mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
# Copyright 2024 the LlamaFactory team.
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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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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Optional, Sequence, Union
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, PreTrainedTokenizer
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from vllm import AsyncLLMEngine
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from ..data import Template
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from ..data.mm_plugin import ImageInput, VideoInput
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from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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@dataclass
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class Response:
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response_text: str
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response_length: int
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prompt_length: int
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finish_reason: Literal["stop", "length"]
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class BaseEngine(ABC):
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r"""
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Base class for inference engine of chat models.
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Must implements async methods: chat(), stream_chat() and get_scores().
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"""
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model: Union["PreTrainedModel", "AsyncLLMEngine"]
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tokenizer: "PreTrainedTokenizer"
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can_generate: bool
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template: "Template"
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generating_args: Dict[str, Any]
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@abstractmethod
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def __init__(
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self,
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model_args: "ModelArguments",
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data_args: "DataArguments",
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finetuning_args: "FinetuningArguments",
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generating_args: "GeneratingArguments",
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) -> None:
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r"""
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Initializes an inference engine.
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"""
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...
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@abstractmethod
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async def chat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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images: Optional[Sequence["ImageInput"]] = None,
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videos: Optional[Sequence["VideoInput"]] = None,
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**input_kwargs,
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) -> List["Response"]:
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r"""
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Gets a list of responses of the chat model.
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"""
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...
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@abstractmethod
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async def stream_chat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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images: Optional[Sequence["ImageInput"]] = None,
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videos: Optional[Sequence["VideoInput"]] = None,
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**input_kwargs,
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) -> AsyncGenerator[str, None]:
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r"""
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Gets the response token-by-token of the chat model.
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"""
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...
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@abstractmethod
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async def get_scores(
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self,
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batch_input: List[str],
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**input_kwargs,
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) -> List[float]:
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r"""
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Gets a list of scores of the reward model.
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"""
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...
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