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# Copyright (c) 2020, NVIDIA CORPORATION. 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 dataclasses import dataclass
from functools import partial
from typing import Any, Dict, Optional
@dataclass
class SchedulerParams:
"""
Base configuration for all schedulers.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
last_epoch: int = -1
@dataclass
class SquareRootConstantSchedulerParams(SchedulerParams):
"""
Base configuration for all schedulers.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
constant_steps: Optional[float] = None
constant_ratio: Optional[float] = None
@dataclass
class WarmupSchedulerParams(SchedulerParams):
"""
Base configuration for all schedulers.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
max_steps: int = 0
warmup_steps: Optional[float] = None
warmup_ratio: Optional[float] = None
@dataclass
class WarmupHoldSchedulerParams(WarmupSchedulerParams):
"""
Base configuration for all schedulers.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
hold_steps: Optional[float] = None
hold_ratio: Optional[float] = None
min_lr: float = 0.0
@dataclass
class WarmupAnnealingHoldSchedulerParams(WarmupSchedulerParams):
"""
Base configuration for all schedulers.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
constant_steps: Optional[float] = None
constant_ratio: Optional[float] = None
min_lr: float = 0.0
@dataclass
class SquareAnnealingParams(WarmupSchedulerParams):
"""
Square Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
min_lr: float = 1e-5
@dataclass
class SquareRootAnnealingParams(WarmupSchedulerParams):
"""
Square Root Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
min_lr: float = 0.0
@dataclass
class CosineAnnealingParams(WarmupAnnealingHoldSchedulerParams):
"""
Cosine Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
min_lr: float = 0.0
@dataclass
class NoamAnnealingParams(WarmupSchedulerParams):
"""
Cosine Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
min_lr: float = 0.0
@dataclass
class NoamHoldAnnealingParams(WarmupHoldSchedulerParams):
"""
Polynomial Hold Decay Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
decay_rate: float = 0.5
@dataclass
class WarmupAnnealingParams(WarmupSchedulerParams):
"""
Warmup Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
warmup_ratio: Optional[float] = None
@dataclass
class InverseSquareRootAnnealingParams(WarmupSchedulerParams):
"""
Inverse Square Root Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
@dataclass
class PolynomialDecayAnnealingParams(WarmupSchedulerParams):
"""
Polynomial Decay Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
power: float = 1.0
cycle: bool = False
@dataclass
class PolynomialHoldDecayAnnealingParams(WarmupSchedulerParams):
"""
Polynomial Hold Decay Annealing parameter config
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
power: float = 1.0
cycle: bool = False
"""
Pytorch Optimizers
"""
@dataclass
class StepLRParams(SchedulerParams):
"""
Config for StepLR.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
step_size: float = 0.1
gamma: float = 0.1
@dataclass
class ExponentialLRParams(SchedulerParams):
"""
Config for ExponentialLR.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
gamma: float = 0.9
@dataclass
class ReduceLROnPlateauParams:
"""
Config for ReduceLROnPlateau.
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
mode: str = 'min'
factor: float = 0.1
patience: int = 10
verbose: bool = False
threshold: float = 1e-4
threshold_mode: str = 'rel'
cooldown: int = 0
min_lr: float = 0
eps: float = 1e-8
@dataclass
class CyclicLRParams(SchedulerParams):
"""
Config for CyclicLR.
NOTE:
# `scale_fn` is not supported
It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
"""
base_lr: float = 0.001
max_lr: float = 0.1
step_size_up: int = 2000
step_size_down: Optional[int] = None
mode: str = 'triangular'
gamma: float = 1.0
scale_mode: str = 'cycle'
# scale_fn is not supported
cycle_momentum: bool = True
base_momentum: float = 0.8
max_momentum: float = 0.9
def register_scheduler_params(name: str, scheduler_params: SchedulerParams):
"""
Checks if the schduler config name exists in the registry, and if it doesnt, adds it.
This allows custom schedulers to be added and called by name during instantiation.
Args:
name: Name of the optimizer. Will be used as key to retrieve the optimizer.
scheduler_params: SchedulerParams class
"""
if name in AVAILABLE_SCHEDULER_PARAMS:
raise ValueError(f"Cannot override pre-existing optimizers. Conflicting optimizer name = {name}")
AVAILABLE_SCHEDULER_PARAMS[name] = scheduler_params
def get_scheduler_config(name: str, **kwargs: Optional[Dict[str, Any]]) -> SchedulerParams:
"""
Convenience method to obtain a SchedulerParams class and partially instantiate it with optimizer kwargs.
Args:
name: Name of the SchedulerParams in the registry.
kwargs: Optional kwargs of the optimizer used during instantiation.
Returns:
a partially instantiated SchedulerParams
"""
if name not in AVAILABLE_SCHEDULER_PARAMS:
raise ValueError(
f"Cannot resolve scheduler parameters '{name}'. Available scheduler parameters are : "
f"{AVAILABLE_SCHEDULER_PARAMS.keys()}"
)
scheduler_params = AVAILABLE_SCHEDULER_PARAMS[name]
scheduler_params = partial(scheduler_params, **kwargs)
return scheduler_params
AVAILABLE_SCHEDULER_PARAMS = {
'SchedulerParams': SchedulerParams,
'WarmupPolicyParams': WarmupSchedulerParams,
'WarmupHoldPolicyParams': WarmupHoldSchedulerParams,
'WarmupAnnealingHoldSchedulerParams': WarmupAnnealingHoldSchedulerParams,
'SquareAnnealingParams': SquareAnnealingParams,
'SquareRootAnnealingParams': SquareRootAnnealingParams,
'InverseSquareRootAnnealingParams': InverseSquareRootAnnealingParams,
'SquareRootConstantSchedulerParams': SquareRootConstantSchedulerParams,
'CosineAnnealingParams': CosineAnnealingParams,
'NoamAnnealingParams': NoamAnnealingParams,
'NoamHoldAnnealingParams': NoamHoldAnnealingParams,
'WarmupAnnealingParams': WarmupAnnealingParams,
'PolynomialDecayAnnealingParams': PolynomialDecayAnnealingParams,
'PolynomialHoldDecayAnnealingParams': PolynomialHoldDecayAnnealingParams,
'ReduceLROnPlateauParams': ReduceLROnPlateauParams,
}
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