feat: hnsw embedding server and csr format
This commit is contained in:
Andy Lee
@@ -3,7 +3,7 @@ import os
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import json
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import struct
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from pathlib import Path
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from typing import Dict
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from typing import Dict, Any
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import contextlib
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import threading
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import time
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@@ -12,9 +12,7 @@ import socket
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import subprocess
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import sys
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# 文件: packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
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# ... (其他 import 保持不变) ...
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from .convert_to_csr import convert_hnsw_graph_to_csr
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from leann.registry import register_backend
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from leann.interface import (
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@@ -28,7 +26,7 @@ def get_metric_map():
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return {
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"mips": faiss.METRIC_INNER_PRODUCT,
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"l2": faiss.METRIC_L2,
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"cosine": faiss.METRIC_INNER_PRODUCT, # Will need normalization
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"cosine": faiss.METRIC_INNER_PRODUCT,
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}
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def _check_port(port: int) -> bool:
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@@ -69,12 +67,11 @@ class HNSWEmbeddingServerManager:
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try:
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command = [
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sys.executable,
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"-m", "packages.leann-backend-hnsw.src.leann_backend_hnsw.hnsw_embedding_server",
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"-m", "leann_backend_hnsw.hnsw_embedding_server",
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"--zmq-port", str(port),
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"--model-name", model_name
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]
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# Add passages file if provided
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if passages_file:
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command.extend(["--passages-file", str(passages_file)])
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@@ -172,7 +169,29 @@ class HNSWBackend(LeannBackendFactoryInterface):
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class HNSWBuilder(LeannBackendBuilderInterface):
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def __init__(self, **kwargs):
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self.build_params = kwargs
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self.build_params = kwargs.copy()
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# --- Configuration defaults with standardized names ---
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# Apply defaults and write them back to the build_params dict
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# so they can be saved in the metadata file by LeannBuilder.
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self.is_compact = self.build_params.setdefault("is_compact", True)
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self.is_recompute = self.build_params.setdefault("is_recompute", True) # Default: prune embeddings
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# --- Additional Options ---
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self.is_skip_neighbors = self.build_params.setdefault("is_skip_neighbors", False)
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self.disk_cache_ratio = self.build_params.setdefault("disk_cache_ratio", 0.0)
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self.external_storage_path = self.build_params.get("external_storage_path", None)
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# --- Standard HNSW parameters ---
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self.M = self.build_params.setdefault("M", 32)
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self.efConstruction = self.build_params.setdefault("efConstruction", 200)
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self.distance_metric = self.build_params.setdefault("distance_metric", "mips")
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if self.is_skip_neighbors and not self.is_compact:
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raise ValueError("is_skip_neighbors can only be used with is_compact=True")
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if self.is_recompute and not self.is_compact:
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raise ValueError("is_recompute requires is_compact=True for efficiency")
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def build(self, data: np.ndarray, index_path: str, **kwargs):
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"""Build HNSW index using FAISS"""
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@@ -189,97 +208,297 @@ class HNSWBuilder(LeannBackendBuilderInterface):
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if not data.flags['C_CONTIGUOUS']:
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data = np.ascontiguousarray(data)
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build_kwargs = {**self.build_params, **kwargs}
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metric_str = build_kwargs.get("distance_metric", "mips").lower()
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metric_str = self.distance_metric.lower()
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metric_enum = get_metric_map().get(metric_str)
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print('metric_enum', metric_enum,' metric_str', metric_str)
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
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# HNSW parameters
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M = build_kwargs.get("M", 32) # Max connections per layer
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efConstruction = build_kwargs.get("efConstruction", 200) # Size of the dynamic candidate list for construction
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M = self.M
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efConstruction = self.efConstruction
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dim = data.shape[1]
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print(f"INFO: Building HNSW index for {data.shape[0]} vectors with metric {metric_enum}...")
