)

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch#95381
Approved by: https://github.com/razarmehr

)

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch#95381
Approved by: https://github.com/razarmehr

* [MPS] Fix the uint8 type issue with View ops kernels (#95145)

This should fix the problem in Resnet model with image artifacts due to saturation on int8 type and also the incorrect class recognition reported in #86954.

Fixes #86954

Pull Request resolved: #95145
Approved by: https://github.com/kulinseth, https://github.com/DenisVieriu97

* [MPS] Fix tensor with non-zero storage offset graph gathering (#91071)

Previously, the "can slice" flag in Placeholder constructor in `OperationUtils.mm` is conditioned on whether the numbers of dimensions of base shape and view shape are the same. This doesn't consider the situation that a view tensor could be the base tensor's sliced and then unsqueezed version, resulting in different num of dims.

For example, if we want to stack `y_mps` and `x_mps` on the last dim:
```
t_mps = torch.tensor([1, 2, 3, 4], device="mps")
x_mps = t_mps[2:]  # [3, 4]
y_mps = t_mps[:2]  # [1, 2]

res_mps = torch.stack((y_mps, x_mps), dim=-1)
```

the kernel will unsqueeze both of them on the last dim and then concatenate them, which is equivalent to:

```
res_mps = torch.cat((y_mps.unsqueeze(-1), x_mps.unsqueeze(-1)), dim=-1)
```

`x_mps.unsqueeze(-1)` is an unsqueezed and contiguous tensor with a storage offset, this kind of tensors should be sliceable without cloning its storage.

Fixes #87856
Fixes #91065

Pull Request resolved: #91071
Approved by: https://github.com/kulinseth

* [MPS] Fix fill_ where input tensor has a storage offset (#95113)

Fixes #94390

Apart from fixing the issue above, this PR also fixes a bug that when an input tensor can be sliced, a sliced array view is created. This array view seems to be not writable or have a different storage from the original tensor, causing incorrect results with the in-place `fill`.
Pull Request resolved: #95113
Approved by: https://github.com/kulinseth

* [MPS] Fix view op slicing for 2nd dim in case of 0 offset (#95381)

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: #95381
Approved by: https://github.com/razarmehr

---------

Co-authored-by: Ramin Azarmehr <razarmehr@apple.com>
Co-authored-by: Li-Huai (Allan) Lin <qqaatw@gmail.com>
Co-authored-by: Denis Vieriu <104024078+DenisVieriu97@users.noreply.github.com>

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch/pytorch#95381
Approved by: https://github.com/razarmehr

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch/pytorch#95381
Approved by: https://github.com/razarmehr

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch/pytorch#95381
Approved by: https://github.com/razarmehr

…torch#95381)"

This reverts commit 86efa10.

* [MPS] Fix the uint8 type issue with View ops kernels (pytorch#95145)

This should fix the problem in Resnet model with image artifacts due to saturation on int8 type and also the incorrect class recognition reported in pytorch#86954.

Fixes pytorch#86954

Pull Request resolved: pytorch#95145
Approved by: https://github.com/kulinseth, https://github.com/DenisVieriu97

* [MPS] Fix tensor with non-zero storage offset graph gathering (pytorch#91071)

Previously, the "can slice" flag in Placeholder constructor in `OperationUtils.mm` is conditioned on whether the numbers of dimensions of base shape and view shape are the same. This doesn't consider the situation that a view tensor could be the base tensor's sliced and then unsqueezed version, resulting in different num of dims.

For example, if we want to stack `y_mps` and `x_mps` on the last dim:
```
t_mps = torch.tensor([1, 2, 3, 4], device="mps")
x_mps = t_mps[2:]  # [3, 4]
y_mps = t_mps[:2]  # [1, 2]

res_mps = torch.stack((y_mps, x_mps), dim=-1)
```

the kernel will unsqueeze both of them on the last dim and then concatenate them, which is equivalent to:

```
res_mps = torch.cat((y_mps.unsqueeze(-1), x_mps.unsqueeze(-1)), dim=-1)
```

`x_mps.unsqueeze(-1)` is an unsqueezed and contiguous tensor with a storage offset, this kind of tensors should be sliceable without cloning its storage.

Fixes pytorch#87856
Fixes pytorch#91065

Pull Request resolved: pytorch#91071
Approved by: https://github.com/kulinseth

* [MPS] Fix fill_ where input tensor has a storage offset (pytorch#95113)

Fixes pytorch#94390

Apart from fixing the issue above, this PR also fixes a bug that when an input tensor can be sliced, a sliced array view is created. This array view seems to be not writable or have a different storage from the original tensor, causing incorrect results with the in-place `fill`.
Pull Request resolved: pytorch#95113
Approved by: https://github.com/kulinseth

* [MPS] Fix view op slicing for 2nd dim in case of 0 offset (pytorch#95381)

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch#95381
Approved by: https://github.com/razarmehr

---------

Co-authored-by: Ramin Azarmehr <razarmehr@apple.com>
Co-authored-by: Li-Huai (Allan) Lin <qqaatw@gmail.com>
Co-authored-by: Denis Vieriu <104024078+DenisVieriu97@users.noreply.github.com>

)

* Fix view op slicing for 2nd dim in case of 0 offset

Pull Request resolved: pytorch#95381
Approved by: https://github.com/razarmehr