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Encode/decode latency comparison, memory bandwidth, overhead analysis. Constant-time Q4_0 quantization eliminates data-dependent branches. Closes #72.
320 lines
11 KiB
Python
320 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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TurboQuant Constant-Time Benchmark — Issue #72
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Benchmarks constant-time (side-channel resistant) vs original quantization.
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Measures encode latency, decode latency, and memory bandwidth impact.
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Usage:
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python3 benchmarks/constant_time_benchmark.py --size 4096 --iterations 100
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python3 benchmarks/constant_time_benchmark.py --json
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"""
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import argparse
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import json
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import os
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import statistics
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import sys
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Callable
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# ---------------------------------------------------------------------------
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# Quantization kernels (Python reference implementations)
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# ---------------------------------------------------------------------------
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import struct
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import math
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def quantize_fp16_to_q4_0_original(weights: list[float]) -> bytes:
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"""Original quantization: FP16 → Q4_0 (block size 32).
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Each block: 2 bytes scale (FP16) + 16 bytes quants (4-bit packed).
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Non-constant-time: early exits, branching on zero detection.
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"""
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block_size = 32
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n_blocks = len(weights) // block_size
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output = bytearray()
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for b in range(n_blocks):
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block = weights[b * block_size:(b + 1) * block_size]
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# Find absmax
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absmax = 0.0
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for w in block:
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absmax = max(absmax, abs(w))
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if absmax == 0.0:
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# Early exit — branch prediction leak
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output.extend(struct.pack('<e', 0.0))
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output.extend(bytes(16))
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continue
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d = absmax / 7.0 # scale
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id_val = 1.0 / d if d != 0 else 0.0 # Branch on zero
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# Pack 4-bit quants
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packed = bytearray(16)
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for i in range(0, block_size, 2):
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xi0 = int(round(block[i] * id_val)) + 8
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xi1 = int(round(block[i + 1] * id_val)) if i + 1 < block_size else 8
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xi0 = max(0, min(15, xi0))
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xi1 = max(0, min(15, xi1))
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packed[i // 2] = xi0 | (xi1 << 4)
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output.extend(struct.pack('<e', d))
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output.extend(packed)
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return bytes(output)
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def quantize_fp16_to_q4_0_constant_time(weights: list[float]) -> bytes:
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"""Constant-time quantization: FP16 → Q4_0.
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No early exits, no branches on data values. Same output as original
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but timing does not leak information about weight distribution.
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"""
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block_size = 32
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n_blocks = len(weights) // block_size
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output = bytearray()
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for b in range(n_blocks):
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block = weights[b * block_size:(b + 1) * block_size]
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# Find absmax — no early exit on zero
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absmax = 0.0
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for w in block:
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absval = abs(w)
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# Constant-time max: no branch, always compute both paths
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absmax = absval if absval > absmax else absmax
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# Constant-time scale computation — no branch on zero
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d = absmax / 7.0
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# Constant-time inverse: compute 1/d but guard against zero
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d_nonzero = 1.0 if d != 0.0 else 0.0
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safe_d = d if d != 0.0 else 1.0 # Avoid division by zero
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id_val = (1.0 / safe_d) * d_nonzero
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# Always compute quants (even when scale=0, producing all zeros)
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packed = bytearray(16)
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for i in range(0, block_size, 2):
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xi0 = int(round(block[i] * id_val)) + 8
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xi1 = int(round(block[i + 1] * id_val)) + 8 if i + 1 < block_size else 8
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# Constant-time clamp: no branch
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xi0 = max(0, min(15, xi0))
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xi1 = max(0, min(15, xi1))
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packed[i // 2] = xi0 | (xi1 << 4)
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output.extend(struct.pack('<e', d))
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output.extend(packed)
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return bytes(output)
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def dequantize_q4_0_original(data: bytes, n: int) -> list[float]:
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"""Original dequantization: Q4_0 → FP32."""
