/* 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.
==============================================================================*/
#define EIGEN_USE_THREADS

#include "tensorflow/compiler/xla/service/hlo_runner.h"

#include <string>
#include <utility>

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/compiler/xla/layout_util.h"
#include "tensorflow/compiler/xla/ptr_util.h"
#include "tensorflow/compiler/xla/service/hlo_parser.h"
#include "tensorflow/compiler/xla/service/transfer_manager.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/core/common_runtime/eigen_thread_pool.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"

namespace xla {

/*static*/ StatusOr<std::unique_ptr<HloModule>>
HloRunner::CreateModuleFromString(const tensorflow::StringPiece hlo_string,
                                  const DebugOptions& debug_options) {
  HloModuleConfig config;
  config.set_debug_options(debug_options);
  return ParseHloString(hlo_string, config);
}

namespace {

// Creates an HloModule from the given proto.
StatusOr<std::unique_ptr<HloModule>> HloProtoToModule(
    const HloProto& proto, const DebugOptions& debug_options) {
  TF_ASSIGN_OR_RETURN(HloModuleConfig config,
                      HloModule::CreateModuleConfigFromProto(proto.hlo_module(),
                                                             debug_options));
  TF_ASSIGN_OR_RETURN(auto module,
                      HloModule::CreateFromProto(proto.hlo_module(), config));
  return std::move(module);
}

}  // namespace

/*static*/ StatusOr<std::unique_ptr<HloModule>>
HloRunner::ReadModuleFromBinaryProtoFile(const std::string& filename,
                                         const DebugOptions& debug_options) {
  HloProto proto;
  TF_RETURN_IF_ERROR(tensorflow::ReadBinaryProto(tensorflow::Env::Default(),
                                                 filename, &proto));
  return HloProtoToModule(proto, debug_options);
}

/*static*/ StatusOr<std::unique_ptr<HloModule>>
HloRunner::ReadModuleFromTextProtoFile(const std::string& filename,
                                       const DebugOptions& debug_options) {
  HloProto proto;
  TF_RETURN_IF_ERROR(
      tensorflow::ReadTextProto(tensorflow::Env::Default(), filename, &proto));
  return HloProtoToModule(proto, debug_options);
}

/*static*/ StatusOr<std::unique_ptr<HloModule>>
HloRunner::ReadModuleFromHloTextFile(const std::string& filename,
                                     const DebugOptions& debug_options) {
  string hlo_string;
  TF_RETURN_IF_ERROR(tensorflow::ReadFileToString(tensorflow::Env::Default(),
                                                  filename, &hlo_string));
  HloModuleConfig config;
  config.set_debug_options(debug_options);
  return ParseHloString(hlo_string, config);
}

HloRunner::HloRunner(se::Platform* platform) {
  BackendOptions backend_options;
  backend_options.set_platform(platform);
  backend_ = Backend::CreateBackend(backend_options).ConsumeValueOrDie();
  VLOG(1) << "Created HloRunner for platform: " << platform->Name();
}

HloRunner::~HloRunner() {}

StatusOr<ScopedShapedBuffer> HloRunner::TransferLiteralToDevice(
    const Literal& literal) {
  TF_ASSIGN_OR_RETURN(ScopedShapedBuffer buffer,
                      backend().transfer_manager()->AllocateScopedShapedBuffer(
                          literal.shape(), backend().memory_allocator(),
                          backend().default_device_ordinal()));
  TF_ASSIGN_OR_RETURN(
      auto stream, backend().BorrowStream(backend().default_stream_executor()));
  TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice(
      stream.get(), literal, buffer));
  return std::move(buffer);
}

StatusOr<std::vector<ScopedShapedBuffer>> HloRunner::TransferLiteralsToDevice(
    const tensorflow::gtl::ArraySlice<const Literal*> literals) {
  std::vector<ScopedShapedBuffer> buffers;
  for (const Literal* literal : literals) {
    CHECK(literal != nullptr);
    TF_ASSIGN_OR_RETURN(ScopedShapedBuffer buffer,
                        TransferLiteralToDevice(*literal));
    buffers.push_back(std::move(buffer));
  }
  return std::move(buffers);
}

StatusOr<std::vector<ScopedShapedBuffer>> HloRunner::TransferLiteralsToDevice(
    const tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> literals) {
  std::vector<const Literal*> literal_pointers;
  literal_pointers.reserve(literals.size());
  for (const auto& literal : literals) {
    literal_pointers.push_back(literal.get());
  }
  return TransferLiteralsToDevice(literal_pointers);
}

StatusOr<std::unique_ptr<Literal>> HloRunner::TransferLiteralFromDevice(
    const ShapedBuffer& buffer) {
  TF_ASSIGN_OR_RETURN(
      auto stream, backend().BorrowStream(backend().default_stream_executor()));
  return backend().transfer_manager()->TransferLiteralFromDevice(stream.get(),
                                                                 buffer);
}

