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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/contrib/tpu/proto/tpu_embedding_configuration.proto
package tensorflow.tpu;
public final class TpuEmbeddingConfiguration {
private TpuEmbeddingConfiguration() {}
public static void registerAllExtensions(
com.google.protobuf.ExtensionRegistryLite registry) {
}
public static void registerAllExtensions(
com.google.protobuf.ExtensionRegistry registry) {
registerAllExtensions(
(com.google.protobuf.ExtensionRegistryLite) registry);
}
public interface TPUEmbeddingConfigurationOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.tpu.TPUEmbeddingConfiguration)
com.google.protobuf.MessageOrBuilder {
/**
* repeated .tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor table_descriptor = 1;
*/
java.util.List
getTableDescriptorList();
/**
* repeated .tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor table_descriptor = 1;
*/
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.TableDescriptor getTableDescriptor(int index);
/**
* repeated .tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor table_descriptor = 1;
*/
int getTableDescriptorCount();
/**
* repeated .tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor table_descriptor = 1;
*/
java.util.List
getTableDescriptorOrBuilderList();
/**
* repeated .tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor table_descriptor = 1;
*/
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.TableDescriptorOrBuilder getTableDescriptorOrBuilder(
int index);
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
int getModeValue();
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode getMode();
/**
*
* Number of samples in each batch of embedding layer activations sent to
* the TensorCore.
*
*
* int32 batch_size_per_tensor_core = 3;
*/
int getBatchSizePerTensorCore();
/**
*
* Number of TPU hosts used for inference/training.
*
*
* int32 num_hosts = 4;
*/
int getNumHosts();
/**
*
* Number of TensorCore used for inference/training.
*
*
* int32 num_tensor_cores = 5;
*/
int getNumTensorCores();
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
int getShardingStrategyValue();
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy getShardingStrategy();
/**
*
* This parameter determines if the execution of the sparse core will be
* pipelined with that of the TensorCore. This parameter only affects results
* when mode=TRAINING. If mode=INFERENCE or BACKWARD_PASS_ONLY, this parameter
* does not affect execution and hence, is a don't care value.
* false: The execution of the sparse core is not pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core is executed
* only after the backward pass of the previous step is complete. And the
* backward pass on the sparse core is executed only after the embedding
* gradients have been computed on the TensorCore on every step. This ensures
* that the activations on every step observe the gradient updates from the
* previous step on both the sparse core and the TensorCore.
* true: The execution of the sparse core is pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core can be
* executed after the forward pass of the previous step is complete without
* waiting for the backward pass. This improves the utilization of the sparse
* core allowing it to process step N+1 while the embedding gradients for step
* N are computed on the TensorCore. The backward pass of every step on the
* sparse core is executed directly after the forward pass for the next step
* is complete. The drawback is that embedding activations for step N+1 do not
* observe the embedding gradient updates from step N. This could affect model
* quality if step N and N+1 involve the same set of embedding IDs. However,
* since the embedding updates are sparse, this is generally not considered a
* problem.
*
*
* bool pipeline_execution_with_tensor_core = 7;
*/
boolean getPipelineExecutionWithTensorCore();
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
boolean hasOutputLayout();
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout getOutputLayout();
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayoutOrBuilder getOutputLayoutOrBuilder();
}
/**
* Protobuf type {@code tensorflow.tpu.TPUEmbeddingConfiguration}
*/
public static final class TPUEmbeddingConfiguration extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.tpu.TPUEmbeddingConfiguration)
TPUEmbeddingConfigurationOrBuilder {
private static final long serialVersionUID = 0L;
// Use TPUEmbeddingConfiguration.newBuilder() to construct.
private TPUEmbeddingConfiguration(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private TPUEmbeddingConfiguration() {
tableDescriptor_ = java.util.Collections.emptyList();
mode_ = 0;
batchSizePerTensorCore_ = 0;
numHosts_ = 0;
numTensorCores_ = 0;
shardingStrategy_ = 0;
pipelineExecutionWithTensorCore_ = false;
}
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public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return tensorflow.tpu.TpuEmbeddingConfiguration.internal_static_tensorflow_tpu_TPUEmbeddingConfiguration_descriptor;
}
@java.lang.Override
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internalGetFieldAccessorTable() {
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tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.class, tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Builder.class);
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/**
*
* Mode. Should the embedding layer program be run for inference (just forward
* pass), training (both forward and backward pass) or just the backward_pass.