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try:
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# Create HNSW index
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if metric_enum == faiss.METRIC_INNER_PRODUCT:
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index = faiss.IndexHNSWFlat(dim, M, metric_enum)
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else: # L2
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index = faiss.IndexHNSWFlat(dim, M, metric_enum)
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# Set construction parameters
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index.hnsw.efConstruction = efConstruction
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# Normalize vectors if using cosine similarity
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if metric_str == "cosine":
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faiss.normalize_L2(data)
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# Add vectors to index
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print('starting to add vectors to index')
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index.add(data.shape[0], faiss.swig_ptr(data))
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print('vectors added to index')
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# Save index
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index_file = index_dir / f"{index_prefix}.index"
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faiss.write_index(index, str(index_file))
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print(f"✅ HNSW index built successfully at '{index_file}'")
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if self.is_compact:
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self._convert_to_csr(index_file)
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# Generate passages file for recompute mode
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if self.is_recompute:
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self._generate_passages_file(index_dir, index_prefix, **kwargs)
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except Exception as e:
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print(f"💥 ERROR: HNSW index build failed. Exception: {e}")
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raise
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def _convert_to_csr(self, index_file: Path):
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"""Convert built index to CSR format"""
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try:
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mode_str = "CSR-pruned" if self.is_recompute else "CSR-standard"
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print(f"INFO: Converting HNSW index to {mode_str} format...")
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csr_temp_file = index_file.with_suffix(".csr.tmp")
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success = convert_hnsw_graph_to_csr(
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str(index_file),
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str(csr_temp_file),
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prune_embeddings=self.is_recompute
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)
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if success:
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print("✅ CSR conversion successful.")
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import shutil
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shutil.move(str(csr_temp_file), str(index_file))
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print(f"INFO: Replaced original index with {mode_str} version at '{index_file}'")
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else:
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# Clean up and fail fast
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if csr_temp_file.exists():
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os.remove(csr_temp_file)
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raise RuntimeError("CSR conversion failed - cannot proceed with compact format")
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except Exception as e:
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print(f"💥 ERROR: CSR conversion failed. Exception: {e}")
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raise
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def _generate_passages_file(self, index_dir: Path, index_prefix: str, **kwargs):
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"""Generate passages file for recompute mode"""
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try:
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chunks = kwargs.get('chunks', [])
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if not chunks:
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print("INFO: No chunks data provided, skipping passages file generation")
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return
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# Generate node_id to text mapping
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passages_data = {}
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for node_id, chunk in enumerate(chunks):
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passages_data[str(node_id)] = chunk["text"]
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# Save passages file
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passages_file = index_dir / f"{index_prefix}.passages.json"
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with open(passages_file, 'w', encoding='utf-8') as f:
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json.dump(passages_data, f, ensure_ascii=False, indent=2)
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print(f"✅ Generated passages file for recompute mode at '{passages_file}' ({len(passages_data)} passages)")
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except Exception as e:
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print(f"💥 ERROR: Failed to generate passages file. Exception: {e}")
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# Don't raise - this is not critical for index building
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pass
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class HNSWSearcher(LeannBackendSearcherInterface):
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def _get_index_storage_status(self, index_file: Path) -> tuple[bool, bool]:
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"""
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Robustly determines the index's storage status by parsing the file.
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Returns:
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A tuple (is_compact, is_pruned).
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"""
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if not index_file.exists():
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return False, False
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with open(index_file, 'rb') as f:
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try:
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def read_struct(fmt):
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size = struct.calcsize(fmt)
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data = f.read(size)
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if len(data) != size:
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raise EOFError(f"File ended unexpectedly reading struct fmt '{fmt}'.")
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return struct.unpack(fmt, data)[0]
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def skip_vector(element_size):
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count = read_struct('<Q')
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f.seek(count * element_size, 1)
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# 1. Read up to the compact flag
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read_struct('<I'); read_struct('<i'); read_struct('<q');
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read_struct('<q'); read_struct('<q'); read_struct('<?')
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metric_type = read_struct('<i')
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if metric_type > 1: read_struct('<f')
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skip_vector(8); skip_vector(4); skip_vector(4)
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# 2. Check if there's a compact flag byte
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# Try to read the compact flag, but handle both old and new formats
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pos_before_compact = f.tell()
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try:
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is_compact = read_struct('<?')
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print(f"INFO: Detected is_compact flag as: {is_compact}")
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except (EOFError, struct.error):
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# Old format without compact flag - assume non-compact
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f.seek(pos_before_compact)
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is_compact = False
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print(f"INFO: No compact flag found, assuming is_compact=False")
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# 3. Read storage FourCC to determine if pruned
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is_pruned = False
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try:
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if is_compact:
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# For compact, we need to skip pointers and scalars to get to the storage FourCC
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skip_vector(8) # level_ptr
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skip_vector(8) # node_offsets
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read_struct('<i'); read_struct('<i'); read_struct('<i');
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read_struct('<i'); read_struct('<i')
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storage_fourcc = read_struct('<I')
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else:
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# For non-compact, we need to read the flag probe, then skip offsets and neighbors
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pos_before_probe = f.tell()
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flag_byte = f.read(1)
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if not (flag_byte and flag_byte == b'\x00'):
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f.seek(pos_before_probe)
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skip_vector(8); skip_vector(4) # offsets, neighbors
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read_struct('<i'); read_struct('<i'); read_struct('<i');
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read_struct('<i'); read_struct('<i')
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# Now we are at the storage. The entire rest is storage blob.
