This enables a check that which a class which only inherits from immutable classes like str, tuple, and NamedTuple, also defined `__slots__` so they don't allocate memory unnecessarily. This also ensure contributors think about how they define their classes with subclass NamedTuples and str, of which we have many in our codebase
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146276
Approved by: https://github.com/aorenste
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
Summary:
The goal is to remove the need to use backend_config when pt2e flow code call this function
Test Plan:
python test/test_quantization.py TestQuantizeFx
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98094
Approved by: https://github.com/jcaip
Summary:
Previously prepare_fx returns an ObservedGraphModule and convert_fx returns a QuantizedGraphModule,
this is to preserve the attributes since torch.fx.GraphModule did not preserve them, after https://github.com/pytorch/pytorch/pull/92062
we are preserving `model.meta`, so we can store the attributes in model.meta now to preserve them.
With this, we don't need to create a new type of GraphModule in these functions and can use GraphModule directly, this
is useful for quantization in pytorch 2.0 flow, if other transformations are using GraphModule as well, the quantization passes will be composable with them
Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
python test/test_quantization.py TestQuantizeFxModels
python test/test_quantization.py TestQuantizePT2E
Imported from OSS
Differential Revision: D42979722
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94412
Approved by: https://github.com/vkuzo
Summary:
att, with the introduction of QConfigMapping, this name is now very confusing, so renamed
it to something clearer
Test Plan:
python test/test_quantization.py TestQuantizeFx
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86861
Approved by: https://github.com/vkuzo
Summary:
- Finishes the second part of https://github.com/pytorch/pytorch/pull/83263
- Removes WEIGHT_INDEX_DICT and BIAS_INDEX_DICT from utils.py
- Moves two funcitons, `node_arg_is_weight` and `node_arg_is_bias` into utils.py from prepare.py
convert.py and _equalize.py now use node_arg_is_weight instead of the dictionaries
- Adds in quantization support for `F.groupnorm`.
Add in missing BackendPatternConfigs for layernorm, instancenorm, and groupnorm
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
```
Reviewers:
Subscribers:
Tasks:
Tags:
ghstack-source-id: 2b157e0dc4f1553be1f4813b4693db952e6fc558
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83848Fixes#83093
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83853
Approved by: https://github.com/jerryzh168, https://github.com/andrewor14
Motivation: each quantization observer only supports a limit qschemes, we need to do this check at the initiation step, rather than at the running step, such as MinMaxObserver with set qscheme with **torch.per_channel_affine**, there will have a runtime error at the running the calibration step:
```
AttributeError: 'MinMaxObserver' object has no attribute 'ch_axis'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80126
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:
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/66058
After the initial migration from `torch.quantization` to `torch.ao.quantization`, some of the files did not change.
This happened because the migration was done in parallel, and some of the files were landed while the others were still in the original location.
This is the last fix in the AO migration phase 1, which completely enables the ao.quantization namespace.
Test Plan: `python test/test_quantization.py`
Reviewed By: vkuzo
Differential Revision: D31366066
Pulled By: z-a-f
fbshipit-source-id: bf4a74885be89d098df2d87e685795a2a64026c5
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
