706 lines
32 KiB
Python
706 lines
32 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 time
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import json
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from typing import Dict, Any, Optional, Union
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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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import msgpack
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from pathlib import Path
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import logging
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RED = "\033[91m"
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# Set up logging based on environment variable
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LOG_LEVEL = os.getenv('LEANN_LOG_LEVEL', 'INFO').upper()
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logging.basicConfig(
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level=getattr(logging, LOG_LEVEL, logging.INFO),
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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RESET = "\033[0m"
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# --- New Passage Loader from HNSW backend ---
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class SimplePassageLoader:
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"""
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Simple passage loader that replaces config.py dependencies
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"""
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def __init__(self, passages_data: Optional[Dict[str, Any]] = None):
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self.passages_data = passages_data or {}
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self._meta_path = ''
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def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
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"""Get passage by ID"""
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str_id = str(passage_id)
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if str_id in self.passages_data:
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return {"text": self.passages_data[str_id]}
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else:
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# Return empty text for missing passages
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return {"text": ""}
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def __len__(self) -> int:
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return len(self.passages_data)
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def keys(self):
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return self.passages_data.keys()
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def load_passages_from_metadata(meta_file: str) -> SimplePassageLoader:
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"""
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Load passages using metadata file with PassageManager for lazy loading
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"""
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# Load metadata to get passage sources
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with open(meta_file, 'r') as f:
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meta = json.load(f)
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# Import PassageManager dynamically to avoid circular imports
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import sys
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from pathlib import Path
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# Find the leann package directory relative to this file
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current_dir = Path(__file__).parent
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leann_core_path = current_dir.parent.parent / "leann-core" / "src"
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sys.path.insert(0, str(leann_core_path))
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try:
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from leann.api import PassageManager
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passage_manager = PassageManager(meta['passage_sources'])
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finally:
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sys.path.pop(0)
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print(f"Initialized lazy passage loading for {len(passage_manager.global_offset_map)} passages")
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class LazyPassageLoader(SimplePassageLoader):
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def __init__(self, passage_manager):
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self.passage_manager = passage_manager
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# Initialize parent with empty data
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super().__init__({})
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def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
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"""Get passage by ID with lazy loading"""
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try:
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int_id = int(passage_id)
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string_id = str(int_id)
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passage_data = self.passage_manager.get_passage(string_id)
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if passage_data and passage_data.get("text"):
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return {"text": passage_data["text"]}
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else:
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raise RuntimeError(f"FATAL: Empty text for ID {int_id} -> {string_id}")
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except Exception as e:
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raise RuntimeError(f"FATAL: Exception getting passage {passage_id}: {e}")
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def __len__(self) -> int:
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return len(self.passage_manager.global_offset_map)
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def keys(self):
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return self.passage_manager.global_offset_map.keys()
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loader = LazyPassageLoader(passage_manager)
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loader._meta_path = meta_file
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return loader
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def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
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"""
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Load passages from a JSONL file with label map support
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Expected format: {"id": "passage_id", "text": "passage_text", "metadata": {...}} (one per line)
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"""
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if not os.path.exists(passages_file):
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raise FileNotFoundError(f"Passages file {passages_file} not found.")
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if not passages_file.endswith('.jsonl'):
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raise ValueError(f"Expected .jsonl file format, got: {passages_file}")
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# Load passages directly by their sequential IDs
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passages_data = {}
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with open(passages_file, 'r', encoding='utf-8') as f:
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for line in f:
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if line.strip():
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passage = json.loads(line)
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passages_data[passage['id']] = passage['text']
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print(f"Loaded {len(passages_data)} passages from JSONL file {passages_file}")
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return SimplePassageLoader(passages_data)
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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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passages_file: Optional[str] = None,
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embedding_mode: str = "sentence-transformers",
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enable_warmup: bool = False,
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):
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"""
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Create and run embedding server in the current thread
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This function is designed to be called in a separate thread
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"""
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logger.info(f"Initializing embedding server thread on port {zmq_port}")
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try:
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# Check if port is already occupied
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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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# Auto-detect mode based on model name if not explicitly set
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if embedding_mode == "sentence-transformers" and model_name.startswith("text-embedding-"):
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embedding_mode = "openai"
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if embedding_mode == "mlx":
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from leann.api import compute_embeddings_mlx
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import torch
