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https://github.com/zebrajr/pytorch.git
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4669c6d3ae
Summary:
Added `set_module_name_qconfig` support to allow users to set configurations based on module name in `X86InductorQuantizer`.
For example, only quantize the `sub`:
```python
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(5, 5)
self.sub = Sub()
def forward(self, x):
x = self.linear(x)
x = self.sub(x)
return x
m = M().eval()
example_inputs = (torch.randn(3, 5),)
# Set config for a specific submodule.
quantizer = X86InductorQuantizer()
quantizer.set_module_name_qconfig("sub", xiq.get_default_x86_inductor_quantization_config())
```
- Added `set_module_name_qconfig` to allow user set the configuration at the `module_name` level.
- Unified the annotation process to follow this order: `module_name_qconfig`, `operator_type_qconfig`, and `global_config`.
- Added `config_checker` to validate all user configurations and prevent mixing of static/dynamic or QAT/non-QAT configs.
- Moved `_get_module_name_filter` from `xnnpack_quantizer.py` into `utils.py` as it common for all quantizer.
Test Plan
```bash
python -m pytest quantization/pt2e/test_x86inductor_quantizer.py -k test_set_module_name
```
@Xia-Weiwen @leslie-fang-intel @jgong5
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126044
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/jerryzh168
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| .. | ||
| ao_migration | ||
| bc | ||
| core | ||
| eager | ||
| fx | ||
| jit | ||
| pt2e | ||
| serialized | ||
| __init__.py | ||