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import argparse |
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import mlx.core as mx |
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import torch |
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from time_utils import measure_runtime |
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def benchmark_scatter_mlx(dst_shape, x_shape, idx_shapes): |
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def scatter(dst, x, idx): |
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dst[tuple(idx)] = x |
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mx.eval(dst) |
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idx = [] |
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for idx_shape in idx_shapes: |
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idx.append(mx.random.randint(0, dst_shape[0] - 1, idx_shape)) |
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x = mx.random.normal(x_shape).astype(mx.float32) |
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dst = mx.random.normal(dst_shape).astype(mx.float32) |
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runtime = measure_runtime(scatter, dst=dst, x=x, idx=idx) |
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print(f"MLX: {runtime:.3f}ms") |
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def benchmark_scatter_torch(dst_shape, x_shape, idx_shapes, device): |
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def scatter(dst, x, idx, device): |
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dst[tuple(idx)] = x |
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if device == torch.device("mps"): |
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torch.mps.synchronize() |
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idx = [] |
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for idx_shape in idx_shapes: |
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idx.append(torch.randint(0, dst_shape[0] - 1, idx_shape).to(device)) |
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x = torch.randn(x_shape, dtype=torch.float32).to(device) |
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dst = torch.randn(dst_shape, dtype=torch.float32).to(device) |
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runtime = measure_runtime(scatter, dst=dst, x=x, idx=idx, device=device) |
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print(f"PyTorch: {runtime:.3f}ms") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser("Gather benchmarks.") |
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parser.add_argument("--cpu", action="store_true", help="Use the CPU.") |
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args = parser.parse_args() |
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if args.cpu: |
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mx.set_default_device(mx.cpu) |
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device = torch.device("cpu") |
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else: |
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device = torch.device("mps") |
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dst_shapes = [ |
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(10, 64), |
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(100_000, 64), |
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(1_000_000, 64), |
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(100_000,), |
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(200_000,), |
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(20_000_000,), |
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(10000, 64), |
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(100, 64), |
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(100, 10_000, 64), |
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(10, 100, 100, 21), |
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(1_000, 1_000, 10), |
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] |
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idx_shapes = [ |
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[(1_000_000,)], |
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[(1_000_000,)], |
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[(100_000,)], |
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[(1_000_000,)], |
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[(20_000_000,)], |
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[(20_000_000,)], |
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[(1000000,)], |
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[(10000000,)], |
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[(1_000,)], |
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[(10_000,)], |
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[(1_000,), (1_000,)], |
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] |
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x_shapes = [ |
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(1_000_000, 64), |
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(1_000_000, 64), |
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(100_000, 64), |
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(1_000_000,), |
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(20_000_000,), |
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(20_000_000,), |
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(1000000, 64), |
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(10000000, 64), |
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(1_000, 10_000, 64), |
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(10_000, 100, 100, 21), |
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(1_000, 10), |
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] |
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for dst_shape, x_shape, idx_shape in zip(dst_shapes, x_shapes, idx_shapes): |
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print("=" * 20) |
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print(f"Dst: {dst_shape}, X {x_shape}, Indices {idx_shape}") |
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benchmark_scatter_mlx(dst_shape, x_shape, idx_shape) |
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benchmark_scatter_torch(dst_shape, x_shape, idx_shape, device=device) |
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