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torch.prod backward for complex types. (#48125)

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Summary:
Fixes https://github.com/pytorch/pytorch/issues/53511
torch.det does depend on torch.prod, which in turn depends on several other functions, and they also depend on torch.prod, so there is a circular relationship, hence this PR will enable complex backward support for several functions at once.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48125

Reviewed By: pbelevich

Differential Revision: D27188589

Pulled By: anjali411

fbshipit-source-id: bbb80f8ecb83a0c3bea2b917627d3cd3b84eb09a
This commit is contained in:
Nikita Vedeneev
2021-03-19 09:42:53 -07:00
committed by Facebook GitHub Bot
parent cc7a28d727
commit 61b074581c
7 changed files with 124 additions and 48 deletions
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6
torch/testing/_internal/common_utils.py
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@ -1644,12 +1644,6 @@ def make_tensor(size, device: torch.device, dtype: torch.dtype, *, low=None, hig
return result
def prod_single_zero(dim_size):
result = torch.randn(dim_size, dim_size)
result[0, 1] = 0
return result
def random_square_matrix_of_rank(l, rank, dtype=torch.double, device='cpu'):
assert rank <= l
A = torch.randn(l, l, dtype=dtype, device=device)