cf17c85607
* fix: diskann zmq port and passages * feat: auto discovery of packages and fix passage gen for diskann
407 lines
16 KiB
Python
407 lines
16 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
Embedding server for leann-backend-diskann - Fixed ZMQ REQ-REP pattern
|
||
"""
|
||
|
||
import pickle
|
||
import argparse
|
||
import threading
|
||
import time
|
||
import json
|
||
from typing import Dict, Any, Optional, Union
|
||
|
||
from transformers import AutoTokenizer, AutoModel
|
||
import os
|
||
from contextlib import contextmanager
|
||
import zmq
|
||
import numpy as np
|
||
|
||
RED = "\033[91m"
|
||
RESET = "\033[0m"
|
||
|
||
# --- New Passage Loader from HNSW backend ---
|
||
class SimplePassageLoader:
|
||
"""
|
||
Simple passage loader that replaces config.py dependencies
|
||
"""
|
||
def __init__(self, passages_data: Optional[Dict[str, Any]] = None):
|
||
self.passages_data = passages_data or {}
|
||
|
||
def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
|
||
"""Get passage by ID"""
|
||
str_id = str(passage_id)
|
||
if str_id in self.passages_data:
|
||
return {"text": self.passages_data[str_id]}
|
||
else:
|
||
# Return empty text for missing passages
|
||
return {"text": ""}
|
||
|
||
def __len__(self) -> int:
|
||
return len(self.passages_data)
|
||
|
||
def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
|
||
"""
|
||
Load passages from a JSON file
|
||
Expected format: {"passage_id": "passage_text", ...}
|
||
"""
|
||
if not os.path.exists(passages_file):
|
||
print(f"Warning: Passages file {passages_file} not found. Using empty loader.")
|
||
return SimplePassageLoader()
|
||
|
||
try:
|
||
with open(passages_file, 'r', encoding='utf-8') as f:
|
||
passages_data = json.load(f)
|
||
print(f"Loaded {len(passages_data)} passages from {passages_file}")
|
||
return SimplePassageLoader(passages_data)
|
||
except Exception as e:
|
||
print(f"Error loading passages from {passages_file}: {e}")
|
||
return SimplePassageLoader()
|
||
|
||
def create_embedding_server_thread(
|
||
zmq_port=5555,
|
||
model_name="sentence-transformers/all-mpnet-base-v2",
|
||
max_batch_size=128,
|
||
passages_file: Optional[str] = None,
|
||
):
|
||
"""
|
||
在当前线程中创建并运行 embedding server
|
||
这个函数设计为在单独的线程中调用
|
||
"""
|
||
print(f"INFO: Initializing embedding server thread on port {zmq_port}")
|
||
|
||
try:
|
||
# 检查端口是否已被占用
|
||
import socket
|
||
def check_port(port):
|
||
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
|
||
return s.connect_ex(('localhost', port)) == 0
|
||
|
||
if check_port(zmq_port):
|
||
print(f"{RED}Port {zmq_port} is already in use{RESET}")
|
||
return
|
||
|
||
# 初始化模型
|
||
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
||
import torch
|
||
|
||
# 选择设备
|
||
mps_available = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
|
||
cuda_available = torch.cuda.is_available()
|
||
|
||
if cuda_available:
|
||
device = torch.device("cuda")
|
||
print("INFO: Using CUDA device")
|
||
elif mps_available:
|
||
device = torch.device("mps")
|
||
print("INFO: Using MPS device (Apple Silicon)")
|
||
else:
|
||
device = torch.device("cpu")
|
||
print("INFO: Using CPU device")
|
||
|
||
# 加载模型
|
||
print(f"INFO: Loading model {model_name}")
|
||
model = AutoModel.from_pretrained(model_name).to(device).eval()
|
||
|
||
# 优化模型
|
||
if cuda_available or mps_available:
|
||
try:
|
||
model = model.half()
|
||
model = torch.compile(model)
|
||
print(f"INFO: Using FP16 precision with model: {model_name}")
|
||
except Exception as e:
|
||
print(f"WARNING: Model optimization failed: {e}")
|
||
|
||
# Load passages from file if provided
|
||
if passages_file and os.path.exists(passages_file):
|
||
passages = load_passages_from_file(passages_file)
|
||
else:
|
||
print("WARNING: No passages file provided or file not found. Using an empty passage loader.")
