Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69547
ScriptModule export introduces duplicated ONNX initializers for shared weights, unnecessarily increases ONNX model size. This PR de-duplicates ONNX initializers for model exported in eval mode, by checking if the underlying tensors share the same `data_ptr`, `strides` and `sizes`.
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32994271
Pulled By: malfet
fbshipit-source-id: 10ac66638b6255890875272472aa9ed07a5b1d9a
Co-authored-by: BowenBao <bowbao@microsoft.com>
(cherry picked from commit d7cbde940c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68491
* Allows implementing symbolic functions for domains other than `aten`, for example `prim`, in symbolic_opset#.py.
* Allows symbolic function to access extra context if needed, through `SymbolicFunctionState`.
* Particularly, the `prim::PythonOp` special case can access node without the need of passing node through inputs. Updates will be made downstreams, and in a follow-up PR we will remove the previous workaround in exporter.
* `prim::Loop`, `prim::If`, etc are now moved outside of `_run_symbolic_function` from utils.py, and to symbolic_opset9.py.
Motivation for this change:
- Better maintainability and reducing complexity. Easier to add symbolic for operators, both simple and complex ones (that need additional context), without the former needing to know the existence of the latter.
- The design idea was long outdated. prim ops are no longer rare special cases, and they shouldn't all be handled inside `_run_symbolic_function`. As a result this function becomes too clumsy. There were also prim ops symbolic added in symbolic_opset#.py with signature `prim_[opname]`, creating separation and confusion.
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D32483782
Pulled By: malfet
fbshipit-source-id: f9affc31b1570af30ffa6668da9375da111fd54a
Co-authored-by: BowenBao <bowbao@microsoft.com>
(cherry picked from commit 1e04ffd2fd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68490
The use of ATEN as a fallback operator during ONNX conversion is important for increasing operator coverage or even provide more efficient implementations over some ONNX ops.
Currently this feature is available through `OperatorExportTypes.ONNX_ATEN_FALLBACK`,
but it also performs changes to the graph that are runnable by Caffe2, only.
This PR introduces restricts caffe2-specific graph transformations for `ONNX_ATEN_FALLBACK`
operator export type for when pytorch is built with caffe2 support (aka BUILD_CAFFE2=1 during build)
The first version of this PR introduced a new operator export type `ONNX_ATEN__STRICT_FALLBACK`,
which essentially is the same as `ONNX_ATEN_FALLBACK` but without caffe2 transformations.
It was preferred to not introduce a new operator export type, but to refine the existing aten fallback one
## BC-breaking note
### The global constant `torch.onnx.PYTORCH_ONNX_CAFFE2_BUNDLE` is removed in favor of
a less visible `torch.onnx._CAFFE2_ATEN_FALLBACK`.
`PYTORCH_ONNX_CAFFE2_BUNDLE` is really a dead code flag always set to False.
One alternative would be fixing it, but #66658 disables Caffe2 build by default.
Making a Caffe2 feature a private one seems to make more sense for future deprecation.
### The method `torch.onnx.export` now defaults to ONNX when `operator_export_type` is not specified.
Previously `torch.onnx.export's operator_export_type` intended to default to `ONNX_ATEN_FALLBACK` when `PYTORCH_ONNX_CAFFE2_BUNDLE` was set, but it would never happen as `PYTORCH_ONNX_CAFFE2_BUNDLE` is always undefined
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D32483781
Pulled By: malfet
fbshipit-source-id: e9b447db9466b369e77d747188685495aec3f124
(cherry picked from commit 5fb1eb1b19)
Cover more cases of scope inferencing where consecutive nodes don't have valid scope information. Usually these nodes are created in some pass where authors forgot to assign meaningful scope to them.
* One rule of `InferScope` is to check if the current node's outputs' users share the same scope. Recursively run `InferScope` on the user nodes if they are missing scope as well. Since the graph is SSA, the depth is finite.
* Fix one pass that missed scope information for a new node.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71897
Export should fail if export_modules_as_functions is set and opset_version<15.
This is because opeset_version < 15 implies IR version < 8, which means no local function support.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71619
Summary:
For some ONNX exported models, the inputs/outputs names have sometimes a numeric value and this makes pretty hard to inspect the generated graphs in the case of large models.
The solution in this PR was initially submitted to our internal utilities library by take-cheeze https://github.com/pfnet/pytorch-pfn-extras/pull/102
Now we would like to upstream this change by adding an extra kwarg when exporting the model to allow replacing these numeric names with actual debuggable ones.
As an example, the following code shows that the module output is `3`
```python
g, p, o = _model_to_graph(module, torch.ones(1, 10))
for n in g.nodes():
for v in n.outputs():
print(v.debugName())
```
output
```
3
```
With this PR
```
v3_Gemm
```
This allows identifying this out as a value from the associated Gemm layer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68976
Reviewed By: jansel
Differential Revision: D33662246
Pulled By: msaroufim
fbshipit-source-id: 45f56eef2a84d9a318db20c6a6de6c2743b9cd99
(cherry picked from commit 513c1d28f1)
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/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/66143
Delete test_list_remove. There's no point in testing conversion of
this model since TorchScript doesn't support it.
Add a link to an issue tracking test_embedding_bag_dynamic_input.
[ONNX] fix docs (#65379)
Mainly fix the sphinx build by inserting empty before
bulleted lists.
Also some minor improvements:
Remove superfluous descriptions of deprecated and ignored args.
The user doesn't need to know anything other than that they are
deprecated and ignored.
Fix custom_opsets description.
Make indentation of Raises section consistent with Args section.
[ONNX] publicize func for discovering unconvertible ops (#65285)
* [ONNX] Provide public function to discover all unconvertible ATen ops
This can be more productive than finding and fixing a single issue at a
time.
