Adds:
```Python
bessel_j0(input, *, out=None) -> Tensor
```
Bessel function of the first kind of order $0$, $J_{0}(\text{input})$.
```Python
bessel_j1(input, *, out=None) -> Tensor
```
Bessel function of the first kind of order $1$, $J_{1}(\text{input})$.
```Python
bessel_j0(input, *, out=None) -> Tensor
```
Bessel function of the second kind of order $0$, $Y_{0}(\text{input})$.
```Python
bessel_j1(input, *, out=None) -> Tensor
```
Bessel function of the second kind of order $1$, $Y_{1}(\text{input})$.
```Python
modified_bessel_i0(input, *, out=None) -> Tensor
```
Modified Bessel function of the first kind of order $0$, $I_{0}(\text{input})$.
```Python
modified_bessel_i1(input, *, out=None) -> Tensor
```
Modified Bessel function of the first kind of order $1$, $I_{1}(\text{input})$.
```Python
modified_bessel_k0(input, *, out=None) -> Tensor
```
Modified Bessel function of the second kind of order $0$, $K_{0}(\text{input})$.
```Python
modified_bessel_k1(input, *, out=None) -> Tensor
```
Modified Bessel function of the second kind of order $1$, $K_{1}(\text{input})$.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78451
Approved by: https://github.com/mruberry
1. Added references `_refs.broadcast_shapes`
2. Added OpInfo test for `torch.broadcast_shapes`
A few minor changes:
- `test_python_ref_meta` and `_ref_test_helper` update to avoid non-tensor outputs
- type annotation update for `_resize_meta`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78612
Approved by: https://github.com/mruberry
Ref #54789
A `bool` has only two valid values, 1 or 0. Any in-memory value
outside of those leads to undefined behavior. So, instead of
`reinterpret_cast`-ing to `bool*` I introduce `c10::load<scalar_t>`
which will read as `unsigned char` and convert to a valid `bool`.
This gets >90% of operators working, but the remaining operators where
skips and xfails have been added will require individual attention.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77122
Approved by: https://github.com/mruberry
Summary:
Some helper functions that generate operator configs based on dtype_configs are reused in native backend and tensorrt, so we
factor out this part to a util file: common_operator_configs.py
Test Plan: buck test mode/opt deeplearning/trt/fx2trt_oss/test/quant:test_quant_trt
Differential Revision: D36728359
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78407
Approved by: https://github.com/vkuzo, https://github.com/andrewor14
Fixes#78490
Following command:
```
conda install pytorch torchvision torchaudio -c pytorch-nightly
```
Installs libiomp . Hence we don't want to package libiomp with conda installs. However, we still keep it for libtorch and wheels.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78632
Approved by: https://github.com/malfet
Summary:
Names of analogous files in quantized directory (previously snake case) were inconsistent with
their non-quantized filename counterparts (pascal case). This is the second of a series of PRs that changes
all files in quantized (and sub-directories) dir to have pascal case.
Some files have not been renamed as it is causing issues related to
custom class with `import torch` at runtime. See
https://github.com/pytorch/pytorch/pull/77037 for additional details
Test Plan:
```
python test/test_quantization.py
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77422
Approved by: https://github.com/jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78563
The profiler assembles a call hierarchy by replaying recorded events. There is an assert to ensure that the events form a well structured tree; however many of the inputs are from external sources and small differences (e.g. recording time in a lower precision) leads to traces which violate that assumption. For now this is acceptable; the post processing can handle resolving these descrepencies. As a result, I am relaxing the assert to only test event types where we expect the framework to be able to enforce these strong structural requirements.
Differential Revision: [D36787787](https://our.internmc.facebook.com/intern/diff/D36787787/)
Approved by: https://github.com/suo
`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
Added operator-, DispatchKeySet::add, and DispatchKeySet::remove.
I wanted to use these in functorch to make a constexpr DispatchKeySet.
Also adds C10_NODISCARD to DispatchKeySet::remove to make it
consistent with DispatchKeySet::add (this will raise a
warning if someone calls remove without assigning the result to a
variable; remove is NOT mutable and this is a pitfall that I run into a
lot)
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78558
Approved by: https://github.com/bdhirsh
Adds the ability for generate_torch_version to grab release versions
based on the current tag. Also includes a regex to check if the tagged
version matches our release pattern (vX.Y.Z) so we don't collide with
ciflow tags
Signed-off-by: Eli Uriegas <eliuriegasfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78584
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Approved by: https://github.com/janeyx99