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logger.info("Using MLX for embeddings")
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# Set device to CPU for compatibility with DeviceTimer class
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device = torch.device("cpu")
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cuda_available = False
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mps_available = False
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elif embedding_mode == "openai":
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from leann.api import compute_embeddings_openai
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import torch
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logger.info("Using OpenAI API for embeddings")
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# Set device to CPU for compatibility with DeviceTimer class
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device = torch.device("cpu")
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cuda_available = False
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mps_available = False
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elif embedding_mode == "sentence-transformers":
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# Initialize model
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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import torch
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# Select device
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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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logger.info("Using CUDA device")
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elif mps_available:
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device = torch.device("mps")
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logger.info("Using MPS device (Apple Silicon)")
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else:
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device = torch.device("cpu")
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logger.info("Using CPU device")
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# Load model
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logger.info(f"Loading model {model_name}")
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model = AutoModel.from_pretrained(model_name).to(device).eval()
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# Optimize model
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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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logger.info(f"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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else:
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raise ValueError(f"Unsupported embedding mode: {embedding_mode}. Supported modes: sentence-transformers, mlx, openai")
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# Load passages from file if provided
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if passages_file and os.path.exists(passages_file):
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# Check if it's a metadata file or a single passages file
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if passages_file.endswith('.meta.json'):
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passages = load_passages_from_metadata(passages_file)
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else:
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# Try to find metadata file in same directory
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passages_dir = Path(passages_file).parent
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meta_files = list(passages_dir.glob("*.meta.json"))
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if meta_files:
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print(f"Found metadata file: {meta_files[0]}, using lazy loading")
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passages = load_passages_from_metadata(str(meta_files[0]))
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else:
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# Fallback to original single file loading (will cause warnings)
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print("WARNING: No metadata file found, using single file loading (may cause missing passage warnings)")
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passages = load_passages_from_file(passages_file)
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else:
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print("WARNING: No passages file provided or file not found. Using an empty passage loader.")
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passages = SimplePassageLoader()
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logger.info(f"Loaded {len(passages)} passages.")
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def client_warmup(zmq_port):
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"""Perform client-side warmup for DiskANN server"""
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time.sleep(2)
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print(f"Performing client-side warmup with model {model_name}...")
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# Get actual passage IDs from the loaded passages
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sample_ids = []
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if hasattr(passages, 'keys') and len(passages) > 0:
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available_ids = list(passages.keys())
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# Take up to 5 actual IDs, but at least 1
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sample_ids = available_ids[:min(5, len(available_ids))]
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print(f"Using actual passage IDs for warmup: {sample_ids}")
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else:
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print("No passages available for warmup, skipping warmup...")
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return
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try:
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context = zmq.Context()
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socket = context.socket(zmq.REQ)
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socket.connect(f"tcp://localhost:{zmq_port}")
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socket.setsockopt(zmq.RCVTIMEO, 30000)
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socket.setsockopt(zmq.SNDTIMEO, 30000)
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try:
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ids_to_send = [int(x) for x in sample_ids]
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except ValueError:
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print("Warning: Could not convert sample IDs to integers, skipping warmup")
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return
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if not ids_to_send:
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print("Skipping warmup send.")
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return
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# Use protobuf format for warmup
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from . import embedding_pb2
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req_proto = embedding_pb2.NodeEmbeddingRequest()
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req_proto.node_ids.extend(ids_to_send)
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request_bytes = req_proto.SerializeToString()
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for i in range(3):
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print(f"Sending warmup request {i + 1}/3 via ZMQ (Protobuf)...")
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socket.send(request_bytes)
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response_bytes = socket.recv()
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resp_proto = embedding_pb2.NodeEmbeddingResponse()
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resp_proto.ParseFromString(response_bytes)
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embeddings_count = resp_proto.dimensions[0] if resp_proto.dimensions else 0
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print(f"Warmup request {i + 1}/3 successful, received {embeddings_count} embeddings")
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time.sleep(0.1)
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print("Client-side Protobuf ZMQ warmup complete")
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socket.close()
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context.term()
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except Exception as e:
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print(f"Error during Protobuf ZMQ warmup: {e}")
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class DeviceTimer:
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"""Device timer"""
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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 embedding_mode == "sentence-transformers" and torch.cuda.is_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 embedding_mode == "sentence-transformers" and torch.cuda.is_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 embedding_mode == "sentence-transformers" and 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 embedding_mode == "sentence-transformers" and torch.cuda.is_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 embedding_mode == "sentence-transformers" and 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 embedding_mode == "sentence-transformers" and torch.cuda.is_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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elapsed = self.elapsed_time()
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print(f"[{self.name}] Elapsed time: {elapsed:.3f}s")