* [ONNX] Reorganize test_utility_funs
Move common functionality into a base class that doesn't define any
tests.
Add a new test for opset-independent tests. This lets us avoid running
the tests repeatedly for each opset.
Use simple inheritance rather than the `type()` built-in. It's more
readable.
* [ONNX] Use TestCase assertions rather than `assert`
This provides better error messages.
* [ONNX] Use double quotes consistently.
[ONNX] Fix code block formatting in doc (#65421)
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D31424093
fbshipit-source-id: 4ced841cc546db8548dede60b54b07df9bb4e36e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66140
* Add new argument to export api to enable users specifying `nn.Module` classes that they wish to be exported as local function in ONNX model.
* Refactor `torch/csrc/jit/serialization/export.cpp`, and remove redundant `EncoderBase` class.
* ~~Contains changes from #63268~~
* Depends on #63716 to update onnx submodule.
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D31424098
fbshipit-source-id: c949d0b01c206c30b4182c2dd1a5b90e32b7a0d3
Co-authored-by: BowenBao <bowbao@microsoft.com>
Summary:
Apex O2 hook state_dict to return fp16 weights as fp32. Exporter cannot identify them as same tensors.
Since this hook is only used by optimizer, it is safe to remove this hook while exporting.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66700
Reviewed By: zou3519
Differential Revision: D31695132
Pulled By: malfet
fbshipit-source-id: 977bdf57240002498f3ad0f1a8046c352e9860e6
Summary:
This moves it to where the user would expect it to be based on the
documentation and all the other public classes in the torch.onnx module.
Also rename it from ONNXCheckerError, since the qualified name
torch.onnx.ONNXCheckerError is otherwise redundant.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66644
Reviewed By: malfet
Differential Revision: D31662559
Pulled By: msaroufim
fbshipit-source-id: bc8a57b99c2980490ede3974279d1124228a7406
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64382
* This `use_external_data_format` parameter is used for large models cannot be exported because of the 2GB protobuf limit.
* When `use_external_data_format` set to True, the model is exported in ONNX external data format, in which case some of the model parameters are stored in external binary files and not in the ONNX model file itself.
* This PR will set this paramter to DEPRECATED and check the model proto sizes by code instead of by user, if the sizes lager than 2GB, then `use_external_data_format = True` automatically.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905265
Pulled By: malfet
fbshipit-source-id: 82b4e17bfa6a8de2bfd700a5282c12f6835603cb
Co-authored-by: hwangdeyu <dejack953@outlook.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/64373
* Fix some bad formatting and clarify things in onnx.rst.
* In `export_to_pretty_string`:
* Add documentation for previously undocumented args.
* Document that `f` arg is ignored and mark it deprecated.
* Update tests to stop setting `f`.
* Warn if `_retain_param_name` is set.
* Use double quotes for string literals in test_operators.py.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905271
Pulled By: malfet
fbshipit-source-id: 3627eeabf40b9516c4a83cfab424ce537b36e4b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64371
As of now, the "strip_doc_string" parameter was described as below:
strip_doc_string (bool, default True): do not include the field
doc_string``` from the exported model. Otherwise the field will mention the source code locations for model``.
This is usually useless to users who want to transform a PyTorch model to ONNX one. Only when the user wants to debug the export process, these source code locations could provide benefits.
To make the export() function more friendly by providing less parameters, we combined "strip_doc_string" into "verbose" parameter. If a user set verbose to True, it means the users need some log information for debugging the export process and this is similar with the purpose of strip_doc_string parameter.
But the usage of these 2 arguments are opposite: setting verbose to True means we want to print log information to help debug, which means strip_doc_string should be False. And this is how we replace strip_doc_string with verbose argument in this PR.
This PR will still keep it in torch.onnx.export() function for backward support while the usage of it has been combined with verbose argument.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905268
Pulled By: malfet
fbshipit-source-id: 2f06eb805c01fe15ff7a1b4f6595c937ba716d60
Co-authored-by: fatcat-z <zhang-ji@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/64369
As of now, the "enable_onnx_checker" parameter was described as below:
enable_onnx_checker (bool, default True): If True the ONNX model checker will be run to ensure the exported model is a valid ONNX model.
An invalid ONNX graph is useless to users so such checker should be done for each call.
In this PR, we will still write the model to an ONNX file even it is invalid. And the exception will be thrown after the ONNX file has been created. This enables user output an invalid ONNX graph for debug.
This PR will still keep it in torch.onnx.export() function for backward support while all backend logic has been changed to work as enable_onnx_checker is set to True.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905267
Pulled By: malfet
fbshipit-source-id: 3ad3f68e77fcec012cc7ef674cc9a61755eebc9e
Co-authored-by: fatcat-z <zhang-ji@outlook.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62763
This PR is to fix the issue that the graph inputs might be updated when we export the model in inference mode.
When a model is export in inference mode, some optimizations will be made. One side effect of these optimizations is: the inputs of graph might be adjusted. Such optimizatiosn include:
1. Conv and BatchNorm op fusion.
2. Do constant folding.
If the user sets export_params=False, or set keep_initializers_as_inputs=True, it's highly possible that the user wants to provide the corresponding parameters or initiliazers as the inputs of the graph.
In such situation, no matter the model is export in inference mode or training mode, exporter needs to prevent above optimizations from adjusting the graph inputs. By this, the inputs of graph could match inputs that users provided.
The changes in this PR, add an additional common judgement to see if the above optimizations needs to be done or not. From the value of export_params and keep_initializers_as_inputs arguments, infer if the graph inputs are allowed to be adjusted.
If no, these optimizations will be ignored, even other requirements are matched.
