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full_type.pb.go
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full_type.pb.go
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// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.28.0
// protoc v3.6.1
// source: tensorflow/core/framework/full_type.proto
package full_type_go_proto
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)
)
// LINT.IfChange
// Experimental. Represents the complete type information of a TensorFlow value.
type FullTypeId int32
const (
// The default represents an uninitialized values.
FullTypeId_TFT_UNSET FullTypeId = 0
// Type variables may serve as placeholder for any other type ID in type
// templates.
//
// Examples:
// TFT_DATASET[TFT_VAR["T"]] is a Dataset returning a type indicated by "T".
// TFT_TENSOR[TFT_VAR["T"]] is a Tensor of n element type indicated by "T".
// TFT_TENSOR[TFT_VAR["T"]], TFT_TENSOR[TFT_VAR["T"]] are two tensors of
// identical element types.
// TFT_TENSOR[TFT_VAR["P"]], TFT_TENSOR[TFT_VAR["Q"]] are two tensors of
// independent element types.
//
FullTypeId_TFT_VAR FullTypeId = 1
// Wildcard type. Describes a parameter of unknown type. In TensorFlow, that
// can mean either a "Top" type (accepts any type), or a dynamically typed
// object whose type is unknown in context.
// Important: "unknown" does not necessarily mean undeterminable!
FullTypeId_TFT_ANY FullTypeId = 2
// The algebraic product type. This is an algebraic type that may be used just
// for logical grouping. Not to confused with TFT_TUPLE which describes a
// concrete object of several elements.
//
// Example:
// TFT_DATASET[TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT64]]]
// is a Dataset producing two tensors, an integer one and a float one.
//
FullTypeId_TFT_PRODUCT FullTypeId = 3
// Represents a named field, with the name stored in the attribute.
//
// Parametrization:
// TFT_NAMED[<type>]{<name>}
// * <type> is the type of the field
// * <name> is the field name, as string (thpugh can theoretically be an int
// as well)
//
// Example:
// TFT_RECORD[
// TFT_NAMED[TFT_TENSOR[TFT_INT32]]{'foo'},
// TFT_NAMED[TFT_TENSOR[TFT_FLOAT32]]{'bar'},
// ]
// is a structure with two fields, an int tensor "foo" and a float tensor
// "bar".
FullTypeId_TFT_NAMED FullTypeId = 4
// Template definition. Expands the variables by repeating a template as
// arguments of container.
//
// Parametrization:
// TFT_FOR_EACH[<container_type>, <template>, <expansions>]
// * <container_type> is the type of the container that the template will be
// expanded into
// * <template> is any type definition that potentially contains type
// variables
// * <expansions> is a TFT_VAR and may include more types in the future
//
// Example:
// TFT_FOR_EACH[
// TFT_PRODUCT,
// TFT_TENSOR[TFT_VAR["t"]],
// TFT_VAR["t"]
// ]
// will substitute a T = TFT_INT32 to TFT_PRODUCT[TFT_TENSOR[TFT_INT32]]
// and a T = (TFT_INT32, TFT_INT64) to
// TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_INT64]].
FullTypeId_TFT_FOR_EACH FullTypeId = 20
// Callable types describe functions and ops.
//
// Parametrization:
// TFT_CALLABLE[<arg type>, <return type>]
// * <arg type> is the type of the arguments; TFT_PRODUCT represents
// multiple
// arguments.
// * <return type> is the return type; TFT_PRODUCT represents multiple
// return values (that means that callables returning multiple things
// don't necessarily return a single tuple).
//
// Example:
// TFT_CALLABLE[
// TFT_ANY,
// TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT64]],
// ]
// is a callable with unspecified (for now) input arguments, and
// two return values of type tensor.
//
FullTypeId_TFT_CALLABLE FullTypeId = 100
// The usual Tensor. This is a parametric type.
//
// Parametrization:
// TFT_TENSOR[<element type>, <shape type>]
// * <element type> is currently limited to one of the element types
// defined below.
