Gradient Pruning for Efficient Machine Learning Model Training
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Solution Overview
Problem
Conventional machine learning model training methods are hindered by the complexity and resource intensity associated with a large number of trainable parameters, leading to inefficiencies in training time, compute resources, and energy usage.
Innovation Solution
The method employs gradient pruning, where a subset of gradients in the gradient tensor is selectively updated based on criteria such as threshold values or percentiles, reducing the number of operations required during each training round and converting the gradient tensor into a sparse representation for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of trainable parameters is increased to improve model expressivity and performance, then model performance is improved, but training complexity and resource consumption increase
Solution Approach 1:
The patent extracts and removes insignificant gradients from the gradient tensor based on magnitude thresholds or percentiles. By identifying and eliminating gradients below a certain significance level, the system reduces the number of parameters that need to be updated during training, thereby reducing training complexity while maintaining model performance.
Solution Approach 2:
The patent applies different update rules to different portions of the gradient tensor based on local gradient magnitudes. Significant gradients (above threshold) are updated using standard gradient descent, while insignificant gradients (below threshold) are either skipped or updated with reduced step sizes, creating a non-uniform update strategy that reduces overall computational complexity.
2Reliability
If the number of trainable parameters is increased to improve model expressivity and performance, then model performance is improved, but training time increases
Solution Approach 1:
By extracting and removing insignificant gradients from the training process, the system reduces the number of parameter updates required per training iteration. This extraction of unnecessary update operations directly reduces training time while preserving the essential learning dynamics needed for model performance.
Solution Approach 2:
The patent applies partial action by updating only a subset of parameters (those with significant gradients) rather than all parameters. This selective updating reduces the computational workload per iteration, thereby reducing training time while maintaining sufficient model performance through the updated parameters.
3Reliability
If the number of trainable parameters is increased to improve model expressivity and performance, then model performance is improved, but compute resource consumption increases
Solution Approach 1:
The system extracts and eliminates insignificant gradients from the computation pipeline, reducing the number of floating-point operations required during each training iteration. This reduction in computational operations directly decreases compute resource consumption and power usage while maintaining model performance.
Solution Approach 2:
By performing partial updates on only the significant portion of parameters rather than all parameters, the system reduces compute resource consumption. The partial action approach maintains essential learning for critical parameters while avoiding wasteful computation on parameters with negligible gradient magnitudes.
4Reliability
If the number of trainable parameters is increased to improve model expressivity and performance, then model performance is improved, but energy use increases
Solution Approach 1:
By extracting and removing insignificant gradients from the training computation, the system reduces the total energy required for training. Fewer parameter updates mean fewer computational operations, which directly translates to reduced energy consumption while preserving model performance.
Solution Approach 2:
The partial updating strategy reduces energy use by avoiding unnecessary computational work on parameters with insignificant gradients. The energy savings come from performing computations only where they are most impactful, maintaining model performance while reducing overall energy consumption.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for improved machine learning using gradient pruning, comprising computing, using a first batch of training data, a first gradient tensor comprising a gradient for each parameter of a parameter tensor for a machine learning model; identifying a first subset of gradients in the first gradient tensor based on a first gradient criteria; and updating a first subset of parameters in the parameter tensor based on the first subset of gradients in the first gradient tensor.