Split-KV: Efficient Context Parallelism for Training Diffusion Drafters

Authors: Geng Zhang , Fangzhao Zhang , Cong Xie , Tiantian Fan , Yang You

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Abstract: Diffusion drafters accelerate speculative decoding by denoising multiple draft tokens in parallel. Their training uses anchor sampling to construct compact draft queries and performs cross-attention with a block-sparse mask over a much longer context. We empirically show that acceptance length remains stable as context scales with a fixed query budget, while increasing the query budget yields diminishing quality gains, jointly establishing a practical regime with queries far shorter than the context. No existing context parallelism (CP) method jointly exploits this query--context asymmetry and meets the nonlinear, sample-dependent load-balancing requirement created by anchor sampling. We present Split-KV, an exact CP method that keeps key/value shards local and limits per-layer attention communication to query-sized tensors, making this communication independent of context length. Its global query view further enables a cost-aware longest-processing-time-first (LPT) strategy that balances context key/value chunks by their realized attention cost. At a CP size of 64, Split-KV retains 85--91\% weak-scaling efficiency and achieves up to $3.67\times$ speedup over the fastest available CP baseline. LPT achieves $<$1\% imbalance and plans three orders of magnitude faster than the best load-balancing baseline. Code is available at this https URL .

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.05926 [cs.AI]

(or

arXiv:2611.05926v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05926

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arXiv-issued DOI via DataCite (pending registration)

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From: Geng Zhang [ view email ]

[v1]

Wed, 4 Nov 2026 04:16:46 UTC (272 KB)

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