refactor: move to apps
This commit is contained in:
Andy Lee
0
apps/benchmarks/__init__.py
Normal file
0
apps/benchmarks/__init__.py
Normal file
338
apps/benchmarks/__main__.py
Normal file
338
apps/benchmarks/__main__.py
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@@ -0,0 +1,338 @@
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#!/usr/bin/env python3
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"""
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Memory comparison between Faiss HNSW and LEANN HNSW backend
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"""
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import logging
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import os
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import sys
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import time
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import psutil
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import gc
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import subprocess
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from pathlib import Path
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from llama_index.core.node_parser import SentenceSplitter
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# Setup logging
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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logger = logging.getLogger(__name__)
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def get_memory_usage():
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"""Get current memory usage in MB"""
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process = psutil.Process()
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return process.memory_info().rss / 1024 / 1024
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def print_memory_stats(stage: str, start_mem: float):
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"""Print memory statistics"""
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current_mem = get_memory_usage()
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diff = current_mem - start_mem
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print(f"[{stage}] Memory: {current_mem:.1f} MB (+{diff:.1f} MB)")
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return current_mem
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class MemoryTracker:
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def __init__(self, name: str):
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self.name = name
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self.start_mem = get_memory_usage()
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self.stages = []
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def checkpoint(self, stage: str):
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current_mem = print_memory_stats(f"{self.name} - {stage}", self.start_mem)
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self.stages.append((stage, current_mem))
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return current_mem
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def summary(self):
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print(f"\n=== {self.name} Memory Summary ===")
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for stage, mem in self.stages:
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print(f"{stage}: {mem:.1f} MB")
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peak_mem = max(mem for _, mem in self.stages)
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print(f"Peak Memory: {peak_mem:.1f} MB")
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print(f"Total Memory Increase: {peak_mem - self.start_mem:.1f} MB")
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return peak_mem
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def test_faiss_hnsw():
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"""Test Faiss HNSW Vector Store in subprocess"""
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print("\n" + "=" * 50)
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print("TESTING FAISS HNSW VECTOR STORE")
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print("=" * 50)
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try:
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# Get the directory of this script
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script_dir = Path(__file__).parent
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faiss_script = script_dir / "faiss_only.py"
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result = subprocess.run(
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[sys.executable, str(faiss_script)],
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capture_output=True,
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text=True,
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timeout=300,
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)
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print(result.stdout)
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if result.stderr:
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print("Stderr:", result.stderr)
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if result.returncode != 0:
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return {
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"peak_memory": float("inf"),
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"error": f"Process failed with code {result.returncode}",
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}
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# Parse peak memory from output
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lines = result.stdout.split("\n")
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peak_memory = 0.0
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for line in lines:
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if "Peak Memory:" in line:
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peak_memory = float(
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line.split("Peak Memory:")[1].split("MB")[0].strip()
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)
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return {"peak_memory": peak_memory}
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except Exception as e:
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return {
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"peak_memory": float("inf"),
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"error": str(e),
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}
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def test_leann_hnsw():
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"""Test LEANN HNSW Search Memory (load existing index)"""
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print("\n" + "=" * 50)
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print("TESTING LEANN HNSW SEARCH MEMORY")
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print("=" * 50)
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tracker = MemoryTracker("LEANN HNSW Search")
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# Import and setup
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tracker.checkpoint("Initial")
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from leann.api import LeannSearcher
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tracker.checkpoint("After imports")
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from llama_index.core import SimpleDirectoryReader
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from leann.api import LeannBuilder, LeannSearcher
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# Load and parse documents
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documents = SimpleDirectoryReader(
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"../documents/data",
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recursive=True,
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encoding="utf-8",
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required_exts=[".pdf", ".txt", ".md"],
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).load_data()
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tracker.checkpoint("After document loading")
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# Parse into chunks
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node_parser = SentenceSplitter(
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chunk_size=256, chunk_overlap=20, separator=" ", paragraph_separator="\n\n"
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)
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all_texts = []
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for doc in documents:
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nodes = node_parser.get_nodes_from_documents([doc])
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for node in nodes:
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all_texts.append(node.get_content())
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tracker.checkpoint("After text chunking")
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# Build LEANN index
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INDEX_DIR = Path("./test_leann_comparison")
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INDEX_PATH = str(INDEX_DIR / "comparison.leann")
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# Check if index already exists
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if os.path.exists(INDEX_PATH + ".meta.json"):
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print("Loading existing LEANN HNSW index...")
