// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.

#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include <ctime>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_sparse_embeddings_forward_layernorm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_sparse_embedding3_forward_layernorm.hpp"

// clang-format off
using EmbType       = ck::half_t;
using IndexType     = int64_t;
using GammaDataType = ck::half_t;
using BetaDataType  = ck::half_t;
using AccDataType   = float;
using OutType       = ck::half_t;
using EmbElementwiseOperation = ck::tensor_operation::element_wise::AddAdd;

using DeviceInstance_fp16_e256   = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  256,   1, 1, 3>;
using DeviceInstance_fp16_e512   = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  512,   1, 2, 3>;
using DeviceInstance_fp16_e768   = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  768,   1, 1, 3>;
using DeviceInstance_fp16_e1024  = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  1024,  1, 2, 3>;
using DeviceInstance_fp16_e1536  = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  1536,  1, 2, 3>;
using DeviceInstance_fp16_e2048  = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  2048,  1, 2, 3>;
using DeviceInstance_fp16_e4096  = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  4096,  1, 8, 3>;
using DeviceInstance_fp16_e8192  = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256,  1,  256, 1,  8192,  1, 8, 3>;

template<typename emb_type, ck::index_t dim> struct emb_kernel{};

template<> struct emb_kernel<ck::half_t, 256>  { using kernel_type = DeviceInstance_fp16_e256; };
template<> struct emb_kernel<ck::half_t, 512>  { using kernel_type = DeviceInstance_fp16_e512; };
template<> struct emb_kernel<ck::half_t, 768>  { using kernel_type = DeviceInstance_fp16_e768; };
template<> struct emb_kernel<ck::half_t, 1024> { using kernel_type = DeviceInstance_fp16_e1024; };
template<> struct emb_kernel<ck::half_t, 1536> { using kernel_type = DeviceInstance_fp16_e1536; };
template<> struct emb_kernel<ck::half_t, 2048> { using kernel_type = DeviceInstance_fp16_e2048; };
template<> struct emb_kernel<ck::half_t, 4096> { using kernel_type = DeviceInstance_fp16_e4096; };
template<> struct emb_kernel<ck::half_t, 8192> { using kernel_type = DeviceInstance_fp16_e8192; };

// clang-format on

int main(int argc, char* argv[])
{
    bool time_kernel = true;

    ck::index_t num_rows          = 65536;
    constexpr auto dims           = ck::Sequence<256, 512, 768, 1024, 1536, 2048, 4096, 8192>{};
    ck::index_t index_length      = 2048;
    ck::index_t dim_mask          = 0xffff;
    constexpr AccDataType epsilon = 1e-4;

    auto f_host_tensor_desc_1d = [](std::size_t len_) { return HostTensorDescriptor({len_}); };

    auto f_host_tensor_desc_2d = [](std::size_t rows_, std::size_t cols_) {
        return HostTensorDescriptor({rows_, cols_});
    };

    using ReferenceInstance =
        ck::tensor_operation::host::ReferenceSparseEmbedding3ForwardLayernorm<EmbType,
                                                                              IndexType,
                                                                              GammaDataType,
                                                                              BetaDataType,
                                                                              AccDataType,
                                                                              OutType>;
    if(argc == 1)
    {
        // Use default value
    }
    else if(argc == 5)
    {
        time_kernel  = std::stoi(argv[1]);
        num_rows     = std::stoi(argv[2]);
        dim_mask     = strtol(argv[3], nullptr, 0);
        index_length = std::stoi(argv[4]);
    }
    else
    {
        std::cout << "Usage of " << argv[0] << std::endl;
        std::cout << "arg1: time kernel (0=no, 1=yes)" << std::endl;
        std::cout << "arg2-4: num_rows dim_mask index_length" << std::endl;
        return 1;
    }

    ck::static_for<0, dims.Size(), 1>{}([&](auto I) {
        if(dim_mask & (1 << I.value))
        {
            std::srand(std::time(nullptr));
            constexpr auto current_dim = dims.At(I);
            Tensor<EmbType> emb_a(f_host_tensor_desc_2d(num_rows, current_dim));
            Tensor<EmbType> emb_b(f_host_tensor_desc_2d(num_rows, current_dim));
            Tensor<EmbType> emb_c(f_host_tensor_desc_2d(num_rows, current_dim));

            Tensor<IndexType> index_a(f_host_tensor_desc_1d(index_length));
            Tensor<IndexType> index_b(f_host_tensor_desc_1d(index_length));
            Tensor<IndexType> index_c(f_host_tensor_desc_1d(index_length));

            Tensor<GammaDataType> gamma(f_host_tensor_desc_1d(current_dim));
            Tensor<BetaDataType> beta(f_host_tensor_desc_1d(current_dim));

