean/median 2.73752/1 intervals per access pattern and mean/median 3.30142/1.99813 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-64-128]: Finished analyzing 21418 access patterns a mean/median 2.73504/1.00003 intervals per access pattern and mean/median 3.06773/1.99969 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-32-64]: Finished analyzing 23529 access patterns a mean/median 2.64333/1.00002 intervals per access pattern and mean/median 3.31854/1.99816 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-64-128]: Finished analyzing 21930 access patterns a mean/median 2.82832/1 intervals per access pattern and mean/median 3.12841/1.99904 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-32-64]: Finished analyzing 23954 access patterns a mean/median 2.72485/1 intervals per access pattern and mean/median 3.35914/1.99758 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-32-64]: Finished analyzing 23825 access patterns a mean/median 2.64185/1.00001 intervals per access pattern and mean/median 3.65485/1.99817 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-64-128]: Finished analyzing 21644 access patterns a mean/median 2.70565/1 intervals per access pattern and mean/median 3.34393/1.99998 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-32-64]: Finished analyzing 24033 access patterns a mean/median 2.61857/1 intervals per access pattern and mean/median 3.68052/1.99829 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-32-64]: Finished analyzing 23820 access patterns a mean/median 2.44156/1 intervals per access pattern and mean/median 3.50126/1.998 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 24649 access patterns a mean/median 2.59207/1.00011 intervals per access pattern and mean/median 3.4651/2.00003 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 24576 access patterns a mean/median 2.58793/1 intervals per access pattern and mean/median 3.48391/1.99999 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-100-105]: Finished analyzing 425319 access patterns a mean/median 2.14058/1 intervals per access pattern and mean/median 3.33048/1.00007 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-97-100]: Finished analyzing 425331 access patterns a mean/median 2.14055/1 intervals per access pattern and mean/median 3.33043/1.00007 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 427461 access patterns a mean/median 2.13487/1 intervals per access pattern and mean/median 3.31506/1.00004 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-105-128]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.33025/1.00006 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-77-96]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.33025/1.00006 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 430430 access patterns a mean/median 2.12704/1 intervals per access pattern and mean/median 3.32905/1.0001 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-68-73]: Finished analyzing 429751 access patterns a mean/median 2.12882/1 intervals per access pattern and mean/median 3.33336/1.00012 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-73-77]: Finished analyzing 429745 access patterns a mean/median 2.12884/1 intervals per access pattern and mean/median 3.33339/1.00007 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-65-68]: Finished analyzing 429754 access patterns a mean/median 2.12881/1 intervals per access pattern and mean/median 3.33334/1.00012 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-1-4]: Finished analyzing 464018 access patterns a mean/median 2.1279/1 intervals per access pattern and mean/median 3.3506/1.00004 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 464024 access patterns a mean/median 2.12542/1 intervals per access pattern and mean/median 3.35063/1.00002 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-33-36]: Finished analyzing 463495 access patterns a mean/median 2.12671/1 intervals per access pattern and mean/median 3.35482/1.00004 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-41-64]: Finished analyzing 463482 access patterns a mean/median 2.12674/1 intervals per access pattern and mean/median 3.3549/1.00001 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-36-41]: Finished analyzing 463492 access patterns a mean/median 2.12671/1 intervals per access pattern and mean/median 3.35484/1.00004 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-9-32]: Finished analyzing 463544 access patterns a mean/median 2.12659/1 intervals per access pattern and mean/median 3.35436/0.999991 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-4-9]: Finished analyzing 464015 access patterns a mean/median 2.12791/1 intervals per access pattern and mean/median 3.35062/1.00002 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 467071 access patterns a mean/median 2.12053/1 intervals per access pattern and mean/median 3.34177/1.00002 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z USER 13755 [WalrusDriver]: anti_dependency_analyzer finished after 2.653 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: curr_vmrss: 11408mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 25\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:00Z USER 13755 [WalrusDriver]: Running dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:01Z INFO 13755 [WalrusDriver]: Inputs to dep_opt: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:02Z INFO 13755 [build_flow_deps]: Start build fdeps. Invocation: 3Sat Sep 30 05:14:02 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:02Z INFO 13755 [build_flow_deps]: Allocs: 88779 instructions: 243534\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [build_flow_deps]: Build fdeps inserted 661744 edges \u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [build_flow_deps]: Done build fdeps 661744 Sat Sep 30 05:14:03 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: dep_opt finished after 2.590 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 11012mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 26\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to report_stats: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [ReportStats]: Data Movement Statistics: sg0000\u001b[0m\n", + "\u001b[34m┌──────────────┬────────────────────────────┬───────┬────────────┐\u001b[0m\n", + "\u001b[34m│ Instruction │ Kind │ Count │ Bytes │\u001b[0m\n", + "\u001b[34m├──────────────┼────────────────────────────┼───────┼────────────┤\u001b[0m\n", + "\u001b[34m│ Load │ Const -> Internal │ 7590 │ 1732837520 │\u001b[0m\n", + "\u001b[34m│ Load │ ExternalInput -> Internal │ 27 │ 848916 │\u001b[0m\n", + "\u001b[34m│ Load │ Internal │ 1378 │ 244028812 │\u001b[0m\n", + "\u001b[34m│ Save │ Internal │ 578 │ 145522828 │\u001b[0m\n", + "\u001b[34m│ Save │ Internal -> ExternalOutput │ 4 │ 65536 │\u001b[0m\n", + "\u001b[34m│ Save (Spill) │ Internal │ 157 │ 71001088 │\u001b[0m\n", + "\u001b[34m└──────────────┴────────────────────────────┴───────┴────────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [ReportStats]: \u001b[0m\n", + "\u001b[34m┌─────────────────────┬───────┐\u001b[0m\n", + "\u001b[34m│ Bytes per partition │ Count │\u001b[0m\n", + "\u001b[34m├─────────────────────┼───────┤\u001b[0m\n", + "\u001b[34m│ 2 │ 5 │\u001b[0m\n", + "\u001b[34m│ 4 │ 142 │\u001b[0m\n", + "\u001b[34m│ 8 │ 70 │\u001b[0m\n", + "\u001b[34m│ 16 │ 65 │\u001b[0m\n", + "\u001b[34m│ 64 │ 70 │\u001b[0m\n", + "\u001b[34m│ 72 │ 1 │\u001b[0m\n", + "\u001b[34m│ 128 │ 748 │\u001b[0m\n", + "\u001b[34m│ 144 │ 1 │\u001b[0m\n", + "\u001b[34m│ 172 │ 1 │\u001b[0m\n", + "\u001b[34m│ 200 │ 1 │\u001b[0m\n", + "\u001b[34m│ 240 │ 1 │\u001b[0m\n", + "\u001b[34m│ 256 │ 1712 │\u001b[0m\n", + "\u001b[34m│ 384 │ 3 │\u001b[0m\n", + "\u001b[34m│ 400 │ 1 │\u001b[0m\n", + "\u001b[34m│ 460 │ 1 │\u001b[0m\n", + "\u001b[34m│ 480 │ 10 │\u001b[0m\n", + "\u001b[34m│ 484 │ 1 │\u001b[0m\n", + "\u001b[34m│ 500 │ 4 │\u001b[0m\n", + "\u001b[34m│ 504 │ 1 │\u001b[0m\n", + "\u001b[34m│ 512 │ 355 │\u001b[0m\n", + "\u001b[34m│ 640 │ 178 │\u001b[0m\n", + "\u001b[34m│ 768 │ 6 │\u001b[0m\n", + "\u001b[34m│ 1024 │ 28 │\u001b[0m\n", + "\u001b[34m│ 1240 │ 15 │\u001b[0m\n", + "\u001b[34m│ 1280 │ 264 │\u001b[0m\n", + "\u001b[34m│ 1792 │ 50 │\u001b[0m\n", + "\u001b[34m│ 1856 │ 1 │\u001b[0m\n", + "\u001b[34m│ 2048 │ 781 │\u001b[0m\n", + "\u001b[34m│ 2268 │ 4 │\u001b[0m\n", + "\u001b[34m│ 2304 │ 2 │\u001b[0m\n", + "\u001b[34m│ 2520 │ 12 │\u001b[0m\n", + "\u001b[34m│ 2560 │ 4128 │\u001b[0m\n", + "\u001b[34m│ 2816 │ 17 │\u001b[0m\n", + "\u001b[34m│ 3072 │ 7 │\u001b[0m\n", + "\u001b[34m│ 3840 │ 428 │\u001b[0m\n", + "\u001b[34m│ 4096 │ 480 │\u001b[0m\n", + "\u001b[34m│ 4160 │ 10 │\u001b[0m\n", + "\u001b[34m│ 5120 │ 68 │\u001b[0m\n", + "\u001b[34m│ 5760 │ 4 │\u001b[0m\n", + "\u001b[34m│ 7680 │ 4 │\u001b[0m\n", + "\u001b[34m│ 8192 │ 38 │\u001b[0m\n", + "\u001b[34m│ 10240 │ 4 │\u001b[0m\n", + "\u001b[34m│ 16644 │ 12 │\u001b[0m\n", + "\u001b[34m└─────────────────────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [ReportStats]: MM Stats: #MatMults 160587 #MatMult-Transposes 42386\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: report_stats finished after 0.056 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 10985mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 27\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to assign_trigger_engine: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [AssignTriggerEngine]: Assigned trigger engine for 735 DMA instructions\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: assign_trigger_engine finished after 0.076 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 10996mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 28\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to alloc_queues: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: alloc_queues finished after 0.038 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 10995mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 29\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to dep_reduction: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [DepReduction]: Start Dependency Reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [DepReduction]: Processing async instrs...\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [DepReduction]: Processing secondary edges per engine...\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing secondary edges per engine, Done. Num edges removed 146530\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing redundant descendants...\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing redundant descendants, Done. Num edges removed 10877\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing async instrs, Done. Num edges removed 157407\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [DepReduction]: Num Async removed: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [DepReduction]: Finished dependency reduction: 1592440 removed, new total 95811\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [DepReduction]: Finished Dependency Reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z USER 13755 [WalrusDriver]: dep_reduction finished after 3.450 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: curr_vmrss: 11044mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Running debug_dump after dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 30\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Done debug_dump after dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z USER 13755 [WalrusDriver]: Running bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Inputs to bir_racecheck: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: bir_racecheck finished after 3.741 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: curr_vmrss: 11768mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump after bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 31\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Done debug_dump after bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: Running lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Inputs to lower_dma: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: lower_dma finished after 0.283 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: curr_vmrss: 11092mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 32\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243538 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: Running alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Inputs to alloc_semaphores: modules=1 functions=1 allocs=88779 blocks=1 instructions=243538 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: alloc_semaphores finished after 0.311 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11087mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 33\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243538 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to expand_inst_late: modules=1 functions=1 allocs=88779 blocks=1 instructions=243538 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: expand_inst_late finished after 0.033 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11087mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 34\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243538 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_sync: modules=1 functions=1 allocs=88779 blocks=1 instructions=243538 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_sync finished after 0.109 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11113mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 35\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253555 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_act: modules=1 functions=1 allocs=88779 blocks=1 instructions=253555 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_act finished after 0.043 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11116mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 36\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_dve: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_dve finished after 0.249 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11155mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 37\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_ap: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_ap finished after 0.071 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11115mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 38\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to alloc_regs: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [AllocRegs]: allocating REG\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [AllocRegs]: main loop iteration 1\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: alloc_regs finished after 0.008 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11115mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 39\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to birverifier: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z USER 13755 [WalrusDriver]: birverifier finished after 0.570 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: curr_vmrss: 11121mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Running debug_dump after birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 40\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Done debug_dump after birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z USER 13755 [WalrusDriver]: Running codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Inputs to codegen: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: Total compiler allocated DRAM tensors: 0.0509834 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: Total un-allocated DRAM tensors by kind:\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────────┬─────────────┐\u001b[0m\n", + "\u001b[34m│ TensorKind │ Size (GB) │\u001b[0m\n", + "\u001b[34m├────────────────┼─────────────┤\u001b[0m\n", + "\u001b[34m│ ExternalInput │ 0.000431065 │\u001b[0m\n", + "\u001b[34m│ ExternalOutput │ 6.10352e-05 │\u001b[0m\n", + "\u001b[34m│ Const │ 1.63908 │\u001b[0m\n", + "\u001b[34m└────────────────┴─────────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: Total runtime managed DRAM tensors: 1.63958 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Instruction Stats:\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────────────┬────────┐\u001b[0m\n", + "\u001b[34m│ Opcode │ Count │\u001b[0m\n", + "\u001b[34m├────────────────────┼────────┤\u001b[0m\n", + "\u001b[34m│ MATMUL │ 171207 │\u001b[0m\n", + "\u001b[34m│ LDWEIGHTS │ 171207 │\u001b[0m\n", + "\u001b[34m│ ACTIVATE │ 47296 │\u001b[0m\n", + "\u001b[34m│ TENSOR_REDUCE │ 16960 │\u001b[0m\n", + "\u001b[34m│ EVENT_SEMAPHORE │ 10017 │\u001b[0m\n", + "\u001b[34m│ PSEUDO_DMA_TRIGGER │ 9734 │\u001b[0m\n", + "\u001b[34m│ TENSOR_TENSOR │ 6259 │\u001b[0m\n", + "\u001b[34m│ RECIPROCAL │ 1202 │\u001b[0m\n", + "\u001b[34m│ MEMSET │ 591 │\u001b[0m\n", + "\u001b[34m│ TENSOR_SCALAR │ 457 │\u001b[0m\n", + "\u001b[34m│ LOAD_MASK_SELECT │ 444 │\u001b[0m\n", + "\u001b[34m│ STREAM_SHUFFLE │ 444 │\u001b[0m\n", + "\u001b[34m│ DRAIN │ 416 │\u001b[0m\n", + "\u001b[34m│ ACT_TABLE_LOAD │ 208 │\u001b[0m\n", + "\u001b[34m│ NOP │ 4 │\u001b[0m\n", + "\u001b[34m│ CAST │ 2 │\u001b[0m\n", + "\u001b[34m│ IOTA │ 2 │\u001b[0m\n", + "\u001b[34m└────────────────────┴────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────┬────────┐\u001b[0m\n", + "\u001b[34m│ Engine │ Count │\u001b[0m\n", + "\u001b[34m├────────────┼────────┤\u001b[0m\n", + "\u001b[34m│ Pool │ 36 │\u001b[0m\n", + "\u001b[34m│ Activation │ 51073 │\u001b[0m\n", + "\u001b[34m│ PE │ 343861 │\u001b[0m\n", + "\u001b[34m│ DVE │ 27666 │\u001b[0m\n", + "\u001b[34m│ SP │ 13814 │\u001b[0m\n", + "\u001b[34m└────────────┴────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Total instructions: 436450 (0.0260144 GB)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Number of DMA descriptors on each queue:\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: \u001b[0m\n", + "\u001b[34m┌───────────────────┬─────────┐\u001b[0m\n", + "\u001b[34m│ Queue │ Count │\u001b[0m\n", + "\u001b[34m├───────────────────┼─────────┤\u001b[0m\n", + "\u001b[34m│ qActSpillReload0 │ 121088 │\u001b[0m\n", + "\u001b[34m│ qDVESpillReload0 │ 48838 │\u001b[0m\n", + "\u001b[34m│ qPoolPIO0 │ 32 │\u001b[0m\n", + "\u001b[34m│ qPoolSpillReload0 │ 5376 │\u001b[0m\n", + "\u001b[34m│ qSPPIO0 │ 177358 │\u001b[0m\n", + "\u001b[34m│ qSPSpillReload0 │ 2129909 │\u001b[0m\n", + "\u001b[34m└───────────────────┴─────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Total descriptors: 2482601 (0.0369936 GB)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [Codegen]: Estimated peak DRAM usage: 1.75357 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z USER 13755 [WalrusDriver]: codegen finished after 2.569 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: curr_vmrss: 11499mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Running debug_dump after codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 41\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Done debug_dump after codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z USER 13755 [WalrusDriver]: Running neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Inputs to neff_packager: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z USER 13755 [WalrusDriver]: neff_packager finished after 0.044 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: curr_vmrss: 11273mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Running debug_dump after neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 42\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Done debug_dump after neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.WalrusDriver.0]: Job #0 finished\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Finished job job.WalrusDriver.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Starting job job.BIRLinker.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.BIRLinker.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmpas37esby/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-sd2ev3s2/sg00\", \"state_id\": \"sg00\"}' --pipeline BIRLinker\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.BIRLinker.0]: BIRLinker cwd: /opt/ml/code/neuronxcc-sd2ev3s2\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.BIRLinker.0]: Linking not needed. Netlist doesnt exist\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Finished job job.BIRLinker.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Starting job job.Kelper.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.Kelper.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmpas37esby/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-sd2ev3s2/sg00\", \"state_id\": \"sg00\"}' --pipeline Kelper\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:27Z INFO 13755 [job.Kelper.0]: IR signature: cae5abfa2b9514e8ed25d48d054e7615 for neff artifacts\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [job.Kelper.0]: neuronxcc version is 2.9.0.40+07376825f, neff version is 1.0 (features 0)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:30Z USER 13755 [job.Kelper.0]: Wrote /tmp/tmpas37esby/graph.neff\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [pipeline.Pipeline.0]: Finished job job.Kelper.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [pipeline.Pipeline.0]: Finished pipeline Pipeline\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [pipeline.Pipeline.0]: Job #0 finished\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:31Z USER 13755 [root]: Compiler status PASS\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:31Z INFO 13691 [root]: Subcommand returned with exitcode=0\u001b[0m\n", + "\u001b[34mDone. Elapsed time: 338407.5057506561ms\u001b[0m\n", + "\u001b[34mLoading pipeline components...: 0%| | 0/6 [00:00 ACT: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [PeepholeOpts]: COPY -> ACT: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [PeepholeOpts]: RECIPROCAL -> ACT: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: peephole_opts finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after peephole_opts\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 18\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after peephole_opts\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_kernel: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Started running LowerKernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Start of kernel lowering pass, number of insts: 26, number of allocs: 22\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Scan BKs time (s): 4e-06\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Lower BKs time (s): 3e-06\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_kernel finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 19\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_kernel\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to build_fdeps: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Start build fdeps. Invocation: 2Sat Sep 30 05:14:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Allocs: 22 instructions: 26\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Build fdeps inserted 40 edges \u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Done build fdeps 40 Sat Sep 30 05:14:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: build_fdeps finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 20\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after build_fdeps\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to remove_redundancies: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove_clobbered_writes\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove_clobbered_writes: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove_useless_insts\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove Useless Instructions: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: remove_redundancies finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 21\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after remove_redundancies\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 0 access patterns a mean/median -nan/0 intervals per access pattern and mean/median -nan/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-96-100]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-64-68]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-32-36]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-0-4]: Finished analyzing 33 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: anti_dependency_analyzer finished after 0.001 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 22\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to post_sched: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Start PosT ScheD 3 sunda Sat Sep 30 05:14:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Tensor CP elimination: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Time-aware hwm post-sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Time-aware simulation time: 6596\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Done PosT ScheD Sat Sep 30 05:14:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: post_sched finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 23\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after post_sched\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to address_rotation_sb: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: PSUM Rotation rotated 0 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: PSUM Rotation rotated 0 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: PSUM Rotation rotated 0 PSUM Banks\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: address_rotation_sb finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 24\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after address_rotation_sb\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 0 access patterns a mean/median -nan/0 intervals per access pattern and mean/median -nan/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-96-100]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-64-68]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-32-36]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-0-4]: Finished analyzing 33 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: anti_dependency_analyzer finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 25\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to dep_opt: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Start build fdeps. Invocation: 3Sat Sep 30 05:14:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Allocs: 22 instructions: 26\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Build fdeps inserted 40 edges \u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Done build fdeps 40 Sat Sep 30 05:14:39 2023\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: dep_opt finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 26\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after dep_opt\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to report_stats: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [ReportStats]: Data Movement Statistics: sg0000\u001b[0m\n", + "\u001b[34m┌─────────────┬────────────────────────────┬───────┬───────┐\u001b[0m\n", + "\u001b[34m│ Instruction │ Kind │ Count │ Bytes │\u001b[0m\n", + "\u001b[34m├─────────────┼────────────────────────────┼───────┼───────┤\u001b[0m\n", + "\u001b[34m│ Load │ Const -> Internal │ 2 │ 48 │\u001b[0m\n", + "\u001b[34m│ Load │ ExternalInput -> Internal │ 4 │ 65536 │\u001b[0m\n", + "\u001b[34m│ Save │ Internal -> ExternalOutput │ 4 │ 65536 │\u001b[0m\n", + "\u001b[34m└─────────────┴────────────────────────────┴───────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [ReportStats]: \u001b[0m\n", + "\u001b[34m┌─────────────────────┬───────┐\u001b[0m\n", + "\u001b[34m│ Bytes per partition │ Count │\u001b[0m\n", + "\u001b[34m├─────────────────────┼───────┤\u001b[0m\n", + "\u001b[34m│ 4 │ 1 │\u001b[0m\n", + "\u001b[34m│ 8 │ 1 │\u001b[0m\n", + "\u001b[34m│ 4096 │ 8 │\u001b[0m\n", + "\u001b[34m└─────────────────────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [ReportStats]: MM Stats: #MatMults 8 #MatMult-Transposes 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: report_stats finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 27\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after report_stats\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to assign_trigger_engine: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AssignTriggerEngine]: Assigned trigger engine for 0 DMA instructions\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: assign_trigger_engine finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 28\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after assign_trigger_engine\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to alloc_queues: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: alloc_queues finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 29\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after alloc_queues\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to dep_reduction: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Start Dependency Reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing async instrs...\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing secondary edges per engine...\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing secondary edges per engine, Done. Num edges removed 18\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing redundant descendants...