This commit improves the export of aten::slice() to ONNX in the following ways:
1. The step size can be an input tensor rather than a constant.
2. Fixes a bug where using a 1-D, 1-element torch tensor as an index created a broken ONNX model.
This commit also adds tests for the new functionality.
Fixes#104314
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104385
Approved by: https://github.com/thiagocrepaldi
Changes:
1. `typing_extensions -> typing-extentions` in dependency. Use dash rather than underline to fit the [PEP 503: Normalized Names](https://peps.python.org/pep-0503/#normalized-names) convention.
```python
import re
def normalize(name):
return re.sub(r"[-_.]+", "-", name).lower()
```
2. Import `Literal`, `Protocal`, and `Final` from standard library as of Python 3.8+
3. Replace `Union[Literal[XXX], Literal[YYY]]` to `Literal[XXX, YYY]`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94490
Approved by: https://github.com/ezyang, https://github.com/albanD
Fixes https://github.com/pytorch/pytorch/issues/84365 and more
This PR addresses not only the issue above, but the entire family of issues related to `torch._C.Value.type()` parsing when `scalarType()` or `dtype()` is not available.
This issue exists before `JitScalarType` was introduced, but the new implementation refactored the bug in because the new api `from_name` and `from_dtype` requires parsing `torch._C.Value.type()` to get proper inputs, which is exactly the root cause for this family of bugs.
Therefore `from_name` and `from_dtype` must be called when the implementor knows the `name` and `dtype` without parsing a `torch._C.Value`. To handle the corner cases hidden within `torch._C.Value`, a new `from_value` API was introduced and it should be used in favor of the former ones for most cases. The new API is safer and doesn't require type parsing from user, triggering JIT asserts in the core of pytorch.
Although CI is passing for all tests, please review carefully all symbolics/helpers refactoring to make sure the meaning/intetion of the old call are not changed in the new call
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87245
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
`_set_opset_version` and `_set_operator_export_type` are previously deprecated. This PR decorates them with the deprecation decorator, so warnings are emitted.
- Remove usage of `_set_opset_version` and `_set_operator_export_type` in favor of setting the globals vars directly in torch.onnx internal
- Update `GLOBALS.operator_export_type`'s default to not be None to tighten types
- Remove usage of `_set_onnx_shape_inference`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85165
Approved by: https://github.com/BowenBao, https://github.com/AllenTiTaiWang
This PR create the `GraphContext` class and relays all graph methods to _C.Graph as well as implements the `g.op` method. The GraphContext object is passed into the symbolic functions in place of _C.Graph for compatibility with existing symbolic functions.
This way (1) we can type annotate all `g` args because the method is defined and (2) we can use additional context information in symbolic functions. (3) no more monkey patching on `_C.Graph`
Also
- Fix return type of `_jit_pass_fixup_onnx_controlflow_node`
- Create `torchscript.py` to house torch.Graph related functions
- Change `GraphContext.op` to create nodes in the Block instead of the Graph
- Create `add_op_with_blocks` to handle scenarios where we need to directly manipulate sub-blocks. Update loop and if symbolic functions to use this function.
## Discussion
Should we put all the context inside `SymbolicContext` and make it an attribute in the `GraphContext` class? This way we only define two attributes `GraphContext.graph` and `GraphContext.context`. Currently all context attributes are directly defined in the class.
### Decision
Keep GraphContext flatand note that it will change in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84728
Approved by: https://github.com/AllenTiTaiWang, https://github.com/BowenBao
Enable runtime type checking for all torch.onnx public apis, symbolic functions and most helpers (minus two that does not have a checkable type: `_.JitType` does not exist) by adding the beartype decorator. Fix type annotations to makes unit tests green.
Profile:
export `torchvision.models.alexnet(pretrained=True)`
```
with runtime type checking: 21.314 / 10 passes
without runtime type checking: 20.797 / 10 passes
+ 2.48%
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84091
Approved by: https://github.com/BowenBao, https://github.com/thiagocrepaldi
Enable runtime type checking for all torch.onnx public apis, symbolic functions and most helpers (minus two that does not have a checkable type: `_.JitType` does not exist) by adding the beartype decorator. Fix type annotations to makes unit tests green.
Profile:
export `torchvision.models.alexnet(pretrained=True)`
```
with runtime type checking: 21.314 / 10 passes
without runtime type checking: 20.797 / 10 passes
+ 2.48%
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84091
Approved by: https://github.com/BowenBao
This PR adds an internal wrapper on the [beartype](https://github.com/beartype/beartype) library to perform runtime type checking in `torch.onnx`. It uses beartype when it is found in the environment and is reduced to a no-op when beartype is not found.
Setting the env var `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=ERRORS` will turn on the feature. setting `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=DISABLED` will disable all checks. When not set and `beartype` is installed, a warning message is emitted.
