Summary:
- `remove_quant_dequant_pairs` removes ops when a `quant` is followed by a `dequant`
- It looks like the quantized implementation of `layer_norm` only supports float weights, so updated the default qconfig to avoid quantizing the weight param.
- Fixes broken test, `test_norm_weight_bias`. This was the only test that broke, because the default qconfig dict we pass in quantizes the weight. I just pulled the native qconfig object and converted it to a dict.
- Adds in qconfig and backend config support for layernorm
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
python test/test_quantization.py TestQuantizeFxModels
```
Reviewers:
Subscribers:
Tasks: Fixes https://github.com/pytorch/pytorch/issues/83110
Tags: quant, fx
Differential Revision: [D39395141](https://our.internmc.facebook.com/intern/diff/D39395141)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84203
Approved by: https://github.com/jerryzh168
Summary:
Some more clarifications for the arguments, including linking to object docs (QConfigMapping, BackendConfig) and adding types
in the doc
Test Plan:
```
cd docs
make html
```
and
visual inspection for the generated docs
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84587
Approved by: https://github.com/vkuzo
Summary: This commit adds qconfigs with special observers for fixed
qparams ops in get_default_qconfig_mapping and
get_default_qat_qconfig_mapping. For correctness, we also require
users to use these special observers if we detect these fixed
qparams ops in prepare.
Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
Reviewers: jerryzh168, vkuzo
Subscribers: jerryzh168, vkuzo
Differential Revision: [D37396379](https://our.internmc.facebook.com/intern/diff/D37396379)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80184
Approved by: https://github.com/jerryzh168
Summary: This follows https://github.com/pytorch/pytorch/pull/78452,
which replaced the qconfig_dict with QConfigMapping. This PR
additionally replaces get_default_*qconfig_dict with
get_default_*qconfig_mapping. For backward compatibility, we
deprecate the old functions instead of removing them.
Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
Reviewers: jerryzh168, vkuzo
Subscribers: jerryzh168, vkuzo, supriyar
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79618
Approved by: https://github.com/jerryzh168
**Summary:** Previously, FX graph mode quantization configurations
were specified through a dictionary of qconfigs. However, this
API was not in line with other core APIs in PyTorch. This commit
replaces this dictionary with a config object that users will
create and pass to prepare and convert. This leads to better
type safety and better user experience in notebook settings
due to improved auto completion.
The new API is as follows:
```
from torch.ao.quantization import QConfigMapping
from torch.ao.quantization.quantize_fx import prepare_fx
qconfig_mapping = QConfigMapping()
.set_global(qconfig)
.set_object_type(torch.nn.Linear, qconfig)
.set_module_name_regex("foo.*bar", qconfig)
.set_module_name("mod", qconfig)
prepare_fx(model, qconfig_mapping)
```
For backwards compatibility, `prepare_fx`, `prepare_qat_fx`,
and `convert_fx` will continue to accept qconfig_dicts, which
will be converted to QuantizationConfigs internally.
Note that this commit does not modify existing tests to use the
new API; they will continue to pass in qconfig_dict as before,
which still works but triggers a deprecation warning. This will
be handled in a future commit.
**Test Plan:**
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
**Reviewers:** jerryzh168, vkuzo
**Subscribers:** jerryzh168, vkuzo
Differential Revision: D36747998
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78452
Approved by: https://github.com/jerryzh168
