Implement Tensor.new_empty_strided(sizes, strides, *, dtype, device, requires_grad) (#47225)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47225
Summary
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This PR implements Tensor.new_empty_strided. Many of our torch.* factory
functions have a corresponding new_* method (e.g., torch.empty and
torch.new_empty), but there is no corresponding method to
torch.empty_strided. This PR adds one.
Motivation
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The real motivation behind this is for vmap to be able to work through
CopySlices. CopySlices shows up a lot in double backwards because a lot
of view functions have backward formulas that perform view+inplace.
https://github.com/pytorch/pytorch/blob/e0fd590ec950cb1e65ea0431c9e765f8cda27908/torch/csrc/autograd/functions/tensor.cpp#L78-L106
To support vmap through CopySlices, the approach in this stack is to:
- add `Tensor.new_empty_strided` and replace `empty_strided` in
CopySlices with that so that we can propagate batch information.
- Make some slight modifications to AsStridedBackward (and add
as_strided batching rule)
Please let me know if it would be better if I squashed everything related to
supporting vmap over CopySlices together into a single big PR.
Test Plan
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- New tests.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D24741688
Pulled By: zou3519
fbshipit-source-id: b688047d2eb3f92998896373b2e9d87caf2c4c39
