添加Baichuan2-7B-Chat模型接口文件 (#404)

* 更新镜像

* 更新镜像信息

* 更新镜像信息

* Create openai_api.py

* Create requirements.txt

files/models/Baichuan2/openai_api.py

+# coding=utf-8
+# Implements API for Baichuan2-7B-Chat in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
+# Usage: python openai_api.py
+
+import gc
+import time
+import torch
+import uvicorn
+from pydantic import BaseModel, Field, validator
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+from typing import Any, Dict, List, Optional, Union
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from sse_starlette.sse import ServerSentEvent, EventSourceResponse
+from transformers.generation.utils import GenerationConfig
+import random
+import string
+
+
+@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[str] = []  # Assuming ModelCard is a string type. Replace with the correct type if not.
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+    @validator('role')
+    def check_role(cls, v):
+        if v not in ["user", "assistant", "system"]:
+            raise ValueError('role must be one of "user", "assistant", "system"')
+        return v
+
+class DeltaMessage(BaseModel):
+    role: Optional[str] = None
+    content: Optional[str] = None
+
+    @validator('role', allow_reuse=True)
+    def check_role(cls, v):
+        if v is not None and v not in ["user", "assistant", "system"]:
+            raise ValueError('role must be one of "user", "assistant", "system"')
+        return v
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatMessage]
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    max_length: Optional[int] = 8192  # max_length should be an integer.
+    stream: Optional[bool] = False
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: str
+
+    @validator('finish_reason')
+    def check_finish_reason(cls, v):
+        if v not in ["stop", "length"]:
+            raise ValueError('finish_reason must be one of "stop" or "length"')
+        return v
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+     index: int
+     delta: DeltaMessage
+     finish_reason: Optional[str]
+
+     @validator('finish_reason', allow_reuse=True)
+     def check_finish_reason(cls, v):
+         if v is not None and v not in ["stop", "length"]:
+             raise ValueError('finish_reason must be one of "stop" or "length"')
+         return v
+
+class ChatCompletionResponse(BaseModel):
+     id:str 
+     object:str 
+     
+     @validator('object')
+     def check_object(cls,v): 
+         if v not in ["chat.completion","chat.completion.chunk"]: 
+             raise ValueError("object must be one of 'chat.completion' or 'chat.completion.chunk'")
+         return v
+     
+     created :Optional[int]=Field(default_factory=lambda:int(time.time()))
+     model:str 
+     choices :List[Union[ChatCompletionResponseChoice,ChatCompletionResponseStreamChoice]]
+
+
+def generate_id():
+    possible_characters = string.ascii_letters + string.digits
+    random_string = ''.join(random.choices(possible_characters, k=29))
+    return 'chatcmpl-' + random_string
+    
+
+@app.get("/v1/models", response_model=ModelList)
+async def list_models():
+    global model_args
+    model_card = ModelCard(id="gpt-3.5-turbo")
+    return ModelList(data=[model_card])
+
+
+@app.post("/v1/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
+    messages = []
+    for message in prev_messages:
+        messages.append({"role": message.role, "content": message.content})
+    
+    messages.append({"role": "user", "content": query})
+    
+    if request.stream:
+        generate = predict(messages, request.model)
+        return EventSourceResponse(generate, media_type="text/event-stream")
+    
+    response = '本接口不支持非stream模式'
+    choice_data = ChatCompletionResponseChoice(
+        index=0,
+        message=ChatMessage(role="assistant", content=response),
+        finish_reason="stop"
+    )
+    id='chatcmpl-7QyqpwdfhqwajicIEznoc6Q47XAyW'
+
+    return ChatCompletionResponse(id=id,model=request.model, choices=[choice_data], object="chat.completion")
+
+
+async def predict(messages: List[List[str]], model_id: str):
+    global model, tokenizer
+    id = generate_id()
+    created = int(time.time())
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0,
+        delta=DeltaMessage(role="assistant",content=""),
+        finish_reason=None
+    )
+    chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
+    yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+
+    current_length = 0
+
+    for new_response in model.chat(tokenizer, messages, stream=True):
+        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(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
+        yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+
+
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0,
+        delta=DeltaMessage(),
+        finish_reason="stop"
+    )
+    chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
+    yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+    yield '[DONE]'
+
+
+def load_models():
+    print("本次加载的大语言模型为: Baichuan-13B-Chat")
+    tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", use_fast=False, trust_remote_code=True)
+    # model = AutoModelForCausalLM.from_pretrained("Baichuan2-13B-Chat", torch_dtype=torch.float32, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", torch_dtype=torch.float16, trust_remote_code=True)
+    model = model.cuda() 
+    model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat") 
+    return tokenizer, model
+
+if __name__ == "__main__":
+    tokenizer, model = load_models()
+    uvicorn.run(app, host='0.0.0.0', port=6006, workers=1)
+
+    while True:
+        try:
+            # 在这里执行您的程序逻辑
+
+            # 检查显存使用情况，如果超过阈值（例如90%），则触发垃圾回收
+            if torch.cuda.is_available():
+                gpu_memory_usage = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
+                if gpu_memory_usage > 0.9:
+                    gc.collect()
+                    torch.cuda.empty_cache()
+        except RuntimeError as e:
+            if "out of memory" in str(e):
+                print("显存不足，正在重启程序...")
+                gc.collect()
+                torch.cuda.empty_cache()
+                time.sleep(5) # 等待一段时间以确保显存已释放
+                tokenizer, model = load_models()
+            else:
+                raise e
+
+    

files/models/Baichuan2/requirements.txt

+protobuf
+transformers==4.30.2
+cpm_kernels
+torch>=2.0
+gradio
+mdtex2html
+sentencepiece
+accelerate
+sse-starlette
+fastapi==0.99.1
+pydantic==1.10.7
+uvicorn==0.21.1
+xformers
+bitsandbytes