Memory Efficient Optimizers with 4-bit States (2309.01507v3)

Abstract: Optimizer states are a major source of memory consumption for training neural networks, limiting the maximum trainable model within given memory budget. Compressing the optimizer states from 32-bit floating points to lower bitwidth is promising to reduce the training memory footprint, while the current lowest achievable bitwidth is 8-bit. In this work, we push optimizer states bitwidth down to 4-bit through a detailed empirical analysis of first and second moments. Specifically, we find that moments have complicated outlier patterns, that current block-wise quantization cannot accurately approximate. We use a smaller block size and propose to utilize both row-wise and column-wise information for better quantization. We further identify a zero point problem of quantizing the second moment, and solve this problem with a linear quantizer that excludes the zero point. Our 4-bit optimizers are evaluated on a wide variety of benchmarks including natural language understanding, machine translation, image classification, and instruction tuning. On all the tasks our optimizers can achieve comparable accuracy with their full-precision counterparts, while enjoying better memory efficiency.

• The paper presents a refined quantization strategy that reduces optimizer state precision from 32-bit to 4-bit while maintaining convergence.
• It resolves the zero point problem in second moment quantization using a linear quantizer to ensure accurate gradient updates.
• Extensive evaluations across NLP, translation, and image classification benchmarks demonstrate significant memory savings without sacrificing accuracy.

Memory Efficient Optimizers with 4-bit States: A Comprehensive Overview

The presented paper introduces a significant advancement in the field of memory-efficient optimization methods for training neural networks, particularly large-scale models. The focus of the research is on reducing the bitwidth of optimizer states, notably the first and second moments used in stateful optimizers like Adam, from the conventional 32-bit floating point to just 4-bit representations.

Motivation and Context

The authors underscore the critical role of optimizer states in the memory consumption profile of large neural networks. With the growing size of such models, memory limitations impose a bottleneck on their effective training. Traditional approaches have capped the quantization of these states at 8 bits, primarily due to challenges in maintaining accuracy and convergence with lower bit precision. The presented work ambitiously pushes this boundary to 4-bit representations.

Key Contributions

To achieve this, the authors undertake a meticulous empirical analysis of the first and second moments intrinsic to optimizers to identify effective quantization strategies. The paper introduces several novel techniques:

1. Refined Quantization Strategy: Recognizing the complexity in the outlier patterns of the moments, the paper proposes a smaller block size for quantization and employs both row-wise and column-wise information to enhance the quantization process.
2. Zero Point Problem Resolution: The paper identifies a 'zero point' problem, particularly acute when quantizing the second moment. This problem leads to inaccuracies in step direction when quantized values unintentionally become zero. A linear quantizer excluding the zero point addresses this issue effectively.
3. Comprehensive Evaluation: The proposed 4-bit optimizers undergo extensive evaluation across various benchmarks including natural language understanding, machine translation, image classification, and instruction tuning. The optimizers demonstrate comparable accuracy to their full-precision counterparts while achieving superior memory efficiency.

Numerical Results and Implications

Numerically, the 4-bit optimizers maintain accuracy across examined tasks and manage to reduce the memory footprints significantly compared to existing solutions. This yields practical benefits as the optimizers can be deployed to train larger models under fixed memory constraints. This research opens avenues for further optimization in memory-hungry domains like deep learning and paves the way for new applications and models that would have been previously constrained by memory limitations.

Future Directions

The findings inspire multiple directions for future research, especially in further improving the efficiency and applicability of low-bit optimizers. Exploration could include compounded techniques like integrating these 4-bit optimizers with other memory-saving tactics such as activation quantization or gradient checkpointing. Further, the presented quantization strategies may spur novel developments in specialized hardware acceleration for neural network training.

In conclusion, this paper presents a rigorous approach to pushing the boundaries of memory efficiency in neural network training through innovative quantization techniques. The implications of this work are profound, offering substantial contributions to both theoretical advancement and practical application in AI.