/*
 * SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: Apache-2.0
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
#pragma once

#include "tensorrt_llm/common/cudaBf16Wrapper.h"
#include "tensorrt_llm/common/cudaFp8Utils.h"
#include "tensorrt_llm/common/logger.h"
#include "tensorrt_llm/common/tllmException.h"
#include <cinttypes>
#include <cublasLt.h>
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include <fstream>
#include <iostream>
#include <memory>
#include <string>
#include <vector>

namespace tensorrt_llm::common
{

// workspace for cublas gemm : 32MB
#define CUBLAS_WORKSPACE_SIZE 33554432

typedef struct __align__(4)
{
    half x, y, z, w;
}

half4;

/* **************************** type definition ***************************** */

enum CublasDataType
{
    FLOAT_DATATYPE = 0,
    HALF_DATATYPE = 1,
    BFLOAT16_DATATYPE = 2,
    INT8_DATATYPE = 3,
    FP8_DATATYPE = 4
};

enum TRTLLMCudaDataType
{
    FP32 = 0,
    FP16 = 1,
    BF16 = 2,
    INT8 = 3,
    FP8 = 4
};

enum class OperationType
{
    FP32,
    FP16,
    BF16,
    INT8,
    FP8
};

/* **************************** debug tools ********************************* */
static const char* _cudaGetErrorEnum(cudaError_t error)
{
    return cudaGetErrorString(error);
}

static const char* _cudaGetErrorEnum(cublasStatus_t error)
{
    switch (error)
    {
    case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";

    case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";

    case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";

    case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";

    case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";

    case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";

    case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";

    case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";

    case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";

    case CUBLAS_STATUS_LICENSE_ERROR: return "CUBLAS_STATUS_LICENSE_ERROR";
    }
    return "<unknown>";
}

template <typename T>
void check(T result, char const* const func, const char* const file, int const line)
{
    if (result)
    {
        throw TllmException(
            file, line, fmtstr("[TensorRT-LLM][ERROR] CUDA runtime error in %s: %s", func, _cudaGetErrorEnum(result)));
    }
}

#define check_cuda_error(val) check((val), #val, __FILE__, __LINE__)
#define check_cuda_error_2(val, file, line) check((val), #val, file, line)

inline bool isCudaLaunchBlocking()
{
    static bool firstCall = true;
    static bool result = false;

    if (firstCall)
    {
        const char* env = std::getenv("CUDA_LAUNCH_BLOCKING");
        result = env != nullptr && std::string(env) == "1";
        firstCall = false;
    }

    return result;
}

inline void syncAndCheck(const char* const file, int const line)
{
#ifndef NDEBUG
    const bool checkError = true;
#else
    const bool checkError = isCudaLaunchBlocking();
#endif

    if (checkError)
    {
        cudaError_t result = cudaDeviceSynchronize();
        check(result, "cudaDeviceSynchronize", file, line);
    }
}

#define sync_check_cuda_error() tensorrt_llm::common::syncAndCheck(__FILE__, __LINE__)

#define PRINT_FUNC_NAME_()                                                                                             \
    do                                                                                                                 \
    {                                                                                                                  \
        std::cout << "[TensorRT-LLM][CALL] " << __FUNCTION__ << " " << std::endl;                                      \
    } while (0)

// clang-format off
template<typename T> struct packed_type;
template <>          struct packed_type<float>         { using type = float; }; // we don't need to pack float by default
template <>          struct packed_type<half>          { using type = half2; };

#ifdef ENABLE_BF16
template<>
struct packed_type<__nv_bfloat16> {
    using type = __nv_bfloat162;
};
#endif

#ifdef ENABLE_FP8
template<>
struct packed_type<__nv_fp8_e4m3> {
    using type = __nv_fp8x2_e4m3;
};
#endif

template<typename T> struct num_elems;
template <>          struct num_elems<float>           { static constexpr int value = 1; };
template <>          struct num_elems<float2>          { static constexpr int value = 2; };
template <>          struct num_elems<float4>          { static constexpr int value = 4; };
template <>          struct num_elems<half>            { static constexpr int value = 1; };
template <>          struct num_elems<half2>           { static constexpr int value = 2; };
#ifdef ENABLE_BF16
template <>          struct num_elems<__nv_bfloat16>   { static constexpr int value = 1; };
template <>          struct num_elems<__nv_bfloat162>  { static constexpr int value = 2; };
#endif
#ifdef ENABLE_FP8
template <>          struct num_elems<__nv_fp8_e4m3>   { static constexpr int value = 1; };
template <>          struct num_elems<__nv_fp8x2_e4m3>  { static constexpr int value = 2; };
#endif

