…requires_grad)

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

[ghstack-poisoned]

…e, device, requires_grad)"

Summary
-------
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
----------
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
---------
- New tests.

Differential Revision: [D24741688](https://our.internmc.facebook.com/intern/diff/D24741688)

[ghstack-poisoned]