#pragma once

#include "ggml.h"
#include "ggml-impl.h"
#include "ggml-cuda.h"

#include <cstdint>
#include <memory>

#if defined(GGML_USE_HIP)
#define GGML_COMMON_DECL_HIP
#define GGML_COMMON_IMPL_HIP
#else
#define GGML_COMMON_DECL_CUDA
#define GGML_COMMON_IMPL_CUDA
#if defined(GGML_USE_MUSA)
#define GGML_COMMON_DECL_MUSA
#define GGML_COMMON_IMPL_MUSA
#endif
#endif
#include "ggml-common.h"

#include <array>
#include <cassert>
#include <cfloat>
#include <cstdio>
#include <string>
#include <vector>

#if defined(GGML_USE_HIP)
#include "vendors/hip.h"
#elif defined(GGML_USE_MUSA)
#include "vendors/musa.h"
#else
#include "vendors/cuda.h"
#endif // defined(GGML_USE_HIP)

#define STRINGIZE_IMPL(...) #__VA_ARGS__
#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)

#define WARP_SIZE 32
#define CUDART_HMAX   11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
#define CUDART_HMASK  12000 // CUDA 12.0, min. ver. for half2 -> uint mask comparisons

#define GGML_CUDA_CC_PASCAL          600
#define GGML_CUDA_CC_DP4A            610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
#define GGML_CUDA_CC_VOLTA           700
#define GGML_CUDA_CC_TURING          750
#define GGML_CUDA_CC_AMPERE          800
#define GGML_CUDA_CC_ADA_LOVELACE    890
#define GGML_CUDA_CC_OFFSET_AMD      0x1000000
#define GGML_CUDA_CC_OFFSET_MTHREADS 0x0100000
#define GGML_CUDA_CC_IS_NVIDIA(cc)   (cc < GGML_CUDA_CC_OFFSET_MTHREADS)

// AMD
// GCN/CDNA, wave size is 64
#define GGML_CUDA_CC_GCN4       (GGML_CUDA_CC_OFFSET_AMD + 0x803)  // Tonga, Fiji, Polaris, minimum for fast fp16
#define GGML_CUDA_CC_VEGA       (GGML_CUDA_CC_OFFSET_AMD + 0x900)  // Vega56/64, minimum for fp16 dual issue
#define GGML_CUDA_CC_VEGA20     (GGML_CUDA_CC_OFFSET_AMD + 0x906)  // MI50/Radeon VII, minimum for dp4a
#define GGML_CUDA_CC_CDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x908)  // MI100, minimum for MFMA, acc registers
#define GGML_CUDA_CC_CDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x910)  // MI210, minimum acc register renameing
#define GGML_CUDA_CC_CDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x942)  // MI300

// RDNA removes MFMA, dp4a, xnack, acc registers, wave size is 32
#define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
#define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
#define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
#define GGML_CUDA_CC_RDNA4      (GGML_CUDA_CC_OFFSET_AMD + 0x1200) // RX 9000

#define GGML_CUDA_CC_IS_AMD(cc)   (cc >= GGML_CUDA_CC_OFFSET_AMD)
#define GGML_CUDA_CC_IS_RDNA(cc)  (cc >= GGML_CUDA_CC_RDNA1)
#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA4)
#define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4)
#define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
#define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
#define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)

// Moore Threads
#define GGML_CUDA_CC_QY1 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000
#define GGML_CUDA_CC_QY2 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x220) // MTT S4000
#define GGML_CUDA_CC_NG  (GGML_CUDA_CC_OFFSET_MTHREADS + 0x310) // TBD

#define GGML_CUDA_CC_IS_MTHREADS(cc) (cc >= GGML_CUDA_CC_OFFSET_MTHREADS && cc < GGML_CUDA_CC_OFFSET_AMD)
#define GGML_CUDA_CC_IS_QY1(cc)      (cc >= GGML_CUDA_CC_QY1 && cc < GGML_CUDA_CC_QY2)
#define GGML_CUDA_CC_IS_QY2(cc)      (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_NG)
#define GGML_CUDA_CC_IS_NG(cc)       (cc >= GGML_CUDA_CC_NG)