\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing redundant descendants, Done. Num edges removed 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing async instrs, Done. Num edges removed 18\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Num Async removed: 0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Finished dependency reduction: 22 removed, new total 18\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Finished Dependency Reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: dep_reduction finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 30\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after dep_reduction\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to bir_racecheck: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: bir_racecheck finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 31\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after bir_racecheck\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_dma: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_dma finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 32\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_dma\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 30 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to alloc_semaphores: modules=1 functions=1 allocs=22 blocks=1 instructions=30 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: alloc_semaphores finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 33\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after alloc_semaphores\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 30 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to expand_inst_late: modules=1 functions=1 allocs=22 blocks=1 instructions=30 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: expand_inst_late finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 34\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after expand_inst_late\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 30 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_sync: modules=1 functions=1 allocs=22 blocks=1 instructions=30 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_sync finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 35\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_sync\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 32 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_act: modules=1 functions=1 allocs=22 blocks=1 instructions=32 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_act finished after 0.001 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 36\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_act\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_dve: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_dve finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 37\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_dve\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_ap: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_ap finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 38\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_ap\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to alloc_regs: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AllocRegs]: allocating REG\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AllocRegs]: main loop iteration 1\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: alloc_regs finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 39\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after alloc_regs\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to birverifier: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: birverifier finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 40\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after birverifier\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to codegen: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total compiler allocated DRAM tensors: 0 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total un-allocated DRAM tensors by kind: \u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────────┬─────────────┐\u001b[0m\n", + "\u001b[34m│ TensorKind │ Size (GB) │\u001b[0m\n", + "\u001b[34m├────────────────┼─────────────┤\u001b[0m\n", + "\u001b[34m│ ExternalInput │ 6.10352e-05 │\u001b[0m\n", + "\u001b[34m│ ExternalOutput │ 6.10352e-05 │\u001b[0m\n", + "\u001b[34m│ Const │ 4.47035e-08 │\u001b[0m\n", + "\u001b[34m└────────────────┴─────────────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total runtime managed DRAM tensors: 0.000122115 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Instruction Stats: \u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────────────┬───────┐\u001b[0m\n", + "\u001b[34m│ Opcode │ Count │\u001b[0m\n", + "\u001b[34m├────────────────────┼───────┤\u001b[0m\n", + "\u001b[34m│ PSEUDO_DMA_TRIGGER │ 10 │\u001b[0m\n", + "\u001b[34m│ LDWEIGHTS │ 8 │\u001b[0m\n", + "\u001b[34m│ MATMUL │ 8 │\u001b[0m\n", + "\u001b[34m│ ACTIVATE │ 8 │\u001b[0m\n", + "\u001b[34m│ NOP │ 4 │\u001b[0m\n", + "\u001b[34m│ EVENT_SEMAPHORE │ 2 │\u001b[0m\n", + "\u001b[34m│ DRAIN │ 2 │\u001b[0m\n", + "\u001b[34m│ ACT_TABLE_LOAD │ 1 │\u001b[0m\n", + "\u001b[34m└────────────────────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", + "\u001b[34m┌────────────┬───────┐\u001b[0m\n", + "\u001b[34m│ Engine │ Count │\u001b[0m\n", + "\u001b[34m├────────────┼───────┤\u001b[0m\n", + "\u001b[34m│ Pool │ 8 │\u001b[0m\n", + "\u001b[34m│ Activation │ 12 │\u001b[0m\n", + "\u001b[34m│ PE │ 17 │\u001b[0m\n", + "\u001b[34m│ DVE │ 0 │\u001b[0m\n", + "\u001b[34m│ SP │ 6 │\u001b[0m\n", + "\u001b[34m└────────────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total instructions: 43 (2.563e-06 GB)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Number of DMA descriptors on each queue:\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", + "\u001b[34m┌─────────────────┬───────┐\u001b[0m\n", + "\u001b[34m│ Queue │ Count │\u001b[0m\n", + "\u001b[34m├─────────────────┼───────┤\u001b[0m\n", + "\u001b[34m│ qPoolPIO0 │ 32 │\u001b[0m\n", + "\u001b[34m│ qSPPIO0 │ 32 │\u001b[0m\n", + "\u001b[34m│ qSPSpillReload0 │ 10 │\u001b[0m\n", + "\u001b[34m└─────────────────┴───────┘\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total descriptors: 74 (1.10269e-06 GB)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Estimated peak DRAM usage: 0.000125781 GB\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: codegen finished after 0.002 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 41\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after codegen\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to neff_packager: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: neff_packager finished after 0.000 seconds\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 192mb, ru_maxrss: 192mb (delta=0mb)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 42\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after neff_packager\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.WalrusDriver.0]: Job #0 finished\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished job job.WalrusDriver.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Starting job job.BIRLinker.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.BIRLinker.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp3vi08kcp/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-zanzi8tu/sg00\", \"state_id\": \"sg00\"}' --pipeline BIRLinker\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.BIRLinker.0]: BIRLinker cwd: /opt/ml/code/neuronxcc-zanzi8tu\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.BIRLinker.0]: Linking not needed. Netlist doesnt exist\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished job job.BIRLinker.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Starting job job.Kelper.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.Kelper.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp3vi08kcp/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-zanzi8tu/sg00\", \"state_id\": \"sg00\"}' --pipeline Kelper\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.Kelper.0]: IR signature: 86354cbd21e441ccd6a3e39a830230a7 for neff artifacts\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.Kelper.0]: neuronxcc version is 2.9.0.40+07376825f, neff version is 1.0 (features 0)\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [job.Kelper.0]: Wrote /tmp/tmp3vi08kcp/graph.neff\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished job job.Kelper.0\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished pipeline Pipeline\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Job #0 finished\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z USER 13993 [root]: Compiler status PASS\u001b[0m\n", + "\u001b[34m2023-09-30T05:14:39Z INFO 13929 [root]: Subcommand returned with exitcode=0\u001b[0m\n", + "\u001b[34mDone. Elapsed time: 987.6930713653564ms\u001b[0m\n", + "\u001b[34m2023-09-30 05:14:40,875 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code.\u001b[0m\n", + "\u001b[34m2023-09-30 05:14:40,875 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n", + "\u001b[34m2023-09-30 05:14:40,875 sagemaker-training-toolkit INFO Reporting training SUCCESS\u001b[0m\n", + "\n", + "2023-09-30 05:14:47 Uploading - Uploading generated training model\n", + "2023-09-30 05:16:14 Completed - Training job completed\n", + "Training seconds: 1141\n", + "Billable seconds: 1141\n" + ] + } + ], + "source": [ + "# it takes around 1141 seconds to complete the job on a trn1.32xlarge\n", + "# You will run this just once to compile the model.\n", + "estimator.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "1338cea2-a0ab-4a6f-b4da-c3307d976e13", + "metadata": {}, + "source": [ + "## 5) Deploy the model to inferentia2\n", + "We compiled the model in one region but we'll deploy to another region. So, we need to copy the models artifacts first and then create a PyTorchModel" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d523f809-ff03-4ca8-be29-a92bd4b25a7d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Bucket': 'sagemaker-us-east-1-772327914095', 'Key': 'output/pytorch-training-neuronx-2023-09-30-04-54-35-074/output/model.tar.gz'}\n", + "s3://sagemaker-us-east-2-772327914095/stable-diffusion-neuron-inferentia/model.tar.gz\n" + ] + } + ], + "source": [ + "import boto3\n", + "s3 = boto3.resource('s3', region_name=region_trn1)\n", + "\n", + "model_name=\"stable-diffusion-neuron-inferentia\"\n", + "model_data=f\"s3://{sess_inf2.default_bucket()}/{model_name}/model.tar.gz\"\n", + "copy_source = {\n", + " 'Bucket': sess_trn1.default_bucket(),\n", + " 'Key': estimator.model_data.split('/', 3)[-1]\n", + "}\n", + "print(copy_source)\n", + "print(model_data)\n", + "s3.meta.client.copy(copy_source, sess_inf2.default_bucket(), f'{model_name}/model.tar.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4a202e4f-30cf-4c72-8fd4-f3a6bf408d19", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instance type: ml.inf2.8xlarge. Num SM workers: 1\n" + ] + } + ], + "source": [ + "import logging\n", + "from sagemaker.pytorch.model import PyTorchModel\n", + "from sagemaker.utils import name_from_base\n", + "\n", + "# depending on the inf2 instance you deploy the model you'll have more or less accelerators\n", + "# we'll ask SageMaker to launch 1 worker per accelerator\n", + "model_data=model_data\n", + "instance_type_idx=1 # default ml.inf2.8xlarge\n", + "instance_types=['ml.inf2.xlarge', 'ml.inf2.8xlarge', 'ml.inf2.24xlarge','ml.inf2.48xlarge']\n", + "num_workers=[1,1,6,12]\n", + "\n", + "print(f\"Instance type: {instance_types[instance_type_idx]}. Num SM workers: {num_workers[instance_type_idx]}\")\n", + "pytorch_model = PyTorchModel(\n", + " image_uri=f\"763104351884.dkr.ecr.{region_inf2}.amazonaws.com/{inference_image_name}:{image_tag}\",\n", + " model_data=model_data,\n", + " role=role, \n", + " name=name_from_base('sd-inf2'),\n", + " sagemaker_session=sess_inf2,\n", + " container_log_level=logging.NOTSET,\n", + " model_server_workers=num_workers[instance_type_idx], # 1 worker per inferentia chip\n", + " framework_version=\"1.13.1\",\n", + " env = {\n", + " 'SAGEMAKER_MODEL_SERVER_TIMEOUT' : '3600' \n", + " }\n", + " # for production it is important to define vpc_config and use a vpc_endpoint\n", + " #vpc_config={\n", + " # 'Subnets': ['', ''],\n", + " # 'SecurityGroupIds': ['', '']\n", + " #}\n", + ")\n", + "pytorch_model._is_compiled_model = True" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "733f2b8d-0d21-493d-8d35-b68e85eabc05", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating model with name: sd-inf2-2023-09-30-07-23-44-709\n", + "INFO:sagemaker:Creating endpoint-config with name sd-inf2-ml-inf2-2023-09-30-07-23-50-979\n", + "INFO:sagemaker:Creating endpoint with name sd-inf2-ml-inf2-2023-09-30-07-23-50-979\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------!" + ] + } + ], + "source": [ + "predictor = pytorch_model.deploy(\n", + " initial_instance_count=1,\n", + " instance_type=instance_types[instance_type_idx],\n", + " model_data_download_timeout=3600, # it takes some time to download all the artifacts and load the model\n", + " container_startup_health_check_timeout=1800\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a9fac815-9715-4a17-a748-86f9d1e2dd32", + "metadata": {}, + "source": [ + "## 6) Run a simple test" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c9166516-8b68-409d-b877-a291d9525f83", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.serializers import JSONSerializer\n", + "from sagemaker.deserializers import BytesDeserializer\n", + "predictor.serializer = JSONSerializer()\n", + "predictor.deserializer = BytesDeserializer(accept='image/jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "769b7e0b-045d-442b-911a-5fd045d8e483", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import io\n", + "import time\n", + "from PIL import Image\n", + "import requests\n", + "import base64" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "99228241-0e98-4d97-bfda-b62733722905", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def download_image(url):\n", + " response = requests.get(url)\n", + " encoded_image = base64.b64encode(response.content).decode('UTF-8')\n", + " return encoded_image" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1872a60c-b1c2-44b0-ae34-11599ef56f89", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#prompt = \"a photo of an astronaut riding a horse on mars\"\n", + "prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n", + "img_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n", + "mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n", + "init_image = download_image(img_url)\n", + "mask_image = download_image(mask_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4dee2aff-3d12-4be8-8077-bd9feb161200", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "input_req={\n", + " \"prompt\": prompt,\n", + " \"init_image\": init_image,\n", + " \"mask_image\": mask_image\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "17953fca-c0b8-4d7f-a3c2-e6f3b6dead20", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/jpeg": 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XLt9qUVxckEggcUyVUVV2nuCAK5mNmLs5OcmtwRyzJFtyOKVxWNXTLlZZT7cVLfCRoXReSaoaJbvHM+T3roxAGUk80wOOsNNmW9Lyetdhaw8KKgRU8/YAAa0fLMSBh2oGXo4QqConcK1EVzuiqhPP+8xnFAF4zAN1qU3AC9axJrjbyDUQ1FDkM44osK5rTX6oOvWobe9Ekp571zOp6xBEp/eDIrnJvGBtnPlIWPbFINWeoXUyiInIrEkmUsSCPauJk8bTy2pyhDYrFj8WXe9wy9TxzRdBZs9Zs7hSQAa2IplK15FpPi5os+fkHP1rWXx3GHwFbHrRdD1O9u5gGHIxVaedRH16V5xqPjiYygxoSoqC78ayyWX7sHfjpRdC1PQrm7WKHzM8D0p1hqkcwxuGa8n/AOEyvXgaOSPPGBg1FZ+IbyKRnXO09s0cyCzPZzqkEbEM4H41EdZtWYgSr+deH32t313cbjKy+wNS2l7cIjN5jEkdzS5iuVnrs2uWom2+YM/WsPVvFsNtIAjAn0rz1rqXJYsd3rmqrSl3ySSTRzMfKdenjKcXJfBC+lMvfFc1zGVXjNcpuwKA/ancOVGhLq9weN9Ph1u5jPEhrJc1GHwaB2Nx9cuHGC55qo+pzE9aoq2WFJIcGgLE8l5IxzmqU05PU5p5NQTLkcUASRy5jIzTIchzg1AjYBBqSNuaQy6z5HNRd6bvp8eCaYhysyDOcU0TvvzuNEjVEflNICCzugZF5712lhfx20KsSM4rzy2idea3ElcQAEnpUc1irHp2neIoI4tzNj6mt3T/ABVbXEgjSQZrxUXEgjKhjUdvez2s4lRmBFPnZPKfRkd4kzDB96n+0IGxmvHrDx7JHb+WyEuBgGgeOLtZN7LkegNPnQrM9lWUE8GnmVSvWvIYviBMiMTGSfrVn/hYZESFkbJ60cyFZno8/wA+cNVRYv3ud2a4X/hOxKRsB96kTxxFGCWBzTuhnX3/AJSJywHFYDXyO+N3CnFcfq3i671CUpH8kZ/OqD6lcJFhSd3rSc0Ci2dHq2oR5wGGe9UrDUhFIfmwDXMyzTyEsxOaiE7hsAmlzofKehnUtkQkDAnFZtvrEk2ooucAnk1zS3swh2knFNt7h4pw47U+dC5T2axnRY1wRkitWO8UEZNeT2HiOdHw54xU154tmR1CZ60+ZC5WewLOpTOapyapEkuzcM152vjZks+clsdKwY/Et0bmSaQnLHgelHMgsz1a/wBait48lxj61Uh1lHTzd3FeT32s3V/KAWIQds1LFf3SxbBK2B71LqRQ1CTPWz4gt1QZcc1Sn1OKU5VhzXkst/ciQ5kapU1i5XA3H86pTiDgz0C81RdpjLjp61zNzqsYiKLJnnmufvNTnZuDzisxnkckkmk5oFFnYwTiW2yvrXX6XIjWSl/vAV5XbXk0URUZrWtPEc8ShcHgYoUkJxZ3+nXe26lUjv1rcW7XbwRjvXmmn+IQEkZ/vk0sniuWLcFBNHMg5WeiROj3G4HpV+S8iCFSwzj1ryOHxVeq7EdDVO48Q6i8pfzsE9hRzofIz1iXV7e2jbLgd+tcXqXil/tTGFvlriZtVvJ2zJKxoWVpMAk0nMFDude/iiV4wMEfjWNPq1zJISJCv0NZ8spiAquZyTnFCkPlLkk8s3MjsfqaaVXHNRCQ+VmoWlc1V0FiyxULjioCEzxUDO5BqHdJu6Gi6CxfjVc1OYwoBBrOR3HY4qwZ28sdaV0FiWSIOPeqrw7elSiUkVGzE0XQWGCEEe9W7aAFGxVVCS2KuQ7lRutJyQ0inJFhz9amgAwRUMsnzGmrIQMindBYfKduRUCtzT5iXUMKqbiDSTCxO0mKUPxmqpYk1YQfu+lO4WHFwRULNg8VHI5BwKRAz5OKLgWoGy2aWV6ZbDg5omUjkCi4WGbqbIcqajLEUFwVPNFwKUkpVsCrFsxaqjjdLjtWhbR7QKLgSMMGpIs0kqHg1JCn7tmPalzBYa9NkO4K34UrdKYmCCD2OaGxpEqWRAChavrYnaARW1HbIGGBUssQUcCubnNuQwxYfLTm0seSeO1aqpVpY1280ObBQOUh06RGJx3qRrN+eK6n7OuCQKgMCkninzhyHPxWbFSMUv2ItGVweK6GOBA+MVIlou8+9HOHIc1BayJL04pzQMZDlTXT/ZEVhxUMtoobIFLnDkMKK0JkGRWoLDco4qwkKp1FWFmUEAVLky1FIz5NNGzgVmnT8S9O9dRvVkqkwBk6URkxOKMr7AcdKfFp53dK19oC9KdEV9KrmYuUyTZFWyBUb2JZgcVqStlulSIuFzijmYcqMwWDFeRUT2W3oK3SRt4FV2XJ5FLmYcpmQWPPSrH2TGeKuoNvanMwPapbuUopGPJZ5bpTPsJyMVpwzxy3DRD7w9atCNc9KfM0LlRz0lgzMTinJppI6Vusig9KepUDGKfMw5EYq6aQhGKiOnEV0QTI4FMaMDtS5mHKc9HZsjnFMltX3dK6MQqT0oa2UnpT5xchiRWJ8rNRtZEnpXQ+UoTAFItuDzRzj5DnJNOIHApYbJwRXStAuOlCW6g0c7DkMN7BnAyOaiOmsueOtdUkCHtTmtk44pc7DlRx8tq0aYxVF1I6A12VxZxvVYaVGw6CqVQTgYNlZGY8rmtNNIXaSRWrb2awHAFWMDpUubuNQVjmpdN2j5RUL6dIYwQOa6sQq3WpktkxjAo9oxezOJWxkU8irMdgGPIrqLizQDgVVSBQ1NzuHJYxf7L+cECmyWjKGUV0oiULxUEkSZ6VPOyuVHGSWcnmEYNOjsJGBBBrrhaRsScCpI7OPd0qnUZPszlodNZl2kVHJpDBs4rsTaorcCpFs1bHFL2jHyI4KXTHX+GnQ2bHjFdvcaapXgVVj05Q33elUqjsS4HISaY/mZ29ani01gCMfpXX/YFzyBUgsVB6Ue0YKBxa6cyt0NTnTS6cjmuqeyX0pFt1XtS9ox8hxUulsFPFUvsbDIxXfz2SvHwKypNPAc8ZpqYnA4xLBzN0rSisXCjg10kWnKGyRVn7GuMAUOoL2Zzb2hKjikNo6W7ADqa6UWgI6VI1kpiHApc5XIcY1o4U8VBHC4lIxXYGxUjBFQf2Yokziq9poTyEsbEHpUshyBQIiRTyhwKyuajUjyM1KqEinxYAqVcClcY1UwDTFjyx4qyOaaFwTRcLFV4iHzUqginshIye1LtGzIouFhq5LjNTvFkZqFXH5U5pip68UmwIZYfSqwgYNV0PubFO2A0XCxEsZ2VXCfvOa0olB4qOWABsimmDRFsBWo0j5OKs+V8tJGuGouFiAWzMeRVyK0ymDViNRjNP3hTxSch2Kv2QqDUJt/m6VotICMVWJG6lcLFf7P7Uww4NXsgioXXORRcLHP6jdw2MvmYAY8U7SLl7rfK/AJ+UUup6Uty4d+imprSFY4dqdq0uuUmzuW3G4nFNVTmnoDUqpU3GOiXtSyR96kRacy5GKVx2KqqeacQTTyNrhfWn7MUXAgHWpApxRt+ap1XgUNhYrsCKRc1alCqvNRAKELUJiEiY7qskErUECg/NVxcHihjSKLRktSqu2rjIOtVnwHxSuFhq/OcDrQyHNOhPluTjg1O2Dzii4WK4+UU5ZMGlcYpgAPSgaJnO5Kq7cGrca5GKjdNrGhMViEGq8hO6p9wBpSqt9aLjsVlJzVyBckZqNYhuFTH5CMUNhYnaLoaemKjWUFcGnIcmpHYJmxUI5NWJI9wqMJg1SegrEbNilWYE80SL2qqwKmi4WLZIYcVGyHPSmQNk81byMUrsLECx5U5qs8Qz0q2zjtURcdKaYmiIRfLxTHGKsgYGagk5NFwsMU9qczYSkYbcGg8ihsLEO7FAbdTivaiNMHpTTCw1MbaeVGKrRyZqaRiBxRYRHvAl21IH5rLNx/p20+lXBJxmm0JMvI+GxSs2DVQSgkGpXcdc1NiiZXyCDUZbAKmovOCmlLhgWHpRYRDvYN+NOfcy8U1WDDPvUilWU+1MCSEYxmrJxxUKMpTimNLg9aQFmMEE09jk81EsgMW4daRZQxoGS5wKTgVDJJigSZFAFlZOKhefDVAZNrUjNuGaB3LYlyM0wyAnGaqrLkEUxWIkOTxTSE2aKvxSFwTUIcbcZpN+KkYTfMhxVaxXcXB7Gpy3UU20Kh3prYOpYSPBqQriljIJp8mBzSCwsfFQ3N1Hbgbu5xTlkFU7opI+1gDTQMsRzRu27P0qUsG5FVIhGQBjpU5YKPagQpJp6ybetRBwaY7ZGKQ0RXkjSZCNiq0M0oIhPOe9PkyHpwIVlNWtiHuaUQ2pU0bc9appL8tSJJg1JVy833KqEfval87IxUMj4cUDuPYALmnIwYVCz5QihHAFKw7jpMg4zTBxTmYNimnA60xE0cmKdIu5Saqhwr1ZSQEc1LQ7lUoSD7GnKpxUnALU0OBTELyKeMOvvUbsNvFRxye9FguTgYNSK21qgMgzTDLnAzRYLmgGqNn2tzUSS8YqKaQZGDSsMnkOeahdQaDICvJppcEUxDI2AbGatdU61QBw5NSef2zRYLisx3U5eTULuMg0pl24Ip2C5eAG2qrKA+KBc4FNMgJzSsFxZACABSKOKhWbJOfWnGYDHNOwrisMEGnjHWoZHGOtRLcDGKaQXM2OY76nkuflxntVKPmQ0+RSATg4xV2IKoPmXhbPSrwkJXFZEMha4ZB1J61r+SViBzTYkOSQ7c+lSGVmXio0XJwOakjX5sEVLKRAZGbipWn8m3yTzT1gxISar3UYZSG4p7i2GrOdm4VYt5dytVW1jV1Kk1YjjVGI3UMEWopOKa75bNMXCnrTnGDn1pDHiYrxnrSC4ERwR1prjJFJIueMUgJROshxmljbD4qjjZz3zU8D73osCJLpiuCKi847asTp5iAHiqJ+X6U0BMrkPTWkPmgCpEUEZ61Vu50t2DN2oW4Xsi8jEZqQseKijO+IOO4zUmCcVLKI3lwTUNpcH7SR2NEuckVBEDHIje9UkS2bHm7QMUrSllqGNd7DPSpJGCnAqShFLAn0qvLkuDngVPI4C8daqSFlXLDg00JluDrmpJmwvFQ23IqK5uwkyxEH5qVtQ2RKjk05hjmkVfkz2pepFDGiA5Z8UjZDAVNtAkFE4AYU0JjudoNPQ8Zox8gp3G3ikDJY2y3NR3BPnqB0psRIJpC+6UZp2AV3IOBT0yVyetQyLmTirkYCpzQBAXKmh3JAxTZxjJFOQblH0pMENJNCzFSKHGCBSFM0ATb9y5qMk84pUIAwetIM7iMUANLnbikjPWnlcA8U1cZ4oARmYdKYXIIapZB8tMXBHNAx6yngimSyEN7Un3TSTIWWgBRMHHBpolIOKrpBJE2c5BNS7CTmiwiXdwTTQ3rT1XKUgjypoAa56U1mJXFD5BANSqvy80gIVORSbyDzTlHJFNcelUgId5Eh9KV2O4HtT9m4Zokj4p3FYe+WjyKovJsc+9XUPyYqpLFuc0JiY+KNRIeKbcSlY3GwYq0kfzk4qpfD924HpQtxvYztOt/MdpMYyTW0Yw0PHaqmmwhLMFuprSi2+WRRJ6hFaFGMbWOM5qaBN0mWpzjbyBSwgs/pSvcLD515G08VUuE3Kc9qvuQrBcVFIgZsY60IGZtsmGParKx/NxV6XTzBCJCOtV1BptgiMJ+dS4BUA9aUA5pkuVYYpDJFTfQ6BWAFIpbjFSlGLAmkBVaLLGnRBBNt/iNX1gzzxVZrcR3AfoadwHTJgcVSMe8EVrKFYHNQNABJkdKEDIYoQiYNQy26M2CM81o/L0yMioJEwc4oBjYWRQIz6VOiDNVDHlg4q4hJ2kUmgRBJB89Ub6PZs28nI4rXA2yDd0qrqcYCh42C4POaa3B7DAzBAB1psm4EZqRZFSJcjnFMin82YqV49aQEbOUkjU85NOv2wEXGM1Nb2kF/rVpb3G7yNxdwrEFgOcZHIzXQa54ONzb/AGzQ5GlReWtHbLD/AHWPJ+h/A9qaVwaaVznbU4QCpniilcEgbhzUMHybkIIZTgqRgg+lQGfbqMaZ4Ipa3GaTJtSoMfMO9WZWBTmq6D5ulJMdhZMZGKrzE7gasuNzqoGSegHerj+Hb77P9puTHaRDkeexDN9FAJ/PFVETV9EVlGYRxQq9jUsQ2xYPpUDyYl46GpAeMKpqFSN+alJzHVQE+ZkU0Kxab74x3qdexPSooiC2DU7gbBikxoZcKCOBVXzHjdVVSQxx9KtMcxcdajhP78ZFCBjzHuIzStFtb2qeQAOCOlQuxzTAhlXHIpbeRd+HpZQdmRUax7lB70xF0qu04FUsYfip0bbwTUJcJISelKwBL92owOAasSYdQRxTcDFAyJxwKnVN0W7rS7N0ZpElITYOtILCuq+TyKgUjpU8n+rOaqKSM8UILEuMLSBsCpUIeM1X2/vCM0ALIuWUnpU2NwwKhkJC4p1tIR3oAjZCrnNDpxT5XzJzQoyMGi4rEaYCmhiGSnvEVBPaoVB5FAxFAFQyf6yplGcjNQXACMGqhFpTgk5qjcsTvHtUvmgcZqpLL87A9SKaRLY+zYvCo6YrTQYWs+FfJhT6Vejl3Rk46USQ0OZScU4JswaYJPlz3pGkLKPapsx3JmQNLGB1bA5qCfU9Ptr02+xpWQZMhbCn2AFV7258u2Zs8gYzXKwTF7udyeijr35relBN6mVSTWx28uu2sxVWtWKjgfvDUQ1SxCn/AEI/g5rBhlQxtI6n5eozU9vMk6s4C4HBFdPs49jDnl3NtNRsmUH7G/4PUFzqdhHgm2k3Z+75g/wrNku2VG249go4FZC2z3F21zJKTj7qr0odOHYOeXc6e11i0ucGKxlweRmT/wCtV+S8tjt/cMB67/8A61YEN3JHEWGQenTpUqzSuwLEnv0o9lDsHtJdzo4rm3IyYiMf7f8A9ahpbR+XifOeBv8A/rVjtM3lEAFuOBRb3LiTLcDHTFHsodg9pLubZ+ycHypOnOHH+FVTeWBDDyZsA4P7wcfpVZ7lSGIMgAGcYrDS9nuZWiVcDOd23FDpwXQOeXc6GKWwL7hDOWPXMo4/SpnubMsAbaX8JB/hWLDBIoYkEk+pFTKr7DlDx1IFP2cOwueXc0zPZgEC2lPoBIP8KiOpWkakC0n4/wBsf4VmLIm44znvmqk8rhgRux1wKXsodh+0l3Ns6tZtgm2uAfTetVL3VdPeLLrKi9iZF5P0rnL/AFWWK2KNu+cYGB0qr9mt3ESSSk4TkN6mpdOHYpTl3NWDxAbm6EENnujBPzPIR+WK6W1e0dSXtvmI5CSnj8xWFp9pFbKdgBXscVqCVFjJ2jJGBzWkaULaoiVSXc17RbA38MixzLKjAKwnBHPHIxXSyyzaferIj7fM6ccfiK4XT2M+pW0YYfPKoI/Gu41LSLy9u1a3kWNBgs7H+VefjuWDXKjvwMuZNTehYktNM8QgtcRGG86GWLgn6+v41g3HgZPtqTpqe3bkbTFnP45rqbWyFpGNhLsTkvjGTSXUqwIFZSCWH864J4iSV0a+zi5e7scnJ4buPO8pbmI4HVsj9Ks23hYtJ5dxeInPVBk4/H/PFa94228OBg7cGnwS+fEuRh143Y6isoYqV2mW6SSTKE1/pWgArZQiW66ea53N+fb8KxLvUDN/pWpyOQxwkan/ABrZuPD6tdmaEbS3ODyKwfGenvZ29gCuWZm3HsDxiu3DzVSaUh1pRp0nybjYrzT2yAlxj3df8KiaTTpGJUz5z03L/hWJBHlAHPJ64qVowjA9SOnNer7Gn2PJ9pPubqLYMuN0+PXK8UfZtNH8c+4/7S1ipPKgYEDaR3pySFxkv1NP2MOwvaT7m55dggHzTA/Vafttm4Jm4+lY8TqSF3E/hVtZ1CFSz/iKTo0+we0n3IbzUbRJmgtI7id1+90AFaFsltsEjpOrAeqkVnqY4pB5YyXPJIqYXSIAPun0AqfYw7D9rLuXjJaSMR++DDnoKgZrTP35c56YFVEu33n5xx0qMXJDnJBGO9HsYdg9rI1G+xFdgaUkDnGKhH2SNQd020nAOBVETg7gRgewFZ82opGCArHvT9jDsHtJG65sVYEyzD8F5phfS2fmeYH0wv8AjXMy6iX+byyQmG6Un2lZm8ww9uMZo9lDsHtJHXImnyJkTT4HfaP8ad5On8fvpx/wAf41zcV2wAUKdvuaJL51QgbuemD0o9jDsHtZdzpVWwwR58wH+4P8aYI9ORjm4l3dcbB/jXOJqJMeCwJPB9aT7dtYqGPzetL2EOw/azOic2bISJpPTBT/AOvUQTTmX/j8ZT6GI/41zzXZA4ZsH1qOW4ZlX5iuR1Ipewh2D2szeSW03N5F2kuGKlRkEH6UjNiXJrh7C6eHWpdsmQZTwPeu3cjap61y1YKL0OinLmWo+QhlxUcMRBzUrqPKz0NLCcoc1BfUjaHcxINRqWBI9KleQJ0NRRbmc56UCHJNuyrVGrYfHY01htkOKZyWJx0osMWU7W4qtJJuTB61Ycgg1XWFnbJ6U0JiRld+DUF5EDLGy+tFqfNunx0UUTt/pEYz3q7ak9C3jIwR0FSwSYUoBUkaAsS33cdaajISQBz3pAOXBVgDzTE3E80INjA+pq7FCOXP3RyaAMrWIxFpcjucMTwK5TTTm6n9MLium8SSFrB2PTcABXBw3V2msC3tnRPMUEllz0rej3Mqm52cUuYS7QgKGxkDrTo5lZcCLCn0GKxEuNab5UmtyPeMj+tQyalrqAqGtsA/3SK6LmFjoXk2wkbGOT0xUQmeOLIJHfhelctLr2vBivlQuR3VaRdS8RTwsRaRlP4uP/r0OQ7M6yOeZ1O85Bq1GZCnDt64rhF1TX4xgWq4HT/Oanj8QeIEGBYA/SjnQuVnclZBFje+SOMNjFMWOXeMyzDjn96cVx3/AAkniPGf7NP/AHyaD4k8RN1005/3TRzIOVnbT3ckzKkC8BcFpBk/nVFxJbuAdh3YyBgZrmF17xFtONO259qibV9dZhusOR6ilzIdmdiJQnUKAacbiMHAQEYzkVxZ1jXMDOn8Cg65rZH/ACD+lPnQuVnYR3A3tgnBPO3tSyNld6LlT0JFceuuazgkaf8AkKc3iPXdmPsJAHYLRzIfKzduZoYVxLFuZvamoIcGZiE44yMk1y8uoavOp82ycjOckdKUarq+MHT9wH1qXLUdjt7WQiIfvFKk9xirhmjI2sygn0NcLDrWsKpzpYYY6FjTjqmsumxdIUZ7lz/jVe0VhOJ6P4atxd+IrbaRiMFz+A/xr0a4uvswVY0Msp+6meWP9BXjnw4uNbPi5PtVokVt5Dh2U9OOO/rXrRtzLIZy+0jgMoyfwrxswlKVRKJ34WKUdTM1qLxk6LPpWo2Knr9na2HT/fOc/kKj8K6xqGv3d9o2v2SWup2qrKjIMB0z1xn1x045rpLO3uHT5nKrnq45x9K0I7G2S6W8KB5kTYJMc4PUfTI6VdCEpRtNaCqyUXeL1M97FpZWZlwTgflXnsvjbU7vxJPYeG9Pt7i2gbY8827DEcEgjjFeitqFlqN5f6UXbfCirNtJXAcZwD64qhY+GNN0u0MGmR/Zk7YO7+fNYfVlG7irs3p1l9sh03U9SCH+07ONd38ds5cD6ggEfrWf48i3+Ho5o3BCzKQevByK0nF3aEBpo3TOAOhNcf8AEm7vdO8IRRWsbGWW6UDodqgEn+lKhzRqxv3FW5ZQbRy4kl3gqxdfTHSn75DJkHaemCOtcOur64hDeUc+mBU0Wta2JVdrbdtOdv8Ae+te7zo8zlZ2pmKOeGIxUkN6YAw2E5HpXKP4n1XAB0rA9iaQeKdU2kf2VJg9wDT50LlZ1BvZTyA/J7VKLq5CYw2OvSuRXxTqbYA0l8A8cNUp8Walgk6Q+M+jUudBys6VnumGX3Ann0qMy3DAMxIArmX8VakxJ/sph/wFqb/wk+plf+QZJj2RqOZBys6cefkHd3yeKa3mgEZJHsK5k+KNS5zprjP+waY3ivUeF/s859Npo5kFmdUVlLZB6e9ASTby7D1rkX8V6iB/x4t75Q0n/CWakF/48Xwf9g0cyHZnX/ZtyEFwc9j3qMW5VSqvjb2BrlB4tvwMfYJP++T/AIVG/im+JLGwfnj7p/wo5kLlZ2KSqCFZlJ96eUWTO0jaeQK4aTX7/wAkO+nuEH8ZUj9aIvFd6hCJaszdgCaOZBys7P7GoywYrk5pTY7ypLFWrkX8YX1u22WxKn0Ykf0q5b+Lb+aLzFsAVHH36aaCzOrFm7qeDwOM1E9q5UfNx9KwW8WajtONNGP9+om8W6kEbOnAZ/26d0KzIsNFrsy54Eo7V6GJVWBA3oK8xsdQbULuW8dAjM4yoNeibGe3WTtgVx190dNHqWzcZZQfu1PFyp561kxZmcKDyK04VaNDk9eKxsa3IXjbzCadFkc4pzSbCysevSq5nw23vQBPdKscYPc1Fbsrbt3fvSyo0yrnpUMifZ1zmiwXI2dRIR70glJfA6VE6F1MgogOc5HNOwiDTmREllPUmoZXD3KN2zmixDG3K44JqYwfvQver6kdDSiuF2YI4xU9siSI5A5rKGUbbmtKxmWJCGPJqGixTEX2gHPPSrhOEEKcnvUaypDA0uMsfuiobeRyxfHNMRl+JEZdOAYYy4rgrVWbxLHjtGTXf+KpnewiDAAl6wNIhQaVe3BVfMMwQMeoAXp+tbUnoZT3L9lDvYk+h4qO5tPKhLHqc4q7pYByec45p1/gxlSeh/rXSYHNwqBNIrnaAcA1K80UcPCthv4jkA086e08qykkL52Gx35rs72wsrm0No6oqBfkI42muepKzN4K6OHsbe3ur+KOUOkbsAzA811w8JaWYjjzgx6MJK5IWTiZj5mzy2wGz6d62ptWuWjWNX2ALglepqJczejLVupmz6ZHb3cts0m4o3DK3Uf0NQzRxwRnaXz0602SRUcsn32OTnmnoyvIpliBXPJHcVauQ7dCSztLm+wtrFLIO7dqgFggZmkMxYdVLEY56V6JaXEEUSJAqpCFGAowBXL64Io9WLWrK6SjewB6N3/Pr+NZqbbKcEirpOmafcLI8ybtrYILdKr3FjbLPIIkVkB4yKjkDK+7lM+h61p6dot1qIDq3lQd3bqfoKWqd2x6bWMhLK2klEIjTc5wMjpW/a+F9LWL98Glf67QPyrLm09oLl0Y4mjbGc/e9DWja6u8NoRJHuccD3ole2jBWW5T1Hw9bW0u2JiI3GQM5K//AFqzTpkMRJdmUDrg8GtGW7eWbcyM8jfdReT+VQtFLcZeRuVbGzptI9aab6idi3p3hH7ShuL1nSN/uRg847E1n6ho0VhO0SOCV5DeorooNf8ALsjHOh3qMAgdaxZib65yQfNc9B6elKLlfUbStodV8PGlWSRYraL5/vzMdzH2C16He6glpH5eC8iDO1eFX6kVieDvDr6Lp7TykrNKM4J+6Kt38KTA+ZlYweBjqfp3PuawrVddDWnDuUj4hYZ3S4x3DUq+LWI2h2cjvzWXd2205ztTOAo/h/xNZ5lj5WMFV/U1HtXY05EbUfiGOC4llEOJJzulkA5Y+9aEPiMyfKCc+uK5RQOnVSe1aVjbHzA4GQDU+0Y+RHSW6peSK3l7m7MwIxXO/ESxTVdGFlcNEk8MgliJfAPGMfjXT2Lom0rnPcHtWR4wa1hUm4YBJhkgjINSnaXMhNXVmeIx6bCGBMTcdRu71ah06O4k8qC2Lyt0G4gD3J7CrlyqJPK0HMeeM9xTrO5NtN58XU8Mp/iFehd2ujlsrhB4VuYJjI8kcqH70eSPyqG50tI1aWJXCg4dMnj3rqre9huI96nkdVPUVgXl808sjRghD0qISm3qVKMUjL+xxMcjoR3NdZY6Bpklsh8hXyoy249awbawumtGvViJgV9rY6j3x6VatrmS3YiORgh7DpVTu9ExRSW43UbaxstQkgW2BVeQT9Kostmc/wCjoAe4FWpra4nlDrmcyt1HUE+orprPRoLbTzbTortJzJkd/ak5cqBR5mcW+nWtwC3lIrAHgDrVeK0gtzlrcCT1re1PSptNcSp88BP1I9jVGQiZFGAwBqk76iaLVhoMt7aPOJPJP/LIHo319qozI8E5hmiKsDyCa6bT9UWZVhYCNgMbR04qnr93HcYgSJGdOS/cewqVJ3sU0rXMUxQO3JmB9A5FWLLR7WSJnmlkJzwu85qKK0uJY2lSJnRDyV5I/CnQtIH3K5BH4H8RTd9rkpLcW7sljXYsjhAOFZiePxqGCzvHjL21uzRq2CVHINT3Lt95gzAjk+9dDoUe3R1boZGLEE8+lDk0h2uzlTAu6RpNwcdQy4b9ajs4xMJCVwQ5HH0rb10BrgY+8o6jtWdpUDwwSqckGRiCfTArSk7u5nUQht18p2KjsAaq3UW0NwMjitiRBHA/HG0GqE2HyxA54NbmRyukMyFzj5fMPavXVaN9Nj2nGVH8q8ptUEZmUf8APdh/KvTLfAs4MngoOPwrmrLY3pPcS0XbITV3zS8XXvVWUGPBTv1pyHMeO+axNSO6mxKvc1Z8oSJ5gHNUp0zcqDWhA4A8s80MEJKxRBt7Cqd35vlhjnaavuN0ioB1NV9XZgUjA4700JlYErDg9/SmRxuDuqww/wBHB9KWIfuzk9aAMyzidYgMHGetaSRKbgqDnioIZgbYKox70WjH7QcnJpsSI7iNhL8vQHrUltG80wT9amlIBIxyafYrsZnbtSvoPqFwshmx/CowKlibZg4609ZFYHNJMuAAtAGP4tkU21uB15OKxrBwNCZTjLXBPT0Aq74jZj5YPUKeap2Q26LHkdZX5/Kt6askYzd2zW0uT5Gp84ErKARy4qvZSeWj85zUwPz5zwBniugxM6WT5Nqk5ViQMHrmoZb2edt88xJJ4UdBUgnIlwoyQSasaXpC6jdMLuRliUbvLHGawqLqawfQy/PDSBI1Z2PQCtLTrRbm+SO8kaOM/wAKHqfQmuk1LTIBpgS2gRfJG5do/OuXE29sDJPSojaSLd0zqW0u1Fs1tHAio/fHP51ycmnzQ3Mtu7YVD9/PatObVbp7RUB2hRhnA5P+FY8tzGsgaVicnjnmlGLW43JMvG4cRiJXZ0AwOcZ+tRQb55HjhjMjquWC9hR9mnlUFx5SHtn5j/hXW6bZQWlknkIFDDJb1P1pSlyoaVzh5H3Dcck121hrFrLYI4dIyqgMhOMYrA1W1s4b4iEqQw3EDsazHuFhVmCjHQU3FSQlLlZp6rci8vnuYkwgAUn1xVWV1KDyhukPWptN0+81XBYmG0x8xxy30pt3atp05iZd0R5Rj3Hp9aWmw9dza02yhsYfNYrJMw5f/D2qpqhRENwu3f0Yf3hVK2vZYI3TI8sfdXqajmm3KXuOT/BED+pqVB3uNyVrESBZonVAVGA29ui+w966HwdawXOuQDO4K2efWsG3SYwrPLEVgJO1yMKT/hXVeC22+IYf3O3qCQQR+dOWwo7nqVwm2MY4qhJGCpZsE44rWuog8J57Vz1xNNESFHA9a4qr5ZanRBXRWvLASrsUDJ4OPT0rIl0NY3zn61rLqEqsVCZ7ZHrVT7cXlO4bjnH0rBzjuaqLKyaVtzjkYzV63tSkasOvcetQfbWVTj5jnHFTW8s0x+UY+tT7RFcrZpqFVR2Y8/SqnivTbjV/DrLZjNyjAqvTcO4rQtrY4y5yRVtRsyg4BFaQck+Yzkk1Y8J+wzi6W1uUMUrNtKt1Fa1/4fD/ALyxADBRuTs309DVDUIZLDXZfP3FknLZznPNaF1ryiJktjmUjGR0WvRu3Zo5kkr3MSIvHIeGjlQ4ZT1FC27Xd1HFFhTI2GzwF96gmuiq+UF3vnJc8kGrM9pd2UcLXUWzzVyrA9PY+hrRqxCdzsoYktLZLeP7iLiuY12K2t7tfszYJG54x0B9qLXWZraJkkBkUrhWPUH3rMd2mk+YFnc4x7ms4wad2VKV1oSRXBBVgxGO4PNdBp2sh0eO5bLKuVY8E4/rTn0O3NpDGuUmRcFx3+tY91ZtZTGNpI2z/dNF4y0HZxH3t29zKZHYhP4V7AUy10We7tGvElEbbvkRhww96o3EgyowxOQCBXX208c9unk4RQoGw9sdqp3itCV7z1OWKS20+2RTFIvIU/zFRuwLM7HLEk1va5cKYltQimTO7djJH0rGghmndljtml2gMwXkgU47XZMuyNzSoDFp64IzJyRUGsGGFEUxAzE5B7gd6owTNES0TEe1MvpJL2VZGJWQLtx2NTya3K5tLFUM00vlwxM7EZwvJxUscrREiMlT3wSCDWzoVnHbQmYkGZuvPT2qbWba1exedl2yqPlZeDmhy1sFtLnPTyM2WbLE9zVu2KmzVsYbZ/X/AOtWRNO0RUP0JxxWtbHdAi47MP1NbQ3M5PQlusNbgAA/JzistwUHT5WrTlYC0TucHms6YkRqa2Zkc+gHnTjt57f0r0mCDzNOtm6fIK81Ufvrg/8ATc/yFenWO5tKtcjGEHNc9XobUix9k/0QsTzTWtSqxvHzntT1nLwOh6DpUiyj7MqE/NjIrE2M6aF/tyK/XrV2OBBIMtzVG5nZbuN2PIFJFdeZKWzSaYI24vKWT5ucVnaheQecQyZ9OKiNztjAJ+Ymq0zqWJOMmhAy2rRSxhV4FCW7EZBzg1RjyAewp9rdsJdueKAHW0SSWmF+9im2KNHOwZc81JZ4EI2nk1PYzRGWWJz8/arZI+RYgrP3qCCTc2xaJhsdxniq9vcrFcFiOKlIpkyzBS4PGDSiXKbuT2qgz753diRHnJwO1acPimwFp5EMI24xnaD+NaJXM27GH4gwQO+EHNUrdsaJaqQMlnP61d1RJL21EkOGVlJH0BrN340ewXdyVY/+PGtY9DNl2Fx5bdKnjc4Y54wKy1chOvepo58bhnjFbGZEAQ7MDgEZq7ayyWs8c+48dcng+1V2dUVCEBYrgE9qjM534yzP2xWU5dDSK6mzc6xNcMQD5UZ7KeSKxZ7mOJgIzweuOtTW+nXN7LgsI1HXJ5/KtKXQYBaFIhmbOQxPJrJWRbuzKiUyyqsknlq5APfH1rpIdE06OB1Kea7fxN1rlkt7h90SqU2nB38AVrnVZIoEiUoWUYL9aUrvYcbLcJgYJWhYHKevcdqja8nWAwo7bT/AtUbm+aZy73BaToAf/rVWe5lkXaQQM/w8A1SRLJ2cyTCNEMj/AN0H+dSNCoclzuZTyCMBfYCtuz+z/YUeGNVJADAeveq2pLFsMqkCTuv96lfUdtDQsdZQWYjMe1wMfL3HrVDVL9r+AQiJdoOd56rVK2clcAHkdcVXVJ9QuPs8I+Xv6fUmjlS1C7JTMiD9yAzj70p7fSruk2tszia7dWJ5EbH+f+FSP4faC3DJLvlHJGOCKydpRxjg55BHSjR7Ba252V5qVnZ2QMu11YHCf3vwrG8MXMzeILRYsqpcHagyB7VgahKplUxndk4JznAr0TwH4YSN4tQaQsOqVm0oopO7PTZZBHBuPp0Nc5c3rO7BY1HvWzqxK2ZwcYHWuGuZT5hZifzrgxFRqVjroxTVyeWZkZyXG4DHJ6ZrOM+UMgyOy+/vUTXTeaAuSccDHFKZhNKpdcADGFrmvc22LNvOdgCjBY5G70q/bXPlHggnuKyI51SU/uyVzgGrSzRI24AKDUvQo6i1vBKBv+WrcrKJFKkHiuWhkKuCJMg9hW1FNv2duKv2rcbMnkSd0eXfEGBrTxCzpG22UBy3qa5yC4ypCqQ+Ohr0z4g2sUlhHcFAXTjNeZwQPeXCwwKquOQSeK9XDzvBHDWjaRpeFrI3V491cYCwt8qt3auvuliuInjnUGIjB3VxUNxJbSZUGOZThhn+dXdQ1eW7tkiXKDHz+59PpVyi5O5MWkrGbI8cN3LHBIZIA3ysRT7aYWt5FdBVk2fwseM+tJZ2T6jceXHxj7zdlFa97oiW8Qa1BO1fmTPLe9U2loSk3qWk1c3Fs8hAVgM4H9KwZ5d8hdxuZuetMU4OYyR2K1JB5L3MZnJCA5ZR3pKKjqEm2X7XSbl7E3qkA5BSJhw69z7e1V471opSVBBHBHcfhW5JdmWEeWwKEYAFYepGO5ulWEbZVHzOP60JtvUGrLQbcObiVpVYmRuoPFdPpFvHYWmN6tI/LnNcZ5rxuVkTdj+IdKvWOovE+UYkd1PINU1pYSfU2/EFpaG1NyjeXcngbR9/61zBnkTAcZ9wa0b+8N3MWwQg4FO0e3WQTTsoKn5BkZ9zR8EbsXxSsirb3xifjK1a1C+Fzaoo7H5sUtxpyDMkbBB/dPSqEpWEZJAprllqDvHRj9ISN7yS4cZCAqoPTJqZeLmZQRgSHp7gGqkVwIV+TG3OeKlWYPPI6/xbSfyxVxVpCb0JJGP2b6ZqhIwEIOc1cdh5DjOetZrPiMdwK1ZkZluoe4uAf+ev9BXokLltKtwDgKmDXntjGZZrph0Eoz+I/wDrV3lsv/EpgHJwK56pvS3Hhn8sbT941bvI3tbSOY+lYl1q0GnSxRN88x5VM4/Opn8Qpqdv5IjKuScemBUWe5baI7i48xkOKW2kC7mbtUbAAx5FPjXdBK5HFAFjz0nKbRwKbLzJgDmmQRkIGQYGK0dNtVlaSRzwKkCgC21geAKpRzFLg45rSn8re6g9TUCWIDbycGmtgJoJYwjBTggVUs5i2ou4BCjvV6e0jtIPMyOlZMEyDcm75mbNUtUJmzd3KQpn7xbtVDeJZFB4NJLwgZ+T2qF4rjcj+WxB9Kmw2yyHXEseMgdK4jUruOGaWGLKvnFdghZPN3KQQO9cRrFrJBq4lkU+XKxKnHrWkDOZ1+iXvm+DldsboQ8bZ6jnNYxl3WtmB08r+prnluLi2tJ0inkWOUAOgPBFbLSf6NZKMYFutaRVmQ2Xkk4wM05Hw7fSqyPjHvT0f959etaEGlFbm6uLW2QlTJwWx0Fa2o6db2dtFJAuNjbWOeTnuaxtLvvMZJscwSMBk+n/AOur13qJukMZAVSQcfSsJJtmyasMiuGLKykq6nBNWZ9dcMEjiAYDBY1QMy28ijKhn9T0pps5by4VI5kj3HBLVLRVyK6vJJCXmkPJ7cVXSGe7I8tSEP8AG3SujHhuzihJkd5ZSPvMen0FZMqSWkhj3nI6e9C1BlrT7GG1+cjfL0yap3FsyXTCNSiH5s9qsQXc5HGzI75qrcvIfmkcnHqaSTuDaaJLa6NmrRo5fdzlu1L9viRt7gyP7mqKo08gVGCg9Xf+lb9hpkFsgkwJJf7x5x9KbaQLUz5UvFRJ5IPKglYgKf6+masaffmyuSzqBG3DjH61unZJbFbnAiYc5rnzHbRySDzPPjB+Ttn61Kd9AtqbupaikK4iYNIR8oFczcq4jLu2HPOT0qK6vo7dvlUs7cD6+1aWm6U9wPP1AjJ+5EegGOp96EuUHeTMeK3e5vEbyXUMuCY+d30Fe5+C0EGjxQhmfaOGddpryCWCLTLkRJK7AYKxq4H869K8HamCqxFZRn++2cUpPqCTO4u4Vnt2Q9DXBalphEr7JFbHYGu/lbNqzD+6a8p1C9kW4lK7uCa4MWldHXQvqRuxiJwp3H5VHvUYDpNGCSARz9aILxZ0xJyeoNTysjtGR3Fc0YmzYkjKVwGw27P51JBa3EpU4IH97PH5VBAVOC/YA02fUpZnKRtsQccU3G4KR0lrpRkUEyj6VeiXyplQHIHHNclaX9xDdRx7zluAM11dsdzKWPzVlLsaamH8QoVm0ZS7su05GDwa860S4itp2DLgt/ETXqHjIt/Ykm38eAf5145v2ucHvXrYNXp2PPxDtK50WsvaSlTH/wAfH95eePesN5iX2NlW6Y9altxuYZbBYgAV0w0jS/sbpIBI5GTKTyD7V1aR0MPiMvTL/wCwR7AgKE5JA5Nak2sRpHvj+ZyOAf61g3Fm9lIF8wSxMflcfyPvUckmTsUZbpgdTScE3crnaViVkkvLoLHjzXOcjgUye3ntm2zoUPZuoP0NWLAvYSs7r87DB9hWuJYLuJ45AHRhyD2oba2JSuYlt58X70yfJ0C+p9aa2PMEcZ3u56ipLg4kZVwEXgfSoI5EyCc5PRuRVW6ivqb1jBHaxFDhy33sjrVLU4LKF0MIKzHkqvAxS2126Hlgw9xzWdK0jzs8qncxz0qYxd7lOWlhzyKOJDwe1XrO58jiIgpnOw9Ku+HbCGcyTXESuhG0Kwz+NTax4aggt2urO4WDbyUkb5fwPanKUW7MSTSuipeXRmhCxJgk/NmspYRcXCRSq3zNg8VCt+YyFlGD654P0NXor5MD5gRTUbKyE3d3ZYu9Fttv+iOYGHJUklTVFl2HYCNwUZxVqe9aYjbxxgn1rM85jqIiboYi2T2wRThdbhKw9nIhcE8+mKz5XIXGRz2q/LwpZfu+9ZdwcnPAA7CtmZEejybr++tRklgrAD2//XXaNexaboCzysMIMAep9BXmlveS2WsyXEBAdeORkGrN7f3mpzolxIWAIChRhRWUo3ZpGViK7v555pL2UZkkPy+1begztPdbicmOLbgdAKyL+zkKwiAGQAFQAOSa2/D1obHiXiaTqPQUnaw1ubdzIS0Q7VdtZFNvIG6dKqXIVZU5FI5eO2ZwDjd2rO2hoaqEJHhADSwXBiSRc9T0qnHegQg47VEsjPINnIJ5qbDuXBZ7pBI7YHWo7uQ71SNuelXbxzHFDGeB3qtcRhArIAWIoATUpS0Kx9WJxWS1h5d/wcAAHNajygyJHsy2eDV64t45EDSYBI5qk7CepnSnzFUY5FbdjOkccaSIMZ5NZLRnZtj6Z+9TnaQSLGGPTrSsBbvvIlv5jHgKE5rg9Yf7ZcBUXKR8A+prp0ZhNciVsADBNcjeIv8AaKRW8sh3SA8cc5q4IiRma1b/AGW4EPT92DjGKdZ3Dz4V/wCBQg+gq/4sKHUt75Z0jGTn3rKh3pIHjbaetaxM2bKZOAM1MqPnhT6VTttWuoGwyq+e+BW1b6jIRhgR3q9yTMtLmC0SSOeQRt5rEjvjNSSaraLym9j0Bwa11uwx5Rc+4HNPMwyCUQZ6AAUvZlc5im+0uRT5glye+05qZdZs4VVI2mIHQtGc1tLclcLtx69Kj+3NuO1UyPaj2aDnYxvGlo0OwxSbgME7DWfL4gspiGk8w/8AADxV972Ur91cHrgCojdyEYGAfpS9kg9oyl/benqRtMgH+6aamuaZ5xadZJVH3U2kDPqfWrjXFwRnP6CoJLm5T5t5x9KTpofOLc+INNuip8uRccKyIaSLxPFbhkjkYjHG6M8U+LUZgqhj8v4VLLdPIhMKEkev9KPZIPaMqnxBaSfPLO7yHoWVuPoMUh1jTQeZW5/2G/wqfzJCMhRzzzRtdySQM0eyQc5Xg17S4rs3Od0mABujY7celXf+EvtHY5lH/fsiq0gbP3RjvVeSFXB3RJx7UvYoPaMlm16Cd8m5mx2jUkD64xW74a1cJqKFHuXUkcEcD8MVyv2e2LHdGM+wFaemyLazJ5U7xrnhSSR+VTOnpoNS1Poe0uFudP4z9zGCMdq42PTVuJblXAJya0PC+oCSNYnckleOMZNSmEW2qPnOJOa8ysrtXO2m7XOGurL7BqZh7feH0qw+FSMj+FiPzqv4nu9uv7AcbQBT1ule3kU4yq5qeQrmIjIVhlYkAVoWNh+6EzDr0zXNSXqyqiBvvHpXeWkIkhTcSEVQeO9E42HGRUhsw2oeawGUXge9bVoQZcZ6VRAESzT8hQOKm0xg53AHJ9TXJPc3TujG+I0oXRCBvz2214qt+6vmVWGO+2vb/GzbtNZDIBx90pkfnXjsqhSeB+Fetgn7h5+IXvEkeuWox8+3BBBKHtVtfFCEMgnGCP7v/wBaorXUZQAhI49VFXPtUgbeVU/8AH+Fd1jmZTfXo5EKG5TBPPyn/CpLLW7KAku+JOzhCeKui8lAY5VSR/dFJDqNxvwSAB0O0UcoXIW1mylcnzn/AAjP+FKmqWu75ZZf++D/AIVrRzyHB34J9qlDyZ5lOe1NUxcxjfb7RmG55dvf923+FaEur6dNbLbyRsiqPl2RNlfxqzmQrnzXB+tKBJjPnN0/vUnSuNTsYRvolkwtwXXsxiYfmMVINVgMnDS4Hfy25/SrclxMkhjErh+oyx+Ye1W47ufbzI4OOpY0ezFzD9N8TWtumGSQjP8Azybn9Kl1bVbfU4kjDyKqnJXy25/SoxfTKvMjsP8AeODTXvZ25Er89BuNT7FXuVzu1ilHLYxI0TJvB/vRsf6VVeLTzhoZ5Imzyu0lT+laRM7jLSMc8cNVSfT3k+eNyrDvk0/Z+YuYas43EIrMB/dUn+lVVjne9aVoJBD5ZCsRySTSvFcxgghZV7hhmq6xJ5oKbkdeQjdD+NPkDmLbuzBoyjDvytZlyGGflbI5PFacd3IyOrlvnGOD2rJvITvJ3c1QjEkQrJNIRxkV3WjaZHL4TllEamVwZAcc8dOfwrk518xHUgA4FejaPHjSY4lHyrGAD26VjUehcNzj9MuTJe24aPD8qQenWtn7Ftv/ADfeufnlkj1c5UrMsuAo4BANdojq2zeOeppSKiUpIWaTzB0rQs0WaxdSRuJqNwss3lqe+ac6izU84qCxJHiW1kBQfu+/rWbazF4mMeQc/lVj+0VA8toSVb260KqrG2xNpNUSPnuWlC+Yxyo7U1L/AOfaeSOlLAhU7WXOe9WY7W0EgP8AEal2Grlq1CskagZYHnPapbhlUtubcccCqtvnzS+4Kf7tVo5yl/IJu6naKQye2uoZInRiA2cYq7ZwxkyNJ0A4rHjjjjldzjLc8VbguyLVuPYe9OwkzNviVNzImcZx9ay7dY5dasVxlVO5j74NdBfhI9PLbQWkGD7VzFtJ5eoW5By6gg+9WtiXuZviZCdTbb/F3qRLNMJuzz3HFWNZi8y1knOAQVA/PFWY48BQQCOtbRRlJkEenRtIAH98VdS3TO3PQ1YtIwJmPy9M5pfL2sSAPSrsSAiQAflgUySMn7pwfWn5G7leaRWy+3B5oAcgl2Dc2T1zTUjZHY4IJ/WpjkYUr2poHGecZ700Ihw/nksw8vHSpUQc4Ax+VPK5Y8AA05Auep+lIBpiDY+T9aRrUOuAGH0qfzApGM++Kc0uR0PNMZS/s9O7/pSNCludyEhvWrbSMkRLDgd81SkmMnATKnvSAMblGOM80FWx7mpcgRgkYBHFPQq8ZI6CmBV2kEjr+FROpPb8BVl+TwTUTAlvvUgIDFzwM+nFT2lrI8wYELg9xkVKm3y/vDJ6Z7V0vhHw02v3cskkjxWkHDsnVif4RWc2oxuXFXZo6RetZahaDfvO9QxPoTiu/v4x5itghiePauT8Q+ErDS7A6hFd3CeRh1VzuDkHpwOvpXem3M1ujuOQuST615k1zbHZH3dzxTxoVi8SSrkZVFNYp1UW6Nk/e4qf4nXQtvGkkQ6eUjVw15ftLhQ3SuinTvFGcp2bN0XuGUZ7mva9Jj8zTbfIJZkHP4V86W10XnVWPA/WvqPQLQLo1qxxt8lTn8KjEQ2LpS3MbXGi07Tkhzl5nAyfTrTdM2BQWyp9areN47gavp8zRt9l2lQw6bj6+natqy0a9axDhFAK8Atya82pBuWiOuMko6sytdtHvLR0jkKnHfBFeSajpdxZyuJQDz17GvWZjdHWBpCwuLpl3hG7r/ez6VbPw6S/cNqN2QP7kI5/76P+FdOGnOGjRjWjGWtzwNsqflHTrV21lLKFzXvUHwx8LQHL2TTH1kkJ/lWva+GdB01c2umWsZHQiME/nXf7fyOX2fmfPW5gdrjb9agkiIO5K9F+JbWk1/axW6p5yAlyoAwDjANcUtt8owACPet4PmjcymuV2KMDSrg7j17VpW8rSLliTSfZiMEFeOtOEJGWz34xWiIZLvWMFjnAqQTx7Tg9RVdoiwI4/HNNELIMgrjoRVXERXpVyCn315WnW0kjqd/DDIIJp0kchXOVJHTii02nJ2Y9cmkA5pJc8U1nkYDceMdKslBncDj2ApTCuRzk45FFgK6swfIOAO2aUyNg9alaFWUbTz3JpBb7TjcM+tAESynbgnI96hkG5hlOScCrZhRcZIOByM0jrEFGHGc8UAZ98FiTcB8yelUQwmXcwx7VpTiMHYXBB61nTFQ3ysBxipYzM1BDFiRPpXoOj3K2+iW6Ow81k6d64eQiaIoSDjmuo0mLzrYzyH/VjArGpsaw3MnUwqasr45JA/OtOO5Kj5ufSsO4uPtd6g6nzBgegFaJJEbHuOKVthpmpYsTcb2wKv3RSVwSfoKx7K4WRgCMHbWgrq0eD16A1LWpSehLFEjkb1GQaiuUImBJCp7VEokUHBJ+lWY3VkxKv50AOjlRnCqMjFTWlsplkkkHHakhsgH3o+falWR0kZW4WpGvMpAETs4OVxknNR4+0XrbOdqZzVyHbGHVgCDVrT7a2bzSDhiMAUrlWOflYwx4HJY1ftQ32TOOV5p0OlPdNIityhJpICY7JwzAH1q7kdSvKZtQdbSGJpJXyqgVnHw9qUWuFEtmOz7xBBAzWhZXr2NvNMhHmudquB0Aplr4zOnXEzSwGeSUg5zjArSCRnJle88J6teWDxRxKshYYLNx1qh/wg/ikAYni/7+f/WroE+I+G408f8AfdTf8LHyc/2eD/wOtbIzuc0fBnitFyJ4/fEn/wBakPhHxXnmdP8Av9/9aunb4iqcEWIBPH3qQeP0GQ1iGJ7bqNAOY/4RLxX18xf+/wBSf8In4q/vrn/ruK6mT4hxhQTp5JH+3Sr8RLXeCbB/fDinoByzeFvFeclgf+2wpP8AhFvFfQYwf+mortF+I2nA/NYTfgRxSj4iaSwO6zn/AEpaAcS3hjxYOxJH/TUUDwz4sJ6fnIK7d/iDpDYItp8/Qf41EfHel4P+iznPrRoBxp8JeLCO3/f0Uw+FfFecY/8AIldoPHen4P8AoUmPXNEHxAsEJD2cvXI6U9A1ONPhTxZsK8bfTzRTF8KeK1PAAPf94K75viJpbDH2WYZ69KYPHulAcwTc9eaWganCv4Y8W45xx0/eCmjw14sVcDp/10Fd6/jzSdg/czE9qnTx3pPlc2kpOPajQDzj/hH/ABRnnr7yCj/hHPE5zyP+/gr0ObxrpD8NayY9qb/wmmk5I+zSbfwo0A89fQPFCjovHo4rvvhtr994atLuz1m0neOWXzVmiAcjjBBXr2HSnSeMtJlQDyJP0qA+KdJY/wCqkz67amUVJWZUZNM6bVPiZpZurO3NhdGz84NcPLBjao5BC9+a1br4oeG1gzFcTykj7kdu/p6kV5vc67pUyHasg+qVV/tbTWUKMnHqtZfV49y/avsYnjKS48U+JpNStYGigZFRfNwDwOScVjf8Ite9WnhH4n/Cu4j1LSTtJOOxGKsNq2jk/wCtI9MLmtVBJWRLk27nny+HL6MhlkhOPQn/AAr2zwt4+srfQ7ex1bzoriGMKWRC6tjgHI9q5J9V0Yp8uWz6J1qH+1tIUf6p930AqJ01LccZuJ6NrvxF0GbQruC2Z7u5liaNEEDKNxGASSOMdaZpPxR01dKgW9tL5bpI1RxHBuUkDGQc150uu6ch+W0bH4VNH4jsEbAtWx/dyP8ACp9gu5XtGdTffEJX8UxanbaNeywxWrQDeFRyS2c9+BVsfFK+cfuvDlyf96Zf8K5H/hJ7IdLaTHswpw8VWSAHyJwR6EU/YRF7VnTv8RvEMvyw6BEh7eZP/wDWrG1LxH47v0ZLdbS1B4ynJH51XtvGWnRS7miu/plcVb/4TvSsj9xOPXgU1RghOpI42bwp4ou5zLNe7pHOWYy5JpB4H8SN1vAP+2ldsvjzSQ2TFMM/7IqQeO9GPWOfrn7v/wBetbIi7OFPgXxLj/j7J9f3n/16ki8A+I5V3fbcD3kP+Nds3jrR2Awlxn6f/Xqnd+ObBADAtxjPI4FGgrs5Vvh94jU4+2vn/eNRt4B8Sg/8fb/99Gunl8dxLGJdlyB6FxzUSfEAMplNvLsXoTL1o0DU5k+B/Efe7f8AFjSjwL4hz/x+Yz/00NdYvxCts5+yuQf9upv+Fg2W4E2L/wDfVGganI/8IFr+3J1Aj28w/wCNNPgXX+D9ub/v5/8AXrspPiJZ7h/oD/8AfdN/4WFZgf8AHg//AH1RoF2cn/wr/Xwm/wC2SkezH/GmHwFr5wftFz+v+NdofiNBs+Wwbp/fqH/hYoxkWR68fNRoGpyH/CAa8T/rrg/gf8aVfhzrchJaS4AH+fWurf4jOD8tiP8Avqkb4kXA5Syj9uaNB6nLH4YayeTLL+OP8aif4Y60D8rOfqw/xrpz8Sb3HFrH+fWmn4j3xORbRDNLQNTnLL4e67bXLMYQ6lCPvjr+ddXYeGtUg0v7O8AWQdt461TPxF1MuT5cIGOBtqF/iBqjgn90CepC0nFPcak0UF8Ea/bXZmkt4xGMsSJQcUy/tZrMFJUZCehPQ/SrFx441O8h+zSsnluQG+XnFakjHXontXPzRAGMj071MkiosoJYCK1jmRvmI6VZ8oy2oI6ipVs5YMRnJCilUuIMKuOayLQy2dobUswBNPjkkkTJTjNLNaYtg+7B60tvI6qMrmmBIWkQbk4GKRJfOBDip5WMyqiDGRSpayBdxA+lKw7kdrAXclRlG5JNaCWe2QMi8AdRV23t4rNNpYEEVGk3zuucRnoayLFsrRYJZZf4WUkmsDU1h8h1jPV8AVtxzKvmQM5+YcGuaW3M17IXf5I3JI9atEsz9SlW1jitugCk8VyF1dIbj944TIGMj2rsPEujPc5u4rlFRUzsb2rz68bznGRzwM1tDYxluXoriAEnzwfTmrCT24GTICT71kW9mJpYo1PzO4UZ9zivRNM8K6daLsvrFZmH8RYn61UpWCKuc3DNZ9HIY/WpBLaBgS/0wa7JtE8NE7m01VVuMKTx9aWPwho0oYQ2IYjkbmPNQqiHyM4ySa22lfMUE9+KgiaGMkG4QjPHIruIfBul27yG4skkJPyhicCry+DfD08W6PTow7fw5PH60+dByHAq1qWJMye3IpW+z5bbOhH1ruW8H6CkoV9PjQ+5NRzeEtBjPFrGQw4AJpe0Q+Q4QiI4AkT86UCPPMinHvXbL4T0KQhVsl3Z55NTyeCNEEe5YEz9TR7RB7NnC4j2kBwM+9MWOJf+Wi/nXYDwppIQt9lXah5zmlh8NaPMpb7HEFB755o9og9mzkBHCWyZV/OgpGePNXH1rtx4P0iSQbbFNvc81B/wiukC9kh+yR4HTk0e0QcjOOMcQ43rj61NlAg/ejj3r0Cz8DaLIGaSwQADgbjz+tQL4M0kXXlmxRlDZwSaPaIPZs4YmLklwT9aiYxnkuOvrXeXfhPRkk4sUA4yoJpqeHvDhYR/YVVhwSc4o9p5ByHDqyDd+8Az7007Dkhu/rXcz+HNEBJj06NlXqRmsyfQtImUiKyjU596PaIORnG3ELSSowmwoPTNT4G3hhj0zWNrNo1hqk9tt2qDwPY8itnwlp0Gq3LW0sSyPwRn0q76XJsBHH3gfxpM84BH516XD4R0K2hAl06B2GBucHk96rv4X0J5NyadDsPpkYrP2qK5DzojkfMOPenh/lxkV30vhXREO7+z0Kjryf8AGqd54e0cTJ5FlGqN2BNHtUHIzkVBbGTUqDHJ2jHvXTjw7pKyndaoQRnG44rH8W6Zo9hom62gCXEkgCEE8Acn/PvVRqXBwaKQdAfvJz71DIFb/lov03VzEUZkYAZOTivVLPwrp8Flbwy2UcsuwFnYck96qUrEqNzlY3SPo68epFNZ0Yg71/MV10nh7SI3KtYRZ9MUkGiaT5oB0yBgOcFetRzlcjOSymfvr+dAaMHl1/Ou5l0DSEO7+ybZVPbHSm/8I7ozSADTbfn/AGaPaeQuQ4nzIweHX86ZLJE3G5T9TXef8I3oouAp0+HBGcbavx+E9FV1L6bAFYZ+7R7RByHmB2zMTJKm0dAWpzrEQFEqBR0G4V6tL4T8PPHuhsLcnuAtZkmg6FHNtbT4c/7vSj2i7ByHnQWIDiVOP9qn5h3DMyf99V3Fx4e0pXDRWcRXPTbUh0DSPLcmxgDHoNtHtEHIcGZITgecnH+1SeZDnP2hPzrtToekledPhyDzhakfwxo0iZSzjX1wKftEHIcR59uAP9Jj/Ol+0WwH/HwnWu/GjaMsCxDTrbdjhigzVuDQdIFvtk062J7ER80vaIOQ80FxahvmuR+VDXlnnAmyPpXp8Ph3TVlLDTrdjjgFAaLjQrJSGOn2qA9cRgCl7QfKeWteWanmRj+FRtqFkO7flXqttoGnlT/ocDAHj5BUj6JpgcstjBkD/nmMUe1DkPJW1Ky5wzCmnULPs59+KoXNubjV54kXBaVsAdByaoSKQeAcA9a0uQbkd/btcRqhJYsMcV3Phi4zr3lIc5hYkflXmukWkl3qkEUOPMZsLnpmvR/Cvh+/07Unvr6SJQsZXaj7iSamWxUUdv8AZI2G6QDJ5xVG50l3fMK4GOlE1zINrI24E4FaD3ciQxyYAyec1jdo10Mm18P3JlD3EmUB4Wn31uYm8tIsY4JArXuNQCqjZAHepkvbeaNX4Y9xTuKxyhEkQ4Bz6kVZgL3C7FYkjrW+93aPKVeIBQM0+E2JcyxIB9KLhYybmH7Z88RIcjkY4FZbpNAGUvuHYg1sLqqvHIjRkcYHGCKwbpJXYqrYGelICRbed9spPK8FfWsy6t7yKZ2EL7T1IFPuLm4DoUkJIIBwa3F1Rha7HUdPmz3p7Ac5PubT3UkklSDXnNxHmfAxmvV7h7aeGSZYyhZT8vvXl1wNtxnvWtPYynuR6bn7Xbt6Sqf1r1aG/FxfG3ZGO4cgV5dpn/HxbtjgSqT+der2GnrPLJcRNgf3vSpqblQLUyW1viAAb35z1wRVm3lEJSMlVcjk1Xu0NvbLJJGGBbk96il/ehWtht4GCe5rIsuSAIZFaYuAMj2NUI7ueO4GFIYdTnhhWrpkKMsi3EZaQDJOeKtx6NZXRLJvBboQeFpoZmf2pBfRtb3EeD0DHsax306W3vWLSs0Y5TnrXYR+FrURfNI5weakfSLAyITI4K8AZ607iZy9m0qyMSCqjjn1q9BYzyzDzsogGc+tbUek2/zuknBPIPUUXNu8MLDJEIAPI5PtSbGZV7pskAVB80Uncc4qJ7JIUjIOeeBW9b3PmKB5ZKED8KSfTpGnzEwVQOjDNIdzGEkm4BAVUHB4qpcwhrx5UB3ADpXWpAqHDhSrDpjpR/ZVs7+avGByKB3MO1aeRlXY/FWJ5WRiNmH7cda6KBVG1EAAHGcUx4UM2ZEB29OKBXOcbTJroGYjDccGql5Y4KZQbvUV2rhNqkoQcUG0gmjPyAU7CuedvbXYlGxdoHXB61PNpWHhdUxvXJ9jXYSWFvC6nYW+lN+xo+VRiV9+1PfcVzxf4haMyGDUo0OB+7l46DsawPDOoDR9bgnfIiJ2uR/dPevc9c0VLnT5oZf3sTJtYYrw/WNHl0i7MEgJXrG/94VpF6WM3o7ntcjRXVuqhldXXIbt7VkrbvFM27IUdPSuG8M+MZ9JRbW5Bltv4fVPp6iuwi1e11Jt8NypXrt6H8qiSsWmmWhLLLHsxkd6rxx8tEUz1xVrYzEKhwOpqlearY6e4eSUM+PuJyTULsi7k76cIojNKwiCAszN0ArzHxTqv9q6kEhBMa/JGvt6/jxWn4l8YXWqDyEPlwjkRg9fdj3rI0TTJprhJFjMtzKdsCepP8R9hW8Y2V2ZSlfQv+F9EN1r0EZXMdr+9lIGQSOg/P8AlXqN1cxrbtIP3Ug9RTdD8PRaLpi2bSr9pc75ZR1Zvb2q01tb3EvkzSb9vUkY5rOTuNKxzcUvnz73yw5Ga1bU2cSHz2B/3R0q+NChkg3RSM3PSPjFQQ6Bbs53TtnqEY5NIor3KQKnmRyqcnoapGW6umZIIixHAxxXWQ6Fb28QLoGDdMjOKvw6ZHGhljCn0xxRqBzml6O5Imml+YEfL/OukvrBby1j2kbgOcccUkNtHFG5yAc9D2qxGCThTwaEwOeew+xStIpJBPAzx0rLuIXnO/acnqa7aW3ikYRFPlxnOayp9MnjUmJQ4HWlqM523hRpAp+6OKvx6fFuJfGD3qpewz2yl1jbaOTjtV3TFmurbc4P1NFwK09kIZeUGw96fGltbxFiMlq1p7cTRi3kYKcdRWdNocrQs8Mhb2o3EV3jtpBvyBjtTlmRZhhNoA4z3qG20bUZFIaMge9TJpuoDqisAOvpS1AuxoH3SJJsz2qjc3cSsVdvmxgA96srprz3GzzSAFHzDpU1xpkRgWJ/9Zng45pa2EMLrFChRguR0pDKojYnbuIJzU0mjBUBkuG6elNn0eNLZn844EZanYdzwDzzBqzzgZIkYj8zV22tEfwnqN0VG9bmJVPpkHNZmd1y/wBTXQQ2c6/D67umUiJ71cH3A/8Ar10PQyRU8JR79etyFzt3H9K9VsbeR3bCFtx59K4T4bwK3iVXkXKJCx/pXsCPuRmjQIi9FA5JrOo9S4Iy5LeHTrTfOgY5yAKaJ7a9s/LchZB0XPNU9TvWlnMMqSbD6jvVKzkSUmVYzEEOCx71mWWruzlmt1Rc5BIzVKytbyzn2MCy+wrVs5ZkkO6TfGzcACtiOCLDSY4b8xTuFiCGGFgpkwMjmllS3jxtYKvoO9PNom13Vsg9x2qu1hC43+Y2E9O9IZft9Qt7h5NkEbfLg5WsfVtIi1EKbOYQzk/MNvSmMgt7028ku5gAQU+XH19agGq3EAmlKiRlkwdnUCncgyh4Yube4Pn3idcbVU/nVs6Sq7t12VwPusuc1o6brsF6NpBUkkEstQarJ5d1tihGWOBJu6fhRcDGk0y8WMyKUljKHlT0/CvNLgYnAI7161LLBEpjSUKNhL15XcLmXd/t1tTd7kTEtLcxfZXORukGPQ816jp9yttFIDhCvB56ivMVmPn20J7SKR7c16tbW1qYwt5tMkn3eMZ9KioEC1czw3UEMe12cgAYrP1qC4tIIyAEK4IXPWtHT4ZbOcbwuc8LnPFaN/EJzGGjVo853ZyVNZ3Zoc9pX2y5aQF3RGALAjpXeWrQpaKI1C8dSK5fVLs6aiPEwKYIbPG6qkfiCd5YVUEIDk7hRcNDpb28xMsMMpJPUjtUptYk2yBuewrB/tOKGFpooXMxJ3NjNPtNRun2s4yTwQTwaQGo7OhdzujiAyxI6mrdtP5kO3Bft8wqjKWuI2GTnjaM8VRmuboIUCcgglgelCA3IsyNlYAqL1B4xRmUtzEN31rCt9TfYbeeRkk3Z3YPSrJuJodshufMVuAm3kmhAbSsBKqNsJI6jtTxcKkqgrt3H0rGkvU84GeFo2C59s+lEuorIp8hwXQfKvrTuBuPNCM4kAycc8YqldSzRyxsjDYWxnOaymgnuHhuZFwj/wALDkH608TrIrxmR02dAozQI2WuzFhcrI2M7falGoIRguqluMdazoyl7GNhHmjg7uM1n3ay2jlnReCAXHajXoO5rFzFOT9oz82doHanPO0FyPnBicc7qyre9kkWdAQrgfK5GM01pmmRklxgdWU8jjtQBe/tWEwSxBg+eoA5ArnNa0yz16xNnsKMvzRyBT8h+v8ASr0M0cTgIpUngFl6j1qNJWSfzHYKVyFGeDTU7MVjyLWNEv8ARJzHeQkR5wkq8o34/wBKoR3LRnMchBr2y4QtbeXOUkDv8wYArg9sVzdx4F0a+kkMUDowYAmB9q8/WtVNdSHE4Ua1ehNvnMRj+9VZ7qWQEM4UHrz1ruv+FcaIrKDfXQ3jIy64469qv2vgrRLZo3Sxe5H9+WQkH3x0p80UKzOG0PQbjXLzybSPcqn95M/CL9T3+leraX4ft9AtWe2UXF0QA8rYBx6D0FPhMNtDHHEgiRVP7tEAwfYCl3yyQt5blkTkris5VL6FKJZ+13G5PMttpxkEckUkiWssbSTpyDnOcVnRT3n2nZLuTdygzkY96tfaLaYN5iFwgz8vUVncosLeqCFswGU+jVIlpI0jPJGhc/xA8ge9VreOG0SPyowFk5Az0q2Jx9oO5ipCjABzn60X7gattJNHbmOTDZ4TjpT7Z5CjI0ZUZwMHrVD+1YrZdwBdz+QqG91eeC38/oo7L3qroDXkaJN3nEfKexotp4pWYIRnH3c1x8HiI3rMnkjBbO7s1aZvQJ9lvENzDGAeaVxnTxOC+GADAYqMNMLk7wvlN+dVhMUtiGXaQODVR9Qs2lUPKwkQdc8VQF6aOONJfkD8btrdxVP7bAUeNVCk/wAI7GnC8guZN6SbgMqQOaa6W6vGfLDuTwPSk2BWtbOSa5Lu4wRj3Fa88iWFqoUF2PGKpG7SOfCggr1AHWmPeiS6RdrEMMqSOBSuBG93elcqgWRuDnoBTJ55lRYoQC7cOTUotTeySGSUowPCqeKH0+RsrnHvSHdFC5vlsQpklXhSWAPtVPS/F1rqshWGYK8echhyRRLoUqPezTz79y7YkB4x3FZ8GkQaVGs6W8aSyNgDPJqhXOoGpFoi7DzAB90daydV1CRNKuZWlCEQuQh69KWR2g6gRyEYUda5nX7V49M1G5nuMuYmUR7uBnFC3E3oefQWQSyW4B/etzn0GRXQlol+F90B80jXi8+gzWBbXZaB4e0aY+pqv9tnOnyWW5jGzByCeMitXFshM7L4dRqNTlYttxB1/EV6Q1xKrlIgWcc7PUeteaeCoZm1BhA21vJ+bjPGa9G2KYGMsmxtvDDrgdqzqfEOOw2WSKVZI7xQGJG1lNY0+l3EaeTCrLH5md/citZ5YmthMYlZDkFjwRWjaSR7VkMozjhTUlo53TJZLCC7EnzFTlA3etUz7kiaGRRM6ZK1JMtlcHDgKeQ1U7jTZIpI2gYFNp24NA0y7BO0h2gq28fPjoKjMZhmL28p2D76dcms+2DW9vsSNizN83UZJq7Pcjy0jMfIHzKOv50AZpa3fTxdNJJ50SfQ46YqlLdgODBGFD/MWNJc3wlmCjHKhGA+6T1rFuod0sgS4WVlz0OM+1MzbNWB0kligijLsCWZs4wKT7Rcu7oWUjfg7ucCsmz1R4UIClgRtHoPUVrWN2hm84jHy5CMBg0WBMkmuIp4mEEYXCkAuM7q82njZ7hUHLFsD3Nd/LqrKS4tvLjcEbMfdrkrVRHqltJwFjfdk89Oa1p7MUtbGOgI1WBGGCJFBB7c16jb38ELL5rgxocsHGSfpXmF1IZfEBkzndNuz9TXf211bBR9tt/MjHQKec1NQInWR6naSsxiAxsyCwzUUmoLPCxtImaTOHIPSsaMw3N4yW7SRfLhFT5vzrQSBbZEmlLq+cEIOo96ysXcsnTra/iT7ZIyuFJBJ/pVIaFN5CGG7glVcne/B+lbEcdu6oUUykH7wPI+tVLq7ghcRGFT83yq+Av1pgQJaXMVxAsWclSWZjxWhOjWdupby2BGCAazbi5sb5vIM0sUqrkjOPyNa9k9r5QZY2OR0PPP1oAzWuAhI2OvGQm7PNWII1eaWdnCvgAoTwRRdo5eQQ7GkyMoB2qykBilBEe4lchG4xU3GV49E+0yG4DkbVyT1AxTGv3SA4OwKSpcjgnPargub+Npo5oY44GjIDKcDNc6lzcxIIdnnOo4HVT+NDYjZsWe5jVXLs27BkYc4rRluYLGIRLHvctwcVz1tqLlfLlMkLodxPUY9MCp5E8+VSbghPvLhSDmmmBPNqbStGskgUKeUzjBpqfbvMDqwXeMgYzmqMkcovABGsp6eYRjmtS2uZUwpO0LjIIzQA2VLwTLggJnIIHLVZni80xPJMEJIyB0NV7y4EERmHyhcHk1nxFr7a8cn7oHedw9P6UxXNNovMeRGwJOxFVxcyLbOhX5g3UL0FSPEDEsiXGQeVIHNMvLXlJd0gyPn7DHvQO5HJdORG3BBYg+49qh2wCZZcMP9huaekEoaOXyxIIuQqjAAPenyRhJzeSHfFGuSFGdv1qLMCG4WPUpHjR3RF4ZVXBb0+lAtHW2ZYlZNnGd/Jq9pm7ziblY3mbBHl8DHb6n3q9JaCQOE+Tcc4YU9RXMW1RYEDPGFkIIG85qWB4ipgaYJGeWCjpTpbVoZT5riSMDjA5BqvbiG3SRJwCzduh9qLsLj7qMxyjG1oiduQSGxV2LyLaZUjYbmXnAzVW9nS2jiliG4cLhqbc3jFVYhYmIxuA4468ij0GOZQlz5cj+Wx+f52wG+lXYoAkjRkom77g7H8axLh4r+13OjebHwshyeM+lSafeyyxujgYA27ueD64osBuG2to3jabllPIBxVW7ubeSc21qP3zfNvPYCsyaSUPGiySsc5BC8YraFvDdKsyvGjDqAMGmIpM7F1Dptw3qPm96vxwpJZqsnCDO49apXkMe6TLovl4OTUlneLHEGMq7evtiktyiWLToCVWHaYlydwGDmlgtoLNhM2d2/PJzinxXSSrJJHhCuAeOKrLDHqAkJnBjjycdKd+wD5tah+1yRqWkdQSFHOR9KhS8jlhYuiKr/wAKjmoClpaxoYcMQSS2Bke3PapLeeKaNzIwRAeZDGBz7VN+gEdrfwwTyxwxnITcU64/GrsOow3B3q/I/MGoYzY+RJJGQ7sCm7aBmqKaTBFbtNCHjJfGXPJPriqC5vQ3SyTfOpDdcHnNUrrU5Yrz5kIQ8hiOF9verECAlAzplTke9WvIjmJEiEORnk8UWEY1xr16kr+RanhMgbfv/StLStWuLuPZPZyRkjGevNQyB4miaLZtySKmAvPODKfLUrknrQgbEubWWWGaRSBIoy0QOQCPSqELvHBBJexgsTmMFuaz1vZLd5zJ95nbLFuHGetMF7FLbpNclnl3FY+cYHsKbYjWu9O+2TRywuY2JDfhWB42ghs/DN24k3SMUUZ68kVan1ES2fyzMvlHaWQHBz656VynjNYotFj8q4eUSTDhieOM046tA2Y/hfR21gX6I4UoqkZ78mqV1ol1Y2s91MCoSURHPfPpW74JuvsFld3IUsWkHQZ6D9OtXPFNxc3GkOslr5aPIsu7OcGtOZ8xNtCbwLkaiXXmTyvlHrzXcyyOjnzoizHJyO3tiuC8MvLZySTW6B2CAEegrpW1aR7uN3TcJIyCnPyn1qJ/EOOxc1HetqxU4B5Qnpjqaz4LqWbyRE28Kc7hxitIahG6qs8RKKuCM8YqS5hj2RrbIFR8H5eSBUPuUUbrfIisju7sMmNTjn6+lX7a7vUhK+QDKgARR3qVNPQW4d2OSwOCOw9attFFF+9wWAB+ZT0oQyiYrtrdnuHTcSCpU4/yaZayWiSSAFg23GHbkmrMyxyhf3zGNBu45qjeQMp8+GIByAFbvg+1O4HNfZbLVJA1vOyysu4LnHNVZNNnSdnO9V25IUZFSaRJY2dukphdG5LEHnHbitizvU8po7qJlUodoccgZ61ZFiCGwCQD/RwsIG4tnOc0zZs+QRRsi8ru4IpbW8WLzoJ5D9mDZUIp249896sX17ZSsfs6lZfLG4AZwPWixI268prUs0Ss5UkqDxnHWuL0+FrnUbeAAkyMVAHfiunub4xWcyM/O0gMRyR2rD8OqJdd00fMcuxwvU8Grhsx9TndWgaz8RtCw2lJQCPxr0SK2WKNd+FV+NpGSfeuB8Wjy/E1ydpXD5wTkiu3gvW8iGcTIvlhW6ZIGOaU9bBE6O1lttNtENvGu8tjeRzUzXjyq0U5jaOQctxla5O8ne7YyG7RLZnwrKCpIz2FU/ttnY3P7y7d1XIDHqw96hRKud9Y3EWf3HlNEox6E1Ne29hqQjVgC45Ax3+teYjV7XzHmTUmWU4G0fdxVuXxKsCxM12kqj5gFOCfajlC51t/p8CwgSNKshYbWVcj8cdqjZnsowrTgp35xx7Vzlv4ta8Vik7kDnGO/oTWdq+szXkg3q0TBvmAOQvvgUcoaHWJqO2O4csd0n+rA6n61YGpSzW6+ZhSowZQcj6D3rjtL1HzAbeW5ZtpDAqOlbMMksrPArJ9nK/MOjZ9c9qXIgOojnM0FuZLkFOjA96X+z/LuV8sp5GNxyK5W481I2AkYFQduOeKgl8QXMUccZkldtu0grwKXKM6fypS9xOWRRkBQoGD+NSQ39uIZYSsbynjAPT3zXOwTXc1oi7lkPP7tT/MVHbPfxCRmsYJ/MJ2ksVPHWny2C50q26h1mjYED5iS2eabBGzB5SuCzEk5JNVrGEtAJblfJYdUXoKr3sscWTBdOsjcKCCQPWjluI044d0jOGwhADB88e9V4oL0TrNBLHtdyrZXIC1nR6o1srxLOZWaPJY8Zx7VB/b91M0a2vlBkXEm7j6GjlA6KGV4rjP3Ag+63G73AqGy8QHUZriCWHyY4myTJxuH0rm5m1ETvI95EABhcnrnv7VXktL9lklN1HCijlg4ff75pqIrncX2pbbULZqshPLY6YqhKsv2VfkXzXY7488NXNTjVBZJ9nDXisA0m0hePTp/Wob2LV5pFnedkUn5VI+7/snijluNs6y2k3xRvLcLGiPtIByPzq0NQnYZhuY5Iwe/cZrk0nvIVS3WDegI3Bcc8ehq1NqzFY3S0jiVk2tuPHvS5AOl8y4umfaoXOQMHJzVP50g8yRVOMqHdecis2C7eKRYop45QTvJPBAPaifUkWARmUsVyU9x3H0qeQDVkdZbZkcI8YYEjBB/nUiy21mVjYB8rkKW4DGuXm1GX7M7Wsu75clOT9M8dKpT22q3lvHJCzMMZIySQfTpVqIXO4iktGMrxrErbclWbqapxXjWpYbA07vhQBkAH1rlrODWGjMM8XlxnBMjkcDGcVueV9lty8lwMlcAY4b3HpRyhc1FmCXg3eWwKfOhwMH61EzwugzNFHu7I3SsZI4NR83zZ9yJ1VTgn1oXQdCWLzpIZdzNgQtIcg/hRyhc6Uy20jiCdTLnqwbaTxVNZIXeWOFd4KbhluAc9Kw5LkadKFS2mVF4DsdxGaltb6dJBcPCipIfvKOw9f/AK1HKwuXFuZPM3z/ACiNjyDg4+laGn3KbxMsiFWByrrjP1rB1G5tpZjd+W0kiEMFThccdfbFRG8PlSsqSSo3OAM7Tn+VLktqgv3OiJe41FPNhiWKT0XOe4zVh7WxaRYmYM2egOR+Ncjaa+wDSKWVkbaoQ8D1rQi1i3s1kLyMu7njBY9OaOUDR1OK3sJjuk2DdwqjPbrVmyngnRElm3iP50IXAP51mjVo5gSmy5frGzjDE1LYutwkYvIhHIrEMVI59KXLrdAXJ9Rt5pUkiiAcMAwDdPzq6moLGh88eauOAo+Y1XuI7WeSKNoxhzyRgD/PvVO98u3AVB5XRN+Rke4p2A07e588lY4TEjdCRyPwqUYt4XZ5TIvTI7VSY3VpDHcOSwORvC5Y01b6OG1HmP5it8+1lwSfQUWsIgS3spIUeXB3dcDjJ9jVSaC1jujArb5FH7sNwAPQVoTpNNGjREYHzFVXkn0qtBcPdyq5RYWbKssi5OB70uUCdoleDyLiKLccbMN1Fef+PVe2Szt22AbmYbe+BivSVktAA0roVQEg7sc/SvM/iZcQyatZpCykC3LHB6Et/wDWq4LUTLfgaF/7L3mENDJI24nqPp+VX/FBll8OzFVAhR1yccn5qwtF1OXT9GthFE8mBuOOBkk1lan4kvb+SW2Z1ELNkqBxwarluw6HV+GXx5qtgDgFifu4zXTxySrAyzQKZg2VcLkkdq87AuGtytnI3m+YGwvHGDnmu2h1g2cQDSAy7R5ihSTnFTUXvXHHY0l0yW+CzNuSI9fmwd3+FXZQlqIypYmNdoMff1zVddXleCFN0ayTHcDjPFTtfwsVildSGG35RjHasyiS2vzIzbiViVckFep9KkWWOYyRtIVVkAK4xiuclluoJWjjilkR33Ix5+X3rQjudk8pCGKUKGZW+6ffNFgLsdwlrb+SpGQeBt/h7VPLdXEgUpbjZs5PHWse+lMs6vslA28lD8pHcZq5ZXrRzIBDsiCE4YdPQHNMEebWn2i8SNYo3LZ+V+3HatONNQlunkEbykJgkvtC+o6fpW6yxLai5t98aqQdkaliAazXkcyKxk2SKQSyjbjPrWq1IJ3uncW8TmIAg7lb5ulPlvI4bdgqo4bGOAdw9PaqsgvUdj+6LO20Mi9jVYaNqK3ZYziNYxu3Lwcfj39qALGtPbyaa2wfvQnTI61z+ku1lqlnJI+wKjP74Iq5qtpPHJJcLMXgIHUYJ/zzWf5wcxTMhd0hKAfj3qkvdF1MfxFJ5uqPLkkN3JrptOAktLcyTAL5YOCvQ/1rkr6KaW4CrHIxx2Q10FlaPZ2VvLcM6Pt3YB7enTiiQI2ZLWx5he4bzDgxB8kIep4xwDmqN1oMOpybhKfl5cjAxSqWP7+ZxDGvJ5JPP+fwprOryI0Zm8r+Fc/0qdhlZ/CcDcRSs2SB6fWnjwtb6bMRdnzI26HOSPatGLVongMUi4LfLvIIKeuKtefBHbxxmQO2dueu7PendiM2S10G3hUwrK0pJH7w4xU1gtgIDPMUJOSAFIJPYGrDWaOySSxTA52qTHwKkazWFTumER3/AHWTPFA0QNaWEDNcTOUuOCYgvQUXN5bxjbAzHziQS5GfWrctnp01y83m3U7A4G0Y59vapLey08LJPJaMjoOHnPJ/D1pOyGjPsbuUlozdiQcjHlYx+NMvhdyEwrGQWxud/ugfz/GtW9Fi0cbKUWV1+UYOSe3TtWZdz3sCzJK8eHUBgD0oQF21sd0SyG6X5l5Ibnj0rQtp51BC+W1og4Y4JIrhbRbqASFXZGLYUE9j6VoRxau8LCEqQRt3NwR+FDQXNXUNXNt8hkcZO45bIrEvdeuJHeSNXKAcFTgD/wCtSrp9wJhJcOhcfKec5P0NXBaPPcbGktmhUbQwwvPpRYLlKGe4v4i1xIImAzndyfxojh8uMSi4zLIcAvya1o4rW08xbqSFNgwkSrwW+tV1vEwS9qsqKxCFSQV/yaYi7aSag5EcgiMZwB5gDFl749K0AkO7yFuo44yQCqqCD/nNYkd9blmlikmilZSNoXKj8anE6wW6hXMhcfPsHOT2ye/0pDNe4byZSgVVYL8px+vFUP7W1VnhjIjljzkZB5I9apS3V+lqzODGpOFZ3GSPaoYLq6aRooZBtJB3tz9aLCuWL3WdYu5VBtlDFgUVP8a1bK1u5iZtQsYGkJyHVj09COlY0V0sckrw27uFO3zAx5Pfitlb/wC0RGORHfbjYuMUMaJL3UbGKZYIYFjMjBWwTk/Sqs1yHhlMaJIzgZMmBj8Kzbn7BKzzzt5bfdURMSF9/ar1lDYaeiBZnkDHl3OMj+dFguXgl4s1u9kkCIF2l15Gcc1L5c8cpk+1wRkdCCTz3wKr3Gpx2e5E5j3fKN2B+pqvOkd3asIowkyknBy2M9yKAJjcpD5/nHznfpIGxuPuKtLNstSjIWOQRuPKn29KybeydLIvcSL+8PzKI8ZPt6VbhWAsohIXAUHIPP5/zpAR216tpes725+YbgoGSR3q3FdRXdzK01hLGpVfLlAIx69f8+1VJYrUzPJFI0bdd7EkH1GaYlut4z5kcSnlGBIyPp2oA1rkqXXyXmDxrghhw34isr+0tkn2e6hIjQbgB6/Wp7aylltnX7UCGwMoeoHY0k2gymTHlErjqGypH0pgCXdg6PsuBEP7pUdc0x4hNE4WdQvVQpxuFNbQo/tESiNt5ByTt+UfSoZNGMCn5pMY6kZX8KQFL7HF5c+EAAPDs3JNE9gZW+zyMpLD7w4/CrVuwS5wyeYwG4jGK1IJJZrkmSCJURQd2PkX6cdadwsjI0rSpoHjCRMQ2eWbIDCtXzJrZnN/iRVxyPl25qne3Fy97lbkxwKeJl+UAnjbWjpzahdRyTXsg3RDjMnK/X1oAuLM7xFYgd6gMo6kD09quJawXMkfnqp3Lgp1xVWJVEu6SZGBG7eSc+tWUZiJMlY8AFSvOaQE1zc2un6c6JGVA5VjknNYkMzmxaSOOV3dz95gDH64q3GLm7bbEVCxA7nljw2fYZxRPaQsn72ZSqEOxGSW9qAGHUZB+5jYiNmGxs7i3HOarS3UZkeV1aFAvBMnX3AqdvLjlDCBRHnK/Nwc96r6hK93OYbe22q7Y244IHfNKwGHcSSefIkUpkikUfvGOMGuI12czarIC+8RqEBAx0r0prGFbZkeHDAjKk4C+/vXnk2jX99fXUsNrLsDkjK8EZ9auKEze01jb6bbuoBPlgeWRktx2FVNTtraHTnYwlZ2IPX7vNdNaWWlQ2yEzyFokUHHTpVHxCNOOkXMlu/zsVK578il1DoV9AK/aXWSRVVgfvd+nArrlsrdlDzXIIcA7HHIB7e9choSvPfOkcYfcmME4HrXVW9pJLdSRzLI5jI2SE/dx2/lRPcI7DXgSULNNeKSGwkeMbgPT3psNpHefaGh3oUOQoJOCO/1q3La2jbkkg/e4wcv29j71FCp01wsdvMsTqSzFh1z6CpGM0zUWhklMs3mYJBjUHb7deM1txaot3HLNKYQHAVQVznHWq8d3BLBIkMcaBF53EZ96kEsbuwiSHYw+cDgjigZYSaC4RooSgfOCO4H0pZZLWKVWUKy7QqqT1/pmsy3htpZZJvspVh8gc/Lu5qxcaYDatHjJZw+Yzx/9alYDAsXjtrf7PKsiKRu2biCwzxx1HtVWW1NpM8oMnOTncGQD29/xp0lskjPPGS+XH7xssV+ntVO+g1Mq4jkSReWwOCR1/OqTJZo2lzaxESywMe+92wTmtBbrzkdArS+Y2FIYAIPX3rmBBqs04R4iqBF+dx1/CrFxqRtX8hsMXBUSKOPy7UwF11fLEi/aFdAoCbe+ef6VS0TypJwj5+51A96r6pqMNzDGsChVyMnHp79+9Jpd4tncK2OPLIH8+Ku3uk9TonRQvnJKFfGEjVSQo9SaqmO6SEqs4ZpW6leO/H61FJrG244V/LfqqDA3e9ObxR5N3kwIYohzkYO71AqLF3JpdMvniIRFk2jAHJDewFU5TO8gQwsCUBCp/BgetVIvEcgu5ZSVYKcqhJbBP8AEB7Vq215Dc2mFlfczfwj5sY757Z96BDIBcWce37Nbyu+MNMmSp9RmpobKa1cSmcM5JHCbvL9h6f0qzMU8+OEKxQAhZWYEscZHb8KqSMYrmFCzBBndu/h9BjFMC2wuZreRpL5xD90Rlf4j05H9KrT2XnHfLeCfIClgoG44/Q1WuLu9YAbB5J6sB09yO30pbF7aOXgO0i8EK3WgRbS0lhlMRd1bYCpDEgY7/Wr9nB5itFPOr4JYswwf/r1nG5WIzyyvId4Ab5uD2FZY1txcMFVpUK7B3IoGdHcRNsbynAAA+4uCfYGs7f5ji3ZUSUrkmYc+1RQ3d1cx5mJjIGQWw34backZ+WRNqzvnJMp5I9v6UguWXsZreQOBarIiZ+5kHI61Vjs9QeUM11HlRuKIPlX2z61dmuL5yWklTYqhQnXcB656VD5sO5yJAr+Xnap4P59aAIdU021M5kjupY22427cHd3xiiLT7e3RS5kbIBAJGfxpXzKFEk0qFgBk4x/n9aqyyQQTsHu2kbP3wO/+H+c0AWbm2iG8keYTyGOMfnVV5LQRhvNJIP3AhPP19KjnnEsZMVxlVxyFG4n6f56USXu5JGTYUHzBmTkmmArXSCM4jBQ9FPb3pJJsT7YVRhjG3pgVQku57lfMyESQ4+bjBHpirVvK0bgxJsYDG6Qcn3FAAdHW6PnM5bcd33h2q7FNJCoxZo8eCCHOaqzXG52eR3SY87iM5P0pjFrRgzv5rAdVIx70B1LynypF8pI0C8EyHIOfQe1E9xarESH86Q/xMfl/LNVvtcFw0ce4tgcYxyPy/rRcw20TmPO5gAAuMD8PWkA9LiNIZGkiWRc58sDbu9qrSXaxtuaE7T0Dc/hUW5WLAhzsOVZiMD2OKabzdOGkDEHuDkD8KYrlky20y7ZVZ93QbgdntV5L2OBxFEpCqPlZSoYkepxnHtWZPFAdrIZdjAEFVGc/wCe9VGtJVlGHYv12sfm+mKAOlN7C7IGeYMTnOc5+tSTTSsQxfBfkDGQF/Tk1z1rYTuBPM+wDC4HXJ6CrN9cSRR4jDgBuFwMk+1IZsJe5/dIQNxDKAv+eadHqJtm3xRxygDbgjqKwjJHOI54i0mzqqDBH4Hg1GLl4rhAV3x44Zhk49OO9FgudLbau1pPK8dsUaU4AUggfXPSrj67h1zks2AV9/6VyFxql4pUxqflOORjJ9KuLcpK6C8t1XAPzpnFFgubTazN569wvGxFGR+NQvqU6Mh89g56ArkfjWOFgmVEgnPXGFXLH3zR5whneNnA2r8pLZ3D396LBc1381LhXeaFwQOF6HOfy+lWHvsEwGYOHAMYXgk55zXPG5M1moU5ZGOFPy8mpH+8fNmgxEB846j24osFzRS3F0xZ5wnmNy8hz+lWZEMRML3W6YfIwU9/w6VzUGrxvcbVlYRZ27m57+3atCVpL2VpEeWMnrt4NAGg7xIyRiZmaM7lyThj+fariXUylY1O0ucZJ6e9Y32bhAmViiJAaU53U+5ktLVzM8jM2MjaM9aVgLw1UxTtuklRd3Epbbn2phvbZjJOsxjUrgBzu/liqiiCX99PAUVwMeYM4A7+1ZkmnWs5+0WtwwGSrJyee34UCuarX1tEiIGMmE5xwRz6Go5dYkj2yRl2CAkKDjj3rDOmrDA0jSszB+CScnH0qx5DeSwlRkhwNwZevt60wuWoNda6JBZmdWHIO76fSthLsu5/eF3ZQqhgR+VYUOlQQtHKE3lzxg4//VVs3AdvJeLCp82c5x7ZoGX7LMUxa4gKIDkMuOuOO3Tt/WsfxHgaXIEREQMMfKcnn1qVtVidwzLI0mOBuyo+o/z+FQ+INQjutFkVFJbCDccccjpTW4iHQ8PftEZCitGcMpwQeK6e3nFs04F1ulBAcF8KPcetcXbTPaamrxswIH8PXpWyscjK87OYwwG9cgnjp2oktQR1MDbma4kZWCLgIpBJPb696TyJ5Ns7TLJn5mjVsAZHSsFDdyxIiXAAyMswx/Tmr7QXThY1mUsQMFV4HH3s1Fh3NSO6R4GjtbaAykENG0o3be/H+FWUsjK7RwCMRjGAVwSSO+etYdlp11a3Ed9EwLqjbg65J/Hsamubu8lu4V4Yg7zKPugYyVximkMuTSPaag0ElySx+423dhqvBJkbdNdBd0e5V2847dPU4rNF3HPH5kkimKIZyUI3+xPcc0+51khVitbNgZEG4jJb6e1KwGda29/biSMXSOij5snLYOONvtSxs4kuGnWcxxYCcgbvxA4x2qz/AGDEgLQrJHIjlS28liO2QOMUq2eoJGqCMrAcuHUZcsD056D68UhWKkN0s/mCRZF3JwcHax7e+KZBZ29vqjXF4I8SRj5UwQM8d+nat+1sZ7pBI/zonLsR8yD3Aqrqumw6j5dxbWziRchic8NwOBn68e9NA0cRqttC1wyxsERZAoDDqT1xUEsKW97bB2wu85Zew9q6c+HzdNEZ5hDGQTuIydw/lViXw9p11HAJrhmCk7hGuMt6fStOZWsKxjQWkd20kaxO4cbhLnp7HFaD6VZRyorRoRgAqoPIwOtbX2WwsbWJ49kO2MDYEJ+b69aWJvssh3LE80pO3bGTt9AfrUXGYZ8PWkUxZIokYthBvIznp16VSewNvewEFRIuQvltlx7nGePrXYQRPJKZri1JVWIJcHioJobWQzM4lRyhGFGGwf8AOeaLgc5JC5KG6hjMp+bzIHKrz6jsferk1jI0cm2KMDcCnGevbNXo7KBbaKfyFibHAL9ePX1plldXdvNJFHHIIpNq5lAwp+vWmmBiHRbk3MrSXA8vAOQQvuevX8KSSwjW8VWEzbQdhxjIA6H2rcub/SbSZpLy4afa2PICK35HtWfqN6lxaHULGOYREkYY579P/r07hYqy2ZCsg4jIBUYyCffNZ32eNJEI2iXGZAABwOSR71swj7Xbchgp6Adffk9azrnSIhk/bHRmOcbQeewJ7UXETRXLb4hEwEcZDOrYzimzaqqPsfdGMkq2ziojpsis7+dGVwUAOAz+4qax0iaeNiZTsPLBiCV9KQx6TSSKrGYJvbBdBhse4J4FNa5tSflZpJU5UDnoe5q21jILGSO1iVnB8tJSvznvjNVtPt7+0hTzrMxs3LHA+bGetAFS6lllndYzHECQVMhPJ9hmqMtrqYh8pXRyOSqACtwC4nvJHnsleIjIJX7p7YHXrVq3dXtQZ7cq6cM7DZk9sA0AcfL/AGgsYt3tCsgJDED881XhluE8yJfNDZwSvIA9K7f7baO48gRvMQMjOcnocU1PsX2gxxmFsPhnIwPp/OmI5/TrGW4aPasgC52E9Pc+9aKo6wAskbKxwBgls56ilvNZh0+9aAJnH3ZI+QP936UzU9QSbT9+ntJJIhOSMKR6nFAGfPatIMK7GIAE9Tk00K+8DeVRv4SmQ3rzUsj6t9m3lBEo5Y5yCPf0qUXt8IY4/LLocgZUY/zigCdreMiN4lLNtyAOACKAJC5LhFzwn8WD9aI/tTB3MIQNglv89vrV6KOYiNLeJJJE5ZscD/GkMhh08yKkTxqgJy5KnvVe40sIW2x/vOQevNasuWCrLIhkU8jcWHH9arzyM05/0oPsI4Ud/Si4zAli/s8RCQP5hIxnJA/GpI7ljJu+dEIwGx1P8/Wrt3CPlmZmBByAxytUzctHFM0oWQSYCR9cH+lMktLZzvmaO9hlRV3FSSCrds0yRvJjcNINxx95shj2xUJlitLYSyBi7jlSRx9fanQzRrbSStMxcqBlVxnB9+1IYyWRLSVftDLlhtcx8UxYC8M4tSzdG44z/tVLBqjTSl2VSu7JZo+/TIp0skk3lRlpUV+GKY/75PbHSmIr3UcwYNKJNxI2lAOeP1+tKLUm5UTOwATc2CCc/wA6W51UWf3AxAXaFfOSKSS9le0M1tZsC3HAPH9TQA8sbTJYvGQcj5eCKmsIYNRLRqFDgH5gen4fnVCytdQvJBFOksaNkghCc4Hp3rqPKjsFjSF1WRirRsYRyCOvI6mgEVntbdIpYtsjhgpBVuR2yaSTT7eJZYRCPLZFXc45x7f41csEhu4JoS7x7m278AYb+7/P0qtbaJKkxAmSUq2xhuGMAk4H+fzoGctPp0tpHKYN/ll9i8c/n/hVyBbuSMI0gDd0HUD1NdBLpVwEdS6kRgyoHb5lJx271TnVrG+WFtkrSLnc7AH1wfr70CRV+1SsJYsFyEyDk7cd8+9OV5IooRcqWjIGAIz/ADq79pudriIOJW+YBCOR/Q1DdanKQhnUFkUIXxuPHQZPT9aLARyXAEkf2hUKXA+UnqMfyFI1667YUgWMZxtRuaswwpPIzFlEvQGVM8nvUKaes5kuJ7+JlxtwqHJIPv7elFgKVvdzzKPNESqhJAk4yPT61Gl9cfaC7wmaEnjzOeR1rWn01bi4+UxyRxpvGDtLH/8AXWe1vdwbJFt2JOVCJnB9RzRYB0OoSxXPmSxvFH8p2ht4z9cVaF8GEudhLfMcpjg/QVXtt94htrqDynA2qyr1PoeKsW9vDbziRrYpGeNvLM3Hv0qQKclxPI8kgt2EW3JfaB2xWTf+e+nlvK2xgglwOCM9z611sEYis43lR4iWIVc9APXPNTrcRQ6W5htfMdyQd6AjHUU0wONZZIrpJeDgAjn6Vs20glmYzRu0jjcpXk5/w61ftZbO7t3fyBI7IcxgFcY9/wCorUs7fTVEHkztECoyCcA59+hGfb/GqbAjt47goDNJGqt1BQkf45qzE3lSvbtI7ADckpGNw64z6CnNZTLkeb5uHwihucH3qFrO6juTteXKoANwyi/7I/xNQwNS0u4X0+ZJXkmOOAPlPp9axJLm4ii8iG3kWL77FuWJz93sDViSxleS2lhQEBQdqvgfQ4wMfrVgxgzrAZ0JVA7tuP8AjTGOg1CzNvJuiUM6hQpXp64pialPcSoltJGoXIVXGxWx1HSqF7apIBHHFEqPx+7PzE+/pTo7Ce2uYngSR22BQ0jA/jz0pBc6OKOSK2UJMokJyS49eQAPTn0qCee9hZ0mmSWJ8YDADD88D2qy2oK8xndSrf6v5lPH4DqapzxmS7lkSQ+cIzw/zHnnPsR7UhkkDCSeS3CbpBbs+wngfUH+lSDz1gUK6CY/Ntd8gfU1Hp9peFmluJy4SFtpVvmOccnviofObdHvZ5Ix8jG3XqR03egzmgQwCfy3hlBky5ZnBwvI68dveppbeONYWVOQvDZyqDNWxqNugDOyuVXZlwBjn261gQahLEbhBJ5cIk3pgAlgenPpQBo2yvJmf93KqEbHUfdPrzTpkuZohLbyYYE5VwT+grLGs2awIZHPmGRlIDfKCec4H4V0Gm3KmPzTKrDH+rP8QHUfX0xQBkrZXt/GsUyC1kKs3mIcHOe/OO46f0q7b2VvBbKqDy2IAJcFizHr+GaSWeOaUoFlExGFdTlB6cdz+NVRd71QMzSFHLIuzG89xx16UNgWbqK2WFIy213IRJFTlCfxppWxiePKt5gOC6nA47Zz9ayn1N7vUrWNpgzRybtrDKNgd/XFXAxF4UeJpSyBkfI2Y78AcUJgR3VlpV7bmRIyZFbpnGKqw20ptglt5axZJQMAQR1Oe55q/IYJsBFKOGyE2cE+vHXirBtofs6qjFcDqBk/gBRcDKlaaOJI8qZMYPTavpT0HlXGWa2aNsFgRkqf/wBdXYbBTHuTL4GRIM0kGjeYzMruMoVOOpB756/hTvcBHW3ClhDDMDk5c5PNEhWW23WUb2ykqAFAbjvkcZqzDawWttuaQsynAWLknB7/AK1OJ7WO2eWFVId8l5OnSmxleK7kME6xxJti53SvjBz6dKxrzUCisigOjjdGueCf9n6VoX8rmKQXEcCx5UhgQ2R0AHNY9raxpdNGPtMg35YyKVGT2J7Y9vzpBc0Q8k0h3CRsRblRSM578VQvNDtZYP30M7PuLkeaScdeh4FaVxqFppkQjtP9a2UEcnUn0B9M/jWQup3At/PvkhXLbWjOc+yge9HoIrx2MemWyq0fkbnLRNw747Zx+HcVnTwXkKFbZgmH3gE7Wk/vHrzg1svrVusqLd2bxyxIoDbDz1468GntpsF3JJMs++Roz8jkFlGASR6H19apMTOUe2mvZd90mMMcvHgEH3rQgsEtbiMR3UzSSnlQACvH941ox+HnWVLgxs42nl+e3f8Az1qyqX6R+ZDt2qArAAMFGew9aLgRw6hOm1YbY3ChcPvYMpPo2eM0yW5vQFaVfMmm5faMKgHcccH9Ks/YFkucbcypwVjRsY9WwetXI76705Y7V447hVkPyquSc+vpSGVrXa0Uqi4w7rtyBuA+vvV66ItrhY3QCDgLNux8x7464x2qO0lt47q4uYoiHwCoctgkd8ZH+RTINTupo2a8s4ZNpBjm7DH8XXHY9aWwyq2k20lxHJLczAq2cgHy8A84Pp9RVkW9raXawQ2sxSRmzubgqenNNN5cXhmaeAiAru34xF1Hp7ZqS1SJoRHBOxyAVjePICnnORggntzQBWhtrNrkwtcAGRvukbcj1HvUMdqds+26hCpj5Gwvf37VakXT7p0s5l8x+SpRcNj+Hgd8/SqV9DNbqYNynbHsWVh82Dzg/wA6AKGoadHdROSv+qBy6rlSOxz9ax73TruJfLD+cI+flGQR7VPBqM1sksVxKyxO5DYwQcdRz+Fab31ncTxK6OqSAcRHocdCPrVCMUWV0LaHftAaTADZGQf6Vs2dmwD+SYNyDLRkk5Hbj39aviOLzY5JDu3ECOJhu2sOw96v2FrbW148shDTg5fzhkAHt2xj86LisRW3lyTiB7QkKnDLFlc9utTXjxrIDIhkDYXJ+Uq3tjjj1qTzLi7IhW6ZsE5baR5g9AcYGK0TbW0dq0j7URQD8wyMj196VxnONFfLdLGqmMKp538Ekd/X8KhhNxefubrgwJ97spP3eo5FdFd2Vu8RZmL5H3tnzHv69O1Z9vbKbqUrDLE/CsSMgDI6Z60XERQ2Hm2xuIrbbl95O7HbB/CpLuOf7VbQpOUj8rflB/Fj/PNXh5IRZdhLqxQZ+6SD0b2x9KkLwyMlyCWIjIUgZyD3oAx206R/3Mn7qQMH353HI/kOtRixaS7WcrGzcKGkHGPUVt28t2xm3fNGHAO2PcxOOO3A6e1WLhV2Bi0cLvnbuBxnHbrimkBz0NpDPM3kssez5Rt7se4PaqmpaclwhE4/eKdomj9P9of1rqJNPeK3AdVCL95YP4uOhI6ms/8As0XlyiCKVSCMg9JFPY/5FAFW102CGMRSf6Od24MFyrAjuaz7myiDK5WdpD/qQqkhz/u+ldk9hax+XCMCMgYiZT1HbIOaafJjnG22zK5O4FSR3/L+lFwOaiindhDBHtRSGd25AHcYx0qd7jUYn2QRSSx7iysc4PsP510E89ouI5oCRGxJIVsdDgDB+tWGs4ZIjMlwsYAzjBLE+/8AhQBiwTrMYvtSKFkUjDqRz7EDvmo4liSdiFVQuSyMCT6c54x71JcNOsgAgV2DYQFCceufT6mhpJWl+yyMu7GdzqSCO2SOgxj8qm1xmfqOn+dm4sZhkOCFlTJJ9u2PWq9xFv2p9qOzIR/LOMEelalysCRfOrgghmMQOOnYYzj/AOvVizs4JbUmf5IhlmVU+7nv7dRzTA4+V4rebasmbQAAr91j3/EVbtfs0hHlr5TOhWMKSQOeQeM5rr10+xjuIQsNq0hUKryKcMMdRnvTLkWIlUxWizBWwNiYIJ44A/nTbFYht7yUR7E+eRRgtJhcdAeMc8UCVIUlcyNOS2xgQeR1qP8A0SSJ/LieJ93O0/OAOx7A1nJCj+bHukKNJ99wQCR2GO/PNIZoRyyRzidbmNoHwxg+X+pFTxX1gWmO2OOSTjHl98+xqo0VmZUiuEjQPwEKZ9sAemc1TvrJAoEe6RRjhPlIbpnHcUgL8MiiKaSKXcz84CjO7vTZr+WOOM+TJlRghRnn0z1IqOC2MMqpHL158oIAd2Oeepz7VbW9w4RX2hz91uOR1ycUAf/Z", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.open(io.BytesIO(predictor.predict(input_req)))" + ] + }, + { + "cell_type": "markdown", + "id": "95941fa0-2395-4393-ae79-65e2045c7923", + "metadata": {}, + "source": [ + "## 7) Cleanup\n", + "If you don't need the endpoint anymore, run the next cell to delete it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36aba54e-6b61-470f-ae8c-575c4ea58633", + "metadata": {}, + "outputs": [], + "source": [ + "predictor.delete_model()\n", + "predictor.delete_endpoint()" + ] + } + ], + "metadata": { + "availableInstances": [ + { + "_defaultOrder": 0, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.t3.medium", + "vcpuNum": 2 + }, + { + "_defaultOrder": 1, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": 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32 + }, + { + "_defaultOrder": 51, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 52, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + }, + { + "_defaultOrder": 55, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 1152, + "name": "ml.p4d.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 56, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 1152, + "name": "ml.p4de.24xlarge", + "vcpuNum": 96 + } + ], + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (Data Science 3.0)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inference/stable-diffusion/src-inpaint/compile.py b/inference/stable-diffusion/src-inpaint/compile.py new file mode 100644 index 0000000..d148252 --- /dev/null +++ b/inference/stable-diffusion/src-inpaint/compile.py @@ -0,0 +1,193 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +import os +os.environ["NEURON_FUSE_SOFTMAX"] = "1" +import time +import copy +import torch +import shutil +import argparse +import numpy as np +import torch_neuronx +import torch.nn as nn +from wrapper import NeuronTextEncoder, UNetWrap, NeuronUNet, get_attention_scores +from diffusers.models.unet_2d_condition import UNet2DConditionOutput +from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler +from diffusers.models.attention_processor import Attention + +height = 512 // 8 +width = 512 // 8 + +def compile_text_encoder(text_encoder, args): + print("Compiling text encoder...") + base_dir='text_encoder' + os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=True) + os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=True) + t = time.time() + # Apply the wrapper to deal with custom return type + text_encoder = NeuronTextEncoder(text_encoder) + + # Compile text encoder + # This is used for indexing a lookup table in torch.nn.Embedding, + # so using random numbers may give errors (out of range). + emb = torch.tensor([[49406, 18376, 525, 7496, 49407, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0]]) + text_encoder_neuron = torch_neuronx.trace( + text_encoder.neuron_text_encoder, emb, + #compiler_workdir=os.path.join(args.checkpoints_path, base_dir), + ) + + # Save the compiled text encoder + text_encoder_filename = os.path.join(args.model_path, base_dir, 'model.pt') + torch.jit.save(text_encoder_neuron, text_encoder_filename) + + # delete unused objects + del text_encoder + del text_encoder_neuron + print(f"Done. Elapsed time: {(time.time()-t)*1000}ms") + +def compile_vae(decoder, args, dtype): + print("Compiling VAE...") + base_dir='vae_decoder' + os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=True) + os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=True) + t = time.time() + # Compile vae decoder + decoder_in = torch.randn([1, 4, height, width]).type(dtype) + decoder_neuron = torch_neuronx.trace( + decoder, + decoder_in, + #compiler_workdir=os.path.join(args.checkpoints_path, base_dir), + compiler_args=["--verbose", "info"] + ) + + # Save the compiled vae decoder + decoder_filename = os.path.join(args.model_path, base_dir, 'model.pt') + torch.jit.save(decoder_neuron, decoder_filename) + + # delete unused objects + del decoder + del decoder_neuron + print(f"Done. Elapsed time: {(time.time()-t)*1000}ms") + +def compile_unet(unet, args, dtype): + print("Compiling U-Net...") + base_dir='unet' + os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=True) + os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=True) + t = time.time() + # Compile unet - BF16 + sample_1b = torch.randn([1, 9, height, width]).type(dtype) + timestep_1b = torch.tensor(999).type(dtype).expand((1,)) + encoder_hidden_states_1b = torch.randn([1, 77, 1024]).type(dtype) + example_inputs = sample_1b, timestep_1b, encoder_hidden_states_1b + + + unet_neuron = torch_neuronx.trace( + unet, + example_inputs, + #compiler_workdir=os.path.join(args.checkpoints_path, base_dir), + compiler_args=["--model-type=unet-inference", "--verbose=info"] + ) + + # save compiled unet + unet_filename = os.path.join(args.model_path, base_dir, 'model.pt') + torch.jit.save(unet_neuron, unet_filename) + + # delete unused objects + del unet + del unet_neuron + print(f"Done. Elapsed time: {(time.time()-t)*1000}ms") + +def compile_vae_post_quant_conv(post_quant_conv, args, dtype): + print("Compiling Post Quant Conv...") + base_dir='vae_post_quant_conv' + os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=True) + os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=True) + t = time.time() + + # # Compile vae post_quant_conv + post_quant_conv_in = torch.randn([1, 4, height, width]).type(dtype) + post_quant_conv_neuron = torch_neuronx.trace( + post_quant_conv, + post_quant_conv_in, + #compiler_workdir=os.path.join(args.checkpoints_path, base_dir), + compiler_args=["--verbose", "info"] + ) + + # # Save the compiled vae post_quant_conv + post_quant_conv_filename = os.path.join(args.model_path, base_dir, 'model.pt') + torch.jit.save(post_quant_conv_neuron, post_quant_conv_filename) + + # delete unused objects + del post_quant_conv + del post_quant_conv_neuron + print(f"Done. Elapsed time: {(time.time()-t)*1000}ms") + +if __name__=='__main__': + parser = argparse.ArgumentParser(description='Train the UNet on images and target masks') + parser.add_argument('--model-path', type=str, help="Path where we'll save the model", default=os.environ["SM_MODEL_DIR"]) + parser.add_argument('--checkpoints-path', type=str, help="Path where we'll save the best model and cache", default='/opt/ml/checkpoints') + parser.add_argument('--dtype', type=str, help="Datatype of the weights", default='fp32') + + args = parser.parse_args() + + # make sure the checkpoint path exists + os.makedirs(args.checkpoints_path, exist_ok=True) + + # Model ID for SD version pipeline + model_id = "stabilityai/stable-diffusion-2-inpainting" + + # --- Compile CLIP text encoder and save --- + + dtype = torch.float32 + # Only keep the model being compiled in RAM to minimze memory pressure + pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype) + text_encoder = copy.deepcopy(pipe.text_encoder) + del pipe + compile_text_encoder(text_encoder, args) + + # --- Compile VAE decoder and save --- + + # Only keep the model being compiled in RAM to minimze memory pressure + pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype) + decoder = copy.deepcopy(pipe.vae.decoder) + del pipe + compile_vae(decoder, args, dtype) + + # --- Compile UNet and save --- + + pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype) + + # Replace original cross-attention module with custom cross-attention module for better performance + Attention.get_attention_scores = get_attention_scores + + # Apply double wrapper to deal with custom return type + pipe.unet = NeuronUNet(UNetWrap(pipe.unet)) + + # Only keep the model being compiled in RAM to minimze memory pressure + unet = copy.deepcopy(pipe.unet.unetwrap) + del pipe + compile_unet(unet, args, dtype) + + # --- Compile VAE post_quant_conv and save --- + + # Only keep the model being compiled in RAM to minimze memory pressure + pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype) + post_quant_conv = copy.deepcopy(pipe.vae.post_quant_conv) + del pipe + compile_vae_post_quant_conv(post_quant_conv, args, dtype) + + code_path = os.path.join(args.model_path, 'code') + os.makedirs(code_path, exist_ok=True) + + shutil.copyfile('inference.py', os.path.join(code_path, 'inference.py')) + #shutil.copyfile('wrapper.py', os.path.join(code_path, 'wrapper.py')) + shutil.copyfile('requirements.txt', os.path.join(code_path, 'requirements.txt')) \ No newline at end of file diff --git a/inference/stable-diffusion/src-inpaint/inference.py b/inference/stable-diffusion/src-inpaint/inference.py new file mode 100644 index 0000000..c030671 --- /dev/null +++ b/inference/stable-diffusion/src-inpaint/inference.py @@ -0,0 +1,155 @@ +import os +os.environ['NEURON_RT_NUM_CORES'] = '2' +import torch +import torch.nn as nn +import torch_neuronx +import time +from diffusers import StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler +from diffusers.models.unet_2d_condition import UNet2DConditionOutput +from diffusers.models.attention_processor import Attention + +import threading +import argparse +import sys +import copy +import PIL +import math +import json +import requests +import io +from io import BytesIO +import base64 +from PIL import Image + + +model_id = "stabilityai/stable-diffusion-2-inpainting" +dtype = torch.float32 + +class UNetWrap(nn.Module): + def __init__(self, unet): + super().