Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new Buffer class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the register_buffer method has not been changed. The persistent parameter in the Buffer type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new Buffer type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the Buffer type can be used as a drop in replacement for register_buffer as it just leads to register_buffer being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125971
Approved by: https://github.com/albanD, https://github.com/anijain2305, https://github.com/mlazos
Summary:
After QAT is completed or given pre-tuned weight observer via tunable PTQ algorithm, it should not over-write again with a given weight, at least for static QAT never.
Dynamic QAT also does not require to re-run weight observer again by design.
This is a fix
Test Plan: Signals
Differential Revision: D57747749
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127309
Approved by: https://github.com/jerryzh168
Updates flake8 to v6.1.0 and fixes a few lints using sed and some ruff tooling.
- Replace `assert(0)` with `raise AssertionError()`
- Remove extraneous parenthesis i.e.
- `assert(a == b)` -> `assert a == b`
- `if(x > y or y < z):`->`if x > y or y < z:`
- And `return('...')` -> `return '...'`
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116591
Approved by: https://github.com/albanD, https://github.com/malfet
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new `Buffer` class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the `register_buffer` method has not been changed. The `persistent` parameter in the `Buffer` type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new `Buffer` type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the `Buffer` type can be used as a drop in replacement for `register_buffer` as it just leads to `register_buffer` being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104069
Approved by: https://github.com/mikaylagawarecki
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86259
Add assertion to make sure backend is one of "fbgemm", "x86", "qnnpack" and "onednn"
for get_default_qconfig, get_default_qat_qconfig, get_default_qconfig_mapping and get_default_qat_qconfig_mapping
Test Plan:
python test/test_quantization.py -k test_get_default_qconfig_mapping
Imported from OSS
Reviewed By: jcaip
Differential Revision: D40236474
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87331
Approved by: https://github.com/andrewor14
Summary:
A more stable conv_bn fusion for qat training:
* Existing implementation may cause QAT training loss become NaN. This could happen when the fused conv for qat (torch/nn/intrinsic/qat/modules/conv_fused.py) is used and is independent of if fake_quant is enabled.
* This is caused by the unscaling for the conv output (`conv_orig = conv / scale_factor` where `scale_factor = bn.weight / running_std`) when there is 0 in `bn.weight`.
* This implementation follows the [white paper](https://arxiv.org/pdf/1806.08342.pdf) better and fixed the issue by scaling `running_std / std_Y` instead and compute the fused output accordingly (see comments in conv_fused.py for more details):
* It comes at the cost of running conv twice (one to update bn statistics and one to compute fake quant for fused weights).
* It does not need to use conv bias for back prop.
* It uses the bn statistics computed with the current input batch, while the existing code uses the statistics without the current batch.
* The implementation could be enabled by setting the flag `_enable_slow_path_for_better_numerical_stability` to True after the model is prepared for QAT.
* Unit test
* Added test case for zero `bn.weight`.
* Added test case for conv to has bias.
Test Plan: buck run mode/dev-nosan //caffe2/test:quantization -- -r quantization.eager.test_quantize_eager_qat
Differential Revision: D29506778
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85744
Approved by: https://github.com/vkuzo
Fixes https://github.com/pytorch/pytorch/issues/83505
BC-breaking message:
- Previously we only required input and weight to have the same dtype on cpu (when input is non-complex). After this change, the dtype of bias is now also expected to have the same dtype. This change was necessary to improve the error message for certain combinations of inputs. This behavior now also matches that of convolution on cuda.
<details>
<summary>
Old plan
</summary>
Previously convolution (at least for slow_conv2d) did not perform type promotion, i.e. the output of `conv(int, int, float)` is an int, and that leads to the autograd assert.
This PR adds type promotion handling at the `at::native::conv2d` (this is a composite) level. We also need to correct or remove many tests that assume that conv errors when input types are mixed
Pros:
- Doing type promotion at this level avoids the complex path from having any special handling for mixed dtypes, and can potentially speed up mixed dtype inputs to now dispatch to faster kernels which are only capable of handling floats.
Cons:
- Doing type promotion at this level has the risk of introducing extra overhead when we would've dispatched to a kernel capable of handle mixed type anyway. I don't know if any of these exist at all though - it is possible that inputs with any non-float arguments are dispatched to the slow path.
If this approach is OK, we can proceed with the other convolutions as well:
</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83686
Approved by: https://github.com/ngimel
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [X] [Current PR] `torch.nn.qat` → `torch.ao.nn.qat`
- [X] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [X] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861197/)!
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78716
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] [Current PR] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
- [BC test](test/quantization/bc/test_backward_compatibility.py) @vkuzo
- [IR emitter](torch/csrc/jit/frontend/ir_emitter.cpp) @jamesr66a
- [JIT serialization](torch/csrc/jit/serialization/import_source.cpp) @IvanKobzarev @jamesr66a
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860660/)!
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78714
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [X] [Current PR] `torch.nn.qat` → `torch.ao.nn.qat`
- [X] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [X] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861197/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78716
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] [Current PR] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
- [BC test](test/quantization/bc/test_backward_compatibility.py) @vkuzo
- [IR emitter](torch/csrc/jit/frontend/ir_emitter.cpp) @jamesr66a
- [JIT serialization](torch/csrc/jit/serialization/import_source.cpp) @IvanKobzarev @jamesr66a
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860660/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78714
Approved by: https://github.com/jerryzh168
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74507
* This is the default symmetric qat qconfigs for qnnpack.
* Support for symmetric quantization is not available from other backends.
* Observers are similar to symmetric PTQ qconfigs for qnnpack.
Reviewed By: jerryzh168
Differential Revision: D34804808
fbshipit-source-id: 22c11b89242a98f54029ac195f7b984e42809164
(cherry picked from commit ea751ded1174ba2c2f061bafc81573faaf248a9a)
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/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/69880
Making the test cases more standardized, in general we would like to have
```
TestQuantizeEager,
TestQuantizeEagerOps,
TestQuantizeEagerModels,
```
but currently since we have separate ptq static, ptq dynamic and qat static apis, we only partially cleaned
up the test cases, we can merge all of them later when we merge all the apis
Test Plan:
python test/test_quantization.py
Imported from OSS
Reviewed By: supriyar
Differential Revision: D33081418
fbshipit-source-id: fcb96559b76bbc51eb1b0625e0d4b193dbb37532
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68390
Observer zero_point's dtype can be float, in the specific case of `torch.per_channel_affine_float_qparams`.
This change sets FakeQuant's zero_point dtype accordingly.
Test Plan:
`pytest test/quantization/core/test_workflow_module.py -v -k "embedding"`
`pytest test/quantization/eager/test_quantize_eager_qat.py -v -k "embedding"`
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32446405
fbshipit-source-id: cca7aade68ff171887eeeae42801f77d934dad4c
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/66274
Removed outdated context manager in unit test.
* The linked issue (https://github.com/pytorch/pytorch/issues/23825) seemed have been be fixed in 2020.
Test Plan: buck run mode/dev-nosan //caffe2/test:quantization -- -r quantization.eager.test_quantize_eager_qat
Reviewed By: vkuzo
Differential Revision: D29507087
fbshipit-source-id: e8fa04c9527023a5adaf1a012b2c393ce0c5cd97
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59007
Create folders for each test category and move the tests.
Will follow-up with a cleanup of test_quantization.py
Test Plan:
python test/test_quantization.py
Imported from OSS
Reviewed By: HDCharles
Differential Revision: D28718742
fbshipit-source-id: 4c2dbbf36db35d289df9708565b7e88e2381ff04
