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syntax = "proto3";
package tensorflow;
option cc_enable_arenas = true;
option java_outer_classname = "OpDefProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "";
import "tensorflow/core/framework/attr_value.proto";
import "tensorflow/core/framework/types.proto";
// Defines an operation. A NodeDef in a GraphDef specifies an Op by
// using the "op" field which should match the name of a OpDef.
// LINT.IfChange
message OpDef {
// Op names starting with an underscore are reserved for internal use.
// Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
string name = 1;
// For describing inputs and outputs.
message ArgDef {
// Name for the input/output. Should match the regexp "[a-z][a-z0-9_]*".
string name = 1;
// Human readable description.
string description = 2;
// Describes the type of one or more tensors that are accepted/produced
// by this input/output arg. The only legal combinations are:
// * For a single tensor: either the "type" field is set or the
// "type_attr" field is set to the name of an attr with type "type".
// * For a sequence of tensors with the same type: the "number_attr"
// field will be set to the name of an attr with type "int", and
// either the "type" or "type_attr" field will be set as for
// single tensors.
// * For a sequence of tensors, the "type_list_attr" field will be set
// to the name of an attr with type "list(type)".
DataType type = 3;
string type_attr = 4; // if specified, attr must have type "type"
string number_attr = 5; // if specified, attr must have type "int"
// If specified, attr must have type "list(type)", and none of
// type, type_attr, and number_attr may be specified.
string type_list_attr = 6;
// For inputs: if true, the inputs are required to be refs.
// By default, inputs can be either refs or non-refs.
// For outputs: if true, outputs are refs, otherwise they are not.
bool is_ref = 16;
// Description of the input(s).
repeated ArgDef input_arg = 2;
// Description of the output(s).
repeated ArgDef output_arg = 3;
// Description of the graph-construction-time configuration of this
// Op. That is to say, this describes the attr fields that will
// be specified in the NodeDef.
message AttrDef {
// A descriptive name for the argument. May be used, e.g. by the
// Python client, as a keyword argument name, and so should match
// the regexp "[a-z][a-z0-9_]+".
string name = 1;
// One of the type names from attr_value.proto ("string", "list(string)",
// "int", etc.).
string type = 2;
// A reasonable default for this attribute if the user does not supply
// a value. If not specified, the user must supply a value.
AttrValue default_value = 3;
// Human-readable description.
string description = 4;
// TODO(josh11b): bool is_optional?
// --- Constraints ---
// These constraints are only in effect if specified. Default is no
// constraints.
// For type == "int", this is a minimum value. For "list(___)"
// types, this is the minimum length.
bool has_minimum = 5;
int64 minimum = 6;
// The set of allowed values. Has type that is the "list" version
// of the "type" field above (uses the "list" field of AttrValue).
// If type == "type" or "list(type)" above, then the "type" field
// of "allowed_values.list" has the set of allowed DataTypes.
// If type == "string" or "list(string)", then the "s" field of
// "allowed_values.list" has the set of allowed strings.
AttrValue allowed_values = 7;
repeated AttrDef attr = 4;
// Optional deprecation based on GraphDef versions.
OpDeprecation deprecation = 8;
// One-line human-readable description of what the Op does.
string summary = 5;
// Additional, longer human-readable description of what the Op does.
string description = 6;
// -------------------------------------------------------------------------
// Which optimizations this operation can participate in.
// True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
bool is_commutative = 18;
// If is_aggregate is true, then this operation accepts N >= 2
// inputs and produces 1 output all of the same type. Should be
// associative and commutative, and produce output with the same
// shape as the input. The optimizer may replace an aggregate op
// taking input from multiple devices with a tree of aggregate ops
// that aggregate locally within each device (and possibly within
// groups of nearby devices) before communicating.
// TODO(josh11b): Implement that optimization.
bool is_aggregate = 16; // for things like add
// Other optimizations go here, like
// can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
// -------------------------------------------------------------------------
// Optimization constraints.
// Ops are marked as stateful if their behavior depends on some state beyond
// their input tensors (e.g. variable reading op) or if they have
// a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
// must always produce the same output for the same input and have
// no side-effects.
// By default Ops may be moved between devices. Stateful ops should
// either not be moved, or should only be moved if that state can also
// be moved (e.g. via some sort of save / restore).
// Stateful ops are guaranteed to never be optimized away by Common
// Subexpression Elimination (CSE).
bool is_stateful = 17; // for things like variables, queue
// -------------------------------------------------------------------------
// Non-standard options.
// By default, all inputs to an Op must be initialized Tensors. Ops
// that may initialize tensors for the first time should set this
// field to true, to allow the Op to take an uninitialized Tensor as
// input.
bool allows_uninitialized_input = 19; // for Assign, etc.
// LINT.ThenChange(
// Information about version-dependent deprecation of an op
message OpDeprecation {
// First GraphDef version at which the op is disallowed.
int32 version = 1;
// Explanation of why it was deprecated and what to use instead.
string explanation = 2;
// A collection of OpDefs
message OpList {
repeated OpDef op = 1;