mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-05 01:50:27 +08:00
feat: Enhance BinaryANNIndex with vectorized search and performance benchmarking
This commit is contained in:
catlog22
209
codex-lens/benchmarks/binary_search_microbenchmark.py
Normal file
209
codex-lens/benchmarks/binary_search_microbenchmark.py
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@@ -0,0 +1,209 @@
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#!/usr/bin/env python
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"""Micro-benchmark for BinaryANNIndex search performance.
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Measures the actual speedup of vectorized Hamming distance computation.
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"""
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from __future__ import annotations
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import gc
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import statistics
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import sys
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import time
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from pathlib import Path
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# Add src to path
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sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
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import numpy as np
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def old_search_implementation(query_arr: np.ndarray, vectors: dict, id_list: list, top_k: int):
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"""Original O(N) loop-based implementation for comparison."""
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packed_dim = len(query_arr)
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distances = []
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for vec_id in id_list:
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vec = vectors[vec_id]
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vec_arr = np.frombuffer(vec, dtype=np.uint8)
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xor = np.bitwise_xor(query_arr, vec_arr)
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dist = int(np.unpackbits(xor).sum())
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distances.append((vec_id, dist))
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distances.sort(key=lambda x: x[1])
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top_results = distances[:top_k]
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ids = [r[0] for r in top_results]
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dists = [r[1] for r in top_results]
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return ids, dists
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def new_search_implementation(query_arr: np.ndarray, vectors_matrix: np.ndarray, ids_array: np.ndarray, top_k: int):
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"""Optimized vectorized implementation."""
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# Broadcast XOR
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xor_result = np.bitwise_xor(query_arr, vectors_matrix)
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# Vectorized popcount using lookup table
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popcount_lut = np.array([bin(i).count('1') for i in range(256)], dtype=np.uint8)
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bit_counts = popcount_lut[xor_result]
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# Sum across packed bytes
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distances = bit_counts.sum(axis=1)
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# Get top-k using argpartition
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n_vectors = len(distances)
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k = min(top_k, n_vectors)
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if k == n_vectors:
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sorted_indices = np.argsort(distances)
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else:
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partition_indices = np.argpartition(distances, k)[:k]
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top_k_distances = distances[partition_indices]
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sorted_order = np.argsort(top_k_distances)
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sorted_indices = partition_indices[sorted_order]
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result_ids = ids_array[sorted_indices].tolist()
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result_dists = distances[sorted_indices].tolist()
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return result_ids, result_dists
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def run_benchmark(n_vectors: int, dim: int = 256, top_k: int = 100, n_iterations: int = 50):
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"""Run benchmark comparing old and new implementations."""
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packed_dim = dim // 8 # 32 bytes for 256-bit
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print(f"\n{'='*60}")
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print(f"Binary Search Micro-Benchmark")
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print(f"{'='*60}")
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print(f"Vectors: {n_vectors}")
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print(f"Dimension: {dim} bits ({packed_dim} bytes packed)")
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print(f"Top-K: {top_k}")
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print(f"Iterations: {n_iterations}")
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print(f"{'='*60}\n")
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# Generate random binary vectors
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print("Generating test data...")
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vectors_dict = {}
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id_list = []
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for i in range(n_vectors):
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vec_bytes = np.random.randint(0, 256, size=packed_dim, dtype=np.uint8).tobytes()
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vectors_dict[i] = vec_bytes
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id_list.append(i)
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# Build matrix for vectorized search
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vectors_matrix = np.empty((n_vectors, packed_dim), dtype=np.uint8)
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ids_array = np.array(id_list, dtype=np.int64)
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for i, vec_id in enumerate(id_list):
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vec_bytes = vectors_dict[vec_id]
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vectors_matrix[i] = np.frombuffer(vec_bytes, dtype=np.uint8)
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# Generate random query
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query_bytes = np.random.randint(0, 256, size=packed_dim, dtype=np.uint8).tobytes()
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query_arr = np.frombuffer(query_bytes, dtype=np.uint8)
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# Warmup
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print("Running warmup...")
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for _ in range(3):
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old_search_implementation(query_arr, vectors_dict, id_list, top_k)
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new_search_implementation(query_arr, vectors_matrix, ids_array, top_k)
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# Benchmark old implementation
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print("Benchmarking old implementation...")
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old_times = []
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for _ in range(n_iterations):
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gc.collect()
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start = time.perf_counter()
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old_ids, old_dists = old_search_implementation(query_arr, vectors_dict, id_list, top_k)
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elapsed = (time.perf_counter() - start) * 1000
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old_times.append(elapsed)
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# Benchmark new implementation
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print("Benchmarking new implementation...")
