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import argparse | ||
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import torch | ||
import pyg_lib | ||
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from time import perf_counter as timestamp | ||
from torch_geometric.utils import scatter, segment | ||
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def softmax_reference(src, ptr, dim=0): | ||
dim = dim + src.dim() if dim < 0 else dim | ||
size = ([1] * dim) + [-1] | ||
count = ptr[1:] - ptr[:-1] | ||
ptr = ptr.view(size) | ||
src_max = segment(src.detach(), ptr, reduce='max') | ||
src_max = src_max.repeat_interleave(count, dim=dim) | ||
out = (src - src_max).exp() | ||
out_sum = segment(out, ptr, reduce='sum') + 1e-16 | ||
out_sum = out_sum.repeat_interleave(count, dim=dim) | ||
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return out / out_sum | ||
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def measure_perf(impl_func, ptr, out_grad, num_warmups, num_steps, backward): | ||
t_fwd = t_bwd = 0 | ||
for i in range(num_warmups + num_steps): | ||
src = torch.randn(num_rows, num_heads) | ||
src.requires_grad = backward | ||
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t_start = timestamp() | ||
out = impl_func(src=src, ptr=ptr) | ||
if i >= num_warmups: | ||
t_fwd += timestamp() - t_start | ||
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if backward: | ||
t_start = timestamp() | ||
out.backward(out_grad) | ||
if i >= num_warmups: | ||
t_bwd += timestamp() - t_start | ||
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return t_fwd, t_bwd | ||
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if __name__ == '__main__': | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument('--backward', action='store_true') | ||
parser.add_argument('--num-heads', type=int, default=4) | ||
args = parser.parse_args() | ||
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num_rows, num_heads = 50000, args.num_heads | ||
num_warmups, num_steps = 100, 500 | ||
group_size = 100 | ||
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ptr = torch.arange(0, num_rows + 1, group_size) | ||
out_grad = torch.randn(num_rows, num_heads) | ||
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func_args = [ptr, out_grad, num_warmups, num_steps, args.backward] | ||
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t_fwd, t_bwd = measure_perf(softmax_reference, *func_args) | ||
print(f'Vanilla forward: {t_fwd:.4f}s') | ||
if args.backward: | ||
print(f'Vanilla backward: {t_bwd:.4f}s') | ||
print('=========================') | ||
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t_fwd, t_bwd = measure_perf(pyg_lib.ops.softmax, *func_args) | ||
print(f'pyg_lib forward: {t_fwd:.4f}s') | ||
if args.backward: | ||
print(f'pyg_lib backward: {t_bwd:.4f}s') | ||
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