397 lines
16 KiB
Python
397 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Embedding server for leann-backend-diskann - Fixed ZMQ REQ-REP pattern
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"""
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import pickle
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import argparse
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import threading
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import time
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from transformers import AutoTokenizer, AutoModel
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import os
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from contextlib import contextmanager
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import zmq
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import numpy as np
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RED = "\033[91m"
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RESET = "\033[0m"
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# 简化的文档存储 - 替代 LazyPassages
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class SimpleDocumentStore:
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"""简化的文档存储，支持任意ID"""
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def __init__(self, documents: dict = None):
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self.documents = documents or {}
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# 默认演示文档
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self.default_docs = {
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0: "Python is a high-level, interpreted language known for simplicity.",
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1: "Machine learning builds systems that learn from data.",
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2: "Data structures like arrays, lists, and graphs organize data.",
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}
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def __getitem__(self, doc_id):
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doc_id = int(doc_id)
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# 优先使用指定的文档
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if doc_id in self.documents:
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return {"text": self.documents[doc_id]}
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# 其次使用默认演示文档
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if doc_id in self.default_docs:
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return {"text": self.default_docs[doc_id]}
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# 对于任意其他ID，返回通用文档
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fallback_docs = [
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"This is a general document about technology and programming concepts.",
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"This document discusses machine learning and artificial intelligence topics.",
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"This content covers data structures, algorithms, and computer science fundamentals.",
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"This is a document about software engineering and development practices.",
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"This content focuses on databases, data management, and information systems."
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]
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# 根据ID选择一个fallback文档
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fallback_text = fallback_docs[doc_id % len(fallback_docs)]
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return {"text": f"[ID:{doc_id}] {fallback_text}"}
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def __len__(self):
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return len(self.documents) + len(self.default_docs)
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def create_embedding_server_thread(
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zmq_port=5555,
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model_name="sentence-transformers/all-mpnet-base-v2",
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max_batch_size=128,
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):
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"""
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在当前线程中创建并运行 embedding server
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这个函数设计为在单独的线程中调用
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"""
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print(f"INFO: Initializing embedding server thread on port {zmq_port}")
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try:
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# 检查端口是否已被占用
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import socket
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def check_port(port):
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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return s.connect_ex(('localhost', port)) == 0
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if check_port(zmq_port):
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print(f"{RED}Port {zmq_port} is already in use{RESET}")
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return
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# 初始化模型
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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import torch
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# 选择设备
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mps_available = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
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cuda_available = torch.cuda.is_available()
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if cuda_available:
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device = torch.device("cuda")
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print("INFO: Using CUDA device")
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elif mps_available:
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device = torch.device("mps")
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print("INFO: Using MPS device (Apple Silicon)")
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else:
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device = torch.device("cpu")
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print("INFO: Using CPU device")
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# 加载模型
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print(f"INFO: Loading model {model_name}")
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model = AutoModel.from_pretrained(model_name).to(device).eval()
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# 优化模型
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if cuda_available or mps_available:
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try:
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model = model.half()
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model = torch.compile(model)
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print(f"INFO: Using FP16 precision with model: {model_name}")
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except Exception as e:
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print(f"WARNING: Model optimization failed: {e}")
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# 默认演示文档
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demo_documents = {
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0: "Python is a high-level, interpreted language known for simplicity.",
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1: "Machine learning builds systems that learn from data.",
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2: "Data structures like arrays, lists, and graphs organize data.",
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}
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passages = SimpleDocumentStore(demo_documents)
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print(f"INFO: Loaded {len(passages)} demo documents")
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class DeviceTimer:
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"""设备计时器"""
