aphrodite-engine/kernels/quantization/gguf/gguf_kernel.cu

#include <cuda_fp16.h>
#include <cuda_runtime.h>

#include <torch/all.h>
#include <torch/python.h>
#include <c10/cuda/CUDAGuard.h>


#define QK_K 256
#define K_QUANTS_PER_ITERATION 2
#define WARP_SIZE 32
#define K_SCALE_SIZE 12
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
#define CUDA_QUANTIZE_BLOCK_SIZE 256
#define GGML_CUDA_DMMV_X 32
#define GGML_CUDA_MMV_Y 1


// Data Structures
// QK = number of values after dequantization
// QR = QK / number of values before dequantization
// QI = number of 32 bit integers before dequantization

#define QK4_0 32
#define QR4_0 2
#define QI4_0 (QK4_0 / (4 * QR4_0))
typedef struct {
    half    d;              // delta
    uint8_t qs[QK4_0 / 2];  // nibbles / quants
} block_q4_0;

#define QK4_1 32
#define QR4_1 2
#define QI4_1 (QK4_1 / (4 * QR4_1))
typedef struct {
    half2   dm;             // dm.x = delta, dm.y = min
    uint8_t qs[QK4_1 / 2];  // nibbles / quants
} block_q4_1;

#define QK5_0 32
#define QR5_0 2
#define QI5_0 (QK5_0 / (4 * QR5_0))
typedef struct {
    half d;                 // delta
    uint8_t qh[4];          // 5-th bit of quants
    uint8_t qs[QK5_0 / 2];  // nibbles / quants
} block_q5_0;

#define QK5_1 32
#define QR5_1 2
#define QI5_1 (QK5_1 / (4 * QR5_1))
typedef struct {
    half2 dm;               // dm.x = delta, dm.y = min
    uint8_t qh[4];          // 5-th bit of quants
    uint8_t qs[QK5_1 / 2];  // nibbles / quants
} block_q5_1;

#define QK8_0 32
#define QR8_0 1
#define QI8_0 (QK8_0 / (4 * QR8_0))
typedef struct {
    half    d;              // delta
    int8_t  qs[QK8_0];      // quants
} block_q8_0;

#define QK8_1 32
#define QR8_1 1
#define QI8_1 (QK8_1 / (4 * QR8_1))
typedef struct {
    half2   ds;             // ds.x = delta, ds.y = sum
    int8_t  qs[QK8_0];      // quants
} block_q8_1;

#define QR2_K 4
#define QI2_K (QK_K / (4*QR2_K))
typedef struct {
    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
    uint8_t qs[QK_K/4];      // quants
    half2 dm;                // super-block scale for quantized scales/mins
} block_q2_K;

#define QR3_K 4
#define QI3_K (QK_K / (4*QR3_K))
typedef struct {
    uint8_t hmask[QK_K/8];     // quants - high bit
    uint8_t qs[QK_K/4];        // quants - low 2 bits
    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
    half d;             // super-block scale
} block_q3_K;

#define QR4_K 2
#define QI4_K (QK_K / (4*QR4_K))
typedef struct {
    half2 dm;                  // super-block scale for quantized scales/mins
    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
    uint8_t qs[QK_K/2];        // 4--bit quants
} block_q4_K;

#define QR5_K 2
#define QI5_K (QK_K / (4*QR5_K))
typedef struct {
    half2 dm;                     // super-block scale for quantized scales/mins
    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
    uint8_t qh[QK_K/8];           // quants, high bit
    uint8_t qs[QK_K/2];           // quants, low 4 bits
} block_q5_K;

#define QR6_K 2
#define QI6_K (QK_K / (4*QR6_K))
typedef struct {
    uint8_t ql[QK_K/2];   // quants, lower 4 bits
    uint8_t qh[QK_K/4];   // quants, upper 2 bits
    int8_t  scales[QK_K/16]; // scales
    half    d;         // delta
} block_q6_K;

#define QR2_XXS 8
#define QI2_XXS (QK_K / (4*QR2_XXS))
typedef struct {
    half d;
    uint16_t qs[QK_K/8];
} block_iq2_xxs;

#define QR2_XS 8
#define QI2_XS (QK_K / (4*QR2_XS))
typedef struct {
    half d;
    uint16_t qs[QK_K/8];
    uint8_t  scales[QK_K/32];
} block_iq2_xs;

#define QR2_S 8
#define QI2_S (QK_K / (4*QR2_S))
typedef struct {
    half d;
    uint8_t qs[QK_K/4];
    uint8_t qh[QK_K/32];
    uint8_t scales[QK_K/32];
} block_iq2_s;

#define QR3_XXS 8
#define QI3_XXS (QK_K / (4*QR3_XXS))
typedef struct {
    half d;
    uint8_t qs[3*(QK_K/8)];
} block_iq3_xxs;

#define QR3_XS 8
#define QI3_XS (QK_K / (4*QR3_XS))
#define IQ3S_N_SCALE QK_K/64
typedef struct {
    half d;
    uint8_t qs[QK_K/4];
    uint8_t qh[QK_K/32];
    uint8_t signs[QK_K/8];
    uint8_t scales[IQ3S_N_SCALE];
} block_iq3_s;

#define QR1_S 8
#define QI1_S (QK_K / (4*QR1_S))
typedef struct {
    half d;
    uint8_t qs[QK_K/8];
    uint8_t scales[QK_K/16];
} block_iq1_s;

#define QK4_NL 32
#define QR4_NL 2
#define QI4_NL (QK4_NL / (4*QR4_NL))
typedef struct {
    half d;
    uint8_t qs[QK4_NL/2];
} block_iq4_nl;

#define QR4_XS 8
#define QI4_XS (QK_K / (4*QR4_XS))
typedef struct {
    half d;
    uint16_t scales_h;
    uint8_t  scales_l[QK_K/64];
    uint8_t  qs[QK_K/2];
} block_iq4_xs;

static const __device__ uint64_t iq2xxs_grid[256] = {
    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
};

static const __device__ uint64_t iq2xs_grid[512] = {
    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808,
    0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819,
    0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819,
    0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808,
    0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b,
    0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b,
    0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908,
    0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908,
    0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919,
    0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808,
    0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919,
    0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908,
    0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b,
    0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908,
    0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08,
    0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808,
    0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808,
    0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819,
    0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908,
    0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819,
    0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808,
    0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b,
    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819,
    0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819,
    0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808,
    0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908,
    0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19,
    0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b,
    0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b,
    0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919,
    0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808,
    0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819,
    0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819,
    0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b,
    0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908,
    0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808,
    0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819,
    0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808,
    0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919,
    0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808,
    0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808,
    0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908,
    0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908,
    0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808,
    0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b,
    0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819,
    0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919,
    0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908,
    0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808,
    0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908,
    0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919,
    0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08,
    0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19,
    0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b,
    0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b,
    0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808,
    0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08,
    0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b,
    0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908,
    0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b,
    0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908,
    0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08,
    0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808,
    0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808,
    0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08,
    0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819,
    0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919,
    0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808,
    0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808,
    0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819,
    0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819,
    0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908,
    0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908,
    0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b,
    0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908,
    0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908,
    0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908,
    0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808,
    0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819,
    0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819,
    0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819,
    0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808,
    0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b,
    0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819,
    0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819,
    0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08,
    0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808,
    0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19,
    0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919,
    0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808,
    0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19,
    0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b,
    0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808,
    0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b,
    0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b,
    0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08,
    0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b,
    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808,
    0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819,
    0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808,
    0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808,
    0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08,
    0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b,
    0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19,
    0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08,
    0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919,
    0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08,
    0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08,
    0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908,
    0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908,
    0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b,
    0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908,
    0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808,
    0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b,
    0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808,
    0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808,
    0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19,
    0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08,
    0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808,
    0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b,
    0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808,
    0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b,
    0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
};

static const __device__ uint64_t iq2s_grid[1024] = {
    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b,
    0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919,
    0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808,
    0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908,
    0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b,
    0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908,
    0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08,
    0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19,
    0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819,
    0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919,
    0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b,
    0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908,
    0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908,
    0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919,
    0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908,
    0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b,
    0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919,
    0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b,
    0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919,
    0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908,
    0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b,
    0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b,
    0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08,
    0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b,
    0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819,
    0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808,
    0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908,
    0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b,
    0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908,
    0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08,
    0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819,
    0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808,
    0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08,
    0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819,
    0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b,
    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908,
    0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919,
    0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b,
    0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919,
    0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808,
    0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819,
    0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919,
    0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919,
    0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808,
    0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819,
    0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b,
    0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908,
    0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b,
    0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908,
    0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919,
    0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b,
    0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919,
    0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b,
    0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819,
    0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919,
    0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908,
    0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b,
    0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908,
    0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b,
    0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908,
    0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08,
    0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908,
    0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819,
    0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819,
    0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808,
    0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08,
    0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19,
    0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819,
    0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808,
    0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819,
    0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919,
    0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808,
    0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19,
    0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08,
    0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b,
    0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908,
    0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808,
    0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819,
    0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908,
    0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819,
    0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808,
    0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808,
    0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819,
    0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908,
    0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08,
    0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819,
    0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b,
    0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b,
    0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08,
    0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19,
    0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819,
    0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919,
    0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908,
    0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808,
    0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808,
    0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908,
    0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808,
    0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08,
    0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08,
    0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908,
    0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919,
    0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808,
    0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819,
    0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908,
    0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08,
    0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819,
    0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808,
    0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808,
    0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819,
    0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808,
    0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908,
    0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b,
    0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819,
    0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808,
    0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b,
    0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808,
    0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b,
    0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19,
    0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819,
    0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08,
    0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b,
    0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908,
    0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b,
    0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b,
    0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919,
    0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808,
    0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819,
    0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908,
    0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08,
    0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08,
    0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819,
    0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919,
    0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908,
    0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b,
    0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908,
    0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b,
    0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908,
    0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08,
    0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819,
    0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808,
    0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819,
    0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919,
    0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808,
    0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808,
    0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08,
    0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819,
    0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919,
    0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808,
    0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819,
    0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919,
    0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808,
    0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b,
    0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908,
    0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808,
    0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908,
    0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b,
    0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908,
    0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b,
    0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908,
    0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b,
    0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908,
    0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08,
    0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908,
    0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b,
    0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908,
    0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08,
    0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819,
    0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919,
    0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808,
    0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19,
    0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b,
    0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919,
    0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808,
    0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819,
    0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908,
    0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919,
    0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808,
    0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808,
    0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b,
    0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919,
    0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808,
    0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b,
    0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808,
    0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919,
    0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b,
    0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08,
    0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919,
    0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808,
    0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b,
    0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908,
    0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808,
    0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808,
    0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808,
    0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908,
    0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808,
    0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808,
    0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b,
    0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908,
    0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808,
    0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808,
    0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819,
    0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919,
    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b,
    0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808,
    0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819,
    0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b,
    0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908,
    0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08,
    0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908,
    0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919,
    0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819,
    0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908,
    0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b,
    0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808,
    0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819,
    0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908,
    0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919,
    0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808,
    0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808,
    0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808,
    0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919,
    0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908,
    0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908,
    0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08,
    0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819,
    0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b,
    0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808,
    0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819,
    0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908,
    0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819,
    0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808,
    0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808,
    0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b,
    0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908,
    0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808,
    0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908,
    0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819,
    0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819,
    0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808,
    0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b,
    0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b,
    0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819,
    0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b,
    0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b,
    0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b,
    0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819,
    0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19,
    0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819,
    0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908,
    0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808,
    0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b,
};

