tf.keras.mixed_precision.experimental.LossScaleOptimizer

TensorFlow 1 version

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An optimizer that applies loss scaling.

Inherits From: Optimizer

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tf.compat.v1.keras.mixed_precision.experimental.LossScaleOptimizer

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(0.25)
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt,
                                                               "dynamic")
var = tf.Variable(1.)
loss_fn = lambda: var ** 2
# 'minimize' applies loss scaling to the loss and updates the loss sale.
opt.minimize(loss_fn, var_list=var)
var.numpy()
0.5

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:

with tf.GradientTape() as tape:
  loss = loss_fn()
  scaled_loss = opt.get_scaled_loss(loss)
scaled_grad = tape.gradient(scaled_loss, var)
(grad,) = opt.get_unscaled_gradients([scaled_grad])
opt.apply_gradients([(grad, var)])  # Loss scale is updated here
var.numpy()
0.25

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, experimental_aggregate_gradients=True
)

Apply gradients to variables.

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

The method sums gradients from all replicas in the presence of tf.distribute.Strategy by default. You can aggregate gradients yourself by passing experimental_aggregate_gradients=False.

Example:

grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
    experimental_aggregate_gradients=False)

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.
experimental_aggregate_gradients	Whether to sum gradients from different replicas in the presense of tf.distribute.Strategy. If False, it's user responsibility to aggregate the gradients. Default to True.

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 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.

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()

Returns the current weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.

For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

opt = tf.keras.optimizers.RMSprop()
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
m.compile(opt, loss='mse')
data = np.arange(100).reshape(5, 20)
labels = np.zeros(5)
print('Training'); results = m.fit(data, labels)
Training ...
len(opt.get_weights())
3

Returns
Weights values as a list of numpy arrays.

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
)

Set the weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.

For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

opt = tf.keras.optimizers.RMSprop()
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
m.compile(opt, loss='mse')
data = np.arange(100).reshape(5, 20)
labels = np.zeros(5)
print('Training'); results = m.fit(data, labels)
Training ...
new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]
opt.set_weights(new_weights)
opt.iterations
<tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>

Arguments
weights	weight values as a list of numpy arrays.

variables

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variables()

Returns variables of this Optimizer based on the order created.