Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
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: same function in observer and quantize, consolidated to a
single function. Note the definitions were slightly different, I've
changed the definition to be maximally inclusive so that the name of the
function is more accurate
Test Plan: python test/test_public_bindings.py
python test/test_quantization.py
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D40709276](https://our.internmc.facebook.com/intern/diff/D40709276)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87520
Approved by: https://github.com/jcaip
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:
This diff adds a way to:
- clone previously observed method
- Add calls to observer's calculate_qparams methods
- Extract the scale and zero point
- Use them to insert quant dequant nodes
Now for forward method we have
- observe_forward
- quantize_forward
observe_forward is used post training to observer statistics. In the
case of dynamic PTQ this requires just running that method once to
update weight observer statistics.
quantize_forward method will be used to use the observer
statistics to calculate quantization parameters and apply that to quant
dequant op.
Subsequent diffs will replace dequant + op with their quantized op
counter parts and replace quantize ops with relevant packed params class
where possible
Test Plan:
To be written
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D38771419](https://our.internmc.facebook.com/intern/diff/D38771419)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83570
Approved by: https://github.com/jerryzh168
Summary:
TO support on device quantization this diff introduces observer
insertion. Specifically observers are inserted by adding new method with
prefix observ_.
Intent is that post training, this method will be run to record
statistics
Test Plan:
test_ondevice_quantization.py
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D38771417](https://our.internmc.facebook.com/intern/diff/D38771417)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83568
Approved by: https://github.com/jerryzh168
Summary:
Add a global custon module support list for the users to specify the modules they want the equalization process support.
To use this list, import it from the _equalize.py file and append module in it.
Unittest passed to check global support list:
https://pxl.cl/28RKG
Test Plan: buck1 test mode/dev //on_device_ai/odai/tests/transforms:test_transforms -- --exact 'on_device_ai/odai/tests/transforms:test_transforms - test_custom_support_list (on_device_ai.odai.tests.transforms.test_input_weight_for_turing.TestInputWeight)'
Reviewed By: jerryzh168
Differential Revision: D38264244
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82606
Approved by: https://github.com/HDCharles
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76637
The previous naming convention `default_affine_fixed_qparams_observer`
and `default_symmetric_fixed_qparams_observer` were uninformative, and users had to read
the definition in order to understand what these observers are. The new
naming convention reveals information about the range of the observers
The analogous changes were also made for
`default_symmetric_fixed_qparams_fake_quant` and
`default_affine_fixed_qparams_fake_quant`
Test Plan:
```
python test/test_quantization.py
```
```
python test/test_quantization.py
```
Differential Revision:
D36054169
D36054169
Reviewed By: vkuzo
Pulled By: dzdang
fbshipit-source-id: 215f7786a4b7abda7327f17cc61735697ec5cca9
(cherry picked from commit 21a4e6eda4467c8adca7fd534a506a14e975f9cf)
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/69959
GraphModule is an implementation detail, We don't want to expose it in quantization apis
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_quantized_model_type
Imported from OSS
Reviewed By: supriyar
Differential Revision: D33119103
fbshipit-source-id: d8736ff08b42ee009d6cfd74dcb3f6150f71f3d2
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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65109
Previously for sub we set the dtype for sub with qconfig since it's matched with a QuantizeHandler,
however this is incorrect, the dtype for sub is decided by whether the output is quantized or not,
so we added a check of is_output_quantized while deciding the dtype for the output of sub.
Later: is_output_quantized now depends on is_reference, which is pretty confusing and it may cause problems down the road, we should remove this dependency in the future.
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_sub_scalar
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D30977826
fbshipit-source-id: 551fd63bd61b43b3c3415944ff73174e3a21cc8a
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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64917
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 from torch.quantization to torch.ao.quantization the following files:
- `_correct_bias.py`
- `_equalize.py`
- `_learnable_fake_quantize.py`
**Note:** These file are migrated completely without any warning. The old location is thus silently deprecated.
Test Plan: `buck test mode/dev //caffe2/test:quantization -- TestBiasCorrection`
Reviewed By: vkuzo
Differential Revision: D30898565
fbshipit-source-id: 1d39be2539dd1adfcb42e16bdcc0daf5c8316bbd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64916
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 quant_type.py from torch.quantization to torch.ao.quantization.
At this point both locations will be supported. Eventually the torch.quantization will be deprecated.
Test Plan: `buck test mode/dev //caffe2/test:quantization -- TestAOMigrationQuantization`
Reviewed By: vkuzo
Differential Revision: D30898422
fbshipit-source-id: 3e6126b49f0565a4136d6928cea9eb25368927ff
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64913
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 fuse_module.py from torch.quantization to torch.ao.quantization.
At this point both locations will be supported. Eventually the torch.quantization will be deprecated.
Test Plan: `buck test mode/dev //caffe2/test:quantization`
Reviewed By: vkuzo
Differential Revision: D30882819
fbshipit-source-id: 1926ad6aa49136aceb5b625dcef4bfde3a2860d4
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
