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234 lines
8.0 KiB
Python
234 lines
8.0 KiB
Python
# coding=utf-8
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# Implements API for Baichuan2-7B-Chat in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
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# Usage: python openai_api.py
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import gc
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import time
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import torch
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import uvicorn
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from pydantic import BaseModel, Field, validator
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
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from typing import Any, Dict, List, Optional, Union
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from sse_starlette.sse import ServerSentEvent, EventSourceResponse
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from transformers.generation.utils import GenerationConfig
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import random
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import string
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@asynccontextmanager
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async def lifespan(app: FastAPI): # collects GPU memory
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yield
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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app = FastAPI(lifespan=lifespan)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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class ModelCard(BaseModel):
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id: str
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object: str = "model"
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created: int = Field(default_factory=lambda: int(time.time()))
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owned_by: str = "owner"
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root: Optional[str] = None
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parent: Optional[str] = None
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permission: Optional[list] = None
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class ModelList(BaseModel):
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object: str = "list"
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data: List[str] = [] # Assuming ModelCard is a string type. Replace with the correct type if not.
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class ChatMessage(BaseModel):
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role: str
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content: str
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@validator('role')
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def check_role(cls, v):
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if v not in ["user", "assistant", "system"]:
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raise ValueError('role must be one of "user", "assistant", "system"')
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return v
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class DeltaMessage(BaseModel):
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role: Optional[str] = None
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content: Optional[str] = None
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@validator('role', allow_reuse=True)
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def check_role(cls, v):
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if v is not None and v not in ["user", "assistant", "system"]:
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raise ValueError('role must be one of "user", "assistant", "system"')
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return v
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class ChatCompletionRequest(BaseModel):
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model: str
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messages: List[ChatMessage]
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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max_length: Optional[int] = 8192 # max_length should be an integer.
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stream: Optional[bool] = False
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class ChatCompletionResponseChoice(BaseModel):
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index: int
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message: ChatMessage
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finish_reason: str
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@validator('finish_reason')
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def check_finish_reason(cls, v):
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if v not in ["stop", "length"]:
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raise ValueError('finish_reason must be one of "stop" or "length"')
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return v
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class ChatCompletionResponseStreamChoice(BaseModel):
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index: int
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delta: DeltaMessage
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finish_reason: Optional[str]
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@validator('finish_reason', allow_reuse=True)
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def check_finish_reason(cls, v):
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if v is not None and v not in ["stop", "length"]:
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raise ValueError('finish_reason must be one of "stop" or "length"')
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return v
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class ChatCompletionResponse(BaseModel):
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id:str
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object:str
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@validator('object')
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def check_object(cls,v):
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if v not in ["chat.completion","chat.completion.chunk"]:
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raise ValueError("object must be one of 'chat.completion' or 'chat.completion.chunk'")
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return v
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created :Optional[int]=Field(default_factory=lambda:int(time.time()))
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model:str
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choices :List[Union[ChatCompletionResponseChoice,ChatCompletionResponseStreamChoice]]
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def generate_id():
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possible_characters = string.ascii_letters + string.digits
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random_string = ''.join(random.choices(possible_characters, k=29))
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return 'chatcmpl-' + random_string
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@app.get("/v1/models", response_model=ModelList)
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async def list_models():
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global model_args
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model_card = ModelCard(id="gpt-3.5-turbo")
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return ModelList(data=[model_card])
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@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
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async def create_chat_completion(request: ChatCompletionRequest):
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global model, tokenizer
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if request.messages[-1].role != "user":
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raise HTTPException(status_code=400, detail="Invalid request")
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query = request.messages[-1].content
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prev_messages = request.messages[:-1]
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if len(prev_messages) > 0 and prev_messages[0].role == "system":
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query = prev_messages.pop(0).content + query
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messages = []
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for message in prev_messages:
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messages.append({"role": message.role, "content": message.content})
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messages.append({"role": "user", "content": query})
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if request.stream:
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generate = predict(messages, request.model)
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return EventSourceResponse(generate, media_type="text/event-stream")
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response = '本接口不支持非stream模式'
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choice_data = ChatCompletionResponseChoice(
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index=0,
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message=ChatMessage(role="assistant", content=response),
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finish_reason="stop"
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)
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id='chatcmpl-7QyqpwdfhqwajicIEznoc6Q47XAyW'
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return ChatCompletionResponse(id=id,model=request.model, choices=[choice_data], object="chat.completion")
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async def predict(messages: List[List[str]], model_id: str):
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global model, tokenizer
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id = generate_id()
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created = int(time.time())
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant",content=""),
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finish_reason=None
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)
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chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
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yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
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current_length = 0
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for new_response in model.chat(tokenizer, messages, stream=True):
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if len(new_response) == current_length:
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continue
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new_text = new_response[current_length:]
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current_length = len(new_response)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(content=new_text),
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finish_reason=None
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)
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chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
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yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(),
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finish_reason="stop"
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)
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chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
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yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
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yield '[DONE]'
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def load_models():
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print("本次加载的大语言模型为: Baichuan-13B-Chat")
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tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", use_fast=False, trust_remote_code=True)
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# model = AutoModelForCausalLM.from_pretrained("Baichuan2-13B-Chat", torch_dtype=torch.float32, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", torch_dtype=torch.float16, trust_remote_code=True)
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model = model.cuda()
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model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat")
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return tokenizer, model
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if __name__ == "__main__":
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tokenizer, model = load_models()
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uvicorn.run(app, host='0.0.0.0', port=6006, workers=1)
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while True:
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try:
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# 在这里执行您的程序逻辑
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# 检查显存使用情况，如果超过阈值（例如90%），则触发垃圾回收
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if torch.cuda.is_available():
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gpu_memory_usage = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
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if gpu_memory_usage > 0.9:
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gc.collect()
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torch.cuda.empty_cache()
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except RuntimeError as e:
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if "out of memory" in str(e):
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print("显存不足，正在重启程序...")
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gc.collect()
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torch.cuda.empty_cache()
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time.sleep(5) # 等待一段时间以确保显存已释放
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tokenizer, model = load_models()
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else:
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raise e
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