/*
 * Copyright (c) 2022-2023, NVIDIA CORPORATION.  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 "tensorrt_llm/runtime/worldConfig.h"

#include "tensorrt_llm/common/assert.h"
#include "tensorrt_llm/common/stringUtils.h"
#include "tensorrt_llm/runtime/tllmLogger.h"
#include "tensorrt_llm/runtime/utils/multiDeviceUtils.h"

#include <csignal>
#include <cstdlib>
#include <mpi.h>

using namespace tensorrt_llm::runtime;
namespace tc = tensorrt_llm::common;

namespace
{

bool mpiInitialized = false;

void initMpi(nvinfer1::ILogger& logger, int threadMode = MPI_THREAD_FUNNELED)
{
    if (mpiInitialized)
    {
        return;
    }

    int initialized = 0;
    TLLM_MPI_CHECK(MPI_Initialized(&initialized));
    if (!initialized)
    {
        logger.log(
            nvinfer1::ILogger::Severity::kINFO, tc::fmtstr("Initializing MPI with thread mode %d", threadMode).c_str());
        int providedMode;
        TLLM_MPI_CHECK(MPI_Init_thread(nullptr, nullptr, threadMode, &providedMode));
        TLLM_CHECK_WITH_INFO(providedMode >= threadMode, "MPI_Init_thread failed");
        std::atexit([]() { MPI_Finalize(); });

        auto previousHandler = std::signal(SIGABRT, [](int signal) { MPI_Abort(MPI_COMM_WORLD, EXIT_FAILURE); });
        TLLM_CHECK_WITH_INFO(previousHandler != SIG_ERR, "Signal handler setup failed");
    }

    mpiInitialized = true;
}

} // namespace

bool WorldConfig::validConfig(nvinfer1::ILogger& logger, SizeType tensorParallelism, SizeType pipelineParallelism)
{
    initMpi(logger);

    int mpiSize;
    TLLM_MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &mpiSize));
    return mpiSize == tensorParallelism * pipelineParallelism;
}

WorldConfig WorldConfig::mpi(nvinfer1::ILogger& logger, SizeType gpusPerNode, std::optional<SizeType> tensorParallelism,
    std::optional<SizeType> pipelineParallelism)
{
    initMpi(logger);

    int mpiSize, mpiRank;
    TLLM_MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &mpiSize));
    TLLM_MPI_CHECK(MPI_Comm_rank(MPI_COMM_WORLD, &mpiRank));
    logger.log(nvinfer1::ILogger::Severity::kINFO, tc::fmtstr("MPI size: %d, rank: %d", mpiSize, mpiRank).c_str());

    auto pp = pipelineParallelism.value_or(1);
    auto tp = tensorParallelism.value_or(mpiSize / pp);
    TLLM_CHECK(mpiSize == tp * pp);
    return WorldConfig{tp, pp, mpiRank, gpusPerNode};
}

WorldConfig WorldConfig::mpi(
    SizeType gpusPerNode, std::optional<SizeType> tensorParallelism, std::optional<SizeType> pipelineParallelism)
{
    TllmLogger logger{};
    return mpi(logger, gpusPerNode, tensorParallelism, pipelineParallelism);
}

std::vector<SizeType> WorldConfig::getPipelineParallelGroup() const
{
    auto const pp = getPipelineParallelism();
    auto const tp = getTensorParallelism();
    auto const worldSize = getSize();
    std::vector<SizeType> group;
    group.reserve(pp);
    for (SizeType idx = getTensorParallelRank(); idx < worldSize; idx += tp)
    {
        group.push_back(idx);
    }
    return group;
}