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storage_fourcc = struct.unpack('<I', f.read(4))[0]
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NULL_INDEX_FOURCC = int.from_bytes(b'null', 'little')
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if storage_fourcc == NULL_INDEX_FOURCC:
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is_pruned = True
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except (EOFError, struct.error):
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# Cannot determine pruning status, assume not pruned
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pass
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print(f"INFO: Detected is_pruned as: {is_pruned}")
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return is_compact, is_pruned
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except (EOFError, struct.error) as e:
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print(f"WARNING: Could not parse index file to detect format: {e}. Assuming standard, not pruned.")
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return False, False
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def __init__(self, index_path: str, **kwargs):
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from . import faiss
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path = Path(index_path)
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index_dir = path.parent
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index_prefix = path.stem
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metric_str = kwargs.get("distance_metric", "mips").lower()
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# Store configuration and paths for later use
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self.config = kwargs.copy()
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self.config["index_path"] = index_path
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self.index_dir = index_dir
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self.index_prefix = index_prefix
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metric_str = self.config.get("distance_metric", "mips").lower()
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metric_enum = get_metric_map().get(metric_str)
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
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dimensions = kwargs.get("dimensions")
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dimensions = self.config.get("dimensions")
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if not dimensions:
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raise ValueError("Vector dimension not provided to HNSWSearcher.")
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try:
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# Load FAISS HNSW index
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index_file = index_dir / f"{index_prefix}.index"
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if not index_file.exists():
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raise FileNotFoundError(f"HNSW index file not found at {index_file}")
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self._index = faiss.read_index(str(index_file))
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self.metric_str = metric_str
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self.embedding_server_manager = HNSWEmbeddingServerManager()
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print("✅ HNSW index loaded successfully.")
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except Exception as e:
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print(f"💥 ERROR: Failed to load HNSW index. Exception: {e}")
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raise
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index_file = index_dir / f"{index_prefix}.index"
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if not index_file.exists():
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raise FileNotFoundError(f"HNSW index file not found at {index_file}")
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def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, any]:
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self.is_compact, self.is_pruned = self._get_index_storage_status(index_file)
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# Validate configuration constraints
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if not self.is_compact and self.config.get("is_skip_neighbors", False):
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raise ValueError("is_skip_neighbors can only be used with is_compact=True")
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if self.config.get("is_recompute", False) and self.config.get("external_storage_path"):
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raise ValueError("Cannot use both is_recompute and external_storage_path simultaneously")
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hnsw_config = faiss.HNSWIndexConfig()
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hnsw_config.is_compact = self.is_compact
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# Apply additional configuration options with strict validation
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hnsw_config.is_skip_neighbors = self.config.get("is_skip_neighbors", False)
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# If index is pruned, force recompute mode regardless of user setting
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hnsw_config.is_recompute = self.is_pruned or self.config.get("is_recompute", False)
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hnsw_config.disk_cache_ratio = self.config.get("disk_cache_ratio", 0.0)
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hnsw_config.external_storage_path = self.config.get("external_storage_path")
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hnsw_config.zmq_port = self.config.get("zmq_port", 5557)
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# CRITICAL ASSERTION: If index is pruned, recompute MUST be enabled
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if self.is_pruned and not hnsw_config.is_recompute:
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raise RuntimeError("Index is pruned (embeddings removed) but recompute is disabled. This is impossible - recompute must be enabled for pruned indices.")
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print(f"INFO: Loading index with is_compact={self.is_compact}, is_pruned={self.is_pruned}")
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print(f"INFO: Config - skip_neighbors={hnsw_config.is_skip_neighbors}, recompute={hnsw_config.is_recompute}")
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self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
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if self.is_compact:
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print("✅ Compact CSR format HNSW index loaded successfully.")
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else:
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print("✅ Standard HNSW index loaded successfully.")
