// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include <algorithm>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <typeinfo>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp"

#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp"

namespace ck {
namespace profiler {

template <ck::index_t NDimSpatial,
          typename InLayout,
          typename WeiLayout,
          typename OutLayout,
          typename InDataType,
          typename WeiDataType,
          typename OutDataType,
          typename ComputeTypeA = InDataType,
          typename ComputeTypeB = ComputeTypeA>
bool profile_grouped_conv_bwd_weight_impl(int do_verification,
                                          int init_method,
                                          bool do_log,
                                          bool time_kernel,
                                          const ck::utils::conv::ConvParam& conv_param,
                                          ck::index_t split_k)
{
    using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
    using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
    using OutElementOp = ck::tensor_operation::element_wise::PassThrough;

    const auto in_element_op  = InElementOp{};
    const auto wei_element_op = WeiElementOp{};
    const auto out_element_op = OutElementOp{};

    const auto in_g_n_c_wis_desc =
        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);

    const auto wei_g_k_c_xs_desc =
        ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);

    const auto out_g_n_k_wos_desc =
        ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);

    Tensor<InDataType> input(in_g_n_c_wis_desc);
    Tensor<WeiDataType> weight_host_result(wei_g_k_c_xs_desc);
    Tensor<WeiDataType> weight_device_result(wei_g_k_c_xs_desc);
    Tensor<OutDataType> output(out_g_n_k_wos_desc);

    std::cout << "input: " << input.mDesc << std::endl;
    std::cout << "weight: " << weight_host_result.mDesc << std::endl;
    std::cout << "output: " << output.mDesc << std::endl;

    switch(init_method)
    {
    case 0: break;
    case 1:
        input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
        output.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
        break;
    default:
        input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
        output.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-0.5, 0.5});
    }

    DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpaceSize());
    DeviceMem wei_device_buf(sizeof(WeiDataType) *
                             weight_device_result.mDesc.GetElementSpaceSize());
    DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpaceSize());

    in_device_buf.ToDevice(input.mData.data());
    out_device_buf.ToDevice(output.mData.data());

    if(do_verification)
    {
        auto ref_conv     = ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial,
                                                                           InDataType,
                                                                           WeiDataType,
                                                                           OutDataType,
                                                                           InElementOp,
                                                                           WeiElementOp,
                                                                           OutElementOp>{};
        auto ref_invoker  = ref_conv.MakeInvoker();
        auto ref_argument = ref_conv.MakeArgument(input,
                                                  weight_host_result,
                                                  output,
                                                  conv_param.conv_filter_strides_,
                                                  conv_param.conv_filter_dilations_,
                                                  conv_param.input_left_pads_,
                                                  conv_param.input_right_pads_,
                                                  in_element_op,
                                                  wei_element_op,
                                                  out_element_op);

        ref_invoker.Run(ref_argument);
    }

    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvBwdWeight<NDimSpatial,
                                                                              InLayout,
                                                                              WeiLayout,
                                                                              OutLayout,
                                                                              InDataType,
                                                                              WeiDataType,
                                                                              OutDataType,
                                                                              InElementOp,
                                                                              WeiElementOp,
                                                                              OutElementOp,
                                                                              ComputeTypeA,
                                                                              ComputeTypeB>;

    // get device op instances
    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
        DeviceOp>::GetInstances();

    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;

    std::string best_op_name;
    float best_avg_time   = 0;
    float best_tflops     = 0;
    float best_gb_per_sec = 0;

    // profile device Conv instances
    bool all_pass = true;

    std::array<ck::index_t, NDimSpatial + 3> input_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> filter_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> output_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> input_strides{};
    std::array<ck::index_t, NDimSpatial + 3> weights_strides{};
    std::array<ck::index_t, NDimSpatial + 3> output_strides{};
    std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
    std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
    std::array<ck::index_t, NDimSpatial> input_left_pads{};
    std::array<ck::index_t, NDimSpatial> input_right_pads{};

    auto range_copy = [](const auto& from, auto to) { std::copy(begin(from), end(from), to); };

    range_copy(in_g_n_c_wis_desc.GetLengths(), begin(input_lengths));
    range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides));
    range_copy(wei_g_k_c_xs_desc.GetLengths(), begin(filter_lengths));
    range_copy(wei_g_k_c_xs_desc.GetStrides(), begin(weights_strides));
    range_copy(out_g_n_k_wos_desc.GetLengths(), begin(output_lengths));
    range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides));
    range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
    range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
    range_copy(conv_param.input_left_pads_, begin(input_left_pads));
    range_copy(conv_param.input_right_pads_, begin(input_right_pads));

    for(auto& op_ptr : op_ptrs)
    {
        auto argument_ptr =
            op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
                                        static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
                                        static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
                                        input_lengths,
                                        input_strides,
                                        filter_lengths,
                                        weights_strides,
                                        output_lengths,
                                        output_strides,
                                        conv_filter_strides,
                                        conv_filter_dilations,
                                        input_left_pads,
                                        input_right_pads,
                                        in_element_op,
                                        wei_element_op,
                                        out_element_op,
                                        split_k);

        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
        {
            // using atomic add, so need to reset input
            wei_device_buf.SetZero();

            std::string op_name = op_ptr->GetTypeString();

            auto invoker_ptr = op_ptr->MakeInvokerPointer();

            float avg_time =
                invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});

            std::size_t flop      = conv_param.GetFlops();
            std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();

            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
            float gb_per_sec = num_btype / 1.E6 / avg_time;

            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
                      << gb_per_sec << " GB/s, " << op_name << std::endl;

            if(tflops > best_tflops)
            {
                best_op_name    = op_name;
                best_tflops     = tflops;
                best_avg_time   = avg_time;
                best_gb_per_sec = gb_per_sec;
            }

            if(do_verification)
            {
                wei_device_buf.FromDevice(weight_device_result.mData.data());

                bool pass = ck::utils::check_err(weight_device_result, weight_host_result);

                if(!pass)
                {
                    std::cout << "Fail info: " << op_ptr->GetTypeString() << std::endl;
                }

                all_pass &= pass;

                if(do_log)
                {
                    LogRangeAsType<float>(std::cout << "output : ", output.mData, ",") << std::endl;
                    ;
                    LogRangeAsType<float>(
                        std::cout << "weight (device): ", weight_device_result.mData, ",")
                        << std::endl;
                    ;
                    LogRangeAsType<float>(
                        std::cout << "weight (host): ", weight_host_result.mData, ",")
                        << std::endl;
                    ;
                    LogRangeAsType<float>(std::cout << "input: ", input.mData, ",") << std::endl;
                    ;
                }
            }
        }
        else
        {
            std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
        }
    }

    std::cout << "Best configuration parameters:"
              << "\nname: " << best_op_name << "\navg_time: " << best_avg_time
              << "\ntflops: " << best_tflops << "\nGB/s: " << best_gb_per_sec << std::endl;

    return all_pass;
}

} // namespace profiler
} // namespace ck
