Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64981
this would have cause errors when observer.py was moved to ao.
see: D30391189
ghstack-source-id: 138118430
Test Plan:
buck test mode/opt //caffe2/test:quantization -- --exact 'caffe2/test:quantization - test_dynamic_quant_multi_uses (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses)'
buck test mode/opt //caffe2/test:quantization -- --exact 'caffe2/test:quantization - test_save_load_state_dict_script (quantization.core.test_workflow_module.TestObserver)'
Reviewed By: supriyar
Differential Revision: D30432008
fbshipit-source-id: 754727a89c78f6ceada6f8ff92c304f3953f38fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64814
1. move the file
```
hg mv caffe2/torch/quantization/fake_quantize.py caffe2/torch/ao/quantization/
```
2. create a new file in the old location and copy the imports
3. fix all callsites inside `torch`
Test Plan:
```
buck test mode/dev //caffe2/test:quantization
```
Reviewed By: z-a-f
Differential Revision: D30866792
fbshipit-source-id: 7a221cb46c0ab01f1c5de9be061f09ecc83ce23e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63513
updating these names per Jerry's nits in the previous pr
Test Plan: Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D30406710
fbshipit-source-id: a9f1577a2b8c4a93f5005e0f6278b7d7348d8b66
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63384
In some cases the changes to qconfig on module would cause the
fusions to fail. This bugfix solves that problem by adding a
qconfig_function_comparison that compares the functions within the
qconfig rather than the modules the qconfigs are on. The comparison
looks at the partial object within QConfig.activation/weight.p and
compares args, keywords and func. This is necessary to do mannually
because partial doesn't have __eq__ implemented and so == reverts to is.
Test Plan:
python test/test_quantization.py
TestFuseFx.test_problematic_fuse_example
Imported from OSS
Reviewed By: supriyar, ejguan
Differential Revision: D30386264
fbshipit-source-id: 51e358c021c39d6f48dc12ad2a82b2838677b9de
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63043
In version 1 we use the fused module/operator during QAT. Making this the default for all QAT runs going forward.
Older models saved after prepare_qat_fx can still load their state_dict into a model prepared using version 1.
The state_dict will still have the same attribute for the observer/fake_quant modules.
There may be some numerics difference between the old observer code in observer.py and the new fused module that was
re-written in C++/CUDA to perform observe + fake_quantize.
This PR also updates the test to check for the new module instead of the default FakeQuantize module.
Note: there are also some changes to make the operator work for multi-dim per-channel quantization + updated the test for that.
Test Plan:
python test/test_quantization.py TestSerialization.test_default_qat_qconfig
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D30232222
fbshipit-source-id: f3553a1926ab7c663bbeed6d574e30a7e90dfb5b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62702
Expose the qconfig to the user to speed up training by leveraging the fused module.
The module currently supports per-tensor/per-channel moving avg observer and fake-quantize.
For details on perf benefits, refer to https://github.com/pytorch/pytorch/pull/61691
Test Plan: Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D30093719
fbshipit-source-id: b78deb7810f5b597474b9b9a0395d361d04eb46a
Summary:
This PR enables gpu only quantization, best used with is_reference since
there are not many gpu kernels for ops as of now.
This PR mainly changes how qconfigs and their obs constructors operate once they
on modules qconfig. The function add_module_to_qconfig_obs_ctr takes the obs constructors on the original
qconfig, and configures them so that when invoked, the created obs will
be on whatever device the module occupies. (Once observers are created,
module.to(device) is already setup so that it moves any observers). To do this,
a new method and a few small chanegs were added to the _PartialWrapper class that
our observers already use to create constructors (without changing the
existing functionality). These changes work in
concert with changes to the prepare flow such that when the qconfigs are
propagated to the moduels (in quantize.py and qconfig_utils.py) they are configured using add_module_to_qconfig_obs_ctr.
Ideally this would work on other models but the is_reference support for
a lot of modules isn't there yet, those tests should be added in a
future PR
Test Plan:
python test/test_quantization.py TestQuantizeFxModels.test_static_gpu_convert_basic
python test/test_quantization.py TestQuantizeFxModels.test_switch_device_prepare_convert
python test/test_quantization.py TestQuantizeFxModels.test_prepare_serialize_switch_device_convert
python test/test_quantization.py TestQuantizeFx.test_qconfig_precedence
Reviewed By: vkuzo
Differential Revision: D29684114
fbshipit-source-id: 19fefb8e1998eaf212723e836276ccf39467f2e7
Summary:
Generally wildcard imports are bad for the reasons described here: https://www.flake8rules.com/rules/F403.html
This PR replaces wildcard imports with an explicit list of imported items where possible, and adds a `# noqa: F403` comment in the other cases (mostly re-exports in `__init__.py` files).
This is a prerequisite for https://github.com/pytorch/pytorch/issues/55816, because currently [`tools/codegen/dest/register_dispatch_key.py` simply fails if you sort its imports](https://github.com/pytorch/pytorch/actions/runs/742505908).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55838
Test Plan: CI. You can also run `flake8` locally.