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self.metric_str = metric_str
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self.embedding_server_manager = HNSWEmbeddingServerManager()
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def _get_index_file(self, index_dir: Path, index_prefix: str) -> Path:
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"""Get the appropriate index file path based on format"""
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# We always use the same filename now, format is detected internally
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return index_dir / f"{index_prefix}.index"
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def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
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"""Search using HNSW index with optional recompute functionality"""
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from . import faiss
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ef = kwargs.get("ef", 200) # Size of the dynamic candidate list for search
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# Merge config with search-time kwargs
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search_config = self.config.copy()
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search_config.update(kwargs)
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ef = search_config.get("ef", 200) # Size of the dynamic candidate list for search
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# Recompute parameters
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recompute_neighbor_embeddings = kwargs.get("recompute_neighbor_embeddings", False)
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zmq_port = kwargs.get("zmq_port", 5556)
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embedding_model = kwargs.get("embedding_model", "sentence-transformers/all-mpnet-base-v2")
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passages_file = kwargs.get("passages_file", None)
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zmq_port = search_config.get("zmq_port", 5557)
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embedding_model = search_config.get("embedding_model", "sentence-transformers/all-mpnet-base-v2")
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passages_file = search_config.get("passages_file", None)
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if recompute_neighbor_embeddings:
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print(f"INFO: HNSW ZMQ mode enabled - ensuring embedding server is running")
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# For recompute mode, try to find the passages file automatically
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if self.is_pruned and not passages_file:
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potential_passages_file = self.index_dir / f"{self.index_prefix}.passages.json"
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print(f"DEBUG: Checking for passages file at: {potential_passages_file}")
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if potential_passages_file.exists():
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passages_file = str(potential_passages_file)
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print(f"INFO: Found passages file for recompute mode: {passages_file}")
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else:
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print(f"WARNING: No passages file found for recompute mode at {potential_passages_file}")
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# If index is pruned (embeddings removed), we MUST start embedding server for recompute
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if self.is_pruned:
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print(f"INFO: Index is pruned - starting embedding server for recompute")
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if not self.embedding_server_manager.start_server(zmq_port, embedding_model, passages_file):
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print(f"WARNING: Failed to start HNSW embedding server, falling back to standard search")
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kwargs['recompute_neighbor_embeddings'] = False
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# CRITICAL: Check passages file exists - fail fast if not
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if not passages_file:
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raise RuntimeError(f"FATAL: Index is pruned but no passages file found. Cannot proceed with recompute mode.")
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# Check if server is already running first
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if _check_port(zmq_port):
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print(f"INFO: Embedding server already running on port {zmq_port}")
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else:
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if not self.embedding_server_manager.start_server(zmq_port, embedding_model, passages_file):
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raise RuntimeError(f"Failed to start HNSW embedding server on port {zmq_port}")
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# Give server extra time to fully initialize
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print(f"INFO: Waiting for embedding server to fully initialize...")
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time.sleep(3)
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# Final verification
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if not _check_port(zmq_port):
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raise RuntimeError(f"Embedding server failed to start listening on port {zmq_port}")
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else:
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print(f"INFO: Index has embeddings stored - no recompute needed")
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if query.dtype != np.float32:
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query = query.astype(np.float32)
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@@ -299,23 +518,14 @@ class HNSWSearcher(LeannBackendSearcherInterface):
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distances = np.empty((batch_size, top_k), dtype=np.float32)
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labels = np.empty((batch_size, top_k), dtype=np.int64)
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if recompute_neighbor_embeddings:
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# Use custom search with recompute
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# This would require implementing custom HNSW search logic
|
||||
# For now, we'll fall back to standard search
|
||||
print("WARNING: Recompute functionality for HNSW not yet implemented, using standard search")
|
||||
self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels))
|
||||
else:
|
||||
# Standard FAISS search using SWIG API
|
||||
self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels))
|
||||
# Use standard FAISS search - recompute is handled internally by FAISS
|
||||
self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels))
|
||||
|
||||
return {"labels": labels, "distances": distances}
|
||||
|
||||
except Exception as e:
|
||||
print(f"💥 ERROR: HNSW search failed. Exception: {e}")
|
||||
batch_size = query.shape[0]
|
||||
return {"labels": np.full((batch_size, top_k), -1, dtype=np.int64),
|
||||
"distances": np.full((batch_size, top_k), float('inf'), dtype=np.float32)}
|
||||
raise
|
||||
|
||||
def __del__(self):
|
||||
if hasattr(self, 'embedding_server_manager'):
|
||||
|
||||
Reference in New Issue
Block a user