deepspeedai / deepspeedai/DeepSpeed
[QUESTION] How to do p2p send/recv a tensor with stride info in pipeline parallelism
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 43.1k
- Forks
- 5k
- Avg merge
- 4d 15h
- Merged PRs (30d)
- 112
Description
I'm curious about how deepspeed pipeline strategy handles the stride information when do p2p communication. I've seen DDP handle these stride issues with 'bucket_view' and 'as_strided' api , so the tensor with specific stride could be reconstructed correctly. But I can't find the similar operations in p2p communication implementations.
Does anyone know the reason for this? I would be very grateful if anyone can give some answers or related ideas.
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
Review DeepSpeed's p2p communication implementation and compare it with the DDP behavior involving bucket_view and as_strided described in the issue. Done means documenting whether stride information is preserved or reconstructed during pipeline-parallel tensor transfer, and explaining the reason.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100