Summary:
We have tests testing package level migration correctness for torch AO migration.
After reading the code, I noticed that these tests are not testing anything
additional on top of the function level tests we already have.
An upcoming user warning PR will break this test, and it doesn't seem worth fixing.
As long as the function level tests pass, 100% of user functionality will
be tested. Removing this in a separate PR to keep PRs small.
Test plan:
```
python test/test_quantization.py -k AOMigration
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94422
Approved by: https://github.com/jcaip
Summary: This commit renames fx/quantization_patterns.py
to fx/quantize_handler.py, and fx/fusion_patterns.py to
fx/fuse_handler.py. This is because these files contain
only QuantizeHandler and FuseHandler respectively, so the
new names are more descriptive. A future commit will
further break BC by removing all the empty *QuantizeHandler
classes.
BC-breaking notes:
The following classes under the
`torch.ao.quantization.fx.quantization_patterns` namespace
are migrated to the `torch.ao.quantization.fx.quantize_handler`
namespace:
```
QuantizeHandler
BinaryOpQuantizeHandler
CatQuantizeHandler
ConvReluQuantizeHandler
LinearReLUQuantizeHandler
BatchNormQuantizeHandler
EmbeddingQuantizeHandler
RNNDynamicQuantizeHandler
DefaultNodeQuantizeHandler
FixedQParamsOpQuantizeHandler
CopyNodeQuantizeHandler
GeneralTensorShapeOpQuantizeHandler
CustomModuleQuantizeHandler
StandaloneModuleQuantizeHandler
```
The following classes under the
`torch.ao.quantization.fx.fusion_patterns` namespace are
migrated to the `torch.ao.quantization.fx.fuse_handler`
namespace:
```
DefaultFuseHandler
FuseHandler
```
Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
Reviewers: jerryzh168, vkuzo
Subscribers: jerryzh168, vkuzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89872
Approved by: https://github.com/jerryzh168
Summary: the main problem with this was that the different objects
defined simply as 'Any' should theoretically be public but making them
public either A) results in an error about the module being 'typing'
rather than whatever module it should be or B) you set the module
manually, thereby changing the module for the original 'Any' class.
note: QuantizeHandler has a similar issue where its simply defined as
'Any'
Pattern was defined in multiple places which was causing issues so i just moved it to a single
place given the note at the top of quantization_types.py indicating
these definitions should be moved to utils at some point anyway.
Finally i changed any references to these objects to point at the
correct locations. Note: i didn't see any fb internal references to
NodePattern or QuantizerCls that would cause issues.
Test Plan: python test/test_public_bindings.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86031
Approved by: https://github.com/jerryzh168
Summary:
Refactors `find_matches` function to only find subgraph
matches and not assign qconfigs to them. Moves the qconfig assignment
outside of the function. No logic change.
This will useful for prototyping future tools for quantizing
parts of the model. These tools will need to know the matches
and will reuse the `find_matches` function,
but they will assign their own qconfigs to them using a different
strategy.
Test plan:
```
python test/test_quantization.py -k Fx
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79713
Approved by: https://github.com/jerryzh168
Summary:
Following https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md we implemented
the backend configuration for fbgemm/qnnpack backend, currently it was under fx folder, but we'd like to use this for all different
workflows, including eager, fx graph and define by run quantization, this PR moves it to torch.ao.quantization namespace so that
it can be shared by different workflows
Also moves some utility functions specific to fx to fx/backend_config_utils.py and some files are kept in fx folder (quantize_handler.py and fuse_handler.py)
Test Plan:
python test/teset_quantization.py TestQuantizeFx
python test/teset_quantization.py TestQuantizeFxOps
python test/teset_quantization.py TestQuantizeFxModels
python test/test_quantization.py TestAOMigrationQuantization
python test/test_quantization.py TestAOMigrationQuantizationFx
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75823
Approved by: https://github.com/vkuzo
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74276
Removing convert.py since we have rerouted the traffic to _convert_do_not_use, we'll do a rename in the follow up PR
Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D34914261
fbshipit-source-id: 09ad520d95fa91c525222a69474930efb3571088
(cherry picked from commit 8aeb33206f3572132356fe78395aa3ce6aff11cd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73274
As noticed in https://discuss.pytorch.org/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/6
and related to https://github.com/pytorch/pytorch/issues/72698 when using fx quantizaiton, if an op like view was used in a
model and the index parameters were passed in to the ops with a
variable rather than
hard coded, fx would mistakenly insert observers for them, leading to an
error when the observer tried to do tensor only operations on a
non-tensor. To fix this, an API was added to specify non tensor
arguments for various ops to enable better dtype propagation.
