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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(0.25)
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
add_slot(
var, slot_name, initializer='zeros'
)
Add a new slot variable for var
.
add_weight
add_weight(
name, shape, dtype=None, initializer='zeros', trainable=None,
synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.compat.v1.VariableAggregation.NONE
)
apply_gradients
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
@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
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
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
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
get_slot(
var, slot_name
)
get_slot_names
get_slot_names()
A list of names for this optimizer's slots.
get_unscaled_gradients
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,