static const __device__ uint32_t iq3xxs_grid[256] = {
    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};

static const __device__ uint32_t iq3xs_grid[512] = {
    0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
    0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
    0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
    0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
    0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
    0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
    0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
    0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
    0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
    0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
    0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
    0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
    0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
    0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
    0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
    0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
    0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
    0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
    0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
    0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
    0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
    0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
    0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
    0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
    0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
    0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
    0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
    0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
    0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
    0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
    0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
    0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
    0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
    0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
    0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
    0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
    0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
    0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
    0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
    0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
    0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
    0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
    0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
    0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
    0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
    0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
    0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
    0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
    0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
    0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
    0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
    0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
    0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
    0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
    0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
    0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
    0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
    0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
    0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
    0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
    0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
    0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
    0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
    0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
};

static const __device__ uint64_t iq1s_grid[512] = {
    0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
    0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01,
    0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100,
    0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00,
    0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101,
    0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100,
    0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00,
    0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff,
    0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000,
    0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000,
    0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001,
    0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff,
    0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01,
    0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001,
    0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00,
    0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001,
    0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100,
    0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000,
    0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000,
    0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000,
    0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff,
    0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff,
    0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01,
    0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100,
    0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff,
    0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000,
    0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101,
    0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff,
    0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff,
    0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001,
    0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01,
    0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101,
    0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100,
    0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00,
    0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001,
    0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff,
    0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000,
    0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000,
    0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100,
    0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100,
    0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01,
    0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff,
    0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101,
    0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000,
    0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff,
    0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000,
    0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff,
    0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00,
    0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101,
    0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000,
    0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000,
    0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000,
    0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100,
    0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000,
    0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001,
    0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff,
    0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000,
    0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000,
    0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000,
    0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000,
    0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff,
    0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000,
    0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001,
    0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01,
    0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100,
    0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000,
    0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00,
    0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100,
    0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000,
    0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001,
    0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00,
    0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff,
    0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100,
    0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff,
    0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000,
    0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff,
    0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff,
    0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00,
    0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001,
    0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001,
    0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01,
    0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000,
    0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101,
    0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00,
    0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100,
    0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101,
    0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101,
    0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000,
    0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff,
    0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff,
    0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101,
    0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff,
    0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101,
    0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001,
    0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff,
    0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff,
    0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01,
    0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff,
    0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100,
    0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001,
    0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00,
    0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff,
    0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff,
    0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000,
    0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000,
    0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101,
    0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001,
    0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000,
    0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101,
    0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000,
    0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001,
    0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000,
    0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100,
    0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000,
    0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000,
    0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100,
    0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff,
    0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff,
    0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00,
    0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101,
    0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000,
    0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00,
    0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000,
    0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff,
    0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101,
    0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff,
    0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00,
    0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff,
};

static const __device__ uint8_t ksigns_iq2xs[128] = {
      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
};

static const __device__ uint64_t ksigns64[128] = {
    0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff,
    0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff,
    0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff,
    0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff,
    0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff,
    0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff,
    0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff,
    0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff,
    0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff,
    0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff,
    0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff,
    0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff,
    0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff,
    0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff,
    0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff,
    0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff,
    0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff,
    0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff,
    0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff,
    0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff,
    0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff,
    0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff,
    0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff,
    0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff,
    0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff,
    0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff,
    0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff,
    0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff,
    0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff,
    0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff,
    0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff,
    0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff,
};

static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};


typedef half dfloat; // dequantize float
typedef half2 dfloat2;
typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
typedef void (*to_fp16_cuda_t)(const void * __restrict__ x, dfloat * __restrict__ y, int k, cudaStream_t stream);
typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
typedef void (*load_tiles_cuda_t)(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
typedef float (*vec_dot_q_mul_mat_cuda_t)(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);

// Utility function

#if defined(USE_ROCM)

#ifndef __has_builtin
    #define __has_builtin(x) 0
#endif

typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
#if __has_builtin(__builtin_elementwise_sub_sat)
    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
    return reinterpret_cast<const int &>(c);
#else
    int8x4_t c;
    int16_t tmp;
#pragma unroll
    for (int i = 0; i < 4; i++) {
        tmp = va[i] - vb[i];
        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
        c[i] = tmp;
    }
    return reinterpret_cast<int &>(c);
#endif // __has_builtin(__builtin_elementwise_sub_sat)
}

static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
#if __has_builtin(__builtin_amdgcn_sdot4)
    c = __builtin_amdgcn_sdot4(a, b, c, false);
#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;
}
#endif // defined(USE_ROCM)

static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
    int x32 = 0;
    x32 |= x16[0] <<  0;
    x32 |= x16[1] << 16;
    return x32;
}

static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
    int x32 = 0;
    x32 |= x16[0] <<  0;
    x32 |= x16[1] << 16;
    return x32;
}

static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
}

static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
}

// Dequant functions
static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
    const block_q4_0 * x = (const block_q4_0 *) vx;

    const dfloat d = x[ib].d;

    const int vui = x[ib].qs[iqs];

    v.x = __int2half_rn(vui & 0xF);
    v.y = __int2half_rn(vui >> 4);

    v = __hsub2(v, __floats2half2_rn(8.0f, 8.0f));
    v = __hmul2(v, {d, d});
}

static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
    const block_q4_1 * x = (const block_q4_1 *) vx;

    const dfloat d = __low2half(x[ib].dm);
    const dfloat m = __high2half(x[ib].dm);

    const int vui = x[ib].qs[iqs];

    v.x = __int2half_rn(vui & 0xF);
    v.y = __int2half_rn(vui >> 4);

    v = __hmul2(v, {d, d});
    v = __hadd2(v, {m, m});
}

static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
    const block_q5_0 * x = (const block_q5_0 *) vx;

    const dfloat d = x[ib].d;

    uint32_t qh;
    memcpy(&qh, x[ib].qh, sizeof(qh));

    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;

    v.x = __int2half_rn((x[ib].qs[iqs] & 0xf) | xh_0);
    v.y = __int2half_rn((x[ib].qs[iqs] >>  4) | xh_1);

    v = __hsub2(v, __floats2half2_rn(16.0f, 16.0f));
    v = __hmul2(v, {d, d});
}

static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
    const block_q5_1 * x = (const block_q5_1 *) vx;

    const dfloat d = __low2half(x[ib].dm);
    const dfloat m = __high2half(x[ib].dm);

    uint32_t qh;
    memcpy(&qh, x[ib].qh, sizeof(qh));

    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;

    v.x = __int2half_rn((x[ib].qs[iqs] & 0xf) | xh_0);
    v.y = __int2half_rn((x[ib].qs[iqs] >>  4) | xh_1);

    v = __hmul2(v, {d, d});
    v = __hadd2(v, {m, m});
}

static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
    const block_q8_0 * x = (const block_q8_0 *) vx;

    const dfloat d = x[ib].d;

    v.x = __int2half_rn(x[ib].qs[iqs + 0]);
    v.y = __int2half_rn(x[ib].qs[iqs + 1]);

    v = __hmul2(v, {d, d});
}

template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
    const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);

    if (i >= k) {
        return;
    }

    const int ib = i/qk; // block index
    const int iqs = (i%qk)/qr; // quant index
    const int iybs = i - i%qk; // y block start index
    const int y_offset = qr == 1 ? 1 : qk/2;

    // dequantize
    dfloat2 v;
    dequantize_kernel(vx, ib, iqs, v);

    y[iybs + iqs + 0]        = v.x;
    y[iybs + iqs + y_offset] = v.y;
}

template<typename dst_t>
static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_q2_K * x = (const block_q2_K *) vx;

    const int tid = threadIdx.x;
    const int n   = tid/32;
    const int l   = tid - 32*n;
    const int is  = 8*n + l/16;

    const uint8_t q = x[i].qs[32*n + l];
    dst_t * y = yy + i*QK_K + 128*n;

    half dall = __low2half(x[i].dm);
    half dmin = __high2half(x[i].dm);
    y[l+ 0] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+0] & 0xF) * ((q >> 0) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+0] >> 4)));
    y[l+32] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+2] & 0xF) * ((q >> 2) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+2] >> 4)));
    y[l+64] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+4] & 0xF) * ((q >> 4) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+4] >> 4)));
    y[l+96] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+6] & 0xF) * ((q >> 6) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+6] >> 4)));
}

template<typename dst_t>
static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i = blockIdx.x;
    const block_q3_K * x = (const block_q3_K *) vx;

    const int r = threadIdx.x/4;
    const int tid = r/2;
    const int is0 = r%2;
    const int l0 = 16*is0 + 4*(threadIdx.x%4);
    const int n = tid / 4;
    const int j = tid - 4*n;

    uint8_t m = 1 << (4*n + j);
    int is = 8*n + 2*j + is0;
    int shift = 2*j;

    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
    half d_all = x[i].d;
    half dl = __hmul(d_all,  __int2half_rn(us - 32));

    dst_t * y = yy + i*QK_K + 128*n + 32*j;
    const uint8_t * q = x[i].qs + 32*n;
    const uint8_t * hm = x[i].hmask;

    for (int l = l0; l < l0+4; ++l) y[l] = __hmul(dl,  __int2half_rn((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)));
}

static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
    if (j < 4) {
        d = q[j] & 63; m = q[j + 4] & 63;
    } else {
        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
    }
}

template<typename dst_t>
static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
    const block_q4_K * x = (const block_q4_K *) vx;

    const int i = blockIdx.x;

