// This file contains primitives that expose the tensor core PTX instructions for CUDA code.
// The primitives can be used in a similar way as the nvcuda::wmma interface but with a well-defined memory layout.
// The documentation for the PTX instructions can be found under:
//   https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-multiply-accumulate-operation-using-mma-instruction
//
// Like with nvcuda::wmma there are three types of matrix tiles: A, B, and C with A @ B = C.
// A is a row-major matrix with shape M x K.
// B is a column-major matrix with shape K x N.
// C is a column-major matrix with shape M x N.
// A, B, and C are represented using the same fundamental data type: a row-major matrix with I rows and J columns.
// Note that J is measured in physical 32 bit elements instead of logical elements.
// The methods get_i and get_j can be used to get the physical 32 bit index of the lth element of a thread within a tile.
// All matrix tiles have ne physical 32 bit elements per warp.
//
// As described in the PTX documentation, all pointers for load_ldmatrix must be to shared memory and aligned to 16 bytes.
// The API in this file also assumes that the pointers for load_generic are aligned to 16 bytes, unaligned pointers are considered undefined behavior.

#include "common.cuh"


#if CUDART_VERSION >= 11080

static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
    int ret = 0;

#ifdef NEW_MMA_AVAILABLE
    asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
        : "=r"(ret) : "r"(x));
#else
    GGML_UNUSED(x);
    NO_DEVICE_CODE;
#endif // defined(NEW_MMA_AVAILABLE)
    return ret;
}

#else

static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
    // Imagine transposing row-major matrix to column-major matrix.
    const int src_i_low  = 2 * (threadIdx.x % 4);
    const int src_i_high = src_i_low + 1;
    const int src_j      = threadIdx.x / 4;

    const int src_laneid_low  = src_i_low  * 4 + src_j / 2;
    const int src_laneid_high = src_i_high * 4 + src_j / 2;

    const int shift_low  = ((src_j + 0) % 2) * 16;
    const int shift_high = ((src_j + 1) % 2) * 16;

    const int ret_low  = (__shfl_sync(0xFFFFFFFF, x, src_laneid_low,  WARP_SIZE) >> shift_low)  & 0x0000FFFF;
    const int ret_high = (__shfl_sync(0xFFFFFFFF, x, src_laneid_high, WARP_SIZE) << shift_high) & 0xFFFF0000;

    return ret_low | ret_high;
}

#endif // CUDART_VERSION >= 11080

static __device__ __forceinline__ half2 ggml_cuda_movmatrix(const half2 x) {
    half2 ret;
    *((int *) &ret) = ggml_cuda_movmatrix(*((const int *) &x));
    return ret;
}

namespace ggml_cuda_mma {

    template <int I_, int J_, typename T>
    struct tile {
        static constexpr int I  = I_;
        static constexpr int J  = J_;

#if defined(GGML_USE_HIP)
        static constexpr int ne = I * J / 64;
        T x[ne] = {0};

        static __device__ __forceinline__ int get_i(const int l) {
            if constexpr (I == 64 && J == 2) { // Special tile size to load <16, 4> as <16, 8>
                return threadIdx.x % 16;
            } else if constexpr (I == 16 && J == 8) {
                return threadIdx.x % 16;
            } else if constexpr (I == 32 && J == 4) {
                return threadIdx.x % 32;
            } else if constexpr (I == 16 && J == 16) {
                return 4 * (threadIdx.x / 16) + l;
            } else if constexpr (I == 32 && J == 32) {
                return 4 * (threadIdx.x / 32) + 8 * (l / 4) + (l % 4);
            } else {
                static_assert(I == -1 && J == -1, "template specialization not implemented");
            }
        }

        static __device__ __forceinline__ int get_j(const int l) {
            if constexpr (I == 64 && J == 2) { // Special tile size to load <16, 4> as <16, 8>
                return (2 * ((threadIdx.x / 16) % 2) + l);
            } else if constexpr (I == 16 && J == 8) {
                return 2 * (threadIdx.x / 16) + l;
            } else if constexpr (I == 32 && J == 4) {
                return 2 * (threadIdx.x / 32) + l;
            } else if constexpr (I == 16 && J == 16) {
                return threadIdx.x % 16;
            } else if constexpr (I == 32 && J == 32) {
                return threadIdx.x % 32;
            } else {
                static_assert(I == -1 && J == -1, "template specialization not implemented");
            }
        }
#else
        static constexpr int ne = I * J / 32;
        T x[ne] = {0};

