From cd6426a249e29256486f2eadcd44422c4839969e Mon Sep 17 00:00:00 2001 From: x54-729 <45304952+x54-729@users.noreply.github.com> Date: Tue, 19 Sep 2023 13:49:48 +0800 Subject: [PATCH] feat(tools): support openai api (#313) * fix(chat): fix stream_chat to return generator (#123) * fix(configs/7B_sft.py): model dtype float16 to bfloat16 (#302) * fix(convert2hf.py): fix the rotary_emb.inv_freq KeyError (#299) * support openai api to deploy internlm * update README for information os openai_api.py * change example in README_EN.md to English * delete unnecessary print; fix model card typo; fix chat epoch --------- Co-authored-by: yingtongxiong <974106207@qq.com> Co-authored-by: zhjunqin Co-authored-by: huangting4201 <1538303371@qq.com> Co-authored-by: jiangtann <39088437+jiangtann@users.noreply.github.com> --- tools/README.md | 26 ++++++++ tools/README_EN.md | 26 ++++++++ tools/openai_api.py | 157 ++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 209 insertions(+) create mode 100644 tools/openai_api.py diff --git a/tools/README.md b/tools/README.md index 0c78a56..8e42e78 100644 --- a/tools/README.md +++ b/tools/README.md @@ -109,3 +109,29 @@ InternLM 在 GSM8K 数据集中带工具和不带工具的性能表现： | -------- | -------------------- | | w/o tool | 34.5 | | w tool | 39.2 | + +# openai_api.py + +使用 OpenAI 接口实现的流式部署，可以应用于基于 ChatGPT 的应用的后端。部署的命令为： + +```bash +python openai_api.py +``` + +然后可以通过下面代码调用部署好的 api： + +```python +import openai +if __name__ == "__main__": + openai.api_base = "http://localhost:8000/internlm" + openai.api_key = "none" + for chunk in openai.ChatCompletion.create( + model="internlm-chat-7b", + messages=[ + {"role": "user", "content": "你好"}, + ], + stream=True + ): + if hasattr(chunk.choices[0].delta, "content"): + print(chunk.choices[0].delta.content, end="", flush=True) +``` \ No newline at end of file diff --git a/tools/README_EN.md b/tools/README_EN.md index 3105146..8c7e005 100644 --- a/tools/README_EN.md +++ b/tools/README_EN.md @@ -107,3 +107,29 @@ InternLM performance in the GSM8K dataset with and without tools: | -------- | -------------------- | | w/o tool | 34.5 | | w tool | 39.2 | + +# openai_api.py + +`openai_api.py` implements stream deployment with OpenAI APIs which an be used on any applications based on ChatGPT. Below is the command to deploy `internlm`: + +```bash +python openai_api.py +``` + +Then it is able to call the deployed API using the following python code: + +```python +import openai +if __name__ == "__main__": + openai.api_base = "http://localhost:8000/internlm" + openai.api_key = "none" + for chunk in openai.ChatCompletion.create( + model="internlm-chat-7b", + messages=[ + {"role": "user", "content": "Hello!"}, + ], + stream=True + ): + if hasattr(chunk.choices[0].delta, "content"): + print(chunk.choices[0].delta.content, end="", flush=True) +``` diff --git a/tools/openai_api.py b/tools/openai_api.py new file mode 100644 index 0000000..f853329 --- /dev/null +++ b/tools/openai_api.py @@ -0,0 +1,157 @@ +import time +from contextlib import asynccontextmanager +from typing import List, Literal, Optional, Union + +import torch +import uvicorn +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel, Field +from sse_starlette.sse import EventSourceResponse +from transformers import AutoModelForCausalLM, AutoTokenizer + + +@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("/internlm/models", response_model=ModelList) +async def list_models(): + model_card = ModelCard(id="internlm") + return ModelList(data=[model_card]) + + +@app.post("/internlm/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__": + model_name = "internlm/internlm-chat-7b" + tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) + model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) + model.eval() + + uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)