A Sparse Glimpse of the Whole: Train-Free Self-Speculative Decoding
Quick Answer
SparseSpec-L introduces a training-free self-speculative decoding framework that enhances long-context inference efficiency by utilizing a dynamically sparsified KV cache.
Quick Take
This method allows for significant end-to-end acceleration over traditional autoregressive decoding, maintaining output distribution integrity while optimizing speculation length based on an online entropy controller.
Key Points
- SparseSpec-L reduces memory-bandwidth bottlenecks in inference.
- The framework recycles attention statistics to recall critical historical tokens.
- Experiments show consistent acceleration across multiple long-context tasks.
- The method achieves speedup over autoregressive decoding without sacrificing output quality.
- An online controller dynamically adjusts speculation length for efficiency.
Paper Resources
Source Excerpt
Speculative decoding alleviates the memory-bandwidth bottleneck in inference, but its acceleration is jointly constrained by drafting overhead, token acceptance, and speculation length. We present a unified efficiency analysis showing that extending the speculation horizon can reduce rather than improve speedup when the marginal acceptance probability falls below the relative drafting cost. Guided by this analysis, we introduce SparseSpec-L, a training-free self-speculative
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