llama.cpp/ggml-cuda.cu

#include <cstddef>
#include <cstdint>
#include <stdint.h>
#include <stdio.h>
#include <atomic>
#include <assert.h>

#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>

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

static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");

#define CUDA_CHECK(err)                                                                 \
    do {                                                                                \
        cudaError_t err_ = (err);                                                       \
        if (err_ != cudaSuccess) {                                                      \
            fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__,   \
                cudaGetErrorString(err_));                                              \
            exit(1);                                                                    \
        }                                                                               \
    } while (0)

#define CUBLAS_CHECK(err)                                                               \
    do {                                                                                \
        cublasStatus_t err_ = (err);                                                    \
        if (err_ != CUBLAS_STATUS_SUCCESS) {                                            \
            fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__);    \
            exit(1);                                                                    \
        }                                                                               \
    } while (0)

typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, float & v0, float & v1);
typedef void (*to_fp32_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
typedef void (*dequantize_mul_mat_vec_cuda_t)(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream);
typedef void (*dot_kernel_k_t)(const void * vx, const int ib, const int iqs, const float * y, float & v);

// QK = number of values after dequantization
// QR = QK / number of values before dequantization

#define QK4_0 32
#define QR4_0 2
typedef struct {
    half    d;              // delta
    uint8_t qs[QK4_0 / 2];  // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");

#define QK4_1 32
#define QR4_1 2
typedef struct {
    half    d;              // delta
    half    m;              // min
    uint8_t qs[QK4_1 / 2];  // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");

#define QK5_0 32
#define QR5_0 2
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;
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");

#define QK5_1 32
#define QR5_1 2
typedef struct {
    half d;                 // delta
    half m;                 // min
    uint8_t qh[4];          // 5-th bit of quants
    uint8_t qs[QK5_1 / 2];  // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");

#define QK8_0 32
#define QR8_0 1
typedef struct {
    half    d;              // delta
    int8_t  qs[QK8_0];      // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");

//================================= k-quants

#define QK_K 256

typedef struct {
    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
    uint8_t qs[QK_K/4];      // quants
    half d;                  // super-block scale for quantized scales
    half dmin;               // super-block scale for quantized mins
} block_q2_k;
static_assert(sizeof(block_q2_k) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_k block size/padding");

typedef struct {
    uint8_t hmask[QK_K/8];
    uint8_t qs[QK_K/4]; // nibbles / quants
    uint8_t scales[3*QK_K/64];
    half d;
} block_q3_k;
static_assert(sizeof(block_q3_k) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_k block size/padding");

typedef struct {
    half d;                    // super-block scale for quantized scales
    half dmin;                 // super-block scale for quantized 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;
static_assert(sizeof(block_q4_k) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_k block size/padding");

typedef struct {
    half    d;                   // super-block scale for quantized scales
    half    dmin;                // super-block scale for quantized mins
    uint8_t scales[3*QK_K/64];   // scales, 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;
static_assert(sizeof(block_q5_k) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_k block size/padding");

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;
static_assert(sizeof(block_q6_k) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_k block size/padding");

#define WARP_SIZE 32

#define CUDA_MUL_BLOCK_SIZE 256

#define CUDA_DEQUANTIZE_BLOCK_SIZE 256

// dmmv = dequantize_mul_mat_vec
#ifndef GGML_CUDA_DMMV_X
#define GGML_CUDA_DMMV_X 32
#endif
#ifndef GGML_CUDA_DMMV_Y
#define GGML_CUDA_DMMV_Y 1
#endif

static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
    const int i = blockDim.x*blockIdx.x + threadIdx.x;

    if (i >= kx) {
        return;
    }
    dst[i] = x[i] * y[i%ky];
}

static __device__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){
    const block_q4_0 * x = (const block_q4_0 *) vx;

    const float d = x[ib].d;

    const uint8_t vui = x[ib].qs[iqs];

    const int8_t vi0 = vui & 0xF;
    const int8_t vi1 = vui >> 4;

    v0 = (vi0 - 8)*d;
    v1 = (vi1 - 8)*d;
}

static __device__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, float & v0, float & v1){
    const block_q4_1 * x = (const block_q4_1 *) vx;

    const float d = x[ib].d;
    const float m = x[ib].m;

    const uint8_t vui = x[ib].qs[iqs];

    const int8_t vi0 = vui & 0xF;
    const int8_t vi1 = vui >> 4;

    v0 = vi0*d + m;
    v1 = vi1*d + m;
}

static __device__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){
    const block_q5_0 * x = (const block_q5_0 *) vx;

    const float d = x[ib].d;

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

    const uint8_t xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
    const uint8_t xh_1 = ((qh >> (iqs + 12))     ) & 0x10;

    const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16;
    const int32_t x1 = ((x[ib].qs[iqs] >>  4) | xh_1) - 16;

    v0 = x0*d;
    v1 = x1*d;
}

static __device__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, float & v0, float & v1){
    const block_q5_1 * x = (const block_q5_1 *) vx;

    const float d = x[ib].d;
    const float m = x[ib].m;

