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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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