Summary:
cosine_similarity has two non-tensor parameters, needs some special handling. Add the support for its export in this diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21884
Reviewed By: zrphercule
Differential Revision: D15866807
Pulled By: houseroad
fbshipit-source-id: a165fbc00c65c44b276df89ae705ca8960349d48
Summary:
When kwargs are specified in a test defined via common_method_invocations, it doesn't work if there isn't also a positional argument (`{'foo':'foo'}` without a positional arg generates a python call like: `self.method(, foo=foo)`, erroring on the `,`). I wanted to test something in a different PR and noticed I couldn't.
Also fixed some flake8 warnings I was seeing locally.
I replaced `lambda x: x` with `ident` since it seems a bit cleaner to me, but happy to revert that if others don't agree?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21499
Differential Revision: D15826974
Pulled By: nairbv
fbshipit-source-id: a3f37c80ba2303c7d9ae06241df06c7475b64e36
Summary:
When converting pixel_shuffle to reshape + transpose + reshape, the first reshape should
be:
[N, C * r^2, H, W] => [N, C, r, r, H, W]
in order to match pytorch's implementation (see ATen PixelShuffle.cpp).
This previously wasn't caught by the test case, since it uses C = r = 4. Updated test case to
have C = 2, r = 4.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21486
Reviewed By: houseroad
Differential Revision: D15700945
Pulled By: houseroad
fbshipit-source-id: 47019691fdc20e152e867c7f6fd57da104a12948
Summary:
- [x] Add tests after https://github.com/pytorch/pytorch/pull/20256 is merged
- Support exporting ScriptModule with inputs/outputs of arbitrarily constructed tuples.
- Moved the assigning of output shapes to after graph conversion to ONNX is completed. By then all tuples in the IR has already been lowered by the pass ```_jit_pass_lower_all_tuples```. If assigning output shapes is required to happen before that, we'll need to hand parse the tuple structures in the graph, and repeat the same logic in ```_jit_pass_lower_all_tuples```. Handling inputs is easier because all tuple information is encoded within the input tensor type.
- Swap the order of ```_jit_pass_lower_all_tuples``` and ```_jit_pass_erase_number_types```. Ops like ```prim::TupleIndex``` relies on index being a scalar. ```_jit_pass_erase_number_types``` will convert these kind of scalars to tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20784
Reviewed By: zrphercule
Differential Revision: D15484171
Pulled By: houseroad
fbshipit-source-id: 4767a84038244c929f5662758047af6cb92228d3
Summary:
This PR adds support for torch.rand export in the PyTorch ONNX exporter. There are other generator ops that need to be supported for export and they will added in subsequent PRs. This op is needed with priority for a model on our end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20559
Differential Revision: D15379653
Pulled By: houseroad
fbshipit-source-id: d590db04a4cbb256c966f4010a9361ab8eb3ade3
Summary:
~~This is work in progress due to its dependency on multiple pending PRs.~~
- [x] ONNX: Relax constraint on subgraph input/output type & shape check. https://github.com/onnx/onnx/pull/2009
- [x] PyTorch: Add infra to test_pytorch_onnx_caffe2.py to test ScriptModule models. https://github.com/pytorch/pytorch/pull/20256
This PR should partially resolve https://github.com/pytorch/pytorch/issues/17531. However, ideally we shouldn't need to put cast(and reshape) node to help the conversion for loop condition.
- Added cast node for condition values before entering loop node. The ONNX spec only accepts Bool type, while in PyTorch if the condition value is an output from other node it could potentially have any integral type.
- Tidying up the exported ONNX loop subgraph input type & shape. According to ONNX spec, input "M" is exported as 0-d scalar tensor with type int64. input "Cond" is exported as incomplete tensor of type Bool without shape information. This is because through out the iteration, the rank of condition value is dynamic, either 0-d or 1-d, as long as it holds a single value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20445
Differential Revision: D15534188
Pulled By: houseroad
fbshipit-source-id: d174e778529def05ee666afeee4b8fb27786e320
Summary:
Remove Dropout from the opset 10 blacklist.
ONNX Dropout was modified in opset 10, but only the output "mask" was modified, which is not exported in pytorch opset 9. So we can still fallback on the opset 9 op.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20710
Differential Revision: D15571248
Pulled By: houseroad
fbshipit-source-id: 15267eb63308a29a435261034b2f07324db1dea6
Summary:
In onnx spec, the supported input/output type for `And` and `Or` is `Bool` only.
Thus in exporting, cast to/from `Bool` is inserted for input/output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17894
Reviewed By: zrphercule
Differential Revision: D15103148
Pulled By: houseroad
fbshipit-source-id: 3e1068ea236c743260d42882fb11f0e3a21707e6
Summary:
This is a step towards enabling the ONNX constant folding pass by default in the PT->ONNX export. In this change we have enabled test points in `test/onnx/test_pytorch_onnx_caffe2.py` to run with constant folding pass enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20290
Reviewed By: zrphercule
Differential Revision: D15271674
Pulled By: houseroad
fbshipit-source-id: 9e59ab46ae74b4ad8dea1a2200ecc1f3eb8aad75
Summary: This can be used for problems where the action vector must sum to 1
Reviewed By: kittipatv
Differential Revision: D15206348
fbshipit-source-id: 665fbed893d8c52d451a12d3bb2e73b2638b7963
Summary:
As a work around for dynamic shape case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20093
Reviewed By: zrphercule
Differential Revision: D15220661
Pulled By: houseroad
fbshipit-source-id: de271fce542be380bd49a3c74032c61f9aed3b67
Summary:
If JIT constant propagation doesn't work, we have to handle the ListConstructor in symbolic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19102
Reviewed By: zrphercule
Differential Revision: D14875588
Pulled By: houseroad
fbshipit-source-id: d25c847d224d2d32db50aae1751100080e115022
Summary:
Almost there, feel free to review.
these c10 operators are exported to _caffe2 domain.
TODO:
- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210
Reviewed By: zrphercule
Differential Revision: D14600916
Pulled By: houseroad
fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
In discussion with houseroad, because Upsample op is being updated in ONNX https://github.com/onnx/onnx/pull/1773 and these tests are blocking it. These tests will be updated once the ONNX PR goes in.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17696
Differential Revision: D14338845
Pulled By: houseroad
fbshipit-source-id: cfaf8cf1ab578ae69dd3bf21b1c0681b572b9b6f
Summary:
Add support of count_include_pad end to end test for AveragePool
We can export AveragePool from PyTorch with count_include_pad attribute. However, we don't directly support it in Caffe2's ONNX backend.
We also want to check whether we can pass the end to end test for average pool operator with count_include_pad attribute (pytorch => onnx => caffe2)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17034
Reviewed By: houseroad
Differential Revision: D14060186
Pulled By: dwarakrajagopal
fbshipit-source-id: 10dae532611c71f8c8cfc3fa701cc7c1c1c02695
