**Summary**
- Update the quantization document that default qconfig with oneDNN backend is recommended to be used on CPUs with Vector Neural Network Instruction support.
- Add the warning message when user uses default qconfig with oneDNN backend on CPU without Vector Neural Network Instruction support.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103653
Approved by: https://github.com/jgong5, https://github.com/malfet
Summary: The recommended way to use QConfigMapping is through
`get_default_qconfig_mapping`. However, the docs still references
usages that use `QConfigMapping().set_global(...)`. This doesn't
actually work well in practice when the model has fixed qparams
ops for example. This commit updates these usages.
Reviewers: vkuzo
Subscribers: vkuzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87299
Approved by: https://github.com/jerryzh168
`Sparsity` as a term doesn't reflect the tools that are developed by the AO. The `torch/ao/sparsity` also has utilities for structured pruning, which internally we always referred to as just "pruning". To avoid any confusion, we renamed `Sparsity` to `Prune`. We will not be introducing the backwards compatibility, as so far this toolset was kept under silent development.
This change will reflect the changes in the documentation as well.
**TODO:**
- [ ] Change the tutorials
- [ ] Confirm no bc-breakages
- [ ] Reflect the changes in the trackers and RFC docs
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84867
Approved by: https://github.com/supriyar
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [X] [Current PR] `torch.nn.qat` → `torch.ao.nn.qat`
- [X] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [X] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861197/)!
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78716
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] [Current PR] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- `torch/ao/nn/__init__.py` → Changing the imports to lazy.
Differential Revision: [D36861090](https://our.internmc.facebook.com/intern/diff/D36861090/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861090/)!
Differential Revision: [D36861090](https://our.internmc.facebook.com/intern/diff/D36861090)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78717
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] [Current PR] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
- [BC test](test/quantization/bc/test_backward_compatibility.py) @vkuzo
- [IR emitter](torch/csrc/jit/frontend/ir_emitter.cpp) @jamesr66a
- [JIT serialization](torch/csrc/jit/serialization/import_source.cpp) @IvanKobzarev @jamesr66a
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860660/)!
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78714
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [X] [Current PR] `torch.nn.qat` → `torch.ao.nn.qat`
- [X] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [X] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861197/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78716
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] [Current PR] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861090](https://our.internmc.facebook.com/intern/diff/D36861090/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861090/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78717
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] [Current PR] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
- [BC test](test/quantization/bc/test_backward_compatibility.py) @vkuzo
- [IR emitter](torch/csrc/jit/frontend/ir_emitter.cpp) @jamesr66a
- [JIT serialization](torch/csrc/jit/serialization/import_source.cpp) @IvanKobzarev @jamesr66a
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860660/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78714
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] [Current PR] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [ ] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [ ] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
Differential Revision: [D36792967](https://our.internmc.facebook.com/intern/diff/D36792967/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36792967/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78712
Approved by: https://github.com/jerryzh168
Summary: There is currently per channel quantization support for Conv1d,
however this was not highlighted by the documentation for quantization
when discussion which modules have per channel quantization support.
This adds that there is exisiting support for Conv1d, with evidence
reproducable through the test plan below.
Test Plan:
```
class SingleLayerModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1d = torch.nn.Conv1d(5, 5, 1).to(dtype=torch.float)
def forward(self, x):
x = self.conv1d(x)
return x
def get_example_inputs(self):
return (torch.rand(5, 5, 1),)
torch.backends.quantized.engine = "fbgemm"
model = SingleLayerModel()
example_input = model.get_example_inputs()[0]
q_config = q_config_mapping = QConfigMapping()
q_config_mapping.set_global(torch.ao.quantization.get_default_qconfig(torch.backends.quantized.engine))
prepared = quantize_fx.prepare_fx(model, q_config_mapping, example_input)
print(prepared.conv1d.qconfig.weight.p.func)
```
Printing the above lines shows that the Conv1d has a
PerChannelMinMaxObserver. To show that this doesn't work for everything,
if you replace the Conv1d with a ConvTranspose1d, you will see running
the same code above that there is an error thrown about lack of support.
