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8207382 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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
from __future__ import unicode_literals
import copy
import math
import paddle
__all__ = ["build_optimizer"]
class CosineWeightDecayScheduler(object):
"""Cosine-anneal the optimizer's weight decay each step.
wd(t) = end + 0.5 * (start - end) * (1 + cos(pi * t / T))
During warmup the coefficient is held at `start_factor`.
"""
def __init__(
self, optimizer, start_factor, end_factor, total_steps, warmup_steps=0
):
self.optimizer = optimizer
self.start_factor = start_factor
self.end_factor = end_factor
self.total_steps = total_steps
self.warmup_steps = warmup_steps
self._step = 0
def step(self):
self._step += 1
if self._step <= self.warmup_steps:
wd = self.start_factor
else:
progress = (self._step - self.warmup_steps) / max(
1, self.total_steps - self.warmup_steps
)
progress = min(progress, 1.0)
wd = self.end_factor + 0.5 * (self.start_factor - self.end_factor) * (
1 + math.cos(math.pi * progress)
)
self.optimizer.regularization._coeff = wd
def get_wd(self):
return self.optimizer.regularization._coeff
def build_lr_scheduler(lr_config, epochs, step_each_epoch):
from . import learning_rate
lr_config.update({"epochs": epochs, "step_each_epoch": step_each_epoch})
lr_name = lr_config.pop("name", "Const")
lr = getattr(learning_rate, lr_name)(**lr_config)()
return lr
def build_optimizer(config, epochs, step_each_epoch, model):
from . import regularizer, optimizer
config = copy.deepcopy(config)
# step1 build lr
lr = build_lr_scheduler(config.pop("lr"), epochs, step_each_epoch)
# step2 build regularization
wd_scheduler = None
if "regularizer" in config and config["regularizer"] is not None:
reg_config = config.pop("regularizer")
reg_name = reg_config.pop("name")
if not hasattr(regularizer, reg_name):
reg_name += "Decay"
reg_obj = getattr(regularizer, reg_name)(**reg_config)
reg = reg_obj()
# Build weight decay scheduler for CosineL2Decay
if isinstance(reg_obj, regularizer.CosineL2Decay):
warmup_epoch = reg_obj.warmup_epoch
warmup_steps = round(warmup_epoch * step_each_epoch)
total_steps = step_each_epoch * epochs
wd_scheduler = {
"start_factor": reg_obj.start_factor,
"end_factor": reg_obj.end_factor,
"total_steps": total_steps,
"warmup_steps": warmup_steps,
}
elif "weight_decay" in config:
reg = config.pop("weight_decay")
else:
reg = None
# step3 build optimizer
optim_name = config.pop("name")
if "clip_norm" in config:
clip_norm = config.pop("clip_norm")
grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
elif "clip_norm_global" in config:
clip_norm = config.pop("clip_norm_global")
grad_clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=clip_norm)
else:
grad_clip = None
optim = getattr(optimizer, optim_name)(
learning_rate=lr, weight_decay=reg, grad_clip=grad_clip, **config
)
built_optim = optim(model)
# Instantiate the scheduler now that we have the real optimizer
if wd_scheduler is not None:
wd_scheduler = CosineWeightDecayScheduler(built_optim, **wd_scheduler)
return built_optim, lr, wd_scheduler
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