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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";
import "tensorflow/core/framework/attr_value.proto";
import "tensorflow/core/framework/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;
// The body of the function.
repeated Node node = 2; // function.node.ret[*] are unique.
// A node is a multi-value assignment:
// (ret[0], ret[1], ...) = func(arg[0], arg[1], ...)
//
// By convention, "func" is resolved by consulting with a user-defined
// library first. If not resolved, "func" is assumed to be a builtin op.
message Node {
// This node produces multiple outputs. They are named ret[0],
// ret[1], ..., etc.
//
// REQUIRES: function.node.ret[*] are unique across all nodes.
// REQUIRES: ret.size == func/op def's number of output args.
repeated string ret = 1;
// The op/function name.
string op = 2;
// Arguments passed to this func/op.
//
// arg[i] must be either one of
// function.signature.input_args[*].name or one of
// function.node[*].ret[*].
//
// REQUIRES: arg.size == func/op def's number of input args.
repeated string arg = 3;
// Control dependencies.
//
// dep[i] must be one of function.node[*].ret[*] or one of
// function.signature.input_args[*].name.
repeated string dep = 4;
// Attrs.
//
// 'attr' maps names defined by 'func's attr defs to attr values.
// attr values may have placeholders which are substituted
// recursively by concrete values when this node is instantiated.
// These placeholders must name an attr listed in the FunctionDef's
// signature.
map<string, AttrValue> attr = 5;
}
}
// 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.
}
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