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def process_batch_pytorch(texts_batch, ids_batch, missing_ids):
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"""Process text batch"""
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if not texts_batch:
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return np.array([])
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# Filter out empty texts and their corresponding IDs
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valid_texts = []
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valid_ids = []
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for i, text in enumerate(texts_batch):
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if text.strip(): # Only include non-empty texts
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valid_texts.append(text)
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valid_ids.append(ids_batch[i])
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if not valid_texts:
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print("WARNING: No valid texts in batch")
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return np.array([])
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# Tokenize
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token_timer = DeviceTimer("tokenization")
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with token_timer.timing():
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inputs = tokenizer(
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valid_texts,
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padding=True,
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truncation=True,
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max_length=512,
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return_tensors="pt"
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).to(device)
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# Compute embeddings
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embed_timer = DeviceTimer("embedding computation")
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with embed_timer.timing():
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with torch.no_grad():
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outputs = model(**inputs)
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hidden_states = outputs.last_hidden_state
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# Mean pooling
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attention_mask = inputs['attention_mask']
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mask_expanded = 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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embed_timer.print_elapsed()
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return batch_embeddings.cpu().numpy()
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# ZMQ server main loop - modified to use REP socket
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context = zmq.Context()
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socket = context.socket(zmq.ROUTER) # Changed to REP socket
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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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# Set timeouts
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socket.setsockopt(zmq.RCVTIMEO, 5000) # 5 second receive timeout
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socket.setsockopt(zmq.SNDTIMEO, 300000) # 300 second send timeout
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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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# Start warmup thread if enabled
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if enable_warmup and len(passages) > 0:
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import threading
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print(f"Warmup enabled: starting warmup thread")
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warmup_thread = threading.Thread(target=client_warmup, args=(zmq_port,))
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warmup_thread.daemon = True
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warmup_thread.start()
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else:
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print(f"Warmup disabled or no passages available (enable_warmup={enable_warmup}, passages={len(passages)})")
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while True:
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try:
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parts = socket.recv_multipart()
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# --- Restore robust message format detection ---
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# Must check parts length to avoid IndexError
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if len(parts) >= 3:
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identity = parts[0]
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# empty = parts[1] # We usually don't care about the middle empty frame
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message = parts[2]
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elif len(parts) == 2:
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# Can also handle cases without empty frame
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identity = parts[0]
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message = parts[1]
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else:
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# If received message format is wrong, print warning and ignore it instead of crashing
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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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# Handle control messages (MessagePack format)
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try:
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request_payload = msgpack.unpackb(message)
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if isinstance(request_payload, list) and len(request_payload) >= 1:
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if request_payload[0] == "__QUERY_META_PATH__":
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# Return the current meta path being used by the server
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current_meta_path = getattr(passages, '_meta_path', '') if hasattr(passages, '_meta_path') else ''
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response = [current_meta_path]
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socket.send_multipart([identity, b'', msgpack.packb(response)])
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continue
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elif request_payload[0] == "__UPDATE_META_PATH__" and len(request_payload) >= 2:
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# Update the server's meta path and reload passages
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new_meta_path = request_payload[1]
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try:
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print(f"INFO: Updating server meta path to: {new_meta_path}")
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# Reload passages from the new meta file
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passages = load_passages_from_metadata(new_meta_path)
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# Store the meta path for future queries
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passages._meta_path = new_meta_path
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response = ["SUCCESS"]
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print(f"INFO: Successfully updated meta path and reloaded {len(passages)} passages")
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except Exception as e:
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print(f"ERROR: Failed to update meta path: {e}")
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response = ["FAILED", str(e)]
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socket.send_multipart([identity, b'', msgpack.packb(response)])
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continue
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elif request_payload[0] == "__QUERY_MODEL__":
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# Return the current model being used by the server
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response = [model_name]
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socket.send_multipart([identity, b'', msgpack.packb(response)])
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continue
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elif request_payload[0] == "__UPDATE_MODEL__" and len(request_payload) >= 2:
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# Update the server's embedding model
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new_model_name = request_payload[1]
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try:
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print(f"INFO: Updating server model from {model_name} to: {new_model_name}")
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# Clean up old model to free memory
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if not use_mlx:
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print("INFO: Releasing old model from memory...")
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old_model = model
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old_tokenizer = tokenizer
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# Load new tokenizer first
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print(f"Loading new tokenizer for {new_model_name}...")
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tokenizer = AutoTokenizer.from_pretrained(new_model_name, use_fast=True)
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# Load new model
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print(f"Loading new model {new_model_name}...")