__init__() + self.unet = unet + + def forward(self, sample, timestep, encoder_hidden_states, cross_attention_kwargs=None): + out_tuple = self.unet(sample, timestep, encoder_hidden_states, return_dict=False) + return out_tuple + +class NeuronUNet(nn.Module): + def __init__(self, unetwrap): + super().__init__() + self.unetwrap = unetwrap + self.config = unetwrap.unet.config + self.in_channels = unetwrap.unet.in_channels + self.device = unetwrap.unet.device + + def forward(self, sample, timestep, encoder_hidden_states, cross_attention_kwargs=None, return_dict=False): + sample = self.unetwrap(sample, timestep.float().expand((sample.shape[0],)), encoder_hidden_states)[0] + return UNet2DConditionOutput(sample=sample) + +class NeuronTextEncoder(nn.Module): + def __init__(self, text_encoder): + super().__init__() + self.neuron_text_encoder = text_encoder + self.config = text_encoder.config + self.dtype = text_encoder.dtype + self.device = text_encoder.device + + def forward(self, emb, attention_mask = None): + return [self.neuron_text_encoder(emb)['last_hidden_state']] + +# Optimized attention +def get_attention_scores(self, query, key, attn_mask): + dtype = query.dtype + + if self.upcast_attention: + query = query.float() + key = key.float() + + # Check for square matmuls + if(query.size() == key.size()): + attention_scores = custom_badbmm( + key, + query.transpose(-1, -2) + ) + + if self.upcast_softmax: + attention_scores = attention_scores.float() + + attention_probs = torch.nn.functional.softmax(attention_scores, dim=1).permute(0,2,1) + attention_probs = attention_probs.to(dtype) + + else: + attention_scores = custom_badbmm( + query, + key.transpose(-1, -2) + ) + + if self.upcast_softmax: + attention_scores = attention_scores.float() + + attention_probs = torch.nn.functional.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.to(dtype) + + return attention_probs + +def custom_badbmm(a, b): + bmm = torch.bmm(a, b) + scaled = bmm * 0.125 + return scaled + +def model_fn(model_dir, context=None): + global model_id, dtype + print("Loading model parts...") + t=time.time() + + text_encoder_filename = os.path.join(model_dir, 'text_encoder/model.pt') + decoder_filename = os.path.join(model_dir, 'vae_decoder/model.pt') + unet_filename = os.path.join(model_dir, 'unet/model.pt') + post_quant_conv_filename = os.path.join(model_dir, 'vae_post_quant_conv/model.pt') + + pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype) + + # Load the compiled UNet onto two neuron cores. + pipe.unet = NeuronUNet(UNetWrap(pipe.unet)) + device_ids = [0,1] + pipe.unet.unetwrap = torch_neuronx.DataParallel(torch.jit.load(unet_filename), device_ids, set_dynamic_batching=False) + + # Load other compiled models onto a single neuron core. + pipe.text_encoder = NeuronTextEncoder(pipe.text_encoder) + pipe.text_encoder.neuron_text_encoder = torch.jit.load(text_encoder_filename) + pipe.vae.decoder = torch.jit.load(decoder_filename) + pipe.vae.post_quant_conv = torch.jit.load(post_quant_conv_filename) + + print(f"Done. Elapsed time: {(time.time()-t)*1000}ms") + return pipe + +def input_fn(request_body, request_content_type, context=None): + if request_content_type == 'application/json': + req = json.loads(request_body) + prompt = req.get('prompt') + init_image = req.get('init_image') + mask_image = req.get('mask_image') + height = 512 + width = 512 + + if prompt is None or type(prompt) != str or len(prompt) < 5: + raise("Invalid prompt. It needs to be a string > 5") + + return prompt,init_image,mask_image,height,width + else: + raise Exception(f"Unsupported mime type: {request_content_type}. Supported: application/json") + +def predict_fn(input_req, model, context=None): + prompt,init_image,mask_image,height,width = input_req + init_image_input = Image.open(io.BytesIO(base64.b64decode((init_image)))).convert("RGB").resize((width, height)) + mask_image_input = Image.open(io.BytesIO(base64.b64decode((mask_image)))).convert("RGB").resize((width, height)) + return model(prompt,image=init_image_input, mask_image=mask_image_input, height=height, width=width).images[0] + +def output_fn(image, accept, context=None): + if accept!='image/jpeg': + raise Exception(f'Invalid data type. Expected image/jpeg, got {accept}') + + buffer = io.BytesIO() + image.save(buffer, 'jpeg', icc_profile=image.info.get('icc_profile')) + buffer.seek(0) + return buffer.read() diff --git a/inference/stable-diffusion/src-inpaint/requirements.txt b/inference/stable-diffusion/src-inpaint/requirements.txt new file mode 100644 index 0000000..2a1e641 --- /dev/null +++ b/inference/stable-diffusion/src-inpaint/requirements.txt @@ -0,0 +1,6 @@ +diffusers==0.20.2 +transformers==4.33.1 +accelerate==0.22.0 +safetensors==0.3.1 +matplotlib +Pillow \ No newline at end of file diff --git a/inference/stable-diffusion/src-inpaint/wrapper.py b/inference/stable-diffusion/src-inpaint/wrapper.py new file mode 100644 index 0000000..310a085 --- /dev/null +++ b/inference/stable-diffusion/src-inpaint/wrapper.py @@ -0,0 +1,76 @@ +import torch +import torch.nn as nn +from diffusers.models.unet_2d_condition import UNet2DConditionOutput +#from diffusers.models.attention_processor import Attention + +class UNetWrap(nn.Module): + def __init__(self, unet): + super().__init__() + self.unet = unet + + def forward(self, sample, timestep, encoder_hidden_states, cross_attention_kwargs=None): + out_tuple = self.unet(sample, timestep, encoder_hidden_states, return_dict=False) + return out_tuple + +class NeuronUNet(nn.Module): + def __init__(self, unetwrap): + super().__init__() + self.unetwrap = unetwrap + self.config = unetwrap.unet.config + self.in_channels = unetwrap.unet.in_channels + self.device = unetwrap.unet.device + + def forward(self, sample, timestep, encoder_hidden_states, cross_attention_kwargs=None, return_dict=False): + sample = self.unetwrap(sample, timestep.float().expand((sample.shape[0],)), encoder_hidden_states)[0] + return UNet2DConditionOutput(sample=sample) + +class NeuronTextEncoder(nn.Module): + def __init__(self, text_encoder): + super().__init__() + self.neuron_text_encoder = text_encoder + self.config = text_encoder.config + self.dtype = text_encoder.dtype + self.device = text_encoder.device + + def forward(self, emb, attention_mask = None): + return [self.neuron_text_encoder(emb)['last_hidden_state']] + +# Optimized attention +def get_attention_scores(self, query, key, attn_mask): + dtype = query.dtype + + if self.upcast_attention: + query = query.float() + key = key.float() + + # Check for square matmuls + if(query.size() == key.size()): + attention_scores = custom_badbmm( + key, + query.transpose(-1, -2) + ) + + if self.upcast_softmax: + attention_scores = attention_scores.float() + + attention_probs = torch.nn.functional.softmax(attention_scores, dim=1).permute(0,2,1) + attention_probs = attention_probs.to(dtype) + + else: + attention_scores = custom_badbmm( + query, + key.transpose(-1, -2) + ) + + if self.upcast_softmax: + attention_scores = attention_scores.float() + + attention_probs = torch.nn.functional.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.to(dtype) + + return attention_probs + +def custom_badbmm(a, b): + bmm = torch.bmm(a, b) + scaled = bmm * 0.125 + return scaled \ No newline at end of file From 84816a7e7a203d0c02df149ff8fe4c749907c525 Mon Sep 17 00:00:00 2001 From: eric80116 Date: Sun, 1 Oct 2023 10:09:45 +0800 Subject: [PATCH 2/2] content modified --- .../StableDiffusion2_1-inpaint.ipynb | 10919 +--------------- 1 file changed, 27 insertions(+), 10892 deletions(-) diff --git a/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb b/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb index dc46516..359030a 100644 --- a/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb +++ b/inference/stable-diffusion/StableDiffusion2_1-inpaint.ipynb @@ -18,7 +18,7 @@ "**Inference:** After compiling the model it is time to deploy. You'll create a SageMaker real-time Endpoint hosted on an **inf2** instance. SageMaker exposes your model as a webservice and allow you to invoke it with a simple API call.\n", "\n", "\n", - "The compilation mechanism supports datatypes in FP32 or BF16. BF16 will give you a lower latency and it is selected by default." + "The compilation mechanism supports datatypes in FP32 and it is selected by default." ] }, { @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2084775d-958a-4c6d-9f23-9b946f630008", "metadata": { "collapsed": true, @@ -43,74 +43,7 @@ "scrolled": true, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: sagemaker in /opt/conda/lib/python3.10/site-packages (2.184.0)\n", - "Collecting sagemaker\n", - " Downloading sagemaker-2.188.0.tar.gz (892 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m892.2/892.2 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n", - "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: attrs<24,>=23.1.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (23.1.0)\n", - "Requirement already satisfied: boto3<2.0,>=1.26.131 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.28.42)\n", - "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (2.2.1)\n", - "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.2.0)\n", - "Requirement already satisfied: numpy<2.0,>=1.9.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.25.2)\n", - "Requirement already satisfied: protobuf<5.0,>=3.12 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.24.2)\n", - "Requirement already satisfied: smdebug_rulesconfig==1.0.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.0.1)\n", - "Requirement already satisfied: importlib-metadata<7.0,>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.11.3)\n", - "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (21.3)\n", - "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.4.4)\n", - "Requirement already satisfied: pathos in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.3.1)\n", - "Requirement already satisfied: schema in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.7.5)\n", - "Requirement already satisfied: PyYAML~=6.0 in /opt/conda/lib/python3.10/site-packages/PyYAML-6.0-py3.10-linux-x86_64.egg (from sagemaker) (6.0)\n", - "Requirement already satisfied: jsonschema in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.19.0)\n", - "Requirement already satisfied: platformdirs in /opt/conda/lib/python3.10/site-packages (from sagemaker) (2.5.2)\n", - "Requirement already satisfied: tblib==1.7.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.7.0)\n", - "Requirement already satisfied: botocore<1.32.0,>=1.31.42 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (1.31.42)\n", - "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (0.10.0)\n", - "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (0.6.0)\n", - "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.10/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.8.0)\n", - "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->sagemaker) (3.0.9)\n", - "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from google-pasta->sagemaker) (1.16.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (2023.7.1)\n", - "Requirement already satisfied: referencing>=0.28.4 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (0.30.2)\n", - "Requirement already satisfied: rpds-py>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (0.10.2)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2022.1)\n", - "Requirement already satisfied: ppft>=1.7.6.7 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (1.7.6.7)\n", - "Requirement already satisfied: dill>=0.3.7 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.3.7)\n", - "Requirement already satisfied: pox>=0.3.3 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.3.3)\n", - "Requirement already satisfied: multiprocess>=0.70.15 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.70.15)\n", - "Requirement already satisfied: contextlib2>=0.5.5 in /opt/conda/lib/python3.10/site-packages (from schema->sagemaker) (21.6.0)\n", - "Collecting urllib3<1.27,>=1.25.4 (from botocore<1.32.0,>=1.31.42->boto3<2.0,>=1.26.131->sagemaker)\n", - " Obtaining dependency information for urllib3<1.27,>=1.25.4 from https://files.pythonhosted.org/packages/c5/05/c214b32d21c0b465506f95c4f28ccbcba15022e000b043b72b3df7728471/urllib3-1.26.16-py2.py3-none-any.whl.metadata\n", - " Using cached urllib3-1.26.16-py2.py3-none-any.whl.metadata (48 kB)\n", - "Using cached urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n", - "Building wheels for collected packages: sagemaker\n", - " Building wheel for sagemaker (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for sagemaker: filename=sagemaker-2.188.0-py2.py3-none-any.whl size=1193902 sha256=9363f36c103f4d81b0ab5eb19315d02f68706f68e5eba7587dff04ae8d20f130\n", - " Stored in directory: /root/.cache/pip/wheels/94/00/3e/af9dde735e7ee826b11724be1de1a009a4179984f236a7a928\n", - "Successfully built sagemaker\n", - "Installing collected packages: urllib3, sagemaker\n", - " Attempting uninstall: urllib3\n", - " Found existing installation: urllib3 2.0.4\n", - " Uninstalling urllib3-2.0.4:\n", - " Successfully uninstalled urllib3-2.0.4\n", - " Attempting uninstall: sagemaker\n", - " Found existing installation: sagemaker 2.184.0\n", - " Uninstalling sagemaker-2.184.0:\n", - " Successfully uninstalled sagemaker-2.184.0\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", - "distributed 2022.7.0 requires tornado<6.2,>=6.0.3, but you have tornado 6.3.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed sagemaker-2.188.0 urllib3-1.26.16\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install -U sagemaker" ] @@ -126,32 +59,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "9d5d9d4d-b3ed-4e7c-968b-c0c95c41f028", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", - "2.188.0\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n", - "sagemaker role arn: arn:aws:iam::772327914095:role/service-role/AmazonSageMaker-ExecutionRole-20220822T202655\n", - "sagemaker bucket trn1: sagemaker-us-east-1-772327914095\n", - "sagemaker bucket trn1: sagemaker-us-east-2-772327914095\n", - "sagemaker session regions. trn1: us-east-1 inf2: us-east-2\n" - ] - } - ], + "outputs": [], "source": [ "import boto3\n", "import sagemaker\n", @@ -198,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "73dd42ba-7ec5-4404-a144-c5b1ccb96bb1", "metadata": { "collapsed": true, @@ -208,97 +121,14 @@ "scrolled": true, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36munet_2d_condition\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m UNet2DConditionOutput\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m#from diffusers.models.attention_processor import Attention\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mUNetWrap\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unet):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.unet = unet\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " out_tuple = \u001b[36mself\u001b[39;49;00m.unet(sample, timestep, encoder_hidden_states, return_dict=\u001b[34mFalse\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m out_tuple\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronUNet\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unetwrap):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.unetwrap = unetwrap\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.config = unetwrap.unet.config\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.in_channels = unetwrap.unet.in_channels\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.device = unetwrap.unet.device\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m, return_dict=\u001b[34mFalse\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " sample = \u001b[36mself\u001b[39;49;00m.unetwrap(sample, timestep.float().expand((sample.shape[\u001b[34m0\u001b[39;49;00m],)), encoder_hidden_states)[\u001b[34m0\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m UNet2DConditionOutput(sample=sample)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronTextEncoder\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, text_encoder):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.neuron_text_encoder = text_encoder\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.config = text_encoder.config\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.dtype = text_encoder.dtype\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.device = text_encoder.device\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, emb, attention_mask = \u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m [\u001b[36mself\u001b[39;49;00m.neuron_text_encoder(emb)[\u001b[33m'\u001b[39;49;00m\u001b[33mlast_hidden_state\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]]\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m# Optimized attention\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_attention_scores\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, query, key, attn_mask): \u001b[37m\u001b[39;49;00m\n", - " dtype = query.dtype\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_attention:\u001b[37m\u001b[39;49;00m\n", - " query = query.float()\u001b[37m\u001b[39;49;00m\n", - " key = key.float()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Check for square matmuls\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m(query.size() == key.size()):\u001b[37m\u001b[39;49;00m\n", - " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", - " key,\u001b[37m\u001b[39;49;00m\n", - " query.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", - " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " attention_probs = torch.nn.functional.softmax(attention_scores, dim=\u001b[34m1\u001b[39;49;00m).permute(\u001b[34m0\u001b[39;49;00m,\u001b[34m2\u001b[39;49;00m,\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", - " query,\u001b[37m\u001b[39;49;00m\n", - " key.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", - " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " attention_probs = torch.nn.functional.softmax(attention_scores, dim=-\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m attention_probs\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mcustom_badbmm\u001b[39;49;00m(a, b):\u001b[37m\u001b[39;49;00m\n", - " bmm = torch.bmm(a, b)\u001b[37m\u001b[39;49;00m\n", - " scaled = bmm * \u001b[34m0.125\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m scaled\u001b[37m\u001b[39;49;00m\n" - ] - } - ], + "outputs": [], "source": [ "!pygmentize src-inpaint/wrapper.py" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e063c730-5b62-4600-950d-51f1646c3686", "metadata": { "collapsed": true, @@ -308,214 +138,14 @@ "scrolled": true, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[37m# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m# SPDX-License-Identifier: MIT-0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mNEURON_FUSE_SOFTMAX\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m] = \u001b[33m\"\u001b[39;49;00m\u001b[33m1\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtime\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcopy\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mshutil\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch_neuronx\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mwrapper\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m NeuronTextEncoder, UNetWrap, NeuronUNet, get_attention_scores\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36munet_2d_condition\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m UNet2DConditionOutput\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m StableDiffusionPipeline, StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mattention_processor\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Attention\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "height = \u001b[34m512\u001b[39;49;00m // \u001b[34m8\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "width = \u001b[34m512\u001b[39;49;00m // \u001b[34m8\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_text_encoder\u001b[39;49;00m(text_encoder, args):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling text encoder...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33mtext_encoder\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " t = time.time()\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Apply the wrapper to deal with custom return type\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " text_encoder = NeuronTextEncoder(text_encoder)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Compile text encoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# This is used for indexing a lookup table in torch.nn.Embedding,\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# so using random numbers may give errors (out of range).\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " emb = torch.tensor([[\u001b[34m49406\u001b[39;49;00m, \u001b[34m18376\u001b[39;49;00m, \u001b[34m525\u001b[39;49;00m, \u001b[34m7496\u001b[39;49;00m, \u001b[34m49407\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n", - " \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m, \u001b[34m0\u001b[39;49;00m]])\u001b[37m\u001b[39;49;00m\n", - " text_encoder_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", - " text_encoder.neuron_text_encoder, emb,\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Save the compiled text encoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " text_encoder_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " torch.jit.save(text_encoder_neuron, text_encoder_filename)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m text_encoder\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m text_encoder_neuron\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_vae\u001b[39;49;00m(decoder, args, dtype):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling VAE...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33mvae_decoder\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " t = time.time()\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Compile vae decoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " decoder_in = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m4\u001b[39;49;00m, height, width]).type(dtype)\u001b[37m\u001b[39;49;00m\n", - " decoder_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", - " decoder,\u001b[37m\u001b[39;49;00m\n", - " decoder_in,\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " compiler_args=[\u001b[33m\"\u001b[39;49;00m\u001b[33m--verbose\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33minfo\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Save the compiled vae decoder\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " decoder_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " torch.jit.save(decoder_neuron, decoder_filename)\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m decoder\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m decoder_neuron\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_unet\u001b[39;49;00m(unet, args, dtype):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling U-Net...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33munet\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " t = time.time()\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Compile unet - BF16\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " sample_1b = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m9\u001b[39;49;00m, height, width]).type(dtype)\u001b[37m\u001b[39;49;00m\n", - " timestep_1b = torch.tensor(\u001b[34m999\u001b[39;49;00m).type(dtype).expand((\u001b[34m1\u001b[39;49;00m,))\u001b[37m\u001b[39;49;00m\n", - " encoder_hidden_states_1b = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m77\u001b[39;49;00m, \u001b[34m1024\u001b[39;49;00m]).type(dtype)\u001b[37m\u001b[39;49;00m\n", - " example_inputs = sample_1b, timestep_1b, encoder_hidden_states_1b\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " unet_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", - " unet,\u001b[37m\u001b[39;49;00m\n", - " example_inputs,\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " compiler_args=[\u001b[33m\"\u001b[39;49;00m\u001b[33m--model-type=unet-inference\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33m--verbose=info\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# save compiled unet\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " unet_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " torch.jit.save(unet_neuron, unet_filename)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m unet\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m unet_neuron\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mcompile_vae_post_quant_conv\u001b[39;49;00m(post_quant_conv, args, dtype):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mCompiling Post Quant Conv...