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#include <miopen/buffer_info.hpp>
#include <miopen/datatype.hpp>
#include <miopen/kernel_build_params.hpp>
#include <miopen/mlo_internal.hpp>
#include <miopen/tensor_view_utils.hpp>
#include <miopen/prelu/invoke_params.hpp>
#include <miopen/prelu/solvers.hpp>
#include <miopen/prelu/utils.hpp>

#define VIEW_DIMS 5

#define LOCAL_SIZE_SW_BWD 256
#define LOCAL_SIZE_SW_REDUCE_BWD 256

namespace miopen {

namespace solver {

namespace prelu {

namespace {
MultiBufferWorkspaceTraits GetMultiBufferWorkspaceTraits(const TensorDescriptor& inputDesc)
{
    auto size = inputDesc.GetElementSize();
    return MultiBufferWorkspaceTraits{size * get_data_size(miopenFloat),
                                      AlignUp(size, LOCAL_SIZE_SW_REDUCE_BWD) /
                                          LOCAL_SIZE_SW_REDUCE_BWD * get_data_size(miopenFloat)};
}
} // namespace

bool SingleWeightBackward::IsApplicable(
    const ExecutionContext& /*context*/,
    const miopen::prelu::BackwardProblemDescription& problem) const
{
    if(problem.GetdInputDesc().GetVectorLength() > VIEW_DIMS)
        return false;
    if(!problem.IsSingleWeight())
        return false;
    return true;
}

ConvSolution
SingleWeightBackward::GetSolution(const ExecutionContext& /*context*/,
                                  const miopen::prelu::BackwardProblemDescription& problem) const
{
    auto result = ConvSolution{miopenStatusSuccess};

    auto dtype        = problem.GetdInputDesc().GetType();
    auto input_dtype  = miopen::GetDataType(problem.GetdInputDesc().GetType());
    auto output_dtype = miopen::GetDataType(problem.GetdOuputDesc().GetType());

    /* Phase 1: Calc gradient for each elements. */
    {
        auto size         = problem.GetdInputDesc().GetElementSize();
        auto build_params = KernelBuildParameters{
            {"MIOPEN_USE_FP16", static_cast<int>(dtype == miopenHalf)},
            {"MIOPEN_USE_FP32", static_cast<int>(dtype == miopenFloat)},
            {"MIOPEN_USE_FP64", static_cast<int>(dtype == miopenDouble)},
            {"MIOPEN_USE_BFP16", static_cast<int>(dtype == miopenBFloat16)},
            {"VIEW_DIMS", VIEW_DIMS},
            {"INPUT_TYPE", input_dtype == "bfloat16" ? "ushort" : input_dtype},
            {"OUTPUT_TYPE", output_dtype == "bfloat16" ? "ushort" : output_dtype},
        };
        result.construction_params.push_back(make_hip_kernel(
            {LOCAL_SIZE_SW_BWD}, {size}, "MIOpenPReLU.cpp", "PReLUSWBackward", build_params));
    }

    /* Phase 2: Reduce gradient. */
    {
        auto size         = problem.GetdInputDesc().GetElementSize();
        auto build_params = KernelBuildParameters{
            {"MIOPEN_USE_FP16", static_cast<int>(dtype == miopenHalf)},
            {"MIOPEN_USE_FP32", static_cast<int>(dtype == miopenFloat)},
            {"MIOPEN_USE_FP64", static_cast<int>(dtype == miopenDouble)},
            {"MIOPEN_USE_BFP16", static_cast<int>(dtype == miopenBFloat16)},
            {"REDUCE_SIZE", LOCAL_SIZE_SW_REDUCE_BWD},
        };
        while(size > LOCAL_SIZE_SW_REDUCE_BWD)
        {
            result.construction_params.push_back(make_hip_kernel({LOCAL_SIZE_SW_REDUCE_BWD},
                                                                 {size},
                                                                 "MIOpenReduceSum.cpp",
                                                                 "ReduceSumFLOATACCUM",
                                                                 build_params));
            size = (size + LOCAL_SIZE_SW_REDUCE_BWD - 1) / LOCAL_SIZE_SW_REDUCE_BWD;
        }
        result.construction_params.push_back(make_hip_kernel(
            {LOCAL_SIZE_SW_REDUCE_BWD}, {size}, "MIOpenReduceSum.cpp", "ReduceSum", build_params));
    }

