### Description
Since the major changes for `_TypedStorage` and `_UntypedStorage` are now complete, they can be renamed to be public.
`TypedStorage._untyped()` is renamed to `TypedStorage.untyped()`.
Documentation for storages is improved as well.
### Issue
Fixes#82436
### Testing
N/A
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82438
Approved by: https://github.com/ezyang
unflatten now has a free function version in torch.flatten in addition to
the method in torch.Tensor.flatten.
Updated docs to reflect this and polished them a little.
For consistency, changed the signature of the int version of unflatten in
native_functions.yaml.
Some override tests were failing because unflatten has unusual
characteristics in terms of the .int and .Dimname versions having
different number of arguments so this required some changes
to test/test_override.py
Removed support for using mix of integer and string arguments
when specifying dimensions in unflatten.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81399
Approved by: https://github.com/Lezcano, https://github.com/ngimel
### The problem
This original regex abuses .* in combination with `re.DOTALL` and leads to a catastrophic backtracking perf issue when there is no match. When it happens, test_doc_template will run "forever" and timeout. Here is an example timeout test https://github.com/pytorch/pytorch/runs/7413337595
Another minor issue with this regex is that it won't matches concatenated doc string like `"""FOO""" + """BAR"""`, which is used for some API `_torch_docs.py`
### The fix
* Remove most of the match all .* usage. I have tested to make sure that the test finishes even when there is no match, i.e. it fails successfully
* Update the regex to match all the following cases before and after linting (You can also try it out on https://pythex.org):
BEFORE
```
add_docstr(torch.abs, r"""
abs(input, *, out=None) -> Tensor
Computes the absolute value of each element in :attr:`input`.
.. math::
\text{out}_{i} = |\text{input}_{i}|
""" + r"""
Args:
{input}
Keyword args:
{out}
Example::
>>> torch.abs(torch.tensor([-1, -2, 3]))
tensor([ 1, 2, 3])
""".format(**common_args))
add_docstr(torch.absolute,
r"""
absolute(input, *, out=None) -> Tensor
Alias for :func:`torch.abs`
""")
```
AFTER
```
add_docstr(
torch.abs,
r"""
abs(input, *, out=None) -> Tensor
Computes the absolute value of each element in :attr:`input`.
.. math::
\text{out}_{i} = |\text{input}_{i}|
"""
+ r"""
Args:
{input}
Keyword args:
{out}
Example::
>>> torch.abs(torch.tensor([-1, -2, 3]))
tensor([ 1, 2, 3])
""".format(
**common_args
),
)
add_docstr(
torch.absolute,
r"""
absolute(input, *, out=None) -> Tensor
Alias for :func:`torch.abs`
""",
)
```
This will unblock https://github.com/pytorch/pytorch/pull/81643
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81755
Approved by: https://github.com/atalman
This PR:
- Corrects the forward AD formula of `torch.sgn`.
- The reason why we can't use `auto_element_wise` for this operations is rather subtle. I left a comment.
- This, in turn, fixes a problem we had in forward-over-backward for `linalg.svd` and other spectral decompositions (and `norm`, `linalg.norm`, `linalg.matrix_norm`) that were using `torch.abs` (whose derivative is given by `torch.sgn`.
- Implement the formula for a number of missing operations `nansum`, `amax`, `amin`...
- Simplified a few formulas, most notably the forward AD for `div` and the derivative of `norm`, `linalg.norm` and `vector_norm` for `ord=+-inf`.
- Correct the formula for `mean`, `std_mean`, `var_mean` when `dim` is provided and equal to `()` (or `None`)
- A few minor improvements to `sum_backward`, `unsqueeze_multiple` and formulas depending on them
- Fix the derivatives of `std_mean` and `std_var` (complex support,
ASAN, forward AD...)
Fixes: https://github.com/pytorch/pytorch/issues/67539
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80082
Approved by: https://github.com/zou3519
`chalf` is experimental as the op coverage is low.
Following script raises 6 warnings if `set_warn_always(True)` else raises only 1 warning.
```python
import torch
torch.set_warn_always(True)
device='cpu'
t = torch.randn(3, dtype=torch.chalf, device=device)
y = torch.rand(3, dtype=torch.chalf, device=device)
# Allocates new tensor for result
t + y
device='cuda'
t = torch.randn(3, dtype=torch.chalf, device=device)
y = torch.rand(3, dtype=torch.chalf, device=device)
# Allocates new tensor for result
t + y
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78245
Approved by: https://github.com/anjali411
Summary:
While running those tests on
- my local windows GPU machine
- a dev server
- an on-demand GPU
I noticed a few test failures and here's some tentative fixes
Test Plan:
Ran tests on:
- my local windows GPU machine
- a linux dev server w/o GPU
- an linux on-demand GPU server
Note that when using CUDA11, the tests crashes (segfaults) on calls to torch.nn.ConvTranspose3d. Fails on master, but works with CUDA10.
Differential Revision: D36377288
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77531
Approved by: https://github.com/ezyang
Fixes#73298
I don't know whether `where` kernel actually supports type promotion, nor am I in the mood to find out, so it's manual type promotion.
Edit: nah, i can't tell TI to "promote to common dtype" because of bool condition, so manual type promotion is our only option.
I'll see what tests start failing and fix.
Uses some parts from #62084
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76691
Approved by: https://github.com/mruberry
crossref is a new strategy for performing tests when you want
to run a normal PyTorch API call, separately run some variation of
the API call (e.g., same thing but all the arguments are meta tensors)
and then cross-reference the results to see that they are consistent.
Any logic you add to CrossRefMode will get run on *every* PyTorch API
call that is called in the course of PyTorch's test suite. This can
be a good choice for correctness testing if OpInfo testing is not
exhaustive enough.
For now, the crossref test doesn't do anything except verify that
we can validly push a mode onto the torch function mode stack for all
functions.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75988
Approved by: https://github.com/seemethere
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74226
Update signature of `scatter_reduce_` to match `scatter_/scatter_add_`
`Tensor.scatter_reduce_(int64 dim, Tensor index, Tensor src, str reduce)`
- Add new reduction options in ScatterGatherKernel.cpp and update `scatter_reduce` to call into the cpu kernel for `scatter.reduce`
- `scatter_reduce` now has the same shape constraints as `scatter_` and `scatter_add_`
- Migrate `test/test_torch.py:test_scatter_reduce` to `test/test_scatter_gather_ops.py`
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D35222842
Pulled By: mikaylagawarecki
fbshipit-source-id: 84930add2ad30baf872c495251373313cb7428bd
(cherry picked from commit 1b45139482e22eb0dc8b6aec2a7b25a4b58e31df)