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " base_dir=\u001b[33m'\u001b[39;49;00m\u001b[33mvae_post_quant_conv\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.checkpoints_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(os.path.join(args.model_path, base_dir), exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " t = time.time()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# # Compile vae post_quant_conv\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv_in = torch.randn([\u001b[34m1\u001b[39;49;00m, \u001b[34m4\u001b[39;49;00m, height, width]).type(dtype)\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv_neuron = torch_neuronx.trace(\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv,\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv_in,\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m#compiler_workdir=os.path.join(args.checkpoints_path, base_dir),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " compiler_args=[\u001b[33m\"\u001b[39;49;00m\u001b[33m--verbose\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33minfo\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# # Save the compiled vae post_quant_conv\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv_filename = os.path.join(args.model_path, base_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " torch.jit.save(post_quant_conv_neuron, post_quant_conv_filename)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# delete unused objects\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m post_quant_conv\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m post_quant_conv_neuron\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mif\u001b[39;49;00m \u001b[31m__name__\u001b[39;49;00m==\u001b[33m'\u001b[39;49;00m\u001b[33m__main__\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " parser = argparse.ArgumentParser(description=\u001b[33m'\u001b[39;49;00m\u001b[33mTrain the UNet on images and target masks\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--model-path\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mPath where we\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mll save the model\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_MODEL_DIR\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n", - " parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--checkpoints-path\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mPath where we\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mll save the best model and cache\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, default=\u001b[33m'\u001b[39;49;00m\u001b[33m/opt/ml/checkpoints\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--dtype\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mDatatype of the weights\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, default=\u001b[33m'\u001b[39;49;00m\u001b[33mfp32\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " args = parser.parse_args()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# make sure the checkpoint path exists\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(args.checkpoints_path, exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Model ID for SD version pipeline\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " model_id = \u001b[33m\"\u001b[39;49;00m\u001b[33mstabilityai/stable-diffusion-2-inpainting\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# --- Compile CLIP text encoder and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " dtype = torch.float32\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", - " text_encoder = copy.deepcopy(pipe.text_encoder)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", - " compile_text_encoder(text_encoder, args)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# --- Compile VAE decoder and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", - " decoder = copy.deepcopy(pipe.vae.decoder)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", - " compile_vae(decoder, args, dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# --- Compile UNet and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Replace original cross-attention module with custom cross-attention module for better performance\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " Attention.get_attention_scores = get_attention_scores\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Apply double wrapper to deal with custom return type\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " pipe.unet = NeuronUNet(UNetWrap(pipe.unet))\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " unet = copy.deepcopy(pipe.unet.unetwrap)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", - " compile_unet(unet, args, dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# --- Compile VAE post_quant_conv and save ---\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Only keep the model being compiled in RAM to minimze memory pressure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv = copy.deepcopy(pipe.vae.post_quant_conv)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdel\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", - " compile_vae_post_quant_conv(post_quant_conv, args, dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " code_path = os.path.join(args.model_path, \u001b[33m'\u001b[39;49;00m\u001b[33mcode\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " os.makedirs(code_path, exist_ok=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " shutil.copyfile(\u001b[33m'\u001b[39;49;00m\u001b[33minference.py\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, os.path.join(code_path, \u001b[33m'\u001b[39;49;00m\u001b[33minference.py\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m#shutil.copyfile('wrapper.py', os.path.join(code_path, 'wrapper.py'))\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " shutil.copyfile(\u001b[33m'\u001b[39;49;00m\u001b[33mrequirements.txt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, os.path.join(code_path, \u001b[33m'\u001b[39;49;00m\u001b[33mrequirements.txt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n" - ] - } - ], + "outputs": [], "source": [ "!pygmentize src-inpaint/compile.py" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "85d0251e-4d73-4986-8b71-10faf2bef429", "metadata": { "collapsed": true, @@ -525,169 +155,7 @@ "scrolled": true, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "os.environ[\u001b[33m'\u001b[39;49;00m\u001b[33mNEURON_RT_NUM_CORES\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m] = \u001b[33m'\u001b[39;49;00m\u001b[33m2\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch_neuronx\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtime\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36munet_2d_condition\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m UNet2DConditionOutput\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdiffusers\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mattention_processor\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Attention\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mthreading\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msys\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcopy\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mPIL\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mmath\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mjson\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mrequests\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mio\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mio\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m BytesIO\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mbase64\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mPIL\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Image\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "model_id = \u001b[33m\"\u001b[39;49;00m\u001b[33mstabilityai/stable-diffusion-2-inpainting\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "dtype = torch.float32\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mUNetWrap\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unet):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.unet = unet\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " out_tuple = \u001b[36mself\u001b[39;49;00m.unet(sample, timestep, encoder_hidden_states, return_dict=\u001b[34mFalse\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m out_tuple\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronUNet\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, unetwrap):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.unetwrap = unetwrap\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.config = unetwrap.unet.config\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.in_channels = unetwrap.unet.in_channels\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.device = unetwrap.unet.device\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, sample, timestep, encoder_hidden_states, cross_attention_kwargs=\u001b[34mNone\u001b[39;49;00m, return_dict=\u001b[34mFalse\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " sample = \u001b[36mself\u001b[39;49;00m.unetwrap(sample, timestep.float().expand((sample.shape[\u001b[34m0\u001b[39;49;00m],)), encoder_hidden_states)[\u001b[34m0\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m UNet2DConditionOutput(sample=sample)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mNeuronTextEncoder\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, text_encoder):\u001b[37m\u001b[39;49;00m\n", - " \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.neuron_text_encoder = text_encoder\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.config = text_encoder.config\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.dtype = text_encoder.dtype\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mself\u001b[39;49;00m.device = text_encoder.device\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, emb, attention_mask = \u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m [\u001b[36mself\u001b[39;49;00m.neuron_text_encoder(emb)[\u001b[33m'\u001b[39;49;00m\u001b[33mlast_hidden_state\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]]\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m# Optimized attention\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_attention_scores\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, query, key, attn_mask): \u001b[37m\u001b[39;49;00m\n", - " dtype = query.dtype\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_attention:\u001b[37m\u001b[39;49;00m\n", - " query = query.float()\u001b[37m\u001b[39;49;00m\n", - " key = key.float()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Check for square matmuls\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m(query.size() == key.size()):\u001b[37m\u001b[39;49;00m\n", - " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", - " key,\u001b[37m\u001b[39;49;00m\n", - " query.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", - " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " attention_probs = torch.nn.functional.softmax(attention_scores, dim=\u001b[34m1\u001b[39;49;00m).permute(\u001b[34m0\u001b[39;49;00m,\u001b[34m2\u001b[39;49;00m,\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " attention_scores = custom_badbmm(\u001b[37m\u001b[39;49;00m\n", - " query,\u001b[37m\u001b[39;49;00m\n", - " key.transpose(-\u001b[34m1\u001b[39;49;00m, -\u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " )\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.upcast_softmax:\u001b[37m\u001b[39;49;00m\n", - " attention_scores = attention_scores.float()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " attention_probs = torch.nn.functional.softmax(attention_scores, dim=-\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " attention_probs = attention_probs.to(dtype)\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m attention_probs\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mcustom_badbmm\u001b[39;49;00m(a, b):\u001b[37m\u001b[39;49;00m\n", - " bmm = torch.bmm(a, b)\u001b[37m\u001b[39;49;00m\n", - " scaled = bmm * \u001b[34m0.125\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m scaled\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mmodel_fn\u001b[39;49;00m(model_dir, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mglobal\u001b[39;49;00m model_id, dtype\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mLoading model parts...\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " t=time.time()\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " text_encoder_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mtext_encoder/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " decoder_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mvae_decoder/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " unet_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33munet/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " post_quant_conv_filename = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mvae_post_quant_conv/model.pt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, torch_dtype=dtype)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Load the compiled UNet onto two neuron cores.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " pipe.unet = NeuronUNet(UNetWrap(pipe.unet))\u001b[37m\u001b[39;49;00m\n", - " device_ids = [\u001b[34m0\u001b[39;49;00m,\u001b[34m1\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n", - " pipe.unet.unetwrap = torch_neuronx.DataParallel(torch.jit.load(unet_filename), device_ids, set_dynamic_batching=\u001b[34mFalse\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[37m# Load other compiled models onto a single neuron core.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " pipe.text_encoder = NeuronTextEncoder(pipe.text_encoder)\u001b[37m\u001b[39;49;00m\n", - " pipe.text_encoder.neuron_text_encoder = torch.jit.load(text_encoder_filename)\u001b[37m\u001b[39;49;00m\n", - " pipe.vae.decoder = torch.jit.load(decoder_filename)\u001b[37m\u001b[39;49;00m\n", - " pipe.vae.post_quant_conv = torch.jit.load(post_quant_conv_filename)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mDone. Elapsed time: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m(time.time()-t)*\u001b[34m1000\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33mms\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m pipe\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32minput_fn\u001b[39;49;00m(request_body, request_content_type, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m request_content_type == \u001b[33m'\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " req = json.loads(request_body)\u001b[37m\u001b[39;49;00m\n", - " prompt = req.get(\u001b[33m'\u001b[39;49;00m\u001b[33mprompt\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " init_image = req.get(\u001b[33m'\u001b[39;49;00m\u001b[33minit_image\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " mask_image = req.get(\u001b[33m'\u001b[39;49;00m\u001b[33mmask_image\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " height = \u001b[34m512\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - " width = \u001b[34m512\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m prompt \u001b[35mis\u001b[39;49;00m \u001b[34mNone\u001b[39;49;00m \u001b[35mor\u001b[39;49;00m \u001b[36mtype\u001b[39;49;00m(prompt) != \u001b[36mstr\u001b[39;49;00m \u001b[35mor\u001b[39;49;00m \u001b[36mlen\u001b[39;49;00m(prompt) < \u001b[34m5\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mraise\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mInvalid prompt. It needs to be a string > 5\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m prompt,init_image,mask_image,height,width\u001b[37m\u001b[39;49;00m\n", - " \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mraise\u001b[39;49;00m \u001b[36mException\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mUnsupported mime type: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mrequest_content_type\u001b[33m}\u001b[39;49;00m\u001b[33m. Supported: application/json\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32mpredict_fn\u001b[39;49;00m(input_req, model, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " prompt,init_image,mask_image,height,width = input_req\u001b[37m\u001b[39;49;00m\n", - " init_image_input = Image.open(io.BytesIO(base64.b64decode((init_image)))).convert(\u001b[33m\"\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m).resize((width, height))\u001b[37m\u001b[39;49;00m\n", - " mask_image_input = Image.open(io.BytesIO(base64.b64decode((mask_image)))).convert(\u001b[33m\"\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m).resize((width, height))\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m model(prompt,image=init_image_input, mask_image=mask_image_input, height=height, width=width).images[\u001b[34m0\u001b[39;49;00m] \u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - "\u001b[34mdef\u001b[39;49;00m \u001b[32moutput_fn\u001b[39;49;00m(image, accept, context=\u001b[34mNone\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mif\u001b[39;49;00m accept!=\u001b[33m'\u001b[39;49;00m\u001b[33mimage/jpeg\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mraise\u001b[39;49;00m \u001b[36mException\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mInvalid data type. Expected image/jpeg, got \u001b[39;49;00m\u001b[33m{\u001b[39;49;00maccept\u001b[33m}\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - "\u001b[37m\u001b[39;49;00m\n", - " buffer = io.BytesIO()\u001b[37m\u001b[39;49;00m\n", - " image.save(buffer, \u001b[33m'\u001b[39;49;00m\u001b[33mjpeg\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, icc_profile=image.info.get(\u001b[33m'\u001b[39;49;00m\u001b[33micc_profile\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n", - " buffer.seek(\u001b[34m0\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n", - " \u001b[34mreturn\u001b[39;49;00m buffer.read()\u001b[37m\u001b[39;49;00m\n" - ] - } - ], + "outputs": [], "source": [ "!pygmentize src-inpaint/inference.py" ] @@ -702,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "03717dd9-53de-4b0b-b892-53ccb89c0680", "metadata": { "tags": [] @@ -736,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f401d597-ed80-4fbc-ab1a-8c63d263976d", "metadata": { "collapsed": true, @@ -745,10293 +213,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Creating training-job with name: pytorch-training-neuronx-2023-09-30-04-54-35-074\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using provided s3_resource\n", - "2023-09-30 04:54:35 Starting - Starting the training job.........\n", - "2023-09-30 04:55:57 Starting - Preparing the instances for training.........\n", - "2023-09-30 04:57:32 Downloading - Downloading input data\n", - "2023-09-30 04:57:32 Training - Downloading the training image.....................\n", - "2023-09-30 05:00:43 Training - Training image download completed. Training in progress....\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n", - "\u001b[34mbash: no job control in this shell\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:22,877 sagemaker-training-toolkit INFO Imported framework sagemaker_pytorch_container.training\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:22,878 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:23,307 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:23,316 sagemaker_pytorch_container.training INFO Block until all host DNS lookups succeed.\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:23,317 sagemaker_pytorch_container.training INFO Invoking user training script.\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:24,280 sagemaker-training-toolkit INFO Installing dependencies from requirements.txt:\u001b[0m\n", - "\u001b[34m/usr/local/bin/python3.10 -m pip install -r requirements.txt\u001b[0m\n", - "\u001b[34mLooking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\u001b[0m\n", - "\u001b[34mCollecting diffusers==0.20.2 (from -r requirements.txt (line 1))\u001b[0m\n", - "\u001b[34mDownloading diffusers-0.20.2.tar.gz (989 kB)\u001b[0m\n", - "\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 989.1/989.1 kB 71.4 MB/s eta 0:00:00\u001b[0m\n", - "\u001b[34mInstalling build dependencies: started\u001b[0m\n", - "\u001b[34mInstalling build dependencies: finished with status 'done'\u001b[0m\n", - "\u001b[34mGetting requirements to build wheel: started\u001b[0m\n", - "\u001b[34mGetting requirements to build wheel: finished with status 'done'\u001b[0m\n", - "\u001b[34mPreparing metadata (pyproject.toml): started\u001b[0m\n", - "\u001b[34mPreparing metadata (pyproject.toml): finished with status 'done'\u001b[0m\n", - "\u001b[34mRequirement already satisfied: transformers==4.33.1 in /usr/local/lib/python3.10/site-packages (from -r requirements.txt (line 2)) (4.33.1)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: accelerate==0.22.0 in /usr/local/lib/python3.10/site-packages (from -r requirements.txt (line 3)) (0.22.0)\u001b[0m\n", - "\u001b[34mCollecting safetensors==0.3.1 (from -r requirements.txt (line 4))\u001b[0m\n", - "\u001b[34mDownloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\u001b[0m\n", - "\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.3/1.3 MB 96.5 MB/s eta 0:00:00\u001b[0m\n", - "\u001b[34mRequirement already satisfied: matplotlib in /usr/local/lib/python3.10/site-packages (from -r requirements.txt (line 5)) (3.7.2)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: Pillow in /usr/local/lib/python3.10/site-packages (from -r requirements.txt (line 6)) (10.0.0)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/site-packages (from diffusers==0.20.2->-r requirements.txt (line 1)) (6.8.0)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: filelock in /usr/local/lib/python3.10/site-packages (from diffusers==0.20.2->-r requirements.txt (line 1)) (3.12.3)\u001b[0m\n", - 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"\u001b[34mRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 5)) (1.16.0)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.22.0->-r requirements.txt (line 3)) (11.7.99)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.22.0->-r requirements.txt (line 3)) (8.5.0.96)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.22.0->-r requirements.txt (line 3)) (11.10.3.66)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.22.0->-r requirements.txt (line 3)) (11.7.99)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: setuptools in /usr/local/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.10.0->accelerate==0.22.0->-r requirements.txt (line 3)) (68.2.1)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: wheel in /usr/local/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.10.0->accelerate==0.22.0->-r requirements.txt (line 3)) (0.41.2)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/site-packages (from importlib-metadata->diffusers==0.20.2->-r requirements.txt (line 1)) (3.16.2)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/site-packages (from requests->diffusers==0.20.2->-r requirements.txt (line 1)) (3.2.0)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/site-packages (from requests->diffusers==0.20.2->-r requirements.txt (line 1)) (3.4)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/site-packages (from requests->diffusers==0.20.2->-r requirements.txt (line 1)) (1.26.16)\u001b[0m\n", - "\u001b[34mRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/site-packages (from requests->diffusers==0.20.2->-r requirements.txt (line 1)) (2023.7.22)\u001b[0m\n", - "\u001b[34mBuilding wheels for collected packages: diffusers\u001b[0m\n", - "\u001b[34mBuilding wheel for diffusers (pyproject.toml): started\u001b[0m\n", - "\u001b[34mBuilding wheel for diffusers (pyproject.toml): finished with status 'done'\u001b[0m\n", - "\u001b[34mCreated wheel for diffusers: filename=diffusers-0.20.2-py3-none-any.whl size=1342633 sha256=e88357d4078229ab564d5d0ef6a2a61686052e27e2a507b39b33a01131c1861a\u001b[0m\n", - "\u001b[34mStored in directory: /root/.cache/pip/wheels/dc/8b/d9/34f7a1936109e05e9bba0cc2241a6f8cd89e25959dc7aae942\u001b[0m\n", - "\u001b[34mSuccessfully built diffusers\u001b[0m\n", - "\u001b[34mInstalling collected packages: safetensors, diffusers\u001b[0m\n", - "\u001b[34mAttempting uninstall: safetensors\u001b[0m\n", - "\u001b[34mFound existing installation: safetensors 0.3.3\u001b[0m\n", - "\u001b[34mUninstalling safetensors-0.3.3:\u001b[0m\n", - "\u001b[34mSuccessfully uninstalled safetensors-0.3.3\u001b[0m\n", - "\u001b[34mSuccessfully installed diffusers-0.20.2 safetensors-0.3.1\u001b[0m\n", - "\u001b[34mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:31,830 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code.\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:31,830 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:31,831 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:32,284 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:32,295 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:32,756 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:32,766 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:33,224 sagemaker-training-toolkit INFO Found 32 neurons on this instance\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:33,234 sagemaker-training-toolkit INFO Invoking user script\u001b[0m\n", - "\u001b[34mTraining Env:\u001b[0m\n", - "\u001b[34m{\n", - " \"additional_framework_parameters\": {},\n", - " \"channel_input_dirs\": {},\n", - " \"current_host\": \"algo-1\",\n", - " \"current_instance_group\": \"homogeneousCluster\",\n", - " \"current_instance_group_hosts\": [\n", - " \"algo-1\"\n", - " ],\n", - " \"current_instance_type\": \"ml.trn1.32xlarge\",\n", - " \"distribution_hosts\": [],\n", - " \"distribution_instance_groups\": [],\n", - " \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n", - " \"hosts\": [\n", - " \"algo-1\"\n", - " ],\n", - " \"hyperparameters\": {\n", - " \"dtype\": \"fp32\"\n", - " },\n", - " \"input_config_dir\": \"/opt/ml/input/config\",\n", - " \"input_data_config\": {},\n", - " \"input_dir\": \"/opt/ml/input\",\n", - " \"instance_groups\": [\n", - " \"homogeneousCluster\"\n", - " ],\n", - " \"instance_groups_dict\": {\n", - " \"homogeneousCluster\": {\n", - " \"instance_group_name\": \"homogeneousCluster\",\n", - " \"instance_type\": \"ml.trn1.32xlarge\",\n", - " \"hosts\": [\n", - " \"algo-1\"\n", - " ]\n", - " }\n", - " },\n", - " \"is_hetero\": false,\n", - " \"is_master\": true,\n", - " \"is_modelparallel_enabled\": null,\n", - " \"is_smddpmprun_installed\": false,\n", - " \"job_name\": \"pytorch-training-neuronx-2023-09-30-04-54-35-074\",\n", - " \"log_level\": 20,\n", - " \"master_hostname\": \"algo-1\",\n", - " \"model_dir\": \"/opt/ml/model\",\n", - " \"module_dir\": \"s3://sagemaker-us-east-1-772327914095/pytorch-training-neuronx-2023-09-30-04-54-35-074/source/sourcedir.tar.gz\",\n", - " \"module_name\": \"compile\",\n", - " \"network_interface_name\": \"eth0\",\n", - " \"num_cpus\": 128,\n", - " \"num_gpus\": 0,\n", - " \"num_neurons\": 32,\n", - " \"output_data_dir\": \"/opt/ml/output/data\",\n", - " \"output_dir\": \"/opt/ml/output\",\n", - " \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n", - " \"resource_config\": {\n", - " \"current_host\": \"algo-1\",\n", - " \"current_instance_type\": \"ml.trn1.32xlarge\",\n", - " \"current_group_name\": \"homogeneousCluster\",\n", - " \"hosts\": [\n", - " \"algo-1\"\n", - " ],\n", - " \"instance_groups\": [\n", - " {\n", - " \"instance_group_name\": \"homogeneousCluster\",\n", - " \"instance_type\": \"ml.trn1.32xlarge\",\n", - " \"hosts\": [\n", - " \"algo-1\"\n", - " ]\n", - " }\n", - " ],\n", - " \"network_interface_name\": \"eth0\"\n", - " },\n", - " \"user_entry_point\": \"compile.py\"\u001b[0m\n", - "\u001b[34m}\u001b[0m\n", - "\u001b[34mEnvironment variables:\u001b[0m\n", - "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n", - "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n", - "\u001b[34mSM_HPS={\"dtype\":\"fp32\"}\u001b[0m\n", - "\u001b[34mSM_USER_ENTRY_POINT=compile.py\u001b[0m\n", - "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n", - "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.trn1.32xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n", - "\u001b[34mSM_INPUT_DATA_CONFIG={}\u001b[0m\n", - "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n", - "\u001b[34mSM_CHANNELS=[]\u001b[0m\n", - "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n", - "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.trn1.32xlarge\u001b[0m\n", - "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n", - "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n", - "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n", - "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}}\u001b[0m\n", - "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n", - "\u001b[34mSM_IS_HETERO=false\u001b[0m\n", - "\u001b[34mSM_MODULE_NAME=compile\u001b[0m\n", - "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n", - "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n", - "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n", - "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n", - "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n", - "\u001b[34mSM_NUM_CPUS=128\u001b[0m\n", - "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n", - "\u001b[34mSM_NUM_NEURONS=32\u001b[0m\n", - "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n", - "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-1-772327914095/pytorch-training-neuronx-2023-09-30-04-54-35-074/source/sourcedir.tar.gz\u001b[0m\n", - "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.trn1.32xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"dtype\":\"fp32\"},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":false,\"job_name\":\"pytorch-training-neuronx-2023-09-30-04-54-35-074\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-772327914095/pytorch-training-neuronx-2023-09-30-04-54-35-074/source/sourcedir.tar.gz\",\"module_name\":\"compile\",\"network_interface_name\":\"eth0\",\"num_cpus\":128,\"num_gpus\":0,\"num_neurons\":32,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.trn1.32xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.trn1.32xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"compile.py\"}\u001b[0m\n", - "\u001b[34mSM_USER_ARGS=[\"--dtype\",\"fp32\"]\u001b[0m\n", - "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n", - "\u001b[34mSM_HP_DTYPE=fp32\u001b[0m\n", - "\u001b[34mPYTHONPATH=/opt/ml/code:/usr/local/bin:/usr/local/lib/python310.zip:/usr/local/lib/python3.10:/usr/local/lib/python3.10/lib-dynload:/usr/local/lib/python3.10/site-packages\u001b[0m\n", - "\u001b[34mInvoking script with the following command:\u001b[0m\n", - "\u001b[34m/usr/local/bin/python3.10 compile.py --dtype fp32\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:33,235 sagemaker-training-toolkit INFO Exceptions not imported for SageMaker Debugger as it is not installed.\u001b[0m\n", - "\u001b[34m2023-09-30 05:01:33,235 sagemaker-training-toolkit INFO Exceptions not imported for SageMaker TF as Tensorflow is not installed.\u001b[0m\n", - "\u001b[34mDownloading (…)ain/model_index.json: 0%| | 0.00/544 [00:00 ACT: 22320\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [PeepholeOpts]: COPY -> ACT: 69317\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [PeepholeOpts]: RECIPROCAL -> ACT: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: peephole_opts finished after 1.269 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: curr_vmrss: 4415mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump after peephole_opts\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 18\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Done debug_dump after peephole_opts\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663600 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: Running lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Inputs to lower_kernel: modules=1 functions=1 allocs=159647 blocks=1 instructions=663600 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Started running LowerKernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Start of kernel lowering pass, number of insts: 663600, number of allocs: 159647\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Scan BKs time (s): 0.113715\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [LowerKernel]: Lower BKs time (s): 0.001777\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: lower_kernel finished after 0.165 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: curr_vmrss: 4410mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 19\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663600 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z USER 7014 [WalrusDriver]: Running build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [WalrusDriver]: Inputs to build_fdeps: modules=1 functions=1 allocs=159647 blocks=1 instructions=663600 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [build_flow_deps]: Start build fdeps. Invocation: 2Sat Sep 30 05:06:26 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:26Z INFO 7014 [build_flow_deps]: Allocs: 159647 instructions: 663600\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [build_flow_deps]: Build fdeps inserted 2562527 edges \u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [build_flow_deps]: Done build fdeps 2562527 Sat Sep 30 05:06:28 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z USER 7014 [WalrusDriver]: build_fdeps finished after 1.758 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: curr_vmrss: 5094mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump after build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 20\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Done debug_dump after build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663600 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z USER 7014 [WalrusDriver]: Running remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Inputs to remove_redundancies: modules=1 functions=1 allocs=159647 blocks=1 instructions=663600 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove_clobbered_writes\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove_clobbered_writes: 384\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove_useless_insts\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [RemoveRedundancies]: remove Useless Instructions: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z USER 7014 [WalrusDriver]: remove_redundancies finished after 0.681 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: curr_vmrss: 5082mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump after remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 21\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Done debug_dump after remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:28Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663216 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:29Z USER 7014 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:29Z INFO 7014 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=159647 blocks=1 instructions=663216 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:29Z INFO 7014 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:29Z INFO 7014 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 70298 access patterns a mean/median 1.41111/1 intervals per access pattern and mean/median 3.40997/3.03362 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-32-128]: Finished analyzing 59139 access patterns a mean/median 1.02969/1 intervals per access pattern and mean/median 2.83166/2.42382 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-32-128]: Finished analyzing 59486 access patterns a mean/median 1.02407/1 intervals per access pattern and mean/median 2.76871/2.43956 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-32-128]: Finished analyzing 58752 access patterns a mean/median 1.02243/1 intervals per access pattern and mean/median 3.12924/2.5462 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-32-128]: Finished analyzing 58828 access patterns a mean/median 1.02121/1 intervals per access pattern and mean/median 2.83007/2.58563 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-32-128]: Finished analyzing 58486 access patterns a mean/median 1.01932/1 intervals per access pattern and mean/median 2.66362/2.47247 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 69290 access patterns a mean/median 1.41192/1 intervals