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

#include "tensorflow/compiler/xla/python/py_executable.h"

#include "absl/algorithm/container.h"

namespace xla {

namespace py = pybind11;

PyExecutable::PyExecutable(std::shared_ptr<PyClient> client,
                           std::unique_ptr<PjRtExecutable> executable,
                           std::unique_ptr<Traceback> traceback)
    : client_(std::move(client)),
      executable_(std::move(executable)),
      traceback_(std::move(traceback)) {}

std::vector<ClientAndPtr<Device>> PyExecutable::LocalDevices() const {
  std::vector<ClientAndPtr<Device>> devices;
  devices.reserve(executable_->local_devices().size());
  for (Device* device : executable_->local_devices()) {
    devices.push_back(WrapWithClient(client_, device));
  }
  return devices;
}

StatusOr<std::vector<std::unique_ptr<PyBuffer>>> PyExecutable::Execute(
    absl::Span<PyBuffer* const> args) {
  auto traceback = Traceback::Get();
  py::gil_scoped_release gil_release;
  ExecuteOptions options;
  options.untuple_result = true;
  std::vector<PjRtBuffer*> arg_buffers(args.size());
  absl::c_transform(args, arg_buffers.begin(),
                    [](PyBuffer* buf) { return buf->buffer(); });
  TF_ASSIGN_OR_RETURN(std::vector<std::unique_ptr<PjRtBuffer>> output_buffers,
                      executable_->Execute(arg_buffers, options));
  std::vector<std::unique_ptr<PyBuffer>> outputs;
  outputs.reserve(output_buffers.size());
  for (auto& buffer : output_buffers) {
    outputs.push_back(std::make_unique<PyBuffer>(client_, std::move(buffer),
                                                 std::move(traceback)));
  }
  return outputs;
}

StatusOr<std::vector<std::vector<std::unique_ptr<PyBuffer>>>>
PyExecutable::ExecuteOnLocalDevices(
    absl::Span<const std::vector<PyBuffer*>> args) {
  auto traceback = Traceback::Get();
  py::gil_scoped_release gil_release;
  ExecuteOptions options;
  options.untuple_result = true;
  std::vector<std::vector<PjRtBuffer*>> arg_buffers(args.size());
  for (int computation = 0; computation < args.size(); ++computation) {
    arg_buffers[computation].resize(args[computation].size());
    absl::c_transform(args[computation], arg_buffers[computation].begin(),
                      [](PyBuffer* buf) { return buf->buffer(); });
  }
  TF_ASSIGN_OR_RETURN(
      std::vector<std::vector<std::unique_ptr<PjRtBuffer>>> output_buffers,
      executable_->ExecuteOnLocalDevices(arg_buffers, options));
  std::vector<std::vector<std::unique_ptr<PyBuffer>>> outputs;
  outputs.resize(output_buffers.size());
  for (int computation = 0; computation < output_buffers.size();
       ++computation) {
    for (auto& buffer : output_buffers[computation]) {
      outputs[computation].push_back(std::make_unique<PyBuffer>(
          client_, std::move(buffer), std::move(traceback)));
    }
  }
  return outputs;
}

StatusOr<std::vector<std::shared_ptr<HloModule>>> PyExecutable::HloModules()
    const {
  std::vector<std::shared_ptr<HloModule>> modules;
  modules.reserve(executable_->executables().size());
  for (const auto& local_exec : executable_->executables()) {
    if (!local_exec->executable()->has_module()) {
      return InvalidArgument("Executable does not have HLO modules.");
    }
    modules.push_back(local_exec->executable()->shared_module());
  }
  return std::move(modules);
}

}  // namespace xla