#ifdef __CUDA_ARCH_LIST__
constexpr bool ggml_cuda_has_arch_impl(int) {
    return false;
}

template<class ... Archs>
constexpr bool ggml_cuda_has_arch_impl(const int arch, const int first, Archs... rest) {
    return arch == first || ggml_cuda_has_arch_impl(arch, rest...);
}

constexpr bool ggml_cuda_has_arch(const int arch) {
    return ggml_cuda_has_arch_impl(arch, __CUDA_ARCH_LIST__);
}

constexpr int ggml_cuda_highest_compiled_arch_impl(const int arch, const int cur) {
    if (cur == 0) {
        GGML_ABORT("ggml was not compiled with any CUDA arch <= %d", arch);
    }
    return cur;
}

template<class ... Archs>
constexpr int ggml_cuda_highest_compiled_arch_impl(const int arch, const int cur, const int first, Archs... rest) {
    if (first <= arch && first > cur) {
        return ggml_cuda_highest_compiled_arch_impl(arch, first, rest...);
    } else {
        return ggml_cuda_highest_compiled_arch_impl(arch, cur, rest...);
    }
}

constexpr int ggml_cuda_highest_compiled_arch(const int arch) {
    return ggml_cuda_highest_compiled_arch_impl(arch, 0, __CUDA_ARCH_LIST__);
}
#else
static int ggml_cuda_highest_compiled_arch(const int arch) {
    return arch;
}
#endif // __CUDA_ARCH_LIST__

// ---------------------------------------------------------------------------------------------------------

#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses

#define GGML_CUDA_MAX_STREAMS 8

[[noreturn]]
void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg);

#define CUDA_CHECK_GEN(err, success, error_fn)                                      \
     do {                                                                           \
        auto err_ = (err);                                                          \
        if (err_ != (success)) {                                                    \
            ggml_cuda_error(#err, __func__, __FILE__, __LINE__, error_fn(err_));    \
        }                                                                           \
    } while (0)

#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)

#if CUDART_VERSION >= 12000 || defined(GGML_USE_MUSA)
    static const char * cublas_get_error_str(const cublasStatus_t err) {
        return cublasGetStatusString(err);
    }
#else
    static const char * cublas_get_error_str(const cublasStatus_t err) {
        switch (err) {
            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";
            default: return "unknown error";
        }
    }
#endif // CUDART_VERSION >= 12000

#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)

#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
static const char * cu_get_error_str(CUresult err) {
    const char * err_str;
    cuGetErrorString(err, &err_str);
    return err_str;
}
#define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
#endif

#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
#    define CUDA_SET_SHARED_MEMORY_LIMIT(kernel, nbytes)                                                       \
        do {                                                                                                   \
            static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = { false };                         \
            const int   id                                                = ggml_cuda_get_device();            \
            if (!shared_memory_limit_raised[id]) {                                                             \
                CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes)); \
                shared_memory_limit_raised[id] = true;                                                         \
            }                                                                                                  \
        } while (0)
#else
#    define CUDA_SET_SHARED_MEMORY_LIMIT(kernel, nbytes) \
        do {                                             \
            GGML_UNUSED(nbytes);                         \
        } while (0)
#endif // !(defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)

#if CUDART_VERSION >= 11010 || defined(GGML_USE_MUSA)
#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
#else
#define GGML_CUDA_ASSUME(x)
#endif // CUDART_VERSION >= 11010

#ifdef GGML_CUDA_F16
typedef half dfloat; // dequantize float
typedef half2 dfloat2;
#else
typedef float dfloat; // dequantize float
typedef float2 dfloat2;
#endif // GGML_CUDA_F16

#if (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
#define GGML_USE_VMM
#endif // (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))

#if defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
#define FP16_AVAILABLE
#endif // defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL

#if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
#define FAST_FP16_AVAILABLE
#endif // defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610

#if (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA)
#define FP16_MMA_AVAILABLE
#endif // (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA)

#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
#define FP16_MMA_AVAILABLE
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))

#if defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
#define AMD_MFMA_AVAILABLE
#endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)

#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
#define TURING_MMA_AVAILABLE
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING

