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TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
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abstract
With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and high latency. While various compression methods for KV cache have been proposed to alleviate this issue, they suffer from degradation in generation quality. We introduce TriForce, a hierarchical speculative decoding system that is scalable for long sequence generation. This approach leverages the original model weights and dynamic sparse KV cache via retrieval as a draft model, which serves as an intermediate layer in the hierarchy and is further speculated by a smaller model to reduce its drafting latency. TriForce not only facilitates impressive speedups for Llama2-7B-128K, achieving up to 2.31$\times$ on an A100 GPU but also showcases scalability in handling even longer contexts. For the offloading setting on two RTX 4090 GPUs, TriForce achieves 0.108s/token$\unicode{x2014}$only half as slow as the auto-regressive baseline on an A100, which attains 7.78$\times$ on our optimized offloading system. Additionally, TriForce performs 4.86$\times$ than DeepSpeed-Zero-Inference on a single RTX 4090 GPU. TriForce's robustness is highlighted by its consistently outstanding performance across various temperatures. The code is available at https://github.com/Infini-AI-Lab/TriForce.
Forward citations
Cited by 10 Pith papers
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Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding
Graft combines pruning and retrieval in a sequential mechanism to build hybrid draft trees for speculative decoding, delivering up to 5.41× speedup and 21.8% better average speedup than EAGLE-3 on large models.
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A Sparse Glimpse of the Whole: Train-Free Self-Speculative Decoding
A training-free self-speculative decoding system with a recallable sparse KV cache and entropy-guided adaptive speculation achieves up to 2.79× speedup while preserving the target distribution.
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Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes
Lossy speculative-decoding verification splits into truncation-based and collaborative methods; truncation-based methods underperform their matched baselines, and capping draft overshoot preserves quality.
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Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding
Dustin reports 27.85x self-attention and 9.17x end-to-end speedups at 32k length on Qwen2.5-72B using draft-augmented sparse verification with negligible accuracy loss on PG-19 and LongBench.
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DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation
DREAM-S combines neural architecture search, target-aware supernet training, and attention-entropy-guided distillation to accelerate speculative decoding in VLMs, reporting up to 3.85x speedup over standard methods.
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Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference
Ada-KV is the first head-wise adaptive KV cache budget allocator for LLMs, using a theoretical loss upper bound to allocate eviction differently per attention head and yielding higher quality than uniform methods on l...
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KAP: Bridging the Knowledge Selection-Runtime Consumption Gap in LLM Systems
KAP compiles structured knowledge priors into runtime access plans that cut proposal-time KV access to 5.5% at 128K while matching full-context QA quality and yielding up to 1.19× decode throughput.
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Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM
K-VEC is a coverage-aware KV-cache eviction strategy using cross-head and cross-layer modules that improves performance by up to 10.35 points over prior methods on LongBench subsets at fixed memory budget.
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BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding
BudgetDraft applies multi-view sparse training with an acceptance-aware full-cache loss branch to produce one budget-robust drafter that recovers acceptance rates across sparsity levels in speculative decoding for 4K-...
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DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution
DREAM-R introduces RL-based draft alignment, ratio-based verification, and parallel execution to accelerate speculative reasoning in multimodal models while preserving accuracy.
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