huajsj commented on pull request #6049:
URL: https://github.com/apache/incubator-tvm/pull/6049#issuecomment-658449313
Hi @t-vi @masahi , @liangfu
Thanks for the review, following are my comments
Regards
Hua
#1 about use case and why copy_ , from my understanding copy_ is not only a
shortcut of .to, the biggest difference between copy_ and .clone , .to is
that the in place variable would keep it's stride&storage and size, for example
for following use case, .clone and .to would cause b lost it's stride/storage
information, running environment is pytorch 1.5.
a = torch.from_numpy(np.array((11.0,12.0, 13.0)).astype('float32'))
a = a.expand(2)
b = torch.from_numpy(np.array((1.0, 2.0,3.0)).astype('float32'))
b = b.repeat(2)
b.copy_(a)
print(b.stride())
b = torch.clone(a)
print(b.stride())
b = a.to("cpu")
print(b.stride())
#2. about use case t.diag().copy_(a) , seems like currently pytorch front
end not support diag operator, we may can not
test it with copy_
#3 about the copy operator, just like @t-vi mentioned, clone is the non in
place one.
#4. i agree with that clone is not capture the semantics of copy_ and find
some problem during testing, would set this
PR into WIP and push the fix and test case later.
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