tf.keras.dtensor.experimental.optimizers.Adadelta

DTensor specific optimizers.

Inherits From: Adadelta, Optimizer, Module

tf.keras.dtensor.experimental.optimizers.Adadelta(
    learning_rate=0.001,
    rho=0.95,
    epsilon=1e-07,
    gradients_clip_option=None,
    ema_option=None,
    name='Adadelta',
    mesh=None
)

The major changes for this class is that all the variable init logic will be mesh/layout aware.

Optimizer that implements the Adadelta algorithm.

Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks:

  • The continual decay of learning rates throughout training.
  • The need for a manually selected global learning rate.

Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, the initial learning rate can be set, as in most other Keras optimizers.

learning_rate Initial value for the learning rate: either a floating point value, or a tf.keras.optimizers.schedules.LearningRateSchedule instance. Defaults to 0.001. Note that Adadelta tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0.
rho A Tensor or a floating point value. The decay rate. Defaults to 0.95.
epsilon Small floating point value used to maintain numerical stability. Defaults to 1e-7.
name String. The name to use for momentum accumulator weights created by the optimizer.
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.
jit_compile Boolean, defaults to True. If True, the optimizer will use XLA compilation. jit_compile cannot be True when training with tf.distribute.experimental.ParameterServerStrategy. Additionally, if no GPU device is found, this flag will be ignored.
**kwargs keyword arguments only used for backward compatibility.

iterations The number of training steps this optimizer has run.

By default, iterations would be incremented by one every time apply_gradients() is called.

learning_rate

Methods

add_variable

View source

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

Create an optimizer variable.

Args
shape A list of integers, a tuple of integers, or a 1-D Tensor of type int32. Defaults to scalar if unspecified.
dtype The DType of the optimizer variable to be created. Defaults to tf.keras.backend.floatx if unspecified.
initializer string or callable. Initializer instance.
name The name of the optimizer variable to be created.

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

add_variable_from_reference

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add_variable_from_reference(
    model_variable, variable_name, initial_value=None
)

Create an optimizer variable from 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
model_variable The corresponding model variable to the optimizer variable to be created.
variable_name The name prefix of the optimizer variable to be created. The create variables name will follow the pattern {variable_name}/{model_variable.name}, e.g., momemtum/dense_1.
initial_value The initial value of the optimizer variable, if None, the value will be default to 0.

Returns
An optimizer variable.

apply_gradients

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apply_gradients(
    grads_and_vars
)

Apply gradients to variables.

Args
grads_and_vars List of (gradient, variable) pairs.

Returns
None

Raises
TypeError If grads_and_vars is malformed.

build

View source

build(
    var_list
)

Initialize the optimizer's variables, such as momemtum variables.

This function has to be implemented by subclass optimizers, and subclass optimizers need to call super().build(var_list).

Args
var_list List of model variables to build optimizers on. For example, SGD optimizer with momentum will store one momentum variable corresponding to each model variable.

compute_gradients

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compute_gradients(
    loss, var_list, tape=None
)

Compute gradients of loss on trainable variables.

Args
loss Tensor or callable. If a callable, loss should take no arguments and return the value to minimize.
var_list list or tuple of Variable objects to update to minimize loss.
tape (Optional) tf.GradientTape. If loss is provided as a Tensor, the tape that computed the loss must be provided.

Returns
A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.

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
)

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.

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.

minimize

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minimize(
    loss, var_list, tape=None
)

Minimize loss by updating var_list.

This method simply computes gradient using tf.GradientTape and calls apply_gradients(). If you want to process the gradient before applying then call tf.GradientTape and apply_gradients() explicitly instead of using this function.

Args
loss Tensor or callable. If a callable, loss should take no arguments and return the value to minimize.
var_list list or tuple of Variable objects to update to minimize loss.
tape (Optional) tf.GradientTape.

Returns
None

update_step

View source

update_step(
    grad, variable
)

Update step given gradient and the associated model variable.