// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

template <ck::index_t NDimSpatial,
          typename InDataType,
          typename WeiDataType,
          typename OutDataType,
          typename InElementOp,
          typename WeiElementOp,
          typename OutElementOp,
          typename DeviceConvNDFwdInstance>
bool run_grouped_conv_fwd(bool do_verification,
                          bool time_kernel,
                          const ck::utils::conv::ConvParam& conv_param,
                          const HostTensorDescriptor& in_g_n_c_wis_desc,
                          const HostTensorDescriptor& wei_g_k_c_xs_desc,
                          const HostTensorDescriptor& bias_g_k_desc,
                          const HostTensorDescriptor& out_g_n_k_wos_desc,
                          const InElementOp& in_element_op,
                          const WeiElementOp& wei_element_op,
                          const OutElementOp& out_element_op)
{
    Tensor<InDataType> in(in_g_n_c_wis_desc);
    Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
    Tensor<BiasDataType> bias(bias_g_k_desc);
    Tensor<OutDataType> out_host(out_g_n_k_wos_desc);
    Tensor<OutDataType> out_device(out_g_n_k_wos_desc);

    std::cout << "in: " << in.mDesc << std::endl;
    std::cout << "wei: " << wei.mDesc << std::endl;
    std::cout << "bias: " << bias.mDesc << std::endl;
    std::cout << "out: " << out_host.mDesc << std::endl;

    in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
    wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
    bias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5});

    DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
    DeviceMem bias_device_buf(sizeof(BiasDataType) * bias.mDesc.GetElementSpaceSize());
    DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());

    in_device_buf.ToDevice(in.mData.data());
    wei_device_buf.ToDevice(wei.mData.data());
    bias_device_buf.ToDevice(bias.mData.data());

    std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
    std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
    std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_strides{};
    std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
    std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_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 copy = [](auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };

    copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
    copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
    copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
    copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
    copy(bias_g_k_desc.GetLengths(), d0_g_n_k_wos_lengths);
    copy(bias_g_k_desc.GetStrides(), d0_g_n_k_wos_strides);
    copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
    copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
    copy(conv_param.conv_filter_strides_, conv_filter_strides);
    copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
    copy(conv_param.input_left_pads_, input_left_pads);
    copy(conv_param.input_right_pads_, input_right_pads);

    // do Conv
    auto conv     = DeviceConvNDFwdInstance{};
    auto invoker  = conv.MakeInvoker();
    auto argument = conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
                                      wei_device_buf.GetDeviceBuffer(),
                                      {bias_device_buf.GetDeviceBuffer()},
                                      out_device_buf.GetDeviceBuffer(),
                                      a_g_n_c_wis_lengths,
                                      a_g_n_c_wis_strides,
                                      b_g_k_c_xs_lengths,
                                      b_g_k_c_xs_strides,
                                      {d0_g_n_k_wos_lengths},
                                      {d0_g_n_k_wos_strides},
                                      e_g_n_k_wos_lengths,
                                      e_g_n_k_wos_strides,
                                      conv_filter_strides,
                                      conv_filter_dilations,
                                      input_left_pads,
                                      input_right_pads,
                                      in_element_op,
                                      wei_element_op,
                                      out_element_op);

    if(!conv.IsSupportedArgument(argument))
    {
        throw std::runtime_error(
            "wrong! device_conv with the specified compilation parameters does "
            "not support this Conv problem");
    }

    float avg_time = invoker.Run(argument, 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: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
              << conv.GetTypeString() << std::endl;

    bool pass = true;

    if(do_verification)
    {
        Tensor<CShuffleDataType> c_host(out_g_n_k_wos_desc);

        auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
                                                                     InDataType,
                                                                     WeiDataType,
                                                                     CShuffleDataType,
                                                                     InElementOp,
                                                                     WeiElementOp,
                                                                     PassThrough>();

        auto ref_invoker  = ref_conv.MakeInvoker();
        auto ref_argument = ref_conv.MakeArgument(in,
                                                  wei,
                                                  c_host,
                                                  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,
                                                  PassThrough{});

        ref_invoker.Run(ref_argument);

        // TODO: implement elementwise operation for host
        out_host.ForEach(
            [&](auto&, auto idx) { out_element_op(out_host(idx), c_host(idx), bias(idx)); });

        out_device_buf.FromDevice(out_device.mData.data());

        pass &=
            ck::utils::check_err(out_device, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
    }

    return (pass ? 0 : 1);
}

int run_conv2d_fwd_bias_relu_perlayer_quantization_example()
{
    bool do_verification           = true;
    bool time_kernel               = true;
    const ck::index_t ndim_spatial = 2;

    ck::utils::conv::ConvParam conv_param{
        ndim_spatial, // n_dim
        1,            // group
        4,            // batch
        64,           // output channels
        192,          // input chanels
        {3, 3},       // weight HW
        {71, 71},     // x HW
        {2, 2},       // strides
        {1, 1},       // dilations
        {1, 1},       // left_pads
        {1, 1}        // right_pads
    };

    const auto in_element_op  = InElementOp{};
    const auto wei_element_op = WeiElementOp{};
    const auto out_element_op = OutElementOp{0.5f, ActivationOp{}};

    using InLayout   = ck::tensor_layout::convolution::GNHWC;
    using WeiLayout  = ck::tensor_layout::convolution::GKYXC;
    using BiasLayout = ck::tensor_layout::convolution::G_K;
    using OutLayout  = ck::tensor_layout::convolution::GNHWK;

    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);

    // TODO - make_bias_host_tensor_descriptor_g_n_k_wos_packed()
    const auto bias_g_k_desc = HostTensorDescriptor({conv_param.G_,
                                                     conv_param.N_,
                                                     conv_param.K_,
                                                     conv_param.output_spatial_lengths_[0],
                                                     conv_param.output_spatial_lengths_[1]},
                                                    {
                                                        conv_param.K_, // g
                                                        0,             // n
                                                        1,             // k
                                                        0,             // ho
                                                        0              // wo
                                                    });

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

    return run_grouped_conv_fwd<
        ndim_spatial,
        InDataType,
        WeiDataType,
        OutDataType,
        InElementOp,
        WeiElementOp,
        OutElementOp,
        DeviceGroupedConvNDFwdInstance<ndim_spatial, InLayout, WeiLayout, BiasLayout, OutLayout>>(
        do_verification,
        time_kernel,
        conv_param,
        in_g_n_c_wis_desc,
        wei_g_k_c_xs_desc,
        bias_g_k_desc,
        out_g_n_k_wos_desc,
        in_element_op,
        wei_element_op,
        out_element_op);
}