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def __init__(self, name="", device=device):
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self.name = name
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self.device = device
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self.start_time = 0
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self.end_time = 0
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if cuda_available:
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self.start_event = torch.cuda.Event(enable_timing=True)
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self.end_event = torch.cuda.Event(enable_timing=True)
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else:
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self.start_event = None
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self.end_event = None
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@contextmanager
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def timing(self):
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self.start()
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yield
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self.end()
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def start(self):
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if cuda_available:
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torch.cuda.synchronize()
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self.start_event.record()
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else:
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if self.device.type == "mps":
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torch.mps.synchronize()
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self.start_time = time.time()
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def end(self):
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if cuda_available:
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self.end_event.record()
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torch.cuda.synchronize()
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else:
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if self.device.type == "mps":
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torch.mps.synchronize()
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self.end_time = time.time()
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def elapsed_time(self):
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if cuda_available:
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return self.start_event.elapsed_time(self.end_event) / 1000.0
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else:
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return self.end_time - self.start_time
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def print_elapsed(self):
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print(f"Time taken for {self.name}: {self.elapsed_time():.6f} seconds")
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def process_batch(texts_batch, ids_batch, missing_ids):
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"""处理文本批次"""
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batch_size = len(texts_batch)
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print(f"INFO: Processing batch of size {batch_size}")
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tokenize_timer = DeviceTimer("tokenization (batch)", device)
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to_device_timer = DeviceTimer("transfer to device (batch)", device)
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embed_timer = DeviceTimer("embedding (batch)", device)
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pool_timer = DeviceTimer("mean pooling (batch)", device)
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with tokenize_timer.timing():
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encoded_batch = tokenizer.batch_encode_plus(
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texts_batch,
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padding="max_length",
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truncation=True,
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max_length=256,
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return_tensors="pt",
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return_token_type_ids=False,
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)
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tokenize_timer.print_elapsed()
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seq_length = encoded_batch["input_ids"].size(1)
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print(f"Batch size: {batch_size}, Sequence length: {seq_length}")
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with to_device_timer.timing():
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enc = {k: v.to(device) for k, v in encoded_batch.items()}
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to_device_timer.print_elapsed()
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with torch.no_grad():
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with embed_timer.timing():
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out = model(enc["input_ids"], enc["attention_mask"])
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embed_timer.print_elapsed()
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with pool_timer.timing():
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hidden_states = out.last_hidden_state if hasattr(out, "last_hidden_state") else out
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mask_expanded = enc["attention_mask"].unsqueeze(-1).expand(hidden_states.size()).float()
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sum_embeddings = torch.sum(hidden_states * mask_expanded, 1)
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sum_mask = torch.clamp(mask_expanded.sum(1), min=1e-9)
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batch_embeddings = sum_embeddings / sum_mask
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pool_timer.print_elapsed()
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return batch_embeddings.cpu().numpy()
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# ZMQ server 主循环 - 修改为REP套接字
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context = zmq.Context()
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socket = context.socket(zmq.ROUTER) # 改为REP套接字
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socket.bind(f"tcp://127.0.0.1:{zmq_port}")
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print(f"INFO: ZMQ ROUTER server listening on port {zmq_port}")
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# 设置超时
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socket.setsockopt(zmq.RCVTIMEO, 5000) # 5秒接收超时
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socket.setsockopt(zmq.SNDTIMEO, 300000) # 300秒发送超时
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from . import embedding_pb2
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print(f"INFO: Embedding server ready to serve requests")
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while True:
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try:
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parts = socket.recv_multipart()
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# --- 恢复稳健的消息格式判断 ---
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# 必须检查 parts 的长度，避免 IndexError
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if len(parts) >= 3:
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identity = parts[0]
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# empty = parts[1] # 中间的空帧我们通常不关心
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message = parts[2]
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elif len(parts) == 2:
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# 也能处理没有空帧的情况
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identity = parts[0]
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message = parts[1]
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else:
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# 如果收到格式错误的消息，打印警告并忽略它，而不是崩溃
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print(f"WARNING: Received unexpected message format with {len(parts)} parts. Ignoring.")