Now when users call an api with invalid arguments e.g.
```python
torch.onnx.export(conv, y, path, export_params=True, training=False)
# traning should take TrainingModel, not bool
```
they get
```
Traceback (most recent call last):
File "bisect_m1_error.py", line 63, in <module>
main()
File "bisect_m1_error.py", line 59, in main
reveal_error()
File "bisect_m1_error.py", line 32, in reveal_error
torch.onnx.export(conv, y, cpu_model_path, export_params=True, training=False)
File "<@beartype(torch.onnx.utils.export) at 0x1281f5a60>", line 136, in export
File "pytorch/venv/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception
raise exception_cls( # type: ignore[misc]
beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter training=False violates type hint <class 'torch._C._onnx.TrainingMode'>, as False not instance of <protocol "torch._C._onnx.TrainingMode">.
```
when `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK` is not set and `beartype` is installed, a warning message is emitted.
```
>>> torch.onnx.export("foo", "bar", "f")
<stdin>:1: CallHintViolationWarning: Traceback (most recent call last):
File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 54, in _coerce_beartype_exceptions_to_warnings
return beartyped(*args, **kwargs)
File "<@beartype(torch.onnx.utils.export) at 0x7f1d4ab35280>", line 39, in export
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception
raise exception_cls( # type: ignore[misc]
beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter model='foo' violates type hint typing.Union[torch.nn.modules.module.Module, torch.jit._script.ScriptModule, torch.jit.ScriptFunction], as 'foo' not <protocol "torch.jit.ScriptFunction">, <protocol "torch.nn.modules.module.Module">, or <protocol "torch.jit._script.ScriptModule">.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 63, in _coerce_beartype_exceptions_to_warnings
return func(*args, **kwargs)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 482, in export
_export(
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 1422, in _export
with exporter_context(model, training, verbose):
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 177, in exporter_context
with select_model_mode_for_export(
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 95, in select_model_mode_for_export
originally_training = model.training
AttributeError: 'str' object has no attribute 'training'
```
We see the error is caught right when the type mismatch happens, improving from what otherwise would become `AttributeError: 'str' object has no attribute 'training'`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83673
Approved by: https://github.com/BowenBao
Replace runtime errors in torch.onnx with `errors.SymbolicValueError` for more context around jit values.
- Extend `_unimplemented`, `_onnx_unsupported`, `_onnx_opset_unsupported`, `_onnx_opset_unsupported_detailed` errors to include JIT value information
- Replace plain RuntimeError with `errors.SymbolicValueError`
- Clean up: Use `_is_bool` to replace string comparison on jit types
- Clean up: Remove the todo `Remove type ignore after #81112`
#77316
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83332
Approved by: https://github.com/AllenTiTaiWang, https://github.com/thiagocrepaldi, https://github.com/BowenBao
In some scenarios, by combining a traced model with a scripted function in it, a `%74 : Tensor?[] = prim::ListConstruct(%35, %y_int, %x_int)` (aka List of Optional Tensor) can be generated, which will make `symbolic_helper._is_fp()` fail to read the data type of the specified input.
In such scenario, something like `type = value.type().scalarType()` raises `RuntimeError: r INTERNAL ASSERT FAILED at "/github/pytorch/aten/src/ATen/core/jit_type_base.h":545, please report a bug to PyTorch. ` that refers to
```
template <typename T>
T& expectRef() {
auto* r = castRaw<T>();
AT_ASSERT(r);
return *r;
}
```
What happens is that for `torch._C.TypeList` in this repro, `input.type()` is `torch._C.TypeList` which does not have `scalarType()` method. Instead, `value.type().getElementType().dtype()` should be used to get the underlying type.
This PR tries to use `value.type().getElementType().dtype()` when `isinstance(value.type(), torch.ListType)`.
A unit test is proposed along with the fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81386
Approved by: https://github.com/BowenBao
### Description
- Clearer error messages with more context
- Created `SymbolicValueError` which adds context of the value to the error message
- Type annotation
example error message:
```
torch.onnx.errors.SymbolicValueError: ONNX symbolic does not understand the Constant node '%1 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 3 3 [ CPULongType{2} ]]()
' specified with descriptor 'is'. [Caused by the value '1 defined in (%1 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 3 3 [ CPULongType{2} ]]()
)' (type 'Tensor') in the TorchScript graph. The containing node has kind 'onnx::Constant'.]
Inputs:
Empty
Outputs:
#0: 1 defined in (%1 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 3 3 [ CPULongType{2} ]]()
) (type 'Tensor')
```
### Issue
- #77316 (Runtime error during symbolic conversion)
### Testing
Unit tested
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83007
Approved by: https://github.com/BowenBao
Legacy code has onnx_shape_inference=False by default, which is misleading
as every other export api sets it to True unless otherwise overriden by caller.
