torch.compile crashes when sizes are unbacked SymInts. #243
Description
Describe the bug
Unbacked SymInts (i.e. when the size isnt known at trace time) cause SegmentedPolynomial to crash. This was just the first call I tried it on, I would expect the same issue is present elsewhere too
To Reproduce
Run the following script. Note that my code hits the same issue without setting torch._dynamo.config.capture_dynamic_output_shape_ops = True (so the failure is not limited to that case), but it was significantly easier to create a repro that did use it
"""Minimal repro: cuequivariance fails with unbacked SymInts from torch.compile.
Bug: segmented_polynomial_fused_tp.py:205 does `if size != 1:` with unbacked SymInt.
"""
import torch
from cuequivariance import Irreps
from cuequivariance.group_theory.descriptors import full_tensor_product
from cuequivariance_torch import SegmentedPolynomial
torch._dynamo.config.capture_dynamic_output_shape_ops = True
def create_cg_module(device):
lhs_irreps = Irreps("SO3", [(4, 0), (2, 1)])
rhs_irreps = Irreps("SO3", [(3, 0)])
equation = full_tensor_product(lhs_irreps, rhs_irreps, None)
return SegmentedPolynomial(equation.polynomial, method="fused_tp").to(device)
@torch.compile(fullgraph=True)
def test_with_cuequivariance(positions, radius, cg_module, call_cg):
# Create unbacked SymInt via nonzero
dist = torch.cdist(positions, positions)
mask = dist < radius
edge_indices = torch.nonzero(mask)
num_edges = edge_indices.shape[0] # unbacked SymInt 'u0'
# Create features with dynamic shape
lhs = torch.randn(num_edges, 10, device=positions.device)
rhs = torch.randn(num_edges, 3, device=positions.device)
if not call_cg:
return lhs.sum() + rhs.sum() # Works fine with unbacked SymInts
# FAILS HERE: cuequivariance can't handle unbacked SymInt
result = cg_module([lhs, rhs])
return result[0]
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cg_module = create_cg_module(device)
positions = torch.randn(1, 50, 3, device=device)
# Test 1: Without cuequivariance - works
print("Test 1 (no cuequivariance):", end=" ")
result = test_with_cuequivariance(positions, 0.5, cg_module, False)
print(f"✓ Pass")
# Test 2: With cuequivariance - fails
print("Test 2 (with cuequivariance):", end=" ")
result = test_with_cuequivariance(positions, 0.5, cg_module, True)
print(f"✗ Unexpected pass")
It crashes with error
ryan@ryan-dev-box:~/src/env$ TORCHDYNAMO_VERBOSE=1 python /home/ryan/src/env/cuequivariance_bug_final_repro.py 2>&1
/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial_fused_tp.py:89: UserWarning: `math_dtype` is not provided for method `fused_tp`: using float32.
warnings.warn(
Test 1 (no cuequivariance): ✓ Pass
Test 2 (with cuequivariance): Traceback (most recent call last):
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/tensor.py", line 1410, in evaluate_expr
return guard_scalar(self.sym_num)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 1519, in guard_scalar
return guard_bool(a)
^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 1711, in guard_bool
return a.node.guard_bool("", 0) # NB: uses Python backtrace
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 538, in guard_bool
r = self.evaluate()
^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 512, in evaluate
return self.shape_env.evaluate_sym_node(self, size_oblivious)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7223, in evaluate_sym_node
return self.evaluate_expr(
^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7323, in evaluate_expr
return self._inner_evaluate_expr(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/recording.py", line 272, in wrapper
return retlog(fn(*args, **kwargs))
^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7346, in _inner_evaluate_expr
return self._evaluate_expr(
^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7570, in _evaluate_expr
raise self._make_data_dependent_error(
torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Ne(u0, 1) (unhinted: Ne(u0, 1)). (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Caused by: if size != 1: # cuequivariance_torch/primitives/segmented_polynomial_fused_tp.py:205 in forward (_dynamo/variables/tensor.py:1410 in evaluate_expr)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
User Stack (most recent call last):
(snipped, see stack below for prefix)
File "/home/ryan/src/env/cuequivariance_bug_final_repro.py", line 37, in test_with_cuequivariance
result = cg_module([lhs, rhs])
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial.py", line 283, in forward
return self.m(inputs, input_indices, output_shapes, output_indices)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial_fused_tp.py", line 205, in forward
if size != 1:
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ryan/src/env/cuequivariance_bug_final_repro.py", line 53, in <module>
result = test_with_cuequivariance(positions, 0.5, cg_module, True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 736, in compile_wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1495, in __call__
return self._torchdynamo_orig_callable(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 629, in __call__
return _compile(
^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1111, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_utils_internal.py", line 97, in wrapper_function
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 793, in compile_inner
return _compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 832, in _compile_inner
out_code = transform_code_object(code, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/bytecode_transformation.py", line 1424, in transform_code_object
transformations(instructions, code_options)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 267, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 753, in transform
tracer.run()
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 3497, in run
super().run()
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1363, in run
while self.step():
^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1267, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 834, in wrapper
return inner_fn(self, inst)
^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2910, in CALL
self._call(inst)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2904, in _call
self.call_function(fn, args, kwargs)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1193, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/lazy.py", line 201, in realize_and_forward
return getattr(self.realize(), name)(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/nn_module.py", line 1000, in call_function
return variables.UserFunctionVariable(fn, source=source).call_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/functions.py", line 529, in call_function
