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Update AutoRound layer_config usage #2331

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Merged
merged 3 commits into from
Nov 11, 2025
Merged

Update AutoRound layer_config usage #2331

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ba1ebc7
update AutoRound layer_config usage
Kaihui-intel Nov 7, 2025
2d76c7f
align format
Kaihui-intel Nov 7, 2025
f9d3ac9
update example
Kaihui-intel Nov 7, 2025
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19 changes: 19 additions & 0 deletions docs/source/3x/PT_WeightOnlyQuant.md
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Original file line number Diff line number Diff line change
Expand Up @@ -178,6 +178,8 @@ model = convert(model, config) # after this step, the model is ready for W4A8 i
| not_use_best_mse (bool) | Whether to use mean squared error | False |
| dynamic_max_gap (int) | The dynamic maximum gap | -1 |
| scale_dtype (str) | The data type of quantization scale to be used, different kernels have different choices | "float16" |
| scheme (str) | A preset scheme that defines the quantization configurations. | "W4A16" |
| layer_config (dict) | Layer-wise quantization config | None |

``` python
# Quantization code
Expand Down Expand Up @@ -283,6 +285,23 @@ quant_config = RTNConfig()
lm_head_config = RTNConfig(dtype="fp32")
quant_config.set_local("lm_head", lm_head_config)
```
3. Example of using `layer_config` for AutoRound
```python
# layer_config = {
# "layer1": {
# "data_type": "int",
# "bits": 3,
# "group_size": 128,
# "sym": True,
# },
# "layer2": {
# "W8A16"
# }
# }
# Use the AutoRound specific 'layer_config' instead of the 'set_local' API.
layer_config = {"lm_head": {"data_type": "int"}}
quant_config = AutoRoundConfig(layer_config=layer_config)
```

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