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https://github.com/hwchase17/langchain.git
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huggingface: Add IPEX models support (#29179)
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Ella Charlaix
GitHub
parent
d6a7aaa97d
commit
6f95db81b7
@ -9,6 +9,14 @@ from langchain_core.language_models.llms import BaseLLM
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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from pydantic import ConfigDict, model_validator
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from ..utils.import_utils import (
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IMPORT_ERROR,
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is_ipex_available,
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is_openvino_available,
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is_optimum_intel_available,
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is_optimum_intel_version,
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)
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DEFAULT_MODEL_ID = "gpt2"
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DEFAULT_TASK = "text-generation"
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VALID_TASKS = (
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@ -18,6 +26,8 @@ VALID_TASKS = (
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"translation",
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)
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DEFAULT_BATCH_SIZE = 4
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_MIN_OPTIMUM_VERSION = "1.21"
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logger = logging.getLogger(__name__)
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@ -126,70 +136,70 @@ class HuggingFacePipeline(BaseLLM):
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_model_kwargs["device_map"] = device_map
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tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
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try:
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if task == "text-generation":
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if backend == "openvino":
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try:
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from optimum.intel.openvino import ( # type: ignore[import]
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OVModelForCausalLM,
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)
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except ImportError:
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raise ValueError(
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"Could not import optimum-intel python package. "
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"Please install it with: "
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"pip install 'optimum[openvino,nncf]' "
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)
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try:
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# use local model
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model = OVModelForCausalLM.from_pretrained(
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model_id, **_model_kwargs
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)
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except Exception:
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# use remote model
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model = OVModelForCausalLM.from_pretrained(
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model_id, export=True, **_model_kwargs
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)
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else:
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model = AutoModelForCausalLM.from_pretrained(
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model_id, **_model_kwargs
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)
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elif task in ("text2text-generation", "summarization", "translation"):
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if backend == "openvino":
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try:
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from optimum.intel.openvino import OVModelForSeq2SeqLM
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except ImportError:
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raise ValueError(
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"Could not import optimum-intel python package. "
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"Please install it with: "
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"pip install 'optimum[openvino,nncf]' "
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)
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try:
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# use local model
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model = OVModelForSeq2SeqLM.from_pretrained(
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model_id, **_model_kwargs
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)
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except Exception:
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# use remote model
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model = OVModelForSeq2SeqLM.from_pretrained(
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model_id, export=True, **_model_kwargs
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)
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else:
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model = AutoModelForSeq2SeqLM.from_pretrained(
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model_id, **_model_kwargs
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)
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else:
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if backend in {"openvino", "ipex"}:
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if task not in VALID_TASKS:
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raise ValueError(
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f"Got invalid task {task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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except ImportError as e:
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raise ValueError(
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f"Could not load the {task} model due to missing dependencies."
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) from e
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err_msg = f'Backend: {backend} {IMPORT_ERROR.format(f"optimum[{backend}]")}'
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if not is_optimum_intel_available():
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raise ImportError(err_msg)
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# TODO: upgrade _MIN_OPTIMUM_VERSION to 1.22 after release
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min_optimum_version = (
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"1.22"
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if backend == "ipex" and task != "text-generation"
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else _MIN_OPTIMUM_VERSION
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)
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if is_optimum_intel_version("<", min_optimum_version):
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raise ImportError(
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f"Backend: {backend} requires optimum-intel>="
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f"{min_optimum_version}. You can install it with pip: "
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"`pip install --upgrade --upgrade-strategy eager "
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f"`optimum[{backend}]`."
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)
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if backend == "openvino":
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if not is_openvino_available():
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raise ImportError(err_msg)
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from optimum.intel import ( # type: ignore[import]
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OVModelForCausalLM,
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OVModelForSeq2SeqLM,
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)
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model_cls = (
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OVModelForCausalLM
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if task == "text-generation"
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else OVModelForSeq2SeqLM
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)
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else:
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if not is_ipex_available():
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raise ImportError(err_msg)
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if task == "text-generation":
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from optimum.intel import (
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IPEXModelForCausalLM, # type: ignore[import]
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)
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model_cls = IPEXModelForCausalLM
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else:
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from optimum.intel import (
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IPEXModelForSeq2SeqLM, # type: ignore[import]
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)
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model_cls = IPEXModelForSeq2SeqLM
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else:
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model_cls = (
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AutoModelForCausalLM
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if task == "text-generation"
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else AutoModelForSeq2SeqLM
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)
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model = model_cls.from_pretrained(model_id, **_model_kwargs)
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if tokenizer.pad_token is None:
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tokenizer.pad_token_id = model.config.eos_token_id
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@ -0,0 +1,116 @@
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import importlib.metadata
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import importlib.util
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import operator as op
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from typing import Union
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from packaging import version
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STR_OPERATION_TO_FUNC = {
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">": op.gt,
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">=": op.ge,
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"==": op.eq,
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"!=": op.ne,
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"<=": op.le,
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"<": op.lt,
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}
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_optimum_available = importlib.util.find_spec("optimum") is not None
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_optimum_version = "N/A"
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if _optimum_available:
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try:
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_optimum_version = importlib.metadata.version("optimum")
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except importlib.metadata.PackageNotFoundError:
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_optimum_available = False
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_optimum_intel_available = (
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_optimum_available and importlib.util.find_spec("optimum.intel") is not None
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)
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_optimum_intel_version = "N/A"
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if _optimum_intel_available:
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try:
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_optimum_intel_version = importlib.metadata.version("optimum-intel")
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except importlib.metadata.PackageNotFoundError:
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_optimum_intel_available = False
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_ipex_available = importlib.util.find_spec("intel_extension_for_pytorch") is not None
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_openvino_available = importlib.util.find_spec("openvino") is not None
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# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319
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def compare_versions(
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library_or_version: Union[str, version.Version],
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operation: str,
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requirement_version: str,
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) -> bool:
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"""
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Compare a library version to some requirement using a given operation.
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Arguments:
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library_or_version (`str` or `packaging.version.Version`):
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A library name or a version to check.
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operation (`str`):
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A string representation of an operator, such as `">"` or `"<="`.
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requirement_version (`str`):
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The version to compare the library version against
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"""
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if operation not in STR_OPERATION_TO_FUNC.keys():
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raise ValueError(
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f"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}"
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f", received {operation}"
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)
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if isinstance(library_or_version, str):
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library_or_version = version.parse(
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importlib.metadata.version(library_or_version)
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)
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return STR_OPERATION_TO_FUNC[operation](
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library_or_version, version.parse(requirement_version)
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)
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def is_optimum_available() -> bool:
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return _optimum_available
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def is_optimum_intel_available() -> bool:
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return _optimum_intel_available
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def is_ipex_available() -> bool:
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return _ipex_available
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def is_openvino_available() -> bool:
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return _openvino_available
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def is_optimum_version(operation: str, reference_version: str) -> bool:
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"""
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Compare the current Optimum version to a given reference with an operation.
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"""
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if not _optimum_version:
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return False
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return compare_versions(
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version.parse(_optimum_version), operation, reference_version
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)
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def is_optimum_intel_version(operation: str, reference_version: str) -> bool:
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"""
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Compare the current Optimum Intel version to a given reference with an operation.
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"""
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if not _optimum_intel_version:
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return False
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return compare_versions(
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version.parse(_optimum_intel_version), operation, reference_version
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)
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IMPORT_ERROR = """
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requires the {0} library but it was not found in your environment.
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You can install it with pip: `pip install {0}`.
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Please note that you may need to restart your runtime after installation.
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"""
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