Besides these code changes, the comments of some parameters below have been updated so that users have more thoughts when they consider how to leverage these parameters for different purposes:
1. export_params
2. training
3. do_constant_folding
4. keep_initializers_as_inputs
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D30375183
Pulled By: msaroufim
fbshipit-source-id: 4db8b9695649eb32a3a0fefa950ee2e5651bdba0
Co-authored-by: fatcat-z <jiz@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60249
* Add introductory paragraph explaining what ONNX is and what the
torch.onnx module does.
* In "Tracing vs Scripting" and doc-string for torch.onnx.export(),
clarify that exporting always happens on ScriptModules and that
tracing and scripting are the two ways to produce a ScriptModule.
* Remove examples of using Caffe2 to run exported models.
Caffe2's website says it's deprecated, so it's probably best not to
encourage people to use it by including it in examples.
* Remove a lot of content that's redundant:
* The example of how to mix tracing and scripting, and instead
link to Introduction to TorchScript, which includes very similar
content.
* "Type annotations" section. Link to TorchScript docs which explain
that in more detail.
* "Using dictionaries to handle Named Arguments as model inputs"
section. It's redundant with the description of the `args` argument
to `export()`, which appears on the same page once the HTML
is generated.
* Remove the list of supported Tensor indexing patterns. If it's not
in the list of unsupported patterns, users can assume it's
supported, so having both is redundant.
* Remove the list of supported operators and models.
I think the list of supported operators is not very useful.
A list of supported model architectures may be useful, but in
reality it's already very out of date. We should add it back if
/ when we have a system for keeping it up to date.
* "Operator Export Type" section. It's redundant with the description
of the `operator_export_type` arg to to `export()`, which appears on
the same page once the HTML is generated.
* "Use external data format" section. It's redundant with the
description of the `use_external_data_format` arg to `export()`.
* "Training" section. It's redundant with the
description of the `training` arg to `export()`.
* Move the content about different operator implementations producing
different results from the "Limitations" section into the doc for the
`operator_export_type` arg.
* Document "quantized" -> "caffe2" behavior of
OperatorExportTypes.ONNX_ATEN_FALLBACK.
* Combing the text about using torch.Tensor.item() and the text about
using NumPy types into a section titled
"Avoid NumPy and built-in Python types", since they're both
fundamentally about the same issue.
* Rename "Write PyTorch model in Torch way" to "Avoiding Pitfalls".
* Lots of minor fixes: spelling, grammar, brevity, fixing links, adding
links.
* Clarify limitation on input and output types. Phrasing it in terms of
PyTorch types is much more accessible than in terms of TorchScript
types. Also clarify what actually happens when dict and str are used
as inputs and outputs.
* In Supported operators, use torch function and class names and link
to them. This is more user friendly than using the internal aten
op names.
* Remove references to VariableType.h, which doesn't appear to contain
the information that it once did. Instead refer to the generated
.pyi files.
* Remove the text in the FAQ about appending to lists within loops.
I think this limitation is no longer present
(perhaps since https://github.com/pytorch/pytorch/pull/51577).
* Minor fixes to some code I read along the way.
* Explain the current rationale for the weird ::prim_PythonOp op name.
Test Plan: Imported from OSS
Reviewed By: zou3519, ZolotukhinM
Differential Revision: D29494912
Pulled By: SplitInfinity
fbshipit-source-id: 7756c010b2320de0692369289604403d28877719
Co-authored-by: Gary Miguel <garymiguel@microsoft.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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57079
Testing onnx 1.9 release, we see that the old bug is triggered for the caffe2 test:
`pytest test/onnx/test_pytorch_onnx_caffe2_quantized.py::TestQuantizedOps::test_small_model`
This is because the graph inputs
```python
graph(%x.1 : Tensor,
%conv1._packed_params : __torch__.torch.classes.quantized.Conv2dPackedParamsBase,
%conv2._packed_params : __torch__.torch.classes.quantized.Conv2dPackedParamsBase,
%fc.bias : Float(10, strides=[1], requires_grad=0, device=cpu),
%fc.weight : Float(10, 72, strides=[72, 1], requires_grad=0, device=cpu)):
```
contains `Conv2dPackedParamsBase` which is a PackedParams.
When we do flatten, we will flatten to several tensors, then the shape inference for input misaligned.
This PR record how may tensors got flattened in PackeParams, and skip by these number rather than 1, then the UT passed.
Note that tuple case should still follow the original logic.
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D28393949
Pulled By: malfet
fbshipit-source-id: 98d48aad27e5ca03fb10d260f8e625478d996ee2
Co-authored-by: David <jiafa@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56172
Enable the standardOps include **Add\Sub\Mul\Div\Gemm\Pow\Mod** with low precision input in ORT
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27866136
Pulled By: SplitInfinity
fbshipit-source-id: f2cf5649fffefd68c0cc7b6dce94198751636727
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53307
This PR did symbolic shape inference, in the onnx pass _jit_pass_onnx_graph_shape_type_inference.
It creates a singleton ConstantValueMap.
It leverages constant folding technique and did a per-op based handling for ConstantValueMap.
As a byproduct, it enables fold_if pass for dynamic axes cases, typically for faster-rcnn etc.
The core change is in `torch/csrc/jit/passes/onnx/shape_type_inference.cpp` and `torch/csrc/jit/passes/onnx/constant_map.cpp`.
We usually need copy tensor to store in the ConstantValueMap, otherwise the underlying value may change. I see this issue in (1) from_blob (2) get value from Constant node.
