Reproduce “ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding” on OpenResearch
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About this paper
Ziyang Ma, Zihong Zhang, Zuchao Li, Lefei Zhang, Baoyuan Qi, Siqi Li, Simin Yu
ECHO improves speculative decoding by assigning frequent candidate exploration to early transformer layers and reserving later layers for authoritative verification. Combining bonus logits, retrieval-based drafting, tree attention, and hidden-state reuse yields reported speedups of roughly 2.4–3.0× across several Llama and CodeLlama settings while preserving the target model’s output distribution.
Key insights
- Intermediate-layer logits provide fresher semantic guidance than stale final-layer logits while costing less than repeated full-model passes.
- Retrieval and model-derived proposals complement one another: retrieval helps with repeated continuations, whereas bonus logits support semantically less predictable text.
- The best configuration balances buffer size and verification frequency; larger speculative batches can raise accepted-token counts but may increase divergence and erase runtime gains.
Summary by alphaXiv.
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