- Rename `test_pytorch_common` -> `pytorch_test_common`, `test_onnx_common` -> `onnx_test_common`, removing the test_ prefix to show that the files are not test cases
- Remove import * in `test_pytorch_common` and adjust to import from `testing._internal.common_utils` (where functions are actually defined) instead
- Import modules only in `test_pytorch_onnx_onnxruntime` (too many to handle in a single PR in other tests) (The skips are exceptions)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81141
Approved by: https://github.com/BowenBao
Use pyupgrade(https://github.com/asottile/pyupgrade) and flynt to modernize python syntax
```sh
pyupgrade --py36-plus --keep-runtime-typing torch/onnx/**/*.py
pyupgrade --py36-plus --keep-runtime-typing test/onnx/**/*.py
flynt torch/onnx/ --line-length 120
```
- Use f-strings for string formatting
- Use the new `super()` syntax for class initialization
- Use dictionary / set comprehension
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77935
Approved by: https://github.com/BowenBao
Currently ONNX exporter symbolics can emit ATen operators when `operator_export_type==ONNX_ATEN_FALLBACK`. However, this is a behavior specific to Caffe2 builds, as the intend use of `ONNX_ATEN_FALLBACK` is to emit ATen operators only when there is no ONNX equivalent.
The reason Caffe2 choses to emit ATen operators when ONNX counterpart exists is for performance on their particular engine implementation, which might not be true for other implementations. e.g. ONNX Runtime can optimize the generated ONNX graph into something more efficient
This PR must be merged only after https://github.com/pytorch/pytorch/pull/73954
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74680
Approved by: https://github.com/garymm, https://github.com/malfet
This PR introduces 3 BC changes:
First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type.
Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set.
Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion.
ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
(cherry picked from commit 03ab65023a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69546
The arg is not used and was previously deprecated.
Also remove torch.onnx._export_to_pretty_string. It's redundant with the
public version.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D32994270
Pulled By: msaroufim
fbshipit-source-id: f8f3933b371a0d868d9247510bcd73c31a9d6fcc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
Follow up work:
1. disallow `default` as an overload name for aten operators.
2. Add a method to obtain a list of all overloads (exclude the ones registered by JIT)
3. Add methods/properties to `OpOverload` to access more schema information (types of input and output args etc)
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33469839
Pulled By: anjali411
fbshipit-source-id: c3fc43460f1c7c9651c64b4d46337be21c400621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67803
* Addresses comments from #63589
[ONNX] remove torch::onnx::PRODUCER_VERSION (#67107)
Use constants from version.h instead.
This simplifies things since we no longer have to update
PRODUCER_VERSION for each release.
Also add TORCH_VERSION to version.h so that a string is available for
this purpose.
[ONNX] Set `ir_version` based on opset_version. (#67128)
This increases the odds that the exported ONNX model will be usable.
Before this change, we were setting the IR version to a value which may
be higher than what the model consumer supports.
Also some minor clean-up in the test code:
* Fix string replacement.
* Use a temporary file so as to not leave files around in the test
current working directory.
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32181306
Pulled By: malfet
fbshipit-source-id: 02f136d34ef8f664ade0bc1985a584f0e8c2b663
Co-authored-by: BowenBao <bowbao@microsoft.com>
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64380
* `example_outputs` used to determine the type and shape of the outputs without tracing the execution of the model. And it must be provided when exporting a ScriptModule or ScriptFunction when using export() function.
* Since we can work out `example_outputs` in internal function instead of being provided by user, so we deprecated this argument in the export() function to increase user experience of calling this function.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905266
Pulled By: malfet
fbshipit-source-id: d00b00d7d02b365d165028288ad915678caa51f2
Co-authored-by: hwangdeyu <dejack953@outlook.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64370
As of now, the "_retain_param_name" parameter has no description in PyTorch docs website. According to code, this argument determines if we keep the original parameter names of PyTorch model in the final ONNX graph. If this is False, those original parameter names will be replaced with a series of integers starting from 1.
Since setting numbers as parameter names make no sense to users, we remove this argument from the torch.onnx.export() function to increase user experience of calling this function.
This PR will still keep it in torch.onnx.export() function for backward support while all backend logic has been changed to work as _retain_param_name is set to True.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905270
Pulled By: malfet
fbshipit-source-id: ca60757ca17daaff937e9f08da42596086795f4a
Co-authored-by: fatcat-z <zhang-ji@outlook.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58695
As PEP8 says: "Pick a rule and stick to it." [1]
[1] https://www.python.org/dev/peps/pep-0008/#string-quotes
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D28714811
Pulled By: SplitInfinity
fbshipit-source-id: c95103aceb1725c17c034dc6fc8216627f189548
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Summary:
Fixes https://github.com/pytorch/pytorch/issues/51652.
In particular:
- the main implementation is in `torch.linalg.det` now. `torch.det` is just a deprecated alias to it
- add a new `OpInfo` for `torch.linalg.det`
- remove the old-style tests for `torch.det` (this is similar to what we did for `torch.linalg.slogdet`, see https://github.com/pytorch/pytorch/issues/49194)
- added a `out=` argument to `torch.linalg.det`, but **not** to `torch.det`.
It is worth noting that I had to skip few tests:
- `TestGradientsCuda::test_fn_gradgrad_linalg_det_cuda_float64`. This is not a regression: the functionality is broken also on master, but the test is not executed properly due to https://github.com/pytorch/pytorch/issues/53361.
