ANN Checkpointing During Propagation
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Solution Overview
Problem
Deep learning models, particularly in natural language processing, require significant computational resources and face challenges in efficiently checkpointing and restarting due to large parameter sizes, leading to high overhead in storing and recovering model states during interruptions.
Innovation Solution
A processor-implemented method for checkpointing artificial neural network (ANN) models that stores information about the model state during forward and backward propagation operations, allowing for optimized checkpointing by determining completion status and performing checkpointing in units of layers, thereby reducing overhead and enabling efficient restarts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If checkpointing is performed frequently to ensure reliable recovery, then reliability is improved, but checkpointing overhead increases
Solution Approach 1:
The system performs preliminary actions by initiating checkpointing operations during forward propagation and backward propagation phases before weight updates are completed. This allows checkpointing to be prepared in advance during computation phases, reducing the impact on overall training time while ensuring reliable recovery points are established.
Solution Approach 2:
The system dynamically adjusts checkpointing behavior by determining completion status at critical time points and selectively performing checkpointing based on whether operations are completed. This dynamic approach allows the system to optimize between reliability and overhead by adapting checkpointing frequency and timing to actual computational state.
2Manufacturing precision
If checkpointing is performed after weight update operation, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
Instead of waiting for weight update completion, the system performs checkpointing during forward propagation and backward propagation operations. This preliminary action approach captures model state information before the weight update phase, enabling checkpointing to occur during computation rather than after, thus improving productivity while maintaining sufficient precision for recovery.
Solution Approach 2:
The system maintains continuity of useful action by performing checkpointing operations during the forward propagation and backward propagation phases, which are part of the normal training computation flow. This allows checkpointing to occur continuously during computation rather than as a separate post-processing step, keeping the training process productive while still capturing necessary model state information.
3Reliability
If full model state is stored for recovery, then reliability is improved, but quantity of substance increases
Solution Approach 1:
The system segments the model state storage by performing checkpointing in units of layers rather than storing the complete model state at once. This segmentation approach divides the large model state into manageable layer-based checkpoints, reducing individual storage requirements while maintaining the ability to recover and continue training from any checkpoint.
Solution Approach 2:
The system applies partial action by performing checkpointing only when necessary and only for the portions of the model state that are currently being computed or updated. Rather than continuously storing the entire model state, the system stores only the necessary state information at critical points, reducing storage requirements while maintaining sufficient reliability for recovery.
Data Source
AI summary
A processor-implemented method with checkpointing includes: performing an operation for learning of an artificial neural network (ANN) model; and performing a checkpointing to store information about a state of the ANN model, simultaneously with performing the operation for the learning of the ANN model.