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tpu_embedding_configuration.pb.go
797 lines (716 loc) · 37.5 KB
/
tpu_embedding_configuration.pb.go
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// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.31.0
// protoc (unknown)
// source: tensorflow/core/protobuf/tpu/tpu_embedding_configuration.proto
package tpu
import (
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
reflect "reflect"
sync "sync"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Mode. Should the embedding layer program be run for inference (just forward
// pass), training (both forward and backward pass) or just the backward_pass.
type TPUEmbeddingConfiguration_Mode int32
const (
TPUEmbeddingConfiguration_UNSPECIFIED TPUEmbeddingConfiguration_Mode = 0
TPUEmbeddingConfiguration_INFERENCE TPUEmbeddingConfiguration_Mode = 1
TPUEmbeddingConfiguration_TRAINING TPUEmbeddingConfiguration_Mode = 2
TPUEmbeddingConfiguration_BACKWARD_PASS_ONLY TPUEmbeddingConfiguration_Mode = 3
)
// Enum value maps for TPUEmbeddingConfiguration_Mode.
var (
TPUEmbeddingConfiguration_Mode_name = map[int32]string{
0: "UNSPECIFIED",
1: "INFERENCE",
2: "TRAINING",
3: "BACKWARD_PASS_ONLY",
}
TPUEmbeddingConfiguration_Mode_value = map[string]int32{
"UNSPECIFIED": 0,
"INFERENCE": 1,
"TRAINING": 2,
"BACKWARD_PASS_ONLY": 3,
}
)
func (x TPUEmbeddingConfiguration_Mode) Enum() *TPUEmbeddingConfiguration_Mode {
p := new(TPUEmbeddingConfiguration_Mode)
*p = x
return p
}
func (x TPUEmbeddingConfiguration_Mode) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (TPUEmbeddingConfiguration_Mode) Descriptor() protoreflect.EnumDescriptor {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_enumTypes[0].Descriptor()
}
func (TPUEmbeddingConfiguration_Mode) Type() protoreflect.EnumType {
return &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_enumTypes[0]
}
func (x TPUEmbeddingConfiguration_Mode) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use TPUEmbeddingConfiguration_Mode.Descriptor instead.
func (TPUEmbeddingConfiguration_Mode) EnumDescriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{0, 0}
}
// 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.vocabulary_size % 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.
type TPUEmbeddingConfiguration_ShardingStrategy int32
const (
TPUEmbeddingConfiguration_DIV_DEFAULT TPUEmbeddingConfiguration_ShardingStrategy = 0
TPUEmbeddingConfiguration_MOD TPUEmbeddingConfiguration_ShardingStrategy = 1
)
// Enum value maps for TPUEmbeddingConfiguration_ShardingStrategy.
var (
TPUEmbeddingConfiguration_ShardingStrategy_name = map[int32]string{
0: "DIV_DEFAULT",
1: "MOD",
}
TPUEmbeddingConfiguration_ShardingStrategy_value = map[string]int32{
"DIV_DEFAULT": 0,
"MOD": 1,
}
)
func (x TPUEmbeddingConfiguration_ShardingStrategy) Enum() *TPUEmbeddingConfiguration_ShardingStrategy {
p := new(TPUEmbeddingConfiguration_ShardingStrategy)
*p = x
return p
}
func (x TPUEmbeddingConfiguration_ShardingStrategy) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (TPUEmbeddingConfiguration_ShardingStrategy) Descriptor() protoreflect.EnumDescriptor {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_enumTypes[1].Descriptor()
}
func (TPUEmbeddingConfiguration_ShardingStrategy) Type() protoreflect.EnumType {
return &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_enumTypes[1]
}
func (x TPUEmbeddingConfiguration_ShardingStrategy) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use TPUEmbeddingConfiguration_ShardingStrategy.Descriptor instead.
func (TPUEmbeddingConfiguration_ShardingStrategy) EnumDescriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{0, 1}
}
type TPUEmbeddingConfiguration struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
TableDescriptor []*TPUEmbeddingConfiguration_TableDescriptor `protobuf:"bytes,1,rep,name=table_descriptor,json=tableDescriptor,proto3" json:"table_descriptor,omitempty"`
Mode TPUEmbeddingConfiguration_Mode `protobuf:"varint,2,opt,name=mode,proto3,enum=tensorflow.tpu.TPUEmbeddingConfiguration_Mode" json:"mode,omitempty"`
// Number of samples in each batch of embedding layer activations sent to
// the TensorCore.
BatchSizePerTensorCore int32 `protobuf:"varint,3,opt,name=batch_size_per_tensor_core,json=batchSizePerTensorCore,proto3" json:"batch_size_per_tensor_core,omitempty"`
// Number of TPU hosts used for inference/training.