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new_times = []
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for _ in range(n_iterations):
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gc.collect()
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start = time.perf_counter()
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new_ids, new_dists = new_search_implementation(query_arr, vectors_matrix, ids_array, top_k)
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elapsed = (time.perf_counter() - start) * 1000
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new_times.append(elapsed)
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# Verify correctness
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print("\nVerifying correctness...")
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# Check that distances are correct (IDs may differ for ties)
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if old_dists == new_dists:
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print("Distances match! (IDs may differ for ties)")
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else:
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# Check if difference is just in tie-breaking
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old_dist_set = set(old_dists)
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new_dist_set = set(new_dists)
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if old_dist_set == new_dist_set:
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print("Distances equivalent (tie-breaking differs, which is acceptable)")
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else:
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print("WARNING: Distance distributions differ!")
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print(f" Old dists (first 5): {old_dists[:5]}")
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print(f" New dists (first 5): {new_dists[:5]}")
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# Calculate statistics
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old_avg = statistics.mean(old_times)
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old_std = statistics.stdev(old_times) if len(old_times) > 1 else 0
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new_avg = statistics.mean(new_times)
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new_std = statistics.stdev(new_times) if len(new_times) > 1 else 0
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speedup = old_avg / new_avg if new_avg > 0 else 0
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# Print results
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print(f"\n{'='*60}")
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print("RESULTS")
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print(f"{'='*60}")
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print(f"{'Metric':<25} {'Old (loop)':>15} {'New (vectorized)':>18}")
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print(f"{'-'*25} {'-'*15} {'-'*18}")
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print(f"{'Avg Latency (ms)':<25} {old_avg:>15.3f} {new_avg:>18.3f}")
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print(f"{'Std Dev (ms)':<25} {old_std:>15.3f} {new_std:>18.3f}")
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print(f"{'Min Latency (ms)':<25} {min(old_times):>15.3f} {min(new_times):>18.3f}")
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print(f"{'Max Latency (ms)':<25} {max(old_times):>15.3f} {max(new_times):>18.3f}")
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print(f"{'P50 (ms)':<25} {sorted(old_times)[len(old_times)//2]:>15.3f} {sorted(new_times)[len(new_times)//2]:>18.3f}")
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print(f"\n{'Speedup:':<25} {speedup:>15.2f}x")
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print(f"{'='*60}\n")
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return {
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"n_vectors": n_vectors,
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"dim": dim,
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"top_k": top_k,
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"old_avg_ms": old_avg,
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"new_avg_ms": new_avg,
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"speedup": speedup,
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}
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def main():
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print("\n" + "="*70)
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print(" BINARY SEARCH OPTIMIZATION MICRO-BENCHMARK")
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print("="*70)
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# Test different vector counts
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results = []
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for n_vectors in [1000, 5000, 10000, 50000]:
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result = run_benchmark(
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n_vectors=n_vectors,
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dim=256,
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top_k=100,
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n_iterations=20,
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)
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results.append(result)
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# Summary
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print("\n" + "="*70)
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print(" SUMMARY")
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print("="*70)
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print(f"{'N Vectors':<12} {'Old (ms)':<12} {'New (ms)':<12} {'Speedup':>10}")
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print("-"*50)
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for r in results:
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print(f"{r['n_vectors']:<12} {r['old_avg_ms']:<12.3f} {r['new_avg_ms']:<12.3f} {r['speedup']:>10.2f}x")
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print("="*70)
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if __name__ == "__main__":
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main()
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@@ -1,30 +1,30 @@
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{
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"timestamp": "2026-01-02 11:22:34",
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"timestamp": "2026-01-02 11:48:33",
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"summaries": {
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"binary": {
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"strategy": "binary",
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"total_queries": 15,
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"successful_queries": 15,
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"avg_latency_ms": 850.328753333209,
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"min_latency_ms": 750.9617999967304,
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"max_latency_ms": 1015.733200001705,
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"p50_latency_ms": 847.9711999971187,
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"p95_latency_ms": 976.768470002571,
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"p99_latency_ms": 1007.9402540018782,
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"avg_results": 0,
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"avg_latency_ms": 1133.4008666667312,
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"min_latency_ms": 959.5361000028788,
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"max_latency_ms": 1330.8978999993997,
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"p50_latency_ms": 1125.8439999946859,
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"p95_latency_ms": 1330.0081999987015,
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"p99_latency_ms": 1330.71995999926,
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"avg_results": 10,
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"errors": []
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},
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"hybrid": {
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"strategy": "hybrid",
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"total_queries": 15,
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"successful_queries": 15,
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"avg_latency_ms": 821.3745733330143,
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"min_latency_ms": 720.5589000004693,
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"max_latency_ms": 943.0299999949057,
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"p50_latency_ms": 819.5875000019441,