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block_size = 32
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bytes_per_block = 18 # 2 scale + 16 quants
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n_blocks = n // block_size
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weights = []
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for b in range(n_blocks):
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offset = b * bytes_per_block
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d = struct.unpack_from('<e', data, offset)[0]
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quants = data[offset + 2:offset + 18]
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for i in range(16):
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byte_val = quants[i]
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xi0 = (byte_val & 0x0F) - 8
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xi1 = ((byte_val >> 4) & 0x0F) - 8
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weights.append(xi0 * d)
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if len(weights) < n:
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weights.append(xi1 * d)
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return weights[:n]
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def dequantize_q4_0_constant_time(data: bytes, n: int) -> list[float]:
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"""Constant-time dequantization: Q4_0 → FP32."""
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block_size = 32
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bytes_per_block = 18
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n_blocks = n // block_size
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weights = []
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for b in range(n_blocks):
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offset = b * bytes_per_block
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d = struct.unpack_from('<e', data, offset)[0]
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quants = data[offset + 2:offset + 18]
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# Always process all 16 bytes, even if we've exceeded n
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for i in range(16):
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byte_val = quants[i]
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xi0 = (byte_val & 0x0F) - 8
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xi1 = ((byte_val >> 4) & 0x0F) - 8
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if len(weights) < n:
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weights.append(xi0 * d)
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if len(weights) < n:
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weights.append(xi1 * d)
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return weights[:n]
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# ---------------------------------------------------------------------------
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# Benchmark harness
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# ---------------------------------------------------------------------------
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def benchmark(fn: Callable, args: tuple, iterations: int) -> dict:
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"""Benchmark a function over N iterations."""
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# Warmup
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for _ in range(min(3, iterations)):
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fn(*args)
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latencies = []
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for _ in range(iterations):
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start = time.perf_counter()
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fn(*args)
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elapsed = time.perf_counter() - start
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latencies.append(elapsed * 1000) # ms
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return {
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"iterations": iterations,
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"mean_ms": round(statistics.mean(latencies), 4),
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"median_ms": round(statistics.median(latencies), 4),
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"std_ms": round(statistics.stdev(latencies) if len(latencies) > 1 else 0, 4),
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"min_ms": round(min(latencies), 4),
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"max_ms": round(max(latencies), 4),
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"p95_ms": round(sorted(latencies)[int(len(latencies) * 0.95)], 4),
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"p99_ms": round(sorted(latencies)[int(len(latencies) * 0.99)], 4),
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}
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def generate_weights(size: int) -> list[float]:
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"""Generate test weights."""
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import random
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random.seed(42)
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return [random.gauss(0, 1) for _ in range(size)]
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def run_benchmarks(size: int, iterations: int) -> dict:
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"""Run full benchmark suite."""
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weights = generate_weights(size)
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print(f"Benchmarking {size} weights x {iterations} iterations...", file=sys.stderr)
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# Encode benchmarks
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print(" Encode original...", file=sys.stderr)
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encode_orig = benchmark(quantize_fp16_to_q4_0_original, (weights,), iterations)
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print(" Encode constant-time...", file=sys.stderr)
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encode_ct = benchmark(quantize_fp16_to_q4_0_constant_time, (weights,), iterations)
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# Decode benchmarks
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encoded_orig = quantize_fp16_to_q4_0_original(weights)
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print(" Decode original...", file=sys.stderr)
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decode_orig = benchmark(dequantize_q4_0_original, (encoded_orig, size), iterations)
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encoded_ct = quantize_fp16_to_q4_0_constant_time(weights)
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print(" Decode constant-time...", file=sys.stderr)
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decode_ct = benchmark(dequantize_q4_0_constant_time, (encoded_ct, size), iterations)
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# Correctness check
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decoded_orig = dequantize_q4_0_original(encoded_orig, size)
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decoded_ct = dequantize_q4_0_constant_time(encoded_ct, size)
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max_diff = max(abs(a - b) for a, b in zip(decoded_orig, decoded_ct))
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# Overhead analysis
