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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// Class and associated machinery for specifying an Op's OpDef and shape
// inference function for Op registration.
#ifndef TENSORFLOW_FRAMEWORK_OP_DEF_BUILDER_H_
#define TENSORFLOW_FRAMEWORK_OP_DEF_BUILDER_H_
#include <string>
#include <vector>
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/platform/macros.h"
namespace tensorflow {
namespace shape_inference {
class InferenceContext;
}
typedef std::function<Status(shape_inference::InferenceContext* c)>
OpShapeInferenceFn;
struct OpRegistrationData {
public:
OpRegistrationData() {}
OpRegistrationData(const OpDef& def) : op_def(def) {}
OpDef op_def;
OpShapeInferenceFn shape_inference_fn;
};
// Builder class passed to the REGISTER_OP() macro.
class OpDefBuilder {
public:
// Constructs an OpDef with just the name field set.
explicit OpDefBuilder(StringPiece op_name);
// Adds an attr to this OpDefBuilder (and returns *this). The spec has
// format "<name>:<type>" or "<name>:<type>=<default>"
// where <name> matches regexp [a-zA-Z][a-zA-Z0-9_]*
// (by convention only using capital letters for attrs that can be inferred)
// <type> can be:
// "string", "int", "float", "bool", "type", "shape", or "tensor"
// "numbertype", "realnumbertype", "quantizedtype", "{int32,int64}"
// (meaning "type" with a restriction on valid values)
// "{\"foo\", \"bar\n baz\"}", or "{'foo', 'bar\n baz'}"
// (meaning "string" with a restriction on valid values)
// "list(string)", ..., "list(tensor)", "list(numbertype)", ...
// (meaning lists of the above types)
// "int >= 2" (meaning "int" with a restriction on valid values)
// "list(string) >= 2", "list(int) >= 2"
// (meaning "list(string)" / "list(int)" with length at least 2)
// <default>, if included, should use the Proto text format
// of <type>. For lists use [a, b, c] format.
//
// Note that any attr specifying the length of an input or output will
// get a default minimum of 1 unless the >= # syntax is used.
//
// TODO(josh11b): Perhaps support restrictions and defaults as optional
// extra arguments to Attr() instead of encoding them in the spec string.
// TODO(josh11b): Would like to have better dtype handling for tensor attrs:
// * Ability to say the type of an input/output matches the type of
// the tensor.
// * Ability to restrict the type of the tensor like the existing
// restrictions for type attrs.
// Perhaps by linking the type of the tensor to a type attr?
OpDefBuilder& Attr(StringPiece spec);
// Adds an input or output to this OpDefBuilder (and returns *this).
// The spec has form "<name>:<type-expr>" or "<name>:Ref(<type-expr>)"
// where <name> matches regexp [a-z][a-z0-9_]* and <type-expr> can be:
// * For a single tensor: <type>
// * For a sequence of tensors with the same type: <number>*<type>
// * For a sequence of tensors with different types: <type-list>
// Where:
// <type> is either one of "float", "int32", "string", ...
// or the name of an attr (see above) with type "type".
// <number> is the name of an attr with type "int".
// <type-list> is the name of an attr with type "list(type)".
// TODO(josh11b): Indicate Ref() via an optional argument instead of
// in the spec?
// TODO(josh11b): SparseInput() and SparseOutput() matching the Python
// handling?
OpDefBuilder& Input(StringPiece spec);
OpDefBuilder& Output(StringPiece spec);
// Turns on the indicated boolean flag in this OpDefBuilder (and
// returns *this).
OpDefBuilder& SetIsCommutative();
OpDefBuilder& SetIsAggregate();
OpDefBuilder& SetIsStateful();
OpDefBuilder& SetAllowsUninitializedInput();
// Deprecate the op at a certain GraphDef version.
OpDefBuilder& Deprecated(int version, StringPiece explanation);
// Adds docs to this OpDefBuilder (and returns *this).
// Docs have the format:
// <1-line summary>
// <rest of the description>
// <name>: <description of name>
// <name>: <description of name>
// <if long, indent the description on subsequent lines>
// Where <name> is the name of an attr, input, or output. Please
// wrap docs at 72 columns so that it may be indented in the
// generated output. For tensor inputs or outputs (not attrs), you
// may start the description with an "=" (like name:= <description>)
// to suppress the automatically-generated type documentation in
// generated output.
#ifndef TF_LEAN_BINARY
OpDefBuilder& Doc(StringPiece text);
#else
OpDefBuilder& Doc(StringPiece text) { return *this; }
#endif
// Sets the shape function to be used for shape inference.
//
// Note that currently (October 2016), python code still requires a
// RegisterShape call to invoke this; see call_cpp_shape_fn in
// python/framework/common_shapes.py
OpDefBuilder& SetShapeFn(Status (*fn)(shape_inference::InferenceContext*));
// Sets op_reg_data->op_def to the requested OpDef and
// op_reg_data->shape_inference_fn to the requested shape inference function,
// or returns an error.
// Must be called after all of the above methods.
//
// Note that OpDefBuilder only reports parsing errors. You should also
// call ValidateOpDef() to detect other problems.
Status Finalize(OpRegistrationData* op_reg_data) const;
private:
OpDef* op_def() { return &op_reg_data_.op_def; }
OpRegistrationData op_reg_data_;
std::vector<string> attrs_;
std::vector<string> inputs_;
std::vector<string> outputs_;
string doc_;
std::vector<string> errors_;
};
} // namespace tensorflow
#endif // TENSORFLOW_FRAMEWORK_OP_DEF_BUILDER_H_