text: /* Copyright 2020 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_INTERFACE_H_
  #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_RUNNER_INTERFACE_H_
  
  #include <map>
  #include <memory>
  #include <set>
  #include <string>
  #include <vector>
  
  #include "absl/types/span.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"
  
  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 HloRunnerInterface {
   public:
    // The options used to configure an 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 boundary 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<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;
  
      // If true, executes on multiple threads using se::Stream::ExecuteOnStream.
      // Otherwise, executes using xla::Executable::ExecuteOnStreams.
      bool use_threads = false;
    };
  
    HloRunnerInterface() = default;
  
    virtual ~HloRunnerInterface() = default;
  
    // Converts an HloModule from the given hlo textual IR string (in
    // HloModule::ToString format).
    static StatusOr<std::unique_ptr<HloModule>> CreateModuleFromString(
        const absl::string_view 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);
  
    // 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<Literal> Execute(std::unique_ptr<HloModule> module,
                              absl::Span<const Literal* const> arguments,
                              bool run_hlo_passes = true) {
      return Execute(std::move(module), arguments, run_hlo_passes, nullptr);
    }
  
    StatusOr<Literal> Execute(std::unique_ptr<HloModule> module,
                              absl::Span<const Literal> arguments,
                              bool run_hlo_passes = true,
                              ExecutionProfile* profile = nullptr);
  
    virtual StatusOr<Literal> Execute(std::unique_ptr<HloModule> module,
                                      absl::Span<const Literal* const> arguments,
                                      bool run_hlo_passes,
                                      ExecutionProfile* profile) = 0;
  
    // Same as above, but with Executable as input.
    StatusOr<Literal> ExecuteWithExecutable(
        std::unique_ptr<Executable> executable,
        absl::Span<const Literal> arguments, ExecutionProfile* profile = nullptr);
  
    StatusOr<Literal> ExecuteWithExecutable(
        std::unique_ptr<Executable> executable,
        absl::Span<const Literal* const> arguments) {
      return ExecuteWithExecutable(std::move(executable), arguments, nullptr);
    }
  
    virtual StatusOr<Literal> ExecuteWithExecutable(
        std::unique_ptr<Executable> executable,
        absl::Span<const Literal* const> arguments,
        ExecutionProfile* profile) = 0;
  
    // 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.
    // TODO(b/172931928): change to non-virtual function.
    virtual StatusOr<std::vector<Literal>> ExecuteReplicated(
        std::unique_ptr<HloModule> module,
        const ReplicatedExecuteOptions& options) = 0;
  
    // Same as above, but with specified device assignment.
    virtual StatusOr<std::vector<Literal>> ExecuteReplicated(
        std::unique_ptr<HloModule> module,
        const ReplicatedExecuteOptions& options,
        DeviceAssignment* device_assignment) = 0;
  };
  
  }  // namespace xla
  
  #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_RUNNER_INTERFACE_H_
  