This PR updates the documentation for `torch.mean()` to explicitly mention that computing the mean over an empty tensor returns `nan`. This clarification helps users understand the behavior and handle it appropriately in their code.
Fixes#141057
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142039
Approved by: https://github.com/albanD
Added documentation note clarifying the rounding behavior of `torch.arange` when using floating-point dtypes, particularly for reduced precision types like `bfloat16`. This helps users understand potential issues like repeated values and provides guidance on using integer dtypes for precise sequences.
## Changes
- Added explanatory note about floating-point rounding behavior and its effects
- Included specific mention of `bfloat16` dtype issues
- Added recommendation to use integer dtypes for precise sequences
Fixes [#137774](https://github.com/pytorch/pytorch/issues/137774)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141655
Approved by: https://github.com/cpuhrsch
This is super duper minor, but I believe this corrects a typo in the documentation of `torch.trapezoid`.
The documentation says the input is a 1-dimensional tensor $y_0, \dots, y_n$, but it uses summations going from 1 to n-1. Since it's summing over terms $y_i - y_{i-1}$, stopping at n-1 excludes the last partition $y_n - y_{n-1}$, which doesn't match the implementation...
```python
# (just showing it does include $y_n - y_{n-1}$)
torch.trapezoid([0, 0, 9999]) == 9999 / 2
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141459
Approved by: https://github.com/colesbury
Fixes#137422
Add parameter type definition in API docs to clarify allowed value type, eliminate users pass `None` as `dim` value directly.
```python
>>> import torch
>>> x = torch.randn(3,1,2)
>>> x.squeeze(dim=None)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: Please look up dimensions by name, got: name = None.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137485
Approved by: https://github.com/albanD
See title. Until now, calling `torch.as_tensor` on a CuPy array would return a CPU tensor, when not providing a device. This is most likely not desired.
Fixes#132553
```python3
import torch
import cupy as cp
cupy_arr = cp.asarray([1, 2, 3])
# Default case
t = torch.as_tensor(cupy_arr)
# New behavior, same device as cupy_arr now, was cpu before
print(t.device) # cuda:0
# Explicitly set device
t = torch.as_tensor(cupy_arr, device='cpu')
print(t.device) # cpu
# Implicit default device
torch.set_default_device('cpu')
t = torch.as_tensor(cupy_arr)
print(t.device) # cpu
# Default device via context manager
torch.set_default_device('cuda')
with torch.device('cpu'):
t = torch.as_tensor(cupy_arr)
print(t.device) # cpu
# Unset default device
torch.set_default_device(None)
t = torch.as_tensor(cupy_arr)
# New behavior, same device as cupy_arr now, was cpu before
print(t.device) # cuda:0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132595
Approved by: https://github.com/ezyang
The default value of `rot90()` in the schema registry is `[0,1]` because we split the function schema by `", "`. There should be no space after `,` in `[0,1]`.
5c9d5272e4/aten/src/ATen/native/native_functions.yaml (L6120-L6126)
Then the the default value is formatted to `(0,1)` in `pyi` files. This PR manually adds an extra whitespace when rerendering the default value to a string.
```python
", ".join(string.split(","))
```
```python
# before
def rot90(input: Tensor, k: _int = 1, dims: _size = (0,1)) -> Tensor: ...
# after
def rot90(input: Tensor, k: _int = 1, dims: _size = (0, 1)) -> Tensor: ...
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129884
Approved by: https://github.com/ezyang
Fixes#120570
## Description
Update torch.nanmean() docstring to mention input dtype requirement as either floating point type or complex.
Previously, the torch.mean() docstring had been updated in #120208 in a similar manner, but the torch.nanmean() docstring was not updated.
## Checklist
- [X] The issue that is being fixed is referred in the description.
- [X] Only one issue is addressed in this pull request.
- [x] Labels from the issue that this PR is fixing are added to this pull request.
- [X] No unnecessary issues are included into this pull request.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128155
Approved by: https://github.com/malfet
Fixes#126012.
`from` is a reserved keyword in Python, thus we can't make the C++ impl available with `from` as function parameter. This PR changes the name to `from_` and also adjusts the docs.
If we want to preserve backwards compatibility, we can leave the C++ name as-is and only fix the docs. However, `torch.can_cast(from_=torch.int, to=torch.int)` won't work then.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126030
Approved by: https://github.com/albanD
Fixes#126012.
`from` is a reserved keyword in Python, thus we can't make the C++ impl available with `from` as function parameter. This PR changes the name to `from_` and also adjusts the docs.
If we want to preserve backwards compatibility, we can leave the C++ name as-is and only fix the docs. However, `torch.can_cast(from_=torch.int, to=torch.int)` won't work then.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126030
Approved by: https://github.com/albanD
See #113541
The PR allows for registering and controlling multiple RNG states using indices, ensuring cudagraph-safe operations, and includes both C++ and Python API changes to support this functionality.
cc @eellison @anijain2305 @jansel @ezyang @ptrblck @csarofeen @mcarilli
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114068
Approved by: https://github.com/ezyang