And the following tests which fails only on ROCm:
- `test_variant_consistency_jit_cuda_{float64,float32}`
- `test_fn_grad_cuda_float64`
I think that the ROCm tests fail because the current linalg.det backward is unstable if the matrix has repeated singular values, see https://github.com/pytorch/pytorch/issues/53364 .
(At the moment of writing some CI jobs are still running but I believe the build will be green, since the only difference wrt the last push is the skip of the ROCm tests)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53119
Reviewed By: H-Huang
Differential Revision: D27441999
Pulled By: mruberry
fbshipit-source-id: 5eab14c4f0a165e0cf9ec626c3f4bb23359f2a9e
Summary:
The args parameter of ONNX export is changed to better support optional arguments such that args is represented as:
args (tuple of arguments or torch.Tensor, a dictionary consisting of named arguments (optional)):
a dictionary to specify the input to the corresponding named parameter:
- KEY: str, named parameter
- VALUE: corresponding input
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47367
Reviewed By: H-Huang
Differential Revision: D25432691
Pulled By: bzinodev
fbshipit-source-id: 9d4cba73cbf7bef256351f181f9ac5434b77eee8
Summary:
BC-breaking NOTE:
In PyTorch 1.6 bool and integral fill values given to torch.full must set the dtype our out keyword arguments. In prior versions of PyTorch these fill values would return float tensors by default, but in PyTorch 1.7 they will return a bool or long tensor, respectively. The documentation for torch.full has been updated to reflect this.
PR NOTE:
This PR causes torch.full to throw a runtime error when it would have inferred a float dtype by being given a boolean or integer value. A versioned symbol for torch.full is added to preserve the behavior of already serialized Torchscript programs. Existing tests for this behavior being deprecated have been updated to reflect it now being unsupported, and a couple new tests have been added to validate the versioned symbol behavior. The documentation of torch.full has also been updated to reflect this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40364
Differential Revision: D22176640
Pulled By: mruberry
fbshipit-source-id: b20158ebbcb4f6bf269d05a688bcf4f6c853a965
Summary:
This PR adds a new operator export type to exporter: ONNX_FALLTHROUGH
This new type allows ops that are not supported to pass through.
This PR also removes all aten ops in ONNX operator export type mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37239
Reviewed By: hl475
Differential Revision: D21440509
Pulled By: houseroad
fbshipit-source-id: 38b826677cf3431ea44868efebefe1ff51c9aa75
Summary:
Add ONNX export support for torch.nn.CrossEntropyLoss.
This PR makes following changes:
1. Updates nll_loss export
2. Makes a post pass for SoftmaxCrossEntropy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34830
Reviewed By: hl475
Differential Revision: D21230712
Pulled By: houseroad
fbshipit-source-id: c81911a41968e23813ba10274340ce4d8ba1ed78
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
Summary:
While ONNX does not currently directly support the Dim operation on a
tensor, we can provide the same functionality with two ONNX operations.
This allows us to support Dim for all opsets. It may be adventageous to
add support for Dim into a future ONNX opset, and use that for more
efficient code.
While testing dim op found that there is an issue with empty blocks
withing if statements. Modified graph generation to prevent generation
of empty if blocks.
Fixes https://github.com/pytorch/pytorch/issues/27569
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31928
Reviewed By: hl475
Differential Revision: D19376602
Pulled By: houseroad
fbshipit-source-id: 111682b058a5341f5cca6c1a950c83ae412a4c6c
Summary:
Updated to export API:
When calling this API, a dict containing the custom opsets (domain and version) used to export the model could be provided.
We allow registering one custom opset (domain, version) per ONNX opset. So, when exporting an operator from a custom domain, users need to pass this pair. Default custom opset version is 1.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29752
Reviewed By: hl475
Differential Revision: D18703662
Pulled By: houseroad
fbshipit-source-id: 84d22557d132b526169051193d730761798fce60
Summary:
Support exporting left/right bitshifts to ONNX for all opset versions.
ONNX has a bitshift operator in opset 11, but it only supports unsigned ints, so it can't be used in PyTorch (since only uint8 is the only uint type).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28210
Reviewed By: hl475
Differential Revision: D18575512
Pulled By: houseroad
fbshipit-source-id: 74161db67f599996a0614981edcc171af6780d21
Summary:
Currently, `keep_initializers_as_input` argument in `torch.onnx.export` API can be used to choose whether to export an ONNX model with IR v3 or v4 semantics. Currently, the implementation does not check for which opset is being used for export. This is an issue because ONNX IR v4 is valid only for opset 9 and above (as listed [here](https://github.com/onnx/onnx/releases/tag/v1.4.0)), and opset 8 or lower export with `keep_initializers_as_input=False` will create a illegal ONNX graph.
This change fixes this by introducing a check on opset version when deciding whether to export ONNX IR v3 or v4.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28990
Reviewed By: hl475
Differential Revision: D18352523
Pulled By: houseroad
fbshipit-source-id: 7e9055d405c3faf52b80a8de0d04186d4c350c15
Summary:
Fix Slice/Select trace arguments. This PR stashes arguments to functions in order to avoid tracing them as constants.
This PR depends on a fix for select op in PR:
https://github.com/pytorch/pytorch/pull/25273
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26549
Reviewed By: hl475
Differential Revision: D17623851
Pulled By: houseroad
fbshipit-source-id: ae314004266688d2c25c5bada2dcedbfc4f39c5b
