The environment friendly deployment of huge language fashions (LLMs) necessitates excessive throughput and low latency. Nonetheless, LLMs’ substantial reminiscence consumption, significantly by the key-value (KV) cache, hinders attaining massive batch sizes and excessive throughput. The KV cache, storing keys and values throughout era, consumes over 30% of GPU reminiscence. Varied approaches equivalent to compressing KV sequences and dynamic cache eviction insurance policies, intention to alleviate this reminiscence burden in LLMs.
Whereas most works focus on compressing KV sequences, vLLM introduces paged consideration to mitigate reminiscence fragmentation. Varied approaches embody compressing prompts, eradicating enter context redundancy, and incrementally compressing token spans. Different strategies contain pruning unimportant tokens, making use of completely different pruning methods to consideration heads, and storing solely essential tokens.
Researchers from the Faculty of Data Science and Know-how, ShanghaiTech College, and Shanghai Engineering Analysis Heart of Clever Imaginative and prescient and Imaging current an environment friendly strategy to scale back reminiscence consumption within the KV cache of transformer decoders by reducing the variety of cached layers. By pairing queries of all layers with keys and values of simply the highest layer, just one layer’s keys and values must be cached, considerably saving reminiscence with out further computation overhead. Impressed by the iterative enchancment means of token illustration, the mannequin attends solely to the highest layer, akin to cross-attention in normal transformers. The mannequin integrates normal consideration for a couple of layers to mitigate efficiency degradation.
The proposed methodology pairs queries of all layers with keys and values of solely the highest layer, eliminating the necessity to cache or compute KVs for different layers, thereby saving reminiscence and computation. This additionally reduces mannequin parameters by eliminating the necessity for weights mapping hidden representations to KVs for these layers. To deal with the cyclic dependency downside attributable to every token attending to itself, the mannequin masks the diagonal of the eye matrix, utilizing zero vectors as dummy KVs for the primary token. Retaining normal consideration for a couple of layers, termed warmup layers, maintains the syntactic-semantic sample noticed in transformers, making certain aggressive efficiency with normal fashions.
Researchers evaluated their methodology utilizing fashions with 1.1B, 7B, and 30B parameters on completely different GPUs, together with NVIDIA GeForce RTX 3090 and A100. The implementation makes use of HuggingFace Transformers with FlashAttention 2, fused RMS norm, fused cross-entropy, and fused SwiGLU. Analysis measures embody latency and throughput, with outcomes indicating considerably bigger batch sizes and better throughput than normal Llama fashions throughout varied settings. Additionally, zero-shot accuracy on commonsense reasoning duties is similar to TinyLlama. Integration with StreamingLLM demonstrates decrease latency and reminiscence consumption, with the power to course of infinite-length tokens successfully. The strategy achieves aggressive efficiency and better inference effectivity, though pre-training requires extra time because of the iterative coaching course of.
This examine presents a sturdy methodology to scale back reminiscence consumption and improve throughput in LLMs by minimizing the variety of layers requiring key and worth computation and caching. Empirical outcomes exhibit substantial reminiscence discount and throughput enchancment with minimal efficiency loss. Additionally, the tactic seamlessly integrates with different memory-saving methods like StreamingLLM.
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