| |
|
|
| 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"), |
| ]: |
| |
| 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() |
|
|