// * <shape type> is not yet defined, and may only be TFT_UNKNOWN for now.
//
// A TFT_SHAPE type will be defined in the future.
//
// Example:
// TFT_TENSOR[TFT_INT32, TFT_UNKNOWN]
// is a Tensor of int32 element type and unknown shape.
//
// TODO(mdan): Define TFT_SHAPE and add more examples.
FullTypeId_TFT_TENSOR FullTypeId = 1000
// Array (or tensorflow::TensorList in the variant type registry).
// Note: this is not to be confused with the deprecated `TensorArray*` ops
// which are not supported by FullType.
// This type represents a random-access list whose elements can be
// described by a single type. Although immutable, Array is expected to
// support efficient mutation semantics (i.e. element update) in the
// user-facing API.
// The element type may be generic or even TFT_ANY for a heterogenous list.
//
// Parametrization:
// TFT_ARRAY[<element type>]
// * <element type> may be any concrete type.
//
// Examples:
// TFT_ARRAY[TFT_TENSOR[TFT_INT32]] is a TensorArray holding int32 Tensors
// of any shape.
// TFT_ARRAY[TFT_TENSOR[TFT_UNKNOWN]] is a TensorArray holding Tensors of
// mixed element types.
// TFT_ARRAY[TFT_UNKNOWN] is a TensorArray holding any element type.
// TFT_ARRAY[] is equivalent to TFT_ARRAY[TFT_UNKNOWN].
// TFT_ARRAY[TFT_ARRAY[]] is an array or arrays (of unknown types).
FullTypeId_TFT_ARRAY FullTypeId = 1001
// Optional (or tensorflow::OptionalVariant in the variant type registry).
// This type represents a value that may either hold an element of a single
// specified type, or nothing at all.
//
// Parametrization:
// TFT_OPTIONAL[<element type>]
// * <element type> may be any concrete type.
//
// Examples:
// TFT_OPTIONAL[TFT_TENSOR[TFT_INT32]] is an Optional holding an int32
// Tensor of any shape.
FullTypeId_TFT_OPTIONAL FullTypeId = 1002
// Literal types describe compile-time constant values.
// Literal types may also participate in dependent types.
//
// Parametrization:
// TFT_LITERAL[<value type>]{<value>}
// * <value type> may be any concrete type compatible that can hold <value>
// * <value> is the type's attribute, and holds the actual literal value
//
// Examples:
// TFT_LITERAL[TFT_INT32]{1} is the compile-time constant 1.
FullTypeId_TFT_LITERAL FullTypeId = 1003
// Encoding types describe a value of a certain type, encoded as a different
// type.
//
// Parametrization:
// TFT_ENCODED[<encoded type>, <encoding type>]
// * <encoded type> may be any type
// * <encoding type> may be any type
//
// Examples:
// TFT_ENCODING[TFT_INT32, TFT_STRING] is an integer encoded as string.
FullTypeId_TFT_ENCODED FullTypeId = 1004
// The bool element type.
// TODO(mdan): Quantized types, legacy representations (e.g. ref)
FullTypeId_TFT_BOOL FullTypeId = 200
// Integer element types.
FullTypeId_TFT_UINT8 FullTypeId = 201
FullTypeId_TFT_UINT16 FullTypeId = 202
FullTypeId_TFT_UINT32 FullTypeId = 203
FullTypeId_TFT_UINT64 FullTypeId = 204
FullTypeId_TFT_INT8 FullTypeId = 205
FullTypeId_TFT_INT16 FullTypeId = 206
FullTypeId_TFT_INT32 FullTypeId = 207
FullTypeId_TFT_INT64 FullTypeId = 208
// Floating-point element types.
FullTypeId_TFT_HALF FullTypeId = 209
FullTypeId_TFT_FLOAT FullTypeId = 210
FullTypeId_TFT_DOUBLE FullTypeId = 211
FullTypeId_TFT_BFLOAT16 FullTypeId = 215
// Complex element types.