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81349
Approved by: https://github.com/andrewor14
The BaseDataScheduler is the abstract scheduler class specifically for the
BaseDataSparsifier class. This class controls a specific hyperparameter of
the sparsifier class and varies it across the training process (or across time).
Args:
data_sparsifier (instance of BaseDataSparsifier)
Implemented class data sparsifier class wherein the update_mask is implemented
schedule_param (str)
A specific hyperparameter of the passed sparsifier that needs to be scheduled/varied
last_epoch (int, default=-1)
This is specifically is passed when training needs to be resumed from a particular
point.
verbose (bool, default=False)
Verbosity of the BaseDataScheduler
The *get_schedule_param()* function needs to be implemented by the user.
Test Plan:
```python test/test_ao_sparsity.py TestBaseDataScheduler```
Differential Revision: [D37358608](https://our.internmc.facebook.com/intern/diff/D37358608)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79817
Approved by: https://github.com/jerryzh168, https://github.com/z-a-f
Summary: per https://github.com/pytorch/pytorch/issues/79135 the code
snippets in the docs don't run. This is a recurring problem since
previously there was no unit test to check that these code snippets
actually ran. This PR adds support for such a test, importing the
snippet as a string and evaluating it to make sure that it actually runs
if the code snippet has user defined code, you can pass in dummy
versions using global_inputs. Sometimes the imports of the code snippets
behave oddly but you can pass them in as in test_quantization_doc_custom
where nnq is passed in.
Test Plan: python test/test_quantization.py TestQuantizationDocs
also see https://github.com/pytorch/pytorch/pull/79994 to see what shows up in CI when the docs get broken
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79923
Approved by: https://github.com/z-a-f, https://github.com/vspenubarthi
Base Data Sparsifier class for all Data sparsifiers.
The abstract class accepts raw torch tensors / embedding / embedding bags (refer to SUPPORTED_TYPES above)
to prepare for sparsification.
In this case, mask (and parametrizations) is owned by the class and not by the user.
Specifically, the container object inside the class maintains the mask and parametrizations of the input data
Test Plan:
```python test/test_ao_sparsity.py TestBaseDataSparsifier```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79251
Approved by: https://github.com/z-a-f, https://github.com/HDCharles
Summary: https://github.com/pytorch/pytorch/pull/78452 replaced
qconfig_dict with QConfigMapping as the default API for prepare_fx,
prepare_qat_fx, and convert_fx. We should update the docs to reflect
this change as well.
Test Plan:
```
cd docs
make html
cd build/html
python -m server.http
```
Reviewers: jerryzh168, vkuzo
Subscribers: jerryzh168, vkuzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78533
Approved by: https://github.com/vkuzo
Summary:
This PR creates a best practices guideline for debugging quantization
accuracy. The content here comes from https://fburl.com/gdoc/nzlzxeaf,
with experimental and Meta-only parts left out.
For now, a lot of the debugging is manual, with the Numeric Suite the
only tool we have to help the user find root causes of quantization
inaccuracies. As we build additional tools for equalization detection,
outlier detection, etc, we will add them to this page
Test plan:
```
cd docs
make html
cd build/html
python -m server.http
// result renders well in browser
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77536
Approved by: https://github.com/hx89
There seems to be a typo in the main quantization docs.
