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full_type.proto
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full_type.proto
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syntax = "proto3";
package tensorboard;
option cc_enable_arenas = true;
option java_outer_classname = "FullTypeProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/full_type_go_proto";
// DISABLED.IfChange
// Experimental. Represents the complete type information of a TensorFlow value.
enum FullTypeId {
// The default represents an uninitialized values.
TFT_UNSET = 0;
// Type symbols. Used to construct more complex type expressions like
// algebraic data types.
// 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.
//
TFT_VAR = 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!
TFT_ANY = 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.
//
TFT_PRODUCT = 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".
TFT_NAMED = 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]].
TFT_FOR_EACH = 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.
//
TFT_CALLABLE = 100;
// Concrete type IDs, representing "proper" data types that can describe
// runtime TensorFlow objects.
// 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.
TFT_TENSOR = 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).
TFT_ARRAY = 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.
TFT_OPTIONAL = 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.
TFT_LITERAL = 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.
TFT_ENCODED = 1004;
// Type attributes. These always appear in the parametrization of a type,
// never alone. For example, there is no such thing as a "bool" TensorFlow
// object (for now).
// The bool element type.
// TODO(mdan): Quantized types, legacy representations (e.g. ref)
TFT_BOOL = 200;
// Integer element types.
TFT_UINT8 = 201;
TFT_UINT16 = 202;
TFT_UINT32 = 203;
TFT_UINT64 = 204;
TFT_INT8 = 205;
TFT_INT16 = 206;
TFT_INT32 = 207;
TFT_INT64 = 208;
// Floating-point element types.
TFT_HALF = 209;
TFT_FLOAT = 210;
TFT_DOUBLE = 211;
TFT_BFLOAT16 = 215;
// Complex element types.
// TODO(mdan): Represent as TFT_COMPLEX[TFT_DOUBLE] instead?
TFT_COMPLEX64 = 212;
TFT_COMPLEX128 = 213;
// The string element type.
TFT_STRING = 214;
// Other types that we don't know yet whether they will become part of the
// core type system or be consisdered third-party (and consequently moved to
// user-defined type mechanisms). Presently, they are effectively in the core
// type system, because key compilation passes like Placer account for their
// existence.
// 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.
TFT_DATASET = 10102;
// A ragged tensor created by tf.ragged ops and APIs.
//
// Parametrization: TFT_RAGGED[<element_type>].
TFT_RAGGED = 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.
TFT_ITERATOR = 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[].
TFT_MUTEX_LOCK = 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.
TFT_LEGACY_VARIANT = 10203;
}
// 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.
message FullTypeDef {
// 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.
FullTypeId type_id = 1;
repeated FullTypeDef args = 2;
// 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.
oneof attr {
string s = 3;
int64 i = 4;
// TODO(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc.
}
}
// DISABLED.ThenChange(../ir/types/attributes.td)