    auto getBuffPart = [ws = GetMultiBufferWorkspaceTraits(problem.GetdInputDesc())](
                           void* buffer, size_t part_idx) {
        return static_cast<void*>(static_cast<std::byte*>(buffer) + ws.GetOffset(part_idx));
    };

    result.invoker_factory = [=](const std::vector<Kernel>& kernels) {
        return [=](const Handle& handle_, const AnyInvokeParams& raw_params) {
            decltype(auto) params = raw_params.CastTo<miopen::prelu::InvokeParams>();

            HipEventPtr start, stop;
            bool profiling = handle_.IsProfilingEnabled();
            if(profiling)
            {
                handle_.EnableProfiling(false);
                (void)hipStreamSynchronize(handle_.GetStream());
                start = miopen::make_hip_event();
                stop  = miopen::make_hip_event();
                (void)hipEventRecord(start.get(), handle_.GetStream());
            }

            auto work_a = getBuffPart(params.GetWorkspace(), 0);
            auto work_b = getBuffPart(params.GetWorkspace(), 1);

            int kernelCnt = 0;

            /* Phase 1: Calc gradient for each elements. */
            {
                auto input_tv         = get_inner_expanded_tv<VIEW_DIMS>(deref(params.inputDesc));
                auto weight_tv        = get_inner_expanded_tv<1>(deref(params.weightDesc));
                auto output_grad_tv   = get_inner_expanded_tv<VIEW_DIMS>(deref(params.doutputDesc));
                auto input_grad_tv    = get_inner_expanded_tv<VIEW_DIMS>(deref(params.dinputDesc));
                decltype(auto) kernel = handle_.Run(kernels[kernelCnt++]);
                kernel(params.input,
                       params.weight,
                       params.doutput,
                       params.dinput,
                       work_a,
                       static_cast<uint64_t>(deref(params.inputDesc).GetElementSize()),
                       input_tv,
                       weight_tv,
                       output_grad_tv,
                       input_grad_tv);
            }

            /* Phase 2: Reduce gradient. */
            {
                uint64_t size = deref(params.inputDesc).GetElementSize();
                while(size > LOCAL_SIZE_SW_REDUCE_BWD)
                {
                    auto kernel = handle_.Run(kernels[kernelCnt++]);
                    kernel(work_a, work_b, size);
                    size = (size + LOCAL_SIZE_SW_REDUCE_BWD - 1) / LOCAL_SIZE_SW_REDUCE_BWD;
                    std::swap(work_a, work_b);
                }
                auto weight_grad_tv = get_inner_expanded_tv<1>(deref(params.dweightDesc));
                handle_.Run(kernels[kernelCnt++])(work_a, params.dweight, size, weight_grad_tv);
            }

            if(profiling)
            {
                float elapsed = 0.0f;
                (void)hipEventRecord(stop.get(), handle_.GetStream());
                handle_.EnableProfiling(true);
                (void)hipEventSynchronize(stop.get());
                (void)hipEventElapsedTime(&elapsed, start.get(), stop.get());
                // Clean up
                (void)hipEventDestroy(start.get());
                (void)hipEventDestroy(stop.get());
                handle_.ResetKernelTime();
                handle_.AccumKernelTime(elapsed);
            };
        };
    };

    return result;
}

std::size_t SingleWeightBackward::GetWorkspaceSize(
    const ExecutionContext& /*context*/,
    const miopen::prelu::BackwardProblemDescription& problem) const
{
    return GetMultiBufferWorkspaceTraits(problem.GetdInputDesc()).GetSize();
}

} // namespace prelu
} // namespace solver
} // namespace miopen
