Sparse CSR: Add torch.sin
#68123
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Sparse CSR: Add torch.sin
#68123
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krshrimali
commented
Nov 12, 2021
krshrimali
commented
Nov 12, 2021
test/test_sparse_csr.py
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| @ops(_sparse_csr_ops) | ||
| def test_sparse_csr_no_error(self, device, dtype, op): | ||
| samples = op.sample_inputs(device, dtype) | ||
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| if len(samples) == 0: | ||
| self.skipTest("Skipped! No sample inputs!") | ||
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| for sample in samples: | ||
| assert isinstance(sample.input, torch.Tensor) | ||
| if sample.input.ndim != 2: | ||
| continue | ||
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| expected = op(sample.input) | ||
| assert torch.is_tensor(expected) | ||
| output = op(sample.input.to_sparse_csr()) | ||
| assert torch.is_tensor(output) |
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The plan is to look at it again, and have a specialized test for sparse CSR tensors ((if everyone agrees)).
I plan to take a look at it in a separate PR next week.
krshrimali
commented
Nov 12, 2021
saketh-are
added
the
triaged
IvanYashchuk
requested changes
Nov 15, 2021
Co-authored-by: Ivan Yashchuk <IvanYashchuk@users.noreply.github.com>
krshrimali
commented
Nov 17, 2021
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| " for self, and ", | ||
| src.layout(), | ||
| " for src"); |
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Earlier, the message ended with something like:
but got self, src: kStridedkStrided
krshrimali
commented
Nov 17, 2021
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| if (result.numel() == 0) { | ||
| at::native::resize_as_sparse_csr_(result, self); | ||
| } |
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at::native::resize_output(result.col_indices(), self.col_indices().sizes());
// OR
result.col_indices().resize_(self.col_indices().sizes());don't resize the result.col_indices() tensor. Is this a bug, or is it expected? cc: @IvanYashchuk @cpuhrsch
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If we write
auto col_indices = result.col_indices();
col_indices.resize_(self.col_indices().sizes());
We would see that col_indices is resized, but result.col_indices() would have the original size. To replace result.col_indices() we would need to use set_member_tensors method (result.unsafeGetTensorImpl()->set_member_tensors(...)`).
It's expected, this issue is related #63549.
krshrimali
commented
Nov 17, 2021
IvanYashchuk
approved these changes
Nov 18, 2021
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Thank you @krshrimali! I've got only two minor comments. In a follow-up PR, we should expand the tests to out and inplace variants.
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@cpuhrsch has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
cpuhrsch
reviewed
Nov 18, 2021
| raise ValueError("Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.") | ||
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| expected = op(sample.input) | ||
| assert torch.is_tensor(expected) |
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nit: it might be better to use self.assertTrue to get a better error message (we can do that in a follow-up PR or together with other changes for more unary ops)
cpuhrsch
approved these changes
Nov 18, 2021
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This PR attempts to add support for
torch.sinfor sparse CSR tensors.This aims to be a revised implementation (in some form) of #68083, and the implementation aims to be similar to that in
SparseTensorMath.cppfileThe tests and
empty_likesupport for sparse CSR tensors (with a minor correction) are borrowed from #68083 temporarily to assist CI with testing this PR. :)cc @nikitaved @pearu @cpuhrsch @IvanYashchuk @krshrimali