Interlocking Backpropagation for Transformer Training Efficiency
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Solution Overview
Problem
Training of large transformer models is inefficient due to global learning methods that cause idle time and excessive communication between accelerators, while local learning compromises model accuracy by limiting inter-layer communication.
Innovation Solution
The implementation of interlocking backpropagation, where auxiliary classification layers are used to optimize local losses across subsets of processing units, allowing for earlier parameter updates and improved information flow within the network, effectively addressing the inefficiencies of global learning and accuracy issues of local learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If global learning is used to train distributed models, then model accuracy is maintained, but training efficiency deteriorates due to idle time and excessive communication
Solution Approach 1:
The model is divided into multiple processing units that can operate independently on different data batches. Each processing unit maintains local gradients and can perform parameter updates without waiting for global gradient synchronization, enabling parallel training operations while maintaining model accuracy through periodic global aggregations.
Solution Approach 2:
Processing units perform preliminary parameter updates using local gradients before global synchronization occurs. This preliminary action allows each unit to make progress on its local data without idle waiting time, and the subsequent global aggregation consolidates these local improvements while maintaining overall model coherence.
2Productivity
If local learning is used to improve training efficiency, then idle time is reduced, but model accuracy deteriorates due to limited inter-layer communication
Solution Approach 1:
A global aggregation mechanism provides feedback to processing units by periodically collecting local gradients and updating global model parameters. This feedback loop ensures that all processing units remain synchronized with the overall model state, maintaining accuracy while allowing local units to operate independently between aggregations.
Solution Approach 2:
Local gradients from multiple processing units are merged through global aggregation to form updated model parameters. This merging process combines the benefits of local independent computation with the coherence of global model state, achieving both efficiency and accuracy.
3Manufacturing precision
If global learning is used to ensure accurate gradient propagation, then communication overhead increases significantly
Solution Approach 1:
Global gradient aggregation and parameter synchronization occur periodically rather than continuously. Processing units perform local computations independently between periodic global aggregations, reducing communication frequency and overhead while maintaining gradient propagation accuracy through these periodic synchronization events.
Data Source
AI summary
A method for training the transformer model that strikes a middle ground between local and global learning by using interlocking backpropagation. Instead of training with one single global objective, or training with each accelerator having its own local objective, the method trains a large-scale network with auxiliary classification layers. The auxiliary classification layers use local losses to optimize a subset of the network. The local losses may be computed based on a group of processing units. Different groups of processing units may contain overlapping processing units such that there is indirect communication flow throughout the network.