Test Plan: Imported from OSS
Reviewed By: pbelevich, malfet
Differential Revision: D26922414
Pulled By: SplitInfinity
fbshipit-source-id: 7654dc13d1de8d9496ad4be89f1454260d7bdeb0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53306
* [ONNX] Fix for sequence of mutations in blocks (#51577)
Fixes consecutive mutations in a tensor inside blocks.
Also, support append and pop in blocks.
* Support inplace operations + indexing
* Clean up old pass for remove mutations
* Add loop test
* Fixes for set attr in loops
* Removing the new jit API flag
* [ONNX] Redesign onnx pass to enable shape type dependent pattern conversion - cont (#51795)
With the introduction of ONNX shape inference, shape and type are inferred on the fly as operators get converted from ATen to ONNX when running symbolic function. This resolves the shape/type requirement for the symbolic functions. The pre-onnx passes however, can not be supported by shape inference, since at that stage the operators in the graph are still ATen operators.
This PR is to update the design of ONNX pass, to enable a mechanism of capturing subgraphs of ATen operators of certain patterns, and convert them later, when shape/type information of upstream operators are available.
The new design will require pre-onnx passes that need shape/type to be written in two parts, encapsulation and conversion.
The encapsulation part will find the nodes of patterns, like how pre-onnx passes were written previously. But instead of converting the nodes, it will encapsulate them into a sub-block of a new placeholder node. This part is called before onnx pass, so it runs before calling symbolic functions.
The conversion part will be called inside the onnx pass. In onnx pass, run_symbolic_func will be called for each node in topological order. When it reaches the placeholder node, the conversion part will be invoked. It will convert the nodes inside the sub-block based on pattern. By that time, it will have shape/type of upstream operators available. After the conversion is complete, the placeholder node will be removed, and nodes inside its sub-block converted. Run_symbolic_func will be called for these nodes, and they will be converted from ATen operator to ONNX operator.
This PR includes several other fixes, listed below.
* ~~replace helper.cpp with onnx_utils.cpp for holding utility functions.~~
* fix EraseNumberTypes on Bool type, the code was outdated that back then Bool type doesn't exist.
* ~~enable onnx shape inference in export with parameter/initializer data.~~
* other code clean ups.
* fix insertion of identity nodes for loop opset 13 sequence output.
~~PR depends on #51603~~
* Fix after merge
* clang
* Fix clang
* Fix clang
* Fix warning message.
* Fixes for non-model param attributes
* Fix for caffe2
* Additional test
* clang
* Skip test for lower opsets
* fix clang-tidy
* Update init.cpp
* Update remove_inplace_ops_for_onnx.cpp
* Update remove_inplace_ops_for_onnx.cpp
* Update remove_inplace_ops_for_onnx.cpp
* Fix for clang formatting
Test Plan: Imported from OSS
Reviewed By: pbelevich, malfet
Differential Revision: D26922416
Pulled By: SplitInfinity
fbshipit-source-id: e7108620b39b6404c594910786c4d275fee59d84
Co-authored-by: Bowen Bao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53304
With the introduction of ONNX shape inference, shape and type are inferred on the fly as operators get converted from ATen to ONNX when running symbolic function. This resolves the shape/type requirement for the symbolic functions. The pre-onnx passes however, can not be supported by shape inference, since at that stage the operators in the graph are still ATen operators.
This PR is to update the design of ONNX pass, to enable a mechanism of capturing subgraphs of ATen operators of certain patterns, and convert them later, when shape/type information of upstream operators are available.
The new design will require pre-onnx passes that need shape/type to be written in two parts, encapsulation and conversion.
The encapsulation part will find the nodes of patterns, like how pre-onnx passes were written previously. But instead of converting the nodes, it will encapsulate them into a sub-block of a new placeholder node. This part is called before onnx pass, so it runs before calling symbolic functions.
The conversion part will be called inside the onnx pass. In onnx pass, run_symbolic_func will be called for each node in topological order. When it reaches the placeholder node, the conversion part will be invoked. It will convert the nodes inside the sub-block based on pattern. By that time, it will have shape/type of upstream operators available. After the conversion is complete, the placeholder node will be removed, and nodes inside its sub-block converted. Run_symbolic_func will be called for these nodes, and they will be converted from ATen operator to ONNX operator.
This PR includes several other fixes, listed below.
* ~~replace helper.cpp with onnx_utils.cpp for holding utility functions.~~
* fix EraseNumberTypes on Bool type, the code was outdated that back then Bool type doesn't exist.
* ~~enable onnx shape inference in export with parameter/initializer data.~~
* other code clean ups.
* fix insertion of identity nodes for loop opset 13 sequence output.
~~PR depends on #51603~~
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D26922417
Pulled By: malfet
fbshipit-source-id: 14ed06158d539e2451c2e5e63ba1b32fb0f75095
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51517
Fix get/set attributes when getting/setting a model parameter.
This PR also fixes inplace ops in If blocks.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26203116
Pulled By: SplitInfinity
fbshipit-source-id: bed6ee6dd92b5b43febc8c584a6872290f8fe33f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50910
Fixing pytorch/vision#3251 (PR #49410 triggers the torch vision test build failure, on three tests test_faster_rcnn, test_mask_rcnn, test_keypoint_rcnn. )
The offending PR is fine on pytorch UT, because the torchvision and pytorch test has a gap when we merge them - we are using different test API on two sides, therefore causing some discrepancy.
This PR bridge the gap for the above three tests, and disable _jit_pass_onnx_fold_if pass until it gets fixed.
Allow _jit_pass_onnx_fold_if only when dynamic_axes is None.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26050886
Pulled By: SplitInfinity
fbshipit-source-id: b765ffe30914261866dcc761f0d0999fd16169e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50905
Adds an additional run of onnx shape inference after constant folding, since initializer may have changed and affected shape inference.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26050881
Pulled By: SplitInfinity
fbshipit-source-id: 9e5d69c52b647133cd3a0781988e2ad1d1a9c09d
Summary:
Handle sequence output shape and type inference.