Reviewed By: jbschlosser
Differential Revision: D27724232
Pulled By: samestep
fbshipit-source-id: 269fb09cb4168f8a51fd65bfaacc6cda7fb87c34
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52534
Currently linear_dynamic_fp16 has a signature that's tied to fbgemm/qnnpack
We'll need to produce a pattern equivalent to linear_dynamic_fp16 to support extensions
to other backends
Test Plan:
python test/test_quantization.py TestQuantizeFxOps.test_linear_dynamic_fp16
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D26557726
fbshipit-source-id: 270c9f781f73c79416a092b7831294cabca84b0c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52844
Fixes a crash in qconfig checking which happened if a model had conv transpose
with qconfig set to None.
Test Plan:
```
python test/test_quantization.py TestPostTrainingStatic.test_convtranspose_per_channel_qconfig_none
```
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D26666043
fbshipit-source-id: e1b62840b4e3c67acbb4dbdcd32514b374efce1e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49899
Per channel weights observer in conv transpose is not supported yet. Adding an
error message which fails instantly instead of making the user wait until after
calibration/training finishes.
Test Plan:
```
python test/test_quantization.py TestPostTrainingStatic.test_convtranspose_per_channel_fails_early
python test/test_quantization.py TestQuantizeFx.test_convtranspose_per_channel_fails_early
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D25717151
fbshipit-source-id: 093e5979030ec185e3e0d56c45d7ce7338bf94b6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48069
also renamed float_qparam_dynamic_qconfig to float_qparam_weight_only_qconfig
It's not used in user code yet so we only need to update the tests.
Test Plan: Imported from OSS
Reviewed By: supriyar
Differential Revision: D25010175
fbshipit-source-id: caa3eaa5358a8bc5c808bf5f64e6ebff3e0b61e8
Summary:
This diff adds FakeQuantizeWithBackward. This works the same way as the regular FakeQuantize module, allowing QAT to occur in the forward pass, except it has an additional quantize_backward parameter. When quantize_backward is enabled, the gradients are fake quantized as well (dynamically, using hard-coded values). This allows the user to see whether there would be a significant loss of accuracy if the gradients were quantized in their model.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40532
Test Plan: The relevant test for this can be run using `python test/test_quantization.py TestQATBackward.test_forward_and_backward`
Reviewed By: supriyar
Differential Revision: D22217029
Pulled By: durumu
fbshipit-source-id: 7055a2cdafcf022f1ea11c3442721ae146d2b3f2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42348
Use the dtype info in placeholderObserver to decide what ops to insert in the graph
In the next PR we can delete NoopObserver
Test Plan:
python test/test_quantization.py
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D22859457
fbshipit-source-id: a5c618f22315534ebd9a2df77b14a0aece196989
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42222
This change adds the necessary passes to perform FP16 dynamic quantization.
We skip inserting observers for activations based on the dtype (torch.float16) and only insert the Fp16Observer for weights
Test Plan:
python test/test_quantization.py TestQuantizeJitOps
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D22849220
fbshipit-source-id: 2c53594ecd2485e9e3dd0b380eceaf7c5ab5fc50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39874
When fbgemm backend is set we make sure reduce_range is set to true to avoid overflow in the operator
Also adds test for per-channel quant with graph mode and compare numerics with eager mode
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D22011205
fbshipit-source-id: 1c7c9b7ab0d84200e3d8d85c34978554c30c0169
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38407
We can still run some quantized tests even when fbgemm/qnnpack isn't enabled
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D21554257
fbshipit-source-id: e4fa8f61f6a6717881c00620ed7938c01ffbf958
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35455
In graph mode we need to observer the activation tensor for dynamic quantization. This observer should behave the same way as the quantization functions called in the dynamic operator.
Currently for qlinear_dynamic we call quant_utils::ChooseQuantizationParams which has its own logic for calculating scale and zero_point.
We mimic those calculations in the new observer.
Test Plan:
python test/test_quantization.py ObserverTest
Imported from OSS
Differential Revision: D20664586
fbshipit-source-id: e987ea71fff777c21e00c498504e6586e92568a2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35265
In graph mode we need to observer the activation tensor for dynamic quantization. This observer should behave the same way as the quantization functions called in the dynamic operator.
Currently for qlinear_dynamic we call quant_utils::ChooseQuantizationParams which has its own logic for calculating scale and zero_point.
We mimic those calculations in the new observer.
Test Plan:
python test/test_quantization.py ObserverTest
Imported from OSS
Differential Revision: D20630988
fbshipit-source-id: 7e7aca77590f965dcb423a705e68d030aaf98550
Summary:
The PR tried to enable the per-channel(row-wise) dynamic quantization for linear operator. Given we have seen some accuracy drop due to the per-tensor quantization, we expect the per-channel could help improve the accuracy.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30122
Differential Revision: D18630541
Pulled By: lly-zero-one
fbshipit-source-id: d52685deec5e7de46cd686ae649a8c8765b9cacf