#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#define AMPERE_MMA_AVAILABLE
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE

#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#define CP_ASYNC_AVAILABLE
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE

#if !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ < 220)
#define FLASH_ATTN_AVAILABLE
#endif // !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ < 220)

static bool fp16_available(const int cc) {
    return ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_PASCAL;
}

static bool fast_fp16_available(const int cc) {
    return (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && cc != 610) || GGML_CUDA_CC_IS_AMD(cc);
}

// To be used for feature selection of external libraries, e.g. cuBLAS.
static bool fast_fp16_hardware_available(const int cc) {
    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_PASCAL && cc != 610) || GGML_CUDA_CC_IS_AMD(cc) ||
        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);
}

// Any FP16 tensor core instructions are available for ggml code.
static bool fp16_mma_available(const int cc) {
#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
    return false;
#else
    if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
        GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) ||
        GGML_CUDA_CC_IS_MTHREADS(cc)) {
        return true;
    } else if (GGML_CUDA_CC_IS_RDNA4(cc)) {
#if defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12)
        return true;
#else
        return false;
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12)
    } else {
        return false;
    }
#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
}

// To be used for feature selection of external libraries, e.g. cuBLAS.
static bool fp16_mma_hardware_available(const int cc) {
    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) ||
        GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc) ||
        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);
}

static bool bf16_mma_hardware_available(const int cc) {
    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE) || GGML_CUDA_CC_IS_CDNA(cc) || cc >= GGML_CUDA_CC_RDNA3;
}

static bool fp32_mma_hardware_available(const int cc) {
    return GGML_CUDA_CC_IS_CDNA(cc);
}

static bool amd_mfma_available(const int cc) {
#if !defined(GGML_HIP_NO_MMQ_MFMA)
    return GGML_CUDA_CC_IS_CDNA(cc);
#else
    return false;
#endif //!defined(GGML_HIP_NO_MMQ_MFMA)
}

// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
static bool turing_mma_available(const int cc) {
    return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
}

static bool ampere_mma_available(const int cc) {
    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
}

static bool cp_async_available(const int cc) {
    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
}

static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
#if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
    return 64;
#else
    return 32;
#endif // defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
}

[[noreturn]]
static __device__ void no_device_code(
    const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {

#if defined(GGML_USE_HIP)
    printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
           file_name, line, function_name, arch);
    GGML_UNUSED(arch_list);
#else
    printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
           file_name, line, function_name, arch, arch_list);
#endif // defined(GGML_USE_HIP)
    __trap();

    GGML_UNUSED(no_device_code); // suppress unused function warning

#if defined(GGML_USE_MUSA)
    __builtin_unreachable();
#endif // defined(GGML_USE_MUSA)
}

#ifdef __CUDA_ARCH__
#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
#else
#define NO_DEVICE_CODE //GGML_ABORT("NO_DEVICE_CODE not valid in host code.")
#endif // __CUDA_ARCH__

// The compiler is always able to unroll loops if they contain continue expressions.
// In such cases loop unrolling can still be achieved via recursion:
template <int n>
struct ggml_cuda_unroll {
    template <typename Func, typename... Args>
    __device__ void operator()(const Func & f, Args... args) const {
        f(n - 1, args...);
        ggml_cuda_unroll<n - 1>{}(f, args...);
    }
};

template <>
struct ggml_cuda_unroll<1> {
    template <typename Func, typename... Args>
    __device__ void operator()(const Func & f, Args... args) const {
        f(0, args...);
    }
};

template<int width = WARP_SIZE>
static __device__ __forceinline__ int warp_reduce_sum(int x) {
#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
    return __reduce_add_sync(0xffffffff, x);
#else
#pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x += __shfl_xor_sync(0xffffffff, x, offset, width);
    }
    return x;
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
}

template<int width = WARP_SIZE>
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x += __shfl_xor_sync(0xffffffff, x, offset, width);
    }
    return x;
}

template<int width = WARP_SIZE>
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        a.x += __shfl_xor_sync(0xffffffff, a.x, offset, width);
        a.y += __shfl_xor_sync(0xffffffff, a.y, offset, width);
    }
    return a;
}

template<int width = WARP_SIZE>
static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#ifdef FP16_AVAILABLE
#pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, width));
    }
    return a;

