Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
(cherry picked from commit 03ab65023a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71110
as mentioned in https://github.com/pytorch/pytorch/issues/70480 the dynamic conv ops are currently missing a key feature to bring their performance in line with other dynamic ops, this diff disables conv/convT from being automatically quantized with convert dynamic
Test Plan: buck test //caffe2/test:quantization --test-selectors test_quantized_module#TestDynamicQuantizedModule
Reviewed By: vkuzo
Differential Revision: D33511152
fbshipit-source-id: 50618fbe734c898664c390f896e70c68f1df3208
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
Follow up work:
1. disallow `default` as an overload name for aten operators.
2. Add a method to obtain a list of all overloads (exclude the ones registered by JIT)
3. Add methods/properties to `OpOverload` to access more schema information (types of input and output args etc)
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33469839
Pulled By: anjali411
fbshipit-source-id: c3fc43460f1c7c9651c64b4d46337be21c400621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
TODO: disallow `default` as an overload name for aten operators.
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33262228
Pulled By: anjali411
fbshipit-source-id: 600dbf511514ea9b41aea3e6b1bc1102dab08909
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66778
This removes the hack of the context manager that would communicate the zeros block shape to the quantization convert.
The conversion will assume that the converted modules have `sparse_params` (which is added by the sparsifier).
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D31835721
Pulled By: z-a-f
fbshipit-source-id: c5fd2da3b09a728a2296765c00ca69275dbca3b1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68176
it should be noted that for the modules, reduce_range is set to
true by default in a similar fashion to linear_dynamic.
Test Plan:
python test/test_quantization.py TestDynamicQuantizedModule
python test/test_quantization.py TestDynamicQuantizedConv
python test/test_quantization.py TestQuantizedConv
Imported from OSS
Reviewed By: kimishpatel
Differential Revision: D32374003
fbshipit-source-id: 011562bd0f4d817387d53bb113df2600aa60a7a3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68175
This slightly alters the way from_float works so it will work
with placeholder observers. It also fixes a but with ConvTranspose3d and
ConvTranspose1d where the parameters like kernel_size, stride...etc
weren't set properly. New tests were added to check for this type of
issue as well.
Test Plan:
python test/test_quantization.py TestQuantizedOps
python test/test_quantization.py TestStaticQuantizedModule
Imported from OSS
Reviewed By: z-a-f
Differential Revision: D32374004
fbshipit-source-id: caaa548d12d433d9c1fa0abc8597a7d31bb4e8af
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/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/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