        static __device__ __forceinline__ int get_i(const int l) {
            if constexpr (I == 8 && (J == 4 || J == 8)) {
                return threadIdx.x / 4;
            } else if constexpr (I == 16 && J == 8) {
                return (l / 2) * 8 + threadIdx.x / 4;
            } else if constexpr (I == 16 && J == 16) {
                return ((l / 2) % 2) * 8 + threadIdx.x / 4;
            } else {
                static_assert(I == -1 && J == -1, "template specialization not implemented");
            }
        }

        static __device__ __forceinline__ int get_j(const int l) {
            if constexpr (I == 8 && J == 4) {
                return threadIdx.x % 4;
            } else if constexpr (I == 8 && J == 8) {
                return 4 * l + threadIdx.x % 4;
            } else if constexpr (I == 16 && J == 8) {
                return 2 * (threadIdx.x % 4) + l % 2;
            } else if constexpr (I == 16 && J == 16) {
                return 8 * (l / 4) + 2 * (threadIdx.x % 4) + l % 2;
            } else {
                static_assert(I == -1 && J == -1, "template specialization not implemented");
            }
        }
#endif // defined(GGML_USE_HIP)
    };

    template <int I_, int J_>
    struct tile<I_, J_, half2> {
        static constexpr int I  = I_;
        static constexpr int J  = J_;
        static constexpr int ne = I * J / WARP_SIZE;
        half2 x[ne] = {{0.0f, 0.0f}};

        static __device__ __forceinline__ int get_i(const int l) {
            if constexpr (I == 8 && J == 8) {
                return threadIdx.x / 4;
            } else if constexpr (I == 16 && J == 4) {
                return l * 8 + threadIdx.x / 4;
            } else if constexpr (I == 16 && J == 8) {
                return (l % 2) * 8 + threadIdx.x / 4;
            } else {
                static_assert(I == -1 && J == -1, "template specialization not implemented");
            }
        }

        static __device__ __forceinline__ int get_j(const int l) {
            if constexpr (I == 8 && J == 8) {
                return l * 4 + threadIdx.x % 4;
            } else if constexpr (I == 16 && J == 4) {
                return threadIdx.x % 4;
            } else if constexpr (I == 16 && J == 8) {
                return (l / 2) * 4 + threadIdx.x % 4;
            } else {
                static_assert(I == -1 && J == -1, "template specialization not implemented");
            }
        }
    };

    template <int I, int J>
    static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
        tile<I, J/2, half2> ret;
#pragma unroll
        for (int l0 = 0; l0 < tile_float.ne; l0 += 2) {
            ret.x[l0/2] = make_half2(tile_float.x[l0 + 0], tile_float.x[l0 + 1]);
        }
        return ret;
    }

    static __device__ __forceinline__ tile<8, 8, half2> get_transposed(const tile<16, 4, half2> & t) {
        tile<8, 8, half2> ret;
        ret.x[0] = ggml_cuda_movmatrix(t.x[0]);
        ret.x[1] = ggml_cuda_movmatrix(t.x[1]);

        return ret;
    }

    template <int I, int J, typename T>
    static __device__ __forceinline__ void load_generic(tile<I, J, T> & t, const T * __restrict__ xs0, const int stride) {
#if defined(AMD_MFMA_AVAILABLE)
        if constexpr (I == 64 && J == 2) { // Special tile size to load <16, 4> as <16, 8>
#pragma unroll
            for (int l = 0; l < t.ne; ++l) {
                t.x[l] = xs0[t.get_i(l)*stride + t.get_j(l)];
            }
        } else {
            int64_t * xi = (int64_t *) t.x;
            const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 2 * (threadIdx.x / t.I));
            xi[0] = xs[0];
        }
#else
#pragma unroll
        for (int l = 0; l < t.ne; ++l) {
            t.x[l] = xs0[t.get_i(l)*stride + t.get_j(l)];
        }
#endif // defined(AMD_MFMA_AVAILABLE)
    }