*
*
* Protobuf enum {@code tensorflow.tpu.TPUEmbeddingConfiguration.Mode}
*/
public enum Mode
implements com.google.protobuf.ProtocolMessageEnum {
/**
* UNSPECIFIED = 0;
*/
UNSPECIFIED(0),
/**
* INFERENCE = 1;
*/
INFERENCE(1),
/**
* TRAINING = 2;
*/
TRAINING(2),
/**
* BACKWARD_PASS_ONLY = 3;
*/
BACKWARD_PASS_ONLY(3),
UNRECOGNIZED(-1),
;
/**
* UNSPECIFIED = 0;
*/
public static final int UNSPECIFIED_VALUE = 0;
/**
* INFERENCE = 1;
*/
public static final int INFERENCE_VALUE = 1;
/**
* TRAINING = 2;
*/
public static final int TRAINING_VALUE = 2;
/**
* BACKWARD_PASS_ONLY = 3;
*/
public static final int BACKWARD_PASS_ONLY_VALUE = 3;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static Mode valueOf(int value) {
return forNumber(value);
}
public static Mode forNumber(int value) {
switch (value) {
case 0: return UNSPECIFIED;
case 1: return INFERENCE;
case 2: return TRAINING;
case 3: return BACKWARD_PASS_ONLY;
default: return null;
}
}
public static com.google.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final com.google.protobuf.Internal.EnumLiteMap<
Mode> internalValueMap =
new com.google.protobuf.Internal.EnumLiteMap() {
public Mode findValueByNumber(int number) {
return Mode.forNumber(number);
}
};
public final com.google.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.getDescriptor().getEnumTypes().get(0);
}
private static final Mode[] VALUES = values();
public static Mode valueOf(
com.google.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
if (desc.getIndex() == -1) {
return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private Mode(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.tpu.TPUEmbeddingConfiguration.Mode)
}
/**
*
* Sharding strategy of the embedding tables among the hosts.
* If the sharding_strategy is "mod", each id is assigned to host
* "id % num_hosts". For instance, 13 ids are split across 5 hosts as:
* [[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]].
* If the sharding_strategy is "div", ids are assigned to hosts in a
* contiguous manner. In this case, 13 ids are split across 5 hosts as:
* [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]].
* In both the strategies, if the id space does not evenly divide the number
* of hosts, each of the first "table_descriptor.num_ids % num_hosts" hosts
* will be assigned one more id.
* This partitioning strategy exactly follows that in the embedding_lookup
* TensorFlow function at tensorflow/python/ops/embedding_ops.py.
*
*
* Protobuf enum {@code tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy}
*/
public enum ShardingStrategy
implements com.google.protobuf.ProtocolMessageEnum {
/**
* DIV_DEFAULT = 0;
*/
DIV_DEFAULT(0),
/**
* MOD = 1;
*/
MOD(1),
UNRECOGNIZED(-1),
;
/**
* DIV_DEFAULT = 0;
*/
public static final int DIV_DEFAULT_VALUE = 0;
/**
* MOD = 1;
*/
public static final int MOD_VALUE = 1;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static ShardingStrategy valueOf(int value) {
return forNumber(value);
}
public static ShardingStrategy forNumber(int value) {
switch (value) {
case 0: return DIV_DEFAULT;
case 1: return MOD;
default: return null;
}
}
public static com.google.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final com.google.protobuf.Internal.EnumLiteMap<
ShardingStrategy> internalValueMap =
new com.google.protobuf.Internal.EnumLiteMap() {
public ShardingStrategy findValueByNumber(int number) {
return ShardingStrategy.forNumber(number);
}
};
public final com.google.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.getDescriptor().getEnumTypes().get(1);
}
private static final ShardingStrategy[] VALUES = values();
public static ShardingStrategy valueOf(
com.google.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
if (desc.getIndex() == -1) {
return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private ShardingStrategy(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy)
}
public interface TableDescriptorOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor)
com.google.protobuf.MessageOrBuilder {
/**
*
* Name of the table.
