File size: 2,053 Bytes
cd0431b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
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,
)
|