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import matplotlib |
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import mlx.core as mx |
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import numpy as np |
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import sympy |
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import torch |
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from time_utils import measure_runtime |
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matplotlib.use("Agg") |
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import matplotlib.pyplot as plt |
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def bandwidth_gb(runtime_ms, system_size): |
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bytes_per_fft = np.dtype(np.complex64).itemsize * 2 |
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bytes_per_gb = 1e9 |
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ms_per_s = 1e3 |
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return system_size * bytes_per_fft / runtime_ms * ms_per_s / bytes_per_gb |
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def run_bench(system_size, fft_sizes, backend="mlx", dim=1): |
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def fft_mlx(x): |
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if dim == 1: |
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out = mx.fft.fft(x) |
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elif dim == 2: |
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out = mx.fft.fft2(x) |
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mx.eval(out) |
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return out |
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def fft_mps(x): |
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if dim == 1: |
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out = torch.fft.fft(x) |
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elif dim == 2: |
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out = torch.fft.fft2(x) |
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torch.mps.synchronize() |
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return out |
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bandwidths = [] |
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for n in fft_sizes: |
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batch_size = system_size // n**dim |
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shape = [batch_size] + [n for _ in range(dim)] |
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if backend == "mlx": |
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x_np = np.random.uniform(size=(system_size // n, n)).astype(np.complex64) |
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x = mx.array(x_np) |
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mx.eval(x) |
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fft = fft_mlx |
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elif backend == "mps": |
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x_np = np.random.uniform(size=(system_size // n, n)).astype(np.complex64) |
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x = torch.tensor(x_np, device="mps") |
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torch.mps.synchronize() |
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fft = fft_mps |
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else: |
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raise NotImplementedError() |
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runtime_ms = measure_runtime(fft, x=x) |
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bandwidth = bandwidth_gb(runtime_ms, np.prod(shape)) |
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print(n, bandwidth) |
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bandwidths.append(bandwidth) |
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return np.array(bandwidths) |
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def time_fft(): |
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x = np.array(range(2, 512)) |
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system_size = int(2**26) |
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print("MLX GPU") |
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with mx.stream(mx.gpu): |
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gpu_bandwidths = run_bench(system_size=system_size, fft_sizes=x) |
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print("MPS GPU") |
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mps_bandwidths = run_bench(system_size=system_size, fft_sizes=x, backend="mps") |
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print("CPU") |
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system_size = int(2**20) |
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with mx.stream(mx.cpu): |
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cpu_bandwidths = run_bench(system_size=system_size, fft_sizes=x) |
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x = np.array(x) |
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all_indices = x - x[0] |
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radix_2to13 = ( |
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np.array([i for i in x if all(p <= 13 for p in sympy.primefactors(i))]) - x[0] |
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) |
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bluesteins = ( |
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np.array([i for i in x if any(p > 13 for p in sympy.primefactors(i))]) - x[0] |
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) |
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for indices, name in [ |
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(all_indices, "All"), |
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(radix_2to13, "Radix 2-13"), |
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(bluesteins, "Bluestein's"), |
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]: |
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print(name) |
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plt.scatter(x[indices], gpu_bandwidths[indices], color="green", label="GPU") |
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plt.scatter(x[indices], mps_bandwidths[indices], color="blue", label="MPS") |
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plt.scatter(x[indices], cpu_bandwidths[indices], color="red", label="CPU") |
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plt.title(f"MLX FFT Benchmark -- {name}") |
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plt.xlabel("N") |
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plt.ylabel("Bandwidth (GB/s)") |
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plt.legend() |
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plt.savefig(f"{name}.png") |
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plt.clf() |
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av_gpu_bandwidth = np.mean(gpu_bandwidths) |
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av_mps_bandwidth = np.mean(mps_bandwidths) |
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av_cpu_bandwidth = np.mean(cpu_bandwidths) |
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print("Average bandwidths:") |
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print("GPU:", av_gpu_bandwidth) |
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print("MPS:", av_mps_bandwidth) |
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print("CPU:", av_cpu_bandwidth) |
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portion_faster = len(np.where(gpu_bandwidths > mps_bandwidths)[0]) / len(x) |
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print("Percent MLX faster than MPS: ", portion_faster * 100) |
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if __name__ == "__main__": |
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time_fft() |
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