tf.keras.dtensor.experimental.optimizers.SGD

DTensor specific optimizers.

Inherits From: SGD, Optimizer, Module

tf.keras.dtensor.experimental.optimizers.SGD(
    learning_rate=0.01,
    momentum=0.0,
    nesterov=False,
    amsgrad=False,
    gradients_clip_option=None,
    ema_option=None,
    jit_compile=False,
    name='SGD',
    mesh=None
)

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

Gradient descent (with momentum) optimizer.

Update rule for parameter w with gradient g when momentum is 0:

w = w - learning_rate * g

Update rule when momentum is larger than 0:

velocity = momentum * velocity - learning_rate * g
w = w + velocity

When nesterov=True, this rule becomes:

velocity = momentum * velocity - learning_rate * g
w = w + momentum * velocity - learning_rate * g

Args
`learning_rate`	A `Tensor`, floating point value, or a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.001.
`momentum`	float hyperparameter >= 0 that accelerates gradient descent in the relevant direction and dampens oscillations. Defaults to 0, i.e., vanilla gradient descent.
`nesterov`	boolean. Whether to apply Nesterov momentum. Defaults to `False`.
`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.

Usage:

opt = tf.keras.optimizers.SGD(learning_rate=0.1)
var = tf.Variable(1.0)
loss = lambda: (var ** 2)/2.0         # d(loss)/d(var1) = var1
step_count = opt.minimize(loss, [var]).numpy()
# Step is `- learning_rate * grad`
var.numpy()
0.9

opt = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2)/2.0         # d(loss)/d(var1) = var1
# First step is `- learning_rate * grad`
step_count = opt.minimize(loss, [var]).numpy()
val1 = var.value()
(val0 - val1).numpy()
0.1
# On later steps, step-size increases because of momentum
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
(val1 - val2).numpy()
0.18

Reference
For `nesterov=True`, See Sutskever et al., 2013.

Reference

For nesterov=True, See Sutskever et al., 2013.

Attributes
`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`

Attributes

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`

View source

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`

View source

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 optimizer variables.

SGD optimizer has one variable momentums, only set if self.momentum is not 0.

Args
`var_list`	list of model variables to build SGD variables on.

`compute_gradients`

View source

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`

View source

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`

View source

@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`

View source

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`

View source

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(
    gradient, variable
)

Update step given gradient and the associated model variable.

tf.keras.dtensor.experimental.optimizers.SGD

Args

Usage:

Reference

Attributes

Methods

add_variable

add_variable_from_reference

apply_gradients

build

compute_gradients

finalize_variable_values

from_config

get_config

minimize

update_step

`add_variable`

`add_variable_from_reference`

`apply_gradients`

`build`

`compute_gradients`

`finalize_variable_values`

`from_config`

`get_config`

`minimize`

`update_step`