For onnx MaxPool with ceil_mode=1, the sliding windows that starts in the right padded region won't be ignored, which causes different output shape with torch.
Therefore, need to add Pad op before and not to set ceil_mode for MaxPool op like what is done in symbolic_opset9 when convertting torch max_pool to onnx MaxPool.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106270
Approved by: https://github.com/thiagocrepaldi
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
- In #102759, the support for `quantized::conv_transposeNd` was introduced. This incorrectly set `output_padding` to all zeros. Turns out, you can specify output_padding in PyTorch, but this parameter was not being unpacked correctly and thus did not show up in the python torch->onnx code.
- This adds unpacking of output_padding in `unpack_quantized_weights.cpp` when needed. It also adds this as a parameter in the python functions and uses that (and removes the all-zero defaults)
- Another issue with #102759 is that it only added these new ops to opset10 without adding the ability to specify axis in opset13. This PR also fixes this.
Fixes#104206
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104207
Approved by: https://github.com/BowenBao
- Extend support:
- quantized::conv1d
- quantized::conv3d
- quantized::conv3d_relu
- quantized::conv_transpose1d
- quantized::conv_transpose2d
- quantized::conv_transpose3d
- Note: quantized::{conv1d_relu,conv2d,conv2d_relu} already supported.
- To support this, quantization unpacking added for:
- conv1d
- conv_transpose1d
- conv_transpose2d
- conv_transpose3d
- Note: conv3d/conv3d_relu already had weights unpacking set up, even though it didn't have torch.onnx support.
- Add tests.
- The 3D tests will fail if run with the qnnpack backend (e.g., on Apple silicon Mac), so added decorator skipIfQuantizationBackendQNNPack.
- Minor fix in `aten/src/ATen/native/quantized/cpu/qconv.cpp` for 3D convolutions (triggered by added tests).
Fixes#102747
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102759
Approved by: https://github.com/BowenBao, https://github.com/thiagocrepaldi, https://github.com/kit1980
* 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
The `_cast_` family of symbolic functions has been created from a template function. Even though it saved some lines, it very much obscured the intention of the code. Since the list doesn't really change and the `_cast_` family are IIRC deprecated, it is safe for us to expand the templates and make the code more readable.
This PR also removes any direct calls to `_cast_` functions to maintain a consistent pattern of directly creating `Cast` nodes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87666
Approved by: https://github.com/BowenBao
According to #38248, quantized::conv1d_relu shares packing parameters with Conv2D (kspatialDim is also 2), and needs a different unpacking way. Therefore, a new `QuantizedParamsType=Conv1D` is used to differentiate the two, and has to extract 1D information from 2D packed parameters.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85997
Approved by: 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
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
- Turn all string comparison on node types into `isinstance` checks
- Update error message in the device op to include the unexpected type's name: `RuntimeError: Unsupported: ONNX export of operator prim::device, output type should be 'DeviceObjType', not '<some unknown type>'. Please feel free to request support or submit a pull request on PyTorch GitHub.`
Tested:
Unit test in `test/onnx/test_pytorch_onnx_onnxruntime.py::TestONNXRuntime_opset13::test_to_device`
Follow up of #78085
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78114
Approved by: https://github.com/garymm
- 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
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
PyTorch restricts activations to be in the range (0, 127).
In ONNX, the supported ranges are (0, 255) and (-128, 127),
respectfully, uint8 and int8. This PR extends support for range
(0, 127), by adding additional clipping when detected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76055
Approved by: https://github.com/garymm
Extending the support for quantization with per channel quantization.
An extra attribute `axis` can be found for per channel quantized tensors,
most commonly in quantized weight of Convolution or Linear module.
The PR adds support to correctly parse the `axis` attribute, and map to
ONNX representation in `QuantizeLinear` and `DequantizeLinear`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76002
Approved by: https://github.com/garymm
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
Original `bias` is float in PyTorch. Quantization is applied in kernel.
To mimic behavior in ONNX, export the `bias` quantization step,
then append the dequantization step to ready `bias` for unquantized operators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73336