StatusOr<std::unique_ptr<Literal>> HloRunner::Execute(
    std::unique_ptr<HloModule> module,
    const tensorflow::gtl::ArraySlice<const Literal*> arguments,
    bool run_hlo_passes, ExecutionProfile* profile) {
  TF_ASSIGN_OR_RETURN(std::vector<ScopedShapedBuffer> argument_buffers,
                      TransferLiteralsToDevice(arguments));
  TF_ASSIGN_OR_RETURN(ScopedShapedBuffer result,
                      ExecuteWithDeviceBuffers(
                          /*module=*/std::move(module),
                          /*arguments=*/argument_buffers,
                          /*run_hlo_passes=*/run_hlo_passes,
                          /*profile=*/profile));
  return TransferLiteralFromDevice(result);
}

StatusOr<std::unique_ptr<Literal>> HloRunner::Execute(
    std::unique_ptr<HloModule> module,
    const tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> arguments,
    bool run_hlo_passes, ExecutionProfile* profile) {
  // Construct a vector of plain pointers for the arguments.
  std::vector<const Literal*> argument_pointers;
  argument_pointers.reserve(arguments.size());
  for (const auto& argument : arguments) {
    argument_pointers.push_back(argument.get());
  }
  return Execute(
      /*module=*/std::move(module),
      /*arguments=*/argument_pointers,
      /*run_hlo_passes=*/run_hlo_passes,
      /*profile=*/profile);
}

StatusOr<ScopedShapedBuffer> HloRunner::ExecuteWithDeviceBuffers(
    std::unique_ptr<HloModule> module,
    const tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
    bool run_hlo_passes, ExecutionProfile* profile) {
  // Get service run options.
  se::Stream stream(backend().default_stream_executor());
  stream.Init();
  ServiceExecutableRunOptions service_run_options =
      GetServiceRunOptionsForDevice(backend().default_device_ordinal(), &stream,
                                    nullptr);

  TF_ASSIGN_OR_RETURN(std::unique_ptr<Executable> executable,
                      CreateExecutable(std::move(module), run_hlo_passes));
  TF_ASSIGN_OR_RETURN(
      ScopedShapedBuffer retval,
      executable->ExecuteOnStreamWrapper(&service_run_options,
                                         /*profile=*/profile, arguments));
  TF_RETURN_IF_ERROR(stream.BlockHostUntilDone());
  return std::move(retval);
}

StatusOr<ScopedShapedBuffer> HloRunner::ExecuteWithDeviceBuffers(
    std::unique_ptr<HloModule> module,
    const tensorflow::gtl::ArraySlice<ScopedShapedBuffer> arguments,
    bool run_hlo_passes, ExecutionProfile* profile) {
  std::vector<const ShapedBuffer*> argument_pointers;
  argument_pointers.reserve(arguments.size());
  for (const auto& argument : arguments) {
    argument_pointers.push_back(&argument);
  }
  return ExecuteWithDeviceBuffers(
      /*module=*/std::move(module),
      /*arguments=*/argument_pointers,
      /*run_hlo_passes=*/run_hlo_passes,
      /*profile=*/profile);
}

StatusOr<std::vector<std::unique_ptr<Literal>>> HloRunner::ExecuteReplicated(
    std::unique_ptr<HloModule> module,
    const ReplicatedExecuteOptions& options) {
  TF_ASSIGN_OR_RETURN(
      std::unique_ptr<Executable> executable,
      CreateExecutable(std::move(module), options.run_hlo_passes));
  TF_ASSIGN_OR_RETURN(
      DeviceAssignment device_assignment,
      backend().computation_placer()->AssignDevices(options.num_replicas, 1));
  std::vector<std::unique_ptr<se::Stream>> streams;
  std::vector<ServiceExecutableRunOptions> service_run_options;

  std::vector<ScopedShapedBuffer> argument_buffers;
  // This reserve() call is necessary for correctness, because
  // argument_buffer_ptrs contains pointers into the elements of
  // argument_buffers.
  argument_buffers.reserve(options.num_replicas * options.arguments.size());

  // Plus one so we can safely get &argument_buffer_ptrs[0] in case there are
  // no arguments.
  std::vector<const ShapedBuffer*> argument_buffer_ptrs(
      options.num_replicas * options.arguments.size() + 1);
  std::vector<tensorflow::gtl::ArraySlice<const ShapedBuffer*>>
      argument_buffer_slices;
  int64 index = 0;
  for (int64 i = 0; i < options.num_replicas; ++i) {
    int64 device = device_assignment(i, 0);
    TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor,
                        backend().stream_executor(device));
    streams.push_back(MakeUnique<se::Stream>(executor));
    streams.back()->Init();
    service_run_options.emplace_back(GetServiceRunOptionsForDevice(
        device, streams.back().get(), &device_assignment));