|
||
passages = SimplePassageLoader()
|
||
|
||
print(f"INFO: Loaded {len(passages)} passages.")
|
||
|
||
class DeviceTimer:
|
||
"""设备计时器"""
|
||
def __init__(self, name="", device=device):
|
||
self.name = name
|
||
self.device = device
|
||
self.start_time = 0
|
||
self.end_time = 0
|
||
|
||
if cuda_available:
|
||
self.start_event = torch.cuda.Event(enable_timing=True)
|
||
self.end_event = torch.cuda.Event(enable_timing=True)
|
||
else:
|
||
self.start_event = None
|
||
self.end_event = None
|
||
|
||
@contextmanager
|
||
def timing(self):
|
||
self.start()
|
||
yield
|
||
self.end()
|
||
|
||
def start(self):
|
||
if cuda_available:
|
||
torch.cuda.synchronize()
|
||
self.start_event.record()
|
||
else:
|
||
if self.device.type == "mps":
|
||
torch.mps.synchronize()
|
||
self.start_time = time.time()
|
||
|
||
def end(self):
|
||
if cuda_available:
|
||
self.end_event.record()
|
||
torch.cuda.synchronize()
|
||
else:
|
||
if self.device.type == "mps":
|
||
torch.mps.synchronize()
|
||
self.end_time = time.time()
|
||
|
||
def elapsed_time(self):
|
||
if cuda_available:
|
||
return self.start_event.elapsed_time(self.end_event) / 1000.0
|
||
else:
|
||
return self.end_time - self.start_time
|
||
|
||
def print_elapsed(self):
|
||
print(f"Time taken for {self.name}: {self.elapsed_time():.6f} seconds")
|
||
|
||
def process_batch(texts_batch, ids_batch, missing_ids):
|
||
"""处理文本批次"""
|
||
batch_size = len(texts_batch)
|
||
print(f"INFO: Processing batch of size {batch_size}")
|
||
|
||
tokenize_timer = DeviceTimer("tokenization (batch)", device)
|
||
to_device_timer = DeviceTimer("transfer to device (batch)", device)
|
||
embed_timer = DeviceTimer("embedding (batch)", device)
|
||
pool_timer = DeviceTimer("mean pooling (batch)", device)
|
||
|
||
with tokenize_timer.timing():
|
||
encoded_batch = tokenizer.batch_encode_plus(
|
||
texts_batch,
|
||
padding="max_length",
|
||
truncation=True,
|
||
max_length=256,
|
||
return_tensors="pt",
|
||
return_token_type_ids=False,
|
||
)
|
||
tokenize_timer.print_elapsed()
|
||
|
||
seq_length = encoded_batch["input_ids"].size(1)
|
||
print(f"Batch size: {batch_size}, Sequence length: {seq_length}")
|
||
|
||
with to_device_timer.timing():
|
||
enc = {k: v.to(device) for k, v in encoded_batch.items()}
|
||
to_device_timer.print_elapsed()
|
||
|
||
with torch.no_grad():
|
||
with embed_timer.timing():
|
||
out = model(enc["input_ids"], enc["attention_mask"])
|
||
embed_timer.print_elapsed()
|
||
|
||
with pool_timer.timing():
|
||
hidden_states = out.last_hidden_state if hasattr(out, "last_hidden_state") else out
|
||
mask_expanded = enc["attention_mask"].unsqueeze(-1).expand(hidden_states.size()).float()
|
||
sum_embeddings = torch.sum(hidden_states * mask_expanded, 1)
|
||
sum_mask = torch.clamp(mask_expanded.sum(1), min=1e-9)
|
||
batch_embeddings = sum_embeddings / sum_mask
|
||
pool_timer.print_elapsed()
|
||
|
||
return batch_embeddings.cpu().numpy()
|
||
|
||
# ZMQ server 主循环 - 修改为REP套接字
|
||
context = zmq.Context()
|
||
socket = context.socket(zmq.ROUTER) # 改为REP套接字
|
||
socket.bind(f"tcp://127.0.0.1:{zmq_port}")
|
||
print(f"INFO: ZMQ ROUTER server listening on port {zmq_port}")
|
||
|
||
# 设置超时
|
||
socket.setsockopt(zmq.RCVTIMEO, 5000) # 5秒接收超时
|
||
socket.setsockopt(zmq.SNDTIMEO, 300000) # 300秒发送超时
|
||
|
||
from . import embedding_pb2
|
||
|
||
print(f"INFO: Embedding server ready to serve requests")
|
||
|
||
while True:
|
||
try:
|
||
parts = socket.recv_multipart()
|
||
|
||
# --- 恢复稳健的消息格式判断 ---
|
||
# 必须检查 parts 的长度，避免 IndexError
|
||
if len(parts) >= 3:
|
||
identity = parts[0]
|
||
# empty = parts[1] # 中间的空帧我们通常不关心
|
||
message = parts[2]
|
||
elif len(parts) == 2:
|
||
# 也能处理没有空帧的情况
|
||
identity = parts[0]
|
||
message = parts[1]
|
||
else:
|
||
# 如果收到格式错误的消息，打印警告并忽略它，而不是崩溃
|
||
print(f"WARNING: Received unexpected message format with {len(parts)} parts. Ignoring.")