// TODO(mdan): Represent as TFT_COMPLEX[TFT_DOUBLE] instead?
FullTypeId_TFT_COMPLEX64 FullTypeId = 212
FullTypeId_TFT_COMPLEX128 FullTypeId = 213
// The string element type.
FullTypeId_TFT_STRING FullTypeId = 214
// Datasets created by tf.data ops and APIs. Datasets have generator/iterable
// semantics, that is, one can construct an iterator from them. Like
// Array, they are considered to return elements that can be described
// by a single type. Unlike Array, they do not support random access or
// mutation, and can potentially produce an infinite number of elements.
// A datasets can produce logical structures (e.g. multiple elements). This
// is expressed using TFT_PRODUCT.
//
//
// Parametrization: TFT_DATASET[<element type>].
// * <element type> may be a concrete type or a type symbol. It represents
// the data type of the elements produced by the dataset.
//
// Examples:
// TFT_DATSET[TFT_TENSOR[TFT_INT32]] is a Dataset producing single int32
// Tensors of unknown shape.
// TFT_DATSET[TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT32]] is
// a Dataset producing pairs of Tensors, one integer and one float.
// Note: The high ID number is to prepare for the eventuality that Datasets
// will be supported by user types in the future.
FullTypeId_TFT_DATASET FullTypeId = 10102
// A ragged tensor created by tf.ragged ops and APIs.
//
// Parametrization: TFT_RAGGED[<element_type>].
FullTypeId_TFT_RAGGED FullTypeId = 10103
// Iterators created by tf.data ops and APIs. Very similar to Datasets, except
// they are mutable.
//
//
// Parametrization: TFT_ITERATOR[<element type>].
// * <element type> may be a concrete type or a type symbol. It represents
// the data type of the elements produced by the dataset.
FullTypeId_TFT_ITERATOR FullTypeId = 10104
// A mutex lock tensor, produced by tf.raw_ops.MutexLock.
// Unlike strict execution models, where ownership of a lock is denoted by
// "running after the lock has been acquired", in non-strict mode, lock
// ownership is in the true sense: "the op argument representing the lock is
// available".
// Mutex locks are the dynamic counterpart of control dependencies.
// TODO(mdan): Properly document this thing.
//
// Parametrization: TFT_MUTEX_LOCK[].
FullTypeId_TFT_MUTEX_LOCK FullTypeId = 10202
// The equivalent of a Tensor with DT_VARIANT dtype, kept here to simplify
// translation. This type should not normally appear after type inference.
// Note that LEGACY_VARIANT != ANY: TENSOR[INT32] is a subtype of ANY, but is
// not a subtype of LEGACY_VARIANT.
FullTypeId_TFT_LEGACY_VARIANT FullTypeId = 10203
)
// Enum value maps for FullTypeId.