template<typename T, int num> struct packed_as;
template<typename T>          struct packed_as<T, 1>              { using type = T; };
template<>                    struct packed_as<half,  2>          { using type = half2; };
template<>                    struct packed_as<float,  2>         { using type = float2; };
template<>                    struct packed_as<int8_t, 2>         { using type = int16_t; };
template<>                    struct packed_as<int32_t, 2>        { using type = int2; };
template<>                    struct packed_as<half2, 1>          { using type = half; };
template<>                    struct packed_as<float2, 1>         { using type = float; };
#ifdef ENABLE_BF16
template<> struct packed_as<__nv_bfloat16,  2> { using type = __nv_bfloat162; };
template<> struct packed_as<__nv_bfloat162, 1> { using type = __nv_bfloat16;  };
#endif
#ifdef ENABLE_FP8
template<> struct packed_as<__nv_fp8_e4m3,  2> { using type = __nv_fp8x2_e4m3; };
template<> struct packed_as<__nv_fp8x2_e4m3, 1> { using type = __nv_fp8_e4m3;  };
template<> struct packed_as<__nv_fp8_e5m2,  2> { using type = __nv_fp8x2_e5m2; };
template<> struct packed_as<__nv_fp8x2_e5m2, 1> { using type = __nv_fp8_e5m2;  };
#endif

inline __device__ float2 operator*(float2 a, float2 b) { return make_float2(a.x * b.x, a.y * b.y); }
inline __device__ float2 operator+(float2 a, float2 b) { return make_float2(a.x + b.x, a.y + b.y); }
inline __device__ float2 operator-(float2 a, float2 b) { return make_float2(a.x - b.x, a.y - b.y); }

inline __device__ float2 operator*(float2 a, float  b) { return make_float2(a.x * b, a.y * b); }
inline __device__ float2 operator+(float2 a, float  b) { return make_float2(a.x + b, a.y + b); }
inline __device__ float2 operator-(float2 a, float  b) { return make_float2(a.x - b, a.y - b); }

// clang-format on

template <typename T>
struct CudaDataType
{
};

template <>
struct CudaDataType<float>
{
    static constexpr cudaDataType_t value = cudaDataType::CUDA_R_32F;
};

template <>
struct CudaDataType<half>
{
    static constexpr cudaDataType_t value = cudaDataType::CUDA_R_16F;
};

#ifdef ENABLE_BF16
template <>
struct CudaDataType<__nv_bfloat16>
{
    static constexpr cudaDataType_t value = cudaDataType::CUDA_R_16BF;
};
#endif

inline int getSMVersion()
{
    int device{-1};
    check_cuda_error(cudaGetDevice(&device));
    int sm_major = 0;
    int sm_minor = 0;
    check_cuda_error(cudaDeviceGetAttribute(&sm_major, cudaDevAttrComputeCapabilityMajor, device));
    check_cuda_error(cudaDeviceGetAttribute(&sm_minor, cudaDevAttrComputeCapabilityMinor, device));
    return sm_major * 10 + sm_minor;
}

inline int getDevice()
{
    int current_dev_id = 0;
    check_cuda_error(cudaGetDevice(&current_dev_id));
    return current_dev_id;
}

inline int getDeviceCount()
{
    int count = 0;
    check_cuda_error(cudaGetDeviceCount(&count));
    return count;
}

/// Get the memory info
/// \return The free and total amount of memory in bytes
inline std::tuple<size_t, size_t> getDeviceMemoryInfo()
{
    size_t free, total;
    check_cuda_error(cudaMemGetInfo(&free, &total));
    return {free, total};
}

inline int getMultiProcessorCount()
{
    int device_id;
    int multi_processor_count;
    check_cuda_error(cudaGetDevice(&device_id));
    check_cuda_error(cudaDeviceGetAttribute(&multi_processor_count, cudaDevAttrMultiProcessorCount, device_id));
    return multi_processor_count;
}

inline int divUp(int a, int n)
{
    return (a + n - 1) / n;
}

template <typename T, typename U, typename = std::enable_if_t<std::is_integral<T>::value>,
    typename = std::enable_if_t<std::is_integral<U>::value>>
auto constexpr ceilDiv(T numerator, U denominator)
{
    return (numerator + denominator - 1) / denominator;
}

template <typename T>
void printAbsMean(const T* buf, uint64_t size, cudaStream_t stream, std::string name = "")
{
    if (buf == nullptr)
    {
        TLLM_LOG_WARNING("%s is an nullptr, skip!", name.c_str());
        return;
    }
    cudaDeviceSynchronize();
    check_cuda_error(cudaGetLastError());
    T* h_tmp = new T[size];
    cudaMemcpyAsync(h_tmp, buf, sizeof(T) * size, cudaMemcpyDeviceToHost, stream);
    cudaDeviceSynchronize();
    check_cuda_error(cudaGetLastError());
    double sum = 0.0f;
    uint64_t zero_count = 0;
    float max_val = -1e10;
    bool find_inf = false;
    for (uint64_t i = 0; i < size; i++)
    {
        if (std::isinf((float) (h_tmp[i])))
        {
            find_inf = true;
            continue;
        }
        sum += abs((double) h_tmp[i]);
        if ((float) h_tmp[i] == 0.0f)
        {
            zero_count++;
        }
        max_val = max_val > abs(float(h_tmp[i])) ? max_val : abs(float(h_tmp[i]));
    }
    TLLM_LOG_INFO("%20s size: %u, abs mean: %f, abs sum: %f, abs max: %f, find inf: %s", name.c_str(), size, sum / size,
        sum, max_val, find_inf ? "true" : "false");
    delete[] h_tmp;
    cudaDeviceSynchronize();
    check_cuda_error(cudaGetLastError());
}

