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| 1 | +# Copyright © 2025 Apple Inc. |
| 2 | + |
| 3 | +import argparse |
| 4 | +import time |
| 5 | + |
| 6 | +import mlx.core as mx |
| 7 | +import numpy as np |
| 8 | + |
| 9 | +MLX_DTYPES = { |
| 10 | + "float16": mx.float16, |
| 11 | + "bfloat16": mx.bfloat16, |
| 12 | + "float32": mx.float32, |
| 13 | +} |
| 14 | + |
| 15 | + |
| 16 | +def parse_cases(cases): |
| 17 | + parsed = [] |
| 18 | + for spec in cases.split(","): |
| 19 | + parts = spec.split("x") |
| 20 | + m, n, k, bs = int(parts[0]), int(parts[1]), int(parts[2]), int(parts[3]) |
| 21 | + sparsity = float(parts[4]) if len(parts) > 4 else 0.5 |
| 22 | + parsed.append((m, n, k, bs, sparsity)) |
| 23 | + return parsed |
| 24 | + |
| 25 | + |
| 26 | +def make_masks(m, n, k, block_size, sparsity, rng): |
| 27 | + """Create block masks with given sparsity (fraction of blocks zeroed).""" |
| 28 | + tm = (m + block_size - 1) // block_size |
| 29 | + tn = (n + block_size - 1) // block_size |
| 30 | + tk = (k + block_size - 1) // block_size |
| 31 | + |
| 32 | + lhs_mask = (rng.random((tm, tk)) >= sparsity).astype(np.bool_) |
| 33 | + rhs_mask = (rng.random((tk, tn)) >= sparsity).astype(np.bool_) |
| 34 | + out_mask = (rng.random((tm, tn)) >= sparsity).astype(np.bool_) |
| 35 | + return lhs_mask, rhs_mask, out_mask |
| 36 | + |
| 37 | + |
| 38 | +def mlx_naive_block_masked_mm(a, b, block_size, out_mask, lhs_mask, rhs_mask): |
| 39 | + """MLX naive: expand masks and use regular matmul.""" |
| 40 | + M, K = a.shape[-2], a.shape[-1] |
| 41 | + N = b.shape[-1] |
| 42 | + |
| 43 | + def expand(mask, rows, cols): |
| 44 | + e = mx.repeat(mx.repeat(mask, block_size, axis=-2), block_size, axis=-1) |
| 45 | + return e[..., :rows, :cols] |
| 46 | + |
| 47 | + a_masked = a * expand(lhs_mask, M, K) |
| 48 | + b_masked = b * expand(rhs_mask, K, N) |
| 49 | + c = a_masked @ b_masked |
| 50 | + c = c * expand(out_mask, M, N) |
| 51 | + return c |
| 52 | + |
| 53 | + |
| 54 | +def bench_mlx(fn, warmup, iters): |
| 55 | + for _ in range(warmup): |
| 56 | + y = fn() |
| 57 | + mx.eval(y) |
| 58 | + mx.synchronize() |
| 59 | + |
| 60 | + start = time.perf_counter() |
| 61 | + for _ in range(iters): |
| 62 | + y = fn() |
| 63 | + mx.eval(y) |
| 64 | + mx.synchronize() |
| 65 | + return (time.perf_counter() - start) * 1e3 / iters |
| 66 | + |
| 67 | + |
| 68 | +def print_table(headers, rows): |
| 69 | + widths = [len(h) for h in headers] |
| 70 | + for row in rows: |
| 71 | + for i, cell in enumerate(row): |
| 72 | + widths[i] = max(widths[i], len(cell)) |
| 73 | + |
| 74 | + def fmt_row(row): |
| 75 | + return ( |
| 76 | + "| " |
| 77 | + + " | ".join(f"{cell:<{widths[i]}}" for i, cell in enumerate(row)) |
| 78 | + + " |" |
| 79 | + ) |
| 80 | + |
| 81 | + sep = "|-" + "-|-".join("-" * w for w in widths) + "-|" |
| 82 | + print(fmt_row(headers)) |
| 83 | + print(sep) |
| 84 | + for row in rows: |
| 85 | + print(fmt_row(row)) |
| 86 | + |
| 87 | + |
| 88 | +def main(): |
| 89 | + parser = argparse.ArgumentParser( |
| 90 | + description="Benchmark block_masked_mm vs naive expand+matmul" |
| 91 | + ) |
| 92 | + parser.add_argument( |
| 93 | + "--cases", |
| 94 | + default=( |
| 95 | + "256x256x256x32x0.5," |
| 96 | + "512x512x512x32x0.5," |
| 97 | + "1024x1024x1024x32x0.5," |
| 98 | + "1024x1024x1024x64x0.5," |
| 99 | + "2048x2048x2048x64x0.5," |
| 100 | + "256x256x256x32x0.0," |
