tf.keras.optimizers.ScheduleFreeAdamW

Optimizer that implements the Schedule-Free AdamW algorithm.

Inherits From: Optimizer

tf.keras.optimizers.ScheduleFreeAdamW(
    learning_rate=0.0025,
    beta_1=0.9,
    beta_2=0.999,
    epsilon=1e-08,
    warmup_steps=0,
    weight_decay=None,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    loss_scale_factor=None,
    gradient_accumulation_steps=None,
    name=None,
    **kwargs
)

Schedule-Free learning is a method that avoids the need for a learning rate schedule by maintaining a combination of interpolation and averaging. This approach eliminates the requirement to specify stopping time in advance and typically matches or outperforms cosine and linear decay schedules.

The optimizer maintains two sets of variables internally:

  • momentum: The sequence where gradient updates are applied
  • x: The averaged sequence used for evaluation

During training, the model parameters are set to an interpolation between momentum and x.

learning_rate A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.0025.
beta_1 A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 1st moment estimates and controls the interpolation between momentum and x. Defaults to 0.9.
beta_2 A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
epsilon A small constant for numerical stability. Defaults to 1e-8.
warmup_steps Number of warmup steps for learning rate warmup. During warmup, the learning rate linearly increases from 0 to the specified learning rate. Defaults to 0.
name String. The name to use for momentum accumulator weights created by the optimizer.
weight_decay Float. If set, weight decay is applied.
clipnorm Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
clipvalue Float. If set, the gradient of each weight is clipped to be no higher than this value.
global_clipnorm Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
use_ema Boolean, defaults to False. If True, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
ema_momentum Float, defaults to 0.99. Only used if use_ema=True. This is the momentum to use when computing the EMA of the model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value.
ema_overwrite_frequency Int or None, defaults to None. Only used if use_ema=True. Every ema_overwrite_frequency steps of iterations, we overwrite the model variable by its moving average. If None, the optimizer does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling optimizer.finalize_variable_values() (which updates the model variables in-place). When using the built-in fit() training loop, this happens automatically after the last epoch, and you don't need to do anything.
loss_scale_factor Float or None. If a float, the scale factor will be multiplied the loss before computing gradients, and the inverse of the scale factor will be multiplied by the gradients before updating variables. Useful for preventing underflow during mixed precision training. Alternately, keras.optimizers.LossScaleOptimizer will automatically set a loss scale factor.
gradient_accumulation_steps Int or None. If an int, model & optimizer variables will not be updated at every step; instead they will be updated every gradient_accumulation_steps steps, using the average value of the gradients since the last update. This is known as "gradient accumulation". This can be useful when your batch size is very small, in order to reduce gradient noise at each update step. EMA frequency will look at "accumulated" iterations value (optimizer steps // gradient_accumulation_steps). Learning rate schedules will look at "real" iterations value (optimizer steps).

References:

Example:

optimizer = keras.optimizers.ScheduleFreeAdamW(learning_rate=0.0025)
model.compile(optimizer=optimizer, loss="mse")
model.fit(x_train, y_train)

iterations

learning_rate

variables

Methods

add_optimizer_variables

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add_optimizer_variables(
    trainable_variables, name, initializer='zeros'
)

Add optimizer variables from the list of trainable model variables.

Create an optimizer variable based on the information of the supplied model variables. For example, in SGD optimizer momemtum, for each model variable, a corresponding momemtum variable is created of the same shape and dtype.

Note that trainable variables with v.overwrite_with_gradient == True will insert None, into the output list, since the optimizer variable will not be used anyways, and could be wasteful.

Args
trainable_variables keras.Variable, the corresponding model variable to the optimizer variable to be created.
name The name prefix(es) of the optimizer variable(s) to be created. Can be a single string or list of strings. If a list of strings, will create an optimizer variable for each prefix. The variable name will follow the pattern {variable_name}_{trainable_variable.name}, e.g., momemtum/dense_1.
initializer Initializer object(s) to use to populate the initial variable value(s), or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "zeros".

Returns
A list of optimizer variables, in the format of keras.Variables. If multiple names are provide, returns a tuple of lists.

add_variable

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add_variable(
    shape,
    initializer='zeros',
    dtype=None,
    aggregation='none',
    layout=None,
    name=None
)

Add a variable to the optimizer.

Args
shape Shape tuple for the variable. Must be fully-defined (no None entries).
initializer Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). Defaults to "zeros".
dtype Dtype of the variable to create, e.g. "float32". If unspecified, defaults to the keras.backend.floatx().
aggregation Optional string, one of None, "none", "mean", "sum" or "only_first_replica". Annotates the variable with the type of multi-replica aggregation to be used for this variable when writing custom data parallel training loops. Defaults to "none".
layout Optional tensor layout. Defaults to None.
name String name of the variable. Useful for debugging purposes.

