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83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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from functools import partial
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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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y = (x - mu) * mx.rsqrt(v + eps)
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if w is not None:
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y = y * w
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if b is not None:
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y = y + b
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return y
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def time_layer_norm(N, dt):
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L = 1024
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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, L, N)).astype(dt)
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w = mx.random.uniform(shape=(N,)).astype(dt)
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b = mx.random.uniform(shape=(N,)).astype(dt)
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y = mx.random.uniform(shape=(8, L, N)).astype(dt)
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mx.eval(x, w, b, y)
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def layer_norm_loop(f, x, w, b):
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for _ in range(32):
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x = f(x, w, b)
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return x
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time_fn(layer_norm_loop, partial(layer_norm, eps=1e-5), x, w, b)
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time_fn(layer_norm_loop, partial(mx.fast.layer_norm, eps=1e-5), x, w, b)
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def layer_norm_grad_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_grad_loop, g1, x, w, b)
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time_fn(layer_norm_grad_loop, g2, x, w, b)
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time_fn(layer_norm_grad_loop, mx.compile(g1), x, w, b)
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time_fn(layer_norm_grad_loop, mx.compile(g2), x, w, b)
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f1 = lambda x, y: (layer_norm(x, None, None, 1e-5) * y).sum()
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f2 = lambda x, y: (mx.fast.layer_norm(x, None, 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, L, N)).astype(dt)
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w = mx.random.uniform(shape=(N,)).astype(dt)
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b = mx.random.uniform(shape=(N,)).astype(dt)
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y = mx.random.uniform(shape=(8, L, N)).astype(dt)
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mx.eval(x, w, b, y)
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def layer_norm_grad_x_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(layer_norm_grad_x_loop, g1, x)
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time_fn(layer_norm_grad_x_loop, g2, x)
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time_fn(layer_norm_grad_x_loop, mx.compile(g1), x)
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time_fn(layer_norm_grad_x_loop, mx.compile(g2), x)
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if __name__ == "__main__":
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for dt in [mx.float32, mx.float16, mx.bfloat16]:
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for n in [1024, 2048, 4096, 8192, 8192 + 1024]:
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print(dt, n)
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time_layer_norm(n, dt)
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