def __post_init__()

in optimum_benchmark/backends/pytorch/config.py [0:0]

• 25 lines of code
• 9 McCabe index (conditional complexity)

    def __post_init__(self):
        super().__post_init__()

        if self.model_kwargs.get("torch_dtype", None) is not None:
            raise ValueError(
                "`torch_dtype` is an explicit argument in the PyTorch backend config. "
                "Please remove it from the `model_kwargs` and set it in the backend config directly."
            )

        if self.torch_dtype is not None and self.torch_dtype not in TORCH_DTYPES:
            raise ValueError(f"`torch_dtype` must be one of {TORCH_DTYPES}. Got {self.torch_dtype} instead.")

        if self.autocast_dtype is not None and self.autocast_dtype not in AMP_DTYPES:
            raise ValueError(f"`autocast_dtype` must be one of {AMP_DTYPES}. Got {self.autocast_dtype} instead.")

        if self.quantization_scheme is not None:
            LOGGER.warning(
                "`backend.quantization_scheme` is deprecated and will be removed in a future version. "
                "Please use `quantization_config.quant_method` instead."
            )
            if self.quantization_config is None:
                self.quantization_config = {"quant_method": self.quantization_scheme}
            else:
                self.quantization_config["quant_method"] = self.quantization_scheme

        if self.quantization_config is not None:
            self.quantization_config = dict(
                QUANTIZATION_CONFIGS.get(self.quantization_scheme, {}),  # default config
                **self.quantization_config,  # user config (overwrites default)
            )