Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models

• URL: http://arxiv.org/abs/2404.09529v1
• Date: Mon, 15 Apr 2024 07:49:10 GMT
• Title: Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models
• Authors: Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover,
• Abstract summary: Prefilling is the computation of the key-value cache for input tokens in the prompt prior to autoregressive generation. For longer input prompt lengths, prefilling incurs a significant overhead on decoding time. We propose Prepacking, a simple yet effective method to optimize prefilling computation.
• Score: 48.592730159983276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: During inference for transformer-based large language models (LLM), prefilling is the computation of the key-value (KV) cache for input tokens in the prompt prior to autoregressive generation. For longer input prompt lengths, prefilling will incur a significant overhead on decoding time. In this work, we highlight the following pitfall of prefilling: for batches containing high-varying prompt lengths, significant computation is wasted by the standard practice of padding sequences to the maximum length. As LLMs increasingly support longer context lengths, potentially up to 10 million tokens, variations in prompt lengths within a batch become more pronounced. To address this, we propose Prepacking, a simple yet effective method to optimize prefilling computation. To avoid redundant computation on pad tokens, prepacking combines prompts of varying lengths into a sequence and packs multiple sequences into a compact batch using a bin-packing algorithm. It then modifies the attention mask and positional encoding to compute multiple prefilled KV-caches for multiple prompts within a single sequence. On standard curated dataset containing prompts with varying lengths, we obtain a significant speed and memory efficiency improvements as compared to the default padding-based prefilling computation within Huggingface across a range of base model configurations and inference serving scenarios.

Related papers

• Squeezed Attention: Accelerating Long Context Length LLM Inference [64.11145320159126]
  We propose Squeezed Attention as a mechanism to accelerate LLM applications where a large portion of the input prompt is fixed. We use K-means clustering offline to group the keys for the fixed context based on semantic similarity and represent each cluster with a single centroid value. We then compute exact attention using only these important keys from the fixed context, thereby reducing bandwidth and computational costs.
  arXiv  Detail & Related papers  (2024-11-14T18:54:19Z)
• CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs [8.649971923487835]
  We propose CritiPrefill, a criticality-based segment-wise prefilling method for long-context processing. CritiPrefill partitions the input sequence's queries and KV cache into segments and blocks, utilizing a segment-wise algorithm to estimate the query criticality. Extensive evaluations on multiple long-context datasets show up to 2.7x speedup on Llama3-8B and 3.0x speedup on Yi-9B for 128K context length on a single A100 GPU.
  arXiv  Detail & Related papers  (2024-09-19T06:09:56Z)
• Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling [53.58854856174773]
  Speculative decoding is an approach to accelerate inference through a guess-and-verify paradigm. Token Recycling stores candidate tokens in an adjacency matrix and employs a breadth-first search algorithm. It significantly outperforms existing train-free methods by 30% and even a training method by 25%.
  arXiv  Detail & Related papers  (2024-08-16T12:20:56Z)
• LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference [30.722379261991563]
  LazyLLM is a method that selectively computes the KV for tokens important for the next token prediction. We show that LazyLLM accelerates the prefilling stage of the LLama 2 7B model by 2.34x while maintaining accuracy.
  arXiv  Detail & Related papers  (2024-07-19T06:34:45Z)
• Let the Code LLM Edit Itself When You Edit the Code [50.46536185784169]
  underlinetextbfPositional textbfIntegrity textbfEncoding (PIE) PIE reduces computational overhead by over 85% compared to the standard full recomputation approach. Results demonstrate that PIE reduces computational overhead by over 85% compared to the standard full recomputation approach.
  arXiv  Detail & Related papers  (2024-07-03T14:34:03Z)
• Training-Free Exponential Context Extension via Cascading KV Cache [49.608367376911694]
  We introduce a novel mechanism that leverages cascading sub-cache buffers to selectively retain the most relevant tokens. Our method reduces prefill stage latency by a factor of 6.8 when compared to flash attention on 1M tokens.
  arXiv  Detail & Related papers  (2024-06-24T03:59:17Z)
• Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum [30.46329559544246]
  We introduce dataset decomposition, a novel variable sequence length training technique. We train an 8k context-length 1B model at the same cost as a 2k context-length model trained with the baseline approach. Experiments on a web-scale corpus demonstrate that our approach significantly enhances performance on standard language evaluations and long-context benchmarks.
  arXiv  Detail & Related papers  (2024-05-21T22:26:01Z)
• Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs [61.40047491337793]
  We present Hierarchical cOntext MERging (HOMER), a new training-free scheme designed to overcome the limitations of large language models. HomeR uses a divide-and-conquer algorithm, dividing long inputs into manageable chunks. A token reduction technique precedes each merging, ensuring memory usage efficiency.
  arXiv  Detail & Related papers  (2024-04-16T06:34:08Z)
• Hydragen: High-Throughput LLM Inference with Shared Prefixes [39.622276190997205]
  Hydragen is a hardware-aware exact implementation of attention with shared prefixes. Our method can improve end-to-end CodeLlama-13b throughput by up to 32x against competitive baselines.
  arXiv  Detail & Related papers  (2024-02-07T18:53:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.