#else
    NO_DEVICE_CODE;
    return a;
#endif // FP16_AVAILABLE
}

// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
template<bool norm>
static __global__ void reduce_rows_f32(const float * x, float * dst, const int ncols) {
    const int row = blockIdx.x;
    const int col = threadIdx.x;

    float sum = 0.0f;
    for (int i = col; i < ncols; i += blockDim.x) {
        sum += x[row * ncols + i];
    }

    sum = warp_reduce_sum(sum);

    if (col != 0) {
        return;
    }

    dst[row] = norm ? sum / ncols : sum;
}

template<int width = WARP_SIZE>
static __device__ __forceinline__ int warp_reduce_all(int x) {
#ifdef GGML_USE_HIP
#pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x = x && __shfl_xor_sync(0xffffffff, x, offset, width);
    }
    return x;
#else
    static_assert(width == WARP_SIZE, "width != WARP_SIZE not implemented");
    return __all_sync(0xffffffff, x);
#endif // GGML_USE_HIP
}

template<int width = WARP_SIZE>
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, width));
    }
    return x;
}

static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
#ifdef FP16_AVAILABLE

#if !defined(GGML_USE_HIP) && CUDART_VERSION < CUDART_HMAX
    return __float2half(fmaxf(__half2float(a), __half2float(b)));
#else
    return __hmax(a, b);
#endif // !defined(GGML_USE_HIP) && CUDART_VERSION < CUDART_HMAX

#else
   NO_DEVICE_CODE;
   GGML_UNUSED(b);
   return a;
#endif // FP16_AVAILABLE
}

static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
#if defined(GGML_USE_HIP) && HIP_VERSION >= 50700000
    return half2(__hmax(a.x, b.x), __hmax(a.y, b.y));
#elif !defined(GGML_USE_HIP) && CUDART_VERSION >= CUDART_HMAX
    return __hmax2(a, b);
#elif !defined(GGML_USE_HIP)
    half2 ret;
    reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a),  __low2float(b)));
    reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b)));
    return ret;
#else
    GGML_UNUSED(a);
    GGML_UNUSED(b);
    NO_DEVICE_CODE;
#endif
}

template<int width = WARP_SIZE>
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
#pragma unroll
   for (int offset = width/2; offset > 0; offset >>= 1) {
       x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, width));
   }
   return x;
#else
   GGML_UNUSED(x);
   NO_DEVICE_CODE;
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
}

#if CUDART_VERSION < CUDART_HMASK
static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
    const uint32_t mask_low  = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
    const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
    return mask_low | mask_high;
}
#endif // CUDART_VERSION < CUDART_HMASK

static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
#if defined(GGML_USE_HIP)
#if defined(CDNA) || defined(RDNA2) || defined(__gfx906__)
    c = __builtin_amdgcn_sdot4(a, b, c, false);
#elif defined(RDNA3) || defined(RDNA4)
    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
#elif defined(RDNA1) || defined(__gfx900__)
    int tmp1;
    int tmp2;
    asm("\n \
        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
        v_add3_u32 %0, %1, %2, %0 \n \
        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
        v_add3_u32 %0, %1, %2, %0 \n \
        "
        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
        : "v"(a), "v"(b)
    );
#else
    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
#endif
    return c;

#else // defined(GGML_USE_HIP)

#if __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
    return __dp4a(a, b, c);
#else // __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
    const int8_t * a8 = (const int8_t *) &a;
    const int8_t * b8 = (const int8_t *) &b;
    return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)