This PR fixes value type of sequence outputs. Prior to this, all model sequence type outputs were unfolded for ONNX models.
This PR also enable shape inference for sequence outputs to represent the dynamic shape of these values.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46542
Reviewed By: ezyang
Differential Revision: D24924236
Pulled By: bzinodev
fbshipit-source-id: 506e70a38cfe31069191d7f40fc6375239c6aafe
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:
Fixes https://github.com/pytorch/pytorch/issues/45215
Still need to resolve a few mypy issues before a review. In special, there is an error which I don't know how to solve, see:
```python
torch/onnx/utils.py:437: error: Name 'is_originally_training' is not defined [name-defined]
if training is None or training == TrainingMode.EVAL or (training == TrainingMode.PRESERVE and not is_originally_training):
```
`is_originally_training` is used but never defined/imported on [`torch/onnx/utils.py`](ab5cc97fb0/torch/onnx/utils.py (L437)),
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45258
Reviewed By: zhangguanheng66
Differential Revision: D25254920
Pulled By: ezyang
fbshipit-source-id: dc9dc036da43dd56b23bd6141e3ab92e1a16e3b8
Summary:
* Enable ONNX shape inference by default.
* ONNX could potentially set inferred shape in output instead of value_infos, checking both to be sure.
* Small fix in symbol_map to avoid overlooking dup symbols.
* Fix scalar_type_analysis to be consistent with PyTorch scalar type promotion logic.
* Correctly handle None dim_param from ONNX inferred shape.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46629
Reviewed By: ailzhang
Differential Revision: D24900171
Pulled By: bzinodev
fbshipit-source-id: 83d37fb9daf83a2c5969d8383e4c8aac986c35fb
Summary:
- rand/randn: the type signature of int[] is different in scripting, thus failing the check.
- where: scripting produces dynamic cases which are supported by `unbind` export of higher opsets.
- test_list_pass: this test fails when using new scripting api, should be fixed by https://github.com/pytorch/pytorch/issues/45369
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45793
Reviewed By: mrshenli
Differential Revision: D24566096
Pulled By: bzinodev
fbshipit-source-id: 6fe0925c66dee342106d71c9cbc3c95cabe639f7
Summary:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
Export of embedding bag with dynamic list of offsets.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44693
Reviewed By: malfet
Differential Revision: D23831980
Pulled By: bzinodev
fbshipit-source-id: 3eaff1a0f20d1bcfb8039e518d78c491be381e1a
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:
* Support sequence type (de)serialization, enables onnx shape inference on sequence nodes.
* Fix shape inference with block input/output: e.g. Loop and If nodes.
* Fix bugs in symbolic discovered by coverage of onnx shape inference.
* Improve debuggability: added more jit logs. For simplicity, the default log level, when jit log is enabled, will not dump ir graphs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43929
Reviewed By: albanD
Differential Revision: D23674604
Pulled By: bzinodev
fbshipit-source-id: ab6aacb16d0e3b9a4708845bce27c6d65e567ba7
Summary:
Follow up to https://github.com/pytorch/pytorch/pull/36404
Adding prim::device and prim::dtype to list of skipped peepholes when we run inlining. In the long term another fix may not be to encode shape / dtype info on the traced graph, because it is not guaranteed to be correct. This is blocked by ONNX currently.
Partial fix for https://github.com/pytorch/pytorch/issues/43134
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43363
Reviewed By: glaringlee
Differential Revision: D23383987
Pulled By: eellison
fbshipit-source-id: 2e9c5160d39d690046bd9904be979d58af8d3a20
Summary:
Duplicate of https://github.com/pytorch/pytorch/issues/41413
This PR initiates the process of updating the torchsciprt backend interface used by ONNX exporter.
Replace jit lower graph pass by freeze module pass
Enable ScriptModule tests for ONNX operator tests (ORT backend) and model tests by default.
Replace jit remove_inplace_ops pass with remove_mutation and consolidation all passes for handling inplace ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43791
Reviewed By: houseroad
Differential Revision: D23421872
Pulled By: bzinodev
fbshipit-source-id: a98710c45ee905748ec58385e2a232de2486331b
Summary:
It is often that the conversion from torch operator to onnx operator requires input rank/dtype/shape to be known. Previously, the conversion depends on tracer to provide these info, leaving a gap in conversion of scripted modules.
We are extending the export with support from onnx shape inference. If enabled, onnx shape inference will be called whenever an onnx node is created. This is the first PR introducing the initial look of the feature. More and more cases will be supported following this PR.
* Added pass to run onnx shape inference on a given node. The node has to have namespace `onnx`.
* Moved helper functions from `export.cpp` to a common place for re-use.
* This feature is currently experimental, and can be turned on through flag `onnx_shape_inference` in internal api `torch.onnx._export`.
* Currently skipping ONNX Sequence ops, If/Loop and ConstantOfShape due to limitations. Support will be added in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40628
Reviewed By: mrshenli
Differential Revision: D22709746
Pulled By: bzinodev
fbshipit-source-id: b52aeeae00667e66e0b0c1144022f7af9a8b2948
Summary:
The ONNX spec for the Squeeze operator:
> Remove single-dimensional entries from the shape of a tensor. Takes a parameter axes with a list of axes to squeeze. If axes is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised.
Currently, as explained in issue https://github.com/pytorch/pytorch/issues/36796, it is possible to export such a model to ONNX, and this results in an exception from ONNX runtime.