NumHosts int32 `protobuf:"varint,4,opt,name=num_hosts,json=numHosts,proto3" json:"num_hosts,omitempty"`
// Number of TensorCore used for inference/training.
NumTensorCores int32 `protobuf:"varint,5,opt,name=num_tensor_cores,json=numTensorCores,proto3" json:"num_tensor_cores,omitempty"`
ShardingStrategy TPUEmbeddingConfiguration_ShardingStrategy `protobuf:"varint,6,opt,name=sharding_strategy,json=shardingStrategy,proto3,enum=tensorflow.tpu.TPUEmbeddingConfiguration_ShardingStrategy" json:"sharding_strategy,omitempty"`
// 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.
PipelineExecutionWithTensorCore bool `protobuf:"varint,7,opt,name=pipeline_execution_with_tensor_core,json=pipelineExecutionWithTensorCore,proto3" json:"pipeline_execution_with_tensor_core,omitempty"`
// Directory where embedding lookup statistics are stored. These statistics
// summarize information about the inputs to the embedding lookup
// operation, in particular, the average number of embedding IDs per example
// and how well the embedding IDs are load balanced across the system. The
// lookup statistics are used during TPU initialization for embedding table
// partitioning. Collection of lookup statistics is done at runtime by
// profiling the embedding inputs: only 3% of input samples are profiled to
// minimize host CPU overhead. Once a suitable number of samples are
// profiled, the lookup statistics are saved to table-specific files in the
// profile data directory generally at the end of a TPU training loop. The
// filename corresponding to each table is obtained by hashing table specific
// parameters (e.g., table name and number of features) and global
// configuration parameters (e.g., sharding strategy and TPU worker task
// count). The same profile data directory can be shared amongst several
// models to reuse embedding lookup statistics.
ProfileDataDirectory string `protobuf:"bytes,9,opt,name=profile_data_directory,json=profileDataDirectory,proto3" json:"profile_data_directory,omitempty"`
FeatureDescriptor []*TPUEmbeddingConfiguration_FeatureDescriptor `protobuf:"bytes,10,rep,name=feature_descriptor,json=featureDescriptor,proto3" json:"feature_descriptor,omitempty"`
SpmdSharding *TPUEmbeddingConfiguration_SpmdSharding `protobuf:"bytes,11,opt,name=spmd_sharding,json=spmdSharding,proto3" json:"spmd_sharding,omitempty"`
}
func (x *TPUEmbeddingConfiguration) Reset() {
*x = TPUEmbeddingConfiguration{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TPUEmbeddingConfiguration) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TPUEmbeddingConfiguration) ProtoMessage() {}
func (x *TPUEmbeddingConfiguration) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TPUEmbeddingConfiguration.ProtoReflect.Descriptor instead.
func (*TPUEmbeddingConfiguration) Descriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{0}
}
func (x *TPUEmbeddingConfiguration) GetTableDescriptor() []*TPUEmbeddingConfiguration_TableDescriptor {
if x != nil {
return x.TableDescriptor
}
return nil
}
func (x *TPUEmbeddingConfiguration) GetMode() TPUEmbeddingConfiguration_Mode {
if x != nil {
return x.Mode
}
return TPUEmbeddingConfiguration_UNSPECIFIED
}
func (x *TPUEmbeddingConfiguration) GetBatchSizePerTensorCore() int32 {
if x != nil {
return x.BatchSizePerTensorCore
}
return 0
}
func (x *TPUEmbeddingConfiguration) GetNumHosts() int32 {
if x != nil {
return x.NumHosts
}
return 0
}
func (x *TPUEmbeddingConfiguration) GetNumTensorCores() int32 {
if x != nil {
return x.NumTensorCores
}
return 0
}
func (x *TPUEmbeddingConfiguration) GetShardingStrategy() TPUEmbeddingConfiguration_ShardingStrategy {
if x != nil {
return x.ShardingStrategy
}
return TPUEmbeddingConfiguration_DIV_DEFAULT
}
func (x *TPUEmbeddingConfiguration) GetPipelineExecutionWithTensorCore() bool {
if x != nil {
return x.PipelineExecutionWithTensorCore
}
return false
}
func (x *TPUEmbeddingConfiguration) GetProfileDataDirectory() string {
if x != nil {
return x.ProfileDataDirectory
}
return ""
}
func (x *TPUEmbeddingConfiguration) GetFeatureDescriptor() []*TPUEmbeddingConfiguration_FeatureDescriptor {
if x != nil {
return x.FeatureDescriptor
}
return nil
}
func (x *TPUEmbeddingConfiguration) GetSpmdSharding() *TPUEmbeddingConfiguration_SpmdSharding {
if x != nil {
return x.SpmdSharding
}
return nil
}
// A placeholder message that is used to define a unique Status payload
// URL for TPU embedding errors.