var (
FullTypeId_name = map[int32]string{
0: "TFT_UNSET",
1: "TFT_VAR",
2: "TFT_ANY",
3: "TFT_PRODUCT",
4: "TFT_NAMED",
20: "TFT_FOR_EACH",
100: "TFT_CALLABLE",
1000: "TFT_TENSOR",
1001: "TFT_ARRAY",
1002: "TFT_OPTIONAL",
1003: "TFT_LITERAL",
1004: "TFT_ENCODED",
200: "TFT_BOOL",
201: "TFT_UINT8",
202: "TFT_UINT16",
203: "TFT_UINT32",
204: "TFT_UINT64",
205: "TFT_INT8",
206: "TFT_INT16",
207: "TFT_INT32",
208: "TFT_INT64",
209: "TFT_HALF",
210: "TFT_FLOAT",
211: "TFT_DOUBLE",
215: "TFT_BFLOAT16",
212: "TFT_COMPLEX64",
213: "TFT_COMPLEX128",
214: "TFT_STRING",
10102: "TFT_DATASET",
10103: "TFT_RAGGED",
10104: "TFT_ITERATOR",
10202: "TFT_MUTEX_LOCK",
10203: "TFT_LEGACY_VARIANT",
}
FullTypeId_value = map[string]int32{
"TFT_UNSET": 0,
"TFT_VAR": 1,
"TFT_ANY": 2,
"TFT_PRODUCT": 3,
"TFT_NAMED": 4,
"TFT_FOR_EACH": 20,
"TFT_CALLABLE": 100,
"TFT_TENSOR": 1000,
"TFT_ARRAY": 1001,
"TFT_OPTIONAL": 1002,
"TFT_LITERAL": 1003,
"TFT_ENCODED": 1004,
"TFT_BOOL": 200,
"TFT_UINT8": 201,
"TFT_UINT16": 202,
"TFT_UINT32": 203,
"TFT_UINT64": 204,
"TFT_INT8": 205,
"TFT_INT16": 206,
"TFT_INT32": 207,
"TFT_INT64": 208,
"TFT_HALF": 209,
"TFT_FLOAT": 210,
"TFT_DOUBLE": 211,
"TFT_BFLOAT16": 215,
"TFT_COMPLEX64": 212,
"TFT_COMPLEX128": 213,
"TFT_STRING": 214,
"TFT_DATASET": 10102,
"TFT_RAGGED": 10103,
"TFT_ITERATOR": 10104,
"TFT_MUTEX_LOCK": 10202,
"TFT_LEGACY_VARIANT": 10203,
}
)
func (x FullTypeId) Enum() *FullTypeId {
p := new(FullTypeId)
*p = x
return p
}
func (x FullTypeId) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (FullTypeId) Descriptor() protoreflect.EnumDescriptor {
return file_tensorflow_core_framework_full_type_proto_enumTypes[0].Descriptor()
}
func (FullTypeId) Type() protoreflect.EnumType {
return &file_tensorflow_core_framework_full_type_proto_enumTypes[0]
}
func (x FullTypeId) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use FullTypeId.Descriptor instead.
func (FullTypeId) EnumDescriptor() ([]byte, []int) {
return file_tensorflow_core_framework_full_type_proto_rawDescGZIP(), []int{0}
}
// Highly experimental and very likely to change.
// This encoding uses tags instead of dedicated messages for regularity. In
// particular the encoding imposes no restrictions on what the parameters of any
// type should be, which in particular needs to be true for type symbols.
type FullTypeDef struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The principal type represented by this object. This may be a concrete type
// (Tensor, Dataset) a type variable (used for dependent types) a type
// symbol (Any, Union). See FullTypeId for details.
TypeId FullTypeId `protobuf:"varint,1,opt,name=type_id,json=typeId,proto3,enum=tensorflow.FullTypeId" json:"type_id,omitempty"`
Args []*FullTypeDef `protobuf:"bytes,2,rep,name=args,proto3" json:"args,omitempty"`
// Literal values of this type object, if the type admits one.
// For example, a type variable admits a string attribute - its name.
// Shape-related types may admit int attributes - their static shape values.
// Fields for more data types to be added as needed.
//
// Types that are assignable to Attr:
// *FullTypeDef_S
// *FullTypeDef_I
Attr isFullTypeDef_Attr `protobuf_oneof:"attr"`
}
func (x *FullTypeDef) Reset() {
*x = FullTypeDef{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_framework_full_type_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *FullTypeDef) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*FullTypeDef) ProtoMessage() {}
func (x *FullTypeDef) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_framework_full_type_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 FullTypeDef.ProtoReflect.Descriptor instead.
