optimizer_config.py

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"""Dataclasses for optimizer configs."""
from typing import List, Optional

import dataclasses
from official.modeling.hyperparams import base_config


@dataclasses.dataclass
class BaseOptimizerConfig(base_config.Config):
  """Base optimizer config.

  Attributes:
    clipnorm: float >= 0 or None. If not None, Gradients will be clipped when
      their L2 norm exceeds this value.
    clipvalue: float >= 0 or None. If not None, Gradients will be clipped when
      their absolute value exceeds this value.
    global_clipnorm: float >= 0 or None. If not None, gradient of all weights is
      clipped so that their global norm is no higher than this value
  """
  clipnorm: Optional[float] = None
  clipvalue: Optional[float] = None
  global_clipnorm: Optional[float] = None


@dataclasses.dataclass
class SGDConfig(BaseOptimizerConfig):
  """Configuration for SGD optimizer.

  The attributes for this class matches the arguments of tf.keras.optimizer.SGD.

  Attributes:
    name: name of the optimizer.
    decay: decay rate for SGD optimizer.
    nesterov: nesterov for SGD optimizer.
    momentum: momentum for SGD optimizer.
  """
  name: str = "SGD"
  decay: float = 0.0
  nesterov: bool = False
  momentum: float = 0.0


@dataclasses.dataclass
class RMSPropConfig(BaseOptimizerConfig):
  """Configuration for RMSProp optimizer.

  The attributes for this class matches the arguments of
  tf.keras.optimizers.RMSprop.

  Attributes:
    name: name of the optimizer.
    rho: discounting factor for RMSprop optimizer.
    momentum: momentum for RMSprop optimizer.
    epsilon: epsilon value for RMSprop optimizer, help with numerical stability.
    centered: Whether to normalize gradients or not.
  """
  name: str = "RMSprop"
  rho: float = 0.9
  momentum: float = 0.0
  epsilon: float = 1e-7
  centered: bool = False


@dataclasses.dataclass
class AdagradConfig(BaseOptimizerConfig):
  """Configuration for Adagrad optimizer.

  The attributes of this class match the arguments of
  tf.keras.optimizer.Adagrad.

  Attributes:
    name: name of the optimizer.
    initial_accumulator_value: A floating point value. Starting value for the
      accumulators, must be non-negative.
    epsilon: A small floating point value to avoid zero denominator.
  """
  name: str = "Adagrad"
  initial_accumulator_value: float = 0.1
  epsilon: float = 1e-07


@dataclasses.dataclass
class AdamConfig(BaseOptimizerConfig):
  """Configuration for Adam optimizer.

  The attributes for this class matches the arguments of
  tf.keras.optimizer.Adam.

  Attributes:
    name: name of the optimizer.
    beta_1: decay rate for 1st order moments.
    beta_2: decay rate for 2st order moments.
    epsilon: epsilon value used for numerical stability in Adam optimizer.
    amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from
      the paper "On the Convergence of Adam and beyond".
  """
  name: str = "Adam"
  beta_1: float = 0.9
  beta_2: float = 0.999
  epsilon: float = 1e-07
  amsgrad: bool = False


@dataclasses.dataclass
class AdamWeightDecayConfig(BaseOptimizerConfig):
  """Configuration for Adam optimizer with weight decay.

  Attributes:
    name: name of the optimizer.
    beta_1: decay rate for 1st order moments.
    beta_2: decay rate for 2st order moments.
    epsilon: epsilon value used for numerical stability in the optimizer.
    amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from
      the paper "On the Convergence of Adam and beyond".
    weight_decay_rate: float. Weight decay rate. Default to 0.
    include_in_weight_decay: list[str], or None. List of weight names to include
      in weight decay.
    exclude_from_weight_decay: list[str], or None. List of weight names to not
      include in weight decay.
    gradient_clip_norm: A positive float. Clips the gradients to this maximum
      L2-norm. Default to 1.0.
  """
  name: str = "AdamWeightDecay"
  beta_1: float = 0.9
  beta_2: float = 0.999
  epsilon: float = 1e-07
  amsgrad: bool = False
  weight_decay_rate: float = 0.0
  include_in_weight_decay: Optional[List[str]] = None
  exclude_from_weight_decay: Optional[List[str]] = None
  gradient_clip_norm: float = 1.0


@dataclasses.dataclass
class LAMBConfig(BaseOptimizerConfig):
  """Configuration for LAMB optimizer.

  The attributes for this class matches the arguments of
  tensorflow_addons.optimizers.LAMB.

  Attributes:
    name: name of the optimizer.
    beta_1: decay rate for 1st order moments.
    beta_2: decay rate for 2st order moments.
    epsilon: epsilon value used for numerical stability in LAMB optimizer.
    weight_decay_rate: float. Weight decay rate. Default to 0.
    exclude_from_weight_decay: List of regex patterns of variables excluded from
      weight decay. Variables whose name contain a substring matching the
      pattern will be excluded.
    exclude_from_layer_adaptation: List of regex patterns of variables excluded
      from layer adaptation. Variables whose name contain a substring matching
      the pattern will be excluded.
  """
  name: str = "LAMB"
  beta_1: float = 0.9
  beta_2: float = 0.999
  epsilon: float = 1e-6
  weight_decay_rate: float = 0.0
  exclude_from_weight_decay: Optional[List[str]] = None
  exclude_from_layer_adaptation: Optional[List[str]] = None


@dataclasses.dataclass
class EMAConfig(BaseOptimizerConfig):
  """Exponential moving average optimizer config.

  Attributes:
    name: 'str', name of the optimizer.
    average_decay: 'float', average decay value.
    start_step: 'int', start step to apply moving average.
    dynamic_decay: 'bool', whether to apply dynamic decay or not.
  """
  name: str = "ExponentialMovingAverage"
  average_decay: float = 0.99
  start_step: int = 0
  dynamic_decay: bool = True


@dataclasses.dataclass
class LARSConfig(BaseOptimizerConfig):
  """Layer-wise adaptive rate scaling config.

  Attributes:
    name: 'str', name of the optimizer.
    momentum: `float` hyperparameter >= 0 that accelerates gradient descent in
      the relevant direction and dampens oscillations. Defaults to 0.9.
    eeta: `float` LARS coefficient as used in the paper. Default set to LARS
      coefficient from the paper. (eeta / weight_decay) determines the highest
      scaling factor in LARS..
    weight_decay_rate: `float` for weight decay.
    nesterov: 'boolean' for whether to use nesterov momentum.
    classic_momentum: `boolean` for whether to use classic (or popular)
      momentum. The learning rate is applied during momentum update in classic
      momentum, but after momentum for popular momentum.
    exclude_from_weight_decay: A list of `string` for variable screening, if any
      of the string appears in a variable's name, the variable will be excluded
      for computing weight decay. For example, one could specify the list like
      ['batch_normalization', 'bias'] to exclude BN and bias from weight decay.
    exclude_from_layer_adaptation: Similar to exclude_from_weight_decay, but for
      layer adaptation. If it is None, it will be defaulted the same as
      exclude_from_weight_decay.
  """
  name: str = "LARS"
  momentum: float = 0.9
  eeta: float = 0.001
  weight_decay_rate: float = 0.0
  nesterov: bool = False
  classic_momentum: bool = True
  exclude_from_weight_decay: Optional[List[str]] = None
  exclude_from_layer_adaptation: Optional[List[str]] = None