Fixes https://github.com/pytorch/pytorch/issues/36796.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38476
Reviewed By: hl475
Differential Revision: D22158024
Pulled By: houseroad
fbshipit-source-id: bed625f3c626eabcbfb2ea83ec2f992963defa19
Summary:
in `_jit_pass_onnx`, symbolic functions are called for each node for conversion. However, there are nodes that cannot be converted without additional context. For example, the number of outputs from split (and whether it is static or dynamic) is unknown until the point where it is unpacked by listUnpack node. This pass does a preprocess, and prepares the nodes such that enough context can be received by the symbolic function.
* After preprocessing, `_jit_pass_onnx` should have enough context to produce valid ONNX nodes, instead of half baked nodes that replies on fixes from later postpasses.
* `_jit_pass_onnx_peephole` should be a pass that does ONNX specific optimizations instead of ONNX specific fixes.
* Producing more valid ONNX nodes in `_jit_pass_onnx` enables better utilization of the ONNX shape inference https://github.com/pytorch/pytorch/issues/40628.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41832
Reviewed By: ZolotukhinM
Differential Revision: D22968334
Pulled By: bzinodev
fbshipit-source-id: 8226f03c5b29968e8197d242ca8e620c6e1d42a5
Summary:
* move both under new file `fixup_onnx_controlflow`
* move the fixup to where the ONNX loop/if node is created, as oppose to running the fixup as postpass. This will help with enable onnx shape inference later.
* move `fuseSequenceSplitConcat` to `Peephole`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40943
Reviewed By: mrshenli
Differential Revision: D22709999
Pulled By: bzinodev
fbshipit-source-id: 51d316991d25dc4bb4047a6bb46ad1e2401d3d2d
Summary:
This PR initiates the process of updating the torchsciprt backend interface used by ONNX exporter.
- Replace jit lower graph pass by freeze module pass
- Enable ScriptModule tests for ONNX operator tests (ORT backend) and model tests by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41413
Reviewed By: VitalyFedyunin
Differential Revision: D22845258
Pulled By: bzinodev
fbshipit-source-id: d57fd4086f27bd0c3bf5f70af7fd0daa39a2814a
Summary:
Add pass that fuses Conv and Batchnormalization nodes into one node Conv.
This pass is only applied in inference mode (training is None or TrainingMode.Eval).
Since this pass needs access to param_dict it is written outside peephole file where these kind of passes (fusing multiple nodes into one) is usually placed.
This PR also adds wrapper skipIfNoEmbed to skip debug_embed_params test:
Pass that fuses Conv and Batchnorm changes the params of resnet model and parameters of onnx and pytorch model won't match. Since parameters are not matching, debug_embed_params test for test_resnet will fail and that is expected, therefore debug_embed_params test for test_resnet should be skipped.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40547
Reviewed By: gchanan
Differential Revision: D22631687
Pulled By: bzinodev
fbshipit-source-id: fe45812400398a32541e797f727fd8697eb6d8c0
Summary:
This PR introduces a warning when user tries to export the model to ONNX in training-amenable mode while constant folding is turned on. We want to warn against any unintentional use because constant folding may fold some parameters that may be intended to be trainable in the exported model.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40546
Reviewed By: hl475
Differential Revision: D22310917
Pulled By: houseroad
fbshipit-source-id: ba83b8e63af7c458b5ecca8ff2ee1c77e2064f90
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38734
As far as I can tell, this pass only exists to canonicalize ops that are generating in the graph fuser, so it's kind of a misnomer.
Test Plan: Imported from OSS
Differential Revision: D21673109
Pulled By: eellison
fbshipit-source-id: b7bedf34ccaf1fcd442bfb2bbb990e64915f51d4
Summary:
Set opset version before model select call - which is used to trigger warnings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37466
Reviewed By: hl475
Differential Revision: D21308796
Pulled By: houseroad
fbshipit-source-id: 0974b9d5b6562d4451f54053138174f663a17aa3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36953
Add support for generic lists as a constant. generic dicts & tuples are already implemented. This is a pretty common pattern and cuts down on the number of non-tensor nodes executed in interpolate tests.
Test Plan: Imported from OSS
Differential Revision: D21160761
Pulled By: eellison
fbshipit-source-id: 1e6b7b25b7580f09067794772d44e615601c60c4
Summary:
With https://github.com/pytorch/pytorch/pull/35562, we are running peephole optimization on inlining to reduce the number of nodes that are copied.
The tracer encodes the sizes in the graph like:
```
graph(%0 : Double(7)):
%1 : Function = prim::Constant[name="tensor_size"]()
%2 : Tensor = prim::CallFunction(%1, %0)
return (%2)
```
however people would like to reuse the graph with different shapes so running size invalidations would invalidate that. long term it might be better for the tracer to not include shape information but there are downstream users of that.
Separates out FuseAddMM from peephole so that now there is a single `disable_size_optimizations` parameter, and onnx explicitly invokes fuseaddmm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36404
Differential Revision: D20968974
Pulled By: eellison
fbshipit-source-id: 56f8f1699e3b0adeeccdfd5a67bb975fd41a2913
Summary:
Since aten;:__interpolate is removed in https://github.com/pytorch/pytorch/pull/34514, we need a pass replace interpolate function with aten::__interpolate for ONNX export.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35744
Reviewed By: hl475
Differential Revision: D20907041
Pulled By: houseroad
fbshipit-source-id: f2d2cdfec47389245c50f538267124eedf682adf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36277
This PR introduce a flag to the tracer that guard the risky behaviors
like adding list/dict as output of the tracer. Currently to ensure not
BC breaking user, we throw warning if the tracer output is list, and
will throw error when the tracer output is dict to enforce using this
flag (next PR)
Test Plan: Imported from OSS
Differential Revision: D20998157
Pulled By: wanchaol
fbshipit-source-id: 0d2c55f1a263a48b1b92dd6ad54407815e0a6f72
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33779
This should eliminate random warnings and print spew from test_jit.