    // assume 32 threads
    const int tid = threadIdx.x;
    const int il  = tid/8;
    const int ir  = tid%8;
    const int is  = 2*il;
    const int n   = 4;

    dst_t * y = yy + i*QK_K + 64*il + n*ir;

    const half dall = __low2half(x[i].dm);
    const half dmin = __high2half(x[i].dm);

    const uint8_t * q = x[i].qs + 32*il + n*ir;

    uint8_t sc, m;
    get_scale_min_k4(is + 0, x[i].scales, sc, m);
    const half d1 = __hmul(dall, __int2half_rn(sc));
    const half m1 = __hmul(dmin,  __int2half_rn(m));
    get_scale_min_k4(is + 1, x[i].scales, sc, m);
    const half d2 = __hmul(dall, __int2half_rn(sc));
    const half m2 = __hmul(dmin, __int2half_rn(m));
    for (int l = 0; l < n; ++l) {
        y[l + 0] = __hsub(__hmul(d1, __int2half_rn(q[l] & 0xF)), m1);
        y[l +32] = __hsub(__hmul(d2,  __int2half_rn(q[l] >> 4)), m2);
    }
}

template<typename dst_t>
static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
    const block_q5_K * x = (const block_q5_K *) vx;

    const int i = blockIdx.x;

    // assume 64 threads - this is very slightly better than the one below
    const int tid = threadIdx.x;
    const int il  = tid/16;   // il is in 0...3
    const int ir  = tid%16;   // ir is in 0...15
    const int is  = 2*il;     // is is in 0...6

    dst_t * y = yy + i*QK_K + 64*il + 2*ir;

    const half dall = __low2half(x[i].dm);
    const half dmin = __high2half(x[i].dm);

    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
    const uint8_t * qh = x[i].qh + 2*ir;

    uint8_t sc, m;
    get_scale_min_k4(is + 0, x[i].scales, sc, m);
    const half d1 = __hmul(dall, __int2half_rn(sc)); const half m1 = __hmul(dmin, __int2half_rn(m));
    get_scale_min_k4(is + 1, x[i].scales, sc, m);
    const half d2 = __hmul(dall, __int2half_rn(sc)); const half m2 = __hmul(dmin, __int2half_rn(m));

    uint8_t   hm  = 1 << (2*il);
    y[ 0] = __hsub(__hmul(d1, __int2half_rn((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0))), m1);
    y[ 1] = __hsub(__hmul(d1, __int2half_rn((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0))), m1);
    hm <<= 1;
    y[32] = __hsub(__hmul(d2, __int2half_rn((ql[0] >>  4) + (qh[0] & hm ? 16 : 0))), m2);
    y[33] = __hsub(__hmul(d2, __int2half_rn((ql[1] >>  4) + (qh[1] & hm ? 16 : 0))), m2);
}

template<typename dst_t>
static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
    const block_q6_K * x = (const block_q6_K *) vx;

    const int i = blockIdx.x;

    // assume 64 threads - this is very slightly better than the one below
    const int tid = threadIdx.x;
    const int ip  = tid/32;   // ip is 0 or 1
    const int il  = tid - 32*ip; // 0...32
    const int is  = 8*ip + il/16;

    dst_t * y = yy + i*QK_K + 128*ip + il;

    const half d = x[i].d;

    const uint8_t * ql = x[i].ql + 64*ip + il;
    const uint8_t   qh = x[i].qh[32*ip + il];
    const int8_t  * sc = x[i].scales + is;

    y[ 0] = __hmul(d, __int2half_rn(sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)));
    y[32] = __hmul(d, __int2half_rn(sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)));
    y[64] = __hmul(d, __int2half_rn(sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32)));
    y[96] = __hmul(d, __int2half_rn(sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32)));
}

template<typename dst_t>
static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
    const uint16_t * q2 = x[i].qs + 4*ib;
    const uint8_t  * aux8 = (const uint8_t *)q2;
    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]);
    const uint32_t aux32 = q2[2] | (q2[3] << 16);
    const float d = __half2float(x[i].d) * (0.5f + (aux32 >> 28)) * 0.25f;
    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
}

template<typename dst_t>
static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq2_xs * x = (const block_iq2_xs *) vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
    const uint16_t * q2 = x[i].qs + 4*ib;
    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
    const float d = __half2float(x[i].d) * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));

}

template<typename dst_t>
static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq2_s * x = (const block_iq2_s *) vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
    const float d = __half2float(x[i].d) * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
}

template<typename dst_t>
static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
    const uint8_t  * q3 = x[i].qs + 8*ib;
    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
    const uint32_t aux32 = gas[0] | (gas[1] << 16);
    const float d = __half2float(x[i].d) * (0.5f + (aux32 >> 28)) * 0.5f;
    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
    for (int j = 0; j < 4; ++j) {
        y[j+0] = __float2half(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
        y[j+4] = __float2half(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
    }
}

template<typename dst_t>
static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq3_s * x = (const block_iq3_s *) vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
    const uint8_t * qs = x[i].qs + 8*ib;
    const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
    const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
    const float d = __half2float(x[i].d) * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f;
    const uint8_t signs = x[i].signs[4*ib + il];
    for (int j = 0; j < 4; ++j) {
        y[j+0] = __float2half(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
        y[j+4] = __float2half(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
    }
}

template<typename dst_t>
static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq1_s * x = (const block_iq1_s  *) vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
    const int i8 = 4*ib+il;
    uint8_t h = x[i].scales[i8/2] >> 4*(i8%2);
    const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5)));
    const float d = __half2float(x[i].d) * (2*(h & 7) + 1);
    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j]);
}

template<typename dst_t>
static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {

    const int i   = blockIdx.x;
    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
    const uint8_t  * q4 = x[ib].qs + 4*il;
    const float d = __half2float(x[ib].d);
    for (int j = 0; j < 4; ++j) {
        y[j+ 0] = __float2half(d * kvalues_iq4nl[q4[j] & 0xf]);
        y[j+16] = __float2half(d * kvalues_iq4nl[q4[j] >>  4]);
    }

}

template<typename dst_t>
static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
    const int i   = blockIdx.x;
    const block_iq4_xs * x = (const block_iq4_xs *)vx;

    const int tid = threadIdx.x;
    const int il = tid/8; // 0...3
    const int ib = tid%8; // 0...7
    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
    const float d = __half2float(x[i].d) * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
    for (int j = 0; j < 4; ++j) {
        y[j+ 0] = __float2half(d * kvalues_iq4nl[q4[j] & 0xf]);
        y[j+16] = __float2half(d * kvalues_iq4nl[q4[j] >>  4]);
    }
}

template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

template<typename dst_t>
static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_iq2_xxs<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_iq2_xs<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_iq2_s<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_iq3_xxs<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_iq3_s<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_iq1_s<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = (k + QK_K - 1) / QK_K;
    dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
}

template<typename dst_t>
static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
    const int nb = (k + QK_K - 1) / QK_K;
    dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
}

static to_fp16_cuda_t ggml_get_to_fp16_cuda(int type) {
    switch (type) {
        case 2:
            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
        case 3:
            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
        case 6:
            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
        case 7:
            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
        case 8:
            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
        case 10:
            return dequantize_row_q2_K_cuda;
        case 11:
            return dequantize_row_q3_K_cuda;
        case 12:
            return dequantize_row_q4_K_cuda;
        case 13:
            return dequantize_row_q5_K_cuda;
        case 14:
            return dequantize_row_q6_K_cuda;
        case 16:
            return dequantize_row_iq2_xxs_cuda;
        case 17:
            return dequantize_row_iq2_xs_cuda;
        case 18:
            return dequantize_row_iq3_xxs_cuda;
        case 19:
            return dequantize_row_iq1_s_cuda;
        case 20:
            return dequantize_row_iq4_nl_cuda;
        case 21:
            return dequantize_row_iq3_s_cuda;
        case 22:
            return dequantize_row_iq2_s_cuda;
        case 23:
            return dequantize_row_iq4_xs_cuda;
        default:
            return nullptr;
    }
}

// GEMV
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, dfloat * __restrict__ dst, const int ncols, const int nrows) {
    // qk = quantized weights per x block
    // qr = number of quantized weights per data value in x block
    const int row = blockIdx.x*blockDim.y + threadIdx.y;

    if (row >= nrows) {
        return;
    }

    const int tid = threadIdx.x;

    const int iter_stride = 2*GGML_CUDA_DMMV_X;
    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
    const int y_offset = qr == 1 ? 1 : qk/2;

    half2 tmp = __floats2half2_rn(0.0f, 0.0f); // two sums for f16 to take advantage of half2 intrinsics

    for (int i = 0; i < ncols; i += iter_stride) {
        const int col = i + vals_per_iter*tid;
        const int ib = (row*ncols + col)/qk; // x block index
        const int iqs = (col%qk)/qr; // x quant index
        const int iybs = col - col%qk; // y block start index

// processing >2 values per i iter is faster for fast GPUs
#pragma unroll
        for (int j = 0; j < vals_per_iter; j += 2) {
            // process 2 vals per j iter

            // dequantize
            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
            dfloat2 v;
            dequantize_kernel(vx, ib, iqs + j/qr, v);

            // matrix multiplication
            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
            tmp = __hadd2(tmp, __hmul2(v, {
                y[iybs + iqs + j/qr + 0],
                y[iybs + iqs + j/qr + y_offset]
            }));
        }
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp = __hadd2(tmp, __shfl_xor_sync(0xffffffff, tmp, mask, 32));
    }

    if (tid == 0) {
        dst[row] = __hadd(tmp.x, tmp.y);
    }
}


static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols, int nrows) {

    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");

    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    if (row > nrows) return;

    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_q2_K * x = (const block_q2_K *)vx + ib0;

    float tmp = 0; // partial sum for thread in warp

    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1

    const int step = 16/K_QUANTS_PER_ITERATION;

    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
    const int in = tid - step*im;                        // 0...15 or 0...7

    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
    const int q_offset = 32*im + l0;
    const int s_offset = 8*im;
    const int y_offset = 128*im + l0;

    uint32_t aux[4];
    const uint8_t * d = (const uint8_t *)aux;
    const uint8_t * m = (const uint8_t *)(aux + 2);

    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {

        const half    * y = yy + i * QK_K + y_offset;
        const uint8_t * q = x[i].qs + q_offset;

        const float dall = __low2float(x[i].dm);
        const float dmin = __high2float(x[i].dm);

