The recent removal of GTest and introduction of custom test framework
requires MPI dependency which is not needed for CodeQL analysis.
Disable test building in CodeQL workflows to fix the build failures.
CodeQL only needs to analyze the core library code, not the tests.
Co-authored-by: chhwang <8018170+chhwang@users.noreply.github.com>
Support --gtest_filter command line argument for test filtering,
compatible with Azure pipeline configurations.
Co-authored-by: chhwang <8018170+chhwang@users.noreply.github.com>
- Move test framework from test/perf/ to test/ for shared use
- Add GTest-compatible macros (TEST, TEST_F, EXPECT_*, ASSERT_*, etc.)
- Remove GTest dependency from CMakeLists.txt
- Add test_framework library for unit and mp_unit tests
- Add code coverage support with lcov (MSCCLPP_ENABLE_COVERAGE option)
- Update perf tests to use shared framework
Co-authored-by: chhwang <8018170+chhwang@users.noreply.github.com>
- Modified test/mp_unit/mp_unit_tests.hpp to use ../framework.hpp instead of gtest/gtest.h
- Enhanced test/framework.hpp with GTest-compatible APIs:
- Added Environment base class for global test setup/teardown
- Added TestInfo and UnitTest classes for test metadata access
- Added GTEST_SKIP macro support via SkipHelper class
- Added namespace alias 'testing' for compatibility
- Added InitGoogleTest and AddGlobalTestEnvironment helper functions
- Updated test/framework.cc with implementations for new classes
- All mp_unit test files now use framework.hpp through mp_unit_tests.hpp
- Formatting applied via lint.sh
Co-authored-by: chhwang <8018170+chhwang@users.noreply.github.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`.
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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
Tune the nThreadsPerBlock for message size in 32KB to 256KB range for FP8 and Half datatype on MI300.
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Co-authored-by: Binyang Li <binyli@microsoft.com>
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>
* Added `port` and `gidIndex` field in the IB endpoint config (and
`deviceIndex` field for future usages)
* Added `MSCCLPP_IBV_SO` env variable to specify a custom libibverbs.so
* Added `--ib_gid_index` CLI option to `mp_unit_tests`
* Other minor fixes
Add an RAII guard that sets a proper GPU device before a CUDA API call.
We may change this stateful in the future to minimize `cudaGetDevice()`
calls. This PR fixes a bug of the tutorial 01.
Minimal fix to make things work. We need a more careful look at
preventing silent fallback of nanobind when it fails to (properly)
construct a C++ STL object with mscclpp instances.
