## Summary
Add CI pipeline support for testing in environments without InfiniBand
(IB) hardware.
## Changes
### IB stubs for no-IB builds (`src/core/ib.cc`)
- Added stub implementations for `IbMr` and `IbQp` classes in the `#else
// !defined(USE_IBVERBS)` block so the library links successfully when
built with `-DMSCCLPP_USE_IB=OFF`.
### Environment variable to disable IB tests
(`MSCCLPP_DISABLE_IB_TESTS`)
- Added `disableIbTests` field to the `Env` class
(`include/mscclpp/env.hpp`, `src/core/env.cpp`), reading from
`MSCCLPP_DISABLE_IB_TESTS` env var.
- Exposed as `disable_ib_tests` in Python bindings
(`python/csrc/env_py.cpp`).
- Updated `python/test/test_mscclpp.py` to skip IB-dependent tests
(`create_group_and_connection` with IB transport, `test_h2h_semaphores`,
`test_h2h_semaphores_gil_release`) when `env().disable_ib_tests` is
true.
### CI pipeline (`ut-no-ib-env.yaml`, `ut.yml`)
The no-IB environment pipeline runs two phases:
1. **No-IB build phase**: Build with `-DMSCCLPP_USE_IB=OFF`, deploy, run
unit tests, multi-process unit tests, and pytests (with
`MSCCLPP_DISABLE_IB_TESTS=1`).
2. **IB build phase**: Rebuild with IB enabled (default), stop the
existing container, redeploy, and run pytests (with
`MSCCLPP_DISABLE_IB_TESTS=1`) — verifying that the full IB-enabled build
works correctly in a non-IB environment when IB tests are skipped.
Also increased the job timeout from 40 to 60 minutes to accommodate the
two-phase pipeline.
This PR adds an example code for switch channel testing. It validates
switch channel on single node and multi node environments. We need to
add the description of the algorithms and the explanation of the code
under doc.
example outputs:
rank0:
./bidir_switch_channel 10.0.5.233:45571 0 0
Rank 0 (GPU 0): Preparing for tests ...
Rank 0 (GPU 0): bytes 4096, elapsed 0.0062328 ms/iter, BW 0.657169 GB/s
Rank 0 (GPU 0): bytes 4.1943e+06, elapsed 0.0164577 ms/iter, BW 254.854
GB/s
Rank 0 (GPU 0): bytes 1.34218e+08, elapsed 0.33628 ms/iter, BW 399.125
GB/s
Rank 0: Succeed!
rank1:
./bidir_switch_channel 10.0.5.233:45571 1 0
Rank 1 (GPU 0): Preparing for tests ...
Rank 1: Succeed!
This pull request updates the way the `nlohmann/json` library is fetched
and upgrades it to a newer version in both the main build and test
configuration files.
Addressed installation issue in some env
This pull request updates the handling of the default flag buffer in the
C++ and Python bindings to ensure proper memory management when
interfacing with Python.
Make sure the buffer will not be deallocated when transfer ownership
from cpp to python
This PR refactors the algorithm selection logic in MSCCL++ and
introduces support for symmetric memory configuration through
environment variables.
1. Algorithm Selection Refactoring
Use separate class for algo selection. Could introduce more complex
logic for algo selection based on message size, arch, if cuda graph is
enabled and memory allocation method
2. Symmetric Memory Support
Introduced symmetricMemory parameter in algorithm context key
generation. Remove disableChannelCache env as is ambiguous
3. Add new args for build_default_algorithms
Add flag_buffer, and flag_buffer_size args to build default algorithm.
Then we could use unified flag buffer for different algorithms, avoid
application hanging when switch algo for different message size.
---------
Co-authored-by: chhwang <8018170+chhwang@users.noreply.github.com>
Co-authored-by: Qinghua Zhou <qinghuazhou@microsoft.com>
Co-authored-by: Caio Rocha <caiorocha@microsoft.com>
Support is being added for fusing the ReadPutPacket operation on DSL,
which reduces the overhead caused by reading packet data multiple times
in the scratch buffer. Fusion will occur when two rppkt operations are
executed consecutively with the same src_buffer:
rppkt(src, dst0) + rppkt(src, dst1) -> rppkt(src, [dst0, dst1]
Co-authored-by: Binyang Li <binyli@microsoft.com>
* Added configurable InfiniBand (IB) signaling mode.
`EndpointConfig::Ib::Mode` enum selects the mode (`Default`, `Host`,
`HostNoAtomic`). `Default` is equivalent to `Host` unless specified
different by envrionment `MSCCLPP_IBV_MODE`. `Host` corresponds to the
previous implementation using RDMA atomics for signaling, while
`HostNoAtomic` uses write-with-immediate instead.
* Regarding updates in Python bindings and API.
- Put the common reduce kernel to reduce_kernel.hpp
- Implement operator overloading for the vector type
- Clean up the duplicated code at `executor_ kernel.hpp` and
`allreduce/common.hpp`
This change makes MSCCL++ automatically select CUDA architectures based
on the build environment. If an NVIDIA GPU is detected, the build
targets the native GPU architecture for optimal performance; otherwise,
it falls back to building for multiple architectures for portability.
When building for the native architecture, FP8 support is automatically
enabled for “a-series” GPUs (e.g., sm_100a), allowing the appropriate
optimized code paths to be picked up.
* Now `NvlsConnection` internally reuses `GpuIpcMem` for multicast
memory handling.
* Removed unnecessary barriers from `connectNvlsCollective()` (CUDA API
handles this automatically).
* Updated `GpuIpcMem::map()` and `GpuIpcMem::mapMulticast()` to return a
shared pointer with custom deleter for unmapping, which prevents misuse
of raw pointers and reduces states to be stored in the `GpuIpcMem`
instance.
* Now for `RuntimeIpc` type handles, for consistency with other types,
`cudaIpcOpenMemHandle` will be called in `GpuIpcMem::map()` instead of
the ctor of `GpuIpcMem`.
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Binyang2014 <9415966+Binyang2014@users.noreply.github.com>
Add `GpuIpcMemHandle` that is a generic GPU memory handle that covers
all existing methods for GPU memory mapping. This PR fixes issues that
fail to properly fallback to a feasible type of memory handle on the
importing environment. It also consolidates code for creating or
destroying various memory handles into a single RAII wrapper.
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Binyang2014 <9415966+Binyang2014@users.noreply.github.com>
* Updated Dockerfiles and the build script to support CUDA 13.0
* Added Python3 venv which is required since Python 3.12
* Updated the default MLNX-OFED version to the LTS version
* Added docker push instruction for multi-arch manifest
- Remove cuda11 support for nccl-test pipeline, since nccl build failed
for cuda11.
- Update to cuda12.9 for CI pipeline. Will consider dropping cuda11
support add cuda13 support in near future
