ROCm: evaluate incremental dual-GPU local-KV sharding for Gemma-4 decode
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- Dominant language
- C++
- Stars
- 423
- Forks
- 53
- Avg merge
- 20h 26m
- Merged PRs (30d)
- 310
Description
Goal
Evaluate an opt-in Gemma-4 ROCm T=1 full-attention decode path that shards the two BF16 KV heads across dual RDNA4 GPUs with local historical KV residency and incremental new-token mirroring.
Scope
- Gemma-4 full-attention layers only
T=1, one request, BF16 KV,Hkv=2,d=512- GPU0 owns KV head 0; GPU1 owns a persistent local mirror of KV head 1
- Mirror only newly written K/V entries after initial backfill
- Run each head against local HBM and merge only peer Q-head outputs
- Default OFF with synchronous fallback to the existing single-GPU split-KV path
Exclusions
- No FP8 KV in the first experiment
- No sliding-window layers
- No remote reads of historical KV
- No full-cache copy per decode token
- No production-default change without measured KEEP evidence
Gates
- Build and default/fallback smoke
- Positive candidate breadcrumb
- Greedy output identity and Paris/arithmetic quality
- Short plus long-context paired, order-alternated A/B
- No GPU faults, page faults, invalid-device errors, NaNs, progressive memory growth, or hidden CPU/offload path
- KEEP only for at least 10% long-context end-to-end decode improvement with no material short-context regression
Evidence origin
Local architecture audit: .agents-external lab reference gemma4-decode-hotpath-architecture-audit-2026-08-12.md; implementation must validate source assumptions against the current dirty lab stack before editing.
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 .agents-external lab reference gemma4-decode-hotpath-architecture-audit-2026-08-12.md, then validate its assumptions against the current dirty lab stack before editing. Run the build and default/fallback smoke checks, followed by the specified paired A/B tests; done requires the listed quality and fault gates plus at least 10% long-context improvement without material short-context regression.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100