LLMs like GPT-4 excel in language comprehension however battle with excessive GPU reminiscence utilization throughout inference, limiting their scalability for real-time functions like chatbots. Present strategies cut back reminiscence by compressing the KV cache however overlook inter-layer dependencies and pre-computation reminiscence calls for. Inference reminiscence utilization primarily comes from mannequin parameters and the KV cache, with the latter consuming considerably extra reminiscence. As an example, a 7 billion parameter mannequin makes use of 14 GB for parameters however 72 GB for the KV cache. This substantial reminiscence requirement restricts the throughput of LLM inference on GPUs.
Researchers from Shanghai Jiao Tong College, Xiaohongshu Inc., and South China College of Expertise developed PyramidInfer, which reinforces LLM inference by compressing the KV cache. In contrast to current strategies that overlook inter-layer dependencies and the reminiscence calls for of pre-computation, PyramidInfer retains solely essential context keys and values layer-by-layer. Impressed by current tokens’ consistency in consideration weights, this method considerably reduces GPU reminiscence utilization. Experiments present PyramidInfer improves throughput by 2.2x and reduces KV cache reminiscence by over 54% in comparison with current strategies, demonstrating its effectiveness throughout numerous duties and fashions.
Environment friendly methods are important to deal with the rising demand for chatbot queries, aiming to maximise throughput by leveraging GPU parallelism. One method is rising GPU reminiscence by way of pipeline parallelism and KV cache offload, using a number of GPUs or RAM. For restricted GPU reminiscence, decreasing the KV cache is another choice. Strategies like FlashAttention 2 and PagedAttention decrease reminiscence waste by optimizing CUDA operations. Strategies akin to StreamingLLM, H2O, and Scissorhands compress the KV cache by specializing in current context or consideration mechanisms however overlook layer variations and prefill section compression. PyramidInfer addresses these gaps by contemplating layer-specific compression in each phases.
Verification of the Inference Context Redundancy (ICR) and Latest Consideration Consistency (RAC) hypotheses impressed the design of PyramidInfer. ICR posits that many context keys and values are redundant throughout inference and are solely vital in coaching to foretell the following token. Experiments with a 40-layer LLaMA 2-13B mannequin revealed that deeper layers have greater redundancy, permitting for important KV cache discount with out affecting output high quality. RAC confirms that sure keys and values are persistently attended by current tokens, enabling the number of pivotal contexts (PVCs) for environment friendly inference. PyramidInfer leverages these insights to compress the KV cache successfully in each prefill and technology phases.
PyramidInfer’s efficiency was evaluated throughout numerous duties and fashions, demonstrating important reductions in GPU reminiscence utilization and elevated throughput whereas sustaining technology high quality. The analysis included language modeling on wikitext-v2, LLM benchmarks like MMLU and BBH, mathematical reasoning with GSM8K, coding through HumanEval, dialog dealing with with MT-Bench, and lengthy textual content summarization utilizing LEval. PyramidInfer was examined on fashions akin to LLaMA 2, LLaMA 2-Chat, Vicuna 1.5-16k, and CodeLLaMA throughout completely different sizes. Outcomes confirmed that PyramidInfer successfully maintained technology high quality with much less GPU reminiscence than full cache strategies and considerably outperformed native methods.
In conclusion, PyramidInfer introduces an environment friendly technique to compress the KV cache throughout each prefill and technology phases, impressed by ICR and RAC. This method considerably reduces GPU reminiscence utilization with out compromising mannequin efficiency, making it ideally suited for deploying giant language fashions in resource-constrained environments. Regardless of its effectiveness, PyramidInfer requires further computation, limiting speedup with small batch sizes. As the primary to compress the KV cache within the prefill section, PyramidInfer is but to be a lossless technique, indicating potential for future enhancements on this space.
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