Fix use-dict-literal pylint suggestions by changing `dict()` to `{}`. This PR should do the change for every Python file except test/jit/test_list_dict.py, where I think the intent is to test the constructor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83718
Approved by: https://github.com/albanD
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75401
This commit removes asserts that require prepare_fx to
be run in eval mode and prepare_qat_fx to be run in training mode.
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_prepare_mode
Imported from OSS
Reviewed By: vkuzo, jerryzh168
Differential Revision: D35457100
fbshipit-source-id: 13a55b13d9e389991f69c06c6a70bc51cdebba36
(cherry picked from commit fb0685e0873dc8e807da3213be403b51e8b4a687)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72735
We use `get_matched_types` to get the (type) pattern from matched modules.
And we need to use MatchAllNode instead of type(MatchAllNode) to query the fuser_method for the pattern
Test Plan:
TODO
Imported from OSS
Reviewed By: raghuramank10000
Differential Revision: D34180705
fbshipit-source-id: db9b6e791a9f26b70079fddc95fce033052199ab
(cherry picked from commit 01d38afabcb1bfc207dee7d49ee13df500d32fdf)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72431
Adds support for a fused QAT observed module for `Linear` followed by
`BatchNorm1d`. In this PR, only the support for prepared module with
fake_quants in the right places is added.
A future PR will add support for `convert`, and tests for eager and FX
graph mode workflows.
Similar to conv-bn, we rescale the weight before applying the fake
quant, and undo the rescaling after the linear operation.
Test Plan:
```
python test/test_quantization.py TestQuantizeEagerQATNumerics.test_linear_bn
```
Imported from OSS
Reviewed By: jerryzh168, raghuramank10000
Differential Revision: D34044427
fbshipit-source-id: 47a519173939ca4824d2c6e6ea7a599764a8ed10
(cherry picked from commit bfc75fe078)
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
**Static Docs Preview: classyvision**
|[Full Site](https://our.intern.facebook.com/intern/staticdocs/eph/D33178977/V36/classyvision/)|
|**Modified Pages**|
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/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/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/64919
AO Team is migrating the existing torch.quantization into torch.ao.quantization. We are doing it one file at a time to make sure that the internal callsites are updated properly. This migrates the quantization utilities.
ghstack-source-id: 138303325
Test Plan: `buck test mode/dev //caffe2/test:quantization`
Reviewed By: jerryzh168
Differential Revision: D30899082
fbshipit-source-id: 85eb38c419e417147e71758b682cd095308dd0c9
