Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57388
It's a bit confusing to have this be a decorator. It's simpler to
just expose it as a function on qhandler.
Test Plan:
```
python test/test_quantization.py TestQuantizeFxOps
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D28129411
fbshipit-source-id: f7316f285e8546c67e8d8cf753462b2c2abb2636
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48343
Annotates the 4 class variables on `Quantizer` with real types,
fixing the small things uncovered by this along the way.
Test Plan:
```
mypy torch/quantization/
python test/test_quantization.py TestQuantizeFx
```
Imported from OSS
Reviewed By: supriyar
Differential Revision: D25136212
fbshipit-source-id: 6ee556c291c395bd8d8765a99f10793ca738086f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48062
When Embedding/EmbeddingBag are configured with static quant we'll skip inserting observer for
them in the graph and keep the op unchanged and print a warning.
This also aligns with eager mode behavior as well.
We'll enforce this behavior for other ops that only supports dynamic/weight_only quant but not static quant as well.
We used a global variable `DEFAULT_NOT_OBSERVED_QUANTIZE_HANDLER`, this is not exposed to user right now,
we can add that later if needed.
Test Plan: Imported from OSS
Reviewed By: supriyar
Differential Revision: D25007537
fbshipit-source-id: 6ab9e025269b44bbfd0d6dd5bb9f95fe3ca9dead
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46337
We plan to pass around the mappings instead of using global registration api to keep
the mappings local to the transformations user is performing
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D24317436
fbshipit-source-id: 81569b88f05eeeaa9595447e482a12827aeb961f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45672
This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: z-a-f
Differential Revision: D24053439
fbshipit-source-id: 03d545e26a36bc22a73349061b751eeb35171e64
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45292
This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23913105
fbshipit-source-id: 4e335286d6de225839daf51d1df54322d52d68e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43914
Renames `matches` function to `is_match`, since there is also
a list named `matches` we are passing around in `Quantizer`,
and would be good to decrease name conflicts.
Test Plan:
```
python test/test_quantization.py TestQuantizeFxOps
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23435601
fbshipit-source-id: 394af11e0120cfb07dedc79d5219247330d4dfd6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43175
This PR added graph mode quantization on fx: https://github.com/pytorch/pytorch/pull/42741
Currently it matches eager mode quantization for torchvision with static/dynamic/qat
ddp/synbn test is still wip
Test Plan:
python test/test_quantization.py TestQuantizeFx
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23178602
fbshipit-source-id: 8e7e0322846fbda2cfa79ad188abd7235326f879
