## Summary
Add ROCm (gfx942) support for the FP8 E4M3B15 data type, including
optimized conversion routines between FP8 E4M3B15 and FP16/FP32 using
inline assembly.
Extends the allpair packet and fullmesh allreduce kernels to support
higher-precision accumulation (e.g., FP16/FP32) when reducing FP8 data,
improving numerical accuracy.
Adds Python tests to verify that higher-precision accumulation is at
least as accurate as native FP8 accumulation across all algorithm
variants.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
## Summary
- **Add `fp8_e4m3b15` datatype**: A software-defined FP8 type with 4
exponent bits, 3 mantissa bits, and bias=15 (max finite value: 0.9375).
Implemented entirely in software with no HW dependency, using
Triton-style bit manipulation through fp16 as intermediate for efficient
conversion.
- **Add mixed-precision accumulation for allreduce**: All allreduce
algorithm variants (packet, NVLS packet, fullmesh, RSAG zero-copy, and
others) now support a configurable `accumDtype` parameter, enabling FP8
inputs to be reduced in float16 or float32 for higher accuracy.
- **Propagate `accumDtype` through the full API**: The new parameter is
threaded from `Algorithm::execute()` → `NativeAlgorithm` → `KernelFunc`
→ dispatch → CUDA kernels, with `DataType::AUTO` as the default
(resolves to input dtype at runtime).
- **Add FP8 accumulation correctness tests**: New `test_fp8_accum.py`
validates that higher-precision accumulation produces results at least
as accurate as native FP8 accumulation across multiple algorithms and
sizes. Skipped on CUDA SM < 89 (pre-Hopper); runs on HIP/ROCm.
- **Add `test_fp8_accum.py` to CI**: Azure Pipeline `ut.yml` now runs
FP8 accumulation tests alongside existing pytests.
- **NCCL shim logging cleanup**: Migrated `printf`-style `WARN`/`INFO`
calls to streaming-style logging.
## Key files
| Area | Files |
|------|-------|
| New datatype + vector ops | `include/mscclpp/gpu_data_types.hpp` |
| Accumulation reduce helpers | `src/core/include/reduce_kernel.hpp` |
| Algorithm API (`accumDtype`) | `include/mscclpp/algorithm.hpp`,
`src/core/algorithm.cc` |
| Allreduce kernels | `src/ext/collectives/allreduce/*.cu` |
| Dispatch + common | `src/ext/collectives/include/allreduce/common.hpp`
|
| Python bindings | `python/csrc/algorithm.cpp`,
`python/mscclpp/_core/algorithm.py` |
| Tests | `python/test/test_fp8_accum.py` |
| CI | `.azure-pipelines/templates/ut.yml` |
## Test plan
- [x] CI passes on H100 (CUDA SM 90) — full FP8 E4M3 + E4M3B15
accumulation tests
- [x] CI passes on A100 (CUDA SM 80) — FP8 tests correctly skipped
- [x] CI passes on MI300X (ROCm) — FP8 tests run via HIP
- [x] Existing `test_mscclpp.py` tests continue to pass
- [x] NCCL shim builds and runs correctly with new `accumDtype` defaults
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
## Summary
This PR addresses a multicast resource leak, fixes `cuMemMap` offset
handling for multicast handles, renames NVLS allreduce algorithm classes
for clarity, and adds a new unit test for `SwitchChannel`.
### Bug Fixes
#### 1. Fix multicast allocation handle leak in `createMulticast()`
(`gpu_ipc_mem.cc`)
`GpuIpcMemHandle::createMulticast()` called
`cuMulticastCreate(&allocHandle, ...)` but never released the local
`allocHandle` after exporting it to shareable handles (POSIX FD /
Fabric). This caused a reference count leak — the multicast object was
never freed even after all mappings and imported handles were released.
Per the [CUDA Driver API docs for
`cuMemRelease`](https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__VA.html):
> *"The memory allocation will be freed when all outstanding mappings to
the memory are unmapped and when all outstanding references to the
handle (including its shareable counterparts) are also released."*
The fix adds `cuMemRelease(allocHandle)` after export, matching the
existing pattern used for regular allocations in
`GpuIpcMemHandle::create()`.