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

    const uint8_t xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
    const uint8_t xh_1 = ((qh >> (iqs + 12))     ) & 0x10;

    const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0);
    const int32_t x1 = ((x[ib].qs[iqs] >>  4) | xh_1);

    v0 = x0*d + m;
    v1 = x1*d + m;
}

static __device__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){
    const block_q8_0 * x = (const block_q8_0 *) vx;

    const float d = x[ib].d;

    const int8_t vi0 = x[ib].qs[iqs + 0];
    const int8_t vi1 = x[ib].qs[iqs + 1];

    v0 = vi0*d;
    v1 = vi1*d;
}

//================================== k-quants

static __global__ void dequantize_block_q2_k(const void * vx, float * yy) {

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

    const block_q2_k * x = (const block_q2_k *) vx;

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

    float dall = x[i].d;
    float dmin = x[i].dmin;
    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);

}

static __device__ void vec_dot_q2_k(const void * vx, const int ib, const int iqs, const float * yy, float & result) {

    const block_q2_k * x = (const block_q2_k *) vx;

    // if n is 0, we want to do the lower 128, else the upper 128,
    // covering y[l+0],  y[l+32], y[l+64], y[l+96] and
    //          y[l+16], y[l+48], y[l+80], y[l+112]
    int n = iqs/128;                // 0 or 1
    int r = iqs - 128*n;            // 0...120 in steps of 8
    int l = r/8;                    // 0...15 in steps of 1

    const float   * y = yy + 128*n + l;
    const uint8_t * q = x[ib].qs + 32*n + l;
    const uint8_t * s = x[ib].scales + 8*n;

    const float dall = x[ib].d;
    const float dmin = x[ib].dmin;

    float sum = y[  0] * (dall * ((s[0] & 0xF) * ((q[ 0] >> 0) & 3)) - dmin * (s[0] >> 4))
              + y[ 32] * (dall * ((s[2] & 0xF) * ((q[ 0] >> 2) & 3)) - dmin * (s[2] >> 4))
              + y[ 64] * (dall * ((s[4] & 0xF) * ((q[ 0] >> 4) & 3)) - dmin * (s[4] >> 4))
              + y[ 96] * (dall * ((s[6] & 0xF) * ((q[ 0] >> 6) & 3)) - dmin * (s[6] >> 4))
              + y[ 16] * (dall * ((s[1] & 0xF) * ((q[16] >> 0) & 3)) - dmin * (s[1] >> 4))
              + y[ 48] * (dall * ((s[3] & 0xF) * ((q[16] >> 2) & 3)) - dmin * (s[3] >> 4))
              + y[ 80] * (dall * ((s[5] & 0xF) * ((q[16] >> 4) & 3)) - dmin * (s[5] >> 4))
              + y[112] * (dall * ((s[7] & 0xF) * ((q[16] >> 6) & 3)) - dmin * (s[7] >> 4));

    result = sum;

}

static __global__ void dequantize_block_q3_k(const void * vx, float * yy) {

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

    const block_q3_k * x = (const block_q3_k *) vx;

    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);
    float d_all = x[i].d;
    float dl = d_all * (us - 32);

    float * 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] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));

}

static __device__ void vec_dot_q3_k(const void * vx, const int ib, const int iqs, const float * yy, float & result) {

    const block_q3_k * x = (const block_q3_k *) vx;

    const uint32_t kmask1 = 0x03030303;
    const uint32_t kmask2 = 0x0f0f0f0f;

    uint32_t aux[3];
    uint32_t utmp[4];

    // if n is 0, we want to do the lower 128, else the upper 128,
    // covering y[l+0],  y[l+32], y[l+64], y[l+96] and
    //          y[l+16], y[l+48], y[l+80], y[l+112]
    int n = iqs/128;                // 0 or 1
    int r = iqs - 128*n;            // 0...120 in steps of 8
    int l = r/8;                    // 0...15 in steps of 1

    const float   * y = yy + 128*n + l;
    const uint8_t * q = x[ib].qs + 32*n + l;
    const uint8_t * hm = x[ib].hmask + l;
    const int8_t  * s = (const int8_t *)utmp + 8*n;

    memcpy(aux, x[ib].scales, 12);
    utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
    utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
    utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
    utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);

    const float dall = x[ib].d;

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

    float sum = y[  0] * (s[0] - 32) * (((q[ 0] >> 0) & 3) - (hm[ 0] & (m << 0) ? 0 : 4))
              + y[ 32] * (s[2] - 32) * (((q[ 0] >> 2) & 3) - (hm[ 0] & (m << 1) ? 0 : 4))
              + y[ 64] * (s[4] - 32) * (((q[ 0] >> 4) & 3) - (hm[ 0] & (m << 2) ? 0 : 4))
              + y[ 96] * (s[6] - 32) * (((q[ 0] >> 6) & 3) - (hm[ 0] & (m << 3) ? 0 : 4))
              + y[ 16] * (s[1] - 32) * (((q[16] >> 0) & 3) - (hm[16] & (m << 0) ? 0 : 4))
              + y[ 48] * (s[3] - 32) * (((q[16] >> 2) & 3) - (hm[16] & (m << 1) ? 0 : 4))
              + y[ 80] * (s[5] - 32) * (((q[16] >> 4) & 3) - (hm[16] & (m << 2) ? 0 : 4))
              + y[112] * (s[7] - 32) * (((q[16] >> 6) & 3) - (hm[16] & (m << 3) ? 0 : 4));

    result = sum * dall;

