deepspeedai / deepspeedai/DeepSpeed
[CUDA Kernel Profiling]
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Description
Thanks for all the great contributions to LLM training and inference.
I have a question regarding DeepSpeed team's development workflow for custom kernels in the context of the FastGen (and more broadly, as well). Specifically, how are kernels tested and profiled on the C++ / CUDA side before being bound to Python?
I see there are kernel-specific tests for ops and inference but these testing already built / bound kernels in Python. I'm interested in learning about tools / techniques and robust development practices for developing high-performance GPU kernels.
Any thoughts would be greatly appreciated!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the kernel-specific tests under ops and inference and the FastGen custom-kernel workflow mentioned in the issue. Investigate how C++/CUDA kernels are tested and profiled before Python binding; done would be clear documentation of the team's tools and development practices.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100