/* 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.
==============================================================================*/

#ifndef TENSORFLOW_FRAMEWORK_FUNCTION_H_
#define TENSORFLOW_FRAMEWORK_FUNCTION_H_

#include <unordered_map>

#include <vector>
#include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/selective_registration.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/protobuf.h"

namespace tensorflow {

class CancellationManager;
class OpKernel;
class ResourceMgr;

// FunctionDefHelper::Create is a convenient helper to construct a
// FunctionDef proto.
// E.g.,
//   FunctionDef my_func = FunctionDefHelper::Create(
//     "my_func_name",
//     {"x:T", "y:T" /* one string per argument */},
//     {"z:T" /* one string per return value */},
//     {"T: {float, double}" /* one string per attribute  */},
//     {
//        {{"o"}, "Mul", {"x", "y"}, {{"T", "$T"}}}
//        /* one entry per function node */
//     },
//     /* Mapping between function returns and function node outputs. */
//     {{"z", "o:z"}});
//
// For the old Function::Node approach, use FunctionDefHelper::Define()
// E.g.,
//   FunctionDef my_func = FunctionDefHelper::Define(
//     "my_func_name",
//     {"x:T", "y:T" /* one string per argument */},
//     {"z:T" /* one string per return value */},
//     {"T: {float, double}" /* one string per attribute  */},
//     {
//        {{"z"}, "Mul", {"x", "y"}, {{"T", "$T"}}}
//        /* one entry per function node */
//     });
class FunctionDefHelper {
 public:
  // AttrValueWrapper has copy constructors for the type T so that
  // it's easy to construct a simple AttrValue proto.
  //
  // If T is a string type (const char*, string, or StringPiece), and
  // it starts with "$", we construct a AttrValue of "placeholder".
  //
  // E.g.,
  //   std::<string, AttrValueWrapper> x = {"T", "$T"}
  // is a named attr value placeholder.
  struct AttrValueWrapper {
    AttrValue proto;

    AttrValueWrapper() {}

    template <typename T>
    AttrValueWrapper(T val) {  // NOLINT(runtime/explicit)
      SetAttrValue(val, &proto);
    }

   private:
    void InitFromString(StringPiece val);
  };

  // Constructs an AttrValue.func given the "name" and "attrs".
  static AttrValueWrapper FunctionRef(
      const string& name,
      gtl::ArraySlice<std::pair<string, AttrValueWrapper>> attrs);
  static AttrValueWrapper FunctionRef(const string& name) {
    return FunctionRef(name, {});
  }

  // Node is used to construct FunctionDef.Node using initialization
  // lists. E.g.,
  //  Node n = {{"z"}, "Mul", {"x", "y"}, {{"T", "$T"}}};  // z = x * y
  struct Node {
    // When constructing a NodeDef, the first entry in ret is used as
    // the node name, the remaining values are ignored.
    std::vector<string> ret;
    string op;
    std::vector<string> arg;
    std::vector<std::pair<string, AttrValueWrapper>> attr;
    std::vector<string> dep;

    FunctionDef::Node ToProto() const;
    NodeDef ToNodeDef() const;
  };

  // The Create() function uses the new NodeDef field.  `ret_def`
  // holds a mapping from the function output names from `out_def` to
  // the node outputs from `node_def`.
  static FunctionDef Create(const string& function_name,
                            gtl::ArraySlice<string> in_def,
                            gtl::ArraySlice<string> out_def,
                            gtl::ArraySlice<string> attr_def,
                            gtl::ArraySlice<Node> node_def,
                            gtl::ArraySlice<std::pair<string, string>> ret_def);

  // The two Define() functions use the old FunctionDef::Node field.
  // TODO(josh11b): Get rid of these and transition to the one above.
  static FunctionDef Define(const string& function_name,
                            gtl::ArraySlice<string> arg_def,
                            gtl::ArraySlice<string> ret_def,
                            gtl::ArraySlice<string> attr_def,
                            gtl::ArraySlice<Node> node_def);

  // Defines an anonymous function. I.e., its name is not relevant.
  static FunctionDef Define(gtl::ArraySlice<string> arg_def,
                            gtl::ArraySlice<string> ret_def,
                            gtl::ArraySlice<string> attr_def,
                            gtl::ArraySlice<Node> node_def);

