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:
Enables the use of NoneType arguments to inputs tuple in the export API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45792
Reviewed By: heitorschueroff
Differential Revision: D24312784
Pulled By: bzinodev
fbshipit-source-id: 1717e856b56062add371af7dc09cdd9c7b5646da
Summary:
Moved description of tool and changes in function name
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44124
Reviewed By: albanD
Differential Revision: D23674618
Pulled By: bzinodev
fbshipit-source-id: 5db0bb14fc106fc96358b1e0590f08e975388c6d
Summary:
solves most of gh-38011 in the framework of solving gh-32703.
These should only be formatting fixes, I did not try to fix grammer and syntax.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41068
Differential Revision: D22411919
Pulled By: zou3519
fbshipit-source-id: 25780316b6da2cfb4028ea8a6f649bb18b746440
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:
xref gh-32838, gh-34032
This is a major refactor of parts of the documentation to split it up using sphinx's `autosummary` feature which will build out `autofuction` and `autoclass` stub files and link to them. The end result is that the top module pages like torch.nn.rst and torch.rst are now more like table-of-contents to the actual single-class or single-function documentations pages.
Along the way, I modified many of the docstrings to eliminate sphinx warnings when building. I think the only thing I changed from a non-documentation perspective is to add names to `__all__` when adding them to `globals()` in `torch.__init__.py`
I do not know the CI system: are the documentation build artifacts available after the build, so reviewers can preview before merging?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37419
Differential Revision: D21337640
Pulled By: ezyang
fbshipit-source-id: d4ad198780c3ae7a96a9f22651e00ff2d31a0c0f
Summary:
Currently constant folding is only enabled for ONNX opset versions 9 to 11. This PR enables it for the new ONNX opset 12.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34823
Reviewed By: hl475
Differential Revision: D20627629
Pulled By: houseroad
fbshipit-source-id: 7501d8ab8295751c0e9a02752d8908a35d8a0454
Summary:
There are large models such as GPT2-large which cannot be exported with the current exporter because of the 2GB protobuf limit (e.g. see https://github.com/pytorch/pytorch/issues/19277). ONNX spec specifies a special format for large (> 2GB) models. This PR adds support for exporting large models in ONNX large model format in the PyTorch-ONNX exporter.
This is the first PR for this feature that enables the end-to-end execution. Tests for large model export have been added. We may need follow-up PRs to refine this workflow based on user feedback.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33062
Reviewed By: hl475
Differential Revision: D19782292
Pulled By: houseroad
fbshipit-source-id: e972fcb066065cae6336aa91c03023d9c41c88bd
Summary:
Included the ONNX model checker code in the ONNX export
this will force onnx checker to run for all models that get exported.
This should help with validating exported models.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32298
Reviewed By: hl475
Differential Revision: D19538251
Pulled By: houseroad
fbshipit-source-id: eb20b124fe59200048f862ddaf20f6c59a0174d5
Summary:
torch.onnx.export docs contain two descriptions for 'example_outputs' arg.
So combined the information for it with the description with the parameters.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31826
Differential Revision: D19274928
Pulled By: zou3519
fbshipit-source-id: cbcce0a79c51784c1d7aa8981aab8aac118ca9b4
Summary:
Upgrade IR version from 4 to 6, below is change doc from ONNX. The upgrade should be backward compatible.
```
// IR VERSION 5 published on March 18, 2019
// - Add message TensorAnnotation.
// - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
IR_VERSION_2019_3_18 = 0x0000000000000005;
// IR VERSION 6 published on Sep 19, 2019
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31025
Reviewed By: hl475
Differential Revision: D18935444
Pulled By: houseroad
fbshipit-source-id: 9ba47f9657fa1a668db291cf04af07d5e8d73c21
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:
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27850
Many of these are real problems in the documentation (i.e., link or
bullet point doesn't display correctly).
Test Plan: - built and viewed the documentation for each change locally.