|
||
continue
|
||
print(f"INFO: Received ZMQ request from client {identity.hex()[:8]}, size {len(message)} bytes")
|
||
|
||
e2e_start = time.time()
|
||
lookup_timer = DeviceTimer("text lookup", device)
|
||
|
||
# 解析请求
|
||
req_proto = embedding_pb2.NodeEmbeddingRequest()
|
||
req_proto.ParseFromString(message)
|
||
node_ids = req_proto.node_ids
|
||
print(f"INFO: Request for {len(node_ids)} node embeddings: {list(node_ids)}")
|
||
|
||
# 添加调试信息
|
||
if len(node_ids) > 0:
|
||
print(f"DEBUG: Node ID range: {min(node_ids)} to {max(node_ids)}")
|
||
|
||
# 查找文本
|
||
texts = []
|
||
missing_ids = []
|
||
with lookup_timer.timing():
|
||
for nid in node_ids:
|
||
txtinfo = passages[nid]
|
||
txt = txtinfo["text"]
|
||
if txt:
|
||
texts.append(txt)
|
||
else:
|
||
# 如果文本为空，我们仍然需要一个占位符来进行批处理，
|
||
# 但将其ID记录为缺失
|
||
texts.append("")
|
||
missing_ids.append(nid)
|
||
lookup_timer.print_elapsed()
|
||
|
||
if missing_ids:
|
||
print(f"WARNING: Missing passages for IDs: {missing_ids}")
|
||
|
||
# 处理批次
|
||
total_size = len(texts)
|
||
print(f"INFO: Total batch size: {total_size}, max_batch_size: {max_batch_size}")
|
||
|
||
all_embeddings = []
|
||
|
||
if total_size > max_batch_size:
|
||
print(f"INFO: Splitting batch of size {total_size} into chunks of {max_batch_size}")
|
||
for i in range(0, total_size, max_batch_size):
|
||
end_idx = min(i + max_batch_size, total_size)
|
||
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}")
|
||
|
||
chunk_texts = texts[i:end_idx]
|
||
chunk_ids = node_ids[i:end_idx]
|
||
|
||
embeddings_chunk = process_batch(chunk_texts, chunk_ids, missing_ids)
|
||
all_embeddings.append(embeddings_chunk)
|
||
|
||
if cuda_available:
|
||
torch.cuda.empty_cache()
|
||
elif device.type == "mps":
|
||
torch.mps.empty_cache()
|
||
|
||
hidden = np.vstack(all_embeddings)
|
||
print(f"INFO: Combined embeddings shape: {hidden.shape}")
|
||
else:
|
||
hidden = process_batch(texts, node_ids, missing_ids)
|
||
|
||
# 序列化响应
|
||
ser_start = time.time()
|
||
|
||
resp_proto = embedding_pb2.NodeEmbeddingResponse()
|
||
hidden_contiguous = np.ascontiguousarray(hidden, dtype=np.float32)
|
||
resp_proto.embeddings_data = hidden_contiguous.tobytes()
|
||
resp_proto.dimensions.append(hidden_contiguous.shape[0])
|
||
resp_proto.dimensions.append(hidden_contiguous.shape[1])
|
||
resp_proto.missing_ids.extend(missing_ids)
|
||
|
||
response_data = resp_proto.SerializeToString()
|
||
|
||
# REP 套接字发送单个响应
|
||
socket.send_multipart([identity, b'', response_data])
|
||
|
||
ser_end = time.time()
|
||
|
||
print(f"INFO: Serialize time: {ser_end - ser_start:.6f} seconds")
|
||
|
||
if device.type == "cuda":
|
||
torch.cuda.synchronize()
|
||
elif device.type == "mps":
|
||
torch.mps.synchronize()
|
||
e2e_end = time.time()
|
||
print(f"INFO: ZMQ E2E time: {e2e_end - e2e_start:.6f} seconds")
|
||
|
||
