// Copyright (c) OpenMMLab. All rights reserved.

#pragma once

// #include "src/turbomind/kernels/gemm_s_f16/gemm_s4_f16.h"
#include "src/turbomind/models/llama/LlamaDenseWeight.h"
#include "src/turbomind/models/llama/llama_kernels.h"
#include "src/turbomind/utils/cublasMMWrapper.h"
#include "src/turbomind/utils/cuda_utils.h"
#include "src/turbomind/utils/logger.h"
#include <type_traits>

namespace turbomind {

template<typename T>
class LlamaLinear {
public:
    enum Type
    {
        kGemm,
        kFusedSiluFfn
    };

    LlamaLinear(cublasMMWrapper* cublas_wrapper, cudaStream_t stream): cublas_wrapper_(cublas_wrapper), stream_(stream)
    {
    }

    void
    forward(T* output_data, const T* input_data, int batch_size, const LlamaDenseWeight<T>& weight, Type type = kGemm)
    {
        switch (weight.type) {
            case WeightType::kFP16:
            case WeightType::kFP32:
            case WeightType::kBF16:
                forwardFp(output_data, input_data, batch_size, weight, type);
                break;
            case WeightType::kINT4:
                forwardInt4(output_data, input_data, batch_size, weight, type);
                break;
            default:
                FT_CHECK(0);
        }
    }

private:
    void forwardFp(T* output_data, const T* input_data, int batch_size, const LlamaDenseWeight<T>& weight, Type type)
    {
        FT_CHECK(type == kGemm);
        cublas_wrapper_->Gemm(CUBLAS_OP_N,
                              CUBLAS_OP_N,
                              weight.output_dims,
                              batch_size,
                              weight.input_dims,
                              (const T*)weight.kernel,
                              weight.output_dims,
                              input_data,
                              weight.input_dims,
                              output_data,
                              weight.output_dims);
        sync_check_cuda_error();
    }

    void forwardInt4(T* output_data, const T* input_data, int batch_size, const LlamaDenseWeight<T>& weight, Type type)
    {
        // if constexpr (std::is_same_v<T, half>) {
        //     gemm_s4_f16_.Run(output_data,
        //                      (const uint*)weight.kernel,
        //                      input_data,
        //                      (const half2*)weight.scales_and_zeros,
        //                      weight.output_dims,
        //                      batch_size,
        //                      weight.input_dims,
        //                      weight.group_size,
        //                      type == kFusedSiluFfn ? GemmS4F16::kFusedSiluFfn : GemmS4F16::kGemm,
        //                      -1,
        //                      stream_);
        //     sync_check_cuda_error();
        // }
        // else {
            FT_CHECK_WITH_INFO(0, "Not implemented");
        // }
    }

private:
    cublasMMWrapper* cublas_wrapper_;
    cudaStream_t     stream_{};
    // GemmS4F16        gemm_s4_f16_;
};

}  // namespace turbomind