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Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

TL;DR

DejaVu accelerates LLM inference by predicting contextual sparsity, reducing OPT-175B latency by over 2x.

cs.LG 🔴 Advanced 2023-10-26 11 views
Zichang Liu Jue Wang Tri Dao Tianyi Zhou Binhang Yuan Zhao Song Anshumali Shrivastava Ce Zhang Yuandong Tian Christopher Re Beidi Chen
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large language models sparsity inference acceleration contextual learning hardware optimization

Key Findings

Methodology

DejaVu system accelerates large language model (LLM) inference by predicting contextual sparsity. It uses a low-cost algorithm to predict sparsity for each layer's input, combined with asynchronous and hardware-aware implementation to enhance efficiency. Core components include sparsity predictors and asynchronous execution mechanisms.

Key Results

  • DejaVu achieves over 2x reduction in inference latency on OPT-175B compared to FasterTransformer, without compromising model quality.
  • Compared to the Hugging Face implementation, inference latency is reduced by over 6x, maintaining contextual learning ability.
  • Experiments validated consistent performance across different datasets, notably on OpenBookQA and Wiki-Text.

Significance

This research introduces contextual sparsity to solve the challenge of accelerating LLM inference without compromising quality. It holds significant academic value and offers more efficient LLM application solutions for industry, especially for latency-sensitive applications.

Technical Contribution

DejaVu's technical contributions include achieving sparsity prediction without retraining the model and realizing practical time acceleration on modern hardware. It provides new theoretical guarantees and engineering implementations, particularly in hardware-optimized sparse matrix multiplication.

Novelty

DejaVu is the first to implement contextual sparsity prediction in LLMs, significantly differing from traditional static sparsity methods. Unlike existing methods, it does not sacrifice contextual learning ability and achieves significant acceleration on hardware.

Limitations

  • The method may fail to accurately predict sparsity in extreme input scenarios, affecting acceleration.
  • Asynchronous execution overhead may offset some acceleration benefits on specific hardware configurations.

Future Work

Future work could explore more complex sparsity prediction algorithms and optimize DejaVu's implementation on more types of hardware. Additionally, researching how to apply contextual sparsity in multi-task learning scenarios is a potential direction.

AI Executive Summary

Large language models (LLMs) like GPT-3 and OPT-175B are computationally expensive at inference time, especially for latency-sensitive applications. Traditional sparsification methods often require retraining or sacrifice the model's contextual learning ability, and they struggle to achieve real-time acceleration on modern hardware.

The DejaVu system addresses this challenge by predicting contextual sparsity. It uses a low-cost algorithm to dynamically predict sparsity at each layer's input and combines asynchronous and hardware-aware implementation to accelerate inference. Experimental results show that DejaVu achieves over 2x reduction in inference latency on OPT-175B compared to FasterTransformer, without compromising model quality.

This breakthrough not only holds significant academic value but also offers more efficient LLM application solutions for industry. Future work will explore more complex sparsity prediction algorithms and applications in multi-task learning scenarios.

Deep Analysis

Background

Large language models (LLMs) like GPT-3 and OPT-175B have made significant advances in natural language processing, but they are computationally expensive at inference time. Traditional sparsification methods, such as weight pruning and quantization, can reduce computation but often require retraining the model or fail to achieve real-time acceleration on modern hardware.

Core Problem

The core problem of LLM inference is how to reduce computational costs without compromising model quality and contextual learning ability. Existing methods struggle to achieve time acceleration on modern hardware and often require sacrificing some model capabilities.

Innovation

DejaVu achieves LLM inference acceleration by predicting contextual sparsity. It does not require retraining the model and achieves sparsity prediction without compromising quality, realizing practical time acceleration on modern hardware.

Methodology

  • �� Use a low-cost algorithm to predict sparsity for each layer's input
  • �� Combine asynchronous and hardware-aware implementation to enhance efficiency
  • �� Conduct experiments on OPT-175B to ensure model quality is maintained

Experiments

Experiments were conducted on OPT-175B, 66B, and 30B models using OpenBookQA and Wiki-Text datasets. Comparisons were made with FasterTransformer and Hugging Face implementations to evaluate inference latency and model quality.

Results

DejaVu achieves over 2x reduction in inference latency on OPT-175B compared to FasterTransformer, without compromising model quality. Compared to the Hugging Face implementation, inference latency is reduced by over 6x.

Applications

DejaVu is suitable for large-scale natural language processing applications requiring efficient inference, especially in latency-sensitive scenarios such as real-time translation and intelligent customer service.

Limitations & Outlook

In some extreme input scenarios, sparsity prediction may be inaccurate, affecting acceleration. Additionally, asynchronous execution overhead may offset some acceleration benefits on specific hardware configurations.

Plain Language Accessible to non-experts

Imagine a factory with many machines, each with its own task. DejaVu is like a smart factory manager who decides which machines need to run based on each order's specific needs, allowing the factory to complete orders more efficiently without wasting resources.

ELI14 Explained like you're 14

Imagine you're playing a game with lots of tasks to complete. DejaVu is like a super-smart assistant that helps you decide which tasks need to be done first and which can wait. This way, you can finish the game faster without wasting time!

Glossary

Contextual Sparsity

Dynamically selects attention heads and MLP parameters based on input to reduce computation.

Used to accelerate LLM inference.

Asynchronous Execution

Executes different tasks at different times to improve efficiency.

Used to optimize the inference process.

Hardware-aware

Optimizes algorithm implementation based on hardware characteristics.

Used to enhance inference efficiency.

OPT-175B

A large language model with 175 billion parameters.

Used as an experimental subject to validate DejaVu's effectiveness.

FasterTransformer

An efficient inference library developed by Nvidia.

Compared with DejaVu for performance evaluation.

Open Questions Unanswered questions from this research

  • 1 How to apply contextual sparsity in multi-task learning?
  • 2 How to further optimize DejaVu's implementation in more complex hardware environments?

Applications

Immediate Applications

Real-time Translation

Accelerate LLM inference with DejaVu to achieve faster translation speeds and enhance user experience.

Long-term Vision

Intelligent Customer Service

Apply DejaVu in intelligent customer service systems to improve response speed and reduce computational resource consumption.

Abstract

Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM's in-context learning ability, or do not yield wall-clock time speedup on modern hardware. We hypothesize that contextual sparsity, which are small, input-dependent sets of attention heads and MLP parameters that yield approximately the same output as the dense model for a given input, can address these issues. We show that contextual sparsity exists, that it can be accurately predicted, and that we can exploit it to speed up LLM inference in wall-clock time without compromising LLM's quality or in-context learning ability. Based on these insights, we propose DejaVu, a system that uses a low-cost algorithm to predict contextual sparsity on the fly given inputs to each layer, along with an asynchronous and hardware-aware implementation that speeds up LLM inference. We validate that DejaVu can reduce the inference latency of OPT-175B by over 2X compared to the state-of-the-art FasterTransformer, and over 6X compared to the widely used Hugging Face implementation, without compromising model quality. The code is available at https://github.com/FMInference/DejaVu.

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