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# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey)
#
# See ../LICENSE for clarification regarding multiple authors
#
# 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.
import logging
from typing import List, Optional, Union
import torch
from torch.optim import Optimizer
class LRScheduler(object):
"""
Base-class for learning rate schedulers where the learning-rate depends on both the
batch and the epoch.
"""
def __init__(self, optimizer: Optimizer, verbose: bool = False):
# Attach optimizer
if not isinstance(optimizer, Optimizer):
raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
self.optimizer = optimizer
self.verbose = verbose
for group in optimizer.param_groups:
group.setdefault("base_lr", group["lr"])
self.base_lrs = [group["base_lr"] for group in optimizer.param_groups]
self.epoch = 0
self.batch = 0
def state_dict(self):
"""Returns the state of the scheduler as a :class:`dict`.
It contains an entry for every variable in self.__dict__ which
is not the optimizer.
"""
return {
# the user might try to override the base_lr, so don't include this in the
# state. previously they were included.
# "base_lrs": self.base_lrs,
"epoch": self.epoch,
"batch": self.batch,
}
def load_state_dict(self, state_dict):
"""Loads the schedulers state.
Args:
state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.
"""
# the things with base_lrs are a work-around for a previous problem
# where base_lrs were written with the state dict.
base_lrs = self.base_lrs
self.__dict__.update(state_dict)
self.base_lrs = base_lrs
def get_last_lr(self) -> List[float]:
"""Return last computed learning rate by current scheduler.
Will be a list of float."""
return self._last_lr
def get_lr(self):
# Compute list of learning rates from self.epoch and self.batch and
# self.base_lrs; this must be overloaded by the user.
# e.g. return [some_formula(self.batch, self.epoch, base_lr)
# for base_lr in self.base_lrs ]
raise NotImplementedError
def step_batch(self, batch: Optional[int] = None) -> None:
# Step the batch index, or just set it. If `batch` is specified, it
# must be the batch index from the start of training, i.e. summed over
# all epochs.
# You can call this in any order; if you don't provide 'batch', it should
# of course be called once per batch.
if batch is not None:
self.batch = batch
else:
self.batch = self.batch + 1
self._set_lrs()
def step_epoch(self, epoch: Optional[int] = None):
# Step the epoch index, or just set it. If you provide the 'epoch' arg, you
# should call this at the start of the epoch; if you don't provide the 'epoch'
# arg, you should call it at the end of the epoch.
if epoch is not None:
self.epoch = epoch
else:
self.epoch = self.epoch + 1
self._set_lrs()
def _set_lrs(self):
values = self.get_lr()
assert len(values) == len(self.optimizer.param_groups)
for i, data in enumerate(zip(self.optimizer.param_groups, values)):
param_group, lr = data
param_group["lr"] = lr
self.print_lr(self.verbose, i, lr)
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
def print_lr(self, is_verbose, group, lr):
"""Display the current learning rate."""
if is_verbose:
logging.warning(
f"Epoch={self.epoch}, batch={self.batch}: adjusting learning rate"
f" of group {group} to {lr:.4e}."
)
class Eden(LRScheduler):
"""
Eden scheduler.
The basic formula (before warmup) is:
lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
(((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup
where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
and then stays constant at 1.
If you don't have the concept of epochs, or one epoch takes a very long time,
you can replace the notion of 'epoch' with some measure of the amount of data
processed, e.g. hours of data or frames of data, with 'lr_epochs' being set to
some measure representing "quite a lot of data": say, one fifth or one third
of an entire training run, but it doesn't matter much. You could also use
Eden2 which has only the notion of batches.
We suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
Args:
optimizer: the optimizer to change the learning rates on
lr_batches: the number of batches after which we start significantly
decreasing the learning rate, suggest 5000.
lr_epochs: the number of epochs after which we start significantly
decreasing the learning rate, suggest 6 if you plan to do e.g.
20 to 40 epochs, but may need smaller number if dataset is huge
and you will do few epochs.
"""
def __init__(
self,
optimizer: Optimizer,
lr_batches: Union[int, float],
lr_epochs: Union[int, float],
warmup_batches: Union[int, float] = 500.0,
warmup_start: float = 0.5,
verbose: bool = False,
):
super(Eden, self).__init__(optimizer, verbose)
self.lr_batches = lr_batches
self.lr_epochs = lr_epochs
self.warmup_batches = warmup_batches
assert 0.0 <= warmup_start <= 1.0, warmup_start
self.warmup_start = warmup_start
def get_lr(self):
factor = (
(self.batch**2 + self.lr_batches**2) / self.lr_batches**2
) ** -0.25 * (
((self.epoch**2 + self.lr_epochs**2) / self.lr_epochs**2) ** -0.25
)
warmup_factor = (
1.0
if self.batch >= self.warmup_batches
else self.warmup_start
+ (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
# else 0.5 + 0.5 * (self.batch / self.warmup_batches)
)
return [x * factor * warmup_factor for x in self.base_lrs]
class FixedLRScheduler(LRScheduler):
"""
Fixed learning rate scheduler.
Args:
optimizer: the optimizer to change the learning rates on
"""
def __init__(
self,
optimizer: Optimizer,
verbose: bool = False,
):
super(FixedLRScheduler, self).__init__(optimizer, verbose)
def get_lr(self):
return [x for x in self.base_lrs]
def _test_eden():
m = torch.nn.Linear(100, 100)
from zipvoice.utils.optim import ScaledAdam
optim = ScaledAdam(m.parameters(), lr=0.03)
scheduler = Eden(optim, lr_batches=100, lr_epochs=2, verbose=True)
for epoch in range(10):
scheduler.step_epoch(epoch) # sets epoch to `epoch`
for step in range(20):
x = torch.randn(200, 100).detach()
x.requires_grad = True
y = m(x)
dy = torch.randn(200, 100).detach()
f = (y * dy).sum()
f.backward()
optim.step()
scheduler.step_batch()
optim.zero_grad()
logging.info(f"last lr = {scheduler.get_last_lr()}")
logging.info(f"state dict = {scheduler.state_dict()}")
if __name__ == "__main__":
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
logging.getLogger().setLevel(logging.INFO)
import subprocess
s = subprocess.check_output(
"git status -uno .; git log -1; git diff HEAD .", shell=True
)
logging.info(s)
_test_eden()
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