        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
        aux[0] = a[0] & 0x0f0f0f0f;
        aux[1] = a[1] & 0x0f0f0f0f;
        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;

        float sum1 = 0, sum2 = 0;
        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
            sum1 += __half2float(y[l+ 0]) * d[0] * ((q[l+ 0] >> 0) & 3)
                  + __half2float(y[l+32]) * d[2] * ((q[l+ 0] >> 2) & 3)
                  + __half2float(y[l+64]) * d[4] * ((q[l+ 0] >> 4) & 3)
                  + __half2float(y[l+96]) * d[6] * ((q[l+ 0] >> 6) & 3)
                  + __half2float(y[l+16]) * d[1] * ((q[l+16] >> 0) & 3)
                  + __half2float(y[l+48]) * d[3] * ((q[l+16] >> 2) & 3)
                  + __half2float(y[l+80]) * d[5] * ((q[l+16] >> 4) & 3)
                  +__half2float(y[l+112]) * d[7] * ((q[l+16] >> 6) & 3);
            sum2 += __half2float(y[l+ 0]) * m[0] + __half2float(y[l+32]) * m[2] + __half2float(y[l+64]) * m[4] + __half2float(y[ l+96]) * m[6]
                  + __half2float(y[l+16]) * m[1] + __half2float(y[l+48]) * m[3] + __half2float(y[l+80]) * m[5] + __half2float(y[l+112]) * m[7];

        }
        tmp += dall * sum1 - dmin * sum2;

    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (threadIdx.x == 0) {
        dst[row] = __float2half(tmp);
    }
}

static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols, int nrows) {

    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    if (row > nrows) return;

    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_q3_K * x = (const block_q3_K *)vx + ib0;

    float tmp = 0; // partial sum for thread in warp

    const uint16_t kmask1 = 0x0303;
    const uint16_t kmask2 = 0x0f0f;

    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1

    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
    const int step = 16/K_QUANTS_PER_ITERATION;
    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
    const int in = tid - step*im;                        // 0....15 or 0...7

    const uint8_t m = 1 << (4*im);

    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
    const int q_offset =  32*im + l0;
    const int y_offset = 128*im + l0;

    uint16_t utmp[4];
    const int8_t * s = (const int8_t *)utmp;

    const uint16_t s_shift = 4*im;

    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {

        const half    * y  = yy + i * QK_K + y_offset;
        const uint8_t * q = x[i].qs + q_offset;
        const uint8_t * h = x[i].hmask + l0;

        const uint16_t * a = (const uint16_t *)x[i].scales;
        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);

        const float d = __half2float(x[i].d);

        float sum = 0;
        for (int l = 0; l < n; ++l) {
            sum += __half2float(y[l+ 0]) * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
                 + __half2float(y[l+32]) * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
                 + __half2float(y[l+64]) * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
                 + __half2float(y[l+96]) * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
            sum += __half2float(y[l+16]) * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
                 + __half2float(y[l+48]) * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
                 + __half2float(y[l+80]) * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
                + __half2float(y[l+112]) * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
        }
        tmp += d * sum;

    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (threadIdx.x == 0) {
        dst[row] = __float2half(tmp);
    }
}

static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols, int nrows) {

    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    if (row > nrows) return;
    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_q4_K * x = (const block_q4_K *)vx + ib0;

    const uint16_t kmask1 = 0x3f3f;
    const uint16_t kmask2 = 0x0f0f;
    const uint16_t kmask3 = 0xc0c0;

    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1

    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4

    const int il  = tid/step;                            // 0...3
    const int ir  = tid - step*il;                       // 0...7 or 0...3
    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4

    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
    const int in = il%2;

    const int l0 = n*(2*ir + in);
    const int q_offset = 32*im + l0;
    const int y_offset = 64*im + l0;

    uint16_t aux[4];
    const uint8_t * sc = (const uint8_t *)aux;

#if K_QUANTS_PER_ITERATION == 2
    uint32_t q32[4];
    const uint8_t * q4 = (const uint8_t *)q32;
#else
    uint16_t q16[4];
    const uint8_t * q4 = (const uint8_t *)q16;
#endif

    float tmp = 0; // partial sum for thread in warp

    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {

        const half   * y1 = yy + i*QK_K + y_offset;
        const half   * y2 = y1 + 128;

        const float dall = __low2float(x[i].dm);
        const float dmin = __high2float(x[i].dm);

        const uint16_t * a = (const uint16_t *)x[i].scales;
        aux[0] = a[im+0] & kmask1;
        aux[1] = a[im+2] & kmask1;
        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);

#if K_QUANTS_PER_ITERATION == 2
        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
        const uint32_t * q2 = q1 + 16;

        q32[0] = q1[0] & 0x0f0f0f0f;
        q32[1] = q1[0] & 0xf0f0f0f0;
        q32[2] = q2[0] & 0x0f0f0f0f;
        q32[3] = q2[0] & 0xf0f0f0f0;

        float4 s = {0.f, 0.f, 0.f, 0.f};
        float smin = 0;
        for (int l = 0; l < 4; ++l) {
            s.x += __half2float(y1[l]) * q4[l+0]; s.y += __half2float(y1[l+32]) * q4[l+ 4];
            s.z += __half2float(y2[l]) * q4[l+8]; s.w += __half2float(y2[l+32]) * q4[l+12];
            smin += __half2float(y1[l]) * sc[2] + __half2float(y1[l+32]) * sc[3] + __half2float(y2[l]) * sc[6] + __half2float(y2[l+32]) * sc[7];
        }
        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
#else
        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
        const uint16_t * q2 = q1 + 32;

        q16[0] = q1[0] & 0x0f0f;
        q16[1] = q1[0] & 0xf0f0;
        q16[2] = q2[0] & 0x0f0f;
        q16[3] = q2[0] & 0xf0f0;

        float4 s = {0.f, 0.f, 0.f, 0.f};
        float smin = 0;
        for (int l = 0; l < 2; ++l) {
            s.x += __half2float(y1[l]) * q4[l+0]; s.y += __half2float(y1[l+32]) * q4[l+2];
            s.z += __half2float(y2[l]) * q4[l+4]; s.w += __half2float(y2[l+32]) * q4[l+6];
            smin += __half2float(y1[l]) * sc[2] + __half2float(y1[l+32]) * sc[3] + __half2float(y2[l]) * sc[6] + __half2float(y2[l+32]) * sc[7];
        }
        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
#endif

    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (tid == 0) {
        dst[row] = __float2half(tmp);
    }
}

static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols) {

    const int row = blockIdx.x;
    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_q5_K * x = (const block_q5_K *)vx + ib0;

    float tmp = 0; // partial sum for thread in warp

    const uint16_t kmask1 = 0x3f3f;
    const uint16_t kmask2 = 0x0f0f;
    const uint16_t kmask3 = 0xc0c0;

    const int tid = threadIdx.x/2;  // 0...15
    const int ix  = threadIdx.x%2;

    const int il  = tid/4;     // 0...3
    const int ir  = tid - 4*il;// 0...3
    const int n   = 2;

    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
    const int in = il%2;

    const int l0 = n*(2*ir + in);
    const int q_offset = 32*im + l0;
    const int y_offset = 64*im + l0;

    const uint8_t hm1  = 1 << (2*im);
    const uint8_t hm2  = hm1 << 4;

    uint16_t aux[4];
    const uint8_t * sc = (const uint8_t *)aux;

    uint16_t q16[8];
    const uint8_t * q4 = (const uint8_t *)q16;

    for (int i = ix; i < num_blocks_per_row; i += 2) {

        const uint8_t * ql1 = x[i].qs + q_offset;
        const uint8_t * qh  = x[i].qh + l0;
        const half    * y1  = yy + i*QK_K + y_offset;
        const half    * y2  = y1 + 128;

        const float dall = __low2float(x[i].dm);
        const float dmin = __high2float(x[i].dm);

        const uint16_t * a = (const uint16_t *)x[i].scales;
        aux[0] = a[im+0] & kmask1;
        aux[1] = a[im+2] & kmask1;
        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);

        float4 sum = {0.f, 0.f, 0.f, 0.f};
        float smin = 0;
        const uint16_t * q1 = (const uint16_t *)ql1;
        const uint16_t * q2 = q1 + 32;
        q16[0] = q1[0] & 0x0f0f;
        q16[1] = q1[8] & 0x0f0f;
        q16[2] = (q1[0] >> 4) & 0x0f0f;
        q16[3] = (q1[8] >> 4) & 0x0f0f;
        q16[4] = q2[0] & 0x0f0f;
        q16[5] = q2[8] & 0x0f0f;
        q16[6] = (q2[0] >> 4) & 0x0f0f;
        q16[7] = (q2[8] >> 4) & 0x0f0f;
        for (int l = 0; l < n; ++l) {
            sum.x += __half2float(y1[l+ 0]) * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
                   + __half2float(y1[l+16]) * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
            sum.y += __half2float(y1[l+32]) * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
                   + __half2float(y1[l+48]) * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
            sum.z += __half2float(y2[l+ 0]) * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
                   + __half2float(y2[l+16]) * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
            sum.w += __half2float(y2[l+32]) * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
                   + __half2float(y2[l+48]) * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
            smin += (__half2float(y1[l]) + __half2float(y1[l+16])) * sc[2] + (__half2float(y1[l+32]) + __half2float(y1[l+48])) * sc[3]
                  + (__half2float(y2[l]) + __half2float(y2[l+16])) * sc[6] + (__half2float(y2[l+32]) + __half2float(y2[l+48])) * sc[7];
        }
        tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (threadIdx.x == 0) {
        dst[row] = __float2half(tmp);
    }
}

static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols, int nrows) {

    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");

    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    if (row > nrows) return;

    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_q6_K * x = (const block_q6_K *)vx + ib0;

    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1

    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8

    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
    const int in = tid - step*im;                        // 0...15 or 0...7

#if K_QUANTS_PER_ITERATION == 1
    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
    const int is = 0;
#else
    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
    const int is = in / 4;
#endif
    const int ql_offset = 64*im + l0;
    const int qh_offset = 32*im + l0;
    const int s_offset  =  8*im + is;
    const int y_offset = 128*im + l0;

    float tmp = 0; // partial sum for thread in warp

    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {

        const half    * y  = yy + i * QK_K + y_offset;
        const uint8_t * ql = x[i].ql + ql_offset;
        const uint8_t * qh = x[i].qh + qh_offset;
        const int8_t  * s  = x[i].scales + s_offset;

        const float d = __half2float(x[i].d);

#if K_QUANTS_PER_ITERATION == 1
        float sum = __half2float(y[ 0]) * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
                  + __half2float(y[16]) * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
                  + __half2float(y[32]) * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
                  + __half2float(y[48]) * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
                  + __half2float(y[64]) * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
                  + __half2float(y[80]) * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
                  + __half2float(y[96]) * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
                  +__half2float(y[112]) * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
        tmp += sum;
#else
        float sum = 0;
        for (int l = 0; l < 4; ++l) {
            sum += __half2float(y[l+ 0]) * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
                 + __half2float(y[l+32]) * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
                 + __half2float(y[l+64]) * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
                 + __half2float(y[l+96]) * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
        }
        tmp += sum;
#endif

    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (tid == 0) {
        dst[row] = __float2half(tmp);
    }
}

static __global__ void dequantize_mul_mat_vec_iq2_xxs(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols, int nrows) {
    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    if (row > nrows) return;