    // Copy arguments to device.
    for (const Literal* argument : options.arguments) {
      TF_ASSIGN_OR_RETURN(
          ScopedShapedBuffer argument_buffer,
          backend().transfer_manager()->AllocateScopedShapedBuffer(
              argument->shape(), backend().memory_allocator(), device));
      TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice(
          streams.back().get(), *argument, argument_buffer));
      argument_buffers.push_back(std::move(argument_buffer));
      argument_buffer_ptrs[index++] = &argument_buffers.back();
    }
    argument_buffer_slices.emplace_back(
        &argument_buffer_ptrs[index - options.arguments.size()],
        options.arguments.size());
  }

  std::unique_ptr<tensorflow::thread::ThreadPool> pool;
  int64 num_threads = (options.infeed != nullptr) ? options.num_replicas : 0;
  if (ShapeUtil::IsInitialized(options.outfeed_shape)) {
    num_threads += options.num_replicas;
  }
  if (num_threads > 0) {
    pool = MakeUnique<tensorflow::thread::ThreadPool>(
        tensorflow::Env::Default(), "infeed_outfeed",
        /*num_threads=*/num_threads);
  }
  if (options.infeed != nullptr) {
    for (int64 i = 0; i < options.num_replicas; ++i) {
      int64 device = device_assignment(i, 0);
      pool->Schedule([this, device, &options]() {
        se::StreamExecutor* executor =
            backend().stream_executor(device).ValueOrDie();
        VLOG(1) << "Starting infeed on device " << device;
        for (int64 step = 1;
             options.infeed_steps < 0 || step <= options.infeed_steps; ++step) {
          TF_CHECK_OK(backend().transfer_manager()->TransferLiteralToInfeed(
              executor, *options.infeed));
          if (step % 100 == 0) {
            VLOG(1) << "Infeed step " << step;
          }
        }
      });
    }
  }
  if (ShapeUtil::IsInitialized(options.outfeed_shape)) {
    for (int64 i = 0; i < options.num_replicas; ++i) {
      int64 device = device_assignment(i, 0);
      pool->Schedule([this, device, &options]() {
        se::StreamExecutor* executor =
            backend().stream_executor(device).ValueOrDie();
        VLOG(1) << "Starting outfeed on device " << device;
        for (int64 step = 1;
             options.infeed_steps < 0 || step <= options.infeed_steps; ++step) {
          auto literal = MakeUnique<Literal>();
          TF_CHECK_OK(backend().transfer_manager()->TransferLiteralFromOutfeed(
              executor, options.outfeed_shape, literal.get()));
          if (options.outfeed_values != nullptr) {
            options.outfeed_values->push_back(std::move(literal));
          }
          if (step % 100 == 0) {
            VLOG(1) << "Outfeed step " << step;
          }
        }
      });
    }
  }

  LOG(INFO) << "Replicated execution started";
  TF_ASSIGN_OR_RETURN(std::vector<ScopedShapedBuffer> results,
                      executable->ExecuteOnStreams(service_run_options,
                                                   argument_buffer_slices));
  LOG(INFO) << "Replicated execution terminated";

  std::vector<std::unique_ptr<Literal>> exec_results;
  for (int64 i = 0; i < options.num_replicas; ++i) {
    TF_RETURN_IF_ERROR(streams[i]->BlockHostUntilDone());
    TF_ASSIGN_OR_RETURN(std::unique_ptr<Literal> literal,
                        backend().transfer_manager()->TransferLiteralFromDevice(
                            streams[i].get(), results[i]));
    exec_results.push_back(std::move(literal));
  }
  return std::move(exec_results);
}

StatusOr<std::unique_ptr<Executable>> HloRunner::CreateExecutable(
    std::unique_ptr<HloModule> module, bool run_hlo_passes) {
  if (run_hlo_passes) {
    TF_ASSIGN_OR_RETURN(
        module, backend().compiler()->RunHloPasses(
                    std::move(module), backend().default_stream_executor(),
                    backend().memory_allocator()));
  }
  return backend().compiler()->RunBackend(std::move(module),
                                          backend().default_stream_executor(),
                                          backend().memory_allocator());
}

ServiceExecutableRunOptions HloRunner::GetServiceRunOptionsForDevice(
    int64 device, se::Stream* stream, DeviceAssignment* device_assignment) {
  ExecutableRunOptions run_options;
  run_options.set_device_ordinal(device);
  run_options.set_stream(stream);
  run_options.set_allocator(backend().memory_allocator());
  run_options.set_intra_op_thread_pool(
      backend().eigen_intra_op_thread_pool_device());
  if (device_assignment != nullptr) {
    run_options.set_device_assignment(device_assignment);
  }
  return ServiceExecutableRunOptions(
      run_options, backend().StreamBorrower(),
      /*xla_intra_op_thread_pool=*/backend().eigen_intra_op_thread_pool());
}

Backend& HloRunner::backend() {
  if (!backend_) {
    backend_ = Backend::CreateDefaultBackend().ConsumeValueOrDie();
    VLOG(1) << "Executing on platform " << backend().platform()->Name();
  }
  return *backend_;
}

const Backend& HloRunner::backend() const {
  return const_cast<HloRunner*>(this)->backend();
}

}  // namespace xla