  // Helpers to construct a constant scalar.
  template <typename T>
  static Node Const(const string& name, const T& val) {
    Node n = {{name}, "Const"};
    const DataType dtype = DataTypeToEnum<T>::value;
    n.attr.push_back({"dtype", dtype});
    Tensor t(dtype, TensorShape({}));
    t.scalar<T>()() = val;
    n.attr.push_back({"value", t});
    return n;
  }

  template <typename T>
  static Node Const(const string& name, gtl::ArraySlice<T> vals) {
    Node n = {{name}, "Const"};
    const DataType dtype = DataTypeToEnum<T>::value;
    n.attr.push_back({"dtype", dtype});
    int64 num = vals.size();
    Tensor t(dtype, TensorShape({num}));
    for (size_t i = 0; i < vals.size(); ++i) {
      t.flat<T>()(i) = vals[i];
    }
    n.attr.push_back({"value", t});
    return n;
  }
};

template <>
inline FunctionDefHelper::AttrValueWrapper::AttrValueWrapper(const char* val) {
  InitFromString(val);
}

template <>
inline FunctionDefHelper::AttrValueWrapper::AttrValueWrapper(
    const string& val) {
  InitFromString(val);
}

template <>
inline FunctionDefHelper::AttrValueWrapper::AttrValueWrapper(StringPiece val) {
  InitFromString(val);
}

// Instantiate a function.
//
// "fdef" encodes a TF function with some attrs in fdef.signature.attr
// containing placeholders.  InstantiateFunction binds these
// placeholders and produces an instantiated function encoded in
// "result.gdef". The value to substitute a placeholder is given by
// "attr_values", which is a map from a placeholder name to an attr
// value.
//
// InstantiateFunction calls "get_function" to find signatures of other
// functions and primitive ops.

// Placeholders in "fdef" is substituted based on "attr_values" here.
typedef ::tensorflow::protobuf::Map<string, AttrValue> InstantiateAttrValueMap;
typedef gtl::ArraySlice<std::pair<string, FunctionDefHelper::AttrValueWrapper>>
    InstantiateAttrValueSlice;

// GetFunctionSignature(func name, opdef) returns OK if the func name is found
// and opdef is filled with a pointer to the corresponding signature
// (a OpDef proto). Otherwise, returns an error.
typedef std::function<Status(const string&, const OpDef**)>
    GetFunctionSignature;

struct InstantiationResult {
  DataTypeVector arg_types;
  DataTypeVector ret_types;
  GraphDef gdef;
};
Status InstantiateFunction(const FunctionDef& fdef,
                           const InstantiateAttrValueMap& attr_values,
                           GetFunctionSignature get_function,
                           InstantiationResult* result);
Status InstantiateFunction(const FunctionDef& fdef,
                           InstantiateAttrValueSlice attr_values,
                           GetFunctionSignature get_function,
                           InstantiationResult* result);

// Returns a debug string for a function definition.
//
// The returned text is multiple-line. It is intended to be
// human-readable rather than being friendly to parsers. It is _NOT_
// intended to be the canonical string representation of "func_def".
// Particularly, it may not include all information presented in
// "func_def" (e.g., comments, description of the function arguments,
// etc.)
string DebugString(const FunctionDef& func_def);
string DebugString(const GraphDef& instantiated_func_def);

// Returns a debug string for a top level graph (the main program and
// its supporting functions defined in its library).
string DebugStringWhole(const GraphDef& gdef);

// Returns a canonicalized string for the instantiation of the
// function of the given "name" and attributes "attrs".
//
// The returned string is guaranteed to be stable within one address
// space. But it may be change as the implementation
// evolves. Therefore, it should not be persisted or compared across
// address spaces.
string Canonicalize(const string& funcname,
                    const InstantiateAttrValueMap& attrs);
string Canonicalize(const string& funcname, InstantiateAttrValueSlice attrs);

// Represents a function call frame. I.e., the data structure used to
// pass arguments to a function and retrieve its results.
//
// Runtime must arrange accesses to one FunctionCallFrame s.t.
//   1. SetArgs() happens before any GetArg();
//   2. GetRetvals happens after all SetRetval();
class FunctionCallFrame {
 public:
  FunctionCallFrame(DataTypeSlice arg_types, DataTypeSlice ret_types);
  ~FunctionCallFrame();

  // Caller methods.
  Status SetArgs(gtl::ArraySlice<Tensor> args);
  Status GetRetvals(std::vector<Tensor>* rets) const;

