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
* CI Test environment to install onnx and onnx-script.
* Add symbolic function for `bitwise_or`, `convert_element_type` and `masked_fill_`.
* Update symbolic function for `slice` and `arange`.
* Update .pyi signature for `_jit_pass_onnx_graph_shape_type_inference`.
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: Ti-Tai Wang <titaiwang@microsoft.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94564
Approved by: https://github.com/abock
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
ONNX and PyTorch has different equation on pooling and different strategy on ceil_mode, which leads to discrepancy on corner case (#71549 ).
Specifically, PyTorch avereage pooling is not following [the equation on documentation](https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html), it allows sliding window to go off-bound instead, if they start within the left padding or the input (in NOTE section). More details can be found in #57178.
This PR changes avgpool in opset 10 and 11 back the way as opset 9, which it stops using ceil_mode and count_include_pad in onnx::AveragePool
A comprehensive test for all combinations of parameters can be found in the next PR. #87893
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87892
Approved by: https://github.com/BowenBao
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
This is the 4th PR in the series of #83787. It enables the use of `@onnx_symbolic` across `torch.onnx`.
- **Backward breaking**: Removed some symbolic functions from `__all__` because of the use of `@onnx_symbolic` for registering the same function on multiple aten names.
- Decorate all symbolic functions with `@onnx_symbolic`
- Move Quantized and Prim ops out from classes to functions defined in the modules. Eliminate the need for `isfunction` checking, speeding up the registration process by 60%.
- Remove the outdated unit test `test_symbolic_opset9.py`
- Symbolic function registration moved from the first call to `_run_symbolic_function` to init time.
- Registration is fast:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84448
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
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
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
Part of #79263
This PR fixes the following matters:
1. Before this fix, the reduced output has `[1]` shape when `dim = None` and `keepdim = False`. Now the output is reduced to `[]` shape, which matches Pytorch's behavior.
2. Before this fix, the output is always casted to `Long`. Now the output is casted to the input's dtype.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79506
Approved by: https://github.com/justinchuby, 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
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
Currently ONNX exporter symbolics can emit ATen operators when `operator_export_type==ONNX_ATEN_FALLBACK`. However, this is a behavior specific to Caffe2 builds, as the intend use of `ONNX_ATEN_FALLBACK` is to emit ATen operators only when there is no ONNX equivalent.
The reason Caffe2 choses to emit ATen operators when ONNX counterpart exists is for performance on their particular engine implementation, which might not be true for other implementations. e.g. ONNX Runtime can optimize the generated ONNX graph into something more efficient
This PR must be merged only after https://github.com/pytorch/pytorch/pull/73954
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74680
Approved by: https://github.com/garymm, https://github.com/malfet
This PR introduces 3 BC changes:
First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type.
Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set.
Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion.
ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
There are a few ONNX operators do not support non-float (e.g., integer) inputs at early versions. For example, Clip supports non-float types until [opset 12](https://github.com/onnx/onnx/blob/main/docs/Changelog.md#type-constraints-280), that said older versions like [opset 6](https://github.com/onnx/onnx/blob/main/docs/Changelog.md#type-constraints-107) cannot deal with integer types.
I initially find such a bug in Clip (https://github.com/pytorch/pytorch/pull/70584), but later found more:
1. Clip < 12;
2. Min/Max < 12;
3. ReLU < 14;
4. Pad < 11;
In PyTorch, if we export Max-11 with integer inputs, actually the exportation will succeed; however, fail when imported by other frameworks like ONNXRuntime.
```python
import torch
class Net(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x: torch.Tensor):
return torch.max(x, x + 1)
net = Net()
onnx_model = 'test.onnx'
torch.onnx.export(net, (torch.zeros((3, 3), dtype=torch.int32),),
onnx_model, verbose=True, opset_version=11)
```
This is an unexpected behavior as we want to ensure that every model exported by PyTorch is valid (https://github.com/pytorch/pytorch/pull/70584#issuecomment-1020636579). Theoretically, we can simply forbid such cases (e.g., `Clip<int>` < 12, `ReLU<int>` < 14). But actually we can enhance the compatibility and flexibility of PyTorch by simply casting inputs of those operators into float tensors, which allows the float operator functions, and then casting it back to original types.
This PR implements the second approach to achieve better compatibility in PyTorch.
@garymm @thiagocrepaldi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72401
Approved by: https://github.com/garymm, https://github.com/thiagocrepaldi
Fixes#74142
Previous check `dim is not None and end_dim == dim - 2` didn't consider `end_dim` being negative. However the error only occurs when input tensor has rank 1, and the rank is known to symbolic function. So a better fix is to return `input` directly when rank is 1.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74595
Approved by: https://github.com/garymm
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)