per access pattern and mean/median 3.47085/3.27302 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 69572 access patterns a mean/median 1.40194/1 intervals per access pattern and mean/median 3.4515/3.08572 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 69592 access patterns a mean/median 1.41283/1 intervals per access pattern and mean/median 3.62958/3.33807 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-32-128]: Finished analyzing 58982 access patterns a mean/median 1.02628/1 intervals per access pattern and mean/median 2.66817/2.31134 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-32-128]: Finished analyzing 58758 access patterns a mean/median 1.02366/1 intervals per access pattern and mean/median 2.89662/2.40036 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 69589 access patterns a mean/median 1.41435/1 intervals per access pattern and mean/median 3.42818/3.04032 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 35937 access patterns a mean/median 1/1 intervals per access pattern and mean/median 2.05256/1.21708 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-32-128]: Finished analyzing 59023 access patterns a mean/median 1.02873/1 intervals per access pattern and mean/median 2.84405/2.39523 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 69899 access patterns a mean/median 1.41743/1 intervals per access pattern and mean/median 3.41474/3.07501 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 69702 access patterns a mean/median 1.40263/1 intervals per access pattern and mean/median 3.3655/3.08343 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:33Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 69944 access patterns a mean/median 1.41788/1 intervals per access pattern and mean/median 3.45172/3.07181 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-3-4]: Finished analyzing 1153303 access patterns a mean/median 1.91116/1 intervals per access pattern and mean/median 1.68452/1.0244 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-35-36]: Finished analyzing 1139792 access patterns a mean/median 1.91446/1 intervals per access pattern and mean/median 1.68163/1.02403 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-67-96]: Finished analyzing 1066076 access patterns a mean/median 2.06705/1 intervals per access pattern and mean/median 1.61466/1.02038 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-36-64]: Finished analyzing 1139784 access patterns a mean/median 1.91447/1 intervals per access pattern and mean/median 1.68164/1.02295 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-33-35]: Finished analyzing 1139924 access patterns a mean/median 1.91436/1 intervals per access pattern and mean/median 1.68259/1.02402 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-100-128]: Finished analyzing 1069167 access patterns a mean/median 2.06443/1 intervals per access pattern and mean/median 1.7063/1.018 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-4-32]: Finished analyzing 1150985 access patterns a mean/median 1.90601/1 intervals per access pattern and mean/median 1.68422/1.02448 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 1081126 access patterns a mean/median 2.05396/1 intervals per access pattern and mean/median 1.7056/1.0057 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 1067170 access patterns a mean/median 2.06622/1 intervals per access pattern and mean/median 1.61574/1.0171 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-65-67]: Finished analyzing 1066208 access patterns a mean/median 2.06691/1 intervals per access pattern and mean/median 1.61553/1.02037 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-97-99]: Finished analyzing 1069545 access patterns a mean/median 2.06406/1 intervals per access pattern and mean/median 1.70748/1.01831 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 1147657 access patterns a mean/median 1.90853/1 intervals per access pattern and mean/median 1.6812/1.01955 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-99-100]: Finished analyzing 1069173 access patterns a mean/median 2.06443/1 intervals per access pattern and mean/median 1.7063/1.01839 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-1-3]: Finished analyzing 1153948 access patterns a mean/median 1.91065/1 intervals per access pattern and mean/median 1.68547/1.02438 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 1208795 access patterns a mean/median 1.86993/1 intervals per access pattern and mean/median 1.68491/1.00014 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z USER 7014 [WalrusDriver]: anti_dependency_analyzer finished after 7.176 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: curr_vmrss: 7023mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 22\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159647 memory location(s), 1 block(s), and 663216 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z USER 7014 [WalrusDriver]: Running post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [WalrusDriver]: Inputs to post_sched: modules=1 functions=1 allocs=159647 blocks=1 instructions=663216 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:36Z INFO 7014 [post_scheduler]: Start PosT ScheD 3 sunda Sat Sep 30 05:06:36 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:48Z INFO 7014 [post_scheduler]: Tensor CP elimination: 384\u001b[0m\n", - "\u001b[34m2023-09-30T05:06:48Z INFO 7014 [post_scheduler]: Time-aware hwm post-sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:01Z INFO 7014 [post_scheduler]: Time-aware simulation time: 97496788\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [post_scheduler]: Done PosT ScheD Sat Sep 30 05:07:06 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z USER 7014 [WalrusDriver]: post_sched finished after 30.064 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: curr_vmrss: 7021mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Running debug_dump after post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 23\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Done debug_dump after post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z USER 7014 [WalrusDriver]: Running address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:06Z INFO 7014 [WalrusDriver]: Inputs to address_rotation_sb: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:13Z INFO 7014 [DMAOptimizationBase]: PSUM Rotation rotated 21101 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:15Z INFO 7014 [DMAOptimizationBase]: PSUM Rotation rotated 18878 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:16Z INFO 7014 [DMAOptimizationBase]: PSUM Rotation rotated 7889 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:17Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 1181 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:19Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 2868 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:22Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 7500 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:31Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 3884 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:40Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 17177 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:42Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 191 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z USER 7014 [WalrusDriver]: address_rotation_sb finished after 37.296 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: curr_vmrss: 6987mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Running debug_dump after address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 24\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Done debug_dump after address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:43Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:44Z USER 7014 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:44Z INFO 7014 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:44Z INFO 7014 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:44Z INFO 7014 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-32-128]: Finished analyzing 59344 access patterns a mean/median 1.02558/1 intervals per access pattern and mean/median 2.72231/2.44076 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-32-128]: Finished analyzing 59403 access patterns a mean/median 1.02326/1 intervals per access pattern and mean/median 3.02721/2.44987 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-32-128]: Finished analyzing 59364 access patterns a mean/median 1.02675/1 intervals per access pattern and mean/median 2.99854/2.59105 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 70236 access patterns a mean/median 1.41469/1 intervals per access pattern and mean/median 3.49664/3.06486 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-32-128]: Finished analyzing 58640 access patterns a mean/median 1.025/1 intervals per access pattern and mean/median 3.00107/2.78138 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 70282 access patterns a mean/median 1.40611/1 intervals per access pattern and mean/median 3.46408/3.04038 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-32-128]: Finished analyzing 58567 access patterns a mean/median 1.02257/1 intervals per access pattern and mean/median 2.78026/2.49181 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 69510 access patterns a mean/median 1.41384/1 intervals per access pattern and mean/median 3.49008/3.03643 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 70491 access patterns a mean/median 1.42797/1 intervals per access pattern and mean/median 3.51453/3.07235 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-32-128]: Finished analyzing 58632 access patterns a mean/median 1.02442/1 intervals per access pattern and mean/median 3.12939/2.54852 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 69519 access patterns a mean/median 1.42049/1 intervals per access pattern and mean/median 3.49695/3.06175 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-32-128]: Finished analyzing 58380 access patterns a mean/median 1.02203/1 intervals per access pattern and mean/median 2.89497/2.62033 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-32-128]: Finished analyzing 59124 access patterns a mean/median 1.02581/1 intervals per access pattern and mean/median 2.88403/2.6009 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 69043 access patterns a mean/median 1.3957/1 intervals per access pattern and mean/median 3.50033/3.03206 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 35937 access patterns a mean/median 1/1 intervals per access pattern and mean/median 2.05256/1.21684 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 68950 access patterns a mean/median 1.39643/1 intervals per access pattern and mean/median 3.38656/3.02634 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:48Z INFO 7014 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 69855 access patterns a mean/median 1.41446/1 intervals per access pattern and mean/median 3.46563/3.07305 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-67-96]: Finished analyzing 1066072 access patterns a mean/median 2.06717/1 intervals per access pattern and mean/median 1.6311/1.02054 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-4-32]: Finished analyzing 1150831 access patterns a mean/median 1.90602/1 intervals per access pattern and mean/median 1.70241/1.02583 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-3-4]: Finished analyzing 1153149 access patterns a mean/median 1.91117/1 intervals per access pattern and mean/median 1.70267/1.02563 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-100-128]: Finished analyzing 1069531 access patterns a mean/median 2.06407/1 intervals per access pattern and mean/median 1.72272/1.01864 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 1083416 access patterns a mean/median 2.05184/1 intervals per access pattern and mean/median 1.72182/1.00459 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-36-64]: Finished analyzing 1139578 access patterns a mean/median 1.91463/1 intervals per access pattern and mean/median 1.69961/1.02557 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-33-35]: Finished analyzing 1139724 access patterns a mean/median 1.91452/1 intervals per access pattern and mean/median 1.7005/1.02556 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-35-36]: Finished analyzing 1139586 access patterns a mean/median 1.91463/1 intervals per access pattern and mean/median 1.6996/1.02557 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 1145317 access patterns a mean/median 1.91043/1 intervals per access pattern and mean/median 1.69986/1.02027 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 1067660 access patterns a mean/median 2.06586/1 intervals per access pattern and mean/median 1.63198/1.0186 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 1207587 access patterns a mean/median 1.87056/1 intervals per access pattern and mean/median 1.70274/1.00016 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-65-67]: Finished analyzing 1066183 access patterns a mean/median 2.06706/1 intervals per access pattern and mean/median 1.63191/1.02053 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-97-99]: Finished analyzing 1069942 access patterns a mean/median 2.06366/1 intervals per access pattern and mean/median 1.72422/1.01764 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-1-3]: Finished analyzing 1153776 access patterns a mean/median 1.91067/1 intervals per access pattern and mean/median 1.70354/1.02562 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [AntiDependencyAnalyzer-SB-99-100]: Finished analyzing 1069537 access patterns a mean/median 2.06406/1 intervals per access pattern and mean/median 1.72272/1.01774 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z USER 7014 [WalrusDriver]: anti_dependency_analyzer finished after 7.545 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: curr_vmrss: 6871mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 25\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z USER 7014 [WalrusDriver]: Running dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:52Z INFO 7014 [WalrusDriver]: Inputs to dep_opt: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:55Z INFO 7014 [build_flow_deps]: Start build fdeps. Invocation: 3Sat Sep 30 05:07:55 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:55Z INFO 7014 [build_flow_deps]: Allocs: 159263 instructions: 662832\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:57Z INFO 7014 [build_flow_deps]: Build fdeps inserted 2268858 edges \u001b[0m\n", - "\u001b[34m2023-09-30T05:07:57Z INFO 7014 [build_flow_deps]: Done build fdeps 2268858 Sat Sep 30 05:07:57 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z USER 7014 [WalrusDriver]: dep_opt finished after 6.447 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: curr_vmrss: 5804mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Running debug_dump after dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 26\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Done debug_dump after dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z USER 7014 [WalrusDriver]: Running report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [WalrusDriver]: Inputs to report_stats: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [ReportStats]: Data Movement Statistics: sg0000\u001b[0m\n", - "\u001b[34m┌──────────────┬────────────────────────────┬───────┬────────────┐\u001b[0m\n", - "\u001b[34m│ Instruction │ Kind │ Count │ Bytes │\u001b[0m\n", - "\u001b[34m├──────────────┼────────────────────────────┼───────┼────────────┤\u001b[0m\n", - "\u001b[34m│ DMACopy │ Internal │ 512 │ 268435456 │\u001b[0m\n", - "\u001b[34m│ Load │ Const -> Internal │ 409 │ 99678476 │\u001b[0m\n", - "\u001b[34m│ Load │ ExternalInput -> Internal │ 8 │ 79872 │\u001b[0m\n", - "\u001b[34m│ Load │ Internal │ 20794 │ 6617424128 │\u001b[0m\n", - "\u001b[34m│ Save │ Internal │ 4498 │ 1198778368 │\u001b[0m\n", - "\u001b[34m│ Save │ Internal -> ExternalOutput │ 256 │ 3145728 │\u001b[0m\n", - "\u001b[34m│ Save (Spill) │ Internal │ 10133 │ 4068137984 │\u001b[0m\n", - "\u001b[34m└──────────────┴────────────────────────────┴───────┴────────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:58Z INFO 7014 [ReportStats]: \u001b[0m\n", - "\u001b[34m┌─────────────────────┬───────┐\u001b[0m\n", - "\u001b[34m│ Bytes per partition │ Count │\u001b[0m\n", - "\u001b[34m├─────────────────────┼───────┤\u001b[0m\n", - "\u001b[34m│ 4 │ 1 │\u001b[0m\n", - "\u001b[34m│ 28 │ 1 │\u001b[0m\n", - "\u001b[34m│ 54 │ 1 │\u001b[0m\n", - "\u001b[34m│ 64 │ 14 │\u001b[0m\n", - "\u001b[34m│ 240 │ 1 │\u001b[0m\n", - "\u001b[34m│ 256 │ 1 │\u001b[0m\n", - "\u001b[34m│ 260 │ 3917 │\u001b[0m\n", - "\u001b[34m│ 320 │ 3814 │\u001b[0m\n", - "\u001b[34m│ 512 │ 14 │\u001b[0m\n", - "\u001b[34m│ 516 │ 549 │\u001b[0m\n", - "\u001b[34m│ 528 │ 741 │\u001b[0m\n", - "\u001b[34m│ 1024 │ 139 │\u001b[0m\n", - "\u001b[34m│ 1028 │ 3948 │\u001b[0m\n", - "\u001b[34m│ 1040 │ 1139 │\u001b[0m\n", - "\u001b[34m│ 1056 │ 118 │\u001b[0m\n", - "\u001b[34m│ 2048 │ 9321 │\u001b[0m\n", - "\u001b[34m│ 2064 │ 2556 │\u001b[0m\n", - "\u001b[34m│ 2080 │ 2509 │\u001b[0m\n", - "\u001b[34m│ 2112 │ 513 │\u001b[0m\n", - "\u001b[34m│ 2304 │ 193 │\u001b[0m\n", - "\u001b[34m│ 2560 │ 6 │\u001b[0m\n", - "\u001b[34m│ 3072 │ 450 │\u001b[0m\n", - "\u001b[34m│ 3080 │ 48 │\u001b[0m\n", - "\u001b[34m│ 3584 │ 192 │\u001b[0m\n", - "\u001b[34m│ 4096 │ 2754 │\u001b[0m\n", - "\u001b[34m│ 5136 │ 13 │\u001b[0m\n", - "\u001b[34m│ 6656 │ 448 │\u001b[0m\n", - "\u001b[34m│ 7192 │ 10 │\u001b[0m\n", - "\u001b[34m│ 7680 │ 4 │\u001b[0m\n", - "\u001b[34m│ 8192 │ 1261 │\u001b[0m\n", - "\u001b[34m│ 9216 │ 224 │\u001b[0m\n", - "\u001b[34m│ 9248 │ 11 │\u001b[0m\n", - "\u001b[34m│ 10240 │ 8 │\u001b[0m\n", - "\u001b[34m│ 11304 │ 11 │\u001b[0m\n", - "\u001b[34m│ 12288 │ 4 │\u001b[0m\n", - "\u001b[34m│ 13360 │ 36 │\u001b[0m\n", - "\u001b[34m│ 14336 │ 2 │\u001b[0m\n", - "\u001b[34m│ 15416 │ 191 │\u001b[0m\n", - "\u001b[34m│ 16384 │ 377 │\u001b[0m\n", - "\u001b[34m│ 24576 │ 2 │\u001b[0m\n", - "\u001b[34m│ 25600 │ 498 │\u001b[0m\n", - "\u001b[34m│ 26624 │ 16 │\u001b[0m\n", - "\u001b[34m│ 27648 │ 6 │\u001b[0m\n", - "\u001b[34m│ 30720 │ 2 │\u001b[0m\n", - "\u001b[34m│ 31744 │ 2 │\u001b[0m\n", - "\u001b[34m│ 32768 │ 544 │\u001b[0m\n", - "\u001b[34m└─────────────────────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [ReportStats]: MM Stats: #MatMults 449876 #MatMult-Transposes 71552\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: report_stats finished after 0.318 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: curr_vmrss: 5722mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump after report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 27\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Done debug_dump after report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: Running assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Inputs to assign_trigger_engine: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [AssignTriggerEngine]: Assigned trigger engine for 14631 DMA instructions\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: assign_trigger_engine finished after 0.340 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: curr_vmrss: 5747mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump after assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 28\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Done debug_dump after assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: Running alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Inputs to alloc_queues: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: alloc_queues finished after 0.177 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: curr_vmrss: 5742mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump after alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 29\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Done debug_dump after alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z USER 7014 [WalrusDriver]: Running dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [WalrusDriver]: Inputs to dep_reduction: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:07:59Z INFO 7014 [DepReduction]: Start Dependency Reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:00Z INFO 7014 [DepReduction]: Processing async instrs...\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:00Z INFO 7014 [DepReduction]: Processing secondary edges per engine...\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:01Z INFO 7014 [DepReduction]: Processing secondary edges per engine, Done. Num edges removed 827616\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:01Z INFO 7014 [DepReduction]: Processing redundant descendants...\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:03Z INFO 7014 [DepReduction]: Processing redundant descendants, Done. Num edges removed 51154\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:03Z INFO 7014 [DepReduction]: Processing async instrs, Done. Num edges removed 878770\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [DepReduction]: Num Async removed: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [DepReduction]: Finished dependency reduction: 5334354 removed, new total 196341\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [DepReduction]: Finished Dependency Reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z USER 7014 [WalrusDriver]: dep_reduction finished after 12.489 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: curr_vmrss: 5897mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Running debug_dump after dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 30\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Done debug_dump after dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:12Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:13Z USER 7014 [WalrusDriver]: Running bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:13Z INFO 7014 [WalrusDriver]: Inputs to bir_racecheck: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z USER 7014 [WalrusDriver]: bir_racecheck finished after 8.019 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: curr_vmrss: 6998mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Running debug_dump after bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 31\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Done debug_dump after bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662832 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z USER 7014 [WalrusDriver]: Running lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:21Z INFO 7014 [WalrusDriver]: Inputs to lower_dma: modules=1 functions=1 allocs=159263 blocks=1 instructions=662832 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z USER 7014 [WalrusDriver]: lower_dma finished after 1.298 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: curr_vmrss: 6072mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 32\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662861 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:22Z USER 7014 [WalrusDriver]: Running alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:23Z INFO 7014 [WalrusDriver]: Inputs to alloc_semaphores: modules=1 functions=1 allocs=159263 blocks=1 instructions=662861 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: alloc_semaphores finished after 1.056 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: curr_vmrss: 6047mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump after alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 33\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Done debug_dump after alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662861 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: Running expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Inputs to expand_inst_late: modules=1 functions=1 allocs=159263 blocks=1 instructions=662861 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: expand_inst_late finished after 0.152 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: curr_vmrss: 6047mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump after expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 34\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Done debug_dump after expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 662861 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: Running lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Inputs to lower_sync: modules=1 functions=1 allocs=159263 blocks=1 instructions=662861 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z USER 7014 [WalrusDriver]: lower_sync finished after 0.445 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: curr_vmrss: 6139mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 35\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:24Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 701954 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z USER 7014 [WalrusDriver]: Running lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Inputs to lower_act: modules=1 functions=1 allocs=159263 blocks=1 instructions=701954 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z USER 7014 [WalrusDriver]: lower_act finished after 0.193 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: curr_vmrss: 6151mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 36\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z USER 7014 [WalrusDriver]: Running lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:25Z INFO 7014 [WalrusDriver]: Inputs to lower_dve: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z USER 7014 [WalrusDriver]: lower_dve finished after 1.093 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: curr_vmrss: 6266mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 37\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z USER 7014 [WalrusDriver]: Running lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:26Z INFO 7014 [WalrusDriver]: Inputs to lower_ap: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: lower_ap finished after 0.285 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: curr_vmrss: 6151mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump after lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 38\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Done debug_dump after lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: Running alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Inputs to alloc_regs: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [AllocRegs]: allocating REG\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [AllocRegs]: main loop iteration 1\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: alloc_regs finished after 0.039 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: curr_vmrss: 6151mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump after alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 39\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Done debug_dump after alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z USER 7014 [WalrusDriver]: Running birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:27Z INFO 7014 [WalrusDriver]: Inputs to birverifier: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z USER 7014 [WalrusDriver]: birverifier finished after 1.950 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: curr_vmrss: 6165mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Running debug_dump after birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 40\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Done debug_dump after birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z USER 7014 [WalrusDriver]: Running codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [WalrusDriver]: Inputs to codegen: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: Total compiler allocated DRAM tensors: 0.492149 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: Total un-allocated DRAM tensors by kind:\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────────┬─────────────┐\u001b[0m\n", - "\u001b[34m│ TensorKind │ Size (GB) │\u001b[0m\n", - "\u001b[34m├────────────────┼─────────────┤\u001b[0m\n", - "\u001b[34m│ ExternalInput │ 6.10352e-05 │\u001b[0m\n", - "\u001b[34m│ ExternalOutput │ 0.00292969 │\u001b[0m\n", - "\u001b[34m│ Const │ 0.0923445 │\u001b[0m\n", - "\u001b[34m└────────────────┴─────────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:29Z INFO 7014 [Codegen]: Total runtime managed DRAM tensors: 0.0953353 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: Instruction Stats:\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: \u001b[0m\n", - "\u001b[34m┌──────────────────────┬────────┐\u001b[0m\n", - "\u001b[34m│ Opcode │ Count │\u001b[0m\n", - "\u001b[34m├──────────────────────┼────────┤\u001b[0m\n", - "\u001b[34m│ MATMUL │ 513016 │\u001b[0m\n", - "\u001b[34m│ LDWEIGHTS │ 513016 │\u001b[0m\n", - "\u001b[34m│ ACTIVATE │ 104649 │\u001b[0m\n", - "\u001b[34m│ TENSOR_TENSOR │ 55507 │\u001b[0m\n", - "\u001b[34m│ EVENT_SEMAPHORE │ 39093 │\u001b[0m\n", - "\u001b[34m│ PSEUDO_DMA_TRIGGER │ 36610 │\u001b[0m\n", - "\u001b[34m│ DRAIN │ 8244 │\u001b[0m\n", - "\u001b[34m│ COPY │ 6136 │\u001b[0m\n", - "\u001b[34m│ TENSOR_REDUCE │ 5952 │\u001b[0m\n", - "\u001b[34m│ ACT_TABLE_LOAD │ 4122 │\u001b[0m\n", - "\u001b[34m│ LOAD_MASK_SELECT │ 2248 │\u001b[0m\n", - "\u001b[34m│ STREAM_SHUFFLE │ 2248 │\u001b[0m\n", - "\u001b[34m│ BATCH_NORM_STATS2 │ 1408 │\u001b[0m\n", - "\u001b[34m│ MEMSET │ 319 │\u001b[0m\n", - "\u001b[34m│ TENSOR_SCALAR │ 64 │\u001b[0m\n", - "\u001b[34m│ RECIPROCAL │ 32 │\u001b[0m\n", - "\u001b[34m│ NOP │ 29 │\u001b[0m\n", - "\u001b[34m│ STREAM_TRANSPOSE │ 20 │\u001b[0m\n", - "\u001b[34m│ BATCH_NORM_AGGREGATE │ 11 │\u001b[0m\n", - "\u001b[34m└──────────────────────┴────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────┬─────────┐\u001b[0m\n", - "\u001b[34m│ Engine │ Count │\u001b[0m\n", - "\u001b[34m├────────────┼─────────┤\u001b[0m\n", - "\u001b[34m│ Pool │ 552 │\u001b[0m\n", - "\u001b[34m│ Activation │ 146952 │\u001b[0m\n", - "\u001b[34m│ PE │ 1028395 │\u001b[0m\n", - "\u001b[34m│ DVE │ 83737 │\u001b[0m\n", - "\u001b[34m│ SP │ 33088 │\u001b[0m\n", - "\u001b[34m└────────────┴─────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:32Z INFO 7014 [Codegen]: Total instructions: 1292724 (0.0770524 GB)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:33Z INFO 7014 [Codegen]: Number of DMA descriptors on each queue:\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:33Z INFO 7014 [Codegen]: \u001b[0m\n", - "\u001b[34m┌───────────────────┬─────────┐\u001b[0m\n", - "\u001b[34m│ Queue │ Count │\u001b[0m\n", - "\u001b[34m├───────────────────┼─────────┤\u001b[0m\n", - "\u001b[34m│ qActSpillReload0 │ 2585854 │\u001b[0m\n", - "\u001b[34m│ qDVESpillReload0 │ 426688 │\u001b[0m\n", - "\u001b[34m│ qPoolPIO0 │ 1536 │\u001b[0m\n", - "\u001b[34m│ qPoolSpillReload0 │ 4864 │\u001b[0m\n", - "\u001b[34m│ qSPPIO0 │ 64 │\u001b[0m\n", - "\u001b[34m│ qSPSpillReload0 │ 6706518 │\u001b[0m\n", - "\u001b[34m└───────────────────┴─────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:33Z INFO 7014 [Codegen]: Total descriptors: 9725524 (0.144922 GB)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:37Z INFO 7014 [Codegen]: Estimated peak DRAM usage: 0.809459 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:38Z USER 7014 [WalrusDriver]: codegen finished after 9.312 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: curr_vmrss: 7387mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Running debug_dump after codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 41\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Done debug_dump after codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:38Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z USER 7014 [WalrusDriver]: Running neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Inputs to neff_packager: modules=1 functions=1 allocs=159263 blocks=1 instructions=706076 Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z USER 7014 [WalrusDriver]: neff_packager finished after 0.201 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: curr_vmrss: 6586mb, ru_maxrss: 10796mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Running debug_dump after neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 42\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Done debug_dump after neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:39Z INFO 7014 [WalrusDriver]: Output has 1 module(s), 1 function(s), 159263 memory location(s), 1 block(s), and 706076 instruction(s). Max writers: 513 Max Readers: 40448\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.WalrusDriver.0]: Job #0 finished\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Finished job job.WalrusDriver.