- Removes the GTest dependency, replacing it with a minimal custom
framework (`test/framework.*`) that covers only what the tests actually
use — a unified `TEST()` macro with SFINAE-based fixture auto-detection,
`EXPECT_*`/`ASSERT_*` assertions, environments, and setup/teardown.
- `--exclude-perf-tests` flag and substring-based negative filtering
- `MSCCLPP_ENABLE_COVERAGE` CMake option with gcov/lcov; CI uploads to
Codecov
- Merges standalone `test/perf/` into main test targets
- Refactors Azure pipelines to reduce redundancies & make more readable
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@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
---------
Co-authored-by: Caio Rocha <caiorocha@microsoft.com>
Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
For mscclpp, to use nvls we require the buffer is allocated by
mscclpp::GpuBuffer. Due to cupy doesn't support bfloat16 yet, we export
the raw buffer to dlpack format.
User can use this feature to create buffer with type supported by
pytorch
```python
buffer = RawGpuBuffer(1024 * 2) # 2 for bfloat16
dl_pack = buffer.to_dlpack(str(torch.bfloat16))
tensor = torch.utils.dlpack.from_dlpack(dl_pack)
```
Documentation update:
*
[`docs/design/mscclpp-dsl.md`](diffhunk://#diff-02a69290fb3e02b8a069bf915fbf5266cfc2ac51c6e9ff8b5b19df51ed909b22L114-R114):
Updated the link to the examples folder to reflect the correct path.
New example script:
*
[`python/examples/allgather_allpairs_multinodes_packets.py`](diffhunk://#diff-ab42c16ecca0680d55b60b82a6913138c5fba4069b9c4493fbe8c72217fe54bcR1-R76):
Added a new example script demonstrating the allgather all-pairs
algorithm across multiple nodes using packet communication.
IR module improvements:
*
[`python/mscclpp/language/ir.py`](diffhunk://#diff-b025796b03fbbd9b2ca9aee2569547efa7a56101743bc4aa05661be0b52aeec9L470-R472):
Refined the sorting criteria for GPU instance channels and thread block
channels to include the channel type, ensuring a more accurate order.
Debugging enhancements:
*
[`src/executor/executor.cc`](diffhunk://#diff-60f7806d111e5cc12ded06358b5d5b09b8521e3858f182d8be81ac05147c535dR439-R441):
Added a debug log to indicate the start of communication collective
execution with details about the execution plan and collective.
*
[`src/include/debug.h`](diffhunk://#diff-24e5fda55e3712277be4bb99b3c348294a77ebd3046bfe716b74bdb32cd203dfR89):
Introduced a new debug log subsystem identifier `MSCCLPP_EXECUTOR` for
logging executor-related information.
First step to merge msccl-tools into mscclpp repo. In this step will
move all msccl related code, pass the current tests and do some
necessary refactor.
Add `mscclpp.language` module
Add `_InstructionOptimizer` and `DagOptimizer` class to optimize the dag
Add `DagLower` to lower dag to intermediate representation
Add documents for mscclpp.language
Remove msccl related code
* Let all CMake option names start with `MSCCLPP_`
* Explain the `MSCCLPP_BUILD_PYTHON_BINDINGS` option in readme
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Now build images by a script with a shared Dockerfile template
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Co-authored-by: Saeed Maleki <saemal@microsoft.com>
