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64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
# 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 rms_norm(x, w, eps):
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ot = x.dtype
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x = x.astype(mx.float32)
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n = mx.rsqrt(x.square().mean(-1, keepdims=True) + eps)
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y = (x * n).astype(ot)
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if w is not None:
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y = y * w
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return y
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def time_rms_norm():
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f1 = lambda x, w, y: (rms_norm(x, w, 1e-5) * y).sum()
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f2 = lambda x, w, y: (mx.fast.rms_norm(x, w, 1e-5) * y).sum()
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g1 = mx.grad(f1, argnums=(0, 1))
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g2 = mx.grad(f2, argnums=(0, 1))
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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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y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
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mx.eval(x, w, y)
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def rms_norm_loop(g, x, w):
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gx, gw = x, w
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for _ in range(32):
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gx, gw = g(gx, gw, y)
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return gx, gw
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time_fn(rms_norm_loop, g1, x, w)
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time_fn(rms_norm_loop, g2, x, w)
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time_fn(rms_norm_loop, mx.compile(g1), x, w)
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time_fn(rms_norm_loop, mx.compile(g2), x, w)
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f1 = lambda x, y: (rms_norm(x, None, 1e-5) * y).sum()
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f2 = lambda x, y: (mx.fast.rms_norm(x, None, 1e-5) * y).sum()
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g1 = mx.grad(f1, argnums=(0,))
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g2 = mx.grad(f2, argnums=(0,))
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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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y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
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mx.eval(x, w, y)
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def rms_norm_loop(g, x):
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gx = x
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for _ in range(32):
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gx = g(gx, y)
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return gx
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time_fn(rms_norm_loop, g1, x)
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time_fn(rms_norm_loop, g2, x)
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time_fn(rms_norm_loop, mx.compile(g1), x)
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time_fn(rms_norm_loop, mx.compile(g2), x)
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if __name__ == "__main__":
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time_rms_norm()
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