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import argparse |
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import math |
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import time |
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import mlx.core as mx |
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import numpy as np |
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import torch |
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N_warmup = 1 |
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N_iter_bench = 10 |
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N_iter_func = 5 |
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mx.set_default_device(mx.cpu) |
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def bench(f, a, b): |
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for i in range(N_warmup): |
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f(a, b) |
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s = time.perf_counter_ns() |
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for i in range(N_iter_bench): |
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f(a, b) |
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e = time.perf_counter_ns() |
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return (e - s) * 1e-9 |
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def make_mx_conv_3D(strides=(1, 1), padding=(0, 0), groups=1): |
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def mx_conv_3D(a, b): |
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ys = [] |
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for i in range(N_iter_func): |
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y = mx.conv3d(a, b, stride=strides, padding=padding, groups=groups) |
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ys.append(y) |
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mx.eval(ys) |
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return ys |
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return mx_conv_3D |
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def make_pt_conv_3D(strides=(1, 1), padding=(0, 0), groups=1): |
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@torch.no_grad() |
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def pt_conv_3D(a, b): |
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ys = [] |
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for i in range(N_iter_func): |
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y = torch.conv3d(a, b, stride=strides, padding=padding, groups=groups) |
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ys.append(y) |
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return ys |
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return pt_conv_3D |
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def bench_shape(N, D, H, W, C, kD, kH, kW, O, strides, padding, groups, np_dtype): |
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scale = 1.0 / math.sqrt(kD * kH * kW * C) |
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a_np = np.random.uniform(0, 0.5, (N, D, H, W, C)).astype(np_dtype) |
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b_np = np.random.uniform(-scale, scale, (O, kD, kH, kW, int(C / groups))).astype( |
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np_dtype |
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) |
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a_mx = mx.array(a_np) |
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b_mx = mx.array(b_np) |
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a_pt = torch.from_numpy(a_np.transpose((0, 4, 1, 2, 3))).to("cpu") |
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b_pt = torch.from_numpy(b_np.transpose((0, 4, 1, 2, 3))).to("cpu") |
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f_mx = make_mx_conv_3D(strides, padding, groups) |
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f_pt = make_pt_conv_3D(strides, padding, groups) |
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time_torch = bench(f_pt, a_pt, b_pt) |
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time_mlx = bench(f_mx, a_mx, b_mx) |
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out_mx = mx.conv3d(a_mx, b_mx, stride=strides, padding=padding, groups=groups) |
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out_pt = torch.conv3d( |
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a_pt.to("cpu"), b_pt.to("cpu"), stride=strides, padding=padding, groups=groups |
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) |
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out_pt = torch.permute(out_pt, (0, 2, 3, 4, 1)) |
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out_pt = out_pt.numpy(force=True) |
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atol = 2e-5 if np_dtype == np.float32 else 1e-4 |
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if not np.allclose(out_pt, out_mx, atol=atol): |
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print( |
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f"Failed at {(N, D, H, W, C)}, {(O, kD, kH, kW, C)} [strides = {strides}, padding = {padding}, groups = {groups}] with max(|a - b|) = {np.max(np.abs(out_pt - out_mx))}" |
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) |
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return time_mlx, time_torch |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="Run conv benchmarks") |
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dtypes = ("float32",) |
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shapes = ( |
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(4, 16, 16, 16, 16, 5, 5, 5, 16, (1, 1, 1), (2, 2, 2), 1), |
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(4, 16, 16, 16, 32, 5, 5, 5, 32, (1, 1, 1), (2, 2, 2), 1), |
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) |
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for dtype in dtypes: |
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print( |
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"(N, D, H, W, C), ( O, kD, kH, kW, C), dtype, stride, pads, groups, diff%" |
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) |
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for N, D, H, W, C, kD, kH, kW, O, strides, padding, groups in shapes: |
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np_dtype = getattr(np, dtype) |
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time_mlx, time_torch = bench_shape( |
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N, D, H, W, C, kD, kH, kW, O, strides, padding, groups, np_dtype |
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) |
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diff = time_torch / time_mlx - 1.0 |
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print( |
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f"({N}, {D:3d}, {H:3d}, {W:3d}, {C:3d}), ({O:3d}, {kD:2d}, {kH:2d}, {kW:2d}, {C:3d}), {dtype}, {strides}, {padding}, {groups:7d}, {100. * diff:+5.2f}%" |
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) |
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if time_mlx >= 2.0 * time_torch: |
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print("ATTENTION ^^^^^^^") |
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