  // Callee methods.
  Status GetArg(int index, Tensor* val) const;
  Status SetRetval(int index, const Tensor& val);

 private:
  DataTypeVector arg_types_;
  DataTypeVector ret_types_;
  gtl::InlinedVector<Tensor, 4> args_;
  struct Retval {
    bool has_val = false;
    Tensor val;
  };
  gtl::InlinedVector<Retval, 4> rets_;

  TF_DISALLOW_COPY_AND_ASSIGN(FunctionCallFrame);
};

// Helper to maintain a map between function names in a given
// FunctionDefLibrary and function definitions.
class FunctionLibraryDefinition : public OpRegistryInterface {
 public:
  explicit FunctionLibraryDefinition(const FunctionLibraryDefinition& lib_def);
  FunctionLibraryDefinition(const OpRegistryInterface* default_registry,
                            const FunctionDefLibrary& lib_def);
  ~FunctionLibraryDefinition() override;

  FunctionLibraryDefinition& operator=(const FunctionLibraryDefinition&) =
      delete;

  // Returns nullptr if "func" is not defined in "lib_def". Otherwise,
  // returns its definition proto.
  const FunctionDef* Find(const string& func) const;

  // Adds function definition 'fdef' to this function library.
  // Returns status 'ok' on success, or error otherwise.
  // If 'fdef' is successfully added to the library, it will be accessible
  // from 'LookUp' and included in the proto returned by 'ToProto'.
  Status AddFunctionDef(const FunctionDef& fdef);

  // If the gradient function for 'func' is specified explicitly in
  // the library, returns the gradient function name.  Otherwise,
  // returns an empty string.
  string FindGradient(const string& func) const;

  // OpRegistryInterface method. Useful for constructing a Graph.
  //
  // If "op" is defined in the library, returns its signature.
  // Otherwise, assume "op" is a primitive op and returns its op
  // signature and shape inference function.
  Status LookUp(const string& op_type_name,
                const OpRegistrationData** op_reg_data) const override;

  // Returns a proto representation of the state of this function library.
  FunctionDefLibrary ToProto() const;

 private:
  // TODO(cwhipkey): support shape functions in FunctionDefLibrary.
  struct FunctionDefAndOpRegistration {
    FunctionDefAndOpRegistration(const FunctionDef& fdef_in)
        : fdef(fdef_in), op_registration_data(fdef.signature()) {}

    FunctionDef fdef;
    OpRegistrationData op_registration_data;
  };

  const OpRegistryInterface* const default_registry_;
  std::unordered_map<string, std::unique_ptr<FunctionDefAndOpRegistration>>
      function_defs_;
  std::unordered_map<string, string> func_grad_;
};

// Forward declare. Defined in common_runtime/function.h
struct FunctionBody;

// Forward declare. Defined in common_runtime/device.h
class Device;

class FunctionLibraryRuntime {
 public:
  virtual ~FunctionLibraryRuntime() {}

  // Instantiate a function with the given "attrs".
  //
  // Returns OK and fills in "handle" if the instantiation succeeds.
  // Otherwise returns an error and "handle" is undefined.
  typedef uint64 Handle;
  virtual Status Instantiate(const string& function_name,
                             const InstantiateAttrValueMap& attrs,
                             Handle* handle) = 0;
  Status Instantiate(const string& function_name,
                     InstantiateAttrValueSlice attrs, Handle* handle);

  // Returns the function body for the instantiated function given its
  // handle 'h'. Returns nullptr if "h" is not found.
  //
  // *this keeps the ownership of the returned object, which remains alive
  // as long as *this.
  virtual const FunctionBody* GetFunctionBody(Handle h) = 0;

  // Asynchronously invokes the instantiated function identified by
  // "handle".
  //
  // If function execution succeeds, "done" is called with OK and
  // "*rets" is filled with the function's return values. Otheriwse,
  // "done" is called with an error status.
  //
  // Does not take ownership of "rets".
  struct Options {
    CancellationManager* cancellation_manager = nullptr;
    // The id of the step that is calling this function.
    int64 step_id = 0;

    // Per-step resource manager. Does not take ownership.
    ResourceMgr* step_resource_manager = nullptr;

    std::function<void(std::function<void()>)>* runner = nullptr;
  };
  typedef std::function<void(const Status&)> DoneCallback;
  virtual void Run(const Options& opts, Handle handle,
                   gtl::ArraySlice<Tensor> args, std::vector<Tensor>* rets,
                   DoneCallback done) = 0;

