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# Copyright © 2024 Apple Inc.
import matplotlib
import mlx.core as mx
import numpy as np
import sympy
import torch
from time_utils import measure_runtime
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def bandwidth_gb(runtime_ms, system_size):
bytes_per_fft = np.dtype(np.complex64).itemsize * 2
bytes_per_gb = 1e9
ms_per_s = 1e3
return system_size * bytes_per_fft / runtime_ms * ms_per_s / bytes_per_gb
def run_bench(system_size, fft_sizes, backend="mlx", dim=1):
def fft_mlx(x):
if dim == 1:
out = mx.fft.fft(x)
elif dim == 2:
out = mx.fft.fft2(x)
mx.eval(out)
return out
def fft_mps(x):
if dim == 1:
out = torch.fft.fft(x)
elif dim == 2:
out = torch.fft.fft2(x)
torch.mps.synchronize()
return out
bandwidths = []
for n in fft_sizes:
batch_size = system_size // n**dim
shape = [batch_size] + [n for _ in range(dim)]
if backend == "mlx":
x_np = np.random.uniform(size=(system_size // n, n)).astype(np.complex64)
x = mx.array(x_np)
mx.eval(x)
fft = fft_mlx
elif backend == "mps":
x_np = np.random.uniform(size=(system_size // n, n)).astype(np.complex64)
x = torch.tensor(x_np, device="mps")
torch.mps.synchronize()
fft = fft_mps
else:
raise NotImplementedError()
runtime_ms = measure_runtime(fft, x=x)
bandwidth = bandwidth_gb(runtime_ms, np.prod(shape))
print(n, bandwidth)
bandwidths.append(bandwidth)
return np.array(bandwidths)
def time_fft():
x = np.array(range(2, 512))
system_size = int(2**26)
print("MLX GPU")
with mx.stream(mx.gpu):
gpu_bandwidths = run_bench(system_size=system_size, fft_sizes=x)
print("MPS GPU")
mps_bandwidths = run_bench(system_size=system_size, fft_sizes=x, backend="mps")
print("CPU")
system_size = int(2**20)
with mx.stream(mx.cpu):
cpu_bandwidths = run_bench(system_size=system_size, fft_sizes=x)
x = np.array(x)
all_indices = x - x[0]
radix_2to13 = (
np.array([i for i in x if all(p <= 13 for p in sympy.primefactors(i))]) - x[0]
)
bluesteins = (
np.array([i for i in x if any(p > 13 for p in sympy.primefactors(i))]) - x[0]
)
for indices, name in [
(all_indices, "All"),
(radix_2to13, "Radix 2-13"),
(bluesteins, "Bluestein's"),
]:
# plot bandwidths
print(name)
plt.scatter(x[indices], gpu_bandwidths[indices], color="green", label="GPU")
plt.scatter(x[indices], mps_bandwidths[indices], color="blue", label="MPS")
plt.scatter(x[indices], cpu_bandwidths[indices], color="red", label="CPU")
plt.title(f"MLX FFT Benchmark -- {name}")
plt.xlabel("N")
plt.ylabel("Bandwidth (GB/s)")
plt.legend()
plt.savefig(f"{name}.png")
plt.clf()
av_gpu_bandwidth = np.mean(gpu_bandwidths)
av_mps_bandwidth = np.mean(mps_bandwidths)
av_cpu_bandwidth = np.mean(cpu_bandwidths)
print("Average bandwidths:")
print("GPU:", av_gpu_bandwidth)
print("MPS:", av_mps_bandwidth)
print("CPU:", av_cpu_bandwidth)
portion_faster = len(np.where(gpu_bandwidths > mps_bandwidths)[0]) / len(x)
print("Percent MLX faster than MPS: ", portion_faster * 100)
if __name__ == "__main__":
time_fft()
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