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 <zhjunqin@users.noreply.github.com>
Co-authored-by: huangting4201 <1538303371@qq.com>
Co-authored-by: jiangtann <39088437+jiangtann@users.noreply.github.com>

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)
+```

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)
+```

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)