optimizer.py

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import math
import paddle
import paddle.nn as nn

import paddle.optimizer as optimizer
import paddle.regularizer as regularizer

from ppdet.core.workspace import register, serializable
import copy

from .adamw import AdamWDL, build_adamwdl

__all__ = ['LearningRate', 'OptimizerBuilder']

from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)


@serializable
class CosineDecay(object):
    """
    Cosine learning rate decay

    Args:
        max_epochs (int): max epochs for the training process.
            if you commbine cosine decay with warmup, it is recommended that
            the max_iters is much larger than the warmup iter
        use_warmup (bool): whether to use warmup. Default: True.
        min_lr_ratio (float): minimum learning rate ratio. Default: 0.
        last_plateau_epochs (int): use minimum learning rate in
            the last few epochs. Default: 0.
    """

    def __init__(self,
                 max_epochs=1000,
                 use_warmup=True,
                 min_lr_ratio=0.,
                 last_plateau_epochs=0):
        self.max_epochs = max_epochs
        self.use_warmup = use_warmup
        self.min_lr_ratio = min_lr_ratio
        self.last_plateau_epochs = last_plateau_epochs

    def __call__(self,
                 base_lr=None,
                 boundary=None,
                 value=None,
                 step_per_epoch=None):
        assert base_lr is not None, "either base LR or values should be provided"

        max_iters = self.max_epochs * int(step_per_epoch)
        last_plateau_iters = self.last_plateau_epochs * int(step_per_epoch)
        min_lr = base_lr * self.min_lr_ratio
        if boundary is not None and value is not None and self.use_warmup:
            # use warmup
            warmup_iters = len(boundary)
            for i in range(int(boundary[-1]), max_iters):
                boundary.append(i)
                if i < max_iters - last_plateau_iters:
                    decayed_lr = min_lr + (base_lr - min_lr) * 0.5 * (math.cos(
                        (i - warmup_iters) * math.pi /
                        (max_iters - warmup_iters - last_plateau_iters)) + 1)
                    value.append(decayed_lr)
                else:
                    value.append(min_lr)
            return optimizer.lr.PiecewiseDecay(boundary, value)
        elif last_plateau_iters > 0:
            # not use warmup, but set `last_plateau_epochs` > 0
            boundary = []
            value = []
            for i in range(max_iters):
                if i < max_iters - last_plateau_iters:
                    decayed_lr = min_lr + (base_lr - min_lr) * 0.5 * (math.cos(
                        i * math.pi / (max_iters - last_plateau_iters)) + 1)
                    value.append(decayed_lr)
                else:
                    value.append(min_lr)
                if i > 0:
                    boundary.append(i)
            return optimizer.lr.PiecewiseDecay(boundary, value)

        return optimizer.lr.CosineAnnealingDecay(
            base_lr, T_max=max_iters, eta_min=min_lr)


@serializable
class PiecewiseDecay(object):
    """
    Multi step learning rate decay

    Args:
        gamma (float | list): decay factor
        milestones (list): steps at which to decay learning rate
    """

    def __init__(self,
                 gamma=[0.1, 0.01],
                 milestones=[8, 11],
                 values=None,
                 use_warmup=True):
        super(PiecewiseDecay, self).__init__()
        if type(gamma) is not list:
            self.gamma = []
            for i in range(len(milestones)):
                self.gamma.append(gamma / 10**i)
        else:
            self.gamma = gamma
        self.milestones = milestones
        self.values = values
        self.use_warmup = use_warmup

    def __call__(self,
                 base_lr=None,
                 boundary=None,
                 value=None,
                 step_per_epoch=None):
        if boundary is not None and self.use_warmup:
            boundary.extend([int(step_per_epoch) * i for i in self.milestones])
        else:
            # do not use LinearWarmup
            boundary = [int(step_per_epoch) * i for i in self.milestones]
            value = [base_lr]  # during step[0, boundary[0]] is base_lr

        # self.values is setted directly in config
        if self.values is not None:
            assert len(self.milestones) + 1 == len(self.values)
            return optimizer.lr.PiecewiseDecay(boundary, self.values)

        # value is computed by self.gamma
        value = value if value is not None else [base_lr]
        for i in self.gamma:
            value.append(base_lr * i)

        return optimizer.lr.PiecewiseDecay(boundary, value)


@serializable
class LinearWarmup(object):
    """
    Warm up learning rate linearly

    Args:
        steps (int): warm up steps
        start_factor (float): initial learning rate factor
        epochs (int|None): use epochs as warm up steps, the priority
            of `epochs` is higher than `steps`. Default: None.
    """

    def __init__(self, steps=500, start_factor=1. / 3, epochs=None):
        super(LinearWarmup, self).__init__()
        self.steps = steps
        self.start_factor = start_factor
        self.epochs = epochs

    def __call__(self, base_lr, step_per_epoch):
        boundary = []
        value = []
        warmup_steps = self.epochs * step_per_epoch \
            if self.epochs is not None else self.steps
        warmup_steps = max(warmup_steps, 1)
        for i in range(warmup_steps + 1):
            if warmup_steps > 0:
                alpha = i / warmup_steps
                factor = self.start_factor * (1 - alpha) + alpha
                lr = base_lr * factor
                value.append(lr)
            if i > 0:
                boundary.append(i)
        return boundary, value