type TPUEmbeddingError struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
}
func (x *TPUEmbeddingError) Reset() {
*x = TPUEmbeddingError{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TPUEmbeddingError) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TPUEmbeddingError) ProtoMessage() {}
func (x *TPUEmbeddingError) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TPUEmbeddingError.ProtoReflect.Descriptor instead.
func (*TPUEmbeddingError) Descriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{1}
}
// Description of the various embedding tables.
type TPUEmbeddingConfiguration_TableDescriptor struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Name of the table.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Size of the vocabulary (i.e., number of rows) in the table.
VocabularySize int64 `protobuf:"varint,2,opt,name=vocabulary_size,json=vocabularySize,proto3" json:"vocabulary_size,omitempty"`
// The embedding dimension (i.e., the width of the embedding table).
Dimension int32 `protobuf:"varint,3,opt,name=dimension,proto3" json:"dimension,omitempty"`
// Number of features mapped to this table.
NumFeatures int32 `protobuf:"varint,4,opt,name=num_features,json=numFeatures,proto3" json:"num_features,omitempty"`
// Details of the learning algorithm used to update the embedding
// parameters.
OptimizationParameters *OptimizationParameters `protobuf:"bytes,5,opt,name=optimization_parameters,json=optimizationParameters,proto3" json:"optimization_parameters,omitempty"`
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) Reset() {
*x = TPUEmbeddingConfiguration_TableDescriptor{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TPUEmbeddingConfiguration_TableDescriptor) ProtoMessage() {}
func (x *TPUEmbeddingConfiguration_TableDescriptor) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TPUEmbeddingConfiguration_TableDescriptor.ProtoReflect.Descriptor instead.
func (*TPUEmbeddingConfiguration_TableDescriptor) Descriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{0, 0}
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) GetVocabularySize() int64 {
if x != nil {
return x.VocabularySize
}
return 0
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) GetDimension() int32 {
if x != nil {
return x.Dimension
}
return 0
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) GetNumFeatures() int32 {
if x != nil {
return x.NumFeatures
}
return 0
}
func (x *TPUEmbeddingConfiguration_TableDescriptor) GetOptimizationParameters() *OptimizationParameters {
if x != nil {
return x.OptimizationParameters
}
return nil
}
// Description of different input features.
type TPUEmbeddingConfiguration_FeatureDescriptor struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Name of the input feature.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Index of the corresponding table in the TableDescriptor list.
TableId int32 `protobuf:"varint,2,opt,name=table_id,json=tableId,proto3" json:"table_id,omitempty"`
// Static shape of the inputs (excluding the reduction axis). Note that
// the shape of the actual inputs provided using the infeed op must be
// strictly smaller than input_shape. The outputs received at the TensorCore
// will have rank = input_shape.size() + 1. The innermost axis corresponds
// to the embedding dimension. If the input has shape [m, n, k] (excluding
// the reduction axis) and the embedding dimension is d, the output received
// at the TensorCore will have shape [m, n, k, d].
InputShape []int32 `protobuf:"varint,3,rep,packed,name=input_shape,json=inputShape,proto3" json:"input_shape,omitempty"`
}
func (x *TPUEmbeddingConfiguration_FeatureDescriptor) Reset() {
*x = TPUEmbeddingConfiguration_FeatureDescriptor{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TPUEmbeddingConfiguration_FeatureDescriptor) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TPUEmbeddingConfiguration_FeatureDescriptor) ProtoMessage() {}
func (x *TPUEmbeddingConfiguration_FeatureDescriptor) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TPUEmbeddingConfiguration_FeatureDescriptor.ProtoReflect.Descriptor instead.
func (*TPUEmbeddingConfiguration_FeatureDescriptor) Descriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{0, 1}
}
func (x *TPUEmbeddingConfiguration_FeatureDescriptor) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *TPUEmbeddingConfiguration_FeatureDescriptor) GetTableId() int32 {
if x != nil {
return x.TableId
}
return 0
}
func (x *TPUEmbeddingConfiguration_FeatureDescriptor) GetInputShape() []int32 {
if x != nil {
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return nil
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// SPMD (Single Program Multiple Data) sharding configuration for
// TPUEmbedding. When model parallelism is used on the TensorCore, the number
// of cores per replica must be passed to TPUEmbedding so that the right
// shapes can be computed in the TF/XLA bridge.
type TPUEmbeddingConfiguration_SpmdSharding struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Whether SPMD sharding is enabled.
