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/73947
The original implementation of memoryless observers used MinMaxObservers and
a memoryless argument to manipulate the behavior of the observer such that it wouldn't
keep track of previously observed min and max's. It was later pointed
out that this was equivalent to a movingaverageobserver with averaging_constant=1
which is requires less overhead and no 1 off args (memoryless) so this PR refactors
the memoryless arg and uses MovingAverage observers instead, although the memoryless
adjective is still used, a complete definintion was also added to clarify error
messages given these changes.
TestPlan
python test/test_quantization.py TestQuantizeEagerQAT
python test/test_quantization.py TestObserver
Test Plan: Imported from OSS
Reviewed By: andrewor14
Differential Revision: D34732080
Pulled By: HDCharles
fbshipit-source-id: 227a1ab29d18adae55093a684ea35ac34523d07a
(cherry picked from commit 5238e70e8f90f3219c36f9c64b647951dcf64b5a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69864
att, will have a follow up PR that removes QConfigDynamic in the api
Test Plan:
regression tests
```
python test/test_quantization.py TestPostTrainingStatic
python test/test_quantization.py TestPostTrainingDynamic
python test/test_quantization.py TestQuantizeFx
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D33073235
fbshipit-source-id: 6c1a1647032453803c55cdad7c04154502f085db
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69333
Original PR reverted due to break with incompatible qengine on Mac OS, this diff fixes that.
Support QAT workflow by using torch.fx QAT API. e.g. `prepare_qat_fx` and `convert_fx`.
Test Plan:
`pytest test/quantization/fx/test_quantize_fx.py -v -k "test_qat_embedding_linear"`
Imported from OSS
Reviewed By: jingsh
Differential Revision: D32814827
fbshipit-source-id: f7a69d2b596f1276dc5860b397c5d5d07e5b9e16
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69249
This PR added default_replay_qconfig and default_replay_observer which is used
when we want to configure an operator to reuse the observer from input, if the input
Tensor for the operator is not observed, we will not observe the output of this operator either,
if the input Tensor is observed, we will observe the output of the operator with the same observer.
e.g.
```
x1 = x0.reshape()
```
if reshape is configured with default_replay_qconfig:
1. if x0 is observed with observer_0, we'll observe x1 with the same observer instance
2. if x0 is not observed, we won't observe x1 either
Test Plan:
```
python test/test_quantization.py TestQuantizeFx.test_replay_qconfig
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32774723
fbshipit-source-id: 26862b2bc181d0433e2243daeb3b8f7ec3dd33b2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68067
Embedding QAT uses a NoopObserver class for activation,
and a FakeQuant for weight, make sure that qconfig comparison
functions properly for a mix of partial function and class in
qconfig.
Test Plan:
`pytest test/quantization/eager/test_quantize_eager_qat.py -v -k "test_embedding_qat_qconfig_equal"`
Imported from OSS
Reviewed By: HDCharles
Differential Revision: D32318434
fbshipit-source-id: c036eef9cbabe7c247745930501328e9c75a8cb0
Summary:
**Summary:** This commit adds the `torch.nn.qat.dynamic.modules.Linear`
module, the dynamic counterpart to `torch.nn.qat.modules.Linear`.
Functionally these are very similar, except the dynamic version
expects a memoryless observer and is converted into a dynamically
quantized module before inference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67325
Test Plan:
`python3 test/test_quantization.py TestQuantizationAwareTraining.test_dynamic_qat_linear`
**Reviewers:** Charles David Hernandez, Jerry Zhang
**Subscribers:** Charles David Hernandez, Supriya Rao, Yining Lu
**Tasks:** 99696812
**Tags:** pytorch
Reviewed By: malfet, jerryzh168
Differential Revision: D32178739
Pulled By: andrewor14
fbshipit-source-id: 5051bdd7e06071a011e4e7d9cc7769db8d38fd73
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66379
Description:
Creates a quantization API reference and fixes all the docblock errors.
This is #66122 to #66210 squashed together
Test Plan:
```
cd docs
make html
python -m http.server
// open webpage, inspect it, looks good
```
Reviewed By: ejguan
Differential Revision: D31543172
Pulled By: vkuzo
fbshipit-source-id: 9131363d6528337e9f100759654d3f34f02142a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66129
Adds a documentation page for `torch.ao.quantization.QConfig`. It is useful
for this to have a separate page since it shared between Eager and FX graph
mode quantization.
Also, ensures that all important functions and module attributes in this
module have docstrings, so users can discover these without reading the
source code.
Test Plan:
```
cd docs
make html
python -m http.server
// open webpage, inspect it, renders correctly
```
Reviewed By: jerryzh168
Differential Revision: D31447614
Pulled By: vkuzo
fbshipit-source-id: 5d9dd2a4e8647fa17b96cefbaae5299adede619c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65674
Before this PR user had to use the eager mode static quantization APIs to quantize Embedding/EmbeddingBag modules.
With this PR they can use either the static or dynamic quantization APIs for Embedding quantization
The only qconfig supported for embedding quantization is float_qparams_weight_only_qconfig whcih is currently enforced in the from_float
method of the quantized Embedding/Embedding modules.
To combine embedding quantization with Linear dynamic quantization, user can use the qconfig_dict to specify different qconfig for each module type.
The prepare/convert APIs can still be used to quantize Embeddings, with the caveat that user need to ensure input to Embedding ops are FP32.
Addresses Issue #65185
ghstack-source-id: 139935419
Test Plan:
python test/test_quantization.py
Imported from OSS
Reviewed By: gchanan
Differential Revision: D31211199
fbshipit-source-id: 8c747881caee5ccbf8b93c6704b08d132049dea4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66058
After the initial migration from `torch.quantization` to `torch.ao.quantization`, some of the files did not change.
This happened because the migration was done in parallel, and some of the files were landed while the others were still in the original location.
This is the last fix in the AO migration phase 1, which completely enables the ao.quantization namespace.
Test Plan: `python test/test_quantization.py`
Reviewed By: vkuzo
Differential Revision: D31366066
Pulled By: z-a-f
fbshipit-source-id: bf4a74885be89d098df2d87e685795a2a64026c5