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tracker.checkpoint("After loading existing index")
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else:
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print("Building new LEANN HNSW index...")
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# Clean up previous index
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import shutil
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if INDEX_DIR.exists():
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shutil.rmtree(INDEX_DIR)
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="facebook/contriever",
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graph_degree=32,
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complexity=64,
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is_compact=True,
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is_recompute=True,
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num_threads=1,
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)
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tracker.checkpoint("After builder setup")
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print("Building LEANN HNSW index...")
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for chunk_text in all_texts:
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builder.add_text(chunk_text)
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builder.build_index(INDEX_PATH)
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del builder
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gc.collect()
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tracker.checkpoint("After index building")
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# Find existing LEANN index
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index_paths = [
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"./test_leann_comparison/comparison.leann",
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]
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index_path = None
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for path in index_paths:
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if os.path.exists(path + ".meta.json"):
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index_path = path
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break
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if not index_path:
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print("❌ LEANN index not found. Please build it first")
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return {"peak_memory": float("inf"), "error": "Index not found"}
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# Measure runtime memory overhead
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print("\nMeasuring runtime memory overhead...")
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runtime_start_mem = get_memory_usage()
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print(f"Before load memory: {runtime_start_mem:.1f} MB")
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tracker.checkpoint("Before load memory")
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# Load searcher
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searcher = LeannSearcher(index_path)
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tracker.checkpoint("After searcher loading")
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print("Running search queries...")
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queries = [
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"什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发",
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"What is LEANN and how does it work?",
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"华为诺亚方舟实验室的主要研究内容",
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]
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for i, query in enumerate(queries):
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start_time = time.time()
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# Use same parameters as Faiss: top_k=20, ef=120 (complexity parameter)
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_ = searcher.search(query, top_k=20, ef=120)
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query_time = time.time() - start_time
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print(f"Query {i + 1} time: {query_time:.3f}s")
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tracker.checkpoint(f"After query {i + 1}")
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runtime_end_mem = get_memory_usage()
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runtime_overhead = runtime_end_mem - runtime_start_mem
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peak_memory = tracker.summary()
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print(f"Runtime Memory Overhead: {runtime_overhead:.1f} MB")
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# Get storage size before cleanup
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storage_size = 0
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INDEX_DIR = Path(index_path).parent
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if INDEX_DIR.exists():
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total_size = 0
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for dirpath, _, filenames in os.walk(str(INDEX_DIR)):
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for filename in filenames:
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# Only count actual index files, skip text data and backups
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if filename.endswith((".old", ".tmp", ".bak", ".jsonl", ".json")):
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continue
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# Count .index, .idx, .map files (actual index structures)
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if filename.endswith((".index", ".idx", ".map")):
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filepath = os.path.join(dirpath, filename)
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total_size += os.path.getsize(filepath)
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storage_size = total_size / (1024 * 1024) # Convert to MB
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# Clean up
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del searcher
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gc.collect()
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return {
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"peak_memory": peak_memory,
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"storage_size": storage_size,
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}
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def main():
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"""Run comparison tests"""
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print("Storage + Search Memory Comparison: Faiss HNSW vs LEANN HNSW")
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print("=" * 60)
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# Test Faiss HNSW
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faiss_results = test_faiss_hnsw()
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# Force garbage collection
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gc.collect()
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time.sleep(2)
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# Test LEANN HNSW
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leann_results = test_leann_hnsw()
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# Final comparison
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print("\n" + "=" * 60)