It also fixes a bug where we weren't properly comparing captured outputs
(!)
Test Plan: Imported from OSS
Differential Revision: D20124224
Pulled By: suo
fbshipit-source-id: 9241d21fdf9470531b0437427b28e325cdf08d3a
Summary:
We want to run ONNX checker only when selected operator type is ONNX, and nowhere else. This PR updates the logic in the exporter.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33522
Reviewed By: hl475
Differential Revision: D19983954
Pulled By: houseroad
fbshipit-source-id: 15db726321637a96fa110051cc54e9833e201133
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:
Currently, custom ops are registered for a specific opset version.
For example, all torchvision custom ops are registered for opset 11, and cannot be exported into higher opset versions. This PR extends op registration to higher opset versions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32943
Reviewed By: hl475
Differential Revision: D19739406
Pulled By: houseroad
fbshipit-source-id: dd8b616de3a69a529d135fdd02608a17a8e421bc
Summary:
The PR https://github.com/pytorch/pytorch/pull/31791 adds support for float[] constant, which affects some cases of ONNX interpolate support.
This PR adds float[] constants support in ONNX, updates interpolate in ONNX, and re-enable the disabled tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32554
Reviewed By: hl475
Differential Revision: D19566596
Pulled By: houseroad
fbshipit-source-id: 843f62c86126fdf4f9c0117b65965682a776e7e9
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:
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:
- [x] Add more comments and refactor the logic of `ReshapeToAdvancedIndexingFormat`
- [x] Add more description here. Cases that are/aren't supported, and how they are supported.
- [x] Need to merge this PR https://github.com/pytorch/pytorch/issues/27186 to enable testing inplace operators.
We are now supporting exporting aten::copy_ and aten::index_put to ONNX.
Here's a breakdown of the different cases in PyTorch code.
```
# Case 1: Scalar Indices
x[0, 1, 2] = data
# Case 2: Slice Indices
x[1:3, :, ::2] = data
# Case 3: Ellipsis Indices
x[..., 0] = data
# Case 4: Tensor Indices
ind1 = torch.tensor([0, 2])
ind2 = torch.tensor([1, 1])
x[ind1, ind2] = data
# Case 5: Mixing all the above cases
ind1 = torch.tensor([0, 2])
ind2 = torch.tensor([1, 1])
x[1:3, ind1, ind2, ..., 3] = data
```
Limitations:
Tensor indices must be consecutive, and 1-d tensors.
```
# Supported
ind1 = torch.tensor([0, 2])
ind2 = torch.tensor([1, 1])
x[ind1, ind2] = data
# Not supported
ind1 = torch.tensor([0, 2])
ind2 = torch.tensor([1, 1])
ind3 = torch.tensor([[0], [1]])
x[ind1, :, ind2] = data
x[ind3] = data
```
Negative indices are not supported.
```
# Not supported
x[-1] = data
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26941
Differential Revision: D17951030
Pulled By: houseroad
fbshipit-source-id: 4357777072f53aa0bc4b297aa1ee53457a7f8dec
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30892
Fixes all outstanding lints and actually installs a properly configured
flake8
Test Plan: Imported from OSS
Differential Revision: D18862825
Pulled By: suo
fbshipit-source-id: 08e9083338a7309272e17bb803feaa42e348aa85
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30679
Caffe2 expects quantized ops to be in NHWC format while pytorch inputs are in NCHW.
Add a jit pass to insert permutes to convert from nchw2nhwc before each conv op and add nhwc2nchw permute after the conv op.
Using graph rewriter to find consecutive redundant permutes and remove them from the graph
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps
Imported from OSS
Differential Revision: D18790518
fbshipit-source-id: 4dd39cf0b31b21f5586c0edfdce2260d4e245112
Summary:
The PyTorch exporter does not add any name to the ONNX operators in the exported graph. A common request is to add names to op nodes by default. This helps the readability of the graph in visualization tools such a Netron, or when the ONNX graph is printed as a string. Also, it helps with the debuggability of the ONNX graph.
Therefore this PR adds name to operators in the exporters. The names follow a simple format, <op_type>_<index>. Expect files for tests in `test/onnx/test_operators.py` have been updated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27342
Reviewed By: hl475
Differential Revision: D17790979
Pulled By: houseroad
fbshipit-source-id: 1eaae88b5f51f152735a2ff96e22827837e34d9d
Summary:
This should resolve https://github.com/pytorch/pytorch/issues/29008. This flag has two effects on the tracer.
- Remove the underscroll for inplace operators. E.g.: index_put_ ==> index_put. This is handled in utils.py separately as well.
- Add out as input for backward computation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29466
Reviewed By: hl475
Differential Revision: D18422815
Pulled By: houseroad
fbshipit-source-id: 317b6a3c8a5751fe6fe49d7543e429d281ed0d6d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29694
This PR adds preliminary support required to be able to run quantized pytorch models on a C2 backend.
For quantized ops we use a custom domain name 'caffe2' to register the ops if they are in the "quantized" namespace.
The change also adds JIT pass to unpack the quantized weights and insert the unpacked values into the graph.
The actual tensor values are looked up from the params dict.
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2.py TestQuantizedOps
Imported from OSS
Reviewed By: houseroad
Differential Revision: D18467130
fbshipit-source-id: 53ebd8c43935f7d7e74305dad6c231a2247df176
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:
Currently ONNX constant folding (`do_constant_folding=True` arg in `torch.onnx.export` API) supports only opset 9 and 10 of ONNX. Opset 11 support was recently introduced in the ONNX exporter. For opset 11, it is currently a no-op. This change enables ONNX constant folding for opset 11. Specifically there are three main changes:
1) Turn on constant folding ONNX pass for opset 11.
