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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """lr generator for deeptext"""
- import math
-
- def rsqrt_decay(warmup_steps, current_step):
- return float(max([current_step, warmup_steps])) ** -0.5
-
- def linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
- lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
- learning_rate = float(init_lr) + lr_inc * current_step
- return learning_rate
-
- def a_cosine_learning_rate(current_step, base_lr, warmup_steps, total_steps):
- decay_steps = total_steps - warmup_steps
- linear_decay = (total_steps - current_step) / decay_steps
- cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * current_step / decay_steps))
- decayed = linear_decay * cosine_decay + 0.00001
- learning_rate = decayed * base_lr
- return learning_rate
-
- def dynamic_lr(config, base_step):
- """dynamic learning rate generator"""
- base_lr = config.base_lr
- total_steps = int(base_step * config.num_epochs)
- warmup_steps = int(config.warmup_step)
- lr = []
- for i in range(total_steps):
- if i < warmup_steps:
- lr.append(linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * config.warmup_ratio))
- else:
- lr.append(a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
- return lr
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