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"p95_latency_ms": 916.3381599981221,
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"p99_latency_ms": 937.691631995549,
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"avg_results": 0,
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"avg_latency_ms": 1111.1401133336283,
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"min_latency_ms": 857.0021999985329,
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"max_latency_ms": 1278.8890000010724,
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"p50_latency_ms": 1130.696000000171,
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"p95_latency_ms": 1254.2417899981956,
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"p99_latency_ms": 1273.959558000497,
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"avg_results": 10,
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"errors": []
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}
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},
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@@ -33,121 +33,121 @@
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{
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"strategy": "binary",
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"query": "def search",
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"latency_ms": 862.7266999974381,
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"num_results": 0,
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"top_result": null,
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"latency_ms": 1044.525999997859,
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"num_results": 10,
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\storage\\dir_index.py:0",
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"error": null
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},
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{
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"strategy": "binary",
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"query": "class Engine",
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"latency_ms": 773.8472999990336,
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"num_results": 0,
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"top_result": null,
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"latency_ms": 1052.5979999947594,
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"num_results": 10,
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\parsers\\factory.py:0",
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"error": null
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},
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{
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"strategy": "binary",
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"query": "import numpy",
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"latency_ms": 858.1023000006098,
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"num_results": 0,
|
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"top_result": null,
|
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"latency_ms": 1217.217100005655,
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"num_results": 10,
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\__main__.py:0",
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"error": null
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},
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{
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"strategy": "binary",
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"query": "async def",
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"latency_ms": 877.2815999982413,
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"num_results": 0,
|
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"top_result": null,
|
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"latency_ms": 1276.9802000038908,
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"num_results": 10,
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\semantic\\reranker\\api_reranker.py:0",
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"error": null
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},
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{
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"strategy": "binary",
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"query": "raise ValueError",
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"latency_ms": 824.3320999972639,
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"num_results": 0,
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"top_result": null,
|
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"latency_ms": 1005.9053000004496,
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"num_results": 10,
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\cli\\commands.py:0",
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"error": null
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},
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{
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"strategy": "binary",
|
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"query": "how to parse json",
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"latency_ms": 948.0362000031164,
|
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"num_results": 0,
|
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"top_result": null,
|
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"latency_ms": 1330.8978999993997,
|
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"num_results": 10,
|
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\cli\\commands.py:0",
|
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"error": null
|
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},
|
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{
|
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"strategy": "binary",
|
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"query": "database connection",
|
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"latency_ms": 789.3126000053599,
|
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"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1041.6685000018333,
|
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"num_results": 10,
|
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"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\indexing\\symbol_extractor.py:0",
|
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"error": null
|
||||
},
|
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{
|
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"strategy": "binary",
|
||||
"query": "error handling",
|
||||
"latency_ms": 960.0693000029423,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 959.5361000028788,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\storage\\migrations\\migration_004_dual_fts.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "authentication logic",
|
||||
"latency_ms": 757.247900000948,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1060.9395999999833,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\search\\query_parser.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "file read write",
|
||||
"latency_ms": 750.9617999967304,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 971.8680000005406,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\watcher\\incremental_indexer.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "embedding vector",
|
||||
"latency_ms": 871.1426000008942,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1135.879900000873,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\semantic\\embedder.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "cosine similarity",
|
||||
"latency_ms": 817.1380999992834,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1188.1732000038028,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\semantic\\vector_store.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "binary quantization",
|
||||
"latency_ms": 1015.733200001705,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1259.3522999959532,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\semantic\\vector_store.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "hamming distance",
|
||||
"latency_ms": 847.9711999971187,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1329.6268999984022,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\semantic\\ann_index.py:0",
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"strategy": "binary",
|
||||
"query": "reranking",
|
||||
"latency_ms": 801.028399997449,
|
||||
"num_results": 0,
|
||||
"top_result": null,
|
||||
"latency_ms": 1125.8439999946859,
|
||||
"num_results": 10,
|
||||
"top_result": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\search\\ranking.py:0",
|
||||
"error": null
|
||||
}
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@@ -155,121 +155,121 @@
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|
||||
Reference in New Issue
Block a user