| |
|
|
| from functools import partial |
|
|
| 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) |
| 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(N, dt): |
| L = 1024 |
| 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, L, N)).astype(dt) |
| w = mx.random.uniform(shape=(N,)).astype(dt) |
| b = mx.random.uniform(shape=(N,)).astype(dt) |
| y = mx.random.uniform(shape=(8, L, N)).astype(dt) |
| mx.eval(x, w, b, y) |
|
|
| def layer_norm_loop(f, x, w, b): |
| for _ in range(32): |
| x = f(x, w, b) |
| return x |
|
|
| time_fn(layer_norm_loop, partial(layer_norm, eps=1e-5), x, w, b) |
| time_fn(layer_norm_loop, partial(mx.fast.layer_norm, eps=1e-5), x, w, b) |
|
|
| def layer_norm_grad_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_grad_loop, g1, x, w, b) |
| time_fn(layer_norm_grad_loop, g2, x, w, b) |
| time_fn(layer_norm_grad_loop, mx.compile(g1), x, w, b) |
| time_fn(layer_norm_grad_loop, mx.compile(g2), x, w, b) |
|
|
| 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, L, N)).astype(dt) |
| w = mx.random.uniform(shape=(N,)).astype(dt) |
| b = mx.random.uniform(shape=(N,)).astype(dt) |
| y = mx.random.uniform(shape=(8, L, N)).astype(dt) |
| mx.eval(x, w, b, y) |
|
|
| def layer_norm_grad_x_loop(g, x): |
| gx = x |
| for _ in range(32): |
| gx = g(gx, y) |
| return gx |
|
|
| time_fn(layer_norm_grad_x_loop, g1, x) |
| time_fn(layer_norm_grad_x_loop, g2, x) |
| time_fn(layer_norm_grad_x_loop, mx.compile(g1), x) |
| time_fn(layer_norm_grad_x_loop, mx.compile(g2), x) |
|
|
|
|
| if __name__ == "__main__": |
| for dt in [mx.float32, mx.float16, mx.bfloat16]: |
| for n in [1024, 2048, 4096, 8192, 8192 + 1024]: |
| print(dt, n) |
| time_layer_norm(n, dt) |
|
|