*
*
* string name = 1;
*/
java.lang.String getName();
/**
*
* Name of the table.
*
*
* string name = 1;
*/
com.google.protobuf.ByteString
getNameBytes();
/**
*
* Size of the vocabulary (i.e., number of rows) in the table.
*
*
* int32 vocabulary_size = 2;
*/
int getVocabularySize();
/**
*
* The embedding dimension (i.e., the width of the embedding table).
*
*
* int32 dimension = 3;
*/
int getDimension();
/**
*
* Number of features mapped to this table.
*
*
* int32 num_features = 4;
*/
int getNumFeatures();
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
boolean hasOptimizationParameters();
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
tensorflow.tpu.OptimizationParametersOuterClass.OptimizationParameters getOptimizationParameters();
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
tensorflow.tpu.OptimizationParametersOuterClass.OptimizationParametersOrBuilder getOptimizationParametersOrBuilder();
}
/**
*
* Description of the various embedding tables.
*
*
* Protobuf type {@code tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor}
*/
public static final class TableDescriptor extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor)
TableDescriptorOrBuilder {
private static final long serialVersionUID = 0L;
// Use TableDescriptor.newBuilder() to construct.
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*
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*
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*
*
* int32 vocabulary_size = 2;
*/
public int getVocabularySize() {
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public static final int DIMENSION_FIELD_NUMBER = 3;
private int dimension_;
/**
*
* The embedding dimension (i.e., the width of the embedding table).
*
*
* int32 dimension = 3;
*/
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*
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*
*
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*/
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/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
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}
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
public tensorflow.tpu.OptimizationParametersOuterClass.OptimizationParameters getOptimizationParameters() {
return optimizationParameters_ == null ? tensorflow.tpu.OptimizationParametersOuterClass.OptimizationParameters.getDefaultInstance() : optimizationParameters_;
}
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
public tensorflow.tpu.OptimizationParametersOuterClass.OptimizationParametersOrBuilder getOptimizationParametersOrBuilder() {
return getOptimizationParameters();
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onChanged();
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private int vocabularySize_ ;
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*
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*
*
* int32 vocabulary_size = 2;
*/
public int getVocabularySize() {
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/**
*
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*
*
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public Builder setVocabularySize(int value) {
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onChanged();
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/**
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public Builder clearVocabularySize() {
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onChanged();
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private int dimension_ ;
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*
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*
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*
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onChanged();
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*
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*
*
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*
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*
*
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*
* Details of the learning algorithm used to update the embedding
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*
*
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/**
*
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*/
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if (optimizationParametersBuilder_ == null) {
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}
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onChanged();
} else {
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}
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}
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
public Builder setOptimizationParameters(
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onChanged();
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/**
*
* Details of the learning algorithm used to update the embedding
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*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
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onChanged();
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/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
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if (optimizationParametersBuilder_ == null) {
optimizationParameters_ = null;
onChanged();
} else {
optimizationParameters_ = null;
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/**
*
* Details of the learning algorithm used to update the embedding
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*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
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onChanged();
return getOptimizationParametersFieldBuilder().getBuilder();
}
/**
*
* Details of the learning algorithm used to update the embedding
* parameters.
*
*
* .tensorflow.tpu.OptimizationParameters optimization_parameters = 5;
*/
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if (optimizationParametersBuilder_ != null) {
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} else {
return optimizationParameters_ == null ?
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/**
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int index) {
return tableDescriptor_.get(index);
}
public static final int MODE_FIELD_NUMBER = 2;
private int mode_;
/**
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*/
public int getModeValue() {
return mode_;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
public tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode getMode() {
@SuppressWarnings("deprecation")
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode result = tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode.valueOf(mode_);
return result == null ? tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode.UNRECOGNIZED : result;
}
public static final int BATCH_SIZE_PER_TENSOR_CORE_FIELD_NUMBER = 3;
private int batchSizePerTensorCore_;
/**
*
* Number of samples in each batch of embedding layer activations sent to
* the TensorCore.
