update code positon (#3907)

plugins/model/llm-ChatGLM2/openai_api.py

@@ -0,0 +1,260 @@
+# coding=utf-8
+import argparse
+import time
+from contextlib import asynccontextmanager
+from typing import List, Literal, Optional, Union
+
+import numpy as np
+import tiktoken
+import torch
+import uvicorn
+from fastapi import Depends, FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from sentence_transformers import SentenceTransformer
+from sklearn.preprocessing import PolynomialFeatures
+from sse_starlette.sse import EventSourceResponse
+from starlette.status import HTTP_401_UNAUTHORIZED
+from transformers import AutoModel, 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 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()))
+
+
+async def verify_token(request: Request):
+    auth_header = request.headers.get('Authorization')
+    if auth_header:
+        token_type, _, token = auth_header.partition(' ')
+        if (
+            token_type.lower() == "bearer"
+            and token == "sk-aaabbbcccdddeeefffggghhhiiijjjkkk"
+        ):  # 这里配置你的token
+            return True
+    raise HTTPException(
+        status_code=HTTP_401_UNAUTHORIZED,
+        detail="Invalid authorization credentials",
+    )
+
+
+class EmbeddingRequest(BaseModel):
+    input: List[str]
+    model: str
+
+
+class EmbeddingResponse(BaseModel):
+    data: list
+    model: str
+    object: str
+    usage: dict
+
+
+def num_tokens_from_string(string: str) -> int:
+    """Returns the number of tokens in a text string."""
+    encoding = tiktoken.get_encoding('cl100k_base')
+    num_tokens = len(encoding.encode(string))
+    return num_tokens
+
+
+def expand_features(embedding, target_length):
+    poly = PolynomialFeatures(degree=2)
+    expanded_embedding = poly.fit_transform(embedding.reshape(1, -1))
+    expanded_embedding = expanded_embedding.flatten()
+    if len(expanded_embedding) > target_length:
+        # 如果扩展后的特征超过目标长度，可以通过截断或其他方法来减少维度
+        expanded_embedding = expanded_embedding[:target_length]
+    elif len(expanded_embedding) < target_length:
+        # 如果扩展后的特征少于目标长度，可以通过填充或其他方法来增加维度
+        expanded_embedding = np.pad(
+            expanded_embedding, (0, target_length - len(expanded_embedding))
+        )
+    return expanded_embedding
+
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def create_chat_completion(
+    request: ChatCompletionRequest, token: bool = Depends(verify_token)
+):
+    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]'
+
+
+@app.post("/v1/embeddings", response_model=EmbeddingResponse)
+async def get_embeddings(
+    request: EmbeddingRequest, token: bool = Depends(verify_token)
+):
+    # 计算嵌入向量和tokens数量
+    embeddings = [embeddings_model.encode(text) for text in request.input]
+
+    # 如果嵌入向量的维度不为1536，则使用插值法扩展至1536维度
+    embeddings = [
+        expand_features(embedding, 1536) if len(embedding) < 1536 else embedding
+        for embedding in embeddings
+    ]
+
+    # Min-Max normalization 归一化
+    embeddings = [embedding / np.linalg.norm(embedding) for embedding in embeddings]
+
+    # 将numpy数组转换为列表
+    embeddings = [embedding.tolist() for embedding in embeddings]
+    prompt_tokens = sum(len(text.split()) for text in request.input)
+    total_tokens = sum(num_tokens_from_string(text) for text in request.input)
+
+    response = {
+        "data": [
+            {"embedding": embedding, "index": index, "object": "embedding"}
+            for index, embedding in enumerate(embeddings)
+        ],
+        "model": request.model,
+        "object": "list",
+        "usage": {
+            "prompt_tokens": prompt_tokens,
+            "total_tokens": total_tokens,
+        },
+    }
+
+    return response
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_name", default="16", type=str, help="Model name")
+    args = parser.parse_args()
+
+    model_dict = {
+        "4": "THUDM/chatglm2-6b-int4",
+        "8": "THUDM/chatglm2-6b-int8",
+        "16": "THUDM/chatglm2-6b",
+    }
+
+    model_name = model_dict.get(args.model_name, "THUDM/chatglm2-6b")
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
+    embeddings_model = SentenceTransformer('moka-ai/m3e-large', device='cpu')
+
+    uvicorn.run(app, host='0.0.0.0', port=6006, workers=1)

plugins/model/llm-ChatGLM2/requirements.txt

@@ -0,0 +1,11 @@
+fastapi==0.101.1
+numpy==1.24.3
+pydantic==1.10.7
+scikit_learn==1.2.2
+sentence_transformers==2.2.2
+sse_starlette==1.6.5
+starlette==0.27.0
+tiktoken==0.4.0
+torch==2.0.1
+transformers==4.31.0
+uvicorn==0.23.2