from __future__ import annotations # type: ignore[import-not-found]
import importlib.util
import logging
from typing import Any, Iterator, List, Mapping, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation"
VALID_TASKS = (
"text2text-generation",
"text-generation",
"summarization",
"translation",
)
DEFAULT_BATCH_SIZE = 4
logger = logging.getLogger(__name__)
[docs]class HuggingFacePipeline(BaseLLM):
"""HuggingFace Pipeline API.
To use, you should have the ``transformers`` python package installed.
Only supports `text-generation`, `text2text-generation`, `summarization` and
`translation` for now.
Example using from_model_id:
.. code-block:: python
from langchain_huggingface import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
Example passing pipeline in directly:
.. code-block:: python
from langchain_huggingface import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
"""
pipeline: Any #: :meta private:
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Keyword arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Keyword arguments passed to the pipeline."""
batch_size: int = DEFAULT_BATCH_SIZE
"""Batch size to use when passing multiple documents to generate."""
class Config:
"""Configuration for this pydantic object."""
extra = "forbid"
[docs] @classmethod
def from_model_id(
cls,
model_id: str,
task: str,
backend: str = "default",
device: Optional[int] = -1,
device_map: Optional[str] = None,
model_kwargs: Optional[dict] = None,
pipeline_kwargs: Optional[dict] = None,
batch_size: int = DEFAULT_BATCH_SIZE,
**kwargs: Any,
) -> HuggingFacePipeline:
"""Construct the pipeline object from model_id and task."""
try:
from transformers import ( # type: ignore[import]
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers import pipeline as hf_pipeline # type: ignore[import]
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == "text-generation":
if backend == "openvino":
try:
from optimum.intel.openvino import ( # type: ignore[import]
OVModelForCausalLM,
)
except ImportError:
raise ValueError(
"Could not import optimum-intel python package. "
"Please install it with: "
"pip install 'optimum[openvino,nncf]' "
)
try:
# use local model
model = OVModelForCausalLM.from_pretrained(
model_id, **_model_kwargs
)
except Exception:
# use remote model
model = OVModelForCausalLM.from_pretrained(
model_id, export=True, **_model_kwargs
)
else:
model = AutoModelForCausalLM.from_pretrained(
model_id, **_model_kwargs
)
elif task in ("text2text-generation", "summarization", "translation"):
if backend == "openvino":
try:
from optimum.intel.openvino import OVModelForSeq2SeqLM
except ImportError:
raise ValueError(
"Could not import optimum-intel python package. "
"Please install it with: "
"pip install 'optimum[openvino,nncf]' "
)
try:
# use local model
model = OVModelForSeq2SeqLM.from_pretrained(
model_id, **_model_kwargs
)
except Exception:
# use remote model
model = OVModelForSeq2SeqLM.from_pretrained(
model_id, export=True, **_model_kwargs
)
else:
model = AutoModelForSeq2SeqLM.from_pretrained(
model_id, **_model_kwargs
)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e
if tokenizer.pad_token is None:
tokenizer.pad_token_id = model.config.eos_token_id
if (
(
getattr(model, "is_loaded_in_4bit", False)
or getattr(model, "is_loaded_in_8bit", False)
)
and device is not None
and backend == "default"
):
logger.warning(
f"Setting the `device` argument to None from {device} to avoid "
"the error caused by attempting to move the model that was already "
"loaded on the GPU using the Accelerate module to the same or "
"another device."
)
device = None
if (
device is not None
and importlib.util.find_spec("torch") is not None
and backend == "default"
):
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device_map is not None and device < 0:
device = None
if device is not None and device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 (default) for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
if device is not None and device_map is not None and backend == "openvino":
logger.warning("Please set device for OpenVINO through: `model_kwargs`")
if "trust_remote_code" in _model_kwargs:
_model_kwargs = {
k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
}
_pipeline_kwargs = pipeline_kwargs or {}
pipeline = hf_pipeline(
task=task,
model=model,
tokenizer=tokenizer,
device=device,
device_map=device_map,
batch_size=batch_size,
model_kwargs=_model_kwargs,
**_pipeline_kwargs,
)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs,
batch_size=batch_size,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"model_id": self.model_id,
"model_kwargs": self.model_kwargs,
"pipeline_kwargs": self.pipeline_kwargs,
}
@property
def _llm_type(self) -> str:
return "huggingface_pipeline"
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
# List to hold all results
text_generations: List[str] = []
pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
skip_prompt = kwargs.get("skip_prompt", False)
for i in range(0, len(prompts), self.batch_size):
batch_prompts = prompts[i : i + self.batch_size]
# Process batch of prompts
responses = self.pipeline(
batch_prompts,
**pipeline_kwargs,
)
# Process each response in the batch
for j, response in enumerate(responses):
if isinstance(response, list):
# if model returns multiple generations, pick the top one
response = response[0]
if self.pipeline.task == "text-generation":
text = response["generated_text"]
elif self.pipeline.task == "text2text-generation":
text = response["generated_text"]
elif self.pipeline.task == "summarization":
text = response["summary_text"]
elif self.pipeline.task in "translation":
text = response["translation_text"]
else:
raise ValueError(
f"Got invalid task {self.pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
if skip_prompt:
text = text[len(batch_prompts[j]) :]
# Append the processed text to results
text_generations.append(text)
return LLMResult(
generations=[[Generation(text=text)] for text in text_generations]
)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
from threading import Thread
import torch
from transformers import (
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
skip_prompt = kwargs.get("skip_prompt", True)
if stop is not None:
stop = self.pipeline.tokenizer.convert_tokens_to_ids(stop)
stopping_ids_list = stop or []
class StopOnTokens(StoppingCriteria):
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs: Any,
) -> bool:
for stop_id in stopping_ids_list:
if input_ids[0][-1] == stop_id:
return True
return False
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
inputs = self.pipeline.tokenizer(prompt, return_tensors="pt")
streamer = TextIteratorStreamer(
self.pipeline.tokenizer,
timeout=60.0,
skip_prompt=skip_prompt,
skip_special_tokens=True,
)
generation_kwargs = dict(
inputs,
streamer=streamer,
stopping_criteria=stopping_criteria,
**pipeline_kwargs,
)
t1 = Thread(target=self.pipeline.model.generate, kwargs=generation_kwargs)
t1.start()
for char in streamer:
chunk = GenerationChunk(text=char)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
