text: /* 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_SERVICE_H_
  #define TENSORFLOW_COMPILER_XLA_SERVICE_SERVICE_H_
  
  #include <functional>
  #include <memory>
  #include <string>
  #include <vector>
  
  #include "absl/types/span.h"
  #include "tensorflow/compiler/xla/executable_run_options.h"
  #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
  #include "tensorflow/compiler/xla/service/allocation_tracker.h"
  #include "tensorflow/compiler/xla/service/backend.h"
  #include "tensorflow/compiler/xla/service/channel_tracker.h"
  #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
  #include "tensorflow/compiler/xla/service/executable.h"
  #include "tensorflow/compiler/xla/service/execution_tracker.h"
  #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
  #include "tensorflow/compiler/xla/service/hlo_module.h"
  #include "tensorflow/compiler/xla/service/hlo_module_config.h"
  #include "tensorflow/compiler/xla/service_interface.h"
  #include "tensorflow/compiler/xla/statusor.h"
  #include "tensorflow/compiler/xla/types.h"
  #include "tensorflow/compiler/xla/xla.pb.h"
  #include "tensorflow/compiler/xla/xla_data.pb.h"
  #include "tensorflow/core/platform/logging.h"
  #include "tensorflow/core/platform/macros.h"
  #include "tensorflow/core/platform/stream_executor_no_cuda.h"
  
  namespace xla {
  
  // Options to configure the service when it is created.
  class ServiceOptions {
   public:
    // Set the platform backing the service, or nullptr for the default platform.
    ServiceOptions& set_platform(se::Platform* platform);
    se::Platform* platform() const;
  
    // Set the number of replicas to use when compiling replicated
    // programs.
    ServiceOptions& set_number_of_replicas(int number_of_replicas);
    int number_of_replicas() const;
  
    // Sets the thread pool size for parallel execution of an individual operator.
    ServiceOptions& set_intra_op_parallelism_threads(int num_threads);
    int intra_op_parallelism_threads() const;
  
   private:
    se::Platform* platform_ = nullptr;
    int number_of_replicas_ = 1;
    int intra_op_parallelism_threads_ = -1;
  };
  
  // The XLA service object, which is the same across all platforms. It maintains
  // the service state of computations and allocations, and delegates
  // target-specific requests to the target-specific infrastructure
  // (target-specific compiler, StreamExecutor).
  class Service : public ServiceInterface {
   public:
    // Factory method for creating a new Service.
    static StatusOr<std::unique_ptr<Service>> NewService(
        se::Platform* platform = nullptr);
    static StatusOr<std::unique_ptr<Service>> NewService(
        const ServiceOptions& options);
  
    // Unregisters a previously-allocated global handle.
    //
    // If the handle given is not currently allocated, a NOT_FOUND status is
    // returned.
    Status Unregister(const UnregisterRequest* arg,
                      UnregisterResponse* result) override;
  
    // Deconstructs a tuple. Returns a newly created GlobalDataHandle for each
    // element in the tuple.
    Status DeconstructTuple(const DeconstructTupleRequest* arg,
                            DeconstructTupleResponse* result) override;
  
    // Executes a computation with the provided global data passed as
    // immutable arguments. The request contains the whole computation graph.
    // Returns global data output and execution timing.
    Status ExecuteGraph(const ExecuteGraphRequest* arg,
                        ExecuteResponse* result) override;
  
    // Executes one or more computations in parallel with the provided global data
    // passed as immutable arguments. Returns global data output for each
    // computation.
    Status ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg,
                                ExecuteParallelResponse* result) override;
  
    // Requests one or more device handles from the target.
    //
    // When N device handles are requested and the number of replicas is R, at
    // least N * R devices must be available. The devices are assigned based on
    // the device ordinals such that the first R available devices are assigned to
    // the first set of replicas, and the next R devices to the second set of
    // replicas, etc. Each returned device handle represents the device with the
    // replica id 0.
    Status GetDeviceHandles(const GetDeviceHandlesRequest* arg,
                            GetDeviceHandlesResponse* result) override;
  
