[1/2] Intel GPU Runtime Upstreaming for Stream (#117611)
# Motivation
As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), the second runtime component we would like to upstream is `Stream` which contains the device management functions of Intel GPU's runtime. To facilitate the code review, we split the code changes into 2 PRs. This is one of the 2 PRs and covers the changes under `c10`.
# Design
Intel GPU stream is a wrapper of sycl queue which schedules kernels on a sycl device. In our design, we will maintain a sycl queue pool containing 32 queues per priority per device. And when a queue is requested one of these queues is returned round-robin. The corresponding C++ files related to `Device` will be placed in `c10/xpu` folder. We provide the `c10::xpu::XPUStream` APIs, like
- `XPUStream getStreamFromPool`
- `XPUStream getCurrentXPUStream`
- `void setCurrentXPUStream`
- `void device_synchronize`
# Additional Context
In our plan, 2 PRs should be submitted to PyTorch for `Stream`:
1. for c10
2. for python frontend.
The differences with CUDA:
no default and external stream in XPU and lack of the below API:
- `getDefaultCUDAStream`
- `getStreamFromExternal`
for cuda, `cuda::device_synchronize` can sync all streams on the device, but for xpu, `xpu::sync_streams_on_device` only sync all reserved streams on the device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117611
Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/gujinghui, https://github.com/malfet
