Source code for bigdl.llm.langchain.llms.transformersllm
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# This file is adapted from
# https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_pipeline.py
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import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "gpt2"
[docs]class TransformersLLM(LLM):
"""Wrapper around the BigDL-LLM Transformer-INT4 model
Example:
.. code-block:: python
from bigdl.llm.langchain.llms import TransformersLLM
llm = TransformersLLM.from_model_id(model_id="THUDM/chatglm-6b")
"""
model_id: str = DEFAULT_MODEL_ID
"""Model name or model path to use."""
model_kwargs: Optional[dict] = None
"""Keyword arguments passed to the model."""
model: Any #: :meta private:
"""BigDL-LLM Transformers-INT4 model."""
tokenizer: Any #: :meta private:
"""Huggingface tokenizer model."""
streaming: bool = True
"""Whether to stream the results, token by token."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @classmethod
def from_model_id(
cls,
model_id: str,
model_kwargs: Optional[dict] = None,
device_map: str = 'cpu',
**kwargs: Any,
) -> LLM:
"""
Construct object from model_id
Args:
model_id: Path for the huggingface repo id to be downloaded or
the huggingface checkpoint folder.
model_kwargs: Keyword arguments that will be passed to the model and tokenizer.
kwargs: Extra arguments that will be passed to the model and tokenizer.
Returns:
An object of TransformersLLM.
"""
try:
from bigdl.llm.transformers import (
AutoModel,
AutoModelForCausalLM,
# AutoModelForSeq2SeqLM,
)
from transformers import AutoTokenizer, LlamaTokenizer
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
_model_kwargs = model_kwargs or {}
# TODO: may refactore this code in the future
try:
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
except:
tokenizer = LlamaTokenizer.from_pretrained(model_id, **_model_kwargs)
# TODO: may refactore this code in the future
try:
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, **_model_kwargs)
except:
model = AutoModel.from_pretrained(model_id, load_in_4bit=True, **_model_kwargs)
# TODO: may refactore this code in the future
if 'xpu' in device_map:
model = model.to(device_map)
if "trust_remote_code" in _model_kwargs:
_model_kwargs = {
k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
}
return cls(
model_id=model_id,
model=model,
tokenizer=tokenizer,
model_kwargs=_model_kwargs,
**kwargs,
)
[docs] @classmethod
def from_model_id_low_bit(
cls,
model_id: str,
model_kwargs: Optional[dict] = None,
device_map: str = 'cpu',
**kwargs: Any,
) -> LLM:
"""
Construct low_bit object from model_id
Args:
model_id: Path for the bigdl transformers low-bit model checkpoint folder.
model_kwargs: Keyword arguments that will be passed to the model and tokenizer.
kwargs: Extra arguments that will be passed to the model and tokenizer.
Returns:
An object of TransformersLLM.
"""
try:
from bigdl.llm.transformers import (
AutoModel,
AutoModelForCausalLM,
)
from transformers import AutoTokenizer, LlamaTokenizer
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
_model_kwargs = model_kwargs or {}
# TODO: may refactore this code in the future
try:
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
except:
tokenizer = LlamaTokenizer.from_pretrained(model_id, **_model_kwargs)
# TODO: may refactore this code in the future
try:
model = AutoModelForCausalLM.load_low_bit(model_id, **_model_kwargs)
except:
model = AutoModel.load_low_bit(model_id, **_model_kwargs)
# TODO: may refactore this code in the future
if 'xpu' in device_map:
model = model.to(device_map)
if "trust_remote_code" in _model_kwargs:
_model_kwargs = {
k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
}
return cls(
model_id=model_id,
model=model,
tokenizer=tokenizer,
model_kwargs=_model_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"model_id": self.model_id,
"model_kwargs": self.model_kwargs,
}
@property
def _llm_type(self) -> str:
return "BigDL-llm"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if self.streaming:
from transformers import TextStreamer
input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
input_ids = input_ids.to(self.model.device)
streamer = TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
if stop is not None:
from transformers.generation.stopping_criteria import StoppingCriteriaList
from transformers.tools.agents import StopSequenceCriteria
# stop generation when stop words are encountered
# TODO: stop generation when the following one is stop word
stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(stop,
self.tokenizer)])
else:
stopping_criteria = None
output = self.model.generate(input_ids, streamer=streamer,
stopping_criteria=stopping_criteria, **kwargs)
text = self.tokenizer.decode(output[0], skip_special_tokens=True)
return text
else:
input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
input_ids = input_ids.to(self.model.device)
if stop is not None:
from transformers.generation.stopping_criteria import StoppingCriteriaList
from transformers.tools.agents import StopSequenceCriteria
stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(stop,
self.tokenizer)])
else:
stopping_criteria = None
output = self.model.generate(input_ids, stopping_criteria=stopping_criteria, **kwargs)
text = self.tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt) :]
return text