#endif // defined(GGML_USE_HIP)
}

static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
#if CUDART_VERSION >= 12080
    const nv_bfloat16 e = __nv_cvt_e8m0_to_bf16raw(x);
    return (float) e;
#else
    uint32_t bits;
    if (x == 0) {
        bits = 0x00400000;
    } else {
        bits = (uint32_t) x << 23;
    }

    float result;
    memcpy(&result, &bits, sizeof(float));
    return result;
#endif // CUDART_VERSION >= 12050
}

typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);

static __device__ __forceinline__ float get_alibi_slope(
    const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1
) {
    if (max_bias <= 0.0f) {
        return 1.0f;
    }
    const float base = h < n_head_log2 ? m0 : m1;
    const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;

    return powf(base, exph);
}

template <ggml_type type>
struct ggml_cuda_type_traits;

template<>
struct ggml_cuda_type_traits<GGML_TYPE_F16> {
    static constexpr int qk = 1;
    static constexpr int qr = 1;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q4_0> {
    static constexpr int qk = QK4_0;
    static constexpr int qr = QR4_0;
    static constexpr int qi = QI4_0;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q4_1> {
    static constexpr int qk = QK4_1;
    static constexpr int qr = QR4_1;
    static constexpr int qi = QI4_1;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q5_0> {
    static constexpr int qk = QK5_0;
    static constexpr int qr = QR5_0;
    static constexpr int qi = QI5_0;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q5_1> {
    static constexpr int qk = QK5_1;
    static constexpr int qr = QR5_1;
    static constexpr int qi = QI5_1;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q8_0> {
    static constexpr int qk = QK8_0;
    static constexpr int qr = QR8_0;
    static constexpr int qi = QI8_0;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
    static constexpr int qk = QK_MXFP4;
    static constexpr int qr = QR_MXFP4;
    static constexpr int qi = QI_MXFP4;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR2_K;
    static constexpr int qi = QI2_K;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q3_K> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR3_K;
    static constexpr int qi = QI3_K;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q4_K> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR4_K;
    static constexpr int qi = QI4_K;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q5_K> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR5_K;
    static constexpr int qi = QI5_K;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q6_K> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR6_K;
    static constexpr int qi = QI6_K;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ2_XXS> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR2_XXS;
    static constexpr int qi = QI2_XXS;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ2_XS> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR2_XS;
    static constexpr int qi = QI2_XS;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ2_S> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR2_S;
    static constexpr int qi = QI2_S;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ3_XXS> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR3_XXS;
    static constexpr int qi = QI3_XXS;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ1_S> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR1_S;
    static constexpr int qi = QI1_S;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ1_M> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR1_M;
    static constexpr int qi = QI1_M;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ4_NL> {
    static constexpr int qk = QK4_NL;
    static constexpr int qr = QR4_NL;
    static constexpr int qi = QI4_NL;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ4_XS> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR4_XS;
    static constexpr int qi = QI4_XS;
};

template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ3_S> {
    static constexpr int qk = QK_K;
    static constexpr int qr = QR3_S;
    static constexpr int qi = QI3_S;
};

//////////////////////

struct ggml_cuda_device_info {
    int device_count;

    struct cuda_device_info {
        int     cc;                 // compute capability
        int     nsm;                // number of streaming multiprocessors
        size_t  smpb;               // max. shared memory per block
        size_t  smpbo;              // max. shared memory per block (with opt-in)
        bool    integrated;         // Device is integrated as opposed to discrete
        bool    vmm;                // virtual memory support
        size_t  vmm_granularity;    // granularity of virtual memory
        size_t  total_vram;
        int     warp_size;          // Number of threads in a dispatch
    };

    cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {};

    std::array<float, GGML_CUDA_MAX_DEVICES> default_tensor_split = {};
};

const ggml_cuda_device_info & ggml_cuda_info();

void ggml_cuda_set_device(int device);
int ggml_cuda_get_device();

struct ggml_cuda_pool {
    virtual ~ggml_cuda_pool() = default;

    virtual void * alloc(size_t size, size_t * actual_size) = 0;
    virtual void free(void * ptr, size_t size) = 0;
};

template<typename T>
struct ggml_cuda_pool_alloc {
    ggml_cuda_pool * pool = nullptr;
    T * ptr = nullptr;
    size_t actual_size = 0;

    ggml_cuda_pool_alloc() = default;

    explicit ggml_cuda_pool_alloc(ggml_cuda_pool & pool) : pool(&pool) {
    }

    ggml_cuda_pool_alloc(ggml_cuda_pool & pool, size_t size) : pool(&pool) {
        alloc(size);
    }

