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import argparse |
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import math |
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import os |
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import subprocess |
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import time |
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import mlx.core as mx |
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import numpy as np |
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device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"]) |
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device_name = device_name.decode("utf-8").strip("\n") |
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N_warmup = 5 |
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N_iter_bench = 40 |
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N_iter_func = 8 |
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def bench(f, *args): |
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for i in range(N_warmup): |
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f(*args) |
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s = time.perf_counter_ns() |
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for i in range(N_iter_bench): |
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f(*args) |
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e = time.perf_counter_ns() |
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return (e - s) * 1e-9 |
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def prepare_inputs(B, qL, kL, D, qH, kH, mask, transpose, dtype): |
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np_dtype = getattr(np, dtype) |
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shape_q = (B, qL, qH, D) if transpose else (B, qH, qL, D) |
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shape_kv = (B, kL, kH, D) if transpose else (B, kH, kL, D) |
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scale = 1.0 / math.sqrt(D) |
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q_np = np.random.normal(0.0, 1.0, shape_q).astype(np_dtype) |
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k_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype) |
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v_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype) |
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q_mx = mx.array(q_np) |
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k_mx = mx.array(k_np) |
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v_mx = mx.array(v_np) |
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if mask is not None: |
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if mask == "additive": |
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mask_np = np.random.normal(0.0, 1.0, (B, qH, qL, kL)).astype(np_dtype) |
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mask = mx.array(mask_np) |
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elif mask == "bool": |
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mask_np = np.random.uniform(0.0, 1.0, (B, qH, qL, kL)) < 0.5 |
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mask = mx.array(mask_np) |
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return q_mx, k_mx, v_mx, scale, mask |
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def mlx_ref_attn(q, k, v, scale=1.0, mask=None): |
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q_dtype = q.dtype |
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q = q * mx.array(scale, q_dtype) |
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n_q_heads = q.shape[-3] |
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n_kv_heads = k.shape[-3] |
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n_repeats = n_q_heads // n_kv_heads |
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B = q.shape[0] |
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L = q.shape[2] |
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kL = k.shape[2] |
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if n_repeats > 1: |
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q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1]) |
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k = mx.expand_dims(k, 2) |
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v = mx.expand_dims(v, 2) |
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scores = q @ mx.swapaxes(k, -1, -2) |
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if mask is not None: |
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if mask == "causal": |
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q_offset = max(0, kL - L) |
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q_indices = mx.arange(q_offset, q_offset + L) |
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k_indices = mx.arange(kL) |
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mask = q_indices[:, None] >= k_indices[None] |
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if n_repeats > 1 and mask.ndim >= 3: |
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if mask.shape[-3] == 1: |
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mask = mx.expand_dims(mask, -3) |
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else: |
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mask = mx.unflatten(mask, -3, (n_kv_heads, n_repeats)) |
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if mask.dtype == mx.bool_: |
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scores = mx.where(mask, scores, -np.float32(np.inf)) |
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else: |
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scores += mask |
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scores = mx.softmax(scores, axis=-1, precise=True) |
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out = scores @ v |
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if n_repeats > 1: |
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out = mx.reshape(out, [B, n_q_heads, L, -1]) |
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return out |
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def mlx_fused_attn(q, k, v, scale, mask): |
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return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask) |
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def do_attention(f, q, k, v, scale, mask=None, transpose=False): |
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if transpose: |
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q_t = mx.transpose(q, (0, 2, 1, 3)) |
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k_t = mx.transpose(k, (0, 2, 1, 3)) |
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v_t = mx.transpose(v, (0, 2, 1, 3)) |
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o_t = f(q_t, k_t, v_t, scale=scale, mask=mask) |
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return mx.transpose(o_t, (0, 2, 1, 3)) |
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else: |
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return f(q, k, v, scale=scale, mask=mask) |
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def do_attention_bench(f, q, k, v, scale, mask=None, transpose=False): |
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q_out = q |
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for i in range(N_iter_func): |
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q_out = do_attention(f, q_out, k, v, scale, mask=mask, transpose=transpose) |
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mx.eval(q_out) |
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return q_out |
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def bench_shape( |
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B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, dtype, transpose=True, mask_in=None |
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): |
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q_mx, k_mx, v_mx, scale, mask = prepare_inputs( |
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B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, mask_in, transpose, dtype |
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) |
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time_mlx_unfused = bench( |
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do_attention_bench, mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose |
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) |
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time_mlx_fused = bench( |
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do_attention_bench, mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose |
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) |
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o_mlx_fused = do_attention(mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose) |
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o_mlx_unfused = do_attention( |
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mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose |
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) |
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atol = 1e-5 if dtype == "float32" else 2e-4 |
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if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol, rtol=atol): |
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print( |
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f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}, mask: {mask_in}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}" |
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) |
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return time_mlx_fused, time_mlx_unfused |
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def get_gflop_count(B, M, N, K): |
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return float(2.0 * N_iter_bench * N_iter_func * B * M * N * K) / float(1024.0**3) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="Run gemm benchmarks") |
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dtypes = ("float16", "float32")[:1] |
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transposes = (False,) |
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shapes_64 = ( |
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( 1, 32, 32, 64, 32, 32), |
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( 1, 64, 64, 64, 32, 32), |
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( 1, 128, 128, 64, 32, 32), |
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( 1, 256, 256, 64, 32, 32), |
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( 1, 512, 512, 64, 32, 32), |
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( 1, 1024, 1024, 64, 32, 8), |
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( 1, 2048, 2048, 64, 32, 8), |
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( 1, 4096, 4096, 64, 32, 8), |
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) |
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shapes_80 = ( |
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( 1, 1024, 1024, 80, 32, 8), |
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( 1, 2048, 2048, 80, 32, 8), |
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( 1, 4096, 4096, 80, 32, 8), |
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) |
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shapes_128 = ( |
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( 1, 1024, 1024, 128, 32, 8), |
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( 1, 2048, 2048, 128, 32, 8), |
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( 1, 4096, 4096, 128, 32, 8), |
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) |
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shapes = shapes_64 + shapes_80 + shapes_128 |
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masks = [None, "bool", "causal"] |
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print( |
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" B, qsl, ksl, hdim, n_qh, n_kvh, t, dtype, mask, t_unfs, t_fuse, diff%" |
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) |
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for dtype in dtypes: |
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for transpose in transposes: |
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for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes: |
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for mask_in in masks: |
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time_mlx_fused, time_mlx_unfused = bench_shape( |
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B, |
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qsl, |
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ksl, |
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head_dim, |
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n_q_heads, |
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n_kv_heads, |
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dtype, |
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transpose, |
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mask_in, |
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) |
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diff = time_mlx_unfused / time_mlx_fused - 1.0 |
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t_str = 1 if transpose else 0 |
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print( |
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f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:1d}, {dtype}, {str(mask_in):>8}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%" |
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) |
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