**Impact:** Without this fix, repeated creation/destruction of NVLS
connections causes OOM after ~120 iterations when allocating 1GB
multicast buffers on H100.
#### 2. Fix `cuMemMap` offset for multicast handles (`gpu_ipc_mem.cc`)
`cuMemMap` requires `offset=0` for multicast handles. Previously, the
code attempted to map at a non-zero offset within the multicast object,
leading to errors when binding multiple buffers to the same
`NvlsConnection`. The fix maps the entire range `[0, mcOffset +
bufferSize)` and returns the pointer offset by `mcOffset`. This only
consumes extra virtual address space; no additional physical memory is
used.
### Refactoring
#### 3. Rename NVLS allreduce algorithm classes
Renamed for clarity:
- `AllreduceNvls` → `AllreduceNvlsZeroCopy`
- `AllreduceNvlsWithCopy` → `AllreduceNvlsWarpPipeline`
- `AllreduceNvlsWithCopy2` → `AllreduceNvlsBlockPipeline`
Updated all references in builder, selector, docs, and examples.
#### 4. Move `nvlsConnections` setup to `initialize()`
Moved `nvlsConnections_` from `AlgorithmCtx` (which no longer has this
member) to individual algorithm class members, initialized in their
`initialize()` methods.
### Tests
#### 5. Add `TwoChannelsSameConnection` test
New unit test that creates two `SwitchChannel` instances from the same
`NvlsConnection`, performs reduce operations on both, and verifies
correctness. This exercises the multi-bind path that triggered the
`cuMemMap` offset fix.
### Files Changed
- `src/core/gpu_ipc_mem.cc` — multicast handle leak fix + cuMemMap
offset fix
- `src/ext/collectives/allreduce/allreduce_nvls_zero_copy.cu` (renamed)
- `src/ext/collectives/allreduce/allreduce_nvls_warp_pipeline.cu`
(renamed)
- `src/ext/collectives/allreduce/allreduce_nvls_block_pipeline.cu`
(renamed)
- `src/ext/collectives/allreduce/allreduce_nvls_packet.cu` —
nvlsConnections fix
- `src/ext/collectives/include/allreduce/*.hpp` — renamed headers
- `src/ext/collectives/algorithm_collection_builder.cc` — updated
references
- `src/ext/nccl/algorithm_selector.cc` — updated algorithm names
- `test/mp_unit/switch_channel_tests.cu` — new test
- `docs/guide/mscclpp-torch-integration.md` — updated names
- `examples/torch-integration/customized_comm_with_default_algo.py` —
updated names
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 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>
The key purpose is handling all mscclpp objects' memory internally by
hiding shared pointers from user APIs.
* `Connection` class is now a wrapper of `BaseConnection` class that is
equivalent to the previous `Connection` class
* `connect()` methods now return `Connection` instead of
`std::shared_ptr<Connection>`
* Removed `connectOnSetup()` method
Provides two integration ways for MSCCL++ DSL.
1. Integrate with customized communication group
2. Integrate with NCCL API
Introduce new Python APIs to make it work:
```python
mscclpp.compile # compile dsl to json based execution plan
mscclpp.ExecutionPlanRegistry.register_plan(plan) # register the compiled plan to executionPlanRegistery
mscclpp.ExecutionPlanRegistry.set_selector(selector) # set the selector, the selector will return the best execution plan based on collection, message size, world size....
```
Fix#556
---------
Co-authored-by: Caio Rocha <caiorocha@microsoft.com>
Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
Add FP8 support for Allreduce on both NVIDIA and AMD platform.
Add new data type: fp8_e4m3 and fp8_e5m2
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
- Make nccl interface extensible. Customer can register their own algo
to NCCL API. User can provide customized algo selection function.
- Fallback to NCCL/RCCL if no algo is selected based on algo selection
function
- MSCCLPP interfaces now works for any scale
* Allow CudaIpc connection between GPUs in a single process
* Added an example of connection in a single process
* Minor interface updates
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
