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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
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# Copyright © 2023-2024 Apple Inc.
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import mlx.core as mx
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import mlx.nn as nn
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from time_utils import time_fn
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def layer_norm(x, w, b, eps):
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ot = x.dtype
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x = x.astype(mx.float32)
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mu = mx.mean(x, -1, keepdims=True)
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v = mx.var(x, -1, keepdims=True)
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return (x - mu) * mx.rsqrt(v + eps) * w + b
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def time_layer_norm():
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f1 = lambda x, w, b, y: (layer_norm(x, w, b, 1e-5) * y).sum()
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f2 = lambda x, w, b, y: (mx.fast.layer_norm(x, w, b, 1e-5) * y).sum()
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g1 = mx.grad(f1, argnums=(0, 1, 2))
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g2 = mx.grad(f2, argnums=(0, 1, 2))
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x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
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w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
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b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
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y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
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mx.eval(x, w, b, y)
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def layer_norm_loop(g, x, w, b):
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gx, gw, gb = x, w, b
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for _ in range(32):
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gx, gw, gb = g(gx, gw, gb, y)
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return gx, gw, gb
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time_fn(layer_norm_loop, g1, x, w, b)
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time_fn(layer_norm_loop, g2, x, w, b)
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time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
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time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
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if __name__ == "__main__":
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time_layer_norm()
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