The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs
Sparse attention offers a promising strategy to extend long-context
capabilities in Transformer LLMs, yet its viability, its efficiency-accuracy
trade-offs, and systematic scaling studies remain unexplored. To address this
gap, we perform a careful comparison of training-free sparse attention methods
at varying model scales, sequence lengths, and sparsity levels on a diverse
collection of long-sequence tasks-including novel ones that rely on natural
language while remaining controllable and easy to evaluate. Based on our
experiments, we report a series of key findings: 1) an isoFLOPS analysis
reveals that for very long sequences, larger and highly sparse models are
preferable to smaller and dense ones. 2) The level of sparsity attainable while
statistically guaranteeing accuracy preservation is higher during decoding than
prefilling, and correlates with model size in the former. 3) There is no clear
strategy that performs best across tasks and phases, with different units of
sparsification or budget adaptivity needed for different scenarios. Even
moderate sparsity levels often result in significant performance degradation on
at least one task, highlighting that sparse attention is not a universal
solution. 4) We introduce and validate novel scaling laws specifically tailored
for sparse attention, providing evidence that our findings are likely to hold
true beyond our range of experiments. Through these insights, we demonstrate
that sparse attention is a key tool to enhance the capabilities of Transformer
LLMs for processing longer sequences, but requires careful evaluation of
trade-offs for performance-sensitive applications.
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Piotr Nawrot
admin_assigned
2025-04-29T12:33:38.822Z
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/avatars/0e748d7c91d97526b280e40ccb25c9e0.svg
Piotr Nawrot
user
pnawrot
680f2668db85fd31cd508100
Robert Li
680f2668db85fd31cd508101
Renjie Huang
680f2668db85fd31cd508102
Sebastian Ruder
admin_assigned
2025-04-29T12:34:22.719Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1648719221694-noauth.jpeg
Sebastian Ruder
user
ruder
680f2668db85fd31cd508103
Kelly Marchisio
admin_assigned
2025-04-29T12:34:47.942Z
66356b1e2fcf64c1548a1233
/avatars/9a1fab7da6cd16e8bf2d104c399ae34f.svg
Kelly Marchisio
user
kellycohere
680f2668db85fd31cd508104
Edoardo M. Ponti
claimed_verified
2025-04-28T12:50:11.195Z
60809ad44ad99100d63ce36a
https://cdn-avatars.huggingface.co/v1/production/uploads/1619040921084-noauth.jpeg
Edoardo Maria Ponti
user
ducdauge
The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs
Sparse attention offers a promising strategy to extend long-context
capabilities in Transformer LLMs, yet its viability, its efficiency-accuracy
trade-offs, and systematic scaling studies remain unexplored. To address this
gap, we perform a careful comparison of training-free sparse attention methods
at varying model scales, sequence lengths, and sparsity levels on a diverse
collection of long-sequence tasks-including novel ones that rely on natural
language while remaining controllable and easy to evaluate. Based on our
experiments, we report a series of key findings: 1) an isoFLOPS analysis
reveals that for very long sequences, larger and highly sparse models are
preferable to smaller and dense ones. 2) The level of sparsity attainable while
statistically guaranteeing accuracy preservation is higher during decoding than
prefilling, and correlates with model size in the former. 3) There is no clear
strategy that performs best across tasks and phases, with different units of
sparsification or budget adaptivity needed for different scenarios. Even
moderate sparsity levels often result in significant performance degradation on
at least one task, highlighting that sparse attention is not a universal
solution. 4) We introduce and validate novel scaling laws specifically tailored
for sparse attention, providing evidence that our findings are likely to hold
true beyond our range of experiments. Through these insights, we demonstrate
that sparse attention is a key tool to enhance the capabilities of Transformer
LLMs for processing longer sequences, but requires careful evaluation of
trade-offs for performance-sensitive applications.
Sparse attention methods enable extending long-context capabilities in Transformer LLMs with varying efficiency-accuracy trade-offs across different tasks and scales.
sparse attention Transformer LLMs isoFLOPS prefilling decoding sparsity levels scaling laws