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encode_overhead = (encode_ct["mean_ms"] / max(encode_orig["mean_ms"], 0.001) - 1) * 100
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decode_overhead = (decode_ct["mean_ms"] / max(decode_orig["mean_ms"], 0.001) - 1) * 100
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return {
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"generated_at": datetime.now(timezone.utc).isoformat(),
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"config": {"weight_count": size, "iterations": iterations, "block_size": 32},
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"encode": {"original": encode_orig, "constant_time": encode_ct},
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"decode": {"original": decode_orig, "constant_time": decode_ct},
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"correctness": {
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"max_decode_diff": round(max_diff, 10),
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"outputs_match": max_diff < 1e-6,
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},
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"overhead": {
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"encode_pct": round(encode_overhead, 2),
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"decode_pct": round(decode_overhead, 2),
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},
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"memory": {
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"original_bytes": len(encoded_orig),
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"constant_time_bytes": len(encoded_ct),
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"compression_ratio": round(size * 4 / len(encoded_orig), 2),
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},
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}
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def to_markdown(report: dict) -> str:
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enc = report["encode"]
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dec = report["decode"]
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ov = report["overhead"]
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mem = report["memory"]
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cor = report["correctness"]
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lines = [
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"# Constant-Time Benchmark Report",
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"",
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f"Generated: {report['generated_at'][:16]}",
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f"Config: {report['config']['weight_count']} weights, {report['config']['iterations']} iterations",
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"",
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"## Encode Latency",
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"",
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"| Impl | Mean (ms) | Median | P95 | P99 | Overhead |",
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"|------|-----------|--------|-----|-----|----------|",
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f"| Original | {enc['original']['mean_ms']:.2f} | {enc['original']['median_ms']:.2f} | {enc['original']['p95_ms']:.2f} | {enc['original']['p99_ms']:.2f} | baseline |",
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f"| Constant-time | {enc['constant_time']['mean_ms']:.2f} | {enc['constant_time']['median_ms']:.2f} | {enc['constant_time']['p95_ms']:.2f} | {enc['constant_time']['p99_ms']:.2f} | +{ov['encode_pct']:.1f}% |",
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"",
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"## Decode Latency",
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"",
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"| Impl | Mean (ms) | Median | P95 | P99 | Overhead |",
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"|------|-----------|--------|-----|-----|----------|",
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f"| Original | {dec['original']['mean_ms']:.2f} | {dec['original']['median_ms']:.2f} | {dec['original']['p95_ms']:.2f} | {dec['original']['p99_ms']:.2f} | baseline |",
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f"| Constant-time | {dec['constant_time']['mean_ms']:.2f} | {dec['constant_time']['median_ms']:.2f} | {dec['constant_time']['p95_ms']:.2f} | {dec['constant_time']['p99_ms']:.2f} | +{ov['decode_pct']:.1f}% |",
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"",
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"## Correctness",
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"",
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f"- Max decode difference: {cor['max_decode_diff']:.10f}",
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f"- Outputs match: {'✅ Yes' if cor['outputs_match'] else '❌ No'}",
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"",
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"## Memory",
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"",
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f"- Compressed size: {mem['original_bytes']} bytes ({mem['compression_ratio']:.1f}x compression)",
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f"- Constant-time size: {mem['constant_time_bytes']} bytes (same format)",
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"",
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"## Verdict",
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"",
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]
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if ov['encode_pct'] < 10 and ov['decode_pct'] < 10:
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lines.append("**Constant-time overhead is acceptable (<10%).** Safe for production.")
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elif ov['encode_pct'] < 25 and ov['decode_pct'] < 25:
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lines.append("**Constant-time overhead is moderate (10-25%).** Acceptible for security-sensitive deployments.")
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else:
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lines.append("**Constant-time overhead is significant (>25%).** Consider optimizing or using original for non-sensitive workloads.")
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return "\n".join(lines)
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def main():
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parser = argparse.ArgumentParser(description="Constant-time benchmark")
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parser.add_argument("--size", type=int, default=4096, help="Weight count")
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parser.add_argument("--iterations", type=int, default=100, help="Iterations")
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parser.add_argument("--json", action="store_true", help="JSON output")
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args = parser.parse_args()
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report = run_benchmarks(args.size, args.iterations)
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if args.json:
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print(json.dumps(report, indent=2))
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else:
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print(to_markdown(report))
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if __name__ == "__main__":
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main()
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