Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
Fixes#91351
As for unit tests - in this PR I only fixed LSTM unit test to properly use dynamic axes and expose export issue by running test with same ONNX for additional inputs.
If the changes approved, we should also fix the rest of the tests (RNN/GRU and beyond).
I have verified the following updated tests are working with new code and failing with the old code:
test/onnx/test_pytorch_onnx_onnxruntime.py::TestONNXRuntime_opset_version_14_is_script_False_keep_initializers_as_inputs_True::test_rnn_name_lstm_nonlinearity_None_unilayer_bidirectional_no_initial_state_with_variable_length_sequences_with_dropout
test/onnx/test_pytorch_onnx_onnxruntime.py::TestONNXRuntime_opset_version_14_is_script_False_keep_initializers_as_inputs_True::test_rnn_name_lstm_nonlinearity_None_unilayer_bidirectional_with_initial_state_with_variable_length_sequences_with_dropout
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92970
Approved by: https://github.com/titaiwangms, https://github.com/kit1980
__What?__
Per discussion at #94634, deprecate `masked_fill` with non-bool masks. Deprecation warnings were previously added by #22261, but not for Apple MPS. I can revert the MPS changes if deprecation warnings are wanted first tho. See also #96112.
Fixes#85063 and #89320.
__Further Development?__
- Fixed the mask dtype checking for the cuda dispatch for `masked_fill` in `aten/src/ATen/native/cuda/Indexing.cu`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96594
Approved by: https://github.com/malfet, https://github.com/ngimel
This PR addresses issue [#81075](https://github.com/pytorch/pytorch/issues/81075), making `torch.stft` compatible with ONNX Opset 17's STFT operator.
The conversion works for _most_ of `torch.stft` functionality:
- Batched or unbatched inputs
- Normalization
- Pre-computed windows
- Rectangular windows
- One-sided returns
- Window centering (implicitly supported)
What is currently _not_ supported is **complex types**, due to the lack of conversion functionality between PyTorch and ONNX (https://github.com/pytorch/pytorch/issues/86746).
Regardless, this is easy to bypass by setting `return_complex=False` when using `torch.stft`.
Note that there is already a draft PR to address this (https://github.com/pytorch/pytorch/pull/83944), but it is currently closed and it only partially addresses the conversion (i.e., most of `torch.stft` functionality is lacking, and unit tests are missing).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92087
Approved by: https://github.com/justinchuby
This commit fixes a bug where the ONNX exporter for circular padding queried the input tensor shape in order to get the correct 'end' index for a slice node. This doesn't work when the axis in question is has dynamic size. The commit fixes this by setting the 'end' index to INT_MAX, which is the recommended way of slicing to the end of a dimension with unknown size per ONNX spec.
See https://onnx.ai/onnx/operators/onnx__Slice.html
Also adds a regression test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95647
Approved by: https://github.com/BowenBao
All this time, PyTorch and ONNX has different strategy for None in output. And in internal test, we flatten the torch outputs to see if the rest of them matched. However, this doesn't work anymore in scripting after Optional node is introduced, since some of None would be kept.
#83184 forces script module to keep all Nones from Pytorch, but in ONNX, the model only keeps the ones generated with Optional node, and deletes those meaningless None.
This PR uses Optional node to keep those meaningless None in output as well, so when it comes to script module result comparison, Pytorch and ONNX should have the same amount of Nones.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84789
Approved by: https://github.com/BowenBao
Optimize unnecessary collection cast calls, unnecessary calls to list, tuple, and dict, and simplify calls to the sorted builtin. This should strictly improve speed and improve readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94323
Approved by: https://github.com/albanD
From PR: https://github.com/pytorch/pytorch/pull/58691, Replacing the second input of `Gather` 0 to 1 affects other innocent Nodes. In Issue #91526 onnx::range starts from 0, the 0 is changed by this mechanism, as it's shared with onnx::Gather. This PR intends to create a whole independent Constant 0 for replacement. NOTE: The PR passes all existing RNN tests locally in case CI doesn't include RNN test.
~~TODO: test~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93120
Approved by: https://github.com/BowenBao
#84624 introduces an update on `torch.norm` [dispatch logic](eaa43d9f25/torch/functional.py (L1489)) which now depends on `layout`. Resulting in regressions to export related operators from TorchScript.
This PR resolves the regression by partially supporting a subset use case of `prim::layout` (only `torch.strided`), `aten::__contains__` (only constants) operators. It requires much more effort to properly support other layouts, e.g. `torch.sparse_coo`. Extending JIT types, and supporting related family of ops like `aten::to_sparse`. This is out of the scope of this PR.
Fixes#83661
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91660
Approved by: https://github.com/justinchuby, https://github.com/kit1980
Fixes https://github.com/pytorch/pytorch/issues/84365 and more
This PR addresses not only the issue above, but the entire family of issues related to `torch._C.Value.type()` parsing when `scalarType()` or `dtype()` is not available.
This issue exists before `JitScalarType` was introduced, but the new implementation refactored the bug in because the new api `from_name` and `from_dtype` requires parsing `torch._C.Value.type()` to get proper inputs, which is exactly the root cause for this family of bugs.
Therefore `from_name` and `from_dtype` must be called when the implementor knows the `name` and `dtype` without parsing a `torch._C.Value`. To handle the corner cases hidden within `torch._C.Value`, a new `from_value` API was introduced and it should be used in favor of the former ones for most cases. The new API is safer and doesn't require type parsing from user, triggering JIT asserts in the core of pytorch.
Although CI is passing for all tests, please review carefully all symbolics/helpers refactoring to make sure the meaning/intetion of the old call are not changed in the new call
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87245
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
According to #38248, quantized::conv1d_relu shares packing parameters with Conv2D (kspatialDim is also 2), and needs a different unpacking way. Therefore, a new `QuantizedParamsType=Conv1D` is used to differentiate the two, and has to extract 1D information from 2D packed parameters.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85997
Approved by: https://github.com/BowenBao
Enable runtime type checking for all torch.onnx public apis, symbolic functions and most helpers (minus two that does not have a checkable type: `_.JitType` does not exist) by adding the beartype decorator. Fix type annotations to makes unit tests green.
Profile:
export `torchvision.models.alexnet(pretrained=True)`
```
with runtime type checking: 21.314 / 10 passes
without runtime type checking: 20.797 / 10 passes
+ 2.48%
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84091
Approved by: https://github.com/BowenBao, https://github.com/thiagocrepaldi
`torch.norm` is very odd. Some notable issues are:
- The default value of `"fro"` in `torch.norm` has an odd behaviour when `dim=None`. This is handled in the new dispatch
- The treatment of the `dtype` argument in `torch.norm` was completely wrong. This should fix it
- Some `out=` variants in the previous implementation were also wrong. This should fix those.
- This new dispatch should make some paths much faster. For example, `torch.norm(x)` where `x` is complex.
I'll try to make the changes in these PRs as incremental as possible as this is a tricky one.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81761
Approved by: https://github.com/ngimel
fix#78119
Why:
As in onnx tests verification code, we used to only consider tracing output, which ignores None type, this PR enables runtime test to keep None type in torch in script mode.
1. Move Optional Type tests from no runtime to runtime, as it's supported by ONNXRUNTIME.
2. Add ignoreNone flag for output comparison of internal tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83184
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
