beartype has served us well in identifying type errors and ensuring we call internal functions with the correct arguments (thanks!). However, the value of having beartype is diminished because of the following:
1. When beartype improves support for better Dict[] type checking, it discovered typing mistakes in some functions that were previously uncaught. This caused the exporter to fail with newer versions beartype when it used to succeed. Since we cannot fix PyTorch and release a new version just because of this, it creates confusion for users that have beartype in their environment from using torch.onnx
2. beartype adds an additional call line in the traceback, which makes the already thick dynamo stack even larger, affecting readability when users diagnose errors with the traceback.
3. Since the typing annotations need to be evaluated, we cannot use new syntaxes like `|` because we need to maintain compatibility with Python 3.8. We don't want to wait for PyTorch take py310 as the lowest supported Python before using the new typing syntaxes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130484
Approved by: https://github.com/titaiwangms
beartype has served us well in identifying type errors and ensuring we call internal functions with the correct arguments (thanks!). However, the value of having beartype is diminished because of the following:
1. When beartype improves support for better Dict[] type checking, it discovered typing mistakes in some functions that were previously uncaught. This caused the exporter to fail with newer versions beartype when it used to succeed. Since we cannot fix PyTorch and release a new version just because of this, it creates confusion for users that have beartype in their environment from using torch.onnx
2. beartype adds an additional call line in the traceback, which makes the already thick dynamo stack even larger, affecting readability when users diagnose errors with the traceback.
3. Since the typing annotations need to be evaluated, we cannot use new syntaxes like `|` because we need to maintain compatibility with Python 3.8. We don't want to wait for PyTorch take py310 as the lowest supported Python before using the new typing syntaxes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130484
Approved by: https://github.com/titaiwangms
beartype has served us well in identifying type errors and ensuring we call internal functions with the correct arguments (thanks!). However, the value of having beartype is diminished because of the following:
1. When beartype improves support for better Dict[] type checking, it discovered typing mistakes in some functions that were previously uncaught. This caused the exporter to fail with newer versions beartype when it used to succeed. Since we cannot fix PyTorch and release a new version just because of this, it creates confusion for users that have beartype in their environment from using torch.onnx
2. beartype adds an additional call line in the traceback, which makes the already thick dynamo stack even larger, affecting readability when users diagnose errors with the traceback.
3. Since the typing annotations need to be evaluated, we cannot use new syntaxes like `|` because we need to maintain compatibility with Python 3.8. We don't want to wait for PyTorch take py310 as the lowest supported Python before using the new typing syntaxes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130484
Approved by: https://github.com/titaiwangms
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
## Summary
The change brings the new registry for symbolic functions in ONNX. The `SymbolicRegistry` class in `torch.onnx._internal.registration` replaces the dictionary and various functions defined in `torch.onnx.symbolic_registry`.
The new registry
- Has faster lookup by storing only functions in the opset version they are defined in
- Is easier to manage and interact with due to its class design
- Builds the foundation for the more flexible registration process detailed in #83787
Implementation changes
- **Breaking**: Remove `torch.onnx.symbolic_registry`
- `register_custom_op_symbolic` and `unregister_custom_op_symbolic` in utils maintain their api for compatibility
- Update _onnx_supported_ops.py for doc generation to include quantized ops.
- Update code to register python ops in `torch/csrc/jit/passes/onnx.cpp`
## Profiling results
-0.1 seconds in execution time. -34% time spent in `_run_symbolic_function`. Tested on the alexnet example in public doc.
### After
```
└─ 1.641 export <@beartype(torch.onnx.utils.export) at 0x7f19be17f790>:1
└─ 1.641 export torch/onnx/utils.py:185
└─ 1.640 _export torch/onnx/utils.py:1331
├─ 0.889 _model_to_graph torch/onnx/utils.py:1005
│ ├─ 0.478 _optimize_graph torch/onnx/utils.py:535
│ │ ├─ 0.214 PyCapsule._jit_pass_onnx_graph_shape_type_inference <built-in>:0
│ │ │ [2 frames hidden] <built-in>
│ │ ├─ 0.190 _run_symbolic_function torch/onnx/utils.py:1670
│ │ │ └─ 0.145 Constant torch/onnx/symbolic_opset9.py:5782
│ │ │ └─ 0.139 _graph_op torch/onnx/_patch_torch.py:18
│ │ │ └─ 0.134 PyCapsule._jit_pass_onnx_node_shape_type_inference <built-in>:0
│ │ │ [2 frames hidden] <built-in>
│ │ └─ 0.033 [self]
```
### Before
### Start up time
The startup process takes 0.03 seconds. Calls to `inspect` will be eliminated when we switch to using decorators for registration in #84448
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84382
Approved by: https://github.com/AllenTiTaiWang, https://github.com/BowenBao
### 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
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
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/34629
Add support for sigmoid in the conversion flow through onnx
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_quantized_sigmoid
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_small_model
Imported from OSS
Differential Revision: D20433680
fbshipit-source-id: 95943e14637d294122e4d102c5c19c06d27064c6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33945
Add mapping for this operator in symbolics
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_max_pool2d
Imported from OSS
Differential Revision: D20433681
fbshipit-source-id: 88f02ade698262a6f8824671830bc1f7d40bbfa6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30887
Support to convert quantized concat from pytorch to caffe2
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_cat
Imported from OSS
Differential Revision: D18855676
fbshipit-source-id: 5d0cf3f03c61819e168b080afa368b1255d0419c
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/30498
Updated Int8SliceOp to accept dim, start and end index similar to Pytorch.
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_slice
Imported from OSS
Differential Revision: D18740519
fbshipit-source-id: 2313f37a4936edb150ce04911b241e591e191801
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30490
Add symbolic mapping to Int8AvgPool2d and Int8Reshape op in C2
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps
Imported from OSS
Differential Revision: D18740520
fbshipit-source-id: 1606125500c4b549fbc984e7929b7fd5204396a0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30202
Pytorch Upsample operator has output_size as an argument.
For quantized tensor inputs we cannot get the input_size to calculate the width and height scale factor.
Instead we pass the output_size directly to caffe2 to calculate the scale factors.
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_upsample
Imported from OSS
Differential Revision: D18631478
fbshipit-source-id: 38a39129bc863f4ecf2293acc068e40ab7edc825
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