    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_iq2_xxs * x = (const block_iq2_xxs *)vx + ib0;

    float tmp = 0; // partial sum for thread in warp

    const int tid = threadIdx.x/4;
    const int ix  = threadIdx.x%4;

    const int q_offset = tid * 4;
    const int y_offset = tid * 32;

    for (int i = ix; i < num_blocks_per_row; i += 4) {

        const half    * y = yy + i * QK_K + y_offset;
        const uint16_t * q = x[i].qs + q_offset;

        const uint8_t  * aux8 = (const uint8_t *)q;
        uint32_t aux32 = q[2] | (q[3] << 16);
        float sumi = 0;
        for (int l = 0; l < 4; ++l) {
            const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
            const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
            for (int j = 0; j < 8; ++j) {
                sumi += __half2float(y[j]) * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
            }
            y += 8;
            aux32 >>= 7;
        }
        tmp += sumi * __half2float(x[i].d) * (0.5f + aux32) * 0.25f;;
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (threadIdx.x == 0) {
        dst[row] = __float2half(tmp);
    }
}

static __global__ void dequantize_mul_mat_vec_iq2_xs(const void * __restrict__ vx, const dfloat * __restrict__ yy, dfloat * __restrict__ dst, const int ncols, int nrows) {
    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    if (row > nrows) return;

    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

    const block_iq2_xs * x = (const block_iq2_xs *)vx + ib0;

    float tmp = 0; // partial sum for thread in warp

    const int tid = threadIdx.x/4;
    const int ix  = threadIdx.x%4;

    const int q_offset = tid * 4;
    const int s_offset = tid;
    const int y_offset = tid * 32;

    for (int i = ix; i < num_blocks_per_row; i += 4) {
        const half    * y = yy + i * QK_K + y_offset;
        const uint16_t * q = x[i].qs + q_offset;
        const uint8_t ls1 = x[i].scales[s_offset] & 0xf;
        const uint8_t ls2 = x[i].scales[s_offset] >>  4;

        float sumi1 = 0;
        for (int l = 0; l < 2; ++l) {
            const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q[l] & 511));
            const uint8_t  signs = ksigns_iq2xs[q[l] >> 9];
            for (int j = 0; j < 8; ++j) {
                sumi1 += __half2float(y[j]) * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
            }
            y += 8;
        }
        float sumi2 = 0;
        for (int l = 2; l < 4; ++l) {
            const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q[l] & 511));
            const uint8_t  signs = ksigns_iq2xs[q[l] >> 9];
            for (int j = 0; j < 8; ++j) {
                sumi2 += __half2float(y[j]) * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
            }
            y += 8;
        }
        const float d = __half2float(x[i].d) * 0.25f;
        tmp += d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);;
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (threadIdx.x == 0) {
        dst[row] = __float2half(tmp);
    }
}

static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
    const int block_num_y = (nrows + ny - 1) / ny;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(32, ny, 1);
    dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int ny = 2 / K_QUANTS_PER_ITERATION;
    const int block_num_y = (nrows + ny - 1) / ny;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(32, ny, 1);
    dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int ny = 2 / K_QUANTS_PER_ITERATION;
    const int block_num_y = (nrows + ny - 1) / ny;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(32, ny, 1);
    dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const dim3 block_dims(32, 1, 1);
    dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int ny = 2 / K_QUANTS_PER_ITERATION;
    const int block_num_y = (nrows + ny - 1) / ny;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(32, ny, 1);
    dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_iq2_xxs_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const dim3 block_dims(32, 1, 1);
    dequantize_mul_mat_vec_iq2_xxs<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

static void dequantize_mul_mat_vec_iq2_xs_cuda(const void * vx, const dfloat * y, dfloat * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const dim3 block_dims(32, 1, 1);
    dequantize_mul_mat_vec_iq2_xs<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

// Q8 gemv
static __global__ void quantize_q8_1(const half * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
    const int ix = blockDim.x*blockIdx.x + threadIdx.x;
    if (ix >= kx_padded) {
        return;
    }
    const int iy = blockDim.y*blockIdx.y + threadIdx.y;
    const int i_padded = iy*kx_padded + ix;

    block_q8_1 * y = (block_q8_1 *) vy;

    const int ib = i_padded / QK8_1; // block index
    const int iqs = i_padded % QK8_1; // quant index

    const float xi = ix < kx ? __half2float(x[iy*kx + ix]) : 0.0f;
    float amax = fabsf(xi);
    float sum = xi;

#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
    }

    const float d = amax / 127;
    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);

    y[ib].qs[iqs] = q;

    if (iqs > 0) {
        return;
    }

    y[ib].ds.x = __float2half(d);
    y[ib].ds.y = __float2half(sum);
}

static void quantize_row_q8_1_cuda(const half * x, void * vy, const int kx, const int ky, cudaStream_t stream) {
    const int64_t kx_padded = (kx + 512 - 1) / 512 * 512;
    const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
    const dim3 num_blocks(block_num_x, ky, 1);
    const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
}

#define VDR_Q4_0_Q8_1_MMVQ 2
#define VDR_Q4_0_Q8_1_MMQ  4

template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
    const int * v, const int * u, const float & d4, const half2 & ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi = 0;

#pragma unroll
    for (int i = 0; i < vdr; ++i) {
        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;

        // SIMD dot product of quantized values
        sumi = __dp4a(vi0, u[2*i+0], sumi);
        sumi = __dp4a(vi1, u[2*i+1], sumi);
    }

    const float2 ds8f = __half22float2(ds8);

    // second part effectively subtracts 8 from each quant value
    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
#endif
}

#define VDR_Q4_1_Q8_1_MMVQ 2
#define VDR_Q4_1_Q8_1_MMQ  4

template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi = 0;

#pragma unroll
    for (int i = 0; i < vdr; ++i) {
        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;

        // SIMD dot product of quantized values
        sumi = __dp4a(vi0, u[2*i+0], sumi);
        sumi = __dp4a(vi1, u[2*i+1], sumi);
    }

    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
    const float d4d8 = tmp.x;
    const float m4s8 = tmp.y;

    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
#endif
}

#define VDR_Q5_0_Q8_1_MMVQ 2
#define VDR_Q5_0_Q8_1_MMQ  4

template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi = 0;

#pragma unroll
    for (int i = 0; i < vdr; ++i) {
        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values

        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
    }

    const float2 ds8f = __half22float2(ds8);

    // second part effectively subtracts 16 from each quant value
    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
#endif
}


#define VDR_Q5_1_Q8_1_MMVQ 2
#define VDR_Q5_1_Q8_1_MMQ  4

template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi = 0;

#pragma unroll
    for (int i = 0; i < vdr; ++i) {
        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values

        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
    }

    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
    const float d5d8 = tmp.x;
    const float m5s8 = tmp.y;

    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
#endif
}

#define VDR_Q8_0_Q8_1_MMVQ 2
#define VDR_Q8_0_Q8_1_MMQ 8

template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
    const int * v, const int * u, const float & d8_0, const float & d8_1) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi = 0;

#pragma unroll
    for (int i = 0; i < vdr; ++i) {
        // SIMD dot product of quantized values
        sumi = __dp4a(v[i], u[i], sumi);
    }
    return d8_0*d8_1 * sumi;
#endif
}

template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610

    int sumi = 0;

#pragma unroll
    for (int i = 0; i < vdr; ++i) {
        // SIMD dot product of quantized values
        sumi = __dp4a(v[i], u[i], sumi);
    }

    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
    const float d8d8 = tmp.x;
    const float m8s8 = tmp.y;

    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
#endif
}

#define VDR_Q2_K_Q8_1_MMVQ 1
#define VDR_Q2_K_Q8_1_MMQ  2

// contiguous v/x values
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
    const half2 & dm2, const float * __restrict__ d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    float sumf_d = 0.0f;
    float sumf_m = 0.0f;

#pragma unroll
    for (int i = 0; i < QR2_K; ++i) {
        const int sc = scales[2*i];

        const int vi = (v >> (2*i)) & 0x03030303;

        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product

        // fill int with 4x m
        int m = sc >> 4;
        m |= m <<  8;
        m |= m << 16;
        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
    }

    const float2 dm2f = __half22float2(dm2);

    return dm2f.x*sumf_d - dm2f.y*sumf_m;
#endif
}

static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
    const half2 & dm2, const float & d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi_d = 0;
    int sumi_m = 0;

#pragma unroll
    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
        int sumi_d_sc = 0;

        const int sc = scales[i0 / (QI8_1/2)];

        // fill int with 4x m
        int m = sc >> 4;
        m |= m <<  8;
        m |= m << 16;

#pragma unroll
        for (int i = i0; i < i0 + QI8_1/2; ++i) {
            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
        }

        sumi_d += sumi_d_sc * (sc & 0xF);
    }

    const float2 dm2f = __half22float2(dm2);

    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
#endif
}

#define VDR_Q3_K_Q8_1_MMVQ 1
#define VDR_Q3_K_Q8_1_MMQ  2

// contiguous v/x values
static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610

    float sumf = 0.0f;

#pragma unroll
    for (int i = 0; i < QR3_K; ++i) {
        const int isc = scale_offset + 2*i;

        const int isc_low = isc % (QK_K/32);
        const int sc_shift_low = 4 * (isc / (QK_K/32));
        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;

        const int isc_high = isc % (QK_K/64);
        const int sc_shift_high = 2 * (isc / (QK_K/64));
        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;

        const int sc = (sc_low | sc_high) - 32;

        const int vil = (vl >> (2*i)) & 0x03030303;

        const int vih = ((vh >> i) << 2) & 0x04040404;

        const int vi = __vsubss4(vil, vih);

        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
    }

    return d3 * sumf;
#endif
}

static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
    const float & d3, const float & d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    int sumi = 0;

#pragma unroll
    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
        int sumi_sc = 0;

        for (int i = i0; i < i0 + QI8_1/2; ++i) {
            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
        }

        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
    }

    return d3*d8 * sumi;
#endif
}

#define VDR_Q4_K_Q8_1_MMVQ 2
#define VDR_Q4_K_Q8_1_MMQ  8

// contiguous v/x values
static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610

    float sumf_d = 0.0f;
    float sumf_m = 0.0f;

#pragma unroll
    for (int i = 0; i < QR4_K; ++i) {
        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;

        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u

        sumf_d += d8[i] * (dot1 * sc[i]);
        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
    }

    const float2 dm4f = __half22float2(dm4);
    return dm4f.x*sumf_d - dm4f.y*sumf_m;
#endif
}

static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    float sumf_d = 0.0f;
    float sumf_m = 0.0f;

#pragma unroll
    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
        int sumi_d = 0;