*
*
* int32 batch_size_per_tensor_core = 3;
*/
public int getBatchSizePerTensorCore() {
return batchSizePerTensorCore_;
}
public static final int NUM_HOSTS_FIELD_NUMBER = 4;
private int numHosts_;
/**
*
* Number of TPU hosts used for inference/training.
*
*
* int32 num_hosts = 4;
*/
public int getNumHosts() {
return numHosts_;
}
public static final int NUM_TENSOR_CORES_FIELD_NUMBER = 5;
private int numTensorCores_;
/**
*
* Number of TensorCore used for inference/training.
*
*
* int32 num_tensor_cores = 5;
*/
public int getNumTensorCores() {
return numTensorCores_;
}
public static final int SHARDING_STRATEGY_FIELD_NUMBER = 6;
private int shardingStrategy_;
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public int getShardingStrategyValue() {
return shardingStrategy_;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy getShardingStrategy() {
@SuppressWarnings("deprecation")
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy result = tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy.valueOf(shardingStrategy_);
return result == null ? tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy.UNRECOGNIZED : result;
}
public static final int PIPELINE_EXECUTION_WITH_TENSOR_CORE_FIELD_NUMBER = 7;
private boolean pipelineExecutionWithTensorCore_;
/**
*
* This parameter determines if the execution of the sparse core will be
* pipelined with that of the TensorCore. This parameter only affects results
* when mode=TRAINING. If mode=INFERENCE or BACKWARD_PASS_ONLY, this parameter
* does not affect execution and hence, is a don't care value.
* false: The execution of the sparse core is not pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core is executed
* only after the backward pass of the previous step is complete. And the
* backward pass on the sparse core is executed only after the embedding
* gradients have been computed on the TensorCore on every step. This ensures
* that the activations on every step observe the gradient updates from the
* previous step on both the sparse core and the TensorCore.
* true: The execution of the sparse core is pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core can be
* executed after the forward pass of the previous step is complete without
* waiting for the backward pass. This improves the utilization of the sparse
* core allowing it to process step N+1 while the embedding gradients for step
* N are computed on the TensorCore. The backward pass of every step on the
* sparse core is executed directly after the forward pass for the next step
* is complete. The drawback is that embedding activations for step N+1 do not
* observe the embedding gradient updates from step N. This could affect model
* quality if step N and N+1 involve the same set of embedding IDs. However,
* since the embedding updates are sparse, this is generally not considered a
* problem.
*
*
* bool pipeline_execution_with_tensor_core = 7;
*/
public boolean getPipelineExecutionWithTensorCore() {
return pipelineExecutionWithTensorCore_;
}
public static final int OUTPUT_LAYOUT_FIELD_NUMBER = 8;
private tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout outputLayout_;
/**
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tableDescriptor_,
((bitField0_ & 0x00000001) == 0x00000001),
getParentForChildren(),
isClean());
tableDescriptor_ = null;
}
return tableDescriptorBuilder_;
}
private int mode_ = 0;
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
public int getModeValue() {
return mode_;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
public Builder setModeValue(int value) {
mode_ = value;
onChanged();
return this;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
public tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode getMode() {
@SuppressWarnings("deprecation")
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode result = tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode.valueOf(mode_);
return result == null ? tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode.UNRECOGNIZED : result;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
public Builder setMode(tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.Mode value) {
if (value == null) {
throw new NullPointerException();
}
mode_ = value.getNumber();
onChanged();
return this;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.Mode mode = 2;
*/
public Builder clearMode() {
mode_ = 0;
onChanged();
return this;
}
private int batchSizePerTensorCore_ ;
/**
*
* Number of samples in each batch of embedding layer activations sent to
* the TensorCore.
*
*
* int32 batch_size_per_tensor_core = 3;
*/
public int getBatchSizePerTensorCore() {
return batchSizePerTensorCore_;
}
/**
*
* Number of samples in each batch of embedding layer activations sent to
* the TensorCore.