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print("STORAGE + SEARCH MEMORY COMPARISON")
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print("=" * 60)
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# Get storage sizes
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faiss_storage_size = 0
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leann_storage_size = leann_results.get("storage_size", 0)
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# Get Faiss storage size using Python
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if os.path.exists("./storage_faiss"):
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total_size = 0
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for dirpath, _, filenames in os.walk("./storage_faiss"):
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for filename in filenames:
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filepath = os.path.join(dirpath, filename)
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total_size += os.path.getsize(filepath)
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faiss_storage_size = total_size / (1024 * 1024) # Convert to MB
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print("Faiss HNSW:")
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if "error" in faiss_results:
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print(f" ❌ Failed: {faiss_results['error']}")
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else:
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print(f" Search Memory: {faiss_results['peak_memory']:.1f} MB")
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print(f" Storage Size: {faiss_storage_size:.1f} MB")
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print("\nLEANN HNSW:")
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if "error" in leann_results:
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print(f" ❌ Failed: {leann_results['error']}")
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else:
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print(f" Search Memory: {leann_results['peak_memory']:.1f} MB")
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print(f" Storage Size: {leann_storage_size:.1f} MB")
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# Calculate improvements only if both tests succeeded
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if "error" not in faiss_results and "error" not in leann_results:
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memory_ratio = faiss_results["peak_memory"] / leann_results["peak_memory"]
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print("\nLEANN vs Faiss Performance:")
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memory_saving = faiss_results["peak_memory"] - leann_results["peak_memory"]
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print(
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f" Search Memory: {memory_ratio:.1f}x less ({memory_saving:.1f} MB saved)"
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)
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# Storage comparison
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if leann_storage_size > faiss_storage_size:
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storage_ratio = leann_storage_size / faiss_storage_size
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print(
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f" Storage Size: {storage_ratio:.1f}x larger (LEANN uses more storage)"
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)
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elif faiss_storage_size > leann_storage_size:
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storage_ratio = faiss_storage_size / leann_storage_size
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print(
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f" Storage Size: {storage_ratio:.1f}x smaller (LEANN uses less storage)"
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)
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else:
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print(" Storage Size: similar")
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else:
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if "error" not in leann_results:
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print("\n✅ LEANN HNSW completed successfully!")
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print(f"📊 Search Memory: {leann_results['peak_memory']:.1f} MB")
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print(f"📊 Storage Size: {leann_storage_size:.1f} MB")
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if "error" not in faiss_results:
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print("\n✅ Faiss HNSW completed successfully!")
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print(f"📊 Search Memory: {faiss_results['peak_memory']:.1f} MB")
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print(f"📊 Storage Size: {faiss_storage_size:.1f} MB")
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if __name__ == "__main__":
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main()
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151
apps/benchmarks/faiss_only.py
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151
apps/benchmarks/faiss_only.py
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@@ -0,0 +1,151 @@
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#!/usr/bin/env python3
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"""Test only Faiss HNSW"""
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import sys
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import time
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import psutil
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import gc
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import os
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def get_memory_usage():
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process = psutil.Process()
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return process.memory_info().rss / 1024 / 1024
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class MemoryTracker:
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def __init__(self, name: str):
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self.name = name
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self.start_mem = get_memory_usage()
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self.stages = []
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def checkpoint(self, stage: str):
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current_mem = get_memory_usage()
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diff = current_mem - self.start_mem
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print(f"[{self.name} - {stage}] Memory: {current_mem:.1f} MB (+{diff:.1f} MB)")
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self.stages.append((stage, current_mem))
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return current_mem
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def summary(self):
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peak_mem = max(mem for _, mem in self.stages)
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print(f"Peak Memory: {peak_mem:.1f} MB")
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return peak_mem
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def main():
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try:
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import faiss
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except ImportError:
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print("Faiss is not installed.")
