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- # coding=utf-8
- # Implements API for ChatGLM2-6B in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
- # Usage: python openai_api.py
- # Visit http://localhost:8000/docs for documents.
-
-
- import time
- import torch
- import uvicorn
- from pydantic import BaseModel, Field
- from fastapi import FastAPI, HTTPException
- from fastapi.middleware.cors import CORSMiddleware
- from contextlib import asynccontextmanager
- from typing import Any, Dict, List, Literal, Optional, Union
- from transformers import AutoTokenizer, AutoModel
- from sse_starlette.sse import ServerSentEvent, EventSourceResponse
-
-
- @asynccontextmanager
- async def lifespan(app: FastAPI): # collects GPU memory
- yield
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
-
-
- app = FastAPI(lifespan=lifespan)
-
- app.add_middleware(
- CORSMiddleware,
- allow_origins=["*"],
- allow_credentials=True,
- allow_methods=["*"],
- allow_headers=["*"],
- )
-
- class ModelCard(BaseModel):
- id: str
- object: str = "model"
- created: int = Field(default_factory=lambda: int(time.time()))
- owned_by: str = "owner"
- root: Optional[str] = None
- parent: Optional[str] = None
- permission: Optional[list] = None
-
-
- class ModelList(BaseModel):
- object: str = "list"
- data: List[ModelCard] = []
-
-
- class ChatMessage(BaseModel):
- role: Literal["user", "assistant", "system"]
- content: str
-
-
- class DeltaMessage(BaseModel):
- role: Optional[Literal["user", "assistant", "system"]] = None
- content: Optional[str] = None
-
-
- class ChatCompletionRequest(BaseModel):
- model: str
- messages: List[ChatMessage]
- temperature: Optional[float] = None
- top_p: Optional[float] = None
- max_length: Optional[int] = None
- stream: Optional[bool] = False
-
-
- class ChatCompletionResponseChoice(BaseModel):
- index: int
- message: ChatMessage
- finish_reason: Literal["stop", "length"]
-
-
- class ChatCompletionResponseStreamChoice(BaseModel):
- index: int
- delta: DeltaMessage
- finish_reason: Optional[Literal["stop", "length"]]
-
-
- class ChatCompletionResponse(BaseModel):
- model: str
- object: Literal["chat.completion", "chat.completion.chunk"]
- choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
- created: Optional[int] = Field(default_factory=lambda: int(time.time()))
-
-
- @app.get("/v1/models", response_model=ModelList)
- async def list_models():
- global model_args
- model_card = ModelCard(id="gpt-3.5-turbo")
- return ModelList(data=[model_card])
-
-
- @app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
- async def create_chat_completion(request: ChatCompletionRequest):
- global model, tokenizer
-
- if request.messages[-1].role != "user":
- raise HTTPException(status_code=400, detail="Invalid request")
- query = request.messages[-1].content
-
- prev_messages = request.messages[:-1]
- if len(prev_messages) > 0 and prev_messages[0].role == "system":
- query = prev_messages.pop(0).content + query
-
- history = []
- if len(prev_messages) % 2 == 0:
- for i in range(0, len(prev_messages), 2):
- if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
- history.append([prev_messages[i].content, prev_messages[i+1].content])
-
- if request.stream:
- generate = predict(query, history, request.model)
- return EventSourceResponse(generate, media_type="text/event-stream")
-
- response, _ = model.chat(tokenizer, query, history=history)
- choice_data = ChatCompletionResponseChoice(
- index=0,
- message=ChatMessage(role="assistant", content=response),
- finish_reason="stop"
- )
-
- return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
-
-
- async def predict(query: str, history: List[List[str]], model_id: str):
- global model, tokenizer
-
- choice_data = ChatCompletionResponseStreamChoice(
- index=0,
- delta=DeltaMessage(role="assistant"),
- finish_reason=None
- )
- chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
- yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
-
- current_length = 0
-
- for new_response, _ in model.stream_chat(tokenizer, query, history):
- if len(new_response) == current_length:
- continue
-
- new_text = new_response[current_length:]
- current_length = len(new_response)
-
- choice_data = ChatCompletionResponseStreamChoice(
- index=0,
- delta=DeltaMessage(content=new_text),
- finish_reason=None
- )
- chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
- yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
-
-
- choice_data = ChatCompletionResponseStreamChoice(
- index=0,
- delta=DeltaMessage(),
- finish_reason="stop"
- )
- chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
- yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
- yield '[DONE]'
-
-
-
- if __name__ == "__main__":
- tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
- model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
- # 多显卡支持,使用下面两行代替上面一行,将num_gpus改为你实际的显卡数量
- # from utils import load_model_on_gpus
- # model = load_model_on_gpus("THUDM/chatglm2-6b", num_gpus=2)
- model.eval()
-
- uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
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