tf.keras.mixed_precision.experimental.LossScaleOptimizer

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An optimizer that applies loss scaling.

Inherits From: Optimizer

tf.keras.mixed_precision.experimental.LossScaleOptimizer(
    optimizer, loss_scale
)

Loss scaling is a process that multiplies the loss by a multiplier called the loss scale, and divides each gradient by the same multiplier. The pseudocode for this process is:

loss = ...
loss *= loss_scale
grads = gradients(loss, vars)
grads /= loss_scale

Mathematically, loss scaling has no effect, but can help avoid numerical underflow in intermediate gradients when float16 tensors are used. By multiplying the loss, each intermediate gradient will have the same multiplier applied.

The loss scale can either be a fixed constant, chosen by the user, or be dynamically determined. Dynamically determining the loss scale is convenient as a loss scale does not have to be explicitly chosen. However it reduces performance.

This optimizer wraps another optimizer and applies loss scaling to it via a LossScale. Loss scaling is applied whenever gradients are computed, either through minimize() or get_gradients(). The loss scale is updated via LossScale.update() whenever gradients are applied, either through minimize() or apply_gradients(). For example:

opt = tf.keras.optimizers.SGD(0.1)
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")
# 'minimize' applies loss scaling to the loss and updates the loss sale.
opt.minimize(loss_fn)

If a tf.GradientTape is used to compute gradients instead of LossScaleOptimizer.minimize or LossScaleOptimizer.get_gradients, the loss and gradients must be scaled manually. This can be done by calling LossScaleOptimizer.get_scaled_loss before passing the loss to tf.GradientTape, and LossScaleOptimizer.get_unscaled_gradients after computing the gradients with tf.GradientTape. For example:

opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(...)
vars = ...
with tf.GradientTape() as tape:
  loss = ...
  scaled_loss = opt.get_scaled_loss(loss)
scaled_grads = tape.gradient(scaled_loss, vars)
grads = opt.get_unscaled_gradients(scaled_grads)
opt.apply_gradients(zip(grads, vars))  # Loss scale will be updated here

Args:

• optimizer: The Optimizer instance to wrap.
• loss_scale: The loss scale to scale the loss and gradients. This can either be an int/float to use a fixed loss scale, the string "dynamic" to use dynamic loss scaling, or an instance of a LossScale. The string "dynamic" equivalent to passing DynamicLossScale(), and passing an int/float is equivalent to passing a FixedLossScale with the given loss scale.

Attributes:

• iterations: Variable. The number of training steps this Optimizer has run.
• learning_rate
• loss_scale: The LossScale instance associated with this optimizer.
• lr
• weights: Returns variables of this Optimizer based on the order created.

Methods

add_slot

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add_slot(
    var, slot_name, initializer='zeros'
)

Add a new slot variable for var.

add_weight

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add_weight(
    name, shape, dtype=None, initializer='zeros', trainable=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.compat.v1.VariableAggregation.NONE
)

apply_gradients

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apply_gradients(
    grads_and_vars, name=None
)

Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

Args:

• grads_and_vars: List of (gradient, variable) pairs.
• name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

Returns:

An Operation that applies the specified gradients. The iterations will be automatically increased by 1.

Raises:

• TypeError: If grads_and_vars is malformed.
• ValueError: If none of the variables have gradients.

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.

Arguments:

• config: A Python dictionary, typically the output of get_config.
• custom_objects: A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter.

Returns:

An optimizer instance.

get_config

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get_config()

Returns the config of the optimimizer.

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.

Returns:

Python dictionary.

get_gradients

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get_gradients(
    loss, params
)

Returns gradients of loss with respect to params.

Arguments:

• loss: Loss tensor.
• params: List of variables.

Returns:

List of gradient tensors.

Raises:

• ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented).

get_scaled_loss

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get_scaled_loss(
    loss
)

Scales the loss by the loss scale.

This method is only needed if you compute gradients manually, e.g. with tf.GradientTape. In that case, call this method to scale the loss before passing the loss to tf.GradientTape. If you use LossScaleOptimizer.minimize or LossScaleOptimizer.get_gradients, loss scaling is automatically applied and this method is unneeded.

If this method is called, get_unscaled_gradients should also be called. See the tf.keras.mixed_precision.experimental.LossScaleOptimizer doc for an example.

Args:

• loss: The loss, which will be multiplied by the loss scale. Can either be a tensor or a callable returning a tensor.

Returns:

loss multiplied by LossScaleOptimizer.loss_scale().

get_slot

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get_slot(
    var, slot_name
)

get_slot_names

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get_slot_names()

A list of names for this optimizer's slots.

get_unscaled_gradients

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get_unscaled_gradients(
    grads
)

Unscales the gradients by the loss scale.

This method is only needed if you compute gradients manually, e.g. with tf.GradientTape. In that case, call this method to unscale the gradients after computing them with tf.GradientTape. If you use LossScaleOptimizer.minimize or LossScaleOptimizer.get_gradients, loss scaling is automatically applied and this method is unneeded.

If this method is called, get_scaled_loss should also be called. See the tf.keras.mixed_precision.experimental.LossScaleOptimizer doc for an example.

Args:

• grads: A list of tensors, each which will be divided by the loss scale. Can have None values, which are ignored.

Returns:

A new list the same size as grads, where every non-None value in grads is divided by LossScaleOptimizer.loss_scale().

get_updates

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get_updates(
    loss, params
)

get_weights

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get_weights()

minimize

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minimize(
    loss, var_list, grad_loss=None, name=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: A callable taking no arguments which returns the value to minimize.
• var_list: list or tuple of Variable objects to update to minimize loss, or a callable returning the list or tuple of Variable objects. Use callable when the variable list would otherwise be incomplete before minimize since the variables are created at the first time loss is called.
• grad_loss: Optional. A Tensor holding the gradient computed for loss.
• name: Optional name for the returned operation.

Returns:

An Operation that updates the variables in var_list. The iterations will be automatically increased by 1.

Raises:

• ValueError: If some of the variables are not Variable objects.

set_weights

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set_weights(
    weights
)

variables

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variables()

Returns variables of this Optimizer based on the order created.