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# coding=utf-8
# Copyright 2018 The Google AI Team Authors.
#
# 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
#
# http://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.
# Lint as: python2, python3
"""Functions and classes related to optimization (weight updates)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import six
import tensorflow.compat.v1 as tf
# pylint: disable=g-direct-tensorflow-import
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
# pylint: enable=g-direct-tensorflow-import
class LAMBOptimizer(tf.train.Optimizer):
"""LAMB (Layer-wise Adaptive Moments optimizer for Batch training)."""
# A new optimizer that includes correct L2 weight decay, adaptive
# element-wise updating, and layer-wise justification. The LAMB optimizer
# was proposed by Yang You, Jing Li, Jonathan Hseu, Xiaodan Song,
# James Demmel, and Cho-Jui Hsieh in a paper titled as Reducing BERT
# Pre-Training Time from 3 Days to 76 Minutes (arxiv.org/abs/1904.00962)
def __init__(self,
learning_rate,
weight_decay_rate=0.0,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=None,
exclude_from_layer_adaptation=None,
name="LAMBOptimizer"):
"""Constructs a LAMBOptimizer."""
super(LAMBOptimizer, self).__init__(False, name)
self.learning_rate = learning_rate
self.weight_decay_rate = weight_decay_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.exclude_from_weight_decay = exclude_from_weight_decay
# exclude_from_layer_adaptation is set to exclude_from_weight_decay if the
# arg is None.
# TODO(jingli): validate if exclude_from_layer_adaptation is necessary.
if exclude_from_layer_adaptation:
self.exclude_from_layer_adaptation = exclude_from_layer_adaptation
else:
self.exclude_from_layer_adaptation = exclude_from_weight_decay
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""See base class."""
assignments = []
for (grad, param) in grads_and_vars:
if grad is None or param is None:
continue
param_name = self._get_variable_name(param.name)
m = tf.get_variable(
name=six.ensure_str(param_name) + "/adam_m",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
v = tf.get_variable(
name=six.ensure_str(param_name) + "/adam_v",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
# Standard Adam update.
next_m = (
tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
next_v = (
tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
tf.square(grad)))
update = next_m / (tf.sqrt(next_v) + self.epsilon)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want ot decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
if self._do_use_weight_decay(param_name):
update += self.weight_decay_rate * param
ratio = 1.0
if self._do_layer_adaptation(param_name):
w_norm = linalg_ops.norm(param, ord=2)
g_norm = linalg_ops.norm(update, ord=2)
ratio = array_ops.where(math_ops.greater(w_norm, 0), array_ops.where(
math_ops.greater(g_norm, 0), (w_norm / g_norm), 1.0), 1.0)
update_with_lr = ratio * self.learning_rate * update
next_param = param - update_with_lr
assignments.extend(
[param.assign(next_param),
m.assign(next_m),
v.assign(next_v)])
return tf.group(*assignments, name=name)
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if not self.weight_decay_rate:
return False
if self.exclude_from_weight_decay:
for r in self.exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
def _do_layer_adaptation(self, param_name):
"""Whether to do layer-wise learning rate adaptation for `param_name`."""
if self.exclude_from_layer_adaptation:
for r in self.exclude_from_layer_adaptation:
if re.search(r, param_name) is not None:
return False
return True
def _get_variable_name(self, param_name):
"""Get the variable name from the tensor name."""
m = re.match("^(.*):\\d+$", six.ensure_str(param_name))
if m is not None:
param_name = m.group(1)
return param_name
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