/* Copyright 2017 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_COMPILER_XLA_SERVICE_HLO_RUNNER_H_
#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_RUNNER_H_

#include <map>
#include <memory>
#include <set>
#include <string>
#include <vector>

#include "tensorflow/compiler/xla/service/backend.h"
#include "tensorflow/compiler/xla/service/compiler.h"
#include "tensorflow/compiler/xla/service/computation_placer.h"
#include "tensorflow/compiler/xla/service/executable.h"
#include "tensorflow/compiler/xla/service/hlo_computation.h"
#include "tensorflow/compiler/xla/service/hlo_module.h"
#include "tensorflow/compiler/xla/status_macros.h"
#include "tensorflow/compiler/xla/statusor.h"
#include "tensorflow/compiler/xla/types.h"
#include "tensorflow/compiler/xla/util.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/stream_executor_no_cuda.h"

namespace xla {

// A base class for running an HloModule. This executes the given HloModule on a
// certain backend directly without using the client interface. HloModule can be
// explicitly built, or loaded from a serialization file (e.g., hlo proto
// file), or parsed from a hlo textual IR string.
class HloRunner {
 public:
  // The options used to configure a ExecuteReplicated() call.
  struct ReplicatedExecuteOptions {
    // The number of devices the HLO module should be replicated onto.
    int64 num_replicas = 1;

    // The arguments to be fed to each replica. Since this is used for a
    // replicated execution, all the arguments are the same for all replicas.
    std::vector<const Literal*> arguments;

    // If the HLO module being run has an infeed instruction, this will be the
    // data which will be fed to it, for as many as infeed_steps steps.
    const Literal* infeed = nullptr;

    // The number of times the infeed literal should be fed to the HLO module.
    // For a clean exit, this should match the iterations-per-loop parameter
    // used when generating the HLO module proto (that is usually the main
    // while bounary counter). A value higher then iterations-per-loop would
    // lead to infeed threads feeding to a gone computation, while a lower
    // value would trigger a stuck ExecuteReplicated() call (the computation
    // will be trying to infeed data which will never come).
    int64 infeed_steps = -1;

    // The shape of the outfeed operation. If empty, the HLO module does not
    // generate any outfeed.
    Shape outfeed_shape;

    // A pointer to a vector where the outfeed values will be stored. If
    // nullptr, the values will be read and discarded.
    std::vector<std::unique_ptr<Literal>>* outfeed_values = nullptr;

    // Whether the HLO passes should be run on the input module. Usually
    // saved modules are coming from after the HLO pass pipeline, so triggering
    // another run will likely cause errors.
    bool run_hlo_passes = false;
  };

  explicit HloRunner(se::Platform* platform);

  ~HloRunner();

  // Converts an HloModule from the given hlo textual IR string (in
  // HloModule::ToString format).
  static StatusOr<std::unique_ptr<HloModule>> CreateModuleFromString(
      const tensorflow::StringPiece hlo_string,
      const DebugOptions& debug_options);

  // Reads the proto file in xla.HloProto format, creates and returns the
  // HloModule.
  static StatusOr<std::unique_ptr<HloModule>> ReadModuleFromBinaryProtoFile(
      const std::string& filename, const DebugOptions& debug_options);
  static StatusOr<std::unique_ptr<HloModule>> ReadModuleFromTextProtoFile(
      const std::string& filename, const DebugOptions& debug_options);

  // Reads the hlo text dump file in HloModule::ToString format, creates and
  // returns the HloModule.
  static StatusOr<std::unique_ptr<HloModule>> ReadModuleFromHloTextFile(
      const std::string& filename, const DebugOptions& debug_options);

  // Transfers data between the host and device.
  StatusOr<ScopedShapedBuffer> TransferLiteralToDevice(const Literal& literal);
  StatusOr<std::vector<ScopedShapedBuffer>> TransferLiteralsToDevice(
      const tensorflow::gtl::ArraySlice<const Literal*> literals);
  StatusOr<std::vector<ScopedShapedBuffer>> TransferLiteralsToDevice(
      const tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> literals);
  StatusOr<std::unique_ptr<Literal>> TransferLiteralFromDevice(
      const ShapedBuffer& buffer);

  // Executes the given module with given literals as input and returns the
  // result as a Literal.
  //
  // If run_hlo_passes is false, the module will be executed without Hlo
  // optimization.
  StatusOr<std::unique_ptr<Literal>> Execute(
      std::unique_ptr<HloModule> module,
      const tensorflow::gtl::ArraySlice<const Literal*> arguments,
      bool run_hlo_passes = true, ExecutionProfile* profile = nullptr);

  StatusOr<std::unique_ptr<Literal>> Execute(
      std::unique_ptr<HloModule> module,
      const tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> arguments,
      bool run_hlo_passes = true, ExecutionProfile* profile = nullptr);

  // As Execute(), but accepts and returns device buffers instead of host
  // buffers.
  StatusOr<ScopedShapedBuffer> ExecuteWithDeviceBuffers(
      std::unique_ptr<HloModule> module,
      const tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
      bool run_hlo_passes = true, ExecutionProfile* profile = nullptr);

  StatusOr<ScopedShapedBuffer> ExecuteWithDeviceBuffers(
      std::unique_ptr<HloModule> module,
      const tensorflow::gtl::ArraySlice<ScopedShapedBuffer> arguments,
      bool run_hlo_passes = true, ExecutionProfile* profile = nullptr);

  // Executes a given HLO module into a set of replicas, and returns a map
  // with the replica number as key, and the corresponding returned literal as
  // value.
  StatusOr<std::vector<std::unique_ptr<Literal>>> ExecuteReplicated(
      std::unique_ptr<HloModule> module,
      const ReplicatedExecuteOptions& options);

  // If backend is not created in the constructor, creates and returns the
  // default backend. If creation fails, crashes the program.
  //
  // This creates the backend lazily so it's possible to instantiate an
  // HloRunner in a program without any backends linked in.
  Backend& backend();
  const Backend& backend() const;

 private:
  // Creates an executable object given an HLO module. If run_hlo_passes is
  // true, the HLO passes will be run before.
  StatusOr<std::unique_ptr<Executable>> CreateExecutable(
      std::unique_ptr<HloModule> module, bool run_hlo_passes);

  // Creates a ServiceExecutableRunOptions object to configure a run on device,
  // using the provided stream object. If device_assignment is not nullptr, it
  // will be used to configure the replication parameters. Replicated executions
  // should pass the device_assignment parameter.
  ServiceExecutableRunOptions GetServiceRunOptionsForDevice(
      int64 device, se::Stream* stream, DeviceAssignment* device_assignment);

  std::unique_ptr<Backend> backend_;
};

}  // namespace xla

#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_RUNNER_H_
