llmcompressor.pipelines.registry

class CalibrationPipeline(ABC, RegistryMixin):
    @staticmethod
    @abstractmethod
    def __call__(
        model: torch.nn.Module,
        dataloader: DataLoader,
        dataset_args: "DatasetArguments",
    ):
        raise NotImplementedError()

    @classmethod
    def from_modifiers(
        cls, modifiers: List[Modifier], user: Optional[str] = None
    ) -> "CalibrationPipeline":
        """
        Infer which calibration pipeline to use based on the available modifiers and
        any user specifications

        :param modifiers: modifiers to apply to model
        :param user: pipeline name passed by user
        :return: CalibrationPipeline instance to be called with data (if not datafree)
        """
        user = standardize_lookup_name(user) if user else None
        inferred = standardize_lookup_name(cls._infer_pipeline(modifiers))
        independent = standardize_lookup_name("independent")

        if user == independent:
            inferred = independent

        if user is not None and user != inferred:
            logger.warning(
                f"Calibration pipeline is set to `{user}`, but it is recommended to "
                f"use `{inferred}`"
            )

        pipeline = user or inferred
        return cls.load_from_registry(pipeline)

    @staticmethod
    def _infer_pipeline(modifiers: List[Modifier]) -> str:
        # only in the case of weight-only qmod quantization can we skip calibration
        if len(modifiers) == 1 and isinstance(modifiers[0], QuantizationModifier):
            config = modifiers[0].resolve_quantization_config()
            if not config.requires_calibration_data():
                return "datafree"

        return "sequential"