Key Points
1. The paper introduces LazyLLM, a method for improving the efficiency of long context language model (LLM) inference by selectively computing the KV for tokens important for the next token prediction and deferring the computation of remaining tokens to later steps.
2. Standard LLM inference consists of two sequential stages: prefilling and decoding. The prefilling stage involves computing and saving the KV cache of each token from the prompt and predicting the first token. However, for long prompts, this prefilling stage can be slow, leading to inefficiencies in LLM inference.
3. LazyLLM allows the model to dynamically select different subsets of tokens from the context in different generation steps, improving the speed of generating the first token without fine-tuning.
4. The paper evaluates LazyLLM on various tasks using large language models such as Llama 2 and XGen. It demonstrates that LazyLLM accelerates the prefilling stage of the LLM model by 2.34× while maintaining accuracy in tasks such as multi-document question-answering.
5. The proposed LazyLLM method is shown to be universal, training-free, and effective. It can be seamlessly integrated with existing transformer-based LLMs to significantly improve inference speed during both prefilling and decoding stages.
6. The paper compares LazyLLM with baseline methods such as prompt compression and static token pruning, showing that LazyLLM consistently achieves better time-to-first-token (TTFT) speedup with negligible accuracy drop across multiple tasks.
7. The study analyzes the impact of different parameters in LazyLLM, such as the number of pruning layers, the locations of these pruning layers, and the number of tokens pruned within these layers. It shows that LazyLLM provides a good trade-off between accuracy and inference speed.
8. LazyLLM is demonstrated to reduce the total computation by not computing all tokens in the prompt, providing additional speedup to the overall generation process across diverse tasks.
9. The paper concludes that LazyLLM is a novel technique for efficient LLM inference, effectively reducing time-to-first-token with negligible performance loss, and can be seamlessly integrated with existing transformer-based LLMs without requiring any fine-tuning. References: - The paper cites multiple other works in the field of LLM inference efficiency, including those related to token pruning, efficient inference techniques, and large language models.
Summary
This research paper proposes a novel method called LazyLLM to address the computation challenges in transformer-based large language models (LLMs), particularly in the prefilling stage where the need to compute the KV cache for all tokens can become a bottleneck.
Inference Challenges of Transformer-based LLMs
The inference of transformer-based LLMs consists of two sequential stages: a prefilling stage to compute the KV cache of prompts and generate the first token, and a decoding stage to generate subsequent tokens. For long prompts, the KV cache must be computed for all tokens during the prefilling stage, which can significantly increase the time needed to generate the first token, making the prefilling stage a potential bottleneck.
LazyLLM Method Details
LazyLLM selectively computes the KV for tokens essential for next token prediction, both in the prefilling and decoding stages. The method allows the language model to dynamically select different subsets of tokens from the context in different generation steps, even though some tokens may have been pruned in previous steps. This selective computation significantly accelerates the generation without fine-tuning, as demonstrated through experiments across various tasks and datasets.
Experimental Results of LazyLLM
Extensive experiments on the LongBench benchmark show that LazyLLM can improve the inference speed of large language models during both the prefilling and decoding stages, without requiring any fine-tuning. For instance, in the multi-document question-answering task, LazyLLM accelerates the prefilling stage of the Llama 2 7B model by 2.34x while maintaining accuracy. Furthermore, LazyLLM reduces the total amount of computation by selectively computing only the tokens that are important for the next token prediction, leading to additional speedups in the overall generation process.
The proposed LazyLLM technique is a generic and training-free method that can be seamlessly integrated with existing transformer-based LLMs to improve their inference efficiency, particularly in long context scenarios where the prefilling stage can become a bottleneck.
Reference: https://arxiv.org/abs/2407.14057