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Starting job job.BIRLinker.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.BIRLinker.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp_qegp74y/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-ta6seopo/sg00\", \"state_id\": \"sg00\"}' --pipeline BIRLinker\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.BIRLinker.0]: BIRLinker cwd: /opt/ml/code/neuronxcc-ta6seopo\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.BIRLinker.0]: Linking not needed. Netlist doesnt exist\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Finished job job.BIRLinker.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [pipeline.Pipeline.0]: Starting job job.Kelper.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:53Z INFO 7014 [job.Kelper.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp_qegp74y/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-ta6seopo/sg00\", \"state_id\": \"sg00\"}' --pipeline Kelper\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:57Z INFO 7014 [job.Kelper.0]: IR signature: 17eb18d15504fbbdaee28a808265cd54 for neff artifacts\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [job.Kelper.0]: neuronxcc version is 2.9.0.40+07376825f, neff version is 1.0 (features 0)\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z USER 7014 [job.Kelper.0]: Wrote /tmp/tmp_qegp74y/graph.neff\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [pipeline.Pipeline.0]: Finished job job.Kelper.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [pipeline.Pipeline.0]: Finished pipeline Pipeline\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z INFO 7014 [pipeline.Pipeline.0]: Job #0 finished\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z USER 7014 [root]: Compiler status PASS\u001b[0m\n", - "\u001b[34m2023-09-30T05:08:58Z INFO 6950 [root]: Subcommand returned with exitcode=0\u001b[0m\n", - "\u001b[34mDone. Elapsed time: 355390.53106307983ms\u001b[0m\n", - "\u001b[34mLoading pipeline components...: 0%| | 0/6 [00:00 ACT: 19969\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [PeepholeOpts]: COPY -> ACT: 17590\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [PeepholeOpts]: RECIPROCAL -> ACT: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: peephole_opts finished after 0.312 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: curr_vmrss: 10544mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump after peephole_opts\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 18\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Done debug_dump after peephole_opts\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: Running lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Inputs to lower_kernel: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Started running LowerKernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Start of kernel lowering pass, number of insts: 243631, number of allocs: 88876\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Scan BKs time (s): 0.039997\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [LowerKernel]: Lower BKs time (s): 5e-06\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: lower_kernel finished after 0.056 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: curr_vmrss: 10544mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 19\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z USER 13755 [WalrusDriver]: Running build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [WalrusDriver]: Inputs to build_fdeps: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [build_flow_deps]: Start build fdeps. Invocation: 2Sat Sep 30 05:13:38 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:38Z INFO 13755 [build_flow_deps]: Allocs: 88876 instructions: 243631\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [build_flow_deps]: Build fdeps inserted 672517 edges \u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [build_flow_deps]: Done build fdeps 672517 Sat Sep 30 05:13:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: build_fdeps finished after 0.644 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: curr_vmrss: 10752mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump after build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 20\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Done debug_dump after build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: Running remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Inputs to remove_redundancies: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove_clobbered_writes\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove_clobbered_writes: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove_useless_insts\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [RemoveRedundancies]: remove Useless Instructions: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: remove_redundancies finished after 0.185 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: curr_vmrss: 10744mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump after remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 21\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Done debug_dump after remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z USER 13755 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:39Z INFO 13755 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-64-128]: Finished analyzing 21295 access patterns a mean/median 2.76877/1 intervals per access pattern and mean/median 2.89342/1.99882 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-32-64]: Finished analyzing 24257 access patterns a mean/median 2.82121/1 intervals per access pattern and mean/median 3.17518/1.85728 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 24176 access patterns a mean/median 2.63584/1.00015 intervals per access pattern and mean/median 3.19031/2.00003 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-64-128]: Finished analyzing 21418 access patterns a mean/median 2.75931/1.00005 intervals per access pattern and mean/median 2.91006/1.98947 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-32-64]: Finished analyzing 23516 access patterns a mean/median 2.67639/1 intervals per access pattern and mean/median 3.12067/1.99893 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 24292 access patterns a mean/median 2.63021/1.00011 intervals per access pattern and mean/median 3.19249/2.00003 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 24271 access patterns a mean/median 2.49681/1 intervals per access pattern and mean/median 3.57787/1.98984 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-64-128]: Finished analyzing 21503 access patterns a mean/median 2.60103/1 intervals per access pattern and mean/median 3.26305/1.98956 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-32-64]: Finished analyzing 23723 access patterns a mean/median 2.52632/1 intervals per access pattern and mean/median 3.66492/1.99851 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-32-64]: Finished analyzing 23718 access patterns a mean/median 2.66435/1.00001 intervals per access pattern and mean/median 3.2163/1.99863 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-64-128]: Finished analyzing 21504 access patterns a mean/median 2.5885/1 intervals per access pattern and mean/median 3.37414/1.97692 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-64-128]: Finished analyzing 21490 access patterns a mean/median 2.44677/1 intervals per access pattern and mean/median 3.55284/1.99841 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 24311 access patterns a mean/median 2.47386/1 intervals per access pattern and mean/median 3.4675/2.00002 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 24326 access patterns a mean/median 2.35271/1 intervals per access pattern and mean/median 3.65296/2.00001 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-64-128]: Finished analyzing 22030 access patterns a mean/median 2.92388/1 intervals per access pattern and mean/median 2.96324/1.82179 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-32-64]: Finished analyzing 23666 access patterns a mean/median 2.38511/1 intervals per access pattern and mean/median 3.71164/1.99849 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-32-64]: Finished analyzing 23694 access patterns a mean/median 2.50692/1 intervals per access pattern and mean/median 3.6265/1.99867 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 24792 access patterns a mean/median 2.78675/1.00035 intervals per access pattern and mean/median 3.1941/1.85633 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 24464 access patterns a mean/median 2.52526/1.00009 intervals per access pattern and mean/median 3.20322/1.99997 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 24610 access patterns a mean/median 2.81353/1.00038 intervals per access pattern and mean/median 3.19392/2.00002 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-64-128]: Finished analyzing 21796 access patterns a mean/median 2.96004/1.00017 intervals per access pattern and mean/median 2.856/1.9988 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-64-128]: Finished analyzing 21850 access patterns a mean/median 2.63735/1 intervals per access pattern and mean/median 3.0524/1.9979 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-32-64]: Finished analyzing 23896 access patterns a mean/median 2.55624/1 intervals per access pattern and mean/median 3.16609/1.99845 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-32-64]: Finished analyzing 23988 access patterns a mean/median 2.8553/1 intervals per access pattern and mean/median 3.17341/1.99852 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:40Z INFO 13755 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 2113 access patterns a mean/median 1/1 intervals per access pattern and mean/median 1.78912/1.08411 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-100-105]: Finished analyzing 425319 access patterns a mean/median 2.14058/1 intervals per access pattern and mean/median 3.30196/1.00023 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-105-128]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.30183/1.00024 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-97-100]: Finished analyzing 425331 access patterns a mean/median 2.14055/1 intervals per access pattern and mean/median 3.30191/1.00023 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 427056 access patterns a mean/median 2.13594/1 intervals per access pattern and mean/median 3.28911/1.00014 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-77-96]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.30183/1.00024 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-68-73]: Finished analyzing 429751 access patterns a mean/median 2.12882/1 intervals per access pattern and mean/median 3.30517/1.00025 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-65-68]: Finished analyzing 429754 access patterns a mean/median 2.12881/1 intervals per access pattern and mean/median 3.30515/1.00028 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-73-77]: Finished analyzing 429745 access patterns a mean/median 2.12884/1 intervals per access pattern and mean/median 3.30521/1.00028 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 430525 access patterns a mean/median 2.12679/1 intervals per access pattern and mean/median 3.30045/1.00027 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-4-9]: Finished analyzing 464067 access patterns a mean/median 2.12778/1 intervals per access pattern and mean/median 3.3226/1.00009 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 464343 access patterns a mean/median 2.12465/1 intervals per access pattern and mean/median 3.321/1.00009 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-1-4]: Finished analyzing 464070 access patterns a mean/median 2.12777/1 intervals per access pattern and mean/median 3.32258/1.00009 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-9-32]: Finished analyzing 463596 access patterns a mean/median 2.12646/1 intervals per access pattern and mean/median 3.32634/1.00009 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-41-64]: Finished analyzing 463534 access patterns a mean/median 2.12661/1 intervals per access pattern and mean/median 3.32687/1.00008 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-33-36]: Finished analyzing 463547 access patterns a mean/median 2.12658/1 intervals per access pattern and mean/median 3.3268/1.00011 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-36-41]: Finished analyzing 463544 access patterns a mean/median 2.12659/1 intervals per access pattern and mean/median 3.32682/1.00009 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 467334 access patterns a mean/median 2.11989/1 intervals per access pattern and mean/median 3.312/1.00006 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z USER 13755 [WalrusDriver]: anti_dependency_analyzer finished after 2.641 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: curr_vmrss: 11269mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 22\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:41Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88876 memory location(s), 1 block(s), and 243631 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:42Z USER 13755 [WalrusDriver]: Running post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:42Z INFO 13755 [WalrusDriver]: Inputs to post_sched: modules=1 functions=1 allocs=88876 blocks=1 instructions=243631 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:42Z INFO 13755 [post_scheduler]: Start PosT ScheD 3 sunda Sat Sep 30 05:13:42 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:45Z INFO 13755 [post_scheduler]: Tensor CP elimination: 97\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:45Z INFO 13755 [post_scheduler]: Time-aware hwm post-sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:47Z INFO 13755 [post_scheduler]: Time-aware simulation time: 31404281\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [post_scheduler]: Done PosT ScheD Sat Sep 30 05:13:48 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z USER 13755 [WalrusDriver]: post_sched finished after 6.600 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: curr_vmrss: 11176mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Running debug_dump after post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 23\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Done debug_dump after post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z USER 13755 [WalrusDriver]: Running address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:48Z INFO 13755 [WalrusDriver]: Inputs to address_rotation_sb: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:50Z INFO 13755 [DMAOptimizationBase]: PSUM Rotation rotated 12187 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:50Z INFO 13755 [DMAOptimizationBase]: PSUM Rotation rotated 11175 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:51Z INFO 13755 [DMAOptimizationBase]: PSUM Rotation rotated 1889 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:51Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 26 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:52Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 1619 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:53Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 172 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:55Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 496 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:56Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 9468 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 14 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [DMAOptimizationBase]: SB Rotation rotated 1 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z USER 13755 [WalrusDriver]: address_rotation_sb finished after 9.336 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: curr_vmrss: 11151mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Running debug_dump after address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 24\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Done debug_dump after address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:57Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:58Z USER 13755 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:58Z INFO 13755 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:58Z INFO 13755 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:58Z INFO 13755 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 2113 access patterns a mean/median 1/1 intervals per access pattern and mean/median 1.78912/1.0593 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 24186 access patterns a mean/median 2.60366/1.00016 intervals per access pattern and mean/median 3.35077/1.99998 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-64-128]: Finished analyzing 21554 access patterns a mean/median 2.71203/1 intervals per access pattern and mean/median 3.15596/1.99833 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 24361 access patterns a mean/median 2.56237/1.00002 intervals per access pattern and mean/median 3.35735/2 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 24307 access patterns a mean/median 2.59028/1.00003 intervals per access pattern and mean/median 3.19146/2 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-64-128]: Finished analyzing 21511 access patterns a mean/median 2.67472/1 intervals per access pattern and mean/median 3.22437/1.99829 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 24312 access patterns a mean/median 2.41757/1 intervals per access pattern and mean/median 3.35383/2.00001 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 24388 access patterns a mean/median 2.67238/1 intervals per access pattern and mean/median 3.35263/2.00003 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 24463 access patterns a mean/median 2.69386/1.00021 intervals per access pattern and mean/median 3.1555/1.99947 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM1-32-64]: Finished analyzing 23784 access patterns a mean/median 2.61995/1 intervals per access pattern and mean/median 3.43968/1.99833 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-64-128]: Finished analyzing 21526 access patterns a mean/median 2.50033/1 intervals per access pattern and mean/median 3.28288/1.99763 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM2-32-64]: Finished analyzing 23748 access patterns a mean/median 2.5974/1 intervals per access pattern and mean/median 3.3895/1.99897 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-64-128]: Finished analyzing 21629 access patterns a mean/median 2.7962/1.00006 intervals per access pattern and mean/median 3.30205/1.99933 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM4-32-64]: Finished analyzing 23765 access patterns a mean/median 2.71092/1.00001 intervals per access pattern and mean/median 3.48949/1.99846 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-64-128]: Finished analyzing 21674 access patterns a mean/median 2.73752/1 intervals per access pattern and mean/median 3.30142/1.99813 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-64-128]: Finished analyzing 21418 access patterns a mean/median 2.73504/1.00003 intervals per access pattern and mean/median 3.06773/1.99969 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM6-32-64]: Finished analyzing 23529 access patterns a mean/median 2.64333/1.00002 intervals per access pattern and mean/median 3.31854/1.99816 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-64-128]: Finished analyzing 21930 access patterns a mean/median 2.82832/1 intervals per access pattern and mean/median 3.12841/1.99904 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM0-32-64]: Finished analyzing 23954 access patterns a mean/median 2.72485/1 intervals per access pattern and mean/median 3.35914/1.99758 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-32-64]: Finished analyzing 23825 access patterns a mean/median 2.64185/1.00001 intervals per access pattern and mean/median 3.65485/1.99817 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-64-128]: Finished analyzing 21644 access patterns a mean/median 2.70565/1 intervals per access pattern and mean/median 3.34393/1.99998 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-32-64]: Finished analyzing 24033 access patterns a mean/median 2.61857/1 intervals per access pattern and mean/median 3.68052/1.99829 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM7-32-64]: Finished analyzing 23820 access patterns a mean/median 2.44156/1 intervals per access pattern and mean/median 3.50126/1.998 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 24649 access patterns a mean/median 2.59207/1.00011 intervals per access pattern and mean/median 3.4651/2.00003 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:13:59Z INFO 13755 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 24576 access patterns a mean/median 2.58793/1 intervals per access pattern and mean/median 3.48391/1.99999 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-100-105]: Finished analyzing 425319 access patterns a mean/median 2.14058/1 intervals per access pattern and mean/median 3.33048/1.00007 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-97-100]: Finished analyzing 425331 access patterns a mean/median 2.14055/1 intervals per access pattern and mean/median 3.33043/1.00007 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-96-97]: Finished analyzing 427461 access patterns a mean/median 2.13487/1 intervals per access pattern and mean/median 3.31506/1.00004 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-105-128]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.33025/1.00006 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-77-96]: Finished analyzing 425291 access patterns a mean/median 2.14066/1 intervals per access pattern and mean/median 3.33025/1.00006 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-64-65]: Finished analyzing 430430 access patterns a mean/median 2.12704/1 intervals per access pattern and mean/median 3.32905/1.0001 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-68-73]: Finished analyzing 429751 access patterns a mean/median 2.12882/1 intervals per access pattern and mean/median 3.33336/1.00012 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-73-77]: Finished analyzing 429745 access patterns a mean/median 2.12884/1 intervals per access pattern and mean/median 3.33339/1.00007 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-65-68]: Finished analyzing 429754 access patterns a mean/median 2.12881/1 intervals per access pattern and mean/median 3.33334/1.00012 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-1-4]: Finished analyzing 464018 access patterns a mean/median 2.1279/1 intervals per access pattern and mean/median 3.3506/1.00004 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-32-33]: Finished analyzing 464024 access patterns a mean/median 2.12542/1 intervals per access pattern and mean/median 3.35063/1.00002 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-33-36]: Finished analyzing 463495 access patterns a mean/median 2.12671/1 intervals per access pattern and mean/median 3.35482/1.00004 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-41-64]: Finished analyzing 463482 access patterns a mean/median 2.12674/1 intervals per access pattern and mean/median 3.3549/1.00001 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-36-41]: Finished analyzing 463492 access patterns a mean/median 2.12671/1 intervals per access pattern and mean/median 3.35484/1.00004 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-9-32]: Finished analyzing 463544 access patterns a mean/median 2.12659/1 intervals per access pattern and mean/median 3.35436/0.999991 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-4-9]: Finished analyzing 464015 access patterns a mean/median 2.12791/1 intervals per access pattern and mean/median 3.35062/1.00002 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [AntiDependencyAnalyzer-SB-0-1]: Finished analyzing 467071 access patterns a mean/median 2.12053/1 intervals per access pattern and mean/median 3.34177/1.00002 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z USER 13755 [WalrusDriver]: anti_dependency_analyzer finished after 2.653 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: curr_vmrss: 11408mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 25\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:00Z USER 13755 [WalrusDriver]: Running dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:01Z INFO 13755 [WalrusDriver]: Inputs to dep_opt: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:02Z INFO 13755 [build_flow_deps]: Start build fdeps. Invocation: 3Sat Sep 30 05:14:02 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:02Z INFO 13755 [build_flow_deps]: Allocs: 88779 instructions: 243534\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [build_flow_deps]: Build fdeps inserted 661744 edges \u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [build_flow_deps]: Done build fdeps 661744 Sat Sep 30 05:14:03 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: dep_opt finished after 2.590 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 11012mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 26\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to report_stats: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [ReportStats]: Data Movement Statistics: sg0000\u001b[0m\n", - "\u001b[34m┌──────────────┬────────────────────────────┬───────┬────────────┐\u001b[0m\n", - "\u001b[34m│ Instruction │ Kind │ Count │ Bytes │\u001b[0m\n", - "\u001b[34m├──────────────┼────────────────────────────┼───────┼────────────┤\u001b[0m\n", - "\u001b[34m│ Load │ Const -> Internal │ 7590 │ 1732837520 │\u001b[0m\n", - "\u001b[34m│ Load │ ExternalInput -> Internal │ 27 │ 848916 │\u001b[0m\n", - "\u001b[34m│ Load │ Internal │ 1378 │ 244028812 │\u001b[0m\n", - "\u001b[34m│ Save │ Internal │ 578 │ 145522828 │\u001b[0m\n", - "\u001b[34m│ Save │ Internal -> ExternalOutput │ 4 │ 65536 │\u001b[0m\n", - "\u001b[34m│ Save (Spill) │ Internal │ 157 │ 71001088 │\u001b[0m\n", - "\u001b[34m└──────────────┴────────────────────────────┴───────┴────────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [ReportStats]: \u001b[0m\n", - "\u001b[34m┌─────────────────────┬───────┐\u001b[0m\n", - "\u001b[34m│ Bytes per partition │ Count │\u001b[0m\n", - "\u001b[34m├─────────────────────┼───────┤\u001b[0m\n", - "\u001b[34m│ 2 │ 5 │\u001b[0m\n", - "\u001b[34m│ 4 │ 142 │\u001b[0m\n", - "\u001b[34m│ 8 │ 70 │\u001b[0m\n", - "\u001b[34m│ 16 │ 65 │\u001b[0m\n", - "\u001b[34m│ 64 │ 70 │\u001b[0m\n", - "\u001b[34m│ 72 │ 1 │\u001b[0m\n", - "\u001b[34m│ 128 │ 748 │\u001b[0m\n", - "\u001b[34m│ 144 │ 1 │\u001b[0m\n", - "\u001b[34m│ 172 │ 1 │\u001b[0m\n", - "\u001b[34m│ 200 │ 1 │\u001b[0m\n", - "\u001b[34m│ 240 │ 1 │\u001b[0m\n", - "\u001b[34m│ 256 │ 1712 │\u001b[0m\n", - "\u001b[34m│ 384 │ 3 │\u001b[0m\n", - "\u001b[34m│ 400 │ 1 │\u001b[0m\n", - "\u001b[34m│ 460 │ 1 │\u001b[0m\n", - "\u001b[34m│ 480 │ 10 │\u001b[0m\n", - "\u001b[34m│ 484 │ 1 │\u001b[0m\n", - "\u001b[34m│ 500 │ 4 │\u001b[0m\n", - "\u001b[34m│ 504 │ 1 │\u001b[0m\n", - "\u001b[34m│ 512 │ 355 │\u001b[0m\n", - "\u001b[34m│ 640 │ 178 │\u001b[0m\n", - "\u001b[34m│ 768 │ 6 │\u001b[0m\n", - "\u001b[34m│ 1024 │ 28 │\u001b[0m\n", - "\u001b[34m│ 1240 │ 15 │\u001b[0m\n", - "\u001b[34m│ 1280 │ 264 │\u001b[0m\n", - "\u001b[34m│ 1792 │ 50 │\u001b[0m\n", - "\u001b[34m│ 1856 │ 1 │\u001b[0m\n", - "\u001b[34m│ 2048 │ 781 │\u001b[0m\n", - "\u001b[34m│ 2268 │ 4 │\u001b[0m\n", - "\u001b[34m│ 2304 │ 2 │\u001b[0m\n", - "\u001b[34m│ 2520 │ 12 │\u001b[0m\n", - "\u001b[34m│ 2560 │ 4128 │\u001b[0m\n", - "\u001b[34m│ 2816 │ 17 │\u001b[0m\n", - "\u001b[34m│ 3072 │ 7 │\u001b[0m\n", - "\u001b[34m│ 3840 │ 428 │\u001b[0m\n", - "\u001b[34m│ 4096 │ 480 │\u001b[0m\n", - "\u001b[34m│ 4160 │ 10 │\u001b[0m\n", - "\u001b[34m│ 5120 │ 68 │\u001b[0m\n", - "\u001b[34m│ 5760 │ 4 │\u001b[0m\n", - "\u001b[34m│ 7680 │ 4 │\u001b[0m\n", - "\u001b[34m│ 8192 │ 38 │\u001b[0m\n", - "\u001b[34m│ 10240 │ 4 │\u001b[0m\n", - "\u001b[34m│ 16644 │ 12 │\u001b[0m\n", - "\u001b[34m└─────────────────────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [ReportStats]: MM Stats: #MatMults 160587 #MatMult-Transposes 42386\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: report_stats finished after 0.056 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 10985mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 27\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to assign_trigger_engine: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [AssignTriggerEngine]: Assigned trigger engine for 735 DMA instructions\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: assign_trigger_engine finished after 0.076 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 10996mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 28\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to alloc_queues: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: alloc_queues finished after 0.038 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: curr_vmrss: 10995mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump after alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 29\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Done debug_dump after alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z USER 13755 [WalrusDriver]: Running dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [WalrusDriver]: Inputs to dep_reduction: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [DepReduction]: Start Dependency Reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [DepReduction]: Processing async instrs...\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:03Z INFO 13755 [DepReduction]: Processing secondary edges per engine...\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing secondary edges per engine, Done. Num edges removed 146530\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing redundant descendants...