Reduce circular dependencies
- Lift constants and flags from `symbolic_helper` to `_constants` and `_globals`
- Standardized constant naming to make it consistant
- Make `utils` strictly dependent on `symbolic_helper`, removing inline imports from symbolic_helper
- Move side effects from `utils` to `_patch_torch`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77142
Approved by: https://github.com/garymm, https://github.com/BowenBao
In `_need_symbolic_context`, when the annotation is postponed evaluated, the annotation is a string and not a type. We need to use get_type_hints to get the real type.
For example,
```python
def g(a: int) -> int: return a
def f(a: "int") -> "int": return a
```
we will get the correct type `int` for both g and f with `typing.get_type_hints`. Otherwise, the type for `a` in `f` will be a string and is not comparable to the type `int` - `issubclass` will complain.
This is necessary as we will use postponed typing evaluation to break circular dependencies.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77365
Approved by: https://github.com/BowenBao
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73284
Some important ops won't support optional type until opset 16,
so we can't fully test things end-to-end, but I believe this should
be all that's needed. Once ONNX Runtime supports opset 16,
we can do more testing and fix any remaining bugs.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D34625646
Pulled By: malfet
fbshipit-source-id: 537fcbc1e9d87686cc61f5bd66a997e99cec287b
Co-authored-by: BowenBao <bowbao@microsoft.com>
Co-authored-by: neginraoof <neginmr@utexas.edu>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
(cherry picked from commit 822e79f31ae54d73407f34f166b654f4ba115ea5)
Updating the docstrings and type annotations as I walk through the code.
- Turned some comments into docstrings.
- Added type annotations for some functions in utils and the registry
- Removed direct function imports; importing functions makes name space collision easier to happen and refactoring/code analysis harder: https://google.github.io/styleguide/pyguide.html#22-imports
- Formatted touched files with black
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76255
Approved by: https://github.com/BowenBao
Currently `torch.onnx.export(.., operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK)` only issues ATen ops through explicit requests (e.g. `g.at()`) calls inside each op symbolic function. This is done based on specific conditions such as `operator_export_type==OperatorExportTypes.ONNX_ATEN_FALLBACK)` or `is_caffe2_aten_fallback()`
This PR extends the ATen fallback mechanism for scenarios when the symbolic function raises `RuntimeError` during export. The idea is that partial implementation of existing ONNX ops can fallback to ATen as a last resort. That is valuable because each operator can have many input combinations and not all are always implemented.
A minor fix was done to make sure the `overload_name` attribute is added to explicit ATen op fallback requests when a symbolic is not registered to a particular op.
ps: The behavior for builds with BUILD_CAFFE2=1 is not changed to ensure BC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74759
Approved by: https://github.com/garymm, https://github.com/msaroufim
Previously pre-tracing model is required for exporting quantized model.
e.g. calling `traced_m = torch.jit.trace(model, inputs)` and export `traced_m`.
The reason was quantized weights are stored in a unique `PackedParam` structure,
and they need to be handled by tracing to be exportable.
This PR enables export api to call tracing underneath if it detects quantization
in the model.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75921
Approved by: https://github.com/garymm
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
Previous logic didn't consider the case for TrainingMode.PRESERVE.
A more direct way is to check `model.training`, which is the accurate
training mode, set by `exporter_context(model, training)`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74247
Approved by: https://github.com/garymm
Summary:
Add ONNX exporter logging facility. Supporting both C++/Python logging api. Logging can be turned on/off. Logging output stream can be either set to `stdout` or `stderr`.
A few other changes:
* When exception is raised in passes, the current IR graph being processed will be logged.
* When exception is raised from `_jit_pass_onnx` (the pass that converts nodes from namespace `ATen` to `ONNX`), both ATen IR graph and ONNX IR graph under construction will be logged.
* Exception message for ConstantFolding is truncated to avoid being too verbose.
* Update the final printed IR graph with node name in ONNX ModelProto as node attribute. Torch IR Node does not have name. Adding this to printed IR graph helps debugging.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71342
Reviewed By: msaroufim
Differential Revision: D34433473
Pulled By: malfet
fbshipit-source-id: 4b137dfd6a33eb681a5f2612f19aadf5dfe3d84a
(cherry picked from commit 67a8ebed5192c266f604bdcca931df6fe589699f)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73280
This PR adds a new attribute overload_name to the Aten node so that third party applications can implement calls to libtorch without using PyTorch source code.
This is necessary because torch's torch::jit::findOperatorFor(fullname) requires a full name, including operator and overload names.
ATen op was originally created for Caffe2, which leveraged the availability of the pytorch yaml files to create calls to the aten oeprators directly, not relying on torch::jit::findOperatorFor
The first part of the PR refactors all symbolics that create Aten ops, so that there is a single helper for this operator. Next all symbolics are updated to pass in the relevant overload name, if empty string is not applicable
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D34625645
Pulled By: malfet
fbshipit-source-id: 37d58cfb5231833768172c122efc42edf7d8609a
(cherry picked from commit e92f09117d3645b38bc3235b30aba4b4c7c71dfa)
Enables local function export to capture annotated attributes.
For example:
```python
class M(torch.nn.Module):
num_layers: int
def __init__(self, num_layers):
super().__init__()
self.num_layers = num_layers
def forward(self, args):
...
```
`num_layers` will now be captured as attribute of local function `M`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72883
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 d7cbde940c5c259a3feff5af870b01dd21fbf3e0)
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 1e04ffd2fd1511d7d144f23afc997bf16fa1d2cc)
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 5fb1eb1b1973e47a8215978e96dd1284570f13e6)
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 513c1d28f1708ccf8224caa92165de702cf43fc3)
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>