#pragma unroll
        for (int j = 0; j < QI8_1; ++j) {
            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
        }

        const float2 ds8f = __half22float2(ds8[i]);

        sumf_d += ds8f.x * (sc[i] * sumi_d);
        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
    }

    const float2 dm4f = __half22float2(dm4);

    return dm4f.x*sumf_d - dm4f.y*sumf_m;
#endif
}

#define VDR_Q5_K_Q8_1_MMVQ 2
#define VDR_Q5_K_Q8_1_MMQ  8

static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610

    float sumf_d = 0.0f;
    float sumf_m = 0.0f;

#pragma unroll
    for (int i = 0; i < QR5_K; ++i) {
        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;

        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;

        const int v0i = vl0i | vh0i;
        const int v1i = vl1i | vh1i;

        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u

        sumf_d += d8[i] * (dot1 * sc[i]);
        sumf_m += d8[i] * (dot2 * m[i]);
    }

    const float2 dm5f = __half22float2(dm5);
    return dm5f.x*sumf_d - dm5f.y*sumf_m;
#endif
}

static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    float sumf_d = 0.0f;
    float sumf_m = 0.0f;

#pragma unroll
    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
        int sumi_d = 0;

#pragma unroll
        for (int j = 0; j < QI8_1; ++j) {
            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
        }

        const float2 ds8f = __half22float2(ds8[i]);

        sumf_d += ds8f.x * (sc[i] * sumi_d);
        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
    }

    const float2 dm4f = __half22float2(dm4);

    return dm4f.x*sumf_d - dm4f.y*sumf_m;
#endif
}

#define VDR_Q6_K_Q8_1_MMVQ 1
#define VDR_Q6_K_Q8_1_MMQ  8

// contiguous v/x values
static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
    const float & d, const float * __restrict__ d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    float sumf = 0.0f;

#pragma unroll
    for (int i = 0; i < QR6_K; ++i) {
        const int sc = scales[4*i];
        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32

        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
    }

    return d*sumf;
#endif
}

static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
    const float & d6, const float * __restrict__ d8) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    float sumf_d = 0.0f;

#pragma unroll
    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale

#pragma unroll
        for (int i = i0; i < i0 + 2; ++i) {
            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product

            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
        }

        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
    }

    return d6 * sumf_d;
#endif
}

static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;

    int v[VDR_Q4_0_Q8_1_MMVQ];
    int u[2*VDR_Q4_0_Q8_1_MMVQ];

#pragma unroll
    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
    }

    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, __half2float(bq4_0->d), bq8_1->ds);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
    *x_ql = tile_x_qs;
    *x_dm = (half2 *) tile_x_d;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI4_0;
    const int kqsx = k % QI4_0;

    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
    float * x_dmf = (float *) x_dm;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
    const int kbxd = k % blocks_per_tile_x_row;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = __half2float(bxi->d);
    }
}

static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    (void)x_qh; (void)x_sc;

    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
    const float * x_dmf = (const float *) x_dm;

    int u[2*VDR_Q4_0_Q8_1_MMQ];

#pragma unroll
    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
    }

    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
}

static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;

    int v[VDR_Q4_1_Q8_1_MMVQ];
    int u[2*VDR_Q4_1_Q8_1_MMVQ];

#pragma unroll
    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
    }

    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
    *x_ql = tile_x_qs;
    *x_dm = tile_x_dm;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI4_1;
    const int kqsx = k % QI4_1;

    const block_q4_1 * bx0 = (const block_q4_1 *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
    const int kbxd = k % blocks_per_tile_x_row;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
    }
}

static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));

    int u[2*VDR_Q4_1_Q8_1_MMQ];

#pragma unroll
    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
    }

    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
}

static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;

    int vl[VDR_Q5_0_Q8_1_MMVQ];
    int vh[VDR_Q5_0_Q8_1_MMVQ];
    int  u[2*VDR_Q5_0_Q8_1_MMVQ];

#pragma unroll
    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
    }

    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, __half2float(bq5_0->d), bq8_1->ds);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];

    *x_ql = tile_x_ql;
    *x_dm = (half2 *) tile_x_d;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI5_0;
    const int kqsx = k % QI5_0;

    const block_q5_0 * bx0 = (const block_q5_0 *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
        const int ql = get_int_from_uint8(bxi->qs, kqsx);
        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));

        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16

        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;

        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16

        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
    const int kbxd = k % blocks_per_tile_x_row;
    float * x_dmf = (float *) x_dm;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = __half2float(bxi->d);
    }
}

static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
    const float * x_dmf = (const float *) x_dm;
    const float * y_df  = (const float *) y_ds;

    int u[2*VDR_Q5_0_Q8_1_MMQ];

#pragma unroll
    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
    }

    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
}

static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;

    int vl[VDR_Q5_1_Q8_1_MMVQ];
    int vh[VDR_Q5_1_Q8_1_MMVQ];
    int  u[2*VDR_Q5_1_Q8_1_MMVQ];

#pragma unroll
    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
    }

    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];

    *x_ql = tile_x_ql;
    *x_dm = tile_x_dm;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI5_1;
    const int kqsx = k % QI5_1;

    const block_q5_1 * bx0 = (const block_q5_1 *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;

        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));

        int qs0 = (ql >>  0) & 0x0F0F0F0F;
        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28

        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;

        int qs1 = (ql >>  4) & 0x0F0F0F0F;
        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28

        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
    const int kbxd = k % blocks_per_tile_x_row;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;

        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
    }
}

static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;

    int u[2*VDR_Q5_1_Q8_1_MMQ];

#pragma unroll
    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
    }

    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
}

static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;

    int v[VDR_Q8_0_Q8_1_MMVQ];
    int u[VDR_Q8_0_Q8_1_MMVQ];

#pragma unroll
    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
    }

    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, __half2float(bq8_0->d), __low2float(bq8_1->ds));
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];

    *x_ql = tile_x_qs;
    *x_dm = (half2 *) tile_x_d;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI8_0;
    const int kqsx = k % QI8_0;
    float * x_dmf = (float *) x_dm;

    const block_q8_0 * bx0 = (const block_q8_0 *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
    const int kbxd = k % blocks_per_tile_x_row;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = __half2float(bxi->d);
    }
}

static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const float * x_dmf = (const float *) x_dm;
    const float * y_df  = (const float *) y_ds;

    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
}

static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q2_K * bq2_K = (const block_q2_K *) vbq;

    const int bq8_offset = QR2_K * (iqs / QI8_1);
    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);

    const uint8_t * scales = bq2_K->scales + scale_offset;

    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
    int    u[QR2_K];
    float d8[QR2_K];

#pragma unroll
    for (int i = 0; i < QR2_K; ++ i) {
        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
    }

    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];

    *x_ql = tile_x_ql;
    *x_dm = tile_x_dm;
    *x_sc = tile_x_sc;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI2_K;
    const int kqsx = k % QI2_K;

    const block_q2_K * bx0 = (const block_q2_K *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
    const int kbxd = k % blocks_per_tile_x_row;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
    }

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
    }
}

static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const int kbx = k / QI2_K;
    const int ky  = (k % QI2_K) * QR2_K;
    const float * y_df = (const float *) y_ds;

    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];

    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));

#pragma unroll
    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
    }

    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;

    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
}

static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q3_K * bq3_K = (const block_q3_K *) vbq;

    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);

    const float d = __half2float(bq3_K->d);

    const int vl = get_int_from_uint8(bq3_K->qs, iqs);

    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;

    int    u[QR3_K];
    float d8[QR3_K];

#pragma unroll
    for (int i = 0; i < QR3_K; ++i) {
        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
    }

    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];

    *x_ql = tile_x_ql;
    *x_dm = tile_x_dm;
    *x_qh = tile_x_qh;
    *x_sc = tile_x_sc;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI3_K;
    const int kqsx = k % QI3_K;

    const block_q3_K * bx0 = (const block_q3_K *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
    const int kbxd = k % blocks_per_tile_x_row;
    float * x_dmf = (float *) x_dm;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = __half2float(bxi->d);
    }

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
    }

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);

        const int ksc = k % (QI3_K/4);

        const int ksc_low = ksc % (QI3_K/8);
        const int shift_low = 4 * (ksc / (QI3_K/8));
        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;

        const int ksc_high = QI3_K/8;
        const int shift_high = 2 * ksc;
        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;

        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);

        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
    }
}

static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {

    const int kbx  = k / QI3_K;
    const int ky  = (k % QI3_K) * QR3_K;
    const float * x_dmf = (const float *) x_dm;
    const float * y_df  = (const float *) y_ds;

    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;

    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];

#pragma unroll
    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
        const int shift = 2 * ((ky % 32) / 8);
        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;

        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
        const int vlh = (vh << 2) & 0x04040404;

        v[l] = __vsubss4(vll, vlh);
    }

    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
}

static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
    const block_q4_K * bq4_K = (const block_q4_K *) vbq;

    int    v[2];
    int    u[2*QR4_K];
    float d8[QR4_K];

    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));

    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108

    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
    v[0] = q4[0];
    v[1] = q4[4];

    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
    uint16_t aux[2];
    const int j = bq8_offset/2;
    if (j < 2) {
        aux[0] = scales[j+0] & 0x3f3f;
        aux[1] = scales[j+2] & 0x3f3f;
    } else {
        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
    }
    const uint8_t * sc = (const uint8_t *)aux;
    const uint8_t * m  = sc + 2;

    for (int i = 0; i < QR4_K; ++i) {
        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
        d8[i] = __low2float(bq8i->ds);

        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
        u[2*i+0] = q8[0];
        u[2*i+1] = q8[4];
    }

    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];

    *x_ql = tile_x_ql;
    *x_dm = tile_x_dm;
    *x_sc = tile_x_sc;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
    const int kqsx = k % QI4_K; // == k if QK_K == 256

    const block_q4_K * bx0 = (const block_q4_K *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }
        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
        if (need_check) {
            i = min(i, i_max);
        }
        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
    }

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);

        const int * scales = (const int *) bxi->scales;

        const int ksc = k % (WARP_SIZE/8);
        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits

        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
    }
}

static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    (void)x_qh;

    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);

    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
}

static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q5_K * bq5_K = (const block_q5_K *) vbq;

    int   vl[2];
    int   vh[2];
    int    u[2*QR5_K];
    float d8[QR5_K];

    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));

    vl[0] = ql[0];
    vl[1] = ql[4];

    vh[0] = qh[0] >> bq8_offset;
    vh[1] = qh[4] >> bq8_offset;

    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
    uint16_t aux[2];
    const int j = bq8_offset/2;
    if (j < 2) {
        aux[0] = scales[j+0] & 0x3f3f;
        aux[1] = scales[j+2] & 0x3f3f;
    } else {
        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
    }
    const uint8_t * sc = (const uint8_t *)aux;
    const uint8_t * m  = sc + 2;

