pytorch

e6561291 - add hack to allow hybrid compressed sparse comparison in assertEqual (#88749)

add hack to allow hybrid compressed sparse comparison in assertEqual (#88749) Hybrid sparse CSR tensors can currently not be compared to strided ones since `.to_dense` does not work: ```py import torch from torch.testing._internal.common_utils import TestCase assertEqual = TestCase().assertEqual actual = torch.sparse_csr_tensor([0, 2, 4], [0, 1, 0, 1], [[1, 11], [2, 12] ,[3, 13] ,[4, 14]]) expected = torch.stack([actual[0].to_dense(), actual[1].to_dense()]) assertEqual(actual, expected) ``` ``` main.py:4: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:54.) actual = torch.sparse_csr_tensor([0, 2, 4], [0, 1, 0, 1], [[1, 11], [2, 12] ,[3, 13] ,[4, 14]]) Traceback (most recent call last): File "/home/philip/git/pytorch/torch/torch/testing/_comparison.py", line 1098, in assert_equal pair.compare() File "/home/philip/git/pytorch/torch/torch/testing/_comparison.py", line 619, in compare actual, expected = self._equalize_attributes(actual, expected) File "/home/philip/git/pytorch/torch/torch/testing/_comparison.py", line 706, in _equalize_attributes actual = actual.to_dense() if actual.layout != torch.strided else actual RuntimeError: sparse_compressed_to_dense: Hybrid tensors are not supported The above exception was the direct cause of the following exception: Traceback (most recent call last): File "main.py", line 10, in <module> assertEqual(actual, expected) File "/home/philip/git/pytorch/torch/torch/testing/_internal/common_utils.py", line 2503, in assertEqual msg=(lambda generated_msg: f"{generated_msg}\n{msg}") if isinstance(msg, str) and self.longMessage else msg, File "/home/philip/git/pytorch/torch/torch/testing/_comparison.py", line 1112, in assert_equal ) from error RuntimeError: Comparing TensorOrArrayPair( id=(), actual=tensor(crow_indices=tensor([0, 2, 4]), col_indices=tensor([0, 1, 0, 1]), values=tensor([[ 1, 11], [ 2, 12], [ 3, 13], [ 4, 14]]), size=(2, 2, 2), nnz=4, layout=torch.sparse_csr), expected=tensor([[[ 1, 11], [ 2, 12]], [[ 3, 13], [ 4, 14]]]), rtol=0.0, atol=0.0, equal_nan=True, check_device=False, check_dtype=True, check_layout=False, check_stride=False, check_is_coalesced=False, ) resulted in the unexpected exception above. If you are a user and see this message during normal operation please file an issue at https://github.com/pytorch/pytorch/issues. If you are a developer and working on the comparison functions, please except the previous error and raise an expressive `ErrorMeta` instead. ``` This adds a temporary hack to `TestCase.assertEqual` to enable this. Basically, we are going through the individual CSR subtensors, call `.to_dense()` on them, and stack everything back together. I opted to not do this in the common machinery, since that way users are not affected by this (undocumented) hack. I also added an xfailed test that will trigger as soon as the behavior is supported natively so we don't forget to remove the hack when it is no longer needed. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88749 Approved by: https://github.com/mruberry, https://github.com/pearu