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[examples/pytorch] Fixed issues in backward pass of conv

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Philippe Tillet
2019-05-19 01:31:08 -04:00
parent b2b55c52c9
commit f33a1f3fe3
9 changed files with 541 additions and 71 deletions
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examples/python/pytorch/test.py Normal file
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import torch
import triton
x = torch.autograd.Variable(torch.randn(16, 64, 8, 8).cuda(), requires_grad=True)
w = torch.autograd.Variable(torch.randn(64, 3, 3, 64).cuda(), requires_grad=True)
cuw = torch.autograd.Variable(w.permute(3,0,1,2).cuda(), requires_grad=True)
y_target = torch.autograd.Variable(torch.randn(16, 64, 6, 6).cuda(), requires_grad=True)
def run(x, w, conv):
y = conv(x, w)
loss = (y - y_target).norm(2)
loss.backward()
return loss, y.clone(), x.grad.clone(), w.grad.clone()
ttyloss, tty, ttdx, ttdw = run(x, w, lambda x, w: triton.ConvFunction.apply(x, w, 0))
x.grad.zero_()
w.grad.zero_()
culoss, cuy, cudx, cudw = run(x, cuw, lambda x, w: torch.nn.functional.conv2d(x, w, padding=0))
print((tty - cuy).norm(2))
print((ttdx - cudx).norm(2))
print((ttdw.permute(3,0,1,2) - cudw).norm(2))