Differential Revision: D17908123
Pulled By: zou3519
fbshipit-source-id: 65c92a352c89b90fb6b508c388b0874233a3817a
Summary:
Bumping up the `producer_version` in exported ONNX models in view of the next release. Updating tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26976
Reviewed By: hl475
Differential Revision: D17631902
Pulled By: houseroad
fbshipit-source-id: 6d58964657402ac23963c49c07fcc813386aabf0
Summary:
This is a follow-up PR for https://github.com/pytorch/pytorch/pull/23284. In that PR we had removed changing the default behavior for `keep_initializers_as_input` argument to the export API. With this PR we are enabling that change in that if `keep_initializers_as_input` is not specified then value/behavior for this argument is chosen automatically depending on whether the export type is ONNX or not.
This was part of the earlier PR was removed for further review. The test points have also been updated.
This change may fail some internal tests which may require explicitly setting `keep_initializers_as_input=True` to preserve old behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26146
Reviewed By: hl475
Differential Revision: D17369677
Pulled By: houseroad
fbshipit-source-id: 2aec2cff50d215714ee8769505ef24d2b7865a11
Summary:
Starting ONNX IR version 4, the initializers in the ONNX graph do not have to be inputs of the graphs. This constraint, which existed in IR version 3 and earlier, was relaxed in IR version 4. This PR provides an API level argument to allow ONNX export with the relaxed constraint of IR version 4, i.e. provides the option to not include initializers as inputs. This allows backends/runtimes to do certain optimizations, such as constant folding, better.
*Edit*: After discussion with houseroad we have the following behavior. For any OperatorExportType, except OperatorExportTypes.ONNX, the current status of export is maintained in this PR by default. However, the user can override it by setting the `keep_initializers_as_inputs` argument to the export API. But when exporting to ONNX, i.e. OperatorExportType is OperatorExportTypes.ONNX, the current status is changed in that by default the initializers are NOT part of the input. Again, the default can be overridden by setting the `keep_initializers_as_inputs` argument.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23284
Differential Revision: D16459961
Pulled By: bddppq
fbshipit-source-id: b8f0270dfaba47cdb8e04bd4cc2d6294f1cb39cf
Summary:
Bumping up the producer_version in exported ONNX models in view of the next release. Updating tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23120
Reviewed By: zrphercule
Differential Revision: D16420917
Pulled By: houseroad
fbshipit-source-id: 6686b10523c102e924ecaf96fd3231240b4219a9
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:
This is probably slow but it should make the traces more understandable and make debugging easier. Any suggestions for how to make it faster (i.e. make it so we don't have to traverse all of locals() and globals()) would be appreciated
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13441
Differential Revision: D12879763
Pulled By: jamesr66a
fbshipit-source-id: b84133dc2ef9ca6cfbfaf2e3f9106784cc42951e
Summary:
there are multiple views of the tensor live.
Also adds recording for copy_ because this is the critical in place
op where these views will cause LHS indexing to fail.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11129
Differential Revision: D9600195
Pulled By: zdevito
fbshipit-source-id: bfd8f5befa47377e36d704dbdb11023c608fe9a3
Summary:
As in the title. Lets us simplify a lot of code.
Depends on #9363, so please review only the last commit.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9414
Reviewed By: zdevito
Differential Revision: D8836496
Pulled By: apaszke
fbshipit-source-id: 9b3c3d1f001a9dc522f8478abc005b6b86cfa3e3
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
* Switch JIT passes to take a graph rather than TracingState
* Add pybind11 binding for ONNX pass from graph
* Fix canonicalize pass
* address comment
* Switch ToONNX to explicitly return new graph
* optimize_graph instead of optimize_trace
Allows you to export an ONNX model as:
Protobuf file (this is what we have now)
Uncompressed zip archive
Compressed zip archive
Directory
* Experimental support for different ONNX export types
* Remove a copy
* Add comment
* Add test cases
* lint
* fix bug
* address comments