template <typename T>
void printToStream(const T* result, const int size, FILE* strm)
{
    const bool split_rows = (strm == stdout);
    if (result == nullptr)
    {
        TLLM_LOG_WARNING("It is an nullptr, skip! \n");
        return;
    }
    T* tmp = reinterpret_cast<T*>(malloc(sizeof(T) * size));
    check_cuda_error(cudaMemcpy(tmp, result, sizeof(T) * size, cudaMemcpyDeviceToHost));
    for (int i = 0; i < size; ++i)
    {
        fprintf(strm, "%f, ", static_cast<float>(tmp[i]));
        if (split_rows && ((i + 1) % 10) == 0)
            fprintf(strm, "\n");
    }
    if (!split_rows || (size % 10) != 0)
    {
        fprintf(strm, "\n");
    }
    free(tmp);
}

template <typename T>
void printToScreen(const T* result, const int size)
{
    printToStream(result, size, stdout);
}

template <typename T>
void print2dToStream(const T* result, const int r, const int c, const int stride, FILE* strm)
{
    if (result == nullptr)
    {
        TLLM_LOG_WARNING("It is an nullptr, skip! \n");
        return;
    }
    for (int ri = 0; ri < r; ++ri)
    {
        const T* ptr = result + ri * stride;
        printToStream(ptr, c, strm);
    }
    fprintf(strm, "\n");
}

template <typename T>
void print2dToScreen(const T* result, const int r, const int c, const int stride)
{
    print2dToStream(result, r, c, stride, stdout);
}

inline void print_float_(float x)
{
    printf("%7.3f ", x);
}

inline void print_element_(float x)
{
    print_float_(x);
}

inline void print_element_(half x)
{
    print_float_((float) x);
}
#ifdef ENABLE_BF16
inline void print_element_(__nv_bfloat16 x)
{
    print_float_((float) x);
}
#endif
inline void print_element_(uint32_t ul)
{
    printf("%7" PRIu32, ul);
}

inline void print_element_(uint64_t ull)
{
    printf("%7" PRIu64, ull);
}

inline void print_element_(int32_t il)
{
    printf("%7" PRId32, il);
}

inline void print_element_(int64_t ill)
{
    printf("%7" PRId64, ill);
}

template <typename T>
inline void printMatrix(const T* ptr, int m, int k, int stride, bool is_device_ptr)
{
    T* tmp;
    if (is_device_ptr)
    {
        // k < stride ; stride = col-dimension.
        tmp = reinterpret_cast<T*>(malloc(m * stride * sizeof(T)));
        check_cuda_error(cudaMemcpy(tmp, ptr, sizeof(T) * m * stride, cudaMemcpyDeviceToHost));
        cudaDeviceSynchronize();
    }
    else
    {
        tmp = const_cast<T*>(ptr);
    }

    for (int ii = -1; ii < m; ++ii)
    {
        if (ii >= 0)
        {
            printf("%07d ", ii);
        }
        else
        {
            printf("   ");
        }

        for (int jj = 0; jj < k; jj += 1)
        {
            if (ii >= 0)
            {
                print_element_(tmp[ii * stride + jj]);
            }
            else
            {
                printf("%7d ", jj);
            }
        }
        printf("\n");
    }
    if (is_device_ptr)
    {
        free(tmp);
    }
}

template void printMatrix(const float* ptr, int m, int k, int stride, bool is_device_ptr);
template void printMatrix(const half* ptr, int m, int k, int stride, bool is_device_ptr);
#ifdef ENABLE_BF16
template void printMatrix(const __nv_bfloat16* ptr, int m, int k, int stride, bool is_device_ptr);
#endif
template void printMatrix(const uint32_t* ptr, int m, int k, int stride, bool is_device_ptr);
template void printMatrix(const uint64_t* ptr, int m, int k, int stride, bool is_device_ptr);
template void printMatrix(const int* ptr, int m, int k, int stride, bool is_device_ptr);

} // namespace tensorrt_llm::common

/*
 * Macros compliant with TensorRT coding conventions
 */
#define TLLM_CUDA_CHECK(stat)                                                                                          \
    do                                                                                                                 \
    {                                                                                                                  \
        tensorrt_llm::common::check((stat), #stat, __FILE__, __LINE__);                                                \
    } while (0)