lm-evaluation/build/lib/lm_eval/models/optimum_lm.py

from importlib.util import find_spec
from pathlib import Path

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


@register_model("openvino")
class OptimumLM(HFLM):
    """
    Optimum Intel provides a simple interface to optimize Transformer models and convert them to \
    OpenVINO™ Intermediate Representation (IR) format to accelerate end-to-end pipelines on \
    Intel® architectures using OpenVINO™ runtime.
    """

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

        self.openvino_device = device

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

    def _create_model(
        self,
        pretrained: str,
        revision="main",
        dtype="auto",
        trust_remote_code=False,
        **kwargs,
    ) -> None:
        if not find_spec("optimum"):
            raise Exception(
                "package `optimum` is not installed. Please install it via `pip install optimum[openvino]`"
            )
        else:
            from optimum.intel.openvino import OVModelForCausalLM

        model_kwargs = kwargs if kwargs else {}
        model_file = Path(pretrained) / "openvino_model.xml"
        if model_file.exists():
            export = False
        else:
            export = True
        kwargs["ov_config"] = {
            "PERFORMANCE_HINT": "LATENCY",
            "NUM_STREAMS": "1",
            "CACHE_DIR": "",
        }

        self._model = OVModelForCausalLM.from_pretrained(
            pretrained,
            revision=revision,
            trust_remote_code=trust_remote_code,
            export=export,
            device=self.openvino_device.upper(),
            **model_kwargs,
        )