LARSV2

Applicability

Product	Supported (√/x)
Atlas 350 Accelerator Card	√
Atlas A3 training product/Atlas A3 inference product	√
Atlas A2 training product/Atlas A2 inference product	√
Atlas 200I/500 A2 inference product	X
Atlas inference product	X
Atlas training product	√

Description

This operator scales gradients based on the norm of weight and the norm of gradient at different levels using different learning rates. It is used to improve the training precision in large batch size scenarios and is used for large-scale cluster training to reduce the training time.

Prototype

def LARSV2(input_weight,
           input_grad,
           weight_decay,
           learning_rate,
           hyperpara=0.001,
           epsilon=0.00001,
           use_clip=False,
           name=None)

Parameters

Parameter	Input/Output	Description
input_weight	Input	Weight tensor of type float.
input_grad	Input	Weight gradient tensor of type float.
weight_decay	Input	Scalar tensor of type float.
learning_rate	Input	Scalar tensor of type float, indicating the learning rate.
hyperpara	Input	Scalar of type float, for the hyperparameter of the operator. Generally it is set to 0.001.
epsilon	Input	A scalar, added to avoid dividing by zero. Generally it is set to 1e-5.
use_clip	Input	A bool. Defaults to False. If this parameter is set to True, the scaling coefficient must be within a specified range.
name	Input	Name of the network layer.

Returns

Result gradient tensor

Example

from npu_bridge.npu_init import *
layers = npu_ops.LARSV2(input_weight , input_grad, weight_decay, learning_rate)

Parent topic: npu_bridge.estimator.npu_ops