Introduce handle cache for AMD platform.
Avoid reaching handle limitation if we open too much IPC handles
For nvidia, we don't need this feature since nvidia will count the
handle reference internally and reuse the same handle if already be
opened
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Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Binyang2014 <9415966+Binyang2014@users.noreply.github.com>
Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
The PR contains following changes:
Python side:
- Channel based DSL implementation: decouple channel with chunk.
- Users create channel explicitly, only need local_rank, remote_rank and
channel_type
- Adjust executor json file, add remote_buffer fields, different op can
use different channel and remote buffers combination.
- Reimplement operation fusion, data dependency check mechanism
- Add new op such as semaphore, pipeline
- Clean code and enhance document
C++ side:
- Support new execution file json format
- Support semaphore and pipeline operation
- code clean, support non-zero copy scenario
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Co-authored-by: Caio Rocha <caiorocha@microsoft.com>
Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
* Renamed and moved mem alloc functions into the `mscclpp::detail::`
namespace (now `mscclpp::detail::gpuCalloc*<T>()`)
* Deprecated constructor-calling mem alloc functions
(`mscclpp::makeShared*<T>()` and `mscclpp::makeUnique*<T>()`)
* Added a new `mscclpp::GpuBuffer<T>()` class that should be used in
general for allocating communication buffers
* Added a new `mscclpp.utils.GpuBuffer` Python class that inherits
`cupy.ndarray` and allocates using `mscclpp::gpuMemAlloc`
* Renamed `mscclpp::memcpyCuda*<T>()` functions into
`mscclpp::gpuMemcpy*<T>()` for name consistency
* A few fixes in NVLS memory allocation
* Tackled minor compiler warnings
