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# Copyright © 2024 Apple Inc.
import argparse
import math
import os
import subprocess
import time
import mlx.core as mx
import numpy as np
device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
device_name = device_name.decode("utf-8").strip("\n")
N_warmup = 5
N_iter_bench = 40
N_iter_func = 8
def bench(f, *args):
for i in range(N_warmup):
f(*args)
s = time.perf_counter_ns()
for i in range(N_iter_bench):
f(*args)
e = time.perf_counter_ns()
return (e - s) * 1e-9
def prepare_inputs(B, qL, kL, D, qH, kH, mask, transpose, dtype):
np_dtype = getattr(np, dtype)
shape_q = (B, qL, qH, D) if transpose else (B, qH, qL, D)
shape_kv = (B, kL, kH, D) if transpose else (B, kH, kL, D)
scale = 1.0 / math.sqrt(D)
q_np = np.random.normal(0.0, 1.0, shape_q).astype(np_dtype)
k_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype)
v_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype)
q_mx = mx.array(q_np)
k_mx = mx.array(k_np)
v_mx = mx.array(v_np)
if mask is not None:
if mask == "additive":
mask_np = np.random.normal(0.0, 1.0, (B, qH, qL, kL)).astype(np_dtype)
mask = mx.array(mask_np)
elif mask == "bool":
mask_np = np.random.uniform(0.0, 1.0, (B, qH, qL, kL)) < 0.5
mask = mx.array(mask_np)
return q_mx, k_mx, v_mx, scale, mask
def mlx_ref_attn(q, k, v, scale=1.0, mask=None):
q_dtype = q.dtype
q = q * mx.array(scale, q_dtype)
n_q_heads = q.shape[-3]
n_kv_heads = k.shape[-3]
n_repeats = n_q_heads // n_kv_heads
B = q.shape[0]
L = q.shape[2]
kL = k.shape[2]
if n_repeats > 1:
q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1])
k = mx.expand_dims(k, 2)
v = mx.expand_dims(v, 2)
scores = q @ mx.swapaxes(k, -1, -2)
if mask is not None:
if mask == "causal":
q_offset = max(0, kL - L)
q_indices = mx.arange(q_offset, q_offset + L)
k_indices = mx.arange(kL)
mask = q_indices[:, None] >= k_indices[None]
if n_repeats > 1 and mask.ndim >= 3:
if mask.shape[-3] == 1:
mask = mx.expand_dims(mask, -3)
else:
mask = mx.unflatten(mask, -3, (n_kv_heads, n_repeats))
if mask.dtype == mx.bool_:
scores = mx.where(mask, scores, -np.float32(np.inf))
else:
scores += mask
scores = mx.softmax(scores, axis=-1, precise=True)
out = scores @ v
if n_repeats > 1:
out = mx.reshape(out, [B, n_q_heads, L, -1])
return out
def mlx_fused_attn(q, k, v, scale, mask):
return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask)
def do_attention(f, q, k, v, scale, mask=None, transpose=False):
if transpose:
q_t = mx.transpose(q, (0, 2, 1, 3))
k_t = mx.transpose(k, (0, 2, 1, 3))
v_t = mx.transpose(v, (0, 2, 1, 3))
o_t = f(q_t, k_t, v_t, scale=scale, mask=mask)
return mx.transpose(o_t, (0, 2, 1, 3))
else:
return f(q, k, v, scale=scale, mask=mask)
def do_attention_bench(f, q, k, v, scale, mask=None, transpose=False):
q_out = q
for i in range(N_iter_func):
q_out = do_attention(f, q_out, k, v, scale, mask=mask, transpose=transpose)
mx.eval(q_out)
return q_out
def bench_shape(
B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, dtype, transpose=True, mask_in=None
):
q_mx, k_mx, v_mx, scale, mask = prepare_inputs(
B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, mask_in, transpose, dtype
)
time_mlx_unfused = bench(
do_attention_bench, mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose
)
time_mlx_fused = bench(
do_attention_bench, mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose
)
o_mlx_fused = do_attention(mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose)
o_mlx_unfused = do_attention(
mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose
)
atol = 1e-5 if dtype == "float32" else 2e-4
if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol, rtol=atol):
print(
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}"
)
return time_mlx_fused, time_mlx_unfused
def get_gflop_count(B, M, N, K):
return float(2.0 * N_iter_bench * N_iter_func * B * M * N * K) / float(1024.0**3)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run gemm benchmarks")
dtypes = ("float16", "float32")[:1]
transposes = (False,)
# fmt: off
shapes_64 = (
# ( B, qsl, ksl, head_dim, n_qh, n_kvh)
( 1, 32, 32, 64, 32, 32),
( 1, 64, 64, 64, 32, 32),
( 1, 128, 128, 64, 32, 32),
( 1, 256, 256, 64, 32, 32),
( 1, 512, 512, 64, 32, 32),
( 1, 1024, 1024, 64, 32, 8),
( 1, 2048, 2048, 64, 32, 8),
( 1, 4096, 4096, 64, 32, 8),
)
shapes_80 = (
# ( B, qsl, ksl, head_dim, n_qh, n_kvh)
( 1, 1024, 1024, 80, 32, 8),
( 1, 2048, 2048, 80, 32, 8),
( 1, 4096, 4096, 80, 32, 8),
)
shapes_128 = (
# ( B, qsl, ksl, head_dim, n_qh, n_kvh)
( 1, 1024, 1024, 128, 32, 8),
( 1, 2048, 2048, 128, 32, 8),
( 1, 4096, 4096, 128, 32, 8),
)
# fmt: on
shapes = shapes_64 + shapes_80 + shapes_128
masks = [None, "bool", "causal"]
print(
" B, qsl, ksl, hdim, n_qh, n_kvh, t, dtype, mask, t_unfs, t_fuse, diff%"
)
for dtype in dtypes:
for transpose in transposes:
for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes:
for mask_in in masks:
time_mlx_fused, time_mlx_unfused = bench_shape(
B,
qsl,
ksl,
head_dim,
n_q_heads,
n_kv_heads,
dtype,
transpose,
mask_in,
)
diff = time_mlx_unfused / time_mlx_fused - 1.0
t_str = 1 if transpose else 0
print(
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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