Returns
An optimizer variable, in the format of keras.Variable.

add_variable_from_reference

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add_variable_from_reference(
    reference_variable, name=None, initializer='zeros'
)

Add an optimizer variable from the model variable.

Create an optimizer variable based on the information of model variable. For example, in SGD optimizer momemtum, for each model variable, a corresponding momemtum variable is created of the same shape and dtype.

Args
reference_variable keras.Variable. The corresponding model variable to the optimizer variable to be created.
name Optional string. The name prefix of the optimizer variable to be created. If not provided, it will be set to "var". The variable name will follow the pattern {variable_name}_{reference_variable.name}, e.g., momemtum/dense_1. Defaults to None.
initializer Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "zeros".

Returns
An optimizer variable, in the format of keras.Variable.

apply

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apply(
    grads, trainable_variables=None
)

Update traininable variables according to provided gradient values.

grads should be a list of gradient tensors with 1:1 mapping to the list of variables the optimizer was built with.

trainable_variables can be provided on the first call to build the optimizer.

apply_gradients

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apply_gradients(
    grads_and_vars
)

assign

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assign(
    variable, value
)

Assign a value to a variable.

This should be used in optimizers instead of variable.assign(value) to support backend specific optimizations. Note that the variable can be a model variable or an optimizer variable; it can be a backend native variable or a Keras variable.

Args
variable The variable to update.
value The value to add to the variable.

assign_add

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assign_add(
    variable, value
)

Add a value to a variable.

This should be used in optimizers instead of variable.assign_add(value) to support backend specific optimizations. Note that the variable can be a model variable or an optimizer variable; it can be a backend native variable or a Keras variable.

Args
variable The variable to update.
value The value to add to the variable.

assign_sub

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assign_sub(
    variable, value
)

Subtract a value from a variable.

This should be used in optimizers instead of variable.assign_sub(value) to support backend specific optimizations. Note that the variable can be a model variable or an optimizer variable; it can be a backend native variable or a Keras variable.

Args
variable The variable to update.
value The value to add to the variable.

build

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build(
    var_list
)

Initialize optimizer variables.

ScheduleFreeAdamW optimizer has the following variables:

  • momentum: Auxiliary variable where gradient updates are applied
  • velocity: Exponential moving average of squared gradients (Adam)

Args
var_list list of model variables to build optimizer variables on.

exclude_from_weight_decay

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exclude_from_weight_decay(
    var_list=None, var_names=None
)

Exclude variables from weight decay.

This method must be called before the optimizer's build method is called. You can set specific variables to exclude out, or set a list of strings as the anchor words, if any of which appear in a variable's name, then the variable is excluded.

Args
var_list A list of Variables to exclude from weight decay.
var_names A list of strings. If any string in var_names appear in the model variable's name, then this model variable is excluded from weight decay. For example, var_names=['bias'] excludes all bias variables from weight decay.

finalize_variable_values

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finalize_variable_values(
    var_list
)

Set the final value of model's trainable variables.

Sometimes there are some extra steps before ending the variable updates, such as overriding the model variables with its average value.

Args
var_list list of model variables.

from_config

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@classmethod
from_config(
    config, custom_objects=None
)

Creates an optimizer from its config.

This method is the reverse of get_config, capable of instantiating the same optimizer from the config dictionary.

Args
config A Python dictionary, typically the output of get_config.
custom_objects A Python dictionary mapping names to additional user-defined Python objects needed to recreate this optimizer.

Returns
An optimizer instance.

get_config

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get_config()

Returns the config of the optimizer.

An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.

Subclass optimizer should override this method to include other hyperparameters.

Returns
Python dictionary.

load_own_variables

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load_own_variables(
    store
)

Set the state of this optimizer object.

save_own_variables

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save_own_variables(
    store
)

Get the state of this optimizer object.

scale_loss

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scale_loss(
    loss
)

Scale the loss before computing gradients.

Scales the loss before gradients are computed in a train_step. This is primarily useful during mixed precision training to prevent numeric underflow.

set_weights

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set_weights(
    weights
)

Set the weights of the optimizer.

stateless_apply

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stateless_apply(
    optimizer_variables, grads, trainable_variables
)

Stateless version of apply that returns modified variables.

Args
optimizer_variables list of tensors containing the current values for the optimizer variables. These are native tensors and not keras.Variables.
grads list of gradients to apply.
trainable_variables list of tensors containing the current values for the model variables. These are native tensors and not keras.Variables.

Returns: A tuple containing two list of tensors, the updated trainable_variables and the updated optimizer_variables.

update_step

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update_step(
    gradient, variable, learning_rate
)

Update step given gradient and the associated model variable.