mlx/benchmarks/python/layer_norm_bench.py

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# Copyright © 2023-2024 Apple Inc.
import mlx.core as mx
import mlx.nn as nn
from time_utils import time_fn
def layer_norm(x, w, b, eps):
ot = x.dtype
x = x.astype(mx.float32)
mu = mx.mean(x, -1, keepdims=True)
v = mx.var(x, -1, keepdims=True)
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y = (x - mu) * mx.rsqrt(v + eps)
if w is not None:
y = y * w
if b is not None:
y = y + b
return y
def time_layer_norm():
f1 = lambda x, w, b, y: (layer_norm(x, w, b, 1e-5) * y).sum()
f2 = lambda x, w, b, y: (mx.fast.layer_norm(x, w, b, 1e-5) * y).sum()
g1 = mx.grad(f1, argnums=(0, 1, 2))
g2 = mx.grad(f2, argnums=(0, 1, 2))
x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
mx.eval(x, w, b, y)
def layer_norm_loop(g, x, w, b):
gx, gw, gb = x, w, b
for _ in range(32):
gx, gw, gb = g(gx, gw, gb, y)
return gx, gw, gb
time_fn(layer_norm_loop, g1, x, w, b)
time_fn(layer_norm_loop, g2, x, w, b)
time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
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f1 = lambda x, y: (layer_norm(x, None, None, 1e-5) * y).sum()
f2 = lambda x, y: (mx.fast.layer_norm(x, None, None, 1e-5) * y).sum()
g1 = mx.grad(f1, argnums=(0,))
g2 = mx.grad(f2, argnums=(0,))
x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
mx.eval(x, w, b, y)
def layer_norm_loop(g, x):
gx = x
for _ in range(32):
gx = g(gx, y)
return gx
time_fn(layer_norm_loop, g1, x)
time_fn(layer_norm_loop, g2, x)
time_fn(layer_norm_loop, mx.compile(g1), x)
time_fn(layer_norm_loop, mx.compile(g2), x)
if __name__ == "__main__":
time_layer_norm()