Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70009
Currently we rely on module.training to decide whether we'll do a qat fusion or ptq fusion, this is
not ideal since training flag has nothing to do with quantization, this PR introduces an extra flag `is_qat`
to control this
Note: currently we still has the constraint that when `is_qat` is True, the modules must be in training mode, we
can relax this constraint later
Test Plan:
```
python test/test_quantization.py TestFuseFx
python test/test_quantization.py TestFusion
```
Imported from OSS
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Reviewed By: mruberry
Differential Revision: D33178977
fbshipit-source-id: 0c1499c45526971140d9ad58e2994d1edf5ad770
(cherry picked from commit 2d51f9fb28)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70022
Add support for fusing ConvTranpose{1,2,3}d with BatchNorm{1,2,3}d. This re-uses the existing fusion logic but adds a "transpose" flag to the fusing function which when enabled will use the appropriate reshape for ConTranspose's transposed weights.
Test Plan: `buck test mode/dev //caffe2/test:quantization -- -r quantization.eager.test_fusion.TestFusion`
Reviewed By: jerryzh168
Differential Revision: D33074405
fbshipit-source-id: 5e9eff1a06d8f98d117e7d18e80da8e842e973b7
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/70006
reland: fixing some mypy errors that was missed before
This PR enables fuse handler for sequence of three ops, and merges all fuse handlers into one
TODO: we can also move this to backend_config_dict folder
Test Plan:
regression fusion test
```
python test/test_quantization.py TestFuseFx
```
Imported from OSS
Imported from OSS
Reviewed By: supriyar
Differential Revision: D33144606
fbshipit-source-id: ca34f282018a0fb4d04c7e35119eaf2d64258e78
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69658
This PR enables fuse handler for sequence of three ops, and merges all fuse handlers into one
TODO: we can also move this to backend_config_dict folder
Test Plan:
regression fusion test
```
python test/test_quantization.py TestFuseFx
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32974907
fbshipit-source-id: ba205e74b566814145f776257c5f5bb3b24547c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69644
This PR cleans up the init of ModuleReLUFuseHandler and moved all `module - relu`
fusion pattern to use this handler
also disabled additional_fuser_method argument temporarily, will enable
after we bring back the simple pattern format
Test Plan:
```
python test/test_quantize_fx.py TestFuseFx
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32974906
fbshipit-source-id: 23483ea4293d569cb3cec6dadfefd4d9f30921a7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69335
This PR added support for configuring fusion with:
"pattern", "fuser_method"
This only works for simple sequence of 2 op patterns currently, will extend this in future PRs
Test Plan:
regresion test on linear-relu fusion:
```
python test/fx2trt/test_quant_trt.py TestQuantizeFxTRTOps
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32816164
fbshipit-source-id: f300b7b96b36908cb94a50a8a17e0e15032509eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68770
Previous fusion only works for a sequnce of ops, which is not general enough for fusion patterns
that is defined by a subgraph, this PR refactors that to make it more general
Test Plan:
```
python test/test_quantization.py TestFuseFx
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32602637
fbshipit-source-id: a7897c62081b9d71c67fb56e78484cf68deaacf6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67876
Previously we miss it when we call obj.convert and this argument would not impact the fusion.
This PR fixes it and adds a test for it
Test Plan:
python test/test_quantization.py TestFuseFx
Imported from OSS
Reviewed By: malfet
Differential Revision: D32191364
fbshipit-source-id: 566bd39461010d70a21de71f611bb929976fe01d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66484https://github.com/pytorch/pytorch/pull/50748 added linear - bn1d fusion
in Eager mode, for PTQ only. This PR also enables this in FX graph mode.
We reuse the existing conv-bn-relu fusion handler, renaming `conv` to
`conv_or_linear` for readability.
The QAT version is saved for a future PR, for both eager and FX graph.
Test Plan:
```
python test/test_quantization.py TestFuseFx.test_fuse_linear_bn_eval
```
Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D31575392
fbshipit-source-id: f69d80ef37c98cbc070099170e335e250bcdf913
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
