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huggingface: init package (#21097)
First Pr for the langchain_huggingface partner Package - Moved some of the hugging face related class from `community` to the new `partner package` Still needed : - Documentation - Tests - Support for the new apply_chat_template in `ChatHuggingFace` - Confirm choice of class to support for embeddings witht he sentence-transformer team. cc : @efriis --------- Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com> Co-authored-by: Erick Friis <erick@langchain.dev>
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Jofthomas
GitHub
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from __future__ import annotations
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import importlib.util
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import logging
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from typing import Any, List, Mapping, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import BaseLLM
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from langchain_core.outputs import Generation, LLMResult
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from langchain_core.pydantic_v1 import Extra
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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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"text2text-generation",
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"text-generation",
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"summarization",
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"translation",
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)
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DEFAULT_BATCH_SIZE = 4
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logger = logging.getLogger(__name__)
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class HuggingFacePipeline(BaseLLM):
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"""HuggingFace Pipeline API.
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To use, you should have the ``transformers`` python package installed.
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Only supports `text-generation`, `text2text-generation`, `summarization` and
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`translation` for now.
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Example using from_model_id:
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.. code-block:: python
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from langchain_community.llms import HuggingFacePipeline
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hf = HuggingFacePipeline.from_model_id(
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model_id="gpt2",
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task="text-generation",
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pipeline_kwargs={"max_new_tokens": 10},
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)
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Example passing pipeline in directly:
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.. code-block:: python
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from langchain_community.llms import HuggingFacePipeline
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model_id = "gpt2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipe = pipeline(
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"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
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)
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hf = HuggingFacePipeline(pipeline=pipe)
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"""
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pipeline: Any #: :meta private:
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model_id: str = DEFAULT_MODEL_ID
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"""Model name to use."""
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model_kwargs: Optional[dict] = None
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"""Keyword arguments passed to the model."""
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pipeline_kwargs: Optional[dict] = None
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"""Keyword arguments passed to the pipeline."""
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batch_size: int = DEFAULT_BATCH_SIZE
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"""Batch size to use when passing multiple documents to generate."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@classmethod
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def from_model_id(
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cls,
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model_id: str,
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task: str,
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backend: str = "default",
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device: Optional[int] = -1,
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device_map: Optional[str] = None,
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model_kwargs: Optional[dict] = None,
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pipeline_kwargs: Optional[dict] = None,
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batch_size: int = DEFAULT_BATCH_SIZE,
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**kwargs: Any,
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) -> HuggingFacePipeline:
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"""Construct the pipeline object from model_id and task."""
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try:
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from transformers import ( # type: ignore[import]
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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)
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from transformers import pipeline as hf_pipeline # type: ignore[import]
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except ImportError:
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raise ValueError(
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"Could not import transformers python package. "
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"Please install it with `pip install transformers`."
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)
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_model_kwargs = model_kwargs or {}
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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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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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if tokenizer.pad_token is None:
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tokenizer.pad_token_id = model.config.eos_token_id
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if (
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(
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getattr(model, "is_loaded_in_4bit", False)
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or getattr(model, "is_loaded_in_8bit", False)
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)
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and device is not None
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and backend == "default"
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):
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logger.warning(
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f"Setting the `device` argument to None from {device} to avoid "
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"the error caused by attempting to move the model that was already "
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"loaded on the GPU using the Accelerate module to the same or "
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"another device."
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)
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device = None
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if (
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device is not None
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and importlib.util.find_spec("torch") is not None
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and backend == "default"
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):
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import torch
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cuda_device_count = torch.cuda.device_count()
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if device < -1 or (device >= cuda_device_count):
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raise ValueError(
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f"Got device=={device}, "
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f"device is required to be within [-1, {cuda_device_count})"
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)
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if device_map is not None and device < 0:
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device = None
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if device is not None and device < 0 and cuda_device_count > 0:
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logger.warning(
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"Device has %d GPUs available. "
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"Provide device={deviceId} to `from_model_id` to use available"
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"GPUs for execution. deviceId is -1 (default) for CPU and "
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"can be a positive integer associated with CUDA device id.",
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cuda_device_count,
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)
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if device is not None and device_map is not None and backend == "openvino":
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logger.warning("Please set device for OpenVINO through: " "'model_kwargs'")
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if "trust_remote_code" in _model_kwargs:
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_model_kwargs = {
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k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
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}
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_pipeline_kwargs = pipeline_kwargs or {}
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pipeline = hf_pipeline(
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task=task,
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model=model,
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tokenizer=tokenizer,
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device=device,
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device_map=device_map,
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batch_size=batch_size,
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model_kwargs=_model_kwargs,
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**_pipeline_kwargs,
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)
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if pipeline.task not in VALID_TASKS:
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raise ValueError(
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f"Got invalid task {pipeline.task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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return cls(
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pipeline=pipeline,
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model_id=model_id,
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model_kwargs=_model_kwargs,
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pipeline_kwargs=_pipeline_kwargs,
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batch_size=batch_size,
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**kwargs,
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)
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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"model_id": self.model_id,
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"model_kwargs": self.model_kwargs,
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"pipeline_kwargs": self.pipeline_kwargs,
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}
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@property
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def _llm_type(self) -> str:
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return "huggingface_pipeline"
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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# List to hold all results
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text_generations: List[str] = []
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pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
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for i in range(0, len(prompts), self.batch_size):
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batch_prompts = prompts[i : i + self.batch_size]
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# Process batch of prompts
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responses = self.pipeline(
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batch_prompts,
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**pipeline_kwargs,
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)
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# Process each response in the batch
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for j, response in enumerate(responses):
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if isinstance(response, list):
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# if model returns multiple generations, pick the top one
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response = response[0]
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if self.pipeline.task == "text-generation":
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text = response["generated_text"]
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elif self.pipeline.task == "text2text-generation":
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text = response["generated_text"]
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elif self.pipeline.task == "summarization":
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text = response["summary_text"]
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elif self.pipeline.task in "translation":
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text = response["translation_text"]
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else:
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raise ValueError(
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f"Got invalid task {self.pipeline.task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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# Append the processed text to results
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text_generations.append(text)
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return LLMResult(
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generations=[[Generation(text=text)] for text in text_generations]
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)
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