function.proto

syntax = "proto3";

package tensorflow;
option cc_enable_arenas = true;
option java_outer_classname = "FunctionProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
import "attr_value.proto";
import "node_def.proto";
import "op_def.proto";

// A library is a set of named functions.
message FunctionDefLibrary {
  repeated FunctionDef function = 1;
  repeated GradientDef gradient = 2;
}

// A function can be instantiated when the runtime can bind every attr
// with a value. When a GraphDef has a call to a function, it must
// have binding for every attr defined in the signature.
//
// TODO(zhifengc):
//   * device spec, etc.
message FunctionDef {
  // The definition of the function's name, arguments, return values,
  // attrs etc.
  OpDef signature = 1;

  // Attributes specific to this function definition.
  map<string, AttrValue> attr = 5;

  // Attributes for function arguments. These attributes are the same set of
  // valid attributes as to _Arg nodes.
  message ArgAttrs {
    map<string, AttrValue> attr = 1;
  }
  map<uint32, ArgAttrs> arg_attr = 7;

  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
  reserved 2;

  // In both of the following fields, there is the need to specify an
  // output that is used as either the input to another node (in
  // `node_def`) or as a return value of the function (in `ret`).
  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
  // list in some cases (instead of just single outputs).  Also, we
  // need to be able to deal with lists of unknown length (so the
  // output index may not be known at function definition time).  So
  // we use the following format instead:
  // * "fun_in" where "fun_in" is the name of a function input arg in
  //   the `signature` field above.  This represents that input, whether
  //   it is a single tensor or a list.
  // * "fun_in:0" gives the first element of a function input arg (a
  //   non-list input is considered a list of length 1 for these
  //   purposes).
  // * "node:out" where "node" is the name of a node in `node_def` and
  //   "out" is the name one of its op's output arguments (the name
  //   comes from the OpDef of the node's op). This represents that
  //   node's output, whether it is a single tensor or a list.
  //   Note: We enforce that an op's output arguments are never
  //   renamed in the backwards-compatibility test.
  // * "node:out:0" gives the first element of a node output arg (a
  //   non-list output is considered a list of length 1 for these
  //   purposes).
  //
  // NOT CURRENTLY SUPPORTED (but may be in the future):
  // * "node:out:-1" gives last element in a node output list
  // * "node:out:1:" gives a list with all but the first element in a
  //   node output list
  // * "node:out::-1" gives a list with all but the last element in a
  //   node output list

  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
  // may have values of type `placeholder` and the `input` field uses
  // the "output" format above.

  // By convention, "op" in node_def is resolved by consulting with a
  // user-defined library first. If not resolved, "func" is assumed to
  // be a builtin op.
  repeated NodeDef node_def = 3;

  // A mapping from the output arg names from `signature` to the
  // outputs from `node_def` that should be returned by the function.
  map<string, string> ret = 4;

  // A mapping from control output names from `signature` to node names in
  // `node_def` which should be control outputs of this function.
  map<string, string> control_ret = 6;
}

// GradientDef defines the gradient function of a function defined in
// a function library.
//
// A gradient function g (specified by gradient_func) for a function f
// (specified by function_name) must follow the following:
//
// The function 'f' must be a numerical function which takes N inputs
// and produces M outputs. Its gradient function 'g', which is a
// function taking N + M inputs and produces N outputs.
//
// I.e. if we have
//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
// then, g is
//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
// loss function). dL/dx_i is the partial derivative of L with respect
// to x_i.
message GradientDef {
  string function_name = 1;  // The function name.
  string gradient_func = 2;  // The gradient function's name.
}