In the table comparing "Eager Mode Quantization" against "FX Graph Mode Quantization", in the row named "Quantization Mode Support" both modes say they are "Quantiztion aware" instead of "Quantization aware"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77300
Approved by: https://github.com/H-Huang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76223
Small formatting fixes that was missed because I didn't check the generated doc last time
Test Plan:
visual inspection of the generated docs for this PR
Imported from OSS
Reviewed By: HDCharles
Differential Revision: D35853174
fbshipit-source-id: 4454a4bf5d0c998d866bbae1d6b5286827082033
(cherry picked from commit 125f60356ccc9cd6888c515889bd27ff9860ec74)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75998
Add more details to user facing docs quantization.rst, which will be displayed in the official quantization doc page: https://pytorch.org/docs/stable/quantization.html
This includes:
* docs for quantization stack (quantized tensor, quantized operator and modules, observer, fake_quantize, QConfig, quantization flow)
* Added support table for quantization mode, quantization flow mode and backend, (also moved around operator support table)
* restructured eager mode and fx mode docs as well
Test Plan:
inspect the doc that's built by github ci
Imported from OSS
Reviewed By: dzdang
Differential Revision: D35739111
fbshipit-source-id: 3762d387479bdd37472cb17d5c49da2f520effbb
(cherry picked from commit db5e6411c52c08dd9c45f841ab86713d36a75d51)
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/75126
Quantization has a high volume of configurations of how to quantize an
op for a reference model representation which is useful for a lowering
step for a backend. An example of this is
```
{'dtype_configs': [{'input_dtype': torch.quint8,
'output_dtype': torch.quint8}],
'observation_type': <ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT: 0>,
'pattern': <class 'torch.nn.modules.conv.ConvTranspose1d'>},
```
These configs are checked into master, and they are created with Python functions.
Therefore, there is no easy way for the user to see what the configs actually
are without running some Python code.
This PR is one approach to document these configs. Here is what this is doing:
1. during documentation build, write a text file of the configs
2. render that text file on a quantization page, with some additional context
In the future, this could be extended to autogenerate better looking tables
such as: op support per backend and dtype, op support per valid quantization settings per backend,
etc.
Test Plan:
```
cd docs
make html
cd html
python -m http.server 8000
// render http://[::]:8000/quantization-backend-configuration.html
// it renders correctly
```
Reviewed By: ejguan
Differential Revision: D35365461
Pulled By: vkuzo
fbshipit-source-id: d60f776ccb57da9db3d09550e4b27bd5e725635a
(cherry picked from commit 14865c0e23bc080120342c8f9278f0fae8eb8fbd)
Summary:
Working towards https://docs.google.com/document/d/10yx2-4gs0gTMOimVS403MnoAWkqitS8TUHX73PN8EjE/edit?pli=1#
This PR:
- Ensure that all the submodules are listed in a rst file (that ensure they are considered by the coverage tool)
- Remove some long deprecated code that just error out on import
- Remove the allow list altogether to ensure nothing gets added back there
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73983
Reviewed By: anjali411
Differential Revision: D34787908
Pulled By: albanD
fbshipit-source-id: 163ce61e133b12b2f2e1cbe374f979e3d6858db7
(cherry picked from commit c9edfead7a01dc45bfc24eaf7220d2a84ab1f62e)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69789
Add details on how to save and load quantized models without hitting errors
Test Plan:
CI autogenerated docs
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D33030991
fbshipit-source-id: 8ec4610ae6d5bcbdd3c5e3bb725f2b06af960d52
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67449
Adds a description of what the current custom module API does
and API examples for Eager mode and FX graph mode to the main
PyTorch quantization documentation page.
Test Plan:
```
cd docs
make html
python -m http.server
// check the docs page, it renders correctly
```
Reviewed By: jbschlosser
Differential Revision: D31994641
Pulled By: vkuzo
fbshipit-source-id: d35a62947dd06e71276eb6a0e37950d3cc5abfc1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66380
Description:
1. creates doc pages for Eager and FX numeric suites
2. adds a link from main quantization doc to (1)
3. formats docblocks in Eager NS to render well
4. adds example code and docblocks to FX numeric suite
Test Plan:
```
cd docs
make html
python -m http.server
// renders well
```
Reviewed By: jerryzh168
Differential Revision: D31543173
Pulled By: vkuzo
fbshipit-source-id: feb291bcbe92747495f45165f738631fa5cbffbd
