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>
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>
