tf.compat.v1.tpu.CrossShardOptimizer

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An optimizer that averages gradients across TPU shards.

Inherits From: Optimizer

tf.compat.v1.tpu.CrossShardOptimizer(
    opt, reduction=losses.Reduction.MEAN, name='CrossShardOptimizer',
    group_assignment=None
)

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".
• group_assignment: Optional 2d int32 lists with shape [num_groups, num_replicas_per_group] which describles how to apply optimizer to subgroups.

Raises:

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

Methods

apply_gradients

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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

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

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

This simply wraps compute_gradients() from the real optimizer. The gradients will be aggregated in 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.

When the CrossShardOptimizer is constructed with reduction == losses.Reduction.MEAN (default), this function scales the loss by 1.0 / num_shards before computing the gradients. Assuming the optimizer uses the default implementation of compute_gradients(), the gradients of the scaled loss are scaled by 1.0 / num_shards compared to the gradients of the original loss. This scaling factor is important because apply_gradients() sums gradients across shards, rather than averaging them. However, the scaling factor must be taken into account when clipping the norm of the gradients or performing other postprocessing.

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 or group_assignment is invalid.

get_name

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get_name()

get_slot

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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

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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

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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 no arguments and computes the value to be minimized. 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

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variables()

Forwarding the variables from the underlying optimizer.

Class Variables

• GATE_GRAPH = 2
• GATE_NONE = 0
• GATE_OP = 1