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Add unit tests and one line reproducers to detect bad pytorch cuda wheels #1663

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Jan 5, 2024
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Add unit tests and one line reproducers to detect bad pytorch cuda wheels #1663

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ffaf2f1
Add one line reproducers and unit tests that would fail when bad wheels
nWEIdia Jan 3, 2024
0bdc4fe
cosmetic fixes
nWEIdia Jan 3, 2024
3ecddcf
fix comments
nWEIdia Jan 3, 2024
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16 changes: 13 additions & 3 deletions .github/scripts/validate_test_ops.sh
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Expand Up @@ -20,6 +20,16 @@ pushd pytorch

pip install expecttest numpy pyyaml jinja2 packaging hypothesis unittest-xml-reporting scipy

# Run test_ops validation
export CUDA_LAUNCH_BLOCKING=1
python3 test/test_ops.py TestCommonCUDA
# Run pytorch cuda wheels validation
# Detect ReduceLogicKernel (ReduceOp and kernel) IMA
python test/test_ops.py -k test_dtypes_all_cuda
# Detect BinaryMulKernel (elementwise binary functor internal mul) IMA
python test/test_torch.py -k test_index_reduce_reduce_prod_cuda_int32
# Detect BinaryBitwiseOpsKernels (at::native::BitwiseAndFunctor) IMA
python test/test_binary_ufuncs.py -k test_contig_vs_every_other___rand___cuda_int32
# Detect MaxMinElementwiseKernel (maximum) IMA
python test/test_schema_check.py -k test_schema_correctness_clamp_cuda_int8
# Detect StepKernel (nextafter) IMA
python -c "import torch; print(torch.nextafter(torch.tensor([-4.5149, -5.9053, -0.9516, -2.3615, 1.5591], device='cuda:0'), torch.tensor(3.8075, device='cuda:0')))"
# Detect BinaryGeometricKernels (atan2) IMA
python -c "import torch; x = (torch.randn((2,1,1), dtype=torch.float, device="cuda")*5).to(torch.float32); y=(torch.randn((), dtype=torch.float, device="cuda")*5).to(torch.float32); print(torch.atan2(x,y))"