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continue
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print(f"INFO: Received ZMQ request from client {identity.hex()[:8]}, size {len(message)} bytes")
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e2e_start = time.time()
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lookup_timer = DeviceTimer("text lookup", device)
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# 解析请求
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req_proto = embedding_pb2.NodeEmbeddingRequest()
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req_proto.ParseFromString(message)
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node_ids = req_proto.node_ids
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print(f"INFO: Request for {len(node_ids)} node embeddings: {list(node_ids)}")
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# 添加调试信息
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if len(node_ids) > 0:
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print(f"DEBUG: Node ID range: {min(node_ids)} to {max(node_ids)}")
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# 查找文本
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texts = []
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missing_ids = []
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with lookup_timer.timing():
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for nid in node_ids:
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txtinfo = passages[nid]
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txt = txtinfo["text"]
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texts.append(txt)
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lookup_timer.print_elapsed()
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if missing_ids:
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print(f"WARNING: Missing passages for IDs: {missing_ids}")
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# 处理批次
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total_size = len(texts)
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print(f"INFO: Total batch size: {total_size}, max_batch_size: {max_batch_size}")
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all_embeddings = []
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if total_size > max_batch_size:
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print(f"INFO: Splitting batch of size {total_size} into chunks of {max_batch_size}")
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for i in range(0, total_size, max_batch_size):
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end_idx = min(i + max_batch_size, total_size)
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print(f"INFO: Processing chunk {i//max_batch_size + 1}/{(total_size + max_batch_size - 1)//max_batch_size}: items {i} to {end_idx-1}")
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chunk_texts = texts[i:end_idx]
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chunk_ids = node_ids[i:end_idx]
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embeddings_chunk = process_batch(chunk_texts, chunk_ids, missing_ids)
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all_embeddings.append(embeddings_chunk)
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if cuda_available:
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torch.cuda.empty_cache()
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elif device.type == "mps":
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torch.mps.empty_cache()
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hidden = np.vstack(all_embeddings)
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print(f"INFO: Combined embeddings shape: {hidden.shape}")
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else:
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hidden = process_batch(texts, node_ids, missing_ids)
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# 序列化响应
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ser_start = time.time()
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resp_proto = embedding_pb2.NodeEmbeddingResponse()
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hidden_contiguous = np.ascontiguousarray(hidden, dtype=np.float32)
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resp_proto.embeddings_data = hidden_contiguous.tobytes()
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resp_proto.dimensions.append(hidden_contiguous.shape[0])
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resp_proto.dimensions.append(hidden_contiguous.shape[1])
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resp_proto.missing_ids.extend(missing_ids)
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response_data = resp_proto.SerializeToString()
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# REP 套接字发送单个响应
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socket.send_multipart([identity, b'', response_data])
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ser_end = time.time()
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print(f"INFO: Serialize time: {ser_end - ser_start:.6f} seconds")
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if device.type == "cuda":
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torch.cuda.synchronize()
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elif device.type == "mps":
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torch.mps.synchronize()
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e2e_end = time.time()
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print(f"INFO: ZMQ E2E time: {e2e_end - e2e_start:.6f} seconds")
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except zmq.Again:
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print("INFO: ZMQ socket timeout, continuing to listen")
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# REP套接字不需要重新创建，只需要继续监听
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continue
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except Exception as e:
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print(f"ERROR: Error in ZMQ server: {e}")
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try:
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# 发送空响应以维持REQ-REP状态
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empty_resp = embedding_pb2.NodeEmbeddingResponse()
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socket.send(empty_resp.SerializeToString())
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except:
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# 如果发送失败，重新创建socket
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socket.close()
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socket = context.socket(zmq.REP)
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socket.bind(f"tcp://127.0.0.1:{zmq_port}")
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socket.setsockopt(zmq.RCVTIMEO, 5000)
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socket.setsockopt(zmq.SNDTIMEO, 300000)
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print("INFO: ZMQ socket recreated after error")
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except Exception as e:
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print(f"ERROR: Failed to start embedding server: {e}")
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raise
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# 保持原有的 create_embedding_server 函数不变，只添加线程化版本
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def create_embedding_server(
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domain="demo",
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load_passages=True,
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load_embeddings=False,
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use_fp16=True,
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use_int8=False,
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use_cuda_graphs=False,
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zmq_port=5555,
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max_batch_size=128,
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lazy_load_passages=False,
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model_name="sentence-transformers/all-mpnet-base-v2",
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):
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"""
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原有的 create_embedding_server 函数保持不变
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这个是阻塞版本，用于直接运行
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"""
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create_embedding_server_thread(zmq_port, model_name, max_batch_size)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Embedding service")
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parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
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parser.add_argument("--domain", type=str, default="demo", help="Domain name")
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parser.add_argument("--load-passages", action="store_true", default=True)
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parser.add_argument("--load-embeddings", action="store_true", default=False)
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parser.add_argument("--use-fp16", action="store_true", default=False)
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parser.add_argument("--use-int8", action="store_true", default=False)
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parser.add_argument("--use-cuda-graphs", action="store_true", default=False)
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parser.add_argument("--max-batch-size", type=int, default=128, help="Maximum batch size before splitting")
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parser.add_argument("--lazy-load-passages", action="store_true", default=True)
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parser.add_argument("--model-name", type=str, default="sentence-transformers/all-mpnet-base-v2",
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help="Embedding model name")
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args = parser.parse_args()
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create_embedding_server(
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domain=args.domain,
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load_passages=args.load_passages,
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load_embeddings=args.load_embeddings,
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use_fp16=args.use_fp16,
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use_int8=args.use_int8,
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use_cuda_graphs=args.use_cuda_graphs,
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zmq_port=args.zmq_port,
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max_batch_size=args.max_batch_size,
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lazy_load_passages=args.lazy_load_passages,
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model_name=args.model_name,
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) |