*
*
* int32 batch_size_per_tensor_core = 3;
*/
public Builder setBatchSizePerTensorCore(int value) {
batchSizePerTensorCore_ = value;
onChanged();
return this;
}
/**
*
* Number of samples in each batch of embedding layer activations sent to
* the TensorCore.
*
*
* int32 batch_size_per_tensor_core = 3;
*/
public Builder clearBatchSizePerTensorCore() {
batchSizePerTensorCore_ = 0;
onChanged();
return this;
}
private int numHosts_ ;
/**
*
* Number of TPU hosts used for inference/training.
*
*
* int32 num_hosts = 4;
*/
public int getNumHosts() {
return numHosts_;
}
/**
*
* Number of TPU hosts used for inference/training.
*
*
* int32 num_hosts = 4;
*/
public Builder setNumHosts(int value) {
numHosts_ = value;
onChanged();
return this;
}
/**
*
* Number of TPU hosts used for inference/training.
*
*
* int32 num_hosts = 4;
*/
public Builder clearNumHosts() {
numHosts_ = 0;
onChanged();
return this;
}
private int numTensorCores_ ;
/**
*
* Number of TensorCore used for inference/training.
*
*
* int32 num_tensor_cores = 5;
*/
public int getNumTensorCores() {
return numTensorCores_;
}
/**
*
* Number of TensorCore used for inference/training.
*
*
* int32 num_tensor_cores = 5;
*/
public Builder setNumTensorCores(int value) {
numTensorCores_ = value;
onChanged();
return this;
}
/**
*
* Number of TensorCore used for inference/training.
*
*
* int32 num_tensor_cores = 5;
*/
public Builder clearNumTensorCores() {
numTensorCores_ = 0;
onChanged();
return this;
}
private int shardingStrategy_ = 0;
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public int getShardingStrategyValue() {
return shardingStrategy_;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public Builder setShardingStrategyValue(int value) {
shardingStrategy_ = value;
onChanged();
return this;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy getShardingStrategy() {
@SuppressWarnings("deprecation")
tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy result = tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy.valueOf(shardingStrategy_);
return result == null ? tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy.UNRECOGNIZED : result;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public Builder setShardingStrategy(tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration.ShardingStrategy value) {
if (value == null) {
throw new NullPointerException();
}
shardingStrategy_ = value.getNumber();
onChanged();
return this;
}
/**
* .tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy sharding_strategy = 6;
*/
public Builder clearShardingStrategy() {
shardingStrategy_ = 0;
onChanged();
return this;
}
private boolean pipelineExecutionWithTensorCore_ ;
/**
*
* This parameter determines if the execution of the sparse core will be
* pipelined with that of the TensorCore. This parameter only affects results
* when mode=TRAINING. If mode=INFERENCE or BACKWARD_PASS_ONLY, this parameter
* does not affect execution and hence, is a don't care value.
* false: The execution of the sparse core is not pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core is executed
* only after the backward pass of the previous step is complete. And the
* backward pass on the sparse core is executed only after the embedding
* gradients have been computed on the TensorCore on every step. This ensures
* that the activations on every step observe the gradient updates from the
* previous step on both the sparse core and the TensorCore.
* true: The execution of the sparse core is pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core can be
* executed after the forward pass of the previous step is complete without
* waiting for the backward pass. This improves the utilization of the sparse
* core allowing it to process step N+1 while the embedding gradients for step
* N are computed on the TensorCore. The backward pass of every step on the
* sparse core is executed directly after the forward pass for the next step
* is complete. The drawback is that embedding activations for step N+1 do not
* observe the embedding gradient updates from step N. This could affect model
* quality if step N and N+1 involve the same set of embedding IDs. However,
* since the embedding updates are sparse, this is generally not considered a
* problem.
