import argparse
import math
import os
import subprocess
import time
import mlx.core as mx
import numpy as np
import torch
device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
device_name = device_name.decode("utf-8").strip("\n")
N_warmup = 10
N_iter_bench = 100
N_iter_func = 5
def bench(f, a, b):
for i in range(N_warmup):
f(a, b)
torch.mps.synchronize()
s = time.perf_counter_ns()
for i in range(N_iter_bench):
f(a, b)
e = time.perf_counter_ns()
return (e - s) * 1e-9
def make_mx_conv_2D(strides=(1, 1), padding=(0, 0), groups=1):
def mx_conv_2D(a, b):
ys = []
for i in range(N_iter_func):
y = mx.conv2d(a, b, stride=strides, padding=padding, groups=groups)
ys.append(y)
mx.eval(ys)
return ys
return mx_conv_2D
def make_pt_conv_2D(strides=(1, 1), padding=(0, 0), groups=1):
@torch.no_grad()
def pt_conv_2D(a, b):
ys = []
for i in range(N_iter_func):
y = torch.conv2d(a, b, stride=strides, padding=padding, groups=groups)
ys.append(y)
torch.mps.synchronize()
return ys
return pt_conv_2D
def bench_shape(N, H, W, C, kH, kW, O, strides, padding, groups, np_dtype):
scale = 1.0 / math.sqrt(kH * kH * C)
a_np = np.random.uniform(0, 0.5, (N, H, W, C)).astype(np_dtype)
b_np = np.random.uniform(-scale, scale, (O, kH, kW, int(C / groups))).astype(
np_dtype
)
a_mx = mx.array(a_np)
b_mx = mx.array(b_np)
a_pt = torch.from_numpy(a_np.transpose((0, 3, 1, 2))).to("mps")
b_pt = torch.from_numpy(b_np.transpose((0, 3, 1, 2))).to("mps")
torch.mps.synchronize()
f_mx = make_mx_conv_2D(strides, padding, groups)
f_pt = make_pt_conv_2D(strides, padding, groups)
time_torch = bench(f_pt, a_pt, b_pt)
time_mlx = bench(f_mx, a_mx, b_mx)
out_mx = mx.conv2d(a_mx, b_mx, stride=strides, padding=padding, groups=groups)
out_pt = torch.conv2d(
a_pt.to("cpu"), b_pt.to("cpu"), stride=strides, padding=padding, groups=groups
)
out_pt = torch.permute(out_pt, (0, 2, 3, 1))
out_pt = out_pt.numpy(force=True)
atol = 2e-5 if np_dtype == np.float32 else 1e-4
if not np.allclose(out_pt, out_mx, atol=atol):
print(
f"Failed at {(N, H, W, C)}, {(O, kH, kW, C)} [strides = {strides}, padding = {padding}, groups = {groups}] with max(|a - b|) = {np.max(np.abs(out_pt - out_mx))}"
)
return time_mlx, time_torch
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run conv benchmarks")
dtypes = ("float32",)
shapes = (
(4, 32, 32, 32, 5, 5, 32, (1, 1), (2, 2), 1),
(4, 32, 32, 64, 5, 5, 64, (1, 1), (2, 2), 1),
(4, 32, 32, 128, 5, 5, 128, (1, 1), (2, 2), 1),
(4, 32, 32, 256, 5, 5, 256, (1, 1), (2, 2), 1),
(4, 32, 32, 512, 5, 5, 512, (1, 1), (2, 2), 1),
(4, 64, 64, 32, 5, 5, 32, (1, 1), (2, 2), 1),
(4, 64, 64, 64, 5, 5, 64, (1, 1), (2, 2), 1),
(4, 64, 64, 128, 5, 5, 128, (1, 1), (2, 2), 1),
(4, 64, 64, 256, 5, 5, 256, (1, 1), (2, 2), 1),
(4, 64, 64, 256, 5, 5, 256, (1, 1), (2, 2), 2),
(4, 64, 64, 256, 5, 5, 256, (1, 1), (2, 2), 16),
(4, 64, 64, 256, 5, 5, 256, (1, 1), (2, 2), 64),
(4, 128, 128, 32, 5, 5, 32, (1, 1), (2, 2), 1),
(4, 128, 128, 64, 5, 5, 64, (1, 1), (2, 2), 1),
(4, 128, 128, 128, 5, 5, 128, (1, 1), (2, 2), 1),
(4, 256, 256, 32, 5, 5, 3, (1, 1), (2, 2), 1),
(4, 256, 256, 3, 5, 5, 32, (1, 1), (2, 2), 1),
(4, 128, 128, 64, 5, 5, 3, (1, 1), (2, 2), 1),
(4, 128, 128, 3, 5, 5, 64, (1, 1), (2, 2), 1),
)
for dtype in dtypes:
print(
"(N, H, W, C), ( O, kH, kW, C), dtype, stride, pads, groups, diff%"
)
for N, H, W, C, kH, kW, O, strides, padding, groups in shapes:
np_dtype = getattr(np, dtype)
time_mlx, time_torch = bench_shape(
N, H, W, C, kH, kW, O, strides, padding, groups, np_dtype
)
diff = time_torch / time_mlx - 1.0
print(
f"({N}, {H:3d}, {W:3d}, {C:3d}), ({O:3d}, {kH:2d}, {kW:2d}, {C:3d}), {dtype}, {strides}, {padding}, {groups:7d}, {100. * diff:+5.2f}%"
)
if time_mlx >= 2.0 * time_torch:
print("ATTENTION ^^^^^^^")