Growing parameter capacity as training progress #1539
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This is done through Optimizer. Two arguments are added for optimizer: capacity_ratio: scheduler for controlling the number of training elements of a parameter. min_capacity: minimal number elements of each parameter being traing To dynamically change capacity, we assign a random number for each element of the parameter. An element is turned on if its assigned random number is less than capacity_ratio. To save memory, we don't store the random numbers. Instead, we save the random number generator state.
Haichao-Zhang
approved these changes
Sep 21, 2023
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This is done through Optimizer. Two arguments are added for optimizer:
capacity_ratio: scheduler for controlling the number of training elements of a parameter.
min_capacity: minimal number elements of each parameter being traing
To dynamically change capacity, we assign a random number for each element of the parameter. An element is turned on if its assigned random number is less than capacity_ratio. To save memory, we don't store the random numbers. Instead, we save the random number generator state.