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482 lines
17 KiB
482 lines
17 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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When training a model, it's often useful to decay the
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learning rate during training process, this is called
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learning_rate_decay. There are many strategies to do
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this, this module will provide some classical method.
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User can also implement their own learning_rate_decay
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strategy according to this module.
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"""
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from __future__ import print_function
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import math
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from . import control_flow
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from . import nn
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from . import ops
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from . import tensor
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from ..initializer import init_on_cpu
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from ..framework import default_main_program, Parameter, unique_name, name_scope
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from ..dygraph import base as imperative_base
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from ..dygraph import learning_rate_scheduler as imperate_lr
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__all__ = [
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'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
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'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS',
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'cosine_decay', 'linear_lr_warmup'
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]
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def _decay_step_counter(begin=0):
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# the first global step is zero in learning rate decay
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global_step = nn.autoincreased_step_counter(
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counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
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global_step = tensor.cast(global_step, 'float32')
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return global_step
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def noam_decay(d_model, warmup_steps):
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"""
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Noam decay method. The numpy implementation of noam decay as follows.
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>>> import numpy as np
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>>> lr_value = np.power(d_model, -0.5) * np.min([
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>>> np.power(current_steps, -0.5),
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>>> np.power(warmup_steps, -1.5) * current_steps])
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Please reference `attention is all you need
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<https://arxiv.org/pdf/1706.03762.pdf>`_.
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Args:
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d_model(Variable): The dimensionality of input and output of model.
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warmup_steps(Variable): A super parameter.
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Returns:
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The decayed learning rate.
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"""
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with default_main_program()._lr_schedule_guard():
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if imperative_base.enabled():
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decay = imperate_lr.NoamDecay(d_model, warmup_steps)
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return decay
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else:
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global_step = _decay_step_counter(1)
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a = global_step**-0.5
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b = (warmup_steps**-1.5) * global_step
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lr_value = (d_model**-0.5) * nn.elementwise_min(a, b)
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return lr_value
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def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
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"""
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Applies exponential decay to the learning rate.
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When training a model, it is often recommended to lower the learning rate as the
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training progresses. By using this function, the learning rate will be decayed by
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'decay_rate' every 'decay_steps' steps.
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>>> if staircase == True:
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>>> decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps)
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>>> else:
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>>> decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
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Args:
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learning_rate(Variable|float): The initial learning rate.
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decay_steps(int): See the decay computation above.
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decay_rate(float): The decay rate. See the decay computation above.
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staircase(Boolean): If True, decay the learning rate at discrete intervals.
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Default: False
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Returns:
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Variable: The decayed learning rate
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Examples:
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.. code-block:: python
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base_lr = 0.1
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sgd_optimizer = fluid.optimizer.SGD(
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learning_rate=fluid.layers.exponential_decay(
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learning_rate=base_lr,
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decay_steps=10000,
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decay_rate=0.5,
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staircase=True))
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sgd_optimizer.minimize(avg_cost)
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"""
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with default_main_program()._lr_schedule_guard():
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if imperative_base.enabled():
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decay = imperate_lr.ExponentialDecay(learning_rate, decay_steps,
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decay_rate, staircase)
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return decay
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else:
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global_step = _decay_step_counter()
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div_res = global_step / decay_steps
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if staircase:
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div_res = ops.floor(div_res)
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decayed_lr = learning_rate * (decay_rate**div_res)
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return decayed_lr
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def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
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"""Applies natural exponential decay to the initial learning rate.
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>>> if not staircase:
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>>> decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
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>>> else:
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>>> decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
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Args:
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learning_rate: A scalar float32 value or a Variable. This
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will be the initial learning rate during training
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decay_steps: A Python `int32` number.
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decay_rate: A Python `float` number.
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staircase: Boolean. If set true, decay the learning rate every decay_steps.
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Returns:
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The decayed learning rate
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"""
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with default_main_program()._lr_schedule_guard():
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if imperative_base.enabled():
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decay = imperate_lr.NaturalExpDecay(learning_rate, decay_steps,
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decay_rate, staircase)
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return decay
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else:
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global_step = _decay_step_counter()
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div_res = global_step / decay_steps
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if staircase:
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div_res = ops.floor(div_res)
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decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
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return decayed_lr
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def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
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"""
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Applies inverse time decay to the initial learning rate.
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When training a model, it is often recommended to lower the learning rate as the
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training progresses. By using this function, an inverse decay function will be
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applied to the initial learning rate.