    // Waits until the specified execution is complete and returns the result.
    // Calling this API multiple times with the same execution handle returns the
    // method with an error since the execution handle is destroyed after the
    // first call.
    Status WaitForExecution(const WaitForExecutionRequest* arg,
                            WaitForExecutionResponse* result) override;
  
    // Requests that global data be transferred to the client in literal form.
    Status TransferToClient(const TransferToClientRequest* arg,
                            TransferToClientResponse* result) override;
  
    // Transfers data from a literal provided by the client, into device memory.
    Status TransferToServer(const TransferToServerRequest* arg,
                            TransferToServerResponse* result) override;
  
    // Transfers data from a literal provided by the client, into the Infeed
    // buffer of the device.
    Status TransferToInfeed(const TransferToInfeedRequest* arg,
                            TransferToInfeedResponse* result) override;
  
    // Transfers data from the Outfeed othe device to the literal provided by the
    // client.
    Status TransferFromOutfeed(const TransferFromOutfeedRequest* arg,
                               TransferFromOutfeedResponse* result) override;
  
    // Resets devices, clearing all existing state on all the devices associated
    // with this service (including memory allocated on the devices).
    //
    // ResetDevice may only be called where no previous Execution state on the
    // device is used by the next Execution.
    //
    // ResetDevice should be called before an Execution that expect the device to
    // be in the reset state. For example, if the prior Execution modifies device
    // state (e.g., architectural state) that the next Execution depends on.
    Status ResetDevice(const ResetDeviceRequest* arg,
                       ResetDeviceResponse* result) override;
  
    Status ComputeConstantGraph(const ComputeConstantGraphRequest* arg,
                                ComputeConstantResponse* result) override;
  
    // Returns the shape (with layout) of an array associated with a given data
    // handle.
    Status GetShape(const GetShapeRequest* arg,
                    GetShapeResponse* result) override;
  
    // Retrieves the statistics of a computation.
    Status GetComputationGraphStats(const ComputationGraphStatsRequest* arg,
                                    ComputationStatsResponse* result) override;
  
    // Creates a unique channel handle that can be used for Send/Recv
    // instructions.
    Status CreateChannelHandle(const CreateChannelHandleRequest* arg,
                               CreateChannelHandleResponse* result) override;
  
    // Returns the backend used to execute computations.
    const Backend& backend() const { return *execute_backend_; }
    Backend* mutable_backend() { return execute_backend_.get(); }
  
   private:
    // A private overload for Service itself, used by other methods within this
    // class.
    StatusOr<std::unique_ptr<HloModuleConfig>> CreateModuleConfig(
        const ProgramShape& program_shape,
        absl::Span<const ShapedBuffer* const> arguments,
        const ExecutionOptions& execution_options);
  
    // Picks a parallel response and fills the result.
    Status PickParallelResponse(const ExecuteParallelResponse& parallel_result,
                                ExecuteResponse* result);
  
    // Prepare the executors for executing parallel.
    StatusOr<std::vector<se::StreamExecutor*>> GetExecutors(
        const ExecutionOptions& execution_options, int64 requests_size,
        int64 request_index) const;
  
    // Prepare the arguments for executing parallel.
    StatusOr<std::vector<std::vector<const ShapedBuffer*>>> GetArguments(
        const ExecutionOptions& execution_options,
        absl::Span<const GlobalDataHandle* const> arguments);
  
   protected:
    friend class LocalExecutable;
  
    // The constructor is private. Use the NewService factory to create new
    // service objects.
    Service(const ServiceOptions& options,
            std::unique_ptr<Backend> execute_backend);
  
    // Resolves the given argument handles in the allocation tracker and returns
    // the corresponding allocations for every replica. The function also verifies
    // that each allocation matches the execution platform and device ordinal of
    // the corresponding replica.
    StatusOr<std::vector<std::vector<const ShapedBuffer*>>>
    ResolveAndValidateArguments(
        absl::Span<const GlobalDataHandle* const> arguments,
        absl::Span<se::StreamExecutor* const> stream_executors);
  