except zmq.Again:
|
||
print("INFO: ZMQ socket timeout, continuing to listen")
|
||
# REP套接字不需要重新创建，只需要继续监听
|
||
continue
|
||
except Exception as e:
|
||
print(f"ERROR: Error in ZMQ server: {e}")
|
||
try:
|
||
# 发送空响应以维持REQ-REP状态
|
||
empty_resp = embedding_pb2.NodeEmbeddingResponse()
|
||
socket.send(empty_resp.SerializeToString())
|
||
except:
|
||
# 如果发送失败，重新创建socket
|
||
socket.close()
|
||
socket = context.socket(zmq.REP)
|
||
socket.bind(f"tcp://127.0.0.1:{zmq_port}")
|
||
socket.setsockopt(zmq.RCVTIMEO, 5000)
|
||
socket.setsockopt(zmq.SNDTIMEO, 300000)
|
||
print("INFO: ZMQ socket recreated after error")
|
||
|
||
except Exception as e:
|
||
print(f"ERROR: Failed to start embedding server: {e}")
|
||
raise
|
||
|
||
|
||
# 保持原有的 create_embedding_server 函数不变，只添加线程化版本
|
||
def create_embedding_server(
|
||
domain="demo",
|
||
load_passages=True,
|
||
load_embeddings=False,
|
||
use_fp16=True,
|
||
use_int8=False,
|
||
use_cuda_graphs=False,
|
||
zmq_port=5555,
|
||
max_batch_size=128,
|
||
lazy_load_passages=False,
|
||
model_name="sentence-transformers/all-mpnet-base-v2",
|
||
passages_file: Optional[str] = None,
|
||
):
|
||
"""
|
||
原有的 create_embedding_server 函数保持不变
|
||
这个是阻塞版本，用于直接运行
|
||
"""
|
||
create_embedding_server_thread(zmq_port, model_name, max_batch_size, passages_file)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser(description="Embedding service")
|
||
parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
|
||
parser.add_argument("--domain", type=str, default="demo", help="Domain name")
|
||
parser.add_argument("--passages-file", type=str, help="JSON file containing passage ID to text mapping")
|
||
parser.add_argument("--load-passages", action="store_true", default=True)
|
||
parser.add_argument("--load-embeddings", action="store_true", default=False)
|
||
parser.add_argument("--use-fp16", action="store_true", default=False)
|
||
parser.add_argument("--use-int8", action="store_true", default=False)
|
||
parser.add_argument("--use-cuda-graphs", action="store_true", default=False)
|
||
parser.add_argument("--max-batch-size", type=int, default=128, help="Maximum batch size before splitting")
|
||
parser.add_argument("--lazy-load-passages", action="store_true", default=True)
|
||
parser.add_argument("--model-name", type=str, default="sentence-transformers/all-mpnet-base-v2",
|
||
help="Embedding model name")
|
||
args = parser.parse_args()
|
||
|
||
create_embedding_server(
|
||
domain=args.domain,
|
||
load_passages=args.load_passages,
|
||
load_embeddings=args.load_embeddings,
|
||
use_fp16=args.use_fp16,
|
||
use_int8=args.use_int8,
|
||
use_cuda_graphs=args.use_cuda_graphs,
|
||
zmq_port=args.zmq_port,
|
||
max_batch_size=args.max_batch_size,
|
||
lazy_load_passages=args.lazy_load_passages,
|
||
model_name=args.model_name,
|
||
passages_file=args.passages_file,
|
||
) |