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Conway's Game of Life Accelerated with Custom Kernels in MLX
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import numpy as np | |
import mlx.core as mx | |
import matplotlib.pyplot as plt | |
from matplotlib.animation import FuncAnimation | |
import tqdm | |
def conway(a: mx.array): | |
source = """ | |
uint i = thread_position_in_grid.x; | |
uint j = thread_position_in_grid.y; | |
uint n = threads_per_grid.x; | |
uint m = threads_per_grid.y; | |
uint down = (i == 0) ? n : (i - 1); | |
uint up = (i + 1) == n ? 0 : (i + 1); | |
uint left = (j == 0) ? m : (j - 1); | |
uint right = (j + 1) == m ? 0 : (j + 1); | |
size_t idx = i * m + j; | |
int count = grid[up * m + right] + grid[up * m + j] | |
+ grid[i * m + right] + grid[up * m + left] + grid[down * m + left] | |
+ grid[down * m + j] + grid[i * m + left] + grid[down * m + right]; | |
if ((grid[idx] && count == 2) || count == 3) { | |
out[idx] = true; | |
} else { | |
out[idx] = false; | |
} | |
""" | |
kernel = mx.fast.metal_kernel( | |
name="conway", | |
input_names=["grid"], | |
output_names=["out"], | |
source=source, | |
) | |
return kernel( | |
inputs=[a], | |
grid=(a.shape[0], a.shape[1], 1), | |
threadgroup=(2, 512, 1), | |
output_shapes=[a.shape], | |
output_dtypes=[a.dtype], | |
)[0] | |
def generator(grid, steps=1000): | |
for i in range(steps): | |
mx.eval(grid) | |
yield (~grid).astype(mx.uint8) * 255 | |
grid = conway(grid) | |
def animate(grid, steps=300, fps=30, save_as="out.mp4"): | |
fig, ax = plt.subplots(figsize=(8, 4)) | |
im = ax.imshow(mx.zeros_like(grid), cmap="gray", vmin=0, vmax=255) | |
progress_bar = tqdm.tqdm(total=steps, desc="Animating", ncols=100) | |
def update(frame): | |
im.set_data(frame) | |
progress_bar.update(1) | |
return [im] | |
ani = FuncAnimation( | |
fig, | |
update, | |
frames=generator(grid, steps=steps), | |
interval=steps // fps, | |
blit=True, | |
cache_frame_data=False, | |
) | |
fig.tight_layout() | |
try: | |
ani.save(save_as, writer="ffmpeg", fps=fps, dpi=300) | |
finally: | |
progress_bar.close() | |
plt.show() | |
if __name__ == "__main__": | |
grid_size = 2048 | |
grid = mx.random.bernoulli(p=0.3, shape=(grid_size, grid_size)) | |
animate(grid) |
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There is a bug with this code in latest mlx 0.18.0