| 101 | + "1024x1024x1024x32x0.0," |
| 102 | + "1024x1024x1024x32x0.9" |
| 103 | + ), |
| 104 | + help="Comma-separated MxNxKxBSxSparsity list. Sparsity=fraction of blocks zeroed.", |
| 105 | + ) |
| 106 | + parser.add_argument( |
| 107 | + "--dtype", |
| 108 | + default="float32", |
| 109 | + choices=["float16", "bfloat16", "float32"], |
| 110 | + ) |
| 111 | + parser.add_argument("--warmup", type=int, default=10) |
| 112 | + parser.add_argument("--iters", type=int, default=50) |
| 113 | + parser.add_argument("--seed", type=int, default=42) |
| 114 | + parser.add_argument("--no-check", action="store_true") |
| 115 | + args = parser.parse_args() |
| 116 | + |
| 117 | + mlx_dtype = MLX_DTYPES[args.dtype] |
| 118 | + |
| 119 | + print(f"dtype={args.dtype} warmup={args.warmup} iters={args.iters}") |
| 120 | + |
| 121 | + headers = [ |
| 122 | + "Case (MxNxKxBS)", |
| 123 | + "Sparsity", |
| 124 | + "MLX ms", |
| 125 | + "Naive ms", |
| 126 | + "Speedup", |
| 127 | + ] |
| 128 | + if not args.no_check: |
| 129 | + headers.append("Max err") |
| 130 | + rows = [] |
| 131 | + |
| 132 | + cases = parse_cases(args.cases) |
| 133 | + for idx, (m, n, k, bs, sparsity) in enumerate(cases): |
| 134 | + rng = np.random.default_rng(args.seed + idx) |
| 135 | + a_np = rng.standard_normal((m, k)).astype(np.float32) |
| 136 | + b_np = rng.standard_normal((k, n)).astype(np.float32) |
| 137 | + lhs_mask_np, rhs_mask_np, out_mask_np = make_masks(m, n, k, bs, sparsity, rng) |
| 138 | + |
| 139 | + a_mx = mx.array(a_np, dtype=mlx_dtype) |
| 140 | + b_mx = mx.array(b_np, dtype=mlx_dtype) |
| 141 | + lhs_mask_mx = mx.array(lhs_mask_np) |
| 142 | + rhs_mask_mx = mx.array(rhs_mask_np) |
| 143 | + out_mask_mx = mx.array(out_mask_np) |
| 144 | + mx.eval(a_mx, b_mx, lhs_mask_mx, rhs_mask_mx, out_mask_mx) |
| 145 | + |
| 146 | + # Correctness check: block_masked_mm vs naive expand+matmul |
| 147 | + err_str = "" |
| 148 | + if not args.no_check: |
| 149 | + y_op = mx.block_masked_mm( |
| 150 | + a_mx, b_mx, bs, out_mask_mx, lhs_mask_mx, rhs_mask_mx |
| 151 | + ) |
| 152 | + y_naive = mlx_naive_block_masked_mm( |
| 153 | + a_mx, b_mx, bs, out_mask_mx, lhs_mask_mx, rhs_mask_mx |
| 154 | + ) |
| 155 | + mx.eval(y_op, y_naive) |
| 156 | + err = float(mx.max(mx.abs(y_op - y_naive)).item()) |
| 157 | + err_str = f"{err:.2e}" |
| 158 | + |
| 159 | + # Benchmark |
| 160 | + t_mlx = bench_mlx( |
| 161 | + lambda: mx.block_masked_mm( |
| 162 | + a_mx, b_mx, bs, out_mask_mx, lhs_mask_mx, rhs_mask_mx |
| 163 | + ), |
| 164 | + args.warmup, |
| 165 | + args.iters, |
| 166 | + ) |
| 167 | + t_naive = bench_mlx( |
| 168 | + lambda: mlx_naive_block_masked_mm( |
| 169 | + a_mx, b_mx, bs, out_mask_mx, lhs_mask_mx, rhs_mask_mx |
| 170 | + ), |
| 171 | + args.warmup, |
| 172 | + args.iters, |
| 173 | + ) |
| 174 | + speedup = f"{t_naive / t_mlx:.2f}x" if t_mlx > 0 else "-" |
| 175 | + |
| 176 | + row = [ |
| 177 | + f"{m}x{n}x{k}x{bs}", |
| 178 | + f"{sparsity:.0%}", |
| 179 | + f"{t_mlx:.3f}", |
| 180 | + f"{t_naive:.3f}", |
| 181 | + speedup, |
| 182 | + ] |
| 183 | + if not args.no_check: |
| 184 | + row.append(err_str) |
| 185 | + rows.append(row) |
| 186 | + |
| 187 | + print_table(headers, rows) |
| 188 | + if not args.no_check: |
| 189 | + print("err: max|block_masked_mm - naive_expand_matmul|") |
| 190 | + |
| 191 | + |
| 192 | +if __name__ == "__main__": |
| 193 | + main() |
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