NVIDIA
[dev] DeepSeek V3.2 support
What does this PR do ?
Support DeepSeek V3.2 style sparse attention. Still work in progress.
:warning: For major changes (either in lines of code or in its impact), please make sure to first share discuss a design-doc with the team.
Contribution process
flowchart LR
A[Pre-checks] --> B[PR Tests]
subgraph Code Review/Approval
C1[Expert Review] --> C2[Final Review]
end
B --> C1
C2 --> D[Merge]
Pre-checks
- [ ] I want this PR in a versioned release and have added the appropriate Milestone (e.g.,
Core 0.8) - [ ] I have added relevant unit tests
- [ ] I have added relevant functional tests
- [ ] I have added proper typing to my code Typing guidelines
- [ ] I have added relevant documentation
- [ ] I have run the autoformatter.sh on my PR
Code review
The following process is enforced via the CODEOWNERS file for changes into megatron/core. For changes outside of megatron/core, it is up to the PR author whether or not to tag the Final Reviewer team.
For MRs into `main` branch
(Step 1): Add PR label Expert Review
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:warning: Only proceed to the next step once all reviewers have approved, merge-conflict are resolved and the CI is passing.
Final Review might get declined if these requirements are not fulfilled.
(Step 3): Final Review
- Add
Final Reviewlabel - GitHub auto-assigns final reviewers based on your changes. They will get notified and pick up your PR soon.
(Optional Step 4): Cherry-pick into release branch
If this PR also needs to be merged into core_r* release branches, after this PR has been merged, select Cherry-pick to open a new PR into the release branch.
For MRs into `dev` branch
The proposed review process for `dev` branch is under active discussion.MRs are mergable after one approval by either [email protected] or [email protected].
Merging your PR
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Great work—this has been really helpful to me. Quick question: what are the typical values used for indexer-loss-coeff?
Great work—this has been really helpful to me. Quick question: what are the typical values used for
indexer-loss-coeff?
@iansheng Thanks for your interest. Currently, I don't have relevant experience about indexer loss coefficient, still working on refining this MR. and haven't actually used it to train a model yet.
Thanks for your quick and great work. I've preliminarily view the code and it overall LGTM. Please add UTs for the new features, especially for the functionality of DSA and its parallel correctness. I'll do a second-round review after everything is ready.
Please also do not forget to create a mirror PR to the main branch once ready for review. Thanks!