#pragma unroll
    for (int i = 0; i < QR5_K; ++i) {
        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
        d8[i] = __low2float(bq8i->ds);

        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
        u[2*i+0] = q8[0];
        u[2*i+1] = q8[4];
    }

    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];

    *x_ql = tile_x_ql;
    *x_dm = tile_x_dm;
    *x_sc = tile_x_sc;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
    const int kqsx = k % QI5_K; // == k if QK_K == 256

    const block_q5_K * bx0 = (const block_q5_K *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
        const int ky = QR5_K*kqsx;

        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
        const int ql1 = (ql >> 4) & 0x0F0F0F0F;

        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;

        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);

        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
    }

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);

        const int * scales = (const int *) bxi->scales;

        const int ksc = k % (WARP_SIZE/8);

        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits

        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
    }
}

static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);

    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
}

static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {

    const block_q6_K * bq6_K = (const block_q6_K *) vbq;

    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));

    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;

    const int8_t * scales = bq6_K->scales + scale_offset;

    int    u[QR6_K];
    float d8[QR6_K];

#pragma unroll
    for (int i = 0; i < QR6_K; ++i) {
        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
        d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds);
    }

    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, __half2float(bq6_K->d), d8);
}

template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];

    *x_ql = tile_x_ql;
    *x_dm = tile_x_dm;
    *x_sc = tile_x_sc;
}

template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
    const int kqsx = k % QI6_K; // == k if QK_K == 256

    const block_q6_K * bx0 = (const block_q6_K *) vx;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
        int i = i0 + i_offset;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
        const int ky = QR6_K*kqsx;

        const int ql = get_int_from_uint8(bxi->ql, kqsx);
        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
        const int ql1 = (ql >> 4) & 0x0F0F0F0F;

        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;

        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);

        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
    }

    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
    float * x_dmf = (float *) x_dm;

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;

        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = __half2float(bxi->d);
    }

#pragma unroll
    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;

        if (need_check) {
            i = min(i, i_max);
        }

        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;

        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
    }
}

static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
    const float * x_dmf = (const float *) x_dm;
    const float * y_df  = (const float *) y_ds;

    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);

    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
}

static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;

    const int ib32 = iqs;
    const uint16_t * q2 = bq2->qs + 4*ib32;
    const uint8_t  * aux8 = (const uint8_t *)q2;
    const int8_t   * q8 = bq8_1[ib32].qs;
    uint32_t aux32 = q2[2] | (q2[3] << 16);
    int sumi = 0;
    for (int l = 0; l < 4; ++l) {
        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
        for (int j = 0; j < 8; ++j) {
            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
        }
        q8 += 8;
        aux32 >>= 7;
    }
    const float d = __half2float(bq2->d) * (0.5f + aux32) * __half2float(bq8_1[ib32].ds.x) * 0.25f;
    return d * sumi;
}

static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;

    const int ib32 = iqs;
    const uint16_t * q2 = bq2->qs + 4*ib32;
    const int8_t   * q8 = bq8_1[ib32].qs;
    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
    const uint8_t ls2 = bq2->scales[ib32] >>  4;
    int sumi1 = 0;
    for (int l = 0; l < 2; ++l) {
        const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
        const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
        for (int j = 0; j < 8; ++j) {
            sumi1 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
        }
        q8 += 8;
    }
    int sumi2 = 0;
    for (int l = 2; l < 4; ++l) {
        const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
        const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
        for (int j = 0; j < 8; ++j) {
            sumi2 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
        }
        q8 += 8;
    }
    const float d = __half2float(bq2->d) * __half2float(bq8_1[ib32].ds.x) * 0.25f;
    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
}

static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;

    const int ib32 = iqs;
    const int8_t  * q8 = bq8_1[ib32].qs;
    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
    const uint8_t ls2 = bq2->scales[ib32] >>  4;
    int sumi1 = 0;
    for (int l = 0; l < 2; ++l) {
        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
        q8 += 8;
    }
    int sumi2 = 0;
    for (int l = 2; l < 4; ++l) {
        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
        q8 += 8;
    }
    const float d = __half2float(bq2->d) * __low2float(bq8_1[ib32].ds) * 0.25f;
    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
#endif
}

static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;

    const int ib32 = iqs;
    const uint8_t  * q3 = bq2->qs + 8*ib32;
    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
    const int8_t   * q8 = bq8_1[ib32].qs;
    uint32_t aux32 = gas[0] | (gas[1] << 16);
    int sumi = 0;
    for (int l = 0; l < 4; ++l) {
        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
        const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
        const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
        q8 += 8;
        aux32 >>= 7;
    }
    const float d = __half2float(bq2->d) * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
    return d * sumi;
#endif
}

static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;

    const int ib32 = iqs;
    const uint8_t  * qs = bq2->qs + 8*ib32;
    const int8_t   * q8 = bq8_1[ib32].qs;
    int sumi = 0;
    for (int l = 0; l < 4; ++l) {
        const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
        const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
        uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
        uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
        const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
        const int grid_h = __vsub4(grid2[0] ^ signs1, signs1);
        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
        q8 += 8;
    }
    const float d = __half2float(bq2->d) * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f;
    return d * sumi;
#endif
}

static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;

    const int ib32 = iqs;
    int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0;
    const uint8_t h1 = bq1->scales[2*ib32+0];
    const uint8_t h2 = bq1->scales[2*ib32+1];
    const int * q8 = (const int *)bq8_1[ib32].qs;
    const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5)));
    const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1)));
    const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5)));
    const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1)));
    for (int j = 0; j < 2; ++j) {
        sumi1 = __dp4a(q8[j+0], grid1[j], sumi1);
        sumi2 = __dp4a(q8[j+2], grid2[j], sumi2);
        sumi3 = __dp4a(q8[j+4], grid3[j], sumi3);
        sumi4 = __dp4a(q8[j+6], grid4[j], sumi4);
    }
    const float d = __half2float(bq1->d) * __low2float(bq8_1[ib32].ds);
    return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) +
                sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1));
#endif
}

static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values,
        int & val1, int & val2) {

    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
    aux32 = q4 & 0x0f0f0f0f;
    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
    val1 = v1 | (v2 << 16);
    aux32 = (q4 >> 4) & 0x0f0f0f0f;
    v1 = values[q8[0]] | (values[q8[1]] << 8);
    v2 = values[q8[2]] | (values[q8[3]] << 8);
    val2 = v1 | (v2 << 16);
}

static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610

    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;

    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;

    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;

    int v1, v2;
    int sumi1 = 0, sumi2 = 0;
    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
        get_int_from_table_16(aux, values, v1, v2);
        sumi1 = __dp4a(v1, q8[l+0], sumi1);
        sumi2 = __dp4a(v2, q8[l+4], sumi2);
    }
    const float d = __half2float(bq->d) * __low2float(bq8_1->ds);
    return d * (sumi1 + sumi2);
#endif
}


static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;

    // iqs is 0...7
    const int ib32 = iqs;
    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
    const float d = __half2float(bq4->d) * (ls - 32) * __low2float(bq8_1[ib32].ds);
    int v1, v2;
    int sumi1 = 0, sumi2 = 0;
    for (int j = 0; j < 4; ++j) {
        get_int_from_table_16(q4[j], values, v1, v2);
        sumi1 = __dp4a(v1, q8[j+0], sumi1);
        sumi2 = __dp4a(v2, q8[j+4], sumi2);
    }
    return d * (sumi1 + sumi2);
#endif
}

template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst, const int ncols, const int nrows) {
    const int row = blockIdx.x*blockDim.y + threadIdx.y;

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs  = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1) {
        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
    }

    if (threadIdx.x == 0) {
        dst[row] = __float2half(tmp);
    }
}

static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq2_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq1_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq4_nl_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq4_xs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}

static void mul_mat_vec_iq3_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
    mul_mat_vec_q<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}


template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
static __device__ __forceinline__ void mul_mat_q(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    const int blocks_per_row_x = ncols_x / qk;
    const int blocks_per_col_y = nrows_y / QK8_1;
    const int blocks_per_warp = WARP_SIZE / qi;

    const int & ncols_dst = ncols_y;

    const int row_dst_0 = blockIdx.x*mmq_y;
    const int & row_x_0 = row_dst_0;

    const int col_dst_0 = blockIdx.y*mmq_x;
    const int & col_y_0 = col_dst_0;

    int   * tile_x_ql = nullptr;
    half2 * tile_x_dm = nullptr;
    int   * tile_x_qh = nullptr;
    int   * tile_x_sc = nullptr;

    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);

    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];

    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};

    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {

        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);

#pragma unroll
        for (int ir = 0; ir < qr; ++ir) {
            const int kqs = ir*WARP_SIZE + threadIdx.x;
            const int kbxd = kqs / QI8_1;

#pragma unroll
            for (int i = 0; i < mmq_x; i += nwarps) {
                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
            }

#pragma unroll
            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);

                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
                if (need_sum) {
                    *dsi_dst = *dsi_src;
                } else {
                    float * dfi_dst = (float *) dsi_dst;
                    *dfi_dst = __low2float(*dsi_src);
                }
            }

            __syncthreads();

// #pragma unroll // unrolling this loop causes too much register pressure
            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
#pragma unroll
                for (int j = 0; j < mmq_x; j += nwarps) {
#pragma unroll
                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
                            threadIdx.x + i, threadIdx.y + j, k);
                    }
                }
            }
            __syncthreads();
        }
    }

#pragma unroll
    for (int j = 0; j < mmq_x; j += nwarps) {
        const int col_dst = col_dst_0 + j + threadIdx.y;
        if (col_dst >= ncols_dst) {
            return;
        }

#pragma unroll
        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
            const int row_dst = row_dst_0 + threadIdx.x + i;
            if (row_dst >= nrows_dst) {
                continue;
            }
            dst[col_dst*nrows_dst + row_dst] = __float2half(sum[i/WARP_SIZE][j/nwarps]);
        }
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q4_0  64
#define  MMQ_Y_Q4_0  128
#define NWARPS_Q4_0  8
#else
#define  MMQ_X_Q4_0 4
#define  MMQ_Y_Q4_0 32
#define NWARPS_Q4_0 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q4_0, 2)
#endif
mul_mat_q4_0(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q4_0;
    const int mmq_y  =  MMQ_Y_Q4_0;
    const int nwarps = NWARPS_Q4_0;