\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing redundant descendants, Done. Num edges removed 10877\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:04Z INFO 13755 [DepReduction]: Processing async instrs, Done. Num edges removed 157407\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [DepReduction]: Num Async removed: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [DepReduction]: Finished dependency reduction: 1592440 removed, new total 95811\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [DepReduction]: Finished Dependency Reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z USER 13755 [WalrusDriver]: dep_reduction finished after 3.450 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: curr_vmrss: 11044mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Running debug_dump after dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 30\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Done debug_dump after dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z USER 13755 [WalrusDriver]: Running bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:07Z INFO 13755 [WalrusDriver]: Inputs to bir_racecheck: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: bir_racecheck finished after 3.741 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: curr_vmrss: 11768mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump after bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 31\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Done debug_dump after bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243534 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: Running lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Inputs to lower_dma: modules=1 functions=1 allocs=88779 blocks=1 instructions=243534 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: lower_dma finished after 0.283 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: curr_vmrss: 11092mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 32\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243538 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z USER 13755 [WalrusDriver]: Running alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:11Z INFO 13755 [WalrusDriver]: Inputs to alloc_semaphores: modules=1 functions=1 allocs=88779 blocks=1 instructions=243538 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: alloc_semaphores finished after 0.311 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11087mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 33\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243538 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to expand_inst_late: modules=1 functions=1 allocs=88779 blocks=1 instructions=243538 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: expand_inst_late finished after 0.033 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11087mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 34\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 243538 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_sync: modules=1 functions=1 allocs=88779 blocks=1 instructions=243538 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_sync finished after 0.109 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11113mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 35\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253555 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_act: modules=1 functions=1 allocs=88779 blocks=1 instructions=253555 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_act finished after 0.043 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11116mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 36\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_dve: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_dve finished after 0.249 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11155mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 37\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to lower_ap: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: lower_ap finished after 0.071 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11115mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 38\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to alloc_regs: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [AllocRegs]: allocating REG\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [AllocRegs]: main loop iteration 1\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: alloc_regs finished after 0.008 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: curr_vmrss: 11115mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump after alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 39\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Done debug_dump after alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z USER 13755 [WalrusDriver]: Running birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:12Z INFO 13755 [WalrusDriver]: Inputs to birverifier: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z USER 13755 [WalrusDriver]: birverifier finished after 0.570 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: curr_vmrss: 11121mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Running debug_dump after birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 40\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Done debug_dump after birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z USER 13755 [WalrusDriver]: Running codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [WalrusDriver]: Inputs to codegen: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: Total compiler allocated DRAM tensors: 0.0509834 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: Total un-allocated DRAM tensors by kind:\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────────┬─────────────┐\u001b[0m\n", - "\u001b[34m│ TensorKind │ Size (GB) │\u001b[0m\n", - "\u001b[34m├────────────────┼─────────────┤\u001b[0m\n", - "\u001b[34m│ ExternalInput │ 0.000431065 │\u001b[0m\n", - "\u001b[34m│ ExternalOutput │ 6.10352e-05 │\u001b[0m\n", - "\u001b[34m│ Const │ 1.63908 │\u001b[0m\n", - "\u001b[34m└────────────────┴─────────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:13Z INFO 13755 [Codegen]: Total runtime managed DRAM tensors: 1.63958 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Instruction Stats:\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────────────┬────────┐\u001b[0m\n", - "\u001b[34m│ Opcode │ Count │\u001b[0m\n", - "\u001b[34m├────────────────────┼────────┤\u001b[0m\n", - "\u001b[34m│ MATMUL │ 171207 │\u001b[0m\n", - "\u001b[34m│ LDWEIGHTS │ 171207 │\u001b[0m\n", - "\u001b[34m│ ACTIVATE │ 47296 │\u001b[0m\n", - "\u001b[34m│ TENSOR_REDUCE │ 16960 │\u001b[0m\n", - "\u001b[34m│ EVENT_SEMAPHORE │ 10017 │\u001b[0m\n", - "\u001b[34m│ PSEUDO_DMA_TRIGGER │ 9734 │\u001b[0m\n", - "\u001b[34m│ TENSOR_TENSOR │ 6259 │\u001b[0m\n", - "\u001b[34m│ RECIPROCAL │ 1202 │\u001b[0m\n", - "\u001b[34m│ MEMSET │ 591 │\u001b[0m\n", - "\u001b[34m│ TENSOR_SCALAR │ 457 │\u001b[0m\n", - "\u001b[34m│ LOAD_MASK_SELECT │ 444 │\u001b[0m\n", - "\u001b[34m│ STREAM_SHUFFLE │ 444 │\u001b[0m\n", - "\u001b[34m│ DRAIN │ 416 │\u001b[0m\n", - "\u001b[34m│ ACT_TABLE_LOAD │ 208 │\u001b[0m\n", - "\u001b[34m│ NOP │ 4 │\u001b[0m\n", - "\u001b[34m│ CAST │ 2 │\u001b[0m\n", - "\u001b[34m│ IOTA │ 2 │\u001b[0m\n", - "\u001b[34m└────────────────────┴────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────┬────────┐\u001b[0m\n", - "\u001b[34m│ Engine │ Count │\u001b[0m\n", - "\u001b[34m├────────────┼────────┤\u001b[0m\n", - "\u001b[34m│ Pool │ 36 │\u001b[0m\n", - "\u001b[34m│ Activation │ 51073 │\u001b[0m\n", - "\u001b[34m│ PE │ 343861 │\u001b[0m\n", - "\u001b[34m│ DVE │ 27666 │\u001b[0m\n", - "\u001b[34m│ SP │ 13814 │\u001b[0m\n", - "\u001b[34m└────────────┴────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Total instructions: 436450 (0.0260144 GB)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Number of DMA descriptors on each queue:\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: \u001b[0m\n", - "\u001b[34m┌───────────────────┬─────────┐\u001b[0m\n", - "\u001b[34m│ Queue │ Count │\u001b[0m\n", - "\u001b[34m├───────────────────┼─────────┤\u001b[0m\n", - "\u001b[34m│ qActSpillReload0 │ 121088 │\u001b[0m\n", - "\u001b[34m│ qDVESpillReload0 │ 48838 │\u001b[0m\n", - "\u001b[34m│ qPoolPIO0 │ 32 │\u001b[0m\n", - "\u001b[34m│ qPoolSpillReload0 │ 5376 │\u001b[0m\n", - "\u001b[34m│ qSPPIO0 │ 177358 │\u001b[0m\n", - "\u001b[34m│ qSPSpillReload0 │ 2129909 │\u001b[0m\n", - "\u001b[34m└───────────────────┴─────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:14Z INFO 13755 [Codegen]: Total descriptors: 2482601 (0.0369936 GB)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [Codegen]: Estimated peak DRAM usage: 1.75357 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z USER 13755 [WalrusDriver]: codegen finished after 2.569 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: curr_vmrss: 11499mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Running debug_dump after codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 41\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Done debug_dump after codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:15Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z USER 13755 [WalrusDriver]: Running neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Inputs to neff_packager: modules=1 functions=1 allocs=88779 blocks=1 instructions=253763 Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z USER 13755 [WalrusDriver]: neff_packager finished after 0.044 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: curr_vmrss: 11273mb, ru_maxrss: 14340mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Running debug_dump after neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 42\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Done debug_dump after neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:16Z INFO 13755 [WalrusDriver]: Output has 1 module(s), 1 function(s), 88779 memory location(s), 1 block(s), and 253763 instruction(s). Max writers: 90 Max Readers: 35754\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.WalrusDriver.0]: Job #0 finished\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Finished job job.WalrusDriver.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Starting job job.BIRLinker.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.BIRLinker.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmpas37esby/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-sd2ev3s2/sg00\", \"state_id\": \"sg00\"}' --pipeline BIRLinker\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.BIRLinker.0]: BIRLinker cwd: /opt/ml/code/neuronxcc-sd2ev3s2\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.BIRLinker.0]: Linking not needed. Netlist doesnt exist\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Finished job job.BIRLinker.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [pipeline.Pipeline.0]: Starting job job.Kelper.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:20Z INFO 13755 [job.Kelper.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmpas37esby/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-sd2ev3s2/sg00\", \"state_id\": \"sg00\"}' --pipeline Kelper\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:27Z INFO 13755 [job.Kelper.0]: IR signature: cae5abfa2b9514e8ed25d48d054e7615 for neff artifacts\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [job.Kelper.0]: neuronxcc version is 2.9.0.40+07376825f, neff version is 1.0 (features 0)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:30Z USER 13755 [job.Kelper.0]: Wrote /tmp/tmpas37esby/graph.neff\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [pipeline.Pipeline.0]: Finished job job.Kelper.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [pipeline.Pipeline.0]: Finished pipeline Pipeline\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:30Z INFO 13755 [pipeline.Pipeline.0]: Job #0 finished\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:31Z USER 13755 [root]: Compiler status PASS\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:31Z INFO 13691 [root]: Subcommand returned with exitcode=0\u001b[0m\n", - "\u001b[34mDone. Elapsed time: 338407.5057506561ms\u001b[0m\n", - "\u001b[34mLoading pipeline components...: 0%| | 0/6 [00:00 ACT: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [PeepholeOpts]: COPY -> ACT: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [PeepholeOpts]: RECIPROCAL -> ACT: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: peephole_opts finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after peephole_opts\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 18\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after peephole_opts\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_kernel: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Started running LowerKernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Start of kernel lowering pass, number of insts: 26, number of allocs: 22\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Scan BKs time (s): 4e-06\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [LowerKernel]: Lower BKs time (s): 3e-06\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_kernel finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 19\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_kernel\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to build_fdeps: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Start build fdeps. Invocation: 2Sat Sep 30 05:14:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Allocs: 22 instructions: 26\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Build fdeps inserted 40 edges \u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Done build fdeps 40 Sat Sep 30 05:14:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: build_fdeps finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 20\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after build_fdeps\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to remove_redundancies: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove_clobbered_writes\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove_clobbered_writes: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove_useless_insts\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [RemoveRedundancies]: remove Useless Instructions: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: remove_redundancies finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 21\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after remove_redundancies\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 0 access patterns a mean/median -nan/0 intervals per access pattern and mean/median -nan/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-96-100]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-64-68]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-32-36]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-0-4]: Finished analyzing 33 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: anti_dependency_analyzer finished after 0.001 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 22\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to post_sched: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Start PosT ScheD 3 sunda Sat Sep 30 05:14:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Tensor CP elimination: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Time-aware hwm post-sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Time-aware simulation time: 6596\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [post_scheduler]: Done PosT ScheD Sat Sep 30 05:14:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: post_sched finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 23\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after post_sched\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to address_rotation_sb: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: PSUM Rotation rotated 0 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: PSUM Rotation rotated 0 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: PSUM Rotation rotated 0 PSUM Banks\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DMAOptimizationBase]: SB Rotation rotated 0 Sb address\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: address_rotation_sb finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 24\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after address_rotation_sb\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to anti_dependency_analyzer: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Batch size: 1000\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer]: Analysis types: {DRAM,ALIAS,PSUM,SB}\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-DRAM]: Finished conservative analyzing 0 access patterns a mean/median -nan/0 intervals per access pattern and mean/median -nan/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-96-100]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM0-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM1-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-64-68]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM7-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-32-36]: Finished analyzing 3 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM4-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM3-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM2-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-SB-0-4]: Finished analyzing 33 access patterns a mean/median 1/1 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM5-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AntiDependencyAnalyzer-PSUM6-0-32]: Finished analyzing 2 access patterns a mean/median 1/0 intervals per access pattern and mean/median 0/0 intersections per interval.\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: anti_dependency_analyzer finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 25\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after anti_dependency_analyzer\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to dep_opt: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Start build fdeps. Invocation: 3Sat Sep 30 05:14:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Allocs: 22 instructions: 26\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Build fdeps inserted 40 edges \u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [build_flow_deps]: Done build fdeps 40 Sat Sep 30 05:14:39 2023\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: dep_opt finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 26\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after dep_opt\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to report_stats: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [ReportStats]: Data Movement Statistics: sg0000\u001b[0m\n", - "\u001b[34m┌─────────────┬────────────────────────────┬───────┬───────┐\u001b[0m\n", - "\u001b[34m│ Instruction │ Kind │ Count │ Bytes │\u001b[0m\n", - "\u001b[34m├─────────────┼────────────────────────────┼───────┼───────┤\u001b[0m\n", - "\u001b[34m│ Load │ Const -> Internal │ 2 │ 48 │\u001b[0m\n", - "\u001b[34m│ Load │ ExternalInput -> Internal │ 4 │ 65536 │\u001b[0m\n", - "\u001b[34m│ Save │ Internal -> ExternalOutput │ 4 │ 65536 │\u001b[0m\n", - "\u001b[34m└─────────────┴────────────────────────────┴───────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [ReportStats]: \u001b[0m\n", - "\u001b[34m┌─────────────────────┬───────┐\u001b[0m\n", - "\u001b[34m│ Bytes per partition │ Count │\u001b[0m\n", - "\u001b[34m├─────────────────────┼───────┤\u001b[0m\n", - "\u001b[34m│ 4 │ 1 │\u001b[0m\n", - "\u001b[34m│ 8 │ 1 │\u001b[0m\n", - "\u001b[34m│ 4096 │ 8 │\u001b[0m\n", - "\u001b[34m└─────────────────────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [ReportStats]: MM Stats: #MatMults 8 #MatMult-Transposes 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: report_stats finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 27\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after report_stats\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to assign_trigger_engine: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AssignTriggerEngine]: Assigned trigger engine for 0 DMA instructions\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: assign_trigger_engine finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 28\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after assign_trigger_engine\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to alloc_queues: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: alloc_queues finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 29\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after alloc_queues\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to dep_reduction: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Start Dependency Reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing async instrs...\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing secondary edges per engine...\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing secondary edges per engine, Done. Num edges removed 18\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing redundant descendants...\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing redundant descendants, Done. Num edges removed 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Processing async instrs, Done. Num edges removed 18\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Num Async removed: 0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Finished dependency reduction: 22 removed, new total 18\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [DepReduction]: Finished Dependency Reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: dep_reduction finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 30\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after dep_reduction\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to bir_racecheck: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: bir_racecheck finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 31\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after bir_racecheck\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 26 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_dma: modules=1 functions=1 allocs=22 blocks=1 instructions=26 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_dma finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 32\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_dma\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 30 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to alloc_semaphores: modules=1 functions=1 allocs=22 blocks=1 instructions=30 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: alloc_semaphores finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 190mb, ru_maxrss: 190mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 33\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after alloc_semaphores\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 30 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to expand_inst_late: modules=1 functions=1 allocs=22 blocks=1 instructions=30 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: expand_inst_late finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 34\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after expand_inst_late\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 30 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_sync: modules=1 functions=1 allocs=22 blocks=1 instructions=30 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_sync finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 35\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_sync\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 32 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_act: modules=1 functions=1 allocs=22 blocks=1 instructions=32 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_act finished after 0.001 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 36\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_act\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_dve: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_dve finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 37\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_dve\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to lower_ap: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: lower_ap finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 38\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after lower_ap\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to alloc_regs: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AllocRegs]: allocating REG\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [AllocRegs]: main loop iteration 1\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: alloc_regs finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 39\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after alloc_regs\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to birverifier: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: birverifier finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 40\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after birverifier\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to codegen: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total compiler allocated DRAM tensors: 0 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total un-allocated DRAM tensors by kind: \u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────────┬─────────────┐\u001b[0m\n", - "\u001b[34m│ TensorKind │ Size (GB) │\u001b[0m\n", - "\u001b[34m├────────────────┼─────────────┤\u001b[0m\n", - "\u001b[34m│ ExternalInput │ 6.10352e-05 │\u001b[0m\n", - "\u001b[34m│ ExternalOutput │ 6.10352e-05 │\u001b[0m\n", - "\u001b[34m│ Const │ 4.47035e-08 │\u001b[0m\n", - "\u001b[34m└────────────────┴─────────────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total runtime managed DRAM tensors: 0.000122115 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Instruction Stats: \u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────────────┬───────┐\u001b[0m\n", - "\u001b[34m│ Opcode │ Count │\u001b[0m\n", - "\u001b[34m├────────────────────┼───────┤\u001b[0m\n", - "\u001b[34m│ PSEUDO_DMA_TRIGGER │ 10 │\u001b[0m\n", - "\u001b[34m│ LDWEIGHTS │ 8 │\u001b[0m\n", - "\u001b[34m│ MATMUL │ 8 │\u001b[0m\n", - "\u001b[34m│ ACTIVATE │ 8 │\u001b[0m\n", - "\u001b[34m│ NOP │ 4 │\u001b[0m\n", - "\u001b[34m│ EVENT_SEMAPHORE │ 2 │\u001b[0m\n", - "\u001b[34m│ DRAIN │ 2 │\u001b[0m\n", - "\u001b[34m│ ACT_TABLE_LOAD │ 1 │\u001b[0m\n", - "\u001b[34m└────────────────────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", - "\u001b[34m┌────────────┬───────┐\u001b[0m\n", - "\u001b[34m│ Engine │ Count │\u001b[0m\n", - "\u001b[34m├────────────┼───────┤\u001b[0m\n", - "\u001b[34m│ Pool │ 8 │\u001b[0m\n", - "\u001b[34m│ Activation │ 12 │\u001b[0m\n", - "\u001b[34m│ PE │ 17 │\u001b[0m\n", - "\u001b[34m│ DVE │ 0 │\u001b[0m\n", - "\u001b[34m│ SP │ 6 │\u001b[0m\n", - "\u001b[34m└────────────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total instructions: 43 (2.563e-06 GB)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Number of DMA descriptors on each queue:\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: \u001b[0m\n", - "\u001b[34m┌─────────────────┬───────┐\u001b[0m\n", - "\u001b[34m│ Queue │ Count │\u001b[0m\n", - "\u001b[34m├─────────────────┼───────┤\u001b[0m\n", - "\u001b[34m│ qPoolPIO0 │ 32 │\u001b[0m\n", - "\u001b[34m│ qSPPIO0 │ 32 │\u001b[0m\n", - "\u001b[34m│ qSPSpillReload0 │ 10 │\u001b[0m\n", - "\u001b[34m└─────────────────┴───────┘\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Total descriptors: 74 (1.10269e-06 GB)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [Codegen]: Estimated peak DRAM usage: 0.000125781 GB\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: codegen finished after 0.002 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 191mb, ru_maxrss: 191mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 41\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after codegen\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: Running neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Inputs to neff_packager: modules=1 functions=1 allocs=22 blocks=1 instructions=33 Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [WalrusDriver]: neff_packager finished after 0.000 seconds\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: curr_vmrss: 192mb, ru_maxrss: 192mb (delta=0mb)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump after neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Running debug_dump for Sub Graph with ID: , and Pass ID: 42\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Done debug_dump after neff_packager\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [WalrusDriver]: Output has 1 module(s), 1 function(s), 22 memory location(s), 1 block(s), and 33 instruction(s). Max writers: 4 Max Readers: 8\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.WalrusDriver.0]: Job #0 finished\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished job job.WalrusDriver.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Starting job job.BIRLinker.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.BIRLinker.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp3vi08kcp/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-zanzi8tu/sg00\", \"state_id\": \"sg00\"}' --pipeline BIRLinker\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.BIRLinker.0]: BIRLinker cwd: /opt/ml/code/neuronxcc-zanzi8tu\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.BIRLinker.0]: Linking not needed. Netlist doesnt exist\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished job job.BIRLinker.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Starting job job.Kelper.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.Kelper.0]: Replay this job by calling: /usr/local/bin/neuronx-cc compile --framework XLA --state '{\"model\": [\"/tmp/tmp3vi08kcp/model\"], \"tensormap\": \"tensor_map.json\", \"bir\": \"bir.json\", \"lorean_sg_key\": null, \"input_name_map\": null, \"output_name_map\": null, \"constant_tensors\": null, \"state_dir\": \"/opt/ml/code/neuronxcc-zanzi8tu/sg00\", \"state_id\": \"sg00\"}' --pipeline Kelper\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.Kelper.0]: IR signature: 86354cbd21e441ccd6a3e39a830230a7 for neff artifacts\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [job.Kelper.0]: neuronxcc version is 2.9.0.40+07376825f, neff version is 1.0 (features 0)\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [job.Kelper.0]: Wrote /tmp/tmp3vi08kcp/graph.neff\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished job job.Kelper.0\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Finished pipeline Pipeline\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13993 [pipeline.Pipeline.0]: Job #0 finished\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z USER 13993 [root]: Compiler status PASS\u001b[0m\n", - "\u001b[34m2023-09-30T05:14:39Z INFO 13929 [root]: Subcommand returned with exitcode=0\u001b[0m\n", - "\u001b[34mDone. Elapsed time: 987.6930713653564ms\u001b[0m\n", - "\u001b[34m2023-09-30 05:14:40,875 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code.\u001b[0m\n", - "\u001b[34m2023-09-30 05:14:40,875 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n", - "\u001b[34m2023-09-30 05:14:40,875 sagemaker-training-toolkit INFO Reporting training SUCCESS\u001b[0m\n", - "\n", - "2023-09-30 05:14:47 Uploading - Uploading generated training model\n", - "2023-09-30 05:16:14 Completed - Training job completed\n", - "Training seconds: 1141\n", - "Billable seconds: 1141\n" - ] - } - ], + "outputs": [], "source": [ "# it takes around 1141 seconds to complete the job on a trn1.32xlarge\n", "# You will run this just once to compile the model.\n", @@ -11049,21 +231,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "d523f809-ff03-4ca8-be29-a92bd4b25a7d", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Bucket': 'sagemaker-us-east-1-772327914095', 'Key': 'output/pytorch-training-neuronx-2023-09-30-04-54-35-074/output/model.tar.gz'}\n", - "s3://sagemaker-us-east-2-772327914095/stable-diffusion-neuron-inferentia/model.tar.gz\n" - ] - } - ], + "outputs": [], "source": [ "import boto3\n", "s3 = boto3.resource('s3', region_name=region_trn1)\n", @@ -11081,20 +254,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "4a202e4f-30cf-4c72-8fd4-f3a6bf408d19", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Instance type: ml.inf2.8xlarge. Num SM workers: 1\n" - ] - } - ], + "outputs": [], "source": [ "import logging\n", "from sagemaker.pytorch.model import PyTorchModel\n", @@ -11131,29 +296,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "733f2b8d-0d21-493d-8d35-b68e85eabc05", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Creating model with name: sd-inf2-2023-09-30-07-23-44-709\n", - "INFO:sagemaker:Creating endpoint-config with name sd-inf2-ml-inf2-2023-09-30-07-23-50-979\n", - "INFO:sagemaker:Creating endpoint with name sd-inf2-ml-inf2-2023-09-30-07-23-50-979\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------!" - ] - } - ], + "outputs": [], "source": [ "predictor = pytorch_model.deploy(\n", " initial_instance_count=1,\n", @@ -11173,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "c9166516-8b68-409d-b877-a291d9525f83", "metadata": { "tags": [] @@ -11188,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "769b7e0b-045d-442b-911a-5fd045d8e483", "metadata": { "tags": [] @@ -11204,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "99228241-0e98-4d97-bfda-b62733722905", "metadata": { "tags": [] @@ -11219,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "1872a60c-b1c2-44b0-ae34-11599ef56f89", "metadata": { "tags": [] @@ -11236,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "4dee2aff-3d12-4be8-8077-bd9feb161200", "metadata": { "tags": [] @@ -11252,25 +400,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "17953fca-c0b8-4d7f-a3c2-e6f3b6dead20", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Image.open(io.BytesIO(predictor.predict(input_req)))" ]