*
*
* bool pipeline_execution_with_tensor_core = 7;
*/
public boolean getPipelineExecutionWithTensorCore() {
return pipelineExecutionWithTensorCore_;
}
/**
*
* This parameter determines if the execution of the sparse core will be
* pipelined with that of the TensorCore. This parameter only affects results
* when mode=TRAINING. If mode=INFERENCE or BACKWARD_PASS_ONLY, this parameter
* does not affect execution and hence, is a don't care value.
* false: The execution of the sparse core is not pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core is executed
* only after the backward pass of the previous step is complete. And the
* backward pass on the sparse core is executed only after the embedding
* gradients have been computed on the TensorCore on every step. This ensures
* that the activations on every step observe the gradient updates from the
* previous step on both the sparse core and the TensorCore.
* true: The execution of the sparse core is pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core can be
* executed after the forward pass of the previous step is complete without
* waiting for the backward pass. This improves the utilization of the sparse
* core allowing it to process step N+1 while the embedding gradients for step
* N are computed on the TensorCore. The backward pass of every step on the
* sparse core is executed directly after the forward pass for the next step
* is complete. The drawback is that embedding activations for step N+1 do not
* observe the embedding gradient updates from step N. This could affect model
* quality if step N and N+1 involve the same set of embedding IDs. However,
* since the embedding updates are sparse, this is generally not considered a
* problem.
*
*
* bool pipeline_execution_with_tensor_core = 7;
*/
public Builder setPipelineExecutionWithTensorCore(boolean value) {
pipelineExecutionWithTensorCore_ = value;
onChanged();
return this;
}
/**
*
* This parameter determines if the execution of the sparse core will be
* pipelined with that of the TensorCore. This parameter only affects results
* when mode=TRAINING. If mode=INFERENCE or BACKWARD_PASS_ONLY, this parameter
* does not affect execution and hence, is a don't care value.
* false: The execution of the sparse core is not pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core is executed
* only after the backward pass of the previous step is complete. And the
* backward pass on the sparse core is executed only after the embedding
* gradients have been computed on the TensorCore on every step. This ensures
* that the activations on every step observe the gradient updates from the
* previous step on both the sparse core and the TensorCore.
* true: The execution of the sparse core is pipelined with that of the
* TensorCore. The forward pass of every step on the sparse core can be
* executed after the forward pass of the previous step is complete without
* waiting for the backward pass. This improves the utilization of the sparse
* core allowing it to process step N+1 while the embedding gradients for step
* N are computed on the TensorCore. The backward pass of every step on the
* sparse core is executed directly after the forward pass for the next step
* is complete. The drawback is that embedding activations for step N+1 do not
* observe the embedding gradient updates from step N. This could affect model
* quality if step N and N+1 involve the same set of embedding IDs. However,
* since the embedding updates are sparse, this is generally not considered a
* problem.
*
*
* bool pipeline_execution_with_tensor_core = 7;
*/
public Builder clearPipelineExecutionWithTensorCore() {
pipelineExecutionWithTensorCore_ = false;
onChanged();
return this;
}
private tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout outputLayout_ = null;
private com.google.protobuf.SingleFieldBuilderV3<
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout, tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.Builder, tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayoutOrBuilder> outputLayoutBuilder_;
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public boolean hasOutputLayout() {
return outputLayoutBuilder_ != null || outputLayout_ != null;
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout getOutputLayout() {
if (outputLayoutBuilder_ == null) {
return outputLayout_ == null ? tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.getDefaultInstance() : outputLayout_;
} else {
return outputLayoutBuilder_.getMessage();
}
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public Builder setOutputLayout(tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout value) {
if (outputLayoutBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
outputLayout_ = value;
onChanged();
} else {
outputLayoutBuilder_.setMessage(value);
}
return this;
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public Builder setOutputLayout(
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.Builder builderForValue) {
if (outputLayoutBuilder_ == null) {
outputLayout_ = builderForValue.build();
onChanged();
} else {
outputLayoutBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public Builder mergeOutputLayout(tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout value) {
if (outputLayoutBuilder_ == null) {
if (outputLayout_ != null) {
outputLayout_ =
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.newBuilder(outputLayout_).mergeFrom(value).buildPartial();
} else {
outputLayout_ = value;
}
onChanged();
} else {
outputLayoutBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public Builder clearOutputLayout() {
if (outputLayoutBuilder_ == null) {
outputLayout_ = null;
onChanged();
} else {
outputLayout_ = null;
outputLayoutBuilder_ = null;
}
return this;
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.Builder getOutputLayoutBuilder() {
onChanged();
return getOutputLayoutFieldBuilder().getBuilder();
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
public tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayoutOrBuilder getOutputLayoutOrBuilder() {
if (outputLayoutBuilder_ != null) {
return outputLayoutBuilder_.getMessageOrBuilder();
} else {
return outputLayout_ == null ?
