Fix advanced indexing with negative indices #7345
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Fixes pytorch#7156 Here is some behavior before this PR: ``` In[1]: x = torch.arange(9).view(3, 3).contiguous() x[[0], [-1]] # Should be equivalent to x[0, -1] Out[1]: tensor([ 8]) ``` The bug is that negative indices are added to the computed linear index directly. In the above example, the linear index computed is "-1", which wraps around to "8", giving the last element of a flattened view of `x`. Instead, we should wrap negative indices around before adding them to the linear index.
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@zou3519, can you fix the error messages on this PR? They're missing spaces around the |
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Also please remove the |
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@colesbury my bad, will fix them soon, thank you for the catch |
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Followup to pytorch#7345
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* Fix advanced indexing with negative indices Fixes pytorch#7156 Here is some behavior before this PR: ``` In[1]: x = torch.arange(9).view(3, 3).contiguous() x[[0], [-1]] # Should be equivalent to x[0, -1] Out[1]: tensor([ 8]) ``` The bug is that negative indices are added to the computed linear index directly. In the above example, the linear index computed is "-1", which wraps around to "8", giving the last element of a flattened view of `x`. Instead, we should wrap negative indices around before adding them to the linear index. * Use toCLong()
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Fixes #7156
Here is some behavior before this PR:
However, I would expect the output to be
tensor([2]).The bug is that negative indices are added to the computed linear index
directly. In the above example, the linear index computed is "-1", which
wraps around to "8", giving the last element of a flattened view of
x.Instead, we should wrap negative indices around before adding them to
the linear index.
cc @colesbury