There is only two tests that need updating according to this change.
* test_utility_funs.py::test_constant_fold_shape. The resulting number of nodes
in graph is increased by 1, due to that previously the extra constant node was
added as initializer.
* test_utility_funs.py::test_onnx_function_substitution_pass. Enabling onnx
shape inference discovered discrepancy in test input shape and supplied dynamic
axes arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82767
Approved by: https://github.com/justinchuby, https://github.com/abock
### Description
<!-- What did you change and why was it needed? -->
Remove unused patching methods:
- `torch._C.Graph.constant`
- unpatch `torch._C.Node.__getitem__` and move the helper function to `symbolic_helper`
Add typing annotations
### Issue
<!-- Link to Issue ticket or RFP -->
#76254
### Testing
<!-- How did you test your change? -->
Unit tested
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83006
Approved by: https://github.com/BowenBao
Re-land #81953
Add `_type_utils` for handling data type conversion among JIT, torch and ONNX.
- Replace dictionary / list indexing with methods in ScalarType
- Breaking: **Remove ScalarType from `symbolic_helper`** and move it to `_type_utils`
- Deprecated: "cast_pytorch_to_onnx", "pytorch_name_to_type", "scalar_name_to_pytorch", "scalar_type_to_onnx", "scalar_type_to_pytorch_type" in `symbolic_helper`
- Deprecate the type mappings and lists. Remove all internal references
- Move _cast_func_template to opset 9 and remove its reference elsewhere (clean up). Added documentation for easy discovery
Why: List / dictionary indexing and lookup are error-prone and convoluted.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82995
Approved by: https://github.com/kit1980
Add `_type_utils` for handling data type conversion among JIT, torch and ONNX.
- Replace dictionary / list indexing with methods in ScalarType
- Breaking: **Remove ScalarType from `symbolic_helper`** and move it to `_type_utils`
- Breaking: **Remove "cast_pytorch_to_onnx", "pytorch_name_to_type", "scalar_name_to_pytorch", "scalar_type_to_onnx", "scalar_type_to_pytorch_type"** from `symbolic_helper`
- Deprecate the type mappings and lists. Remove all internal references
- Move _cast_func_template to opset 9 and remove its reference elsewhere (clean up). Added documentation for easy discovery
Why: List / dictionary indexing and lookup are error-prone and convoluted.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81953
Approved by: https://github.com/AllenTiTaiWang, https://github.com/BowenBao
When `TrainingMode.PRESERVE` is set for export, the exporter used to change the model's training mode based on some logic. Now we respect the option and not touch the model's training state.
- Previously `_set_training_mode`'s behavior doesn't match what the global variable expects. This PR removes the deprecated `_set_training_mode` and makes the type correct.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78583
Approved by: https://github.com/BowenBao
- Add quantization support for `interpolate`, `avgpool`, `sigmoid` and `add_relu`
- Return the inputs to ListUnpack if the previous node is ListConstruct so that `ListConstruct` and `ListUnpack` are canceled and removed in the jit passes. ONNX doesn't support them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78103
Approved by: https://github.com/garymm
Add support for decorating functions with variable length arguments in `quantized_args`. This is needed to decorate functions like `symbolic_fn` in `_interpolate_helper` which takes `*args`.
Previously it is not possible to decorate functions like it. Now we can do
```python
@quantized_args(True)
def symbolic_fn(g, input, output_size, *args):
...
```
and the rest of the params are defaulted to non-quantized.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78775
Approved by: https://github.com/garymm
`check_training_mode` always warned that an op is set to training because it was comparing an int `op_train_mode` with an Enum `GLOBALS.training_mode`. This PR fixes the behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78376
Approved by: https://github.com/garymm
Use pyupgrade(https://github.com/asottile/pyupgrade) and flynt to modernize python syntax
```sh
pyupgrade --py36-plus --keep-runtime-typing torch/onnx/**/*.py
pyupgrade --py36-plus --keep-runtime-typing test/onnx/**/*.py
flynt torch/onnx/ --line-length 120
```
- Use f-strings for string formatting
- Use the new `super()` syntax for class initialization
- Use dictionary / set comprehension
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77935
Approved by: https://github.com/BowenBao
Cleaning up onnx module imports to prepare for updating `__init__`.
- Simplify importing the `_C` and `_C._onnx` name spaces
- Remove alias of the symbolic_helper module in imports
- Remove any module level function imports. Import modules instead
- Alias `symbilic_opsetx` as `opsetx`
- Fix some docstrings
Requires:
- https://github.com/pytorch/pytorch/pull/77448
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77423
Approved by: https://github.com/BowenBao
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
