// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.

#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "profiler/profile_grouped_conv_fwd_impl.hpp"

#include "ck/utility/data_type.hpp"
#include "ck/utility/ignore.hpp"
#include "profiler_operation_registry.hpp"

#include <iostream>

enum struct ConvLayout
{
    GNHWC_GKYXC_GNHWK, // 0
    NHWGC_GKYXC_NHWGK, // 1
    NGCHW_GKYXC_NGKHW, // 2
    NGCHW_GKCYX_NGKHW, // 3
};

enum struct ConvDataType
{
    F32_F32_F32,      // 0
    F16_F16_F16,      // 1
    BF16_BF16_BF16,   // 2
    INT8_INT8_INT8,   // 3
    F8_F8_F8,         // 4
    BF8_BF8_F8,       // 5
    F8_BF8_F8,        // 6
    BF8_F8_F8,        // 7
    F32_F32_F32_TF32, // 8
};

enum struct IndexType
{
    INDEX_T,      // 0
    LONG_INDEX_T, // 1
};

#define OP_NAME "grouped_conv_fwd_clamp"
#define OP_DESC "Grouped Convolution Forward+Clamp"

static void print_helper_msg()
{
    std::cout
        // clang-format off
        << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
        << "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
        << "                 1: Input fp16, Weight fp16, Output fp16\n"
        << "                 2: Input bf16, Weight bf16, Output bf16\n"
        << "                 3: Input int8, Weight int8, Output int8\n"
        << "                 4: Input fp8, Weight fp8, Output fp8\n"
        << "                 5: Input bf8, Weight bf8, Output fp8\n"
        << "                 6: Input fp8, Weight bf8, Output fp8\n"
        << "                 7: Input bf8, Weight fp8, Output fp8\n"
        << "                 8: Input fp32, Weight fp32, Output fp32, Compute tf32)\n"
        << "arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
        << "                     1: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]\n"
        << "                     2: Input[N, G, C, Hi, Wi], Weight[G, K, Y, X, C], Output[N, "
        "G, K, Ho, Wo]\n"
        << "                     3: Input[N, G, C, Hi, Wi], Weight[G, K, C, Y, X], Output[N, "
        "G, K, Ho, Wo])\n"
        << "arg4: indexing data type (0: 32-bit, 1: 64-bit)\n"
        << "arg5: verification (0: no, 1: yes)\n"
        << "arg6: initialization (0: no init, 1: integer value, 2: decimal value)\n"
        << "arg7: print tensor value (0: no; 1: yes)\n"
        << "arg8: time kernel (0: no, 1: yes)\n"
        << ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
    // clang-format on
}

int grouped_conv_fwd_clamp(int argc, char* argv[])
{
    // 8 for control, 1 for num_dim_spatial
    if(argc < 10)
    {
        print_helper_msg();
        return 1;
    }

    const auto data_type       = static_cast<ConvDataType>(std::stoi(argv[2]));
    const auto layout          = static_cast<ConvLayout>(std::stoi(argv[3]));
    const auto index_type      = static_cast<IndexType>(std::stoi(argv[4]));
    const bool do_verification = std::stoi(argv[5]);
    const int init_method      = std::stoi(argv[6]);
    const bool do_log          = std::stoi(argv[7]);
    const bool time_kernel     = std::stoi(argv[8]);
    const int num_dim_spatial  = std::stoi(argv[9]);

    // 9 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
    if(argc != 9 + 1 + 4 + 6 * num_dim_spatial)
    {
        print_helper_msg();
        return 1;
    }

    const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 10, argv);

    if(index_type != IndexType::INDEX_T)
    {
        std::cout << "this indexing data type is not implemented" << std::endl;
        return 1;
    }

    using F32  = float;
    using BF16 = ck::bhalf_t;
    using F16  = ck::half_t;
#if defined(__gfx942__)
    using TF32 = ck::tf32_t;
#endif

    using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
    using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
    using NDHWGK = ck::tensor_layout::convolution::NDHWGK;

    using GKYXC = ck::tensor_layout::convolution::GKYXC;
    using NHWGC = ck::tensor_layout::convolution::NHWGC;
    using NHWGK = ck::tensor_layout::convolution::NHWGK;

    constexpr auto I2 = ck::Number<2>{};
    constexpr auto I3 = ck::Number<3>{};

    auto profile = [&](auto num_dim_spatial_tmp,
                       auto in_layout,
                       auto wei_layout,
                       auto out_layout,
                       auto in_type,
                       auto wei_type,
                       auto out_type,
                       auto a_compute_type,
                       auto b_compute_type) {
        constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;

        using InLayout  = decltype(in_layout);
        using WeiLayout = decltype(wei_layout);
        using OutLayout = decltype(out_layout);

        using InDataType  = decltype(in_type);
        using WeiDataType = decltype(wei_type);
        using OutDataType = decltype(out_type);

        using AComputeType = decltype(a_compute_type);
        using BComputeType = decltype(b_compute_type);

        bool pass =
            ck::profiler::profile_grouped_conv_fwd_impl<NDimSpatial,
                                                        InLayout,
                                                        WeiLayout,
                                                        OutLayout,
                                                        InDataType,
                                                        WeiDataType,
                                                        OutDataType,
                                                        AComputeType,
                                                        BComputeType,
                                                        ck::index_t,
                                                        ck::tensor_operation::element_wise::Clamp>(
                do_verification, init_method, do_log, time_kernel, params);

        return pass ? 0 : 1;
    };

    if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
    {
        if(data_type == ConvDataType::F32_F32_F32)
        {
            return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
        }
        else if(data_type == ConvDataType::F16_F16_F16)
        {
            return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
        }
        else if(data_type == ConvDataType::BF16_BF16_BF16)
        {
            return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
        }
        else if(data_type == ConvDataType::F32_F32_F32_TF32)
        {
#if defined(__gfx942__)
            return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}, TF32{}, TF32{});
#endif
        }
    }
    else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
    {
        if(data_type == ConvDataType::F32_F32_F32)
        {
            return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
        }
        else if(data_type == ConvDataType::F16_F16_F16)
        {
            return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
        }
        else if(data_type == ConvDataType::BF16_BF16_BF16)
        {
            return profile(
                I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
        }
        else if(data_type == ConvDataType::F32_F32_F32_TF32)
        {
#if defined(__gfx942__)
            return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, TF32{}, TF32{});
#endif
        }
    }

    std::cout << "this data_type & layout is not implemented" << std::endl;

    return 1;
}

REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, grouped_conv_fwd_clamp);