Optimizing Throughput for Constraint-Aware Execution of Large Language Models

Core Concepts

ExeGPT, a distributed system, finds and runs with an optimal execution schedule to maximize inference throughput while satisfying a given latency constraint by leveraging the distribution of input and output sequences to effectively allocate resources and determine optimal execution configurations.

Abstract

The paper presents ExeGPT, a distributed system designed for constraint-aware large language model (LLM) inference. ExeGPT aims to maximize inference throughput while satisfying a given latency constraint. Key highlights: ExeGPT leverages the distribution of input and output sequences to effectively allocate resources and determine optimal execution configurations, including batch sizes and partial tensor parallelism. It introduces two scheduling strategies: Round-Robin Allocation (RRA) and Workload-Aware Allocation (WAA), which decouple the execution of encoding and decoding to address the challenges of diminishing decoding batches and pipeline bubbles in existing systems. The scheduling strategies provide four control mechanisms (batch size, decoder micro-batch, partial tensor parallelism, and encoding frequency) to enable flexible trade-offs between throughput and latency. The paper formulates an optimization problem to maximize throughput under a given latency constraint and proposes an efficient scheduling algorithm based on the branch-and-bound method, exploiting the monotonicity of the control variables. Extensive evaluation on six LLM instances and five NLP tasks shows that ExeGPT achieves up to 15.2x improvement in throughput and 6x improvement in latency compared to the state-of-the-art FasterTransformer system.

Stats

Generating a single token in LLMs can require hundreds of billions of FLOPs. The correlation coefficient between input and output sequence lengths ranges from 0.08-0.21 for most tasks, except translation which has a high correlation of 0.57-0.94.

Quotes

"Compared to other neural networks, LLM inference is challenging due to their large size and irregular executions." "To mitigate this problem, ORCA proposed iteration-level scheduling that adds a new input data into a running batch to replace a completed one, but it does not consider the cost difference between input encoding and output decoding."

Key Insights Distilled From

ExeGPT

by Hyungjun Oh,... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07947.pdf

Deeper Inquiries

How can the scheduling strategies in ExeGPT be extended to handle dynamic changes in the input/output sequence length distributions during runtime

In order to handle dynamic changes in the input/output sequence length distributions during runtime, the scheduling strategies in ExeGPT can be extended by implementing adaptive algorithms that continuously monitor the sequence length distributions and adjust the scheduling parameters accordingly. Here are some ways to achieve this: Dynamic Workload Adjustment: Implement a mechanism that dynamically adjusts the encoder and decoder batch sizes based on real-time observations of the input and output sequence lengths. By continuously monitoring the sequence distributions and workload variations, the system can adapt its scheduling parameters to optimize resource utilization and maintain consistent throughput. Real-time Profiling: Integrate real-time profiling capabilities into the system to capture changes in the sequence length distributions and adjust the scheduling strategies accordingly. By analyzing the incoming data streams and adapting to the dynamic workload patterns, the system can optimize its scheduling decisions on the fly. Machine Learning Models: Utilize machine learning models to predict changes in the sequence length distributions and optimize the scheduling parameters proactively. By training models on historical data and continuously updating them with real-time information, the system can make informed decisions to handle dynamic changes effectively. Feedback Loop: Implement a feedback loop mechanism that collects performance metrics during runtime and uses this feedback to adjust the scheduling strategies. By continuously evaluating the system's performance against predefined metrics and adapting the scheduling parameters based on the feedback, the system can dynamically respond to changes in the workload.

What are the potential drawbacks or limitations of the branch-and-bound optimization algorithm used in ExeGPT, and how could it be further improved

The branch-and-bound optimization algorithm used in ExeGPT has some potential drawbacks and limitations that could be further improved: Computational Complexity: The branch-and-bound algorithm can be computationally intensive, especially when dealing with a large search space and multiple control variables. This can lead to longer optimization times and increased resource consumption. Local Optima: The algorithm may get stuck in local optima, especially in high-dimensional optimization problems. This can limit the algorithm's ability to find the global optimal solution and may result in suboptimal performance. Sensitivity to Initial Conditions: The algorithm's performance can be sensitive to the initial conditions and parameter settings. Small changes in the initial configuration may lead to significantly different optimization results. To further improve the algorithm, the following strategies could be considered: Enhanced Exploration: Implement strategies to enhance exploration of the search space, such as adaptive sampling techniques or hybrid optimization methods that combine different optimization algorithms. Parallelization: Utilize parallel computing techniques to distribute the optimization tasks across multiple processors or nodes, reducing the overall optimization time and improving scalability. Fine-tuning Parameters: Experiment with different parameter settings and tuning strategies to optimize the algorithm's performance for specific optimization problems.

Given the significant performance gains of ExeGPT, how could its techniques be applied to optimize the inference of other types of large neural networks beyond language models

The techniques and strategies employed in ExeGPT to optimize the inference of large language models can be applied to optimize the inference of other types of large neural networks beyond language models. Here are some ways in which these techniques can be adapted: Image Recognition Models: Similar scheduling strategies can be applied to optimize the inference of large image recognition models, such as convolutional neural networks (CNNs). By decoupling the encoding and decoding phases and dynamically adjusting the workload based on input image sizes, the system can improve resource utilization and inference efficiency. Speech Recognition Models: The scheduling algorithms in ExeGPT can be extended to optimize the inference of large speech recognition models, such as recurrent neural networks (RNNs) or transformer-based models. By adapting the scheduling parameters to the dynamic changes in audio sequence lengths, the system can enhance inference performance and latency. Recommendation Systems: For recommendation systems based on neural networks, the techniques in ExeGPT can be utilized to optimize the inference process for large-scale models. By implementing constraint-aware scheduling and dynamic workload adjustments, the system can improve the efficiency of generating recommendations in real-time scenarios. By applying the principles and methodologies of ExeGPT to a diverse range of neural network models, it is possible to enhance the inference performance and scalability of various AI applications across different domains.