166 lines
6.8 KiB
Python
166 lines
6.8 KiB
Python
import torch
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import torch.nn.functional as F
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from fastapi import FastAPI
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from pydantic import BaseModel
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from typing import List
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from modelscope import AutoTokenizer, AutoModel, AutoModelForCausalLM
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from transformers import BitsAndBytesConfig
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# 在 load_models() 里添加量化配置
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quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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# ===================== 模型路径 =====================
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EMBED_MODEL_PATH = "/home/admin/minor_agent/modelscope/models/Qwen/Qwen3-Embedding-0___6B"
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RERANK_MODEL_PATH = "/home/admin/minor_agent/modelscope/models/Qwen/Qwen3-Reranker-4B"
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# ===================== 初始化 FastAPI =====================
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app = FastAPI(title="RAG Backend with 8-bit Qwen3 Models")
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# ===================== 全局变量（模型、分词器） =====================
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embed_tokenizer = None
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embed_model = None
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rerank_tokenizer = None
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rerank_model = None
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# ===================== 工具函数 =====================
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def last_token_pool(last_hidden_states, attention_mask):
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left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
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if left_padding:
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return last_hidden_states[:, -1]
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else:
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sequence_lengths = attention_mask.sum(dim=1) - 1
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batch_size = last_hidden_states.shape[0]
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return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
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def get_detailed_instruct(task_description: str, query: str) -> str:
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return f'Instruct: {task_description}\nQuery:{query}'
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def format_rerank_instruction(instruction, query, doc):
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if instruction is None:
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instruction = 'Given a web search query, retrieve relevant passages that answer the query'
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return f"<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}"
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# ===================== 启动时加载模型（8‑bit） =====================
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@app.on_event("startup")
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def load_models():
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global embed_tokenizer, embed_model, rerank_tokenizer, rerank_model
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# ---------- 加载 Embedding 模型 ----------
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print("Loading Embedding model with 16-bit quantization...")
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embed_tokenizer = AutoTokenizer.from_pretrained(EMBED_MODEL_PATH, padding_side='left')
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embed_model = AutoModel.from_pretrained(
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EMBED_MODEL_PATH,
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#load_in_8bit=True, # 启用 8-bit 量化
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device_map="auto", # 自动分配到 GPU
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dtype=torch.float16 # 计算精度保持 fp16
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)
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embed_model.eval()
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print("Embedding model loaded.")
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# ---------- 加载 Reranker 模型 ----------
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print("Loading Reranker model with 8-bit quantization...")
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rerank_tokenizer = AutoTokenizer.from_pretrained(RERANK_MODEL_PATH, padding_side='left')
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rerank_model = AutoModelForCausalLM.from_pretrained(
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RERANK_MODEL_PATH,
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quantization_config=quantization_config,
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device_map="auto",
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dtype=torch.float16
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)
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rerank_model.eval()
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# 预计算 Reranker 所需的固定 tokens
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global token_true_id, token_false_id, prefix_tokens, suffix_tokens, max_rerank_length
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token_false_id = rerank_tokenizer.convert_tokens_to_ids("no")
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token_true_id = rerank_tokenizer.convert_tokens_to_ids("yes")
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max_rerank_length = 8192
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prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
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suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
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prefix_tokens = rerank_tokenizer.encode(prefix, add_special_tokens=False)
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suffix_tokens = rerank_tokenizer.encode(suffix, add_special_tokens=False)
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print("Reranker model loaded.")
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# ===================== API 请求体定义 =====================
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class EmbedRequest(BaseModel):
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task_description: str = "Given a web search query, retrieve relevant passages that answer the query"
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queries: List[str]
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documents: List[str] # 可选，若只查询文本可只传 queries
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class EmbedResponse(BaseModel):
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query_embeddings: List[List[float]]
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doc_embeddings: List[List[float]] # 当传入 documents 时返回
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class RerankRequest(BaseModel):
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task_description: str = "Given a web search query, retrieve relevant passages that answer the query"
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query: str
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documents: List[str]
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class RerankResponse(BaseModel):
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scores: List[float]
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# ===================== Embedding 端点 =====================
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@app.post("/embed", response_model=EmbedResponse)
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async def get_embeddings(req: EmbedRequest):
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# 为 query 加上指令，document 不加指令
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instructed_queries = [get_detailed_instruct(req.task_description, q) for q in req.queries]
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# 所有文本拼成一个 batch：先是 queries，后是 documents（如果有）
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input_texts = instructed_queries + req.documents
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# 分词
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batch_dict = embed_tokenizer(
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input_texts,
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padding=True,
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truncation=True,
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max_length=8192,
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return_tensors="pt",
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).to(embed_model.device)
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with torch.no_grad():
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outputs = embed_model(**batch_dict)
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embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
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embeddings = F.normalize(embeddings, p=2, dim=1)
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# 拆分 query 和 document 的嵌入
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nq = len(instructed_queries)
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query_embs = embeddings[:nq].cpu().tolist()
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doc_embs = embeddings[nq:].cpu().tolist() if req.documents else []
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return EmbedResponse(query_embeddings=query_embs, doc_embeddings=doc_embs)
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# ===================== Reranker 端点 =====================
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@app.post("/rerank", response_model=RerankResponse)
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async def rerank_documents(req: RerankRequest):
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# 构造带格式的输入对
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pairs = [
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format_rerank_instruction(req.task_description, req.query, doc)
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for doc in req.documents
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]
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# 处理输入，添加 prefix/suffix tokens 并 pad
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inputs = rerank_tokenizer(
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pairs,
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padding=False,
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truncation='longest_first',
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return_attention_mask=False,
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max_length=max_rerank_length - len(prefix_tokens) - len(suffix_tokens)
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)
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for i, ele in enumerate(inputs['input_ids']):
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inputs['input_ids'][i] = prefix_tokens + ele + suffix_tokens
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inputs = rerank_tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_rerank_length)
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inputs = {k: v.to(rerank_model.device) for k, v in inputs.items()}
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with torch.no_grad():
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batch_scores = rerank_model(**inputs).logits[:, -1, :]
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true_vector = batch_scores[:, token_true_id]
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false_vector = batch_scores[:, token_false_id]
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scores = torch.stack([false_vector, true_vector], dim=1)
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scores = torch.nn.functional.log_softmax(scores, dim=1)
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scores = scores[:, 1].exp().cpu().tolist()
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return RerankResponse(scores=scores)
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# ===================== 健康检查 =====================
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@app.get("/health")
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def health():
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return {"status": "ok"}
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