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>>> if staircase == True:
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>>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
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>>> else:
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>>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
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Args:
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learning_rate(Variable|float): The initial learning rate.
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decay_steps(int): See the decay computation above.
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decay_rate(float): The decay rate. See the decay computation above.
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staircase(Boolean): If True, decay the learning rate at discrete intervals.
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Default: False
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Returns:
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Variable: The decayed learning rate
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Examples:
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.. code-block:: python
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base_lr = 0.1
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sgd_optimizer = fluid.optimizer.SGD(
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learning_rate=fluid.layers.inverse_time_decay(
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learning_rate=base_lr,
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decay_steps=10000,
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decay_rate=0.5,
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staircase=True))
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sgd_optimizer.minimize(avg_cost)
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"""
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with default_main_program()._lr_schedule_guard():
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if imperative_base.enabled():
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decay = imperate_lr.InverseTimeDecay(learning_rate, decay_steps,
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decay_rate, staircase)
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return decay
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else:
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global_step = _decay_step_counter()
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div_res = global_step / decay_steps
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if staircase:
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div_res = ops.floor(div_res)
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decayed_lr = learning_rate / (1 + decay_rate * div_res)
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return decayed_lr
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def polynomial_decay(learning_rate,
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decay_steps,
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end_learning_rate=0.0001,
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power=1.0,
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cycle=False):
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"""
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Applies polynomial decay to the initial learning rate.
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.. code-block:: python
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if cycle:
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decay_steps = decay_steps * ceil(global_step / decay_steps)
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else:
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global_step = min(global_step, decay_steps)
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decayed_learning_rate = (learning_rate - end_learning_rate) *
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(1 - global_step / decay_steps) ^ power + end_learning_rate
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Args:
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learning_rate(Variable|float32): A scalar float32 value or a Variable. This
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will be the initial learning rate during training.
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decay_steps(int32): A Python `int32` number.
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end_learning_rate(float): A Python `float` number.
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power(float): A Python `float` number.
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cycle(bool): If set true, decay the learning rate every decay_steps.
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Returns:
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Variable: The decayed learning rate
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"""
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with default_main_program()._lr_schedule_guard():
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if imperative_base.enabled():
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decay = imperate_lr.PolynomialDecay(learning_rate, decay_steps,
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end_learning_rate, power, cycle)
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return decay
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else:
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global_step = _decay_step_counter()
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if cycle:
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div_res = ops.ceil(global_step / decay_steps)
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zero_var = tensor.fill_constant(
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shape=[1], dtype='float32', value=0.0)
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one_var = tensor.fill_constant(
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shape=[1], dtype='float32', value=1.0)
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with control_flow.Switch() as switch:
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with switch.case(global_step == zero_var):
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tensor.assign(input=one_var, output=div_res)
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decay_steps = decay_steps * div_res
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else:
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decay_steps_var = tensor.fill_constant(
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shape=[1], dtype='float32', value=float(decay_steps))
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global_step = nn.elementwise_min(
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x=global_step, y=decay_steps_var)
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decayed_lr = (learning_rate - end_learning_rate) * \
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((1 - global_step / decay_steps) ** power) + end_learning_rate
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return decayed_lr
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def piecewise_decay(boundaries, values):
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"""Applies piecewise decay to the initial learning rate.
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The algorithm can be described as the code below.
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.. code-block:: python
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boundaries = [10000, 20000]
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values = [1.0, 0.5, 0.1]
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if step < 10000:
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learning_rate = 1.0
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elif 10000 <= step < 20000:
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learning_rate = 0.5
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else:
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learning_rate = 0.1
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Args:
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boundaries: A list of steps numbers.
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values: A list of learning rate values that will be picked during
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different step boundaries.
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Returns:
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The decayed learning rate.
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"""
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with default_main_program()._lr_schedule_guard():
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if len(values) - len(boundaries) != 1:
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raise ValueError("len(values) - len(boundaries) should be 1")
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if imperative_base.enabled():
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decay = imperate_lr.PiecewiseDecay(boundaries, values, 0)
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return decay
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else:
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global_step = _decay_step_counter()
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lr = tensor.create_global_var(
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shape=[1],
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value=0.0,
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dtype='float32',
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persistable=True,
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name="learning_rate")
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with control_flow.Switch() as switch:
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for i in range(len(boundaries)):
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boundary_val = tensor.fill_constant(
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shape=[1],
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dtype='float32',
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value=float(boundaries[i]),
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force_cpu=True)
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value_var = tensor.fill_constant(
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shape=[1], dtype='float32', value=float(values[i]))
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with switch.case(global_step < boundary_val):
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tensor.assign(value_var, lr)
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last_value_var = tensor.fill_constant(
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shape=[1],
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dtype='float32',
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value=float(values[len(values) - 1]))
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with switch.default():
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tensor.assign(last_value_var, lr)
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return lr
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def cosine_decay(learning_rate, step_each_epoch, epochs):
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"""
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Applies cosine decay to the learning rate.