  // Creates a "kernel" for the given node def "ndef".
  //
  // If succeeds, returns OK and the caller takes the ownership of the
  // returned "*kernel". Otherwise, returns an error.
  virtual Status CreateKernel(const NodeDef& ndef, OpKernel** kernel) = 0;

  // Returns true iff 'function' is stateful.
  virtual bool IsStateful(const string& function_name) = 0;

  // Returns the device on which the function executes.
  virtual Device* device() = 0;

  // Returns the function library definition that backs this runtime.
  virtual const FunctionLibraryDefinition* GetFunctionLibraryDefinition()
      const = 0;

  // Returns the environment on which the function executes.
  virtual Env* env() = 0;

  // Returns a debug string showing the definition of the function of
  // 'handle'.
  virtual string DebugString(Handle handle) = 0;
};

// To register a gradient function for a builtin op, one should use
//   REGISTER_OP_GRADIENT(<op_name>, <c++ grad factory>);
//
// Typically, the c++ grad factory is a plan function that can be
// converted into ::tensorflow::gradient::Creator, which is
//   std::function<Status(const AttrSlice&, FunctionDef*)>.
//
// A ::tensorflow::gradient::Creator should populate in FunctionDef* with a
// definition of a brain function which compute the gradient for the
// <op_name> when the <op_name> is instantiated with the given attrs.
//
// E.g.,
//
// Status MatMulGrad(const AttrSlice& attrs, FunctionDef* g) {
//   bool transpose_a;
//   TF_RETURN_IF_ERROR(attrs.Get("transpose_a", &transpose_a));
//   bool transpose_b;
//   TF_RETURN_IF_ERROR(attrs.Get("transpose_b", &transpose_b));
//   DataType dtype;
//   TF_RETURN_IF_ERROR(attrs.Get("dtype", &dtype));
//   if (!transpose_a && !transpose_b) {
//     *g = FunctionDefHelper::Define(
//       "MatMulGrad",
//       {"x:T ", "y:T", "dz:T"},    // Inputs to this function
//       {"dx:T", "dy:T"},           // Outputs from this function
//       {"T: {float, double}"},     // Attributes needed by this function
//       {
//         {{"x_t"}, "Transpose", {"x"}, {{"T", "$T"}}},
//         {{"y_t"}, "Transpose", {"y"}, {{"T", "$T"}}},
//         {{"dx"}, "MatMul", {"dz", "y_t"}, {{"T", "$T"}}},
//         {{"dy"}, "MatMul", {"x_", "dz"}, {{"T", "$T"}}},
//       });
//   } else {
//     ... ...
//   }
//   return Status::OK();
// }
//
// NOTE: $T is substituted with the type variable "T" when the
// gradient function MatMul is instantiated.
//
// TODO(zhifengc): Better documentation somewhere.

// Macros to define a gradient function factory for a primitive
// operation.
#define REGISTER_OP_GRADIENT(name, fn) \
  REGISTER_OP_GRADIENT_UNIQ_HELPER(__COUNTER__, name, fn)

#define REGISTER_OP_NO_GRADIENT(name) \
  REGISTER_OP_GRADIENT_UNIQ_HELPER(__COUNTER__, name, nullptr)

#define REGISTER_OP_GRADIENT_UNIQ_HELPER(ctr, name, fn) \
  REGISTER_OP_GRADIENT_UNIQ(ctr, name, fn)

#define REGISTER_OP_GRADIENT_UNIQ(ctr, name, fn)                 \
  static bool unused_grad_##ctr = SHOULD_REGISTER_OP_GRADIENT && \
                                  ::tensorflow::gradient::RegisterOp(name, fn)

namespace gradient {
// Register a gradient creator for the "op".
typedef std::function<Status(const AttrSlice& attrs, FunctionDef*)> Creator;
bool RegisterOp(const string& op, Creator func);

// Returns OK the gradient creator for the "op" is found (may be
// nullptr if REGISTER_OP_NO_GRADIENT is used.
Status GetOpGradientCreator(const string& op, Creator* creator);
};

}  // end namespace tensorflow

#endif  // TENSORFLOW_FRAMEWORK_FUNCTION_H_
