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27d70c7d9d
* feature complete metal fft * fix contiguity bug * jit fft * simplify rader/bluestein constant computation * remove kernel/utils.h dep * remove bf16.h dep * format --------- Co-authored-by: Alex Barron <abarron22@apple.com>
119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
# Copyright © 2024 Apple Inc.
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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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# plot bandwidths
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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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