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print("Please install it with `uv pip install faiss-cpu`")
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sys.exit(1)
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from llama_index.core import (
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SimpleDirectoryReader,
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VectorStoreIndex,
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StorageContext,
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Settings,
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node_parser,
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Document,
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)
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from llama_index.core.node_parser import SentenceSplitter
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from llama_index.vector_stores.faiss import FaissVectorStore
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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tracker = MemoryTracker("Faiss HNSW")
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tracker.checkpoint("Initial")
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embed_model = HuggingFaceEmbedding(model_name="facebook/contriever")
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Settings.embed_model = embed_model
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tracker.checkpoint("After embedding model setup")
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d = 768
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faiss_index = faiss.IndexHNSWFlat(d, 32)
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faiss_index.hnsw.efConstruction = 64
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tracker.checkpoint("After Faiss index creation")
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documents = SimpleDirectoryReader(
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"../documents/data",
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recursive=True,
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encoding="utf-8",
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required_exts=[".pdf", ".txt", ".md"],
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).load_data()
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tracker.checkpoint("After document loading")
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# Parse into chunks using the same splitter as LEANN
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node_parser = SentenceSplitter(
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chunk_size=256, chunk_overlap=20, separator=" ", paragraph_separator="\n\n"
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)
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tracker.checkpoint("After text splitter setup")
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# Check if index already exists and try to load it
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index_loaded = False
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if os.path.exists("./storage_faiss"):
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print("Loading existing Faiss HNSW index...")
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try:
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# Use the correct Faiss loading pattern from the example
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vector_store = FaissVectorStore.from_persist_dir("./storage_faiss")
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storage_context = StorageContext.from_defaults(
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vector_store=vector_store, persist_dir="./storage_faiss"
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)
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from llama_index.core import load_index_from_storage
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index = load_index_from_storage(storage_context=storage_context)
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print(f"Index loaded from ./storage_faiss")
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tracker.checkpoint("After loading existing index")
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index_loaded = True
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except Exception as e:
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print(f"Failed to load existing index: {e}")
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print("Cleaning up corrupted index and building new one...")
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# Clean up corrupted index
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import shutil
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if os.path.exists("./storage_faiss"):
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shutil.rmtree("./storage_faiss")
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if not index_loaded:
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print("Building new Faiss HNSW index...")
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# Use the correct Faiss building pattern from the example
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vector_store = FaissVectorStore(faiss_index=faiss_index)
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storage_context = StorageContext.from_defaults(vector_store=vector_store)
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index = VectorStoreIndex.from_documents(
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documents,
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storage_context=storage_context,
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transformations=[node_parser]
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)
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tracker.checkpoint("After index building")
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# Save index to disk using the correct pattern
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index.storage_context.persist(persist_dir="./storage_faiss")
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tracker.checkpoint("After index saving")
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# Measure runtime memory overhead
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print("\nMeasuring runtime memory overhead...")
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runtime_start_mem = get_memory_usage()
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print(f"Before load memory: {runtime_start_mem:.1f} MB")
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tracker.checkpoint("Before load memory")
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query_engine = index.as_query_engine(similarity_top_k=20)
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queries = [
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"什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发",
|
||||
"What is LEANN and how does it work?",
|
||||
"华为诺亚方舟实验室的主要研究内容",
|
||||
]
|
||||
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for i, query in enumerate(queries):
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start_time = time.time()
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_ = query_engine.query(query)
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query_time = time.time() - start_time
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print(f"Query {i + 1} time: {query_time:.3f}s")
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tracker.checkpoint(f"After query {i + 1}")
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||||
runtime_end_mem = get_memory_usage()
|
||||
runtime_overhead = runtime_end_mem - runtime_start_mem
|
||||
|
||||
peak_memory = tracker.summary()
|
||||
print(f"Peak Memory: {peak_memory:.1f} MB")
|
||||
print(f"Runtime Memory Overhead: {runtime_overhead:.1f} MB")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user