Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73863
This PR fully aligns the convert function with the design: https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md
and simplifies the implementation of convert function by always produce a reference quantized model (with reference patterns) first,
and then lower the model to a quantized model that is runnable with PyTorch native backend (fbgemm/qnnpack).
This PR makes the convert.py much easier to understand than the previous implementation, and we are able to remove majority of code
in quantization_patterns.py as well (in followup PRs).
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
python test/test_quantization.py TestFXNumericSuiteCoreAPIs
python test/test_quantization.py TestFXNumericSuiteCoreAPIsModels
```
and other internal/oss regression tests
Imported from OSS
Reviewed By: andrewor14
Differential Revision: D34778506
fbshipit-source-id: 0678b66addf736039a8749b352f6f569caca962b
(cherry picked from commit 33ec9caf23f3ab373d827117efbd9db0668b2437)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70757
This is an initial PR on a way to preserve stack traces throughout FX
graph mode quantization. It preserves the stack traces for ops
for all of the quantize handlers. A future PR will add stack traces
for dtype transitions.
Test Plan:
```
python test/test_quantization.py
TestQuantizeFx.test_stack_trace_preserved
```
Note: the above only tests a single case. In a future PR, once we
expand coverage, we can expand the utility functions to check for stack
traces on all tests.
```
python test/test_quantization.py
TestQuantizeFx.test_stack_trace_preserved
```
Imported from OSS
Differential Revision:
D33432485
D33432485
Reviewed By: jerryzh168
Pulled By: vkuzo
fbshipit-source-id: 56c56850393132487430a850fa1def826a9c39c0
(cherry picked from commit c11155b31e)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69720
This function is also useful for DBR quant, moving it from FX utils
to common utils.
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeDBR
```
Reviewed By: jerryzh168
Differential Revision: D33003473
Pulled By: vkuzo
fbshipit-source-id: 20360682c69d614a645c14fc29d3ee023d6b2623
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67537
This PR adds support for quantizing torch.addmm to produce a reference quantized pattern,
and also adds support in the backend_config_dict api that allows people to specify the input, weight and bias input for each input:
```
addmm_config = {
"pattern": torch.addmm,
"observation_type": ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
"dtype_configs": [
weighted_op_qint8_dtype_config,
],
# a map from input type to input index
"input_type_to_index": {
"bias": 0,
"input": 1,
"weight": 2,
}
}
```
This requires some changes in getting weight_dtype and bias_dtype in the type inference stage of prepare, which will be added in the previous PR
Test Plan:
```
pytho test/fx2trt/test_quant_trt.py TestQuantizeFxTRT.test_addmm
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32014998
fbshipit-source-id: 8d96c1e8b7ebb2ab385c08a5b1e43f2d5a2cbcbe
Summary:
- [x] Fix the Pyre type checking errors in `torch/quantization/fx/utils.py`
```
torch/quantization/fx/utils.py:490:4 Incompatible variable type [9]: target_module_type is declared to have type `Type[nn.modules.module.Module]` but is used as type `None`.
```
Fixes the issue: [MLH-Fellowship/pyre-check/issues/75](https://github.com/MLH-Fellowship/pyre-check/issues/75)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66311
Reviewed By: pradeep90
Differential Revision: D31506399
Pulled By: 0xedward
fbshipit-source-id: 3d866fba6005452378d4a2613b8689fa2d7a8b67
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61647
`prepare_fx` currently assumes that bias is always a positional argument to
convolutions, and only a keyword argument to other functions. This happens to work
today due to a quirk in how `__torch_function__` is handled for python
functions but shouldn't be considered stable.
Instead, we should support `bias` for both positional and keyword forms.
cc jerryzh168 jianyuh raghuramank100 jamesr66a vkuzo
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D31401360
Pulled By: albanD
fbshipit-source-id: 1e2f53d80e2176b870f326dc498e251e2386136e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65033
1. Move the file:
```
hg mv caffe2/torch/quantization/fx caffe2/torch/ao/quantization/fx
hg mv caffe2/torch/quantization/quantize_fx.py caffe2/torch/ao/quantization/quantize_fx.py
```
2. Create new files
```
touch caffe2/torch/quantization/quantize_fx.py
touch caffe2/torch/quantization/fx/__init__.py
```
3. import things in the new files
4. add tests to test/quantization/ao_migration/test_quantization_fx.py
this is because we have some fx import in quantize_fx and fx/*.py
Test Plan: buck test mode/dev //caffe2/test:quantization
Reviewed By: vkuzo, z-a-f
Differential Revision: D30949749
fbshipit-source-id: 9e5d4d039c8a0a0820bc9040e224f0d2c26886d3
