mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
237 lines
8.6 KiB
Python
237 lines
8.6 KiB
Python
import asyncio
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import json
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import os
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from contextlib import asynccontextmanager
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from typing import Any, Dict, Sequence
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from pydantic import BaseModel
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from ..chat import ChatModel
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from ..data import Role as DataRole
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from ..extras.misc import torch_gc
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from ..extras.packages import is_fastapi_availble, is_starlette_available, is_uvicorn_available
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from .protocol import (
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ChatCompletionMessage,
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatCompletionResponseStreamChoice,
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ChatCompletionResponseUsage,
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ChatCompletionStreamResponse,
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Finish,
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Function,
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FunctionCall,
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ModelCard,
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ModelList,
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Role,
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ScoreEvaluationRequest,
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ScoreEvaluationResponse,
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)
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if is_fastapi_availble():
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from fastapi import FastAPI, HTTPException, status
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from fastapi.middleware.cors import CORSMiddleware
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if is_starlette_available():
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from sse_starlette import EventSourceResponse
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if is_uvicorn_available():
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import uvicorn
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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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torch_gc()
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def dictify(data: "BaseModel") -> Dict[str, Any]:
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try: # pydantic v2
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return data.model_dump(exclude_unset=True)
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except AttributeError: # pydantic v1
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return data.dict(exclude_unset=True)
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def jsonify(data: "BaseModel") -> str:
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try: # pydantic v2
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return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False)
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except AttributeError: # pydantic v1
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return data.json(exclude_unset=True, ensure_ascii=False)
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def create_app(chat_model: "ChatModel") -> "FastAPI":
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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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semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", 1)))
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role_mapping = {
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Role.USER: DataRole.USER.value,
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Role.ASSISTANT: DataRole.ASSISTANT.value,
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Role.SYSTEM: DataRole.SYSTEM.value,
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Role.FUNCTION: DataRole.FUNCTION.value,
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Role.TOOL: DataRole.OBSERVATION.value,
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}
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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, status_code=status.HTTP_200_OK)
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async def create_chat_completion(request: ChatCompletionRequest):
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if not chat_model.can_generate:
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raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
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if request.messages[0].role == Role.SYSTEM:
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system = request.messages.pop(0).content
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else:
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system = ""
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if len(request.messages) % 2 == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
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input_messages = []
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for i, message in enumerate(request.messages):
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if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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input_messages.append({"role": role_mapping[message.role], "content": message.content})
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tool_list = request.tools
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if isinstance(tool_list, list) and len(tool_list):
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try:
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tools = json.dumps([tool["function"] for tool in tool_list], ensure_ascii=False)
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except Exception:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
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else:
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tools = ""
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async with semaphore:
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loop = asyncio.get_running_loop()
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return await loop.run_in_executor(None, chat_completion, input_messages, system, tools, request)
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def chat_completion(messages: Sequence[Dict[str, str]], system: str, tools: str, request: ChatCompletionRequest):
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if request.stream:
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if tools:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
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generate = stream_chat_completion(messages, system, tools, request)
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return EventSourceResponse(generate, media_type="text/event-stream")
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responses = chat_model.chat(
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messages,
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system,
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tools,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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num_return_sequences=request.n,
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)
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prompt_length, response_length = 0, 0
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choices = []
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for i, response in enumerate(responses):
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if tools:
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result = chat_model.template.format_tools.extract(response.response_text)
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else:
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result = response.response_text
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if isinstance(result, tuple):
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name, arguments = result
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function = Function(name=name, arguments=arguments)
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response_message = ChatCompletionMessage(
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role=Role.ASSISTANT, tool_calls=[FunctionCall(function=function)]
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)
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finish_reason = Finish.TOOL
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else:
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response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
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finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
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choices.append(
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ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason)
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)
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prompt_length = response.prompt_length
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response_length += response.response_length
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usage = ChatCompletionResponseUsage(
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prompt_tokens=prompt_length,
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completion_tokens=response_length,
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total_tokens=prompt_length + response_length,
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)
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return ChatCompletionResponse(model=request.model, choices=choices, usage=usage)
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def stream_chat_completion(
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messages: Sequence[Dict[str, str]], system: str, tools: str, request: ChatCompletionRequest
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):
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choice_data = ChatCompletionResponseStreamChoice(
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index=0, delta=ChatCompletionMessage(role=Role.ASSISTANT, content=""), finish_reason=None
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)
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chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
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yield jsonify(chunk)
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for new_text in chat_model.stream_chat(
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messages,
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system,
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tools,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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):
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if len(new_text) == 0:
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continue
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choice_data = ChatCompletionResponseStreamChoice(
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index=0, delta=ChatCompletionMessage(content=new_text), finish_reason=None
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)
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chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
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yield jsonify(chunk)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
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)
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chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
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yield jsonify(chunk)
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yield "[DONE]"
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@app.post("/v1/score/evaluation", response_model=ScoreEvaluationResponse, status_code=status.HTTP_200_OK)
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async def create_score_evaluation(request: ScoreEvaluationRequest):
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if chat_model.can_generate:
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raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
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async with semaphore:
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loop = asyncio.get_running_loop()
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return await loop.run_in_executor(None, get_score, request)
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def get_score(request: ScoreEvaluationRequest):
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scores = chat_model.get_scores(request.messages, max_length=request.max_length)
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return ScoreEvaluationResponse(model=request.model, scores=scores)
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return app
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if __name__ == "__main__":
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chat_model = ChatModel()
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app = create_app(chat_model)
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uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("API_PORT", 8000)), workers=1)
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