2) Enable constant folding tests in `test/onnx/test_utility_funs.py` and `test/onnx/test_pytorch_onnx_onnxruntime.py` for opset 11.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29011
Reviewed By: hl475
Differential Revision: D18306998
Pulled By: houseroad
fbshipit-source-id: afeed21ca29e01c278612e51dacd93397dd6e2d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28242
There is no reason to have it in a general API of Module/Method - it's
just another graph pass. It was there because some time ago modules were
not first class and all graphs were lowered. After that changed, this
API was added for easier transition, but now we don't need it anymore.
Test Plan: Imported from OSS
Differential Revision: D17986724
Pulled By: ZolotukhinM
fbshipit-source-id: 279a1ec450cd8fac8164ee581515b09f1d755630
Summary:
We currently support exporting traced interpolate ops to ONNX.
Scripting interpolate op invokes aten::__interpolate in the Torch IR (instead of aten::upsample_[mode][dim]d), which we do not support yet.
This PR implements the ONNX symbolic for __interpolate() to support exporting interpolate in scripting scenarios.
Related open issue: https://github.com/pytorch/pytorch/issues/25807
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27566
Reviewed By: hl475
Differential Revision: D17817731
Pulled By: houseroad
fbshipit-source-id: e091793df503e2497f24821cf2954ff157492c75
Summary:
ONNX does not support dictionaries for inputs and output. The reason is that the arg flattening and unflattening does not handle Dictionary types.
This PR adds flattening/unflattening support for dictionaries and strings.
However this feature should be handled with caution for input dictionaries; and users need to verify their dict inputs carefully, and keep in mind that dynamic lookups are not available.
This PR will allow exporting cases where models have dictionnary outputs (detection and segmentation models in torchvision), and where dictionary inputs are used for model configurations (MultiScaleRoiAlign in torchvision).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25889
Reviewed By: hl475
Differential Revision: D17613605
Pulled By: houseroad
fbshipit-source-id: c62da4f35e5dc2aa23a85dfd5e2e11f63e9174db
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26738
someone may use torch._export directly. Here we change the onnx_export_type's default value to None,
and if it's pytorch onnx caffe2 bundle, we set it to ONNX_ATEN_FALLBACK, otherwise, it's ONNX.
Test Plan: ci
Reviewed By: hl475
Differential Revision: D17546452
fbshipit-source-id: 38e53926e2b101484bbbce7b58ebcd6af8c42438
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26487
The way it is implemented currently is bad because while we're inlining
to a graph G, we are also mutating all the graphs that are being
inlined. The problem is that the graphs we're inlining are usually the
original graphs of functions, so we're silently changing them behind the
scenes, and we don't have a way to recover 'unoptimized' graphs
afterwards.
Test Plan: Imported from OSS
Differential Revision: D17485748
Pulled By: ZolotukhinM
fbshipit-source-id: 6094ef56077240e9379d4c53680867df1b6e79ef
Summary:
This pass tries to resolve scalar type mismatch issues between input tensors introduced by the implicit type conversions on scalars.
e.g. https://github.com/pytorch/pytorch/issues/23724
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24378
Reviewed By: hl475
Differential Revision: D17088682
Pulled By: houseroad
fbshipit-source-id: 3de710f70c3b70b9f76fd36a7c4c76e168dbc756
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25052
Previously we would not inline nested functions, now we do.
Test Plan: Imported from OSS
Differential Revision: D16973848
Pulled By: suo
fbshipit-source-id: 94aa0b6f84a2577a663f4e219f930d2c6396d585
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23799
Before, we inlined as part of the initial IR generation process, which
has a few disadvantages:
1. It loses information about what nodes came from which function/method
calls. Other parties who want to implement transformations on the
function/module level don't have a reliable way of doing so.
2. It duplicates a ton of code if we are inlining the same
function/method a tons of times.
After this PR: inline is deferred to the optimization stage, so
optimizations that rely on inlining will still work. But things get
serialized with the function/method calls in.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23799
Differential Revision: D16652819
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Pulled By: suo
fbshipit-source-id: a11af82aec796487586f81f5a9102fefb6c246db
Summary:
Existing code adds two enumerators to the set instead of forming their union.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23974
Differential Revision: D16732762
Pulled By: ezyang
fbshipit-source-id: 787737b7cf4b97ca4e2597e2da4a6ade863ce85c
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:
Don't automatically unwrap top layer DataParalllel for users. Instead, we provide useful error information and tell users what action to take.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23365
Reviewed By: zrphercule
Differential Revision: D16514273
Pulled By: houseroad
fbshipit-source-id: f552de5c53fb44807e9d9ad62126c98873ed106e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23180
This pass needs to be run later because it breaks jit graph invariants and the lower_all_tuples pass still needs a valid jit graph.
Reviewed By: houseroad
Differential Revision: D16427680
fbshipit-source-id: 427c7e74c59a3d7d62f2855ed626cf6258107509
Summary:
This is an extension to the original PR https://github.com/pytorch/pytorch/pull/21765
1. Increase the coverage of different opsets support, comments, and blacklisting.
2. Adding backend tests for both caffe2 and onnxruntime on opset 7 and opset 8.
3. Reusing onnx model tests in caffe2 for onnxruntime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22421
Reviewed By: zrphercule
Differential Revision: D16225518
Pulled By: houseroad
fbshipit-source-id: 01ae3eed85111a83a0124e9e95512b80109d6aee
