Split-KV: Efficient Context Parallelism for Training Diffusion Drafters
Geng Zhang, Fangzhao Zhang, Cong Xie, Tiantian Fan, Yang You
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 https://github.com/zxgx/verl.
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