b827f534d5
* fix low-bit * fix * fix style --------- Co-authored-by: arda <arda@arda-arc12.sh.intel.com>
276 lines
10 KiB
Python
276 lines
10 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This would makes sure Python is aware there is more than one sub-package within bigdl,
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# physically located elsewhere.
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# Otherwise there would be module not found error in non-pip's setting as Python would
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# only search the first bigdl package and end up finding only one sub-package.
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# This file is adapted from
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# https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_pipeline.py
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# The MIT License
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# Copyright (c) Harrison Chase
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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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 ipex_llm.utils.common.log4Error import invalidInputError
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from pydantic import Extra
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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DEFAULT_MODEL_ID = "gpt2"
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class TransformersLLM(LLM):
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"""Wrapper around the BigDL-LLM Transformer-INT4 model
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Example:
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.. code-block:: python
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from ipex_llm.langchain.llms import TransformersLLM
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llm = TransformersLLM.from_model_id(model_id="THUDM/chatglm-6b")
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"""
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model_id: str = DEFAULT_MODEL_ID
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"""Model name or model path 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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model: Any #: :meta private:
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"""BigDL-LLM Transformers-INT4 model."""
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tokenizer: Any #: :meta private:
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"""Huggingface tokenizer model."""
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streaming: bool = True
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"""Whether to stream the results, token by token."""
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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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model_kwargs: Optional[dict] = None,
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device_map: str = 'cpu',
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tokenizer_id: str = None,
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**kwargs: Any,
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) -> LLM:
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"""
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Construct object from model_id
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Args:
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model_id: Path for the huggingface repo id to be downloaded or
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the huggingface checkpoint folder.
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model_kwargs: Keyword arguments that will be passed to the model and tokenizer.
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kwargs: Extra arguments that will be passed to the model and tokenizer.
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Returns:
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An object of TransformersLLM.
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"""
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try:
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from ipex_llm.transformers import (
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AutoModel,
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AutoModelForCausalLM,
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# AutoModelForSeq2SeqLM,
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)
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from transformers import AutoTokenizer, LlamaTokenizer
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except ImportError:
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invalidInputError(
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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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# TODO: may refactore this code in the future
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if tokenizer_id is not None:
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try:
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, **_model_kwargs)
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except:
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tokenizer = LlamaTokenizer.from_pretrained(tokenizer_id, **_model_kwargs)
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else:
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
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except:
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tokenizer = LlamaTokenizer.from_pretrained(model_id, **_model_kwargs)
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# TODO: may refactore this code in the future
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try:
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model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True,
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**_model_kwargs)
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except:
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model = AutoModel.from_pretrained(model_id, load_in_4bit=True, **_model_kwargs)
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# TODO: may refactore this code in the future
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if 'xpu' in device_map:
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model = model.to(device_map)
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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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return cls(
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model_id=model_id,
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model=model,
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tokenizer=tokenizer,
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model_kwargs=_model_kwargs,
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**kwargs,
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)
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@classmethod
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def from_model_id_low_bit(
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cls,
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model_id: str,
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model_kwargs: Optional[dict] = None,
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device_map: str = 'cpu',
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tokenizer_id: str = None,
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**kwargs: Any,
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) -> LLM:
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"""
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Construct low_bit object from model_id
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Args:
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model_id: Path for the bigdl transformers low-bit model checkpoint folder.
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model_kwargs: Keyword arguments that will be passed to the model and tokenizer.
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kwargs: Extra arguments that will be passed to the model and tokenizer.
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Returns:
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An object of TransformersLLM.
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"""
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try:
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from ipex_llm.transformers import (
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AutoModel,
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AutoModelForCausalLM,
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)
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from transformers import AutoTokenizer, LlamaTokenizer
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except ImportError:
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invalidInputError(
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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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# TODO: may refactore this code in the future
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if tokenizer_id is not None:
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try:
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, **_model_kwargs)
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except:
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tokenizer = LlamaTokenizer.from_pretrained(tokenizer_id, **_model_kwargs)
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else:
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
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except:
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tokenizer = LlamaTokenizer.from_pretrained(model_id, **_model_kwargs)
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# TODO: may refactore this code in the future
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try:
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model = AutoModelForCausalLM.load_low_bit(model_id, **_model_kwargs)
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except:
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model = AutoModel.load_low_bit(model_id, **_model_kwargs)
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# TODO: may refactore this code in the future
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if 'xpu' in device_map:
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model = model.to(device_map)
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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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return cls(
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model_id=model_id,
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model=model,
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tokenizer=tokenizer,
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model_kwargs=_model_kwargs,
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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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}
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@property
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def _llm_type(self) -> str:
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return "BigDL-llm"
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def _call(
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self,
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prompt: 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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) -> str:
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if self.streaming:
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from transformers import TextStreamer
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input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
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input_ids = input_ids.to(self.model.device)
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streamer = TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
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if stop is not None:
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from transformers.generation.stopping_criteria import StoppingCriteriaList
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from transformers.tools.agents import StopSequenceCriteria
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# stop generation when stop words are encountered
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# TODO: stop generation when the following one is stop word
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stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(stop,
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self.tokenizer)])
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else:
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stopping_criteria = None
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output = self.model.generate(input_ids, streamer=streamer,
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stopping_criteria=stopping_criteria, **kwargs)
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text = self.tokenizer.decode(output[0], skip_special_tokens=True)
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return text
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else:
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input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
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input_ids = input_ids.to(self.model.device)
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if stop is not None:
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from transformers.generation.stopping_criteria import StoppingCriteriaList
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from transformers.tools.agents import StopSequenceCriteria
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stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(stop,
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self.tokenizer)])
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else:
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stopping_criteria = None
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output = self.model.generate(input_ids, stopping_criteria=stopping_criteria, **kwargs)
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text = self.tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt):]
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return text
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