Contrastive Learning Batch Chunking for Memory-Limited Encoder Training
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Solution Overview
Problem
Neural networks face a memory bottleneck when training large models with large batch sizes due to limited on-device memory, particularly in devices like GPUs and TPUs, which hinders the improvement in representation quality and performance on downstream tasks.
Innovation Solution
The system employs techniques to train image and text encoder neural networks using contrastive learning with large batch sizes by partitioning batches into chunks, performing forward and backward passes on each chunk, and updating parameters without storing intermediate hidden states or cumulative gradients, thus overcoming memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large batch sizes are used during contrastive learning training, then representation quality improves due to more diverse negative examples, but memory consumption exceeds available on-device memory
Solution Approach 1:
The patent divides a large training batch into multiple smaller chunks that can be processed sequentially within available memory constraints. Each chunk is processed independently through forward and backward passes, allowing the system to handle large batch sizes (e.g., 1024 or 2048) by processing them in manageable segments (e.g., 64 or 128 samples per chunk) without exceeding device memory capacity.
2Measurement precision
If large model sizes are used to improve contrastive learning performance, then representation quality improves, but memory consumption for storing gradient vectors exceeds available on-device memory
Solution Approach 1:
The patent segments the gradient computation process by processing samples in chunks and accumulating gradients incrementally. Instead of computing and storing a single large gradient vector for the entire batch, the system computes gradients for each chunk separately and accumulates them, allowing large model training without requiring proportional increases in memory for gradient storage.
Solution Approach 2:
The patent performs multiple forward and backward passes over the same data chunks to accumulate gradients that would otherwise require storing the entire batch in memory. This partial processing approach allows the system to handle larger models by performing repeated computations on smaller data segments rather than attempting to load everything into memory at once.
3Measurement precision
If batch size is increased to provide more negative examples for contrastive learning, then embedding quality improves with better clustering of similar inputs and separation of dissimilar inputs, but training becomes infeasible on devices with limited memory
Solution Approach 1:
The patent enables training feasibility on memory-constrained devices by segmenting the batch processing into chunks that fit within available memory. This allows the system to achieve the benefits of large batch sizes (improved embedding quality through more diverse negative examples) while maintaining ease of operation on standard GPU/TPU hardware with limited memory resources.
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.