    // Create a Hlo module config for the given program shape and arguments.
    // execution_options is optional; if not given a default is used.
    StatusOr<std::unique_ptr<HloModuleConfig>> CreateModuleConfig(
        const ProgramShape& program_shape,
        absl::Span<const Shape* const> argument_shapes,
        const ExecutionOptions* execution_options);
  
    // Builds an Executable for the given parameters.
    //
    // If device_allocator is not null, the compiler may use it to allocate temp
    // buffers, which the compiler is responsible for freeing.  The allocator
    // given here need not match the allocator used when running the executable.
    StatusOr<std::unique_ptr<Executable>> BuildExecutable(
        const HloModuleProto& module_proto,
        std::unique_ptr<HloModuleConfig> module_config, Backend* backend,
        se::StreamExecutor* executor,
        DeviceMemoryAllocator* device_allocator = nullptr);
  
    // Same as BuildExecutable() above, but builds a list of Executables for the
    // given computations that may interact with each other.
    StatusOr<std::vector<std::unique_ptr<Executable>>> BuildExecutables(
        const std::vector<const HloModuleProto*>& module_protos,
        std::vector<std::unique_ptr<HloModuleConfig>> module_configs,
        Backend* backend, std::vector<std::vector<se::StreamExecutor*>> executors,
        DeviceMemoryAllocator* device_allocator);
  
    // Runs the given executable with the given arguments and register the result
    // in the allocation tracker. The handle of the result from the tracker is
    // returned. If the parameter "profile" is not null, it points to an
    // ExecutionProfile object which will be filled in with profile data.
    StatusOr<GlobalDataHandle> ExecuteAndRegisterResult(
        Executable* executable,
        const absl::Span<const std::vector<const ShapedBuffer*>> arguments,
        Backend* backend, const string& result_tag, ExecutionProfile* profile);
  
    // Runs the given executables with the given arguments and register the result
    // from each executable in the allocation tracker. The handles of the result
    // from the tracker are returned.
    StatusOr<std::vector<GlobalDataHandle>> ExecuteParallelAndRegisterResult(
        absl::Span<Executable* const> executables,
        absl::Span<const std::vector<std::vector<const ShapedBuffer*>>> arguments,
        Backend* backend, absl::Span<const DeviceHandle> device_handles,
        absl::Span<const string> result_tags, ExecutionProfile* profile);
  
    // Executes a single computation which has more than one target device.
    // The N devices are expected to all return an empty tuple, but one, which
    // will be the result of this computation.
    Status ExecuteOneToN(const ExecuteGraphRequest* arg, ExecuteResponse* result);
  
    // Convenience function which checks whether the given client_shape
    // (presumably passed by the client to set the result layout) is valid for the
    // given computation result shape.
    Status ValidateResultShape(const Shape& client_shape,
                               const Shape& result_shape) const;
  
    // Returns the stream executors assigned to the replicas represented by the
    // given device handle. Each device_handle is a virtual replicated device that
    // represents a set of physical devices for the replicas.
    StatusOr<std::vector<se::StreamExecutor*>> Replicas(
        const Backend& backend, const DeviceHandle& device_handle) const;
  
    Status MaybeDumpHloModule(const HloModule& module) const;
  
    // Returns the device handle that represents the replicated device for a
    // single computation that is not model-parallelized.
    DeviceHandle SingleComputationDeviceHandle() const;
  
    ServiceOptions options_;
  
    // Tracks channels created via the API.
    ChannelTracker channel_tracker_;
  
    // Tracks allocations made via the API and computation execution.
    AllocationTracker allocation_tracker_;
  
    // Tracks asynchronously launched executions via the API.
    ExecutionTracker execution_tracker_;
  
    // Backend to compile and execute computations on.
    std::unique_ptr<Backend> execute_backend_;
  
    TF_DISALLOW_COPY_AND_ASSIGN(Service);
  };
  
  }  // namespace xla
  
  #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_SERVICE_H_
  