@serializable
class ExpWarmup(object):
    """
    Warm up learning rate in exponential mode
    Args:
        steps (int): warm up steps.
        epochs (int|None): use epochs as warm up steps, the priority
            of `epochs` is higher than `steps`. Default: None.
        power (int): Exponential coefficient. Default: 2.
    """

    def __init__(self, steps=1000, epochs=None, power=2):
        super(ExpWarmup, self).__init__()
        self.steps = steps
        self.epochs = epochs
        self.power = power

    def __call__(self, base_lr, step_per_epoch):
        boundary = []
        value = []
        warmup_steps = self.epochs * step_per_epoch if self.epochs is not None else self.steps
        warmup_steps = max(warmup_steps, 1)
        for i in range(warmup_steps + 1):
            factor = (i / float(warmup_steps))**self.power
            value.append(base_lr * factor)
            if i > 0:
                boundary.append(i)
        return boundary, value


@register
class LearningRate(object):
    """
    Learning Rate configuration

    Args:
        base_lr (float): base learning rate
        schedulers (list): learning rate schedulers
    """
    __category__ = 'optim'

    def __init__(self,
                 base_lr=0.01,
                 schedulers=[PiecewiseDecay(), LinearWarmup()]):
        super(LearningRate, self).__init__()
        self.base_lr = base_lr
        self.schedulers = []

        schedulers = copy.deepcopy(schedulers)
        for sched in schedulers:
            if isinstance(sched, dict):
                # support dict sched instantiate
                module = sys.modules[__name__]
                type = sched.pop("name")
                scheduler = getattr(module, type)(**sched)
                self.schedulers.append(scheduler)
            else:
                self.schedulers.append(sched)

    def __call__(self, step_per_epoch):
        assert len(self.schedulers) >= 1
        if not self.schedulers[0].use_warmup:
            return self.schedulers[0](base_lr=self.base_lr,
                                      step_per_epoch=step_per_epoch)

        # TODO: split warmup & decay
        # warmup
        boundary, value = self.schedulers[1](self.base_lr, step_per_epoch)
        # decay
        decay_lr = self.schedulers[0](self.base_lr, boundary, value,
                                      step_per_epoch)
        return decay_lr


@register
class OptimizerBuilder():
    """
    Build optimizer handles
    Args:
        regularizer (object): an `Regularizer` instance
        optimizer (object): an `Optimizer` instance
    """
    __category__ = 'optim'

    def __init__(self,
                 clip_grad_by_norm=None,
                 clip_grad_by_value=None,
                 regularizer={'type': 'L2',
                              'factor': .0001},
                 optimizer={'type': 'Momentum',
                            'momentum': .9}):
        self.clip_grad_by_norm = clip_grad_by_norm
        self.clip_grad_by_value = clip_grad_by_value
        self.regularizer = regularizer
        self.optimizer = optimizer

    def __call__(self, learning_rate, model=None):
        if self.clip_grad_by_norm is not None:
            grad_clip = nn.ClipGradByGlobalNorm(
                clip_norm=self.clip_grad_by_norm)
        elif self.clip_grad_by_value is not None:
            var = abs(self.clip_grad_by_value)
            grad_clip = nn.ClipGradByValue(min=-var, max=var)
        else:
            grad_clip = None
        if self.regularizer and self.regularizer != 'None':
            reg_type = self.regularizer['type'] + 'Decay'
            reg_factor = self.regularizer['factor']
            regularization = getattr(regularizer, reg_type)(reg_factor)
        else:
            regularization = None

        optim_args = self.optimizer.copy()
        optim_type = optim_args['type']
        del optim_args['type']

        if optim_type == 'AdamWDL':
            return build_adamwdl(model, lr=learning_rate, **optim_args)

        if optim_type != 'AdamW':
            optim_args['weight_decay'] = regularization

        op = getattr(optimizer, optim_type)

        if 'param_groups' in optim_args:
            assert isinstance(optim_args['param_groups'], list), ''

            param_groups = optim_args.pop('param_groups')

            params, visited = [], []
            for group in param_groups:
                assert isinstance(group,
                                  dict) and 'params' in group and isinstance(
                                      group['params'], list), ''
                _params = {
                    n: p
                    for n, p in model.named_parameters()
                    if any([k in n
                            for k in group['params']]) and p.trainable is True
                }
                _group = group.copy()
                _group.update({'params': list(_params.values())})

                params.append(_group)
                visited.extend(list(_params.keys()))

            ext_params = [
                p for n, p in model.named_parameters()
                if n not in visited and p.trainable is True
            ]

            if len(ext_params) < len(model.parameters()):
                params.append({'params': ext_params})

            elif len(ext_params) > len(model.parameters()):
                raise RuntimeError

        else:
            _params = model.parameters()
            params = [param for param in _params if param.trainable is True]

        return op(learning_rate=learning_rate,
                  parameters=params,
                  grad_clip=grad_clip,
                  **optim_args)