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optimization_config.py
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optimization_config.py
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# Copyright 2023 The TensorFlow Authors. 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.
"""Dataclasses for optimization configs.
This file define the dataclass for optimization configs (OptimizationConfig).
It also has two helper functions get_optimizer_config, and get_lr_config from
an OptimizationConfig class.
"""
from typing import Optional
import dataclasses
from official.modeling.hyperparams import base_config
from official.modeling.hyperparams import oneof
from official.modeling.optimization.configs import learning_rate_config as lr_cfg
from official.modeling.optimization.configs import optimizer_config as opt_cfg
@dataclasses.dataclass
class OptimizerConfig(oneof.OneOfConfig):
"""Configuration for optimizer.
Attributes:
type: 'str', type of optimizer to be used, on the of fields below.
sgd: sgd optimizer config.
adam: adam optimizer config.
adamw: adam with weight decay.
lamb: lamb optimizer.
rmsprop: rmsprop optimizer.
lars: lars optimizer.
adagrad: adagrad optimizer.
slide: slide optimizer.
"""
type: Optional[str] = None
sgd: opt_cfg.SGDConfig = dataclasses.field(default_factory=opt_cfg.SGDConfig)
sgd_experimental: opt_cfg.SGDExperimentalConfig = dataclasses.field(
default_factory=opt_cfg.SGDExperimentalConfig
)
adam: opt_cfg.AdamConfig = dataclasses.field(
default_factory=opt_cfg.AdamConfig
)
adam_experimental: opt_cfg.AdamExperimentalConfig = dataclasses.field(
default_factory=opt_cfg.AdamExperimentalConfig
)
adamw: opt_cfg.AdamWeightDecayConfig = dataclasses.field(
default_factory=opt_cfg.AdamWeightDecayConfig
)
adamw_experimental: opt_cfg.AdamWeightDecayExperimentalConfig = (
dataclasses.field(
default_factory=opt_cfg.AdamWeightDecayExperimentalConfig
)
)
lamb: opt_cfg.LAMBConfig = dataclasses.field(
default_factory=opt_cfg.LAMBConfig
)
rmsprop: opt_cfg.RMSPropConfig = dataclasses.field(
default_factory=opt_cfg.RMSPropConfig
)
lars: opt_cfg.LARSConfig = dataclasses.field(
default_factory=opt_cfg.LARSConfig
)
adagrad: opt_cfg.AdagradConfig = dataclasses.field(
default_factory=opt_cfg.AdagradConfig
)
slide: opt_cfg.SLIDEConfig = dataclasses.field(
default_factory=opt_cfg.SLIDEConfig
)
adafactor: opt_cfg.AdafactorConfig = dataclasses.field(
default_factory=opt_cfg.AdafactorConfig
)
@dataclasses.dataclass
class LrConfig(oneof.OneOfConfig):
"""Configuration for lr schedule.
Attributes:
type: 'str', type of lr schedule to be used, one of the fields below.
constant: constant learning rate config.
stepwise: stepwise learning rate config.
exponential: exponential learning rate config.
polynomial: polynomial learning rate config.
cosine: cosine learning rate config.
power: step^power learning rate config.
power_linear: learning rate config of step^power followed by
step^power*linear.
power_with_offset: power decay with a step offset.
step_cosine_with_offset: Step cosine with a step offset.
"""
type: Optional[str] = None
constant: lr_cfg.ConstantLrConfig = dataclasses.field(
default_factory=lr_cfg.ConstantLrConfig
)
stepwise: lr_cfg.StepwiseLrConfig = dataclasses.field(
default_factory=lr_cfg.StepwiseLrConfig
)
exponential: lr_cfg.ExponentialLrConfig = dataclasses.field(
default_factory=lr_cfg.ExponentialLrConfig
)
polynomial: lr_cfg.PolynomialLrConfig = dataclasses.field(
default_factory=lr_cfg.PolynomialLrConfig
)
cosine: lr_cfg.CosineLrConfig = dataclasses.field(
default_factory=lr_cfg.CosineLrConfig
)
power: lr_cfg.DirectPowerLrConfig = dataclasses.field(
default_factory=lr_cfg.DirectPowerLrConfig
)
power_linear: lr_cfg.PowerAndLinearDecayLrConfig = dataclasses.field(
default_factory=lr_cfg.PowerAndLinearDecayLrConfig
)
power_with_offset: lr_cfg.PowerDecayWithOffsetLrConfig = dataclasses.field(
default_factory=lr_cfg.PowerDecayWithOffsetLrConfig
)
step_cosine_with_offset: lr_cfg.StepCosineLrConfig = dataclasses.field(
default_factory=lr_cfg.StepCosineLrConfig
)
@dataclasses.dataclass
class WarmupConfig(oneof.OneOfConfig):
"""Configuration for lr schedule.
Attributes:
type: 'str', type of warmup schedule to be used, one of the fields below.
linear: linear warmup config.
polynomial: polynomial warmup config.
"""
type: Optional[str] = None
linear: lr_cfg.LinearWarmupConfig = dataclasses.field(
default_factory=lr_cfg.LinearWarmupConfig
)
polynomial: lr_cfg.PolynomialWarmupConfig = dataclasses.field(
default_factory=lr_cfg.PolynomialWarmupConfig
)
@dataclasses.dataclass
class OptimizationConfig(base_config.Config):
"""Configuration for optimizer and learning rate schedule.
Attributes:
optimizer: optimizer oneof config.
ema: optional exponential moving average optimizer config, if specified, ema
optimizer will be used.
learning_rate: learning rate oneof config.
warmup: warmup oneof config.
"""
optimizer: OptimizerConfig = dataclasses.field(
default_factory=OptimizerConfig
)
ema: Optional[opt_cfg.EMAConfig] = None
learning_rate: LrConfig = dataclasses.field(default_factory=LrConfig)
warmup: WarmupConfig = dataclasses.field(default_factory=WarmupConfig)