return super().call_function(tx, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/functions.py", line 293, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1210, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 3698, in inline_call
return tracer.inline_call_()
^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 3901, in inline_call_
self.run()
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1363, in run
while self.step():
^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1267, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 834, in wrapper
return inner_fn(self, inst)
^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2910, in CALL
self._call(inst)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2904, in _call
self.call_function(fn, args, kwargs)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1193, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/lazy.py", line 201, in realize_and_forward
return getattr(self.realize(), name)(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/nn_module.py", line 1000, in call_function
return variables.UserFunctionVariable(fn, source=source).call_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/functions.py", line 529, in call_function
return super().call_function(tx, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/functions.py", line 293, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1210, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 3698, in inline_call
return tracer.inline_call_()
^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 3901, in inline_call_
self.run()
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1363, in run
while self.step():
^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1267, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 790, in inner
eval_result = value.evaluate_expr(self.output)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/torch/_dynamo/variables/tensor.py", line 1415, in evaluate_expr
raise UserError( # noqa: B904
torch._dynamo.exc.UserError: Consider annotating your code using torch._check*(). Could not guard on data-dependent expression Ne(u0, 1) (unhinted: Ne(u0, 1)). (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Caused by: if size != 1: # cuequivariance_torch/primitives/segmented_polynomial_fused_tp.py:205 in forward (_dynamo/variables/tensor.py:1410 in evaluate_expr)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
User Stack (most recent call last):
(snipped, see stack below for prefix)
File "/home/ryan/src/env/cuequivariance_bug_final_repro.py", line 37, in test_with_cuequivariance
result = cg_module([lhs, rhs])
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial.py", line 283, in forward
return self.m(inputs, input_indices, output_shapes, output_indices)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial_fused_tp.py", line 205, in forward
if size != 1:
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#constrain-as-size-example
from user code:
File "/home/ryan/src/env/cuequivariance_bug_final_repro.py", line 37, in test_with_cuequivariance
result = cg_module([lhs, rhs])
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial.py", line 283, in forward
return self.m(inputs, input_indices, output_shapes, output_indices)
File "/home/ryan/anaconda3/envs/env/lib/python3.12/site-packages/cuequivariance_torch/primitives/segmented_polynomial_fused_tp.py", line 205, in forward
if size != 1:
Expected behavior
A clear and concise description of what you expected to happen.
Screenshots
If applicable, add screenshots to help explain your problem.
GPU HW/SW(please complete the following information):
Here is the full details on my system, as previously reported for a pytorch bug.
python collect_env.py
--2025-11-21 18:09:41-- https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 30662 (30K) [text/plain]
Saving to: ‘collect_env.py.1’
collect_env.py.1 100%[===================>] 29.94K --.-KB/s in 0s
2025-11-21 18:09:41 (123 MB/s) - ‘collect_env.py.1’ saved [30662/30662]
Collecting environment information...
PyTorch version: 2.8.0+cu129
Is debug build: False
CUDA used to build PyTorch: 12.9
ROCM used to build PyTorch: N/A
OS: Ubuntu 25.10 (x86_64)
GCC version: (Ubuntu 15.2.0-4ubuntu4) 15.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.42
Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct 2 2023, 17:29:18) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.17.0-6-generic-x86_64-with-glibc2.42
Is CUDA available: True
CUDA runtime version: 12.9.86
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5060 Ti
Nvidia driver version: 580.95.05
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 12
On-line CPU(s) list: 0-11
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 5 7600X 6-Core Processor
CPU family: 25
Model: 97
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
Stepping: 2
Frequency boost: enabled
CPU(s) scaling MHz: 70%
CPU max MHz: 5457.1050
CPU min MHz: 427.3640
BogoMIPS: 9381.80
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpuid_fault cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d amd_lbr_pmc_freeze
Virtualization: AMD-V
L1d cache: 192 KiB (6 instances)
L1i cache: 192 KiB (6 instances)
L2 cache: 6 MiB (6 instances)
L3 cache: 32 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-11
Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Old microcode: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsa: Mitigation; Clear CPU buffers
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Mitigation; IBPB before exit to userspace
Versions of relevant libraries:
[pip3] cuequivariance-ops-torch-cu12==0.7.0
[pip3] cuequivariance-torch==0.7.0
[pip3] mypy==1.18.1
[pip3] mypy_extensions==1.1.0
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] torch==2.8.0+cu129
[pip3] torch_cluster==1.6.3+pt28cu129
[pip3] torch_geometric==2.5.2
[pip3] torch_scatter==2.1.2+pt28cu129
[pip3] torchaudio==2.8.0+cu129
[pip3] torchvision==0.23.0+cu129
[pip3] triton==3.4.0
[conda] cuequivariance-ops-torch-cu12 0.7.0 pypi_0 pypi
[conda] cuequivariance-torch 0.7.0 pypi_0 pypi
[conda] numpy 2.2.6 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.9.1.4 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.9.79 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.9.86 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.9.79 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.4.1.4 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.10.19 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.5.82 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.10.65 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.9.86 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.9.79 pypi_0 pypi
[conda] torch 2.8.0+cu129 pypi_0 pypi
[conda] torch-cluster 1.6.3+pt28cu129 pypi_0 pypi
[conda] torch-geometric 2.5.2 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt28cu129 pypi_0 pypi
[conda] torchaudio 2.8.0+cu129 pypi_0 pypi
[conda] torchvision 0.23.0+cu129 pypi_0 pypi
[conda] triton 3.4.0 pypi_0 pypi