lm-evaluation-harness/lm_eval/models/optimum_ipex.py

from importlib.util import find_spec

from lm_eval import utils
from lm_eval.api.registry import register_model
from lm_eval.models.huggingface import HFLM
from lm_eval.models.utils import get_dtype


eval_logger = utils.eval_logger


@register_model("ipex")
class IPEXLM(HFLM):
    """
    using the HuggingFace transformers + optimum-intel ipex backend, can run on intel cpu and intel gpu
    """

    def __init__(
        self,
        **kwargs,
    ) -> None:
        if "backend" in kwargs:
            # currently only supports causal models
            assert kwargs["backend"] == "causal", (
                "Currently, only IPEXModelForCausalLM is supported."
            )

        super().__init__(
            backend=kwargs.pop("backend", "causal"),
            **kwargs,
        )

    def _create_model(
        self,
        pretrained: str,
        revision="main",
        dtype="auto",
        trust_remote_code=False,
        # arguments used for splitting a model across GPUs naively.
        # only used if `parallelize=True`.
        # (accelerate naive PP (device_map) options)
        parallelize=False,
        gpus=None,
        max_memory_per_gpu=None,
        max_cpu_memory=None,
        offload_folder="./offload",
        # PEFT, delta weights and quantization options
        peft=None,
        delta=None,
        autogptq=False,
        gptqmodel=False,
        **kwargs,
    ) -> None:
        if not find_spec("optimum"):
            raise ModuleNotFoundError(
                "package `optimum` is not installed. Please install it via `pip install optimum[ipex]`"
            )
        else:
            from optimum.intel import IPEXModelForCausalLM

        model_kwargs = kwargs if kwargs else {}
        model_kwargs.update(
            self._get_accelerate_args(
                parallelize=parallelize,
                device_map=kwargs.get("device_map", None),
                max_memory_per_gpu=max_memory_per_gpu,
                max_cpu_memory=max_cpu_memory,
                offload_folder=offload_folder,
                gpus=gpus,
            )
        )

        self._model = IPEXModelForCausalLM.from_pretrained(
            pretrained,
            revision=revision,
            torch_dtype=get_dtype(dtype),
            trust_remote_code=trust_remote_code,
            **model_kwargs,
        )