# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['LARS']
from typing import List
import jax.numpy as jn
from objax.module import Module, ModuleList
from objax.typing import JaxArray
from objax.util import class_name
from objax.variable import TrainRef, StateVar, TrainVar, VarCollection
[docs]
class LARS(Module):
"""Layerwise adaptive rate scaling (LARS) optimizer.
See https://arxiv.org/abs/1708.03888
"""
[docs]
def __init__(self, vc: VarCollection,
momentum: float = 0.9,
weight_decay: float = 1e-4,
tc: float = 1e-3,
eps: float = 1e-5):
"""Constructor for LARS optimizer.
Args:
vc: collection of variables to optimize.
momentum: coefficient used for the moving average of the gradient.
weight_decay: weight decay coefficient.
tc: trust coefficient eta ( < 1) for trust ratio computation.
eps: epsilon used for trust ratio computation.
"""
self.momentum = momentum
self.weight_decay = weight_decay
self.tc = tc
self.eps = eps
self.train_vars = ModuleList(TrainRef(x) for x in vc.subset(TrainVar))
self.m = ModuleList(StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
[docs]
def __call__(self, lr: float, grads: List[JaxArray]):
"""Updates variables based on LARS algorithm.
Args:
lr: learning rate. The LARS paper suggests using lr = lr_0 * (1 -t/T)**2,
where t is the current epoch number and T the maximum number of epochs.
grads: the gradients to apply.
"""
assert len(grads) == len(self.train_vars), 'Expecting as many gradients as trainable variables'
for g, p, m in zip(grads, self.train_vars, self.m):
p_norm = jn.linalg.norm(p.value)
g_norm = jn.linalg.norm(g)
trust_ratio = self.tc * p_norm / (g_norm + self.weight_decay * p_norm + self.eps)
local_lr = lr * jn.maximum(jn.logical_or(p_norm == 0, g_norm == 0), trust_ratio)
m.value = self.momentum * m.value + local_lr * (g + self.weight_decay * p.value)
p.value -= m.value
def __repr__(self):
return f'{class_name(self)}(momentum={self.momentum}, weight_decay={self.weight_decay}, ' \
f'tc={self.tc}, eps={self.eps})'