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.getDefaultInstance() : outputLayout_;
}
}
/**
*
* Extended output layout information; if not provided, a compatibility mode
* will use defaults that match the old layout. Providing a value for this
* field is EXPERIMENTAL and most ways of filling it will probably break. Do
* not set it unless you know what you are doing.
*
*
* .tensorflow.tpu.TPUEmbeddingOutputLayout output_layout = 8;
*/
private com.google.protobuf.SingleFieldBuilderV3<
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout, tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.Builder, tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayoutOrBuilder>
getOutputLayoutFieldBuilder() {
if (outputLayoutBuilder_ == null) {
outputLayoutBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout, tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayout.Builder, tensorflow.tpu.TpuEmbeddingOutputLayout.TPUEmbeddingOutputLayoutOrBuilder>(
getOutputLayout(),
getParentForChildren(),
isClean());
outputLayout_ = null;
}
return outputLayoutBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFieldsProto3(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.tpu.TPUEmbeddingConfiguration)
}
// @@protoc_insertion_point(class_scope:tensorflow.tpu.TPUEmbeddingConfiguration)
private static final tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration();
}
public static tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
@java.lang.Override
public TPUEmbeddingConfiguration parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new TPUEmbeddingConfiguration(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public tensorflow.tpu.TpuEmbeddingConfiguration.TPUEmbeddingConfiguration getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
private static final com.google.protobuf.Descriptors.Descriptor
internal_static_tensorflow_tpu_TPUEmbeddingConfiguration_descriptor;
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}
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static {
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"\n>tensorflow/contrib/tpu/proto/tpu_embed" +
"ding_configuration.proto\022\016tensorflow.tpu" +
"\032:tensorflow/contrib/tpu/proto/optimizat" +
"ion_parameters.proto\032>tensorflow/contrib" +
"/tpu/proto/tpu_embedding_output_layout.p" +
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"ingConfiguration.Mode\022\"\n\032batch_size_per_" +
"tensor_core\030\003 \001(\005\022\021\n\tnum_hosts\030\004 \001(\005\022\030\n\020" +
"num_tensor_cores\030\005 \001(\005\022U\n\021sharding_strat" +
"egy\030\006 \001(\0162:.tensorflow.tpu.TPUEmbeddingC" +
"onfiguration.ShardingStrategy\022+\n#pipelin" +
"e_execution_with_tensor_core\030\007 \001(\010\022?\n\rou" +
"tput_layout\030\010 \001(\0132(.tensorflow.tpu.TPUEm" +
"beddingOutputLayout\032\252\001\n\017TableDescriptor\022" +
"\014\n\004name\030\001 \001(\t\022\027\n\017vocabulary_size\030\002 \001(\005\022\021" +
"\n\tdimension\030\003 \001(\005\022\024\n\014num_features\030\004 \001(\005\022" +
"G\n\027optimization_parameters\030\005 \001(\0132&.tenso" +
"rflow.tpu.OptimizationParameters\"L\n\004Mode" +
"\022\017\n\013UNSPECIFIED\020\000\022\r\n\tINFERENCE\020\001\022\014\n\010TRAI" +
"NING\020\002\022\026\n\022BACKWARD_PASS_ONLY\020\003\",\n\020Shardi" +
"ngStrategy\022\017\n\013DIV_DEFAULT\020\000\022\007\n\003MOD\020\001b\006pr" +
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// @@protoc_insertion_point(outer_class_scope)
}