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when training a model, it is often recommended to lower the learning rate as the
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training progresses. By using this function, the learning rate will be decayed by
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following cosine decay strategy.
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decayed_lr = learning_rate * 0.5 * (math.cos(epoch * math.pi / epochs) + 1)
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Args:
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learning_rate(Variable|float): The initial learning rate.
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step_each_epoch(int): the number of steps in an epoch.
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epochs(int): the number of epochs.
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Returns:
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Variable: The decayed learning rate.
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Examples:
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..code-block:: python
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base_lr = 0.1
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lr = fluid.layers.cosine_decay(
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learning_rate = base_lr, step_each_epoch=10000, epochs=120)
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"""
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with default_main_program()._lr_schedule_guard():
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if imperative_base.enabled():
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decay = imperate_lr.CosineDecay(learning_rate, step_each_epoch,
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epochs)
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return decay
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else:
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global_step = _decay_step_counter()
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cur_epoch = ops.floor(global_step / step_each_epoch)
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decayed_lr = learning_rate * 0.5 * (
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ops.cos(cur_epoch * math.pi / epochs) + 1)
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return decayed_lr
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def append_LARS(params_grads, learning_rate, weight_decay):
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"""
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Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
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each layer.
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Args:
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learning_rate: A learning rate Variable. This
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is the global learning rate for LARS.
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weight_decay: A Python `float` number.
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Returns:
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The decayed learning rate
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Examples:
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.. code-block:: python
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learning_rate *= local_gw_ratio * sqrt(sumsq(param))
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/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
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"""
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assert not imperative_base.enabled(
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), "append_LARS is NOT supported in dygraph mode now"
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def _balanced_weight(param_norm, grad_norm):
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if weight_decay == 1.0:
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return grad_norm + param_norm
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else:
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return grad_norm + weight_decay * param_norm
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for param, grad in params_grads:
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with param.block.program.optimized_guard(
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[param, grad]), name_scope("optimizer"):
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param_lr = param.optimize_attr['learning_rate']
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param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
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grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
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if type(param_lr) == float and param_lr == 1.0:
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decayed_lr = learning_rate * param_norm \
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/ _balanced_weight(param_norm, grad_norm)
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else:
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decayed_lr = learning_rate * param_lr * param_norm \
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/ _balanced_weight(param_norm, grad_norm)
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# set back param local learning rate
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param.optimize_attr['learning_rate'] = decayed_lr
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def linear_lr_warmup(learning_rate, warmup_steps, start_lr, end_lr):
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"""
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Applies linear learning rate warmup before the normal learning rate
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scheduling.
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.. code-block:: python
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if global_step < warmup_steps:
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linear_step = end_lr - start_lr
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lr = start_lr + linear_step * (global_step / warmup_steps)
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Args:
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learning_rate (float | Variable): A float value or Variable.
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warmup_steps (int): The warmup steps.
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start_lr (float): The start learning of warmup.
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end_lr (float): The end learning of warmup.
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Returns:
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The decayed learning rate in warmup period.
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Examples:
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.. code-block:: python
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boundaries = [100, 200]
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lr_steps = [0.1, 0.01, 0.001]
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warmup_steps = 50
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start_lr = 1. / 3.
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end_lr = 0.1
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decayed_lr = fluid.layers.linear_lr_warmup(
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fluid.layers.piecewise_decay(boundaries, lr_steps),
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warmup_steps, start_lr, end_lr)
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"""
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assert (isinstance(end_lr, float))
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assert (isinstance(start_lr, float))
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linear_step = end_lr - start_lr
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with default_main_program()._lr_schedule_guard():
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lr = tensor.create_global_var(
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shape=[1],
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value=0.0,
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dtype='float32',
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persistable=True,
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name="learning_rate_warmup")
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global_step = _decay_step_counter()
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with control_flow.Switch() as switch:
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with switch.case(global_step < warmup_steps):
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decayed_lr = start_lr + linear_step * (global_step /
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float(warmup_steps))
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tensor.assign(decayed_lr, lr)
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with switch.default():
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tensor.assign(learning_rate, lr)
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return lr
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