    ~ggml_cuda_pool_alloc() {
        if (ptr != nullptr) {
            pool->free(ptr, actual_size);
        }
    }

    // size is in number of elements
    T * alloc(size_t size) {
        GGML_ASSERT(pool != nullptr);
        GGML_ASSERT(ptr == nullptr);
        ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size);
        return ptr;
    }

    T * alloc(ggml_cuda_pool & pool, size_t size) {
        this->pool = &pool;
        return alloc(size);
    }

    T * get() {
        return ptr;
    }

    ggml_cuda_pool_alloc(const ggml_cuda_pool_alloc &) = delete;
    ggml_cuda_pool_alloc(ggml_cuda_pool_alloc &&) = delete;
    ggml_cuda_pool_alloc& operator=(const ggml_cuda_pool_alloc &) = delete;
    ggml_cuda_pool_alloc& operator=(ggml_cuda_pool_alloc &&) = delete;
};


// backend interface

struct ggml_tensor_extra_gpu {
    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
    cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs
};


#if (defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS)) || defined(GGML_MUSA_GRAPHS)
#define USE_CUDA_GRAPH
#endif

struct ggml_graph_node_properties {
    void * node_address;
    ggml_op node_op;
    int64_t ne[GGML_MAX_DIMS];
    size_t nb[GGML_MAX_DIMS];
    void * src_address[GGML_MAX_SRC];
    int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
};

struct ggml_cuda_graph {
#ifdef USE_CUDA_GRAPH
    ~ggml_cuda_graph() {
        if (instance != nullptr) {
            CUDA_CHECK(cudaGraphExecDestroy(instance));
        }
        if (graph != nullptr) {
            CUDA_CHECK(cudaGraphDestroy(graph));
        }
    }
    cudaGraph_t graph = nullptr;
    cudaGraphExec_t instance = nullptr;
    size_t num_nodes = 0;
    std::vector<cudaGraphNode_t> nodes;
    std::vector<cudaKernelNodeParams> params;
    bool disable_due_to_gpu_arch = false;
    bool disable_due_to_too_many_updates = false;
    bool disable_due_to_failed_graph_capture = false;
    int number_consecutive_updates = 0;
    std::vector<ggml_graph_node_properties> ggml_graph_properties;
    bool use_cpy_indirection = false;
    std::vector<char *> cpy_dest_ptrs;
    char ** dest_ptrs_d;
    int dest_ptrs_size = 0;
    // Index to allow each cpy kernel to be aware of it's position within the graph
    // relative to other cpy nodes.
    int graph_cpynode_index = -1;
#endif
};

struct ggml_backend_cuda_context {
    int device;
    std::string name;
    cudaEvent_t copy_event = nullptr;

    cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } };
    cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};

    std::unique_ptr<ggml_cuda_graph> cuda_graph;

    explicit ggml_backend_cuda_context(int device) :
        device(device),
        name(GGML_CUDA_NAME + std::to_string(device)) {
    }

    ~ggml_backend_cuda_context();

    cudaStream_t stream(int device, int stream) {
        if (streams[device][stream] == nullptr) {
            ggml_cuda_set_device(device);
            CUDA_CHECK(cudaStreamCreateWithFlags(&streams[device][stream], cudaStreamNonBlocking));
        }
        return streams[device][stream];
    }

    cudaStream_t stream() {
        return stream(device, 0);
    }

    cublasHandle_t cublas_handle(int device) {
        if (cublas_handles[device] == nullptr) {
            ggml_cuda_set_device(device);
            CUBLAS_CHECK(cublasCreate(&cublas_handles[device]));
            CUBLAS_CHECK(cublasSetMathMode(cublas_handles[device], CUBLAS_TF32_TENSOR_OP_MATH));
        }
        return cublas_handles[device];
    }

    cublasHandle_t cublas_handle() {
        return cublas_handle(device);
    }

    // pool
    std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES];

    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device);

    ggml_cuda_pool & pool(int device) {
        if (pools[device] == nullptr) {
            pools[device] = new_pool_for_device(device);
        }
        return *pools[device];
    }

    ggml_cuda_pool & pool() {
        return pool(device);
    }
};
