250 lines
7.7 KiB
Python
250 lines
7.7 KiB
Python
# coding=utf-8
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# Implements API for ChatGLM3-6B in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
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# Usage: python openai_api.bak.py
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# Visit http://localhost:8000/docs for documents.
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import time
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from contextlib import asynccontextmanager
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from typing import List, Literal, Optional, Union
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import torch
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from loguru import logger
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from pydantic import BaseModel, Field
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from sse_starlette.sse import EventSourceResponse
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from transformers import AutoTokenizer, AutoModel
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from utils import process_response, generate_chatglm3, generate_stream_chatglm3
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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[ModelCard] = []
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class FunctionCallResponse(BaseModel):
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name: Optional[str] = None
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arguments: Optional[str] = None
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class ChatMessage(BaseModel):
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role: Literal["user", "assistant", "system", "function"]
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content: str = None
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name: Optional[str] = None
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function_call: Optional[FunctionCallResponse] = None
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class DeltaMessage(BaseModel):
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role: Optional[Literal["user", "assistant", "system"]] = None
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content: Optional[str] = None
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function_call: Optional[FunctionCallResponse] = None
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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] = 0.8
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top_p: Optional[float] = 0.8
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max_tokens: Optional[int] = None
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stream: Optional[bool] = False
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functions: Optional[Union[dict, List[dict]]] = None
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# Additional parameters
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max_length: Optional[int] = None
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repetition_penalty: Optional[float] = 1.1
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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: Literal["stop", "length", "function_call"]
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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[Literal["stop", "length", "function_call"]]
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class UsageInfo(BaseModel):
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prompt_tokens: int = 0
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total_tokens: int = 0
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completion_tokens: Optional[int] = 0
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class ChatCompletionResponse(BaseModel):
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model: str
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object: Literal["chat.completion", "chat.completion.chunk"]
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choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
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created: Optional[int] = Field(default_factory=lambda: int(time.time()))
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usage: Optional[UsageInfo] = None
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class History(BaseModel):
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id: int
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messages: List[ChatMessage]
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query: str = None
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contexts: List[str] = None
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@app.get("/v1/models", response_model=ModelList)
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async def list_models():
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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 len(request.messages) < 1 or request.messages[-1].role == "assistant":
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raise HTTPException(status_code=400, detail="Invalid request")
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gen_params = dict(
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messages=request.messages,
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temperature=request.temperature,
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top_p=request.top_p,
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max_tokens=request.max_tokens or 1024,
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max_length=request.max_length,
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echo=False,
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stream=request.stream,
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repetition_penalty=request.repetition_penalty,
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functions=request.functions,
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)
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logger.debug(f"==== request ====\n{gen_params}")
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if request.stream:
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generate = predict(request.model, gen_params)
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return EventSourceResponse(generate, media_type="text/event-stream")
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response = generate_chatglm3(model, tokenizer, gen_params)
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usage = UsageInfo()
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function_call, finish_reason = None, "stop"
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if request.functions:
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try:
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function_call = process_response(response["text"], use_tool=True)
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except:
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logger.warning("Failed to parse tool call")
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if isinstance(function_call, dict):
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finish_reason = "function_call"
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function_call = FunctionCallResponse(**function_call)
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message = ChatMessage(
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role="assistant",
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content=response["text"],
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function_call=function_call if isinstance(function_call, FunctionCallResponse) else None,
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)
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choice_data = ChatCompletionResponseChoice(
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index=0,
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message=message,
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finish_reason=finish_reason,
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)
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task_usage = UsageInfo.parse_obj(response["usage"])
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for usage_key, usage_value in task_usage.dict().items():
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setattr(usage, usage_key, getattr(usage, usage_key) + usage_value)
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return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion", usage=usage)
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async def predict(model_id: str, params: dict):
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global model, tokenizer
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant"),
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finish_reason=None
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)
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chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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yield "{}".format(chunk.json(exclude_unset=True))
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previous_text = ""
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for new_response in generate_stream_chatglm3(model, tokenizer, params):
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decoded_unicode = new_response["text"]
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delta_text = decoded_unicode[len(previous_text):]
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previous_text = decoded_unicode
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finish_reason = new_response["finish_reason"]
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if len(delta_text) == 0 and finish_reason != "function_call":
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continue
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function_call = None
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if finish_reason == "function_call":
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try:
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function_call = process_response(decoded_unicode, use_tool=True)
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except:
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print("Failed to parse tool call")
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if isinstance(function_call, dict):
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function_call = FunctionCallResponse(**function_call)
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delta = DeltaMessage(
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content=delta_text,
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role="assistant",
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function_call=function_call if isinstance(function_call, FunctionCallResponse) else None,
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=delta,
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finish_reason=finish_reason
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)
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chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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yield "{}".format(chunk.json(exclude_unset=True))
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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(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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yield "{}".format(chunk.json(exclude_unset=True))
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yield '[DONE]'
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if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained("../THUDM/chatglm3-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("../THUDM/chatglm3-6b", trust_remote_code=True).cuda()
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# 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
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# from utils import load_model_on_gpus
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# model = load_model_on_gpus("THUDM/chatglm3-6b", num_gpus=2)
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model = model.eval()
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uvicorn.run(app, host='localhost', port=8000, workers=1)
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