    template <typename T>
    static __device__ __forceinline__ void load_ldmatrix(
            tile<8, 8, T> & t, const T * __restrict__ xs0, const int stride) {
#ifdef NEW_MMA_AVAILABLE
        int * xi = (int *) t.x;
        const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + ((threadIdx.x / t.I) * (t.J / 2)) % t.J;
        asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
            : "=r"(xi[0]), "=r"(xi[1])
            : "l"(xs));
#else
        load_generic(t, xs0, stride);
#endif // NEW_MMA_AVAILABLE
    }

    template <typename T>
    static __device__ __forceinline__ void load_ldmatrix(
            tile<16, 4, T> & t, const T * __restrict__ xs0, const int stride) {
#ifdef NEW_MMA_AVAILABLE
        int * xi = (int *) t.x;
        const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride;
        asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
            : "=r"(xi[0]), "=r"(xi[1])
            : "l"(xs));
#else
        load_generic(xs0, stride);
        GGML_UNUSED(t);
#endif // NEW_MMA_AVAILABLE
    }

    template <typename T>
    static __device__ __forceinline__ void load_ldmatrix(
            tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
#if defined(NEW_MMA_AVAILABLE)
        int * xi = (int * ) t.x;
        const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
        asm volatile("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
            : "=r"(xi[0]), "=r"(xi[1]), "=r"(xi[2]), "=r"(xi[3])
            : "l"(xs));
#else
        load_generic(t, xs0, stride);
#endif // NEW_MMA_AVAILABLE
    }

