# Motivation
We propose to support Python with statement on `torch.Stream`. This is a benefit for all accelerators when writing device-agnostic code. The device-specific stream will also be supported because they are generally derived from `torch.Stream`.
With this PR, we can do like this
```python
s1= torch.Stream()
# Set s1 to the current stream
torch.accelerator.set_stream(s1)
with torch.Stream() as s2:
# Inside with statement, we set s2 to the current stream
assert torch.accelerator.current_stream() == s2
# Here the current stream should be s1
assert torch.accelerator.current_stream() == s1
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140138
Approved by: https://github.com/albanD
This PR is to export specific function symbols into .dll shared library on Windows platform to support Windows build for [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
TORCH_API/TORCH_PYTHON_API/PYBIND11_EXPORT are macros that decorate the function as dllexport while compilation, so that the function symbol will be exported into the .dll shared library file on Windows platform. It is necessary for other libraries (such as IPEX) to import and call these functions through dynamic linking of PyTorch on Windows platform.
The code changes of this PR adds decorators to export specific functions used by IPEX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98054
Approved by: https://github.com/ezyang
#75854
A naive attempt at working around the limitations of using a single 64-bit integer to pack `stream_id`, `device_index`, and `device_type`.
Stills needs sanity checks, testing, and minimization of BC-breaking changes.
Currently a Holder for the `StreamData3` struct is used for `IValue` compatibility. While doing this seems to work for `ivalue.h` and `ivalue_inl.h`, this doesn't seem to be naively working for the JIT CUDA stream wrapper? (Something about ambiguous calls if an `intrusive_ptr` to `c10::ivalue::StreamData3Holder` is used as the return type for `pack()`. It turns out that the methods required to access the fields for rematerializing a CUDA Stream are basically already present anyway, so `pack` is simply removed in the wrapper for now and the methods to access the required fields are called directly.
CC @ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81596
Approved by: https://github.com/ezyang
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
The record_stream method was hard coded for CUDA device. Define the record_stream in the native_functions.yaml to enable the dynamic dispatch to different end device.
Fixes https://github.com/pytorch/pytorch/issues/36556
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44301
Reviewed By: glaringlee
Differential Revision: D23763954
Pulled By: ezyang
fbshipit-source-id: e6d24f5e7892b56101fa858a6cad2abc5cdc4293
