🐛 Bug
Various inputs for test_inverse_cpu_* (in test_linalg.py) are resulting in seg faults. The issue is seen for all the datatypes tested. I'm only seeing the seg faults when using torch.linalg.inv (i.e. torch.inverse is fine).
I'm also seeing seg faults in the following tests in test_ops.py:
test_out_linalg_inv_cpu_*test_variant_consistency_eager_linalg_inv_cpu_*test_variant_consistency_jit_linalg_inv_cpu_*test_fn_grad_linalg_inv_cpu_*test_fn_gradgrad_linalg_inv_cpu_*test_supported_dtypes_linalg_inv_cpu_*
I've not fully tested, but the linalg_inv makes me suspect that these are been caused by the same issue.
To Reproduce
Steps to reproduce the behavior:
import torch
n = 0
batches = []
a = random_fullrank_matrix_distinct_singular_value(n, *batches, dtype=torch.float32).to('cpu')
torch.linalg.inv(a)
random_fullrank_matrix_distinct_singular_value being from
|
def random_fullrank_matrix_distinct_singular_value(matrix_size, *batch_dims, |
Fails with n = 0 when batches is one of [], [1], [4], [2, 3].
Expected behavior
No seg fault.
Environment
PyTorch 1.8.0 release - built from source.
Collecting environment information...
PyTorch version: 1.8.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: CentOS Linux release 8.2.2004 (Core) (x86_64)
GCC version: (GCC) 10.2.0
Clang version: Could not collect
CMake version: Could not collect
Python version: 3.8 (64-bit runtime)
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] numpy==1.19.4
[pip3] torch==1.8.0
[conda] Could not collect
Build info
PYTORCH_BUILD_VERSION=1.8.0 PYTORCH_BUILD_NUMBER=1 MAX_JOBS=40 BLAS=Eigen USE_FFMPEG=1 BUILD_CUSTOM_PROTOBUF=0 USE_IBVERBS=1 USE_CUDA=0 USE_METAL=0 /rds/bear-apps/devel/eb-sjb-up/EL8/EL8-cas/software/Python/3.8.6-GCCcore-10.2.0/bin/python setup.py build
- GCC: 10.2.0
- OpenBLAS: 0.3.12
- FFTW: 3.3.8
- CMake: 3.18.4
- Extra C/CXX flags:
-O2 -ftree-vectorize -march=native -fno-math-errno
cc @ezyang @gchanan @zou3519 @bdhirsh @jbschlosser @anjali411 @jianyuh @nikitaved @pearu @mruberry @heitorschueroff @walterddr @IvanYashchuk @VitalyFedyunin
🐛 Bug
Various inputs for
test_inverse_cpu_*(intest_linalg.py) are resulting in seg faults. The issue is seen for all the datatypes tested. I'm only seeing the seg faults when usingtorch.linalg.inv(i.e.torch.inverseis fine).I'm also seeing seg faults in the following tests in
test_ops.py:test_out_linalg_inv_cpu_*test_variant_consistency_eager_linalg_inv_cpu_*test_variant_consistency_jit_linalg_inv_cpu_*test_fn_grad_linalg_inv_cpu_*test_fn_gradgrad_linalg_inv_cpu_*test_supported_dtypes_linalg_inv_cpu_*I've not fully tested, but the
linalg_invmakes me suspect that these are been caused by the same issue.To Reproduce
Steps to reproduce the behavior:
random_fullrank_matrix_distinct_singular_valuebeing frompytorch/torch/testing/_internal/common_utils.py
Line 1676 in 8c798e0
Fails with
n = 0whenbatchesis one of[],[1],[4],[2, 3].Expected behavior
No seg fault.
Environment
PyTorch 1.8.0 release - built from source.
Build info
-O2 -ftree-vectorize -march=native -fno-math-errnocc @ezyang @gchanan @zou3519 @bdhirsh @jbschlosser @anjali411 @jianyuh @nikitaved @pearu @mruberry @heitorschueroff @walterddr @IvanYashchuk @VitalyFedyunin