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c4a471c99d
* Add conv1d grouped convs on CPU * Add GPU support * Parallelize inside metal kernel * clenaup * Update mlx/ops.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * New unfold kernel + remove unused code * Remove copy and refactor * Update vjp and reuse steel gemm * Fixed groups on cpu * Fix metal validation --------- Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
124 lines
3.6 KiB
Python
124 lines
3.6 KiB
Python
import argparse
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import math
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import os
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import subprocess
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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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device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
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device_name = device_name.decode("utf-8").strip("\n")
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N_warmup = 10
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N_iter_bench = 100
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N_iter_func = 5
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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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torch.mps.synchronize()
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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_1D(strides=1, padding=0, groups=1):
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def mx_conv_1D(a, b):
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ys = []
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for _ in range(N_iter_func):
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y = mx.conv1d(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_1D
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def make_pt_conv_1D(strides=1, padding=0, groups=1):
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@torch.no_grad()
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def pt_conv_1D(a, b):
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ys = []
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for _ in range(N_iter_func):
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y = torch.conv1d(a, b, stride=strides, padding=padding, groups=groups)
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ys.append(y)
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torch.mps.synchronize()
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return ys
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return pt_conv_1D
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def bench_shape(N, iH, C, wH, O, strides, padding, np_dtype, groups):
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scale = 1.0 / math.sqrt(wH * C)
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a_np = np.random.uniform(0, 0.5, (N, iH, C)).astype(np_dtype)
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b_np = np.random.uniform(-scale, scale, (O, wH, int(C / groups))).astype(np_dtype)
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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, 2, 1))).to("mps")
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b_pt = torch.from_numpy(b_np.transpose((0, 2, 1))).to("mps")
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torch.mps.synchronize()
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f_mx = make_mx_conv_1D(strides, padding, groups)
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f_pt = make_pt_conv_1D(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.conv1d(a_mx, b_mx, stride=strides, padding=padding, groups=groups)
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out_pt = torch.conv1d(
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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, 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, iH, C)}, {(O, wH, 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, 32, 32, 5, 32, 1, 2, 1),
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(4, 32, 32, 5, 32, 1, 2, 2),
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(4, 32, 32, 5, 32, 1, 2, 4),
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(4, 32, 32, 5, 32, 1, 2, 8),
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(4, 32, 32, 5, 32, 1, 2, 8),
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(4, 32, 32, 5, 32, 1, 2, 16),
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(4, 32, 32, 5, 32, 1, 2, 32),
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(4, 32, 256, 5, 512, 1, 2, 2),
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(4, 32, 256, 5, 512, 1, 2, 128),
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(4, 32, 256, 5, 512, 1, 2, 256),
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)
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for dtype in dtypes:
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print("(N, iH, C), (O, wH, C), dtype, stride, pads, groups, diff%")
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for N, iH, C, wH, 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, iH, C, wH, O, strides, padding, np_dtype, groups
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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}, {iH:3d}, {C:3d}), ({O:3d}, {wH:2d}, {C:3d}), {dtype}, {strides:5d}, {padding:4d}, {groups:6d}, {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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