tf.contrib.tpu.CrossShardOptimizer

Class CrossShardOptimizer

Inherits From: Optimizer

Defined in tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py.

An optimizer that averages gradients across TPU shards.

Methods

__init__

__init__(
    opt,
    reduction=losses.Reduction.MEAN,
    name='CrossShardOptimizer'
)

Construct a new cross-shard optimizer.

Args:

• opt: An existing Optimizer to encapsulate.
• reduction: The reduction to apply to the shard losses.
• name: Optional name prefix for the operations created when applying gradients. Defaults to "CrossShardOptimizer".

Raises:

• ValueError: If reduction is not a valid cross-shard reduction.

apply_gradients

apply_gradients(
    grads_and_vars,
    global_step=None,
    name=None
)

Apply gradients to variables.

Calls tpu_ops.cross_replica_sum() to sum gradient contributions across replicas, and then applies the real optimizer.

Args:

• grads_and_vars: List of (gradient, variable) pairs as returned by compute_gradients().
• global_step: Optional Variable to increment by one after the variables have been updated.
• name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

Returns:

An Operation that applies the gradients. If global_step was not None, that operation also increments global_step.

Raises:

• ValueError: If the grads_and_vars is malformed.

compute_gradients

compute_gradients(
    loss,
    var_list=None,
    **kwargs
)

Compute gradients of "loss" for the variables in "var_list".

This simply wraps the compute_gradients() from the real optimizer. The gradients will be aggregated in the apply_gradients() so that user can modify the gradients like clipping with per replica global norm if needed. The global norm with aggregated gradients can be bad as one replica's huge gradients can hurt the gradients from other replicas.

Args:

• loss: A Tensor containing the value to minimize.
• var_list: Optional list or tuple of tf.Variable to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKey.TRAINABLE_VARIABLES.
• **kwargs: Keyword arguments for compute_gradients().

Returns:

A list of (gradient, variable) pairs.

Raises:

• ValueError: If not within a tpu_shard_context.

get_name

get_name()

get_slot

get_slot(
    *args,
    **kwargs
)

Return a slot named "name" created for "var" by the Optimizer.

This simply wraps the get_slot() from the actual optimizer.

Args:

• *args: Arguments for get_slot().
• **kwargs: Keyword arguments for get_slot().

Returns:

The Variable for the slot if it was created, None otherwise.

get_slot_names

get_slot_names(
    *args,
    **kwargs
)

Return a list of the names of slots created by the Optimizer.

This simply wraps the get_slot_names() from the actual optimizer.

Args:

• *args: Arguments for get_slot().
• **kwargs: Keyword arguments for get_slot().

Returns:

A list of strings.

minimize

minimize(
    loss,
    global_step=None,
    var_list=None,
    gate_gradients=GATE_OP,
    aggregation_method=None,
    colocate_gradients_with_ops=False,
    name=None,
    grad_loss=None
)

Add operations to minimize loss by updating var_list.

This method simply combines calls compute_gradients() and apply_gradients(). If you want to process the gradient before applying them call compute_gradients() and apply_gradients() explicitly instead of using this function.

Args:

• loss: A Tensor containing the value to minimize.
• global_step: Optional Variable to increment by one after the variables have been updated.
• var_list: Optional list or tuple of Variable objects to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.
• gate_gradients: How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
• aggregation_method: Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
• colocate_gradients_with_ops: If True, try colocating gradients with the corresponding op.
• name: Optional name for the returned operation.
• grad_loss: Optional. A Tensor holding the gradient computed for loss.

Returns:

An Operation that updates the variables in var_list. If global_step was not None, that operation also increments global_step.

Raises:

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

Eager Compatibility

When eager execution is enabled, loss should be a Python function that takes elements of var_list as arguments and computes the value to be minimized. If var_list is None, loss should take no arguments. Minimization (and gradient computation) is done with respect to the elements of var_list if not None, else with respect to any trainable variables created during the execution of the loss function. gate_gradients, aggregation_method, colocate_gradients_with_ops and grad_loss are ignored when eager execution is enabled.

variables

variables()

A list of variables which encode the current state of Optimizer.

Includes slot variables and additional global variables created by the optimizer in the current default graph.

Returns:

A list of variables.

Class Members

GATE_GRAPH

GATE_NONE

GATE_OP