Dynamic sparse attention makes LLMs run faster on regular GPUs by being selective about what to focus on
SparseAccelerate introduces dynamic sparse attention patterns that adapt to input characteristics, making LLM inference efficient on mid-range GPUs for long contexts up to 128K tokens.
https://arxiv.org/abs/2412.06198
Original Problem 🤔:
→ Traditional attention mechanisms in LLMs scale quadratically with input length, causing high latency and memory usage
→ An 8B-parameter LLM takes 10-20 seconds to generate the first token for a 32K-token input on dual NVIDIA A5000 GPUs
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Solution in this Paper 🔧:
→ SparseAccelerate uses three dynamic sparsity patterns: Triangular, Interval-Slash, and Block-Cluster
→ A kernel-aware optimization framework selects optimal sparsity patterns at runtime
→ The method dynamically adapts to input-specific attention distributions rather than using static patterns
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Key Insights 💡:
→ Effectiveness threshold starts at 32K tokens with 1.04x latency reduction
→ Memory usage scales more efficiently than competing methods
→ Performance improves as context length increases, unlike traditional approaches
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Results 📊:
→ Processes contexts up to 128K tokens on dual 24GB GPUs
→ Achieves 1.04x TTFT reduction at 32K tokens
→ Uses 26,860 MB memory at 32K tokens vs 41,884 MB for baseline methods
→ Only method capable of handling 64K-128K token sequences
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