NON_TENSOR_ARG_DICT is a nested dict whose first key is a named tuple
which contains matching parameters for ops with nontensor args, the
inner dict's keys are dtypes and the values are a list of those arg indices that
take use such dtypes. Alternatively, instead of a list, the inner dict
value can also be a function that takes the node as an argument and
returns the list of arg indices.
Theoretically this api can support arbitrary functions but the current
implmentation is limited to simpler functions given the particular
issue this fixes seems to be rare.
Note: although torch.unsqueeze and torch.transpose are listed in
quantization_patterns.py, those ops appear to be untraceable by fx. I've
included tests for their cases but fixing this issue is beyond the scope
of this PR
Test Plan:
python test/test_quantization.py test_non_reference_size
...
python test/test_quantization.py test_non_reference_<op>
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D34410122
fbshipit-source-id: fc09949ca8a2d6473876a4b6c214eb91e9a9dae2
(cherry picked from commit 3a1375d677b7c98d62b1f5c839645698c39b32b9)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73470
att, this does not affect user apis since we are only exposing fuse_fx as a public api
Test Plan:
python test/test_quantization.py TestFuseFx
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D34495260
fbshipit-source-id: 3aa253bc7190e50acc7229186f210901ebc5481b
(cherry picked from commit a88517ff6feff7abbece2234d82fd53e33702237)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69720
This function is also useful for DBR quant, moving it from FX utils
to common utils.
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeDBR
```
Reviewed By: jerryzh168
Differential Revision: D33003473
Pulled By: vkuzo
fbshipit-source-id: 20360682c69d614a645c14fc29d3ee023d6b2623
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70006
reland: fixing some mypy errors that was missed before
This PR enables fuse handler for sequence of three ops, and merges all fuse handlers into one
TODO: we can also move this to backend_config_dict folder
Test Plan:
regression fusion test
```
python test/test_quantization.py TestFuseFx
```
Imported from OSS
Imported from OSS
Reviewed By: supriyar
Differential Revision: D33144606
fbshipit-source-id: ca34f282018a0fb4d04c7e35119eaf2d64258e78
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69658
This PR enables fuse handler for sequence of three ops, and merges all fuse handlers into one
TODO: we can also move this to backend_config_dict folder
Test Plan:
regression fusion test
```
python test/test_quantization.py TestFuseFx
```
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D32974907
fbshipit-source-id: ba205e74b566814145f776257c5f5bb3b24547c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66484https://github.com/pytorch/pytorch/pull/50748 added linear - bn1d fusion
in Eager mode, for PTQ only. This PR also enables this in FX graph mode.
We reuse the existing conv-bn-relu fusion handler, renaming `conv` to
`conv_or_linear` for readability.
The QAT version is saved for a future PR, for both eager and FX graph.
Test Plan:
```
python test/test_quantization.py TestFuseFx.test_fuse_linear_bn_eval
```
Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D31575392
fbshipit-source-id: f69d80ef37c98cbc070099170e335e250bcdf913
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65033
1. Move the file:
```
hg mv caffe2/torch/quantization/fx caffe2/torch/ao/quantization/fx
hg mv caffe2/torch/quantization/quantize_fx.py caffe2/torch/ao/quantization/quantize_fx.py
```
2. Create new files
```
touch caffe2/torch/quantization/quantize_fx.py
touch caffe2/torch/quantization/fx/__init__.py
```
3. import things in the new files
4. add tests to test/quantization/ao_migration/test_quantization_fx.py
this is because we have some fx import in quantize_fx and fx/*.py
Test Plan: buck test mode/dev //caffe2/test:quantization
Reviewed By: vkuzo, z-a-f
Differential Revision: D30949749
fbshipit-source-id: 9e5d4d039c8a0a0820bc9040e224f0d2c26886d3
