AdaSplash-2: Faster Differentiable Sparse Attention

• URL: http://arxiv.org/abs/2604.15180v1
• Date: Thu, 16 Apr 2026 16:03:13 GMT
• Title: AdaSplash-2: Faster Differentiable Sparse Attention
• Abstract summary: We introduce AdaSplash-2, which reduces the number of iterations needed to compute $$ to typically 1--2.<n>AdaSplash-2 matches or improves per-step training time relative to FlashAttention-2 when block sparsity is moderate-to-high.<n>On downstream tasks, models trained with our efficient $$-entmax attention match softmax baselines at short-context lengths and achieve substantial gains in long-context settings.
• Score: 14.338708749838881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse attention has been proposed as a way to alleviate the quadratic cost of transformers, a central bottleneck in long-context training. A promising line of work is $α$-entmax attention, a differentiable sparse alternative to softmax that enables input-dependent sparsity yet has lagged behind softmax due to the computational overhead necessary to compute the normalizer $τ$. In this paper, we introduce AdaSplash-2, which addresses this limitation through a novel histogram-based initialization that reduces the number of iterations needed to compute $τ$ to typically 1--2. The key idea is to compute a coarse histogram of attention scores on the fly and store it in on-chip SRAM, yielding a more accurate initialization that enables fast forward and backward computation. Combined with a sparsity-aware GPU implementation that skips zero blocks with low overhead, AdaSplash-2 matches or improves per-step training time relative to FlashAttention-2 when block sparsity is moderate-to-high (e.g., $>$60\%), which often occurs at long-context lengths. On downstream tasks, models trained with our efficient $α$-entmax attention match softmax baselines at short-context lengths and achieve substantial gains in long-context settings.

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