    template <typename T>
    static __device__ __forceinline__ void load_ldmatrix_trans(
            tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
#ifdef NEW_MMA_AVAILABLE
        int * xi = (int * ) t.x;
        const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
        asm volatile("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
            : "=r"(xi[0]), "=r"(xi[2]), "=r"(xi[1]), "=r"(xi[3])
            : "l"(xs));
#else
        GGML_UNUSED(t);
        GGML_UNUSED(xs0);
        GGML_UNUSED(stride);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 8, int> & D, const tile<16, 4, int> & A, const tile<8, 4, int> & B) {
#ifdef NEW_MMA_AVAILABLE
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
        asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
            : "r"(A.x[0]), "r"(A.x[1]), "r"(B.x[0]));
#else
        // On Turing m16n8k16 mma is not available, use 2x m8n8k16 mma instead:
        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
            : "+r"(D.x[0]), "+r"(D.x[1])
            : "r"(A.x[0]), "r"(B.x[0]));
        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
            : "+r"(D.x[2]), "+r"(D.x[3])
            : "r"(A.x[1]), "r"(B.x[0]));
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 8, int> & D, const tile<16, 8, int> & A, const tile<8, 8, int> & B) {
#ifdef NEW_MMA_AVAILABLE
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
        asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
            : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
            : "r"(A.x[0]), "r"(A.x[1]), "r"(A.x[2]), "r"(A.x[3]), "r"(B.x[0]), "r"(B.x[1]));
#else
        // On Turing m16n8k32 mma is not available, use 4x m8n8k16 mma instead:
        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
            : "+r"(D.x[0]), "+r"(D.x[1])
            : "r"(A.x[0]), "r"(B.x[0]));
        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
            : "+r"(D.x[2]), "+r"(D.x[3])
            : "r"(A.x[1]), "r"(B.x[0]));
        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
            : "+r"(D.x[0]), "+r"(D.x[1])
            : "r"(A.x[2]), "r"(B.x[1]));
        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
            : "+r"(D.x[2]), "+r"(D.x[3])
            : "r"(A.x[3]), "r"(B.x[1]));
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 4, half2> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
#ifdef NEW_MMA_AVAILABLE
        const int * Axi = (const int *) A.x;
        const int * Bxi = (const int *) B.x;
        int       * Dxi = (int       *) D.x;
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
        asm("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%0, %1};"
            : "+r"(Dxi[0]), "+r"(Dxi[1])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
#else
        // On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
            : "+r"(Dxi[0]), "+r"(Dxi[1])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
            : "+r"(Dxi[0]), "+r"(Dxi[1])
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 8, half2> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
#ifdef NEW_MMA_AVAILABLE
        const int * Axi = (const int *) A.x;
        const int * Bxi = (const int *) B.x;
        int       * Dxi = (int       *) D.x;
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
        asm("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%0, %1};"
            : "+r"(Dxi[0]), "+r"(Dxi[1])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[2]));
        asm("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%0, %1};"
            : "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]), "r"(Bxi[3]));
#else
        // On Turing m16n8k16 mma is not available, use 4x m8n8k8 mma instead:
        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
            : "+r"(Dxi[0]), "+r"(Dxi[1])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
            : "+r"(Dxi[0]), "+r"(Dxi[1])
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[2]));
        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
            : "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[1]));
        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
            : "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 8, float> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
#ifdef NEW_MMA_AVAILABLE
        const int * Axi = (const int *) A.x;
        const int * Bxi = (const int *) B.x;
        int       * Dxi = (int       *) D.x;
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
        asm("mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
#else
        // On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 16, float> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
#ifdef NEW_MMA_AVAILABLE
        const int * Axi = (const int *) A.x;
        const int * Bxi = (const int *) B.x;
        int       * Dxi = (int       *) D.x;
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
        asm("mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[2]));
        asm("mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
            : "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]), "r"(Bxi[3]));
#else
        // On Turing m16n8k16 mma is not available, use 4x m8n8k8 mma instead:
        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[2]));
        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[1]));
        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
            : "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // NEW_MMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<16, 16, int> & D, const tile<16, 8, int> & A, const tile<16, 8, int> & B) {
#if defined(AMD_MFMA_AVAILABLE)
        using int32x4_t = __attribute__((__vector_size__(4 * sizeof(int)))) int;
        int32x4_t * acc = (int32x4_t *) D.x;
#if defined(CDNA3)
        acc[0] = __builtin_amdgcn_mfma_i32_16x16x32_i8(((int64_t *) A.x)[0],
                                                       ((int64_t *) B.x)[0],
                                                       acc[0],
                                                       0, 0, 0);
#elif defined(CDNA2) || defined(CDNA)
        acc[0] = __builtin_amdgcn_mfma_i32_16x16x16i8(A.x[0],
                                                      B.x[0],
                                                      acc[0],
                                                      0, 0, 0);
        acc[0] = __builtin_amdgcn_mfma_i32_16x16x16i8(A.x[1],
                                                      B.x[1],
                                                      acc[0],
                                                      0, 0, 0);
#endif // defined(CDNA3)
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // AMD_MFMA_AVAILABLE
    }

    static __device__ __forceinline__ void mma(
            tile<32, 32, int> & D, const tile<32, 4, int> & A, const tile<32, 4, int> & B) {
#if defined(AMD_MFMA_AVAILABLE)
        using int32x16_t = __attribute__((__vector_size__(16 * sizeof(int)))) int;
        int32x16_t * acc = (int32x16_t *) D.x;
#if defined(CDNA3)
        acc[0] = __builtin_amdgcn_mfma_i32_32x32x16_i8(((int64_t *) A.x)[0],
                                                       ((int64_t *) B.x)[0],
                                                       acc[0],
                                                       0, 0, 0);
#elif defined(CDNA2) || defined(CDNA)
        acc[0] = __builtin_amdgcn_mfma_i32_32x32x8i8(A.x[0],
                                                     B.x[0],
                                                     acc[0],
                                                     0, 0, 0);
        acc[0] = __builtin_amdgcn_mfma_i32_32x32x8i8(A.x[1],
                                                     B.x[1],
                                                     acc[0],
                                                     0, 0, 0);
#endif // defined(CDNA3)
#else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
        GGML_UNUSED(B);
        NO_DEVICE_CODE;
#endif // AMD_MFMA_AVAILABLE
    }
}