[Usage] How to manually set calibration_function?

[donpromax]

I noticed that modifiers like SmoothQuantModifier have a parameter calibration_function for the forward pass during calibration. However, it's not clear how to set this calibration_function. For example:

recipe = [
    SmoothQuantModifier(smoothing_strength=0.8, calibration_function=model.generate),
    GPTQModifier(targets="Linear", scheme="W8A8", ignore=["lm_head"]),
]

Additionally, I found that initializing SmoothQuantModifier without using YAML for the mappings parameter results in an error #105

mappings = [
    [["re:.*wqkv"], "re:.*attention_norm"],
    [["re:.*w1", "re:.*w3"], "re:.*ffn_norm"]
]

recipe = [
    SmoothQuantModifier(smoothing_strength=0.8, mappings=mappings),
    GPTQModifier(targets="Linear", scheme="W8A8", ignore=["re:.*output", "re:vision_model.*", "re:mlp1.*"]),
]

This results in the following error:

  File "/llm-compressor/src/llmcompressor/recipe/recipe.py", line 601, in _load_json_or_yaml_string
    raise ValueError(f"Could not parse recipe from string {content}") from err
ValueError: Could not parse recipe from string DEFAULT_stage:
  DEFAULT_modifiers:
    SmoothQuantModifier:
      smoothing_strength: 0.8
      mappings:
      - !!python/tuple
        - - re:.*wqkv
        - re:.*attention_norm
      - !!python/tuple
        - - re:.*w1
          - re:.*w3
        - re:.*ffn_norm
    GPTQModifier:
      targets: Linear
      ignore:
      - lm_head
      scheme: W8A8

[HelloCard]

The mappings error is an old bug that was fixed in #37, but strangely the fix has not been merged into the mainline until now.
As stated there, the workaround is to use string mappings, which seems to have no additional pitfalls.