    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q4_0_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {

    int mmq_x  =  MMQ_X_Q4_0;
    int mmq_y  =  MMQ_Y_Q4_0;
    int nwarps = NWARPS_Q4_0;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q4_1 64
#define  MMQ_Y_Q4_1 128
#define NWARPS_Q4_1 8
#else
#define  MMQ_X_Q4_1 4
#define  MMQ_Y_Q4_1 32
#define NWARPS_Q4_1 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q4_1, 2)
#endif
mul_mat_q4_1(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q4_1;
    const int mmq_y  =  MMQ_Y_Q4_1;
    const int nwarps = NWARPS_Q4_1;

    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q4_1_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {

    int mmq_x  =  MMQ_X_Q4_1;
    int mmq_y  =  MMQ_Y_Q4_1;
    int nwarps = NWARPS_Q4_1;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q5_0 64
#define  MMQ_Y_Q5_0 128
#define NWARPS_Q5_0 8
#else
#define  MMQ_X_Q5_0 4
#define  MMQ_Y_Q5_0 32
#define NWARPS_Q5_0 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q5_0, 2)
#endif
mul_mat_q5_0(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q5_0;
    const int mmq_y  =  MMQ_Y_Q5_0;
    const int nwarps = NWARPS_Q5_0;

    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q5_0_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {

    const int mmq_x  =  MMQ_X_Q5_0;
    const int mmq_y  =  MMQ_Y_Q5_0;
    const int nwarps = NWARPS_Q5_0;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q5_1 64
#define  MMQ_Y_Q5_1 128
#define NWARPS_Q5_1 8
#else
#define  MMQ_X_Q5_1 4
#define  MMQ_Y_Q5_1 32
#define NWARPS_Q5_1 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q5_1, 2)
#endif
mul_mat_q5_1(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q5_1;
    const int mmq_y  =  MMQ_Y_Q5_1;
    const int nwarps = NWARPS_Q5_1;

    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q5_1_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
    const int mmq_x  =  MMQ_X_Q5_1;
    const int mmq_y  =  MMQ_Y_Q5_1;
    const int nwarps = NWARPS_Q5_1;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q8_0 64
#define  MMQ_Y_Q8_0 128
#define NWARPS_Q8_0 8
#else
#define  MMQ_X_Q8_0 4
#define  MMQ_Y_Q8_0 32
#define NWARPS_Q8_0 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q8_0, 2)
#endif
mul_mat_q8_0(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q8_0;
    const int mmq_y  =  MMQ_Y_Q8_0;
    const int nwarps = NWARPS_Q8_0;

    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q8_0_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
    const int mmq_x  =  MMQ_X_Q8_0;
    const int mmq_y  =  MMQ_Y_Q8_0;
    const int nwarps = NWARPS_Q8_0;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q2_K 64
#define  MMQ_Y_Q2_K 128
#define NWARPS_Q2_K 8
#else
#define  MMQ_X_Q2_K 4
#define  MMQ_Y_Q2_K 32
#define NWARPS_Q2_K 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q2_K, 2)
#endif
mul_mat_q2_K(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q2_K;
    const int mmq_y  =  MMQ_Y_Q2_K;
    const int nwarps = NWARPS_Q2_K;

    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q2_K_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
    const int mmq_x  =  MMQ_X_Q2_K;
    const int mmq_y  =  MMQ_Y_Q2_K;
    const int nwarps = NWARPS_Q2_K;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q3_K 64
#define  MMQ_Y_Q3_K 128
#define NWARPS_Q3_K 8
#else
#define  MMQ_X_Q3_K 4
#define  MMQ_Y_Q3_K 32
#define NWARPS_Q3_K 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q3_K, 2)
#endif
mul_mat_q3_K(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {

    const int mmq_x  =  MMQ_X_Q3_K;
    const int mmq_y  =  MMQ_Y_Q3_K;
    const int nwarps = NWARPS_Q3_K;

    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q3_K_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {

    const int mmq_x  =  MMQ_X_Q3_K;
    const int mmq_y  =  MMQ_Y_Q3_K;
    const int nwarps = NWARPS_Q3_K;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q4_K 64
#define  MMQ_Y_Q4_K 128
#define NWARPS_Q4_K 8
#else
#define  MMQ_X_Q4_K 4
#define  MMQ_Y_Q4_K 32
#define NWARPS_Q4_K 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q4_K, 2)
#endif
mul_mat_q4_K(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q4_K;
    const int mmq_y  =  MMQ_Y_Q4_K;
    const int nwarps = NWARPS_Q4_K;

    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q4_K_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
    const int mmq_x  =  MMQ_X_Q4_K;
    const int mmq_y  =  MMQ_Y_Q4_K;
    const int nwarps = NWARPS_Q4_K;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q5_K 64
#define  MMQ_Y_Q5_K 128
#define NWARPS_Q5_K 8
#else
#define  MMQ_X_Q5_K 4
#define  MMQ_Y_Q5_K 32
#define NWARPS_Q5_K 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q5_K, 2)
#endif
mul_mat_q5_K(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q5_K;
    const int mmq_y  =  MMQ_Y_Q5_K;
    const int nwarps = NWARPS_Q5_K;

    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q5_K_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {

    const int mmq_x  =  MMQ_X_Q5_K;
    const int mmq_y  =  MMQ_Y_Q5_K;
    const int nwarps = NWARPS_Q5_K;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

#if defined(USE_ROCM)
#define  MMQ_X_Q6_K 64
#define  MMQ_Y_Q6_K 128
#define NWARPS_Q6_K 8
#else
#define  MMQ_X_Q6_K 4
#define  MMQ_Y_Q6_K 32
#define NWARPS_Q6_K 4
#endif

template <bool need_check> static __global__ void
#if defined(USE_ROCM)
__launch_bounds__(WARP_SIZE*NWARPS_Q6_K, 2)
#endif
mul_mat_q6_K(
    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
    const int mmq_x  =  MMQ_X_Q6_K;
    const int mmq_y  =  MMQ_Y_Q6_K;
    const int nwarps = NWARPS_Q6_K;

    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}

static void ggml_mul_mat_q6_K_q8_1_cuda(
    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
    const int mmq_x  =  MMQ_X_Q6_K;
    const int mmq_y  =  MMQ_Y_Q6_K;
    const int nwarps = NWARPS_Q6_K;

    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
    const dim3 block_nums(block_num_x, block_num_y, 1);
    const dim3 block_dims(WARP_SIZE, nwarps, 1);

    if (nrows_x % mmq_y == 0) {
        const bool need_check = false;
        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    } else {
        const bool need_check = true;
        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
    }
}

torch::Tensor ggml_dequantize(
    torch::Tensor W,   // quant weight
    int8_t type,
    int64_t m,
    int64_t n
){
    const at::cuda::OptionalCUDAGuard device_guard(device_of(W));
    auto options = torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
    at::Tensor DW = torch::empty({m, n}, options);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
    const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(type);
    to_fp16_cuda(
        (void*)W.data_ptr(), (half*)DW.data_ptr(), m * n, stream
    );
    return DW;
}

// New quantizations doesn't implement ggml_mul_mat_vec and only use ggml_mul_mat_vec_a8
torch::Tensor ggml_mul_mat_vec(
    torch::Tensor W,  // quant weight
    torch::Tensor X,  // input
    int8_t type,
    int64_t row
){
    size_t col = X.sizes()[1];
    const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
    auto options = torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
    at::Tensor Y = torch::empty({1, row}, options);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
    switch (type) {
        case 2:
            dequantize_mul_mat_vec_q4_0_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 3:
            dequantize_mul_mat_vec_q4_1_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 6:
            dequantize_mul_mat_vec_q5_0_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 7:
            dequantize_mul_mat_vec_q5_1_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 8:
            dequantize_mul_mat_vec_q8_0_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 10:
            dequantize_mul_mat_vec_q2_K_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 11:
            dequantize_mul_mat_vec_q3_K_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 12:
            dequantize_mul_mat_vec_q4_K_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 13:
            dequantize_mul_mat_vec_q5_K_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 14:
            dequantize_mul_mat_vec_q6_K_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 16:
            dequantize_mul_mat_vec_iq2_xxs_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 17:
            dequantize_mul_mat_vec_iq2_xs_cuda((void*)W.data_ptr(), (half*)X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
    }
    return Y;
}

torch::Tensor ggml_mul_mat_vec_a8(
    torch::Tensor W,  // quant weight
    torch::Tensor X,  // input
    int8_t type,
    int64_t row
){
    int col = X.sizes()[1];
    const int padded = (col + 512 - 1) / 512 * 512;
    const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
    auto options = torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
    at::Tensor Y = torch::empty({1, row}, options);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
    options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
    at::Tensor quant_X = torch::empty({1, padded / 32 * 9}, options);
    quantize_row_q8_1_cuda((half*)X.data_ptr(), (void*)quant_X.data_ptr(), col, 1, stream);
    switch (type) {
        case 2:
            mul_mat_vec_q4_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 3:
            mul_mat_vec_q4_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 6:
            mul_mat_vec_q5_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 7:
            mul_mat_vec_q5_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 8:
            mul_mat_vec_q8_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 10:
            mul_mat_vec_q2_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 11:
            mul_mat_vec_q3_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 12:
            mul_mat_vec_q4_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 13:
            mul_mat_vec_q5_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 14:
            mul_mat_vec_q6_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 16:
            mul_mat_vec_iq2_xxs_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 17:
            mul_mat_vec_iq2_xs_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 18:
            mul_mat_vec_iq3_xxs_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 19:
            mul_mat_vec_iq1_s_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 20:
            mul_mat_vec_iq4_nl_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 21:
            mul_mat_vec_iq3_s_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 22:
            mul_mat_vec_iq2_s_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
        case 23:
            mul_mat_vec_iq4_xs_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, stream);
            break;
    }
    return Y;
}

torch::Tensor ggml_mul_mat_a8(
    torch::Tensor W,  // quant weight
    torch::Tensor X,  // input
    int8_t type,
    int64_t row
) {
    int col = X.sizes()[1];
    int padded = (col + 512 - 1) / 512 * 512;
    int batch = X.sizes()[0];
    const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
    auto options = torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
    at::Tensor Y = torch::empty({batch, row}, options);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
    options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
    at::Tensor quant_X = torch::empty({batch, padded / 32 * 9}, options);
    quantize_row_q8_1_cuda((half*)X.data_ptr(), (void*)quant_X.data_ptr(), col, batch, stream);

    switch (type) {
        case 2:
            ggml_mul_mat_q4_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 3:
            ggml_mul_mat_q4_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 6:
            ggml_mul_mat_q5_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 7:
            ggml_mul_mat_q5_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 8:
            ggml_mul_mat_q8_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 10:
            ggml_mul_mat_q2_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 11:
            ggml_mul_mat_q3_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 12:
            ggml_mul_mat_q4_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 13:
            ggml_mul_mat_q5_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
        case 14:
            ggml_mul_mat_q6_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(), col, row, batch, padded, row, stream);
            break;
    }
    return Y;
}