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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Team. 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.
"""Paddle optimization for diffusion models."""
import math
from enum import Enum
from typing import Optional, Union
from paddle.optimizer.lr import LambdaDecay
from .utils import logging
logger = logging.get_logger(__name__)
class SchedulerType(Enum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
def get_constant_schedule(learning_rate: float, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate, using the learning rate set in optimizer.
Args:
learning_rate (`float`):
The base learning rate. It is a python float number.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
"""
return LambdaDecay(learning_rate, lambda _: 1, last_epoch=last_epoch)
def get_constant_schedule_with_warmup(learning_rate: float, num_warmup_steps: int, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.
Args:
learning_rate (`float`):
The base learning rate. It is a python float number.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1.0, num_warmup_steps))
return 1.0
return LambdaDecay(learning_rate, lr_lambda, last_epoch=last_epoch)
def get_linear_schedule_with_warmup(
learning_rate: float, num_warmup_steps: int, num_training_steps: int, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
Args:
learning_rate (`float`):
The base learning rate. It is a python float number.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(
0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
)
return LambdaDecay(learning_rate, lr_lambda, last_epoch)
def get_cosine_schedule_with_warmup(
learning_rate: float, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.
Args:
learning_rate (`float`):
The base learning rate. It is a python float number.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`float`, *optional*, defaults to 0.5):
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
return LambdaDecay(learning_rate, lr_lambda, last_epoch)
def get_cosine_with_hard_restarts_schedule_with_warmup(
learning_rate: float, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.
Args:
learning_rate (`float`):
The base learning rate. It is a python float number.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`int`, *optional*, defaults to 1):
The number of hard restarts to use.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
return LambdaDecay(learning_rate, lr_lambda, last_epoch)
def get_polynomial_decay_schedule_with_warmup(
learning_rate: float,
num_warmup_steps: int,
num_training_steps: int,
lr_end: float = 1e-7,
power: float = 1.0,
last_epoch: int = -1,
):
"""
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.
Args:
learning_rate (`float`):
The base learning rate. It is a python float number.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
lr_end (`float`, *optional*, defaults to 1e-7):
The end LR.
power (`float`, *optional*, defaults to 1.0):
Power factor.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
Return:
`paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
"""
lr_init = learning_rate
if not (lr_init > lr_end):
raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
lr_range = lr_init - lr_end
decay_steps = num_training_steps - num_warmup_steps
pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
decay = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaDecay(learning_rate, lr_lambda, last_epoch)
TYPE_TO_SCHEDULER_FUNCTION = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
}
def get_scheduler(
name: Union[str, SchedulerType],
learning_rate: float = 0.1,
num_warmup_steps: Optional[int] = None,
num_training_steps: Optional[int] = None,
num_cycles: int = 1,
power: float = 1.0,
last_epoch: int = -1,
):
"""
Unified API to get any scheduler from its name.
Args:
name (`str` or `SchedulerType`):
The name of the scheduler to use.
learning_rate (`float`):
The base learning rate. It is a python float number.
num_warmup_steps (`int`, *optional*):
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_training_steps (`int``, *optional*):
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_cycles (`int`, *optional*):
The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler.
power (`float`, *optional*, defaults to 1.0):
Power factor. See `POLYNOMIAL` scheduler
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
"""
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(learning_rate=learning_rate, last_epoch=last_epoch)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(learning_rate=learning_rate, num_warmup_steps=num_warmup_steps, last_epoch=last_epoch)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
learning_rate=learning_rate,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
num_cycles=num_cycles,
last_epoch=last_epoch,
)
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
learning_rate=learning_rate,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
power=power,
last_epoch=last_epoch,
)
return schedule_func(
learning_rate=learning_rate,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
last_epoch=last_epoch,
)
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