Use dlopen to load nccl/rccl Apis from shared library to
enable Allgather, Allreduce, Broadcast, ReduceScatter fallback to nccl/rccl operations.
Add three related environment variables
-x MSCCLPP_ENABLE_NCCL_FALLBACK=TRUE
-x MSCCLPP_NCCL_LIB_PATH=/path/libnccl.so/librccl.so
-x MSCCLPP_FORCE_NCCL_FALLBACK_OPERATION="allreduce,allgather,broadcast,reducescatter" or "all"
By default, if MSCCLPP_FORCE_NCCL_FALLBACK_OPERATION is not specified, all these operations will be fallback to nccl/rccl apis.
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Enhancements to all-gather operation, a temporary solution to fix the
memory overhead when integrating msccl++ with pytorch.
This solution will not register input/output buffer to msccl++, so the
temp output buffer for allgather could be reused by torch automatically.
* Introduced a new `allgather8` kernel function in
`apps/nccl/src/allgather.hpp` to handle larger data sizes more
efficiently. This includes double buffering to hide synchronization
overhead and support for both in-place and out-of-place operations.
* Modified the `allgather` function to decide between `allgather6` and
`allgather8` based on data size and platform, improving performance for
large data sizes.
Configuration and environment improvements:
* Added a new environment variable `MSCCLPP_DISABLE_CHANNEL_CACHE` to
control whether the channel cache is disabled, enhancing
configurability. This variable is now part of the `Env` class and is
logged during environment initialization.
* Removed the redundant global variable `mscclppDisableChannelCache`
from `src/debug.cc` and updated its usage to refer to the new
environment variable.
* 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
* Renamed `ProxyChannel` -> `BaseProxyChannel` and `SimpleProxyChannel`
-> `ProxyChannel`. It makes the interface more consistent by defining
channels to be associated with a certain src/dst memory region:
`ProxyChannel` as "sema + src/dst + fifo" and `SmChannel` as "sema +
src/dst". BaseProxyChannel is not associated with any memory regions, as
"sema + fifo".
* `ProxyChannelDeviceHandle` now inherits from
`BaseProxyChannelDeviceHandle`, instead of having one as a member.
- Support mote datatype for multicast operation
- Add new OP MULTI_LOAD_REDUCE_STORE to support NVLS
- Modify allocSharedPhysicalCuda, which return std::shared_ptr<T>
instead of std::shared_ptr<PhysicalCudaMemory>
- Add Python support for allocSharedPhysicalCuda
Test passed for `allreduce_nvls.json`
- Add C++ executor test
- Fix executor bugs for packet operation
- Enhance executor_test.py
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
MSCCLPP_BITS_REGMEM_HANDLE=8 limits the number registered memories for a
ProxyService to 256. Many use cases, such as KV cache transfer, require
registering more tensors.
This change allows registering up-to 512 memories. Note that this change
uses up the slack bits remaining in the ChannelTrigger struct.
The behavior of `ProxyChannelDeviceHandle::put()` is undefined by design
when each field value is given to exceed the bits limitation (such as
`MSCCLPP_BITS_SIZE`). Even so, we'd better trim exceeding bits of each
field value for safety, so that the invalid usage of a field does not
propagate to other fields.
