mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-23 14:22:51 +08:00
229 lines
7.1 KiB
Python
229 lines
7.1 KiB
Python
# coding=utf-8
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# Implements API for fine-tuned models in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
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# Usage: python api_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
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# Visit http://localhost:8000/docs for document.
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import time
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import torch
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import uvicorn
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from threading import Thread
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from pydantic import BaseModel, Field
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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 transformers import TextIteratorStreamer
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from sse_starlette import EventSourceResponse
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from typing import Any, Dict, List, Literal, Optional
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from utils import (
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Template,
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load_pretrained,
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prepare_infer_args,
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get_logits_processor
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)
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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: Optional[str] = "model"
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created: Optional[int] = Field(default_factory=lambda: int(time.time()))
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owned_by: Optional[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] = []
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class ModelList(BaseModel):
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object: Optional[str] = "list"
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data: Optional[List[ModelCard]] = []
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class ChatMessage(BaseModel):
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role: Literal["user", "assistant", "system"]
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content: str
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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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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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n: Optional[int] = 1
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max_tokens: Optional[int] = None
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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: Literal["stop", "length"]
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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"]] = None
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class ChatCompletionResponseUsage(BaseModel):
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prompt_tokens: int
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completion_tokens: int
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total_tokens: int
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class ChatCompletionResponse(BaseModel):
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id: Optional[str] = "chatcmpl-default"
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object: Literal["chat.completion"]
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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[ChatCompletionResponseChoice]
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usage: ChatCompletionResponseUsage
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class ChatCompletionStreamResponse(BaseModel):
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id: Optional[str] = "chatcmpl-default"
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object: Literal["chat.completion.chunk"]
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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[ChatCompletionResponseStreamChoice]
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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, source_prefix, generating_args
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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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prefix = prev_messages.pop(0).content
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else:
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prefix = source_prefix
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history = []
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if len(prev_messages) % 2 == 0:
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for i in range(0, len(prev_messages), 2):
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if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
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history.append([prev_messages[i].content, prev_messages[i+1].content])
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inputs = tokenizer([prompt_template.get_prompt(query, history, prefix)], return_tensors="pt")
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inputs = inputs.to(model.device)
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gen_kwargs = generating_args.to_dict()
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gen_kwargs.update({
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"input_ids": inputs["input_ids"],
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"temperature": request.temperature if request.temperature else gen_kwargs["temperature"],
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"top_p": request.top_p if request.top_p else gen_kwargs["top_p"],
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"logits_processor": get_logits_processor()
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})
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if request.max_tokens:
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gen_kwargs.pop("max_length", None)
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gen_kwargs["max_new_tokens"] = request.max_tokens
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if request.stream:
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generate = predict(gen_kwargs, request.model)
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return EventSourceResponse(generate, media_type="text/event-stream")
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generation_output = model.generate(**gen_kwargs)
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outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
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response = tokenizer.decode(outputs, skip_special_tokens=True)
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usage = ChatCompletionResponseUsage(
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prompt_tokens=len(inputs["input_ids"][0]),
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completion_tokens=len(outputs),
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total_tokens=len(inputs["input_ids"][0]) + len(outputs)
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)
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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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return ChatCompletionResponse(model=request.model, choices=[choice_data], usage=usage, object="chat.completion")
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async def predict(gen_kwargs: Dict[str, Any], model_id: str):
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global model, tokenizer
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
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gen_kwargs["streamer"] = streamer
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thread = Thread(target=model.generate, kwargs=gen_kwargs)
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thread.start()
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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 = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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yield chunk.json(exclude_unset=True, ensure_ascii=False)
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for new_text in streamer:
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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,
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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 = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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yield 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 = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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yield chunk.json(exclude_unset=True, ensure_ascii=False)
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yield "[DONE]"
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if __name__ == "__main__":
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model_args, data_args, finetuning_args, generating_args = prepare_infer_args()
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model, tokenizer = load_pretrained(model_args, finetuning_args)
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prompt_template = Template(data_args.prompt_template)
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source_prefix = data_args.source_prefix if data_args.source_prefix else ""
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uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
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