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model = AutoModel.from_pretrained(new_model_name).to(device).eval()
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# Optimize new model
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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: {new_model_name}")
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except Exception as e:
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print(f"WARNING: Model optimization failed: {e}")
|
|
|
|
# Now safely delete old model after new one is loaded
|
|
del old_model
|
|
del old_tokenizer
|
|
|
|
# Clear GPU cache if available
|
|
if device.type == "cuda":
|
|
torch.cuda.empty_cache()
|
|
print("INFO: Cleared CUDA cache")
|
|
elif device.type == "mps":
|
|
torch.mps.empty_cache()
|
|
print("INFO: Cleared MPS cache")
|
|
|
|
# Force garbage collection
|
|
import gc
|
|
gc.collect()
|
|
print("INFO: Memory cleanup completed")
|
|
|
|
# Update model name
|
|
model_name = new_model_name
|
|
|
|
response = ["SUCCESS"]
|
|
print(f"INFO: Successfully updated model to: {new_model_name}")
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to update model: {e}")
|
|
response = ["FAILED", str(e)]
|
|
socket.send_multipart([identity, b'', msgpack.packb(response)])
|
|
continue
|
|
except:
|
|
# Not a control message, continue with normal protobuf processing
|
|
pass
|
|
|
|
e2e_start = time.time()
|
|
lookup_timer = DeviceTimer("text lookup")
|
|
|
|
# Parse request
|
|
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)}")
|
|
|
|
# Add debug information
|
|
if len(node_ids) > 0:
|
|
print(f"DEBUG: Node ID range: {min(node_ids)} to {max(node_ids)}")
|
|
|
|
# Look up texts
|
|
texts = []
|
|
missing_ids = []
|
|
with lookup_timer.timing():
|
|
for nid in node_ids:
|
|
txtinfo = passages[nid]
|
|
txt = txtinfo["text"]
|
|
if txt:
|
|
texts.append(txt)
|
|
else:
|
|
# If text is empty, we still need a placeholder for batch processing,
|
|
# but record its ID as missing
|
|
texts.append("")
|
|
missing_ids.append(nid)
|
|
lookup_timer.print_elapsed()
|
|
|
|
if missing_ids:
|
|
print(f"WARNING: Missing passages for IDs: {missing_ids}")
|
|
|
|
# Process batch
|
|
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]
|
|
|
|
if embedding_mode == "mlx":
|
|
embeddings_chunk = compute_embeddings_mlx(chunk_texts, model_name, batch_size=16)
|
|
elif embedding_mode == "openai":
|
|
embeddings_chunk = compute_embeddings_openai(chunk_texts, model_name)
|
|
else: # sentence-transformers
|
|
embeddings_chunk = process_batch_pytorch(chunk_texts, chunk_ids, missing_ids)
|
|
all_embeddings.append(embeddings_chunk)
|
|
|
|
if embedding_mode == "sentence-transformers":
|
|
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:
|
|
if embedding_mode == "mlx":
|
|
hidden = compute_embeddings_mlx(texts, model_name, batch_size=16)
|
|
elif embedding_mode == "openai":
|
|
hidden = compute_embeddings_openai(texts, model_name)
|
|
else: # sentence-transformers
|
|
hidden = process_batch_pytorch(texts, node_ids, missing_ids)
|
|
|
|
# Serialize response
|
|
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 socket sends a single response
|
|
socket.send_multipart([identity, b'', response_data])
|
|
|
|
ser_end = time.time()
|
|
|
|
print(f"INFO: Serialize time: {ser_end - ser_start:.6f} seconds")
|
|
|
|
if embedding_mode == "sentence-transformers":
|
|
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")
|
|
continue
|
|
except Exception as e:
|
|
print(f"ERROR: Error in ZMQ server: {e}")
|
|
try:
|
|
# Send empty response to maintain REQ-REP state
|
|
empty_resp = embedding_pb2.NodeEmbeddingResponse()
|
|
socket.send(empty_resp.SerializeToString())
|
|
except:
|
|
# If sending fails, recreate 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
|
|
|
|
|
|
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,
|
|
embedding_mode: str = "sentence-transformers",
|
|
enable_warmup: bool = False,
|
|
):
|
|
"""
|
|
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, embedding_mode, enable_warmup)
|
|
|
|
|
|
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")
|
|
parser.add_argument("--embedding-mode", type=str, default="sentence-transformers",
|
|
choices=["sentence-transformers", "mlx", "openai"],
|
|
help="Embedding backend mode")
|
|
parser.add_argument("--use-mlx", action="store_true", default=False, help="Use MLX backend for embeddings (deprecated: use --embedding-mode mlx)")
|
|
parser.add_argument("--disable-warmup", action="store_true", default=False, help="Disable warmup requests on server start")
|
|
args = parser.parse_args()
|
|
|
|
# Handle backward compatibility with use_mlx
|
|
embedding_mode = args.embedding_mode
|
|
if args.use_mlx:
|
|
embedding_mode = "mlx"
|
|
|
|
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,
|
|
embedding_mode=embedding_mode,
|
|
enable_warmup=not args.disable_warmup,
|
|
)
|