func (*FullTypeDef) Descriptor() ([]byte, []int) {
return file_tensorflow_core_framework_full_type_proto_rawDescGZIP(), []int{0}
}
func (x *FullTypeDef) GetTypeId() FullTypeId {
if x != nil {
return x.TypeId
}
return FullTypeId_TFT_UNSET
}
func (x *FullTypeDef) GetArgs() []*FullTypeDef {
if x != nil {
return x.Args
}
return nil
}
func (m *FullTypeDef) GetAttr() isFullTypeDef_Attr {
if m != nil {
return m.Attr
}
return nil
}
func (x *FullTypeDef) GetS() string {
if x, ok := x.GetAttr().(*FullTypeDef_S); ok {
return x.S
}
return ""
}
func (x *FullTypeDef) GetI() int64 {
if x, ok := x.GetAttr().(*FullTypeDef_I); ok {
return x.I
}
return 0
}
type isFullTypeDef_Attr interface {
isFullTypeDef_Attr()
}
type FullTypeDef_S struct {
S string `protobuf:"bytes,3,opt,name=s,proto3,oneof"`
}
type FullTypeDef_I struct {
I int64 `protobuf:"varint,4,opt,name=i,proto3,oneof"` // TODO(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc.
}
func (*FullTypeDef_S) isFullTypeDef_Attr() {}
func (*FullTypeDef_I) isFullTypeDef_Attr() {}
var File_tensorflow_core_framework_full_type_proto protoreflect.FileDescriptor
var file_tensorflow_core_framework_full_type_proto_rawDesc = []byte{
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}
var (
file_tensorflow_core_framework_full_type_proto_rawDescOnce sync.Once
file_tensorflow_core_framework_full_type_proto_rawDescData = file_tensorflow_core_framework_full_type_proto_rawDesc
)
func file_tensorflow_core_framework_full_type_proto_rawDescGZIP() []byte {
file_tensorflow_core_framework_full_type_proto_rawDescOnce.Do(func() {
file_tensorflow_core_framework_full_type_proto_rawDescData = protoimpl.X.CompressGZIP(file_tensorflow_core_framework_full_type_proto_rawDescData)
})
return file_tensorflow_core_framework_full_type_proto_rawDescData
}
var file_tensorflow_core_framework_full_type_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_tensorflow_core_framework_full_type_proto_msgTypes = make([]protoimpl.MessageInfo, 1)
var file_tensorflow_core_framework_full_type_proto_goTypes = []interface{}{
(FullTypeId)(0), // 0: tensorflow.FullTypeId
(*FullTypeDef)(nil), // 1: tensorflow.FullTypeDef
}
var file_tensorflow_core_framework_full_type_proto_depIdxs = []int32{
0, // 0: tensorflow.FullTypeDef.type_id:type_name -> tensorflow.FullTypeId
1, // 1: tensorflow.FullTypeDef.args:type_name -> tensorflow.FullTypeDef
2, // [2:2] is the sub-list for method output_type
2, // [2:2] is the sub-list for method input_type
2, // [2:2] is the sub-list for extension type_name
2, // [2:2] is the sub-list for extension extendee
0, // [0:2] is the sub-list for field type_name
}
func init() { file_tensorflow_core_framework_full_type_proto_init() }
func file_tensorflow_core_framework_full_type_proto_init() {
if File_tensorflow_core_framework_full_type_proto != nil {
return
}
if !protoimpl.UnsafeEnabled {
file_tensorflow_core_framework_full_type_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*FullTypeDef); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
file_tensorflow_core_framework_full_type_proto_msgTypes[0].OneofWrappers = []interface{}{
(*FullTypeDef_S)(nil),
(*FullTypeDef_I)(nil),
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_tensorflow_core_framework_full_type_proto_rawDesc,
NumEnums: 1,
NumMessages: 1,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_tensorflow_core_framework_full_type_proto_goTypes,
DependencyIndexes: file_tensorflow_core_framework_full_type_proto_depIdxs,
EnumInfos: file_tensorflow_core_framework_full_type_proto_enumTypes,
MessageInfos: file_tensorflow_core_framework_full_type_proto_msgTypes,
}.Build()
File_tensorflow_core_framework_full_type_proto = out.File
file_tensorflow_core_framework_full_type_proto_rawDesc = nil
file_tensorflow_core_framework_full_type_proto_goTypes = nil
file_tensorflow_core_framework_full_type_proto_depIdxs = nil
}