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06072601ce
Launch 2D grid to eliminate divide and mod in device code, since 64b integer division is very expensive. Github Issue #506 Co-authored-by: Vijay Krishnamoorthy <vijay_krish@apple.com>
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import argparse
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from time import time
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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_gather_mlx(x_shape, idx_shape):
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def gather(x, idx):
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mx.eval(x[idx])
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idx = mx.random.randint(0, x_shape[0] - 1, idx_shape)
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x = mx.random.normal(x_shape).astype(mx.float32)
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runtime = measure_runtime(gather, x=x, idx=idx)
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print(f"MLX: {runtime:.3f}ms")
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def benchmark_gather_torch(x_shape, idx_shape, device):
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def gather(x, idx, device):
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_ = x[idx]
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if device == torch.device("mps"):
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torch.mps.synchronize()
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idx = torch.randint(0, x_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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runtime = measure_runtime(gather, 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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idx_shapes = [(1_000_000,), (100_000,), ()]
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x_shapes = [(100, 64), (100, 1024), (4, 1_000_000)]
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for x_shape, idx_shape in zip(x_shapes, idx_shapes):
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print("=" * 20)
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print(f"X {x_shape}, Indices {idx_shape}")
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benchmark_gather_mlx(x_shape, idx_shape)
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benchmark_gather_torch(x_shape, idx_shape, device=device)
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