            Tensor<OutType> out(f_host_tensor_desc_2d(index_length, current_dim));

            emb_a.GenerateTensorValue(GeneratorTensor_3<EmbType>{0.0, 1.0});
            emb_b.GenerateTensorValue(GeneratorTensor_3<EmbType>{0.0, 1.0});
            emb_c.GenerateTensorValue(GeneratorTensor_3<EmbType>{0.0, 1.0});

            index_a.GenerateTensorValue(GeneratorTensor_2<IndexType>{0, num_rows});
            index_b.GenerateTensorValue(GeneratorTensor_2<IndexType>{0, num_rows});
            index_c.GenerateTensorValue(GeneratorTensor_2<IndexType>{0, num_rows});

            gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
            beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});

            DeviceMem emb_a_dev(sizeof(EmbType) * emb_a.mDesc.GetElementSpaceSize());
            DeviceMem emb_b_dev(sizeof(EmbType) * emb_b.mDesc.GetElementSpaceSize());
            DeviceMem emb_c_dev(sizeof(EmbType) * emb_c.mDesc.GetElementSpaceSize());

            DeviceMem index_a_dev(sizeof(IndexType) * index_a.mDesc.GetElementSpaceSize());
            DeviceMem index_b_dev(sizeof(IndexType) * index_b.mDesc.GetElementSpaceSize());
            DeviceMem index_c_dev(sizeof(IndexType) * index_c.mDesc.GetElementSpaceSize());

            DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
            DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());

            DeviceMem out_dev(sizeof(OutType) * out.mDesc.GetElementSpaceSize());

            emb_a_dev.ToDevice(emb_a.mData.data());
            emb_b_dev.ToDevice(emb_b.mData.data());
            emb_c_dev.ToDevice(emb_c.mData.data());

            index_a_dev.ToDevice(index_a.mData.data());
            index_b_dev.ToDevice(index_b.mData.data());
            index_c_dev.ToDevice(index_c.mData.data());

            gamma_dev.ToDevice(gamma.mData.data());
            beta_dev.ToDevice(beta.mData.data());

            auto device_instance = typename emb_kernel<EmbType, current_dim>::kernel_type{};
            auto argument_ptr    = device_instance.MakeArgumentPointer(
                out_dev.GetDeviceBuffer(),
                {ck::type_convert<EmbType*>(emb_a_dev.GetDeviceBuffer()),
                    ck::type_convert<EmbType*>(emb_b_dev.GetDeviceBuffer()),
                    ck::type_convert<EmbType*>(emb_c_dev.GetDeviceBuffer())},
                {ck::type_convert<IndexType*>(index_a_dev.GetDeviceBuffer()),
                    ck::type_convert<IndexType*>(index_b_dev.GetDeviceBuffer()),
                    ck::type_convert<IndexType*>(index_c_dev.GetDeviceBuffer())},
                gamma_dev.GetDeviceBuffer(),
                beta_dev.GetDeviceBuffer(),
                current_dim,
                index_length,
                epsilon,
                EmbElementwiseOperation{});
            std::cout << "Dim:" << current_dim << ", kernel:" << device_instance.GetTypeString()
                      << std::endl
                      << std::flush;

            if(!device_instance.IsSupportedArgument(argument_ptr.get()))
            {
                std::cerr << device_instance.GetTypeString() << " does not support this problem"
                          << std::endl;
                return;
            }

            auto invoker_ptr = device_instance.MakeInvokerPointer();
            float time_ms =
                invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});

            bool pass = true;
            {
                Tensor<OutType> out_from_dev(f_host_tensor_desc_2d(index_length, current_dim));
                ReferenceInstance ref;
                auto ref_argument = ref.MakeArgument(out,
                                                     emb_a,
                                                     emb_b,
                                                     emb_c,
                                                     index_a,
                                                     index_b,
                                                     index_c,
                                                     gamma,
                                                     beta,
                                                     num_rows,
                                                     current_dim,
                                                     index_length,
                                                     epsilon);
                auto ref_invoker  = ref.MakeInvoker();
                ref_invoker.Run(ref_argument);

                out_dev.FromDevice(out_from_dev.mData.data());
                pass &=
                    ck::utils::check_err(out_from_dev, out, "Error: Incorrect results", 1e-3, 1e-3);
            }

            double total_read = current_dim * index_length * 3 * sizeof(EmbType) +
                                current_dim * sizeof(GammaDataType) +
                                current_dim * sizeof(BetaDataType);
            double total_write = current_dim * index_length * sizeof(OutType);
            double gbps        = (total_read + total_write) / time_ms / 1e6;

            std::cout << ", total bytes:" << (total_read + total_write) << ", time:" << time_ms
                      << ", gbps:" << gbps << ", valid:" << (pass ? "y" : "n") << std::endl
                      << std::flush;
        }
    });

    return 0;
}