Enabled bool `protobuf:"varint,1,opt,name=enabled,proto3" json:"enabled,omitempty"`
// Number of cores per replica.
NumCoresPerReplica int32 `protobuf:"varint,2,opt,name=num_cores_per_replica,json=numCoresPerReplica,proto3" json:"num_cores_per_replica,omitempty"`
}
func (x *TPUEmbeddingConfiguration_SpmdSharding) Reset() {
*x = TPUEmbeddingConfiguration_SpmdSharding{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TPUEmbeddingConfiguration_SpmdSharding) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TPUEmbeddingConfiguration_SpmdSharding) ProtoMessage() {}
func (x *TPUEmbeddingConfiguration_SpmdSharding) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[4]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TPUEmbeddingConfiguration_SpmdSharding.ProtoReflect.Descriptor instead.
func (*TPUEmbeddingConfiguration_SpmdSharding) Descriptor() ([]byte, []int) {
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP(), []int{0, 2}
}
func (x *TPUEmbeddingConfiguration_SpmdSharding) GetEnabled() bool {
if x != nil {
return x.Enabled
}
return false
}
func (x *TPUEmbeddingConfiguration_SpmdSharding) GetNumCoresPerReplica() int32 {
if x != nil {
return x.NumCoresPerReplica
}
return 0
}
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)
func file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescGZIP() []byte {
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescOnce.Do(func() {
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescData = protoimpl.X.CompressGZIP(file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescData)
})
return file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDescData
}
var file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_enumTypes = make([]protoimpl.EnumInfo, 2)
var file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes = make([]protoimpl.MessageInfo, 5)
var file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_goTypes = []interface{}{
(TPUEmbeddingConfiguration_Mode)(0), // 0: tensorflow.tpu.TPUEmbeddingConfiguration.Mode
(TPUEmbeddingConfiguration_ShardingStrategy)(0), // 1: tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy
(*TPUEmbeddingConfiguration)(nil), // 2: tensorflow.tpu.TPUEmbeddingConfiguration
(*TPUEmbeddingError)(nil), // 3: tensorflow.tpu.TPUEmbeddingError
(*TPUEmbeddingConfiguration_TableDescriptor)(nil), // 4: tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor
(*TPUEmbeddingConfiguration_FeatureDescriptor)(nil), // 5: tensorflow.tpu.TPUEmbeddingConfiguration.FeatureDescriptor
(*TPUEmbeddingConfiguration_SpmdSharding)(nil), // 6: tensorflow.tpu.TPUEmbeddingConfiguration.SpmdSharding
(*OptimizationParameters)(nil), // 7: tensorflow.tpu.OptimizationParameters
}
var file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_depIdxs = []int32{
4, // 0: tensorflow.tpu.TPUEmbeddingConfiguration.table_descriptor:type_name -> tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor
0, // 1: tensorflow.tpu.TPUEmbeddingConfiguration.mode:type_name -> tensorflow.tpu.TPUEmbeddingConfiguration.Mode
1, // 2: tensorflow.tpu.TPUEmbeddingConfiguration.sharding_strategy:type_name -> tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy
5, // 3: tensorflow.tpu.TPUEmbeddingConfiguration.feature_descriptor:type_name -> tensorflow.tpu.TPUEmbeddingConfiguration.FeatureDescriptor
6, // 4: tensorflow.tpu.TPUEmbeddingConfiguration.spmd_sharding:type_name -> tensorflow.tpu.TPUEmbeddingConfiguration.SpmdSharding
7, // 5: tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor.optimization_parameters:type_name -> tensorflow.tpu.OptimizationParameters
6, // [6:6] is the sub-list for method output_type
6, // [6:6] is the sub-list for method input_type
6, // [6:6] is the sub-list for extension type_name
6, // [6:6] is the sub-list for extension extendee
0, // [0:6] is the sub-list for field type_name
}
func init() { file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_init() }
func file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_init() {
if File_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto != nil {
return
}
file_tensorflow_core_protobuf_tpu_optimization_parameters_proto_init()
if !protoimpl.UnsafeEnabled {
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TPUEmbeddingConfiguration); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TPUEmbeddingError); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TPUEmbeddingConfiguration_TableDescriptor); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TPUEmbeddingConfiguration_FeatureDescriptor); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TPUEmbeddingConfiguration_SpmdSharding); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDesc,
NumEnums: 2,
NumMessages: 5,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_goTypes,
DependencyIndexes: file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_depIdxs,
EnumInfos: file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_enumTypes,
MessageInfos: file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_msgTypes,
}.Build()
File_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto = out.File
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_rawDesc = nil
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_goTypes = nil
file_tensorflow_core_protobuf_tpu_tpu_embedding_configuration_proto_depIdxs = nil
}