Memory-Optimized Contrastive Learning Through Chunked Batches
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Solution Overview
Problem
Training large neural networks with large batch sizes is bottlenecked by the limited memory of devices like GPUs and TPUs, which limits the improvement in representation quality and performance on downstream tasks.
Innovation Solution
Implement techniques that allow training with large batch sizes by partitioning batches into chunks, performing forward and backward passes on each chunk, and using optimized gradient updates without storing intermediate states or cumulative gradients, thus overcoming memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If batch size is increased to improve representation quality, then embedding quality improves, but memory usage exceeds device memory limits
Solution Approach 1:
The batch is divided into multiple chunks that can be processed separately. Each chunk is processed through forward and backward passes independently, allowing the system to handle larger effective batch sizes by processing smaller segments sequentially, thus reducing peak memory usage while maintaining the benefits of large batch training
Solution Approach 2:
The patent processes gradients in a sequential/streaming manner rather than storing all gradients simultaneously. By processing chunks sequentially and updating parameters incrementally, the system transforms the memory-intensive parallel gradient storage approach into a memory-efficient sequential processing approach
2Measurement precision
If encoder neural network size is increased to improve contrastive learning performance, then representation quality improves, but gradient vector storage requirements exceed available memory
Solution Approach 1:
The gradient computation is segmented into chunk-level gradients rather than computing and storing a single large gradient vector for the entire batch. Each chunk produces its own gradient vector that is processed and applied incrementally, reducing the memory footprint from O(batch_size × parameters) to O(chunk_size × parameters)
Solution Approach 2:
The patent applies gradient updates incrementally based on chunks rather than waiting to compute and apply the full batch gradient. This partial action approach allows large model training by applying updates in smaller steps, reducing peak memory requirements for gradient storage
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using memory-optimized contrastive learning to train image encoder and text encoder neural networks.