}

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);
    }
}

static __global__ void dequantize_block_q4_k(const void * vx, float * yy) {
    const block_q4_k * x = (const block_q4_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;
    //const int ir  = tid%16;
    //const int is  = 2*il;
    //const int n   = 2;

    // 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;

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

    const float dall = x[i].d;
    const float dmin = x[i].dmin;

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

static __device__ void vec_dot_q4_k(const void * vx, const int ib, const int iqs, const float * yy, float & result) {

    const block_q4_k * x = (const block_q4_k *) vx;

                                    // iqs is in 0...248 in steps of 8 =>
    const int j  = iqs / 64;        // j  is in 0...3
    const int ir = (iqs - 64*j)/2;  // ir is in 0...28 in steps of 4
    const int is = 2*j;             // is is in 0...6 in steps of 2

    const float   * y = yy + 64*j + ir;
    const uint8_t * q = x[ib].qs + 32*j + ir;

    const float dall = x[ib].d;
    const float dmin = x[ib].dmin;

    uint8_t sc, m;
    get_scale_min_k4(is + 0, x[ib].scales, sc, m);
    const float d1 = dall * sc;
    const float m1 = dmin * m;
    get_scale_min_k4(is + 1, x[ib].scales, sc, m);
    const float d2 = dall * sc;
    const float m2 = dmin * m;

    float sum = 0;
    for (int k = 0; k < 4; ++k) {
        sum += y[k +  0] * (d1 * (q[k] & 0xF) - m1);
        sum += y[k + 32] * (d2 * (q[k] >>  4) - m2);
    }
    result = sum;

}

static __global__ void dequantize_block_q5_k(const void * vx, float * 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

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

    const float dall = x[i].d;
    const float dmin = x[i].dmin;

    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 float d1 = dall * sc; const float m1 = dmin * m;
    get_scale_min_k4(is + 1, x[i].scales, sc, m);
    const float d2 = dall * sc; const float m2 = dmin * m;

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

static __device__ void vec_dot_q5_k(const void * vx, const int ib, const int iqs, const float * yy, float & result) {

    const block_q5_k * x = (const block_q5_k *) vx;

                                    // iqs is in 0...248 in steps of 8 =>
    const int j  = iqs / 64;        // j  is in 0...3
    const int ir = (iqs - 64*j)/2;  // ir is in 0...28 in steps of 4
    const int is = 2*j;             // is is in 0...6 in steps of 2

    const float   * y  = yy + 64*j + ir;
    const uint8_t * ql = x[ib].qs + 32*j + ir;
    const uint8_t * qh = x[ib].qh + ir;

    const float dall = x[ib].d;
    const float dmin = x[ib].dmin;

    uint8_t sc, m;
    get_scale_min_k4(is + 0, x[ib].scales, sc, m);
    const float d1 = dall * sc;
    const float m1 = dmin * m;
    get_scale_min_k4(is + 1, x[ib].scales, sc, m);
    const float d2 = dall * sc;
    const float m2 = dmin * m;

    uint8_t   hm  = 1 << is;
    float sum = 0;
    for (int k = 0; k < 4; ++k) {
        sum += y[k +  0] * (d1 * ((ql[k] & 0xF) + (qh[k] & hm ? 16 : 0)) - m1);
    }
    hm <<= 1;
    for (int k = 0; k < 4; ++k) {
        sum += y[k + 32] * (d2 * ((ql[k] >>  4) + (qh[k] & hm ? 16 : 0)) - m2);
    }
    result = sum;

}

static __global__ void dequantize_block_q6_k(const void * vx, float * 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;

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

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

static __device__ void vec_dot_q6_k(const void * vx, const int ib, const int iqs, const float * yy, float & result) {

    const block_q6_k * x = (const block_q6_k *) vx;

    const int ip = iqs / 128;        // 0 or 1
    const int il = (iqs - 128*ip)/8; // 0...15
    const int is = 8*ip;

    const float * y = yy + 128*ip + il;

    const float d = x[ib].d;

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

    result = y[  0] * d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh[ 0] >> 0) & 3) << 4)) - 32)
           + y[ 32] * d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh[ 0] >> 2) & 3) << 4)) - 32)
           + y[ 64] * d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh[ 0] >> 4) & 3) << 4)) - 32)
           + y[ 96] * d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh[ 0] >> 6) & 3) << 4)) - 32)
           + y[ 16] * d * sc[1] * ((int8_t)((ql[16] & 0xF) | (((qh[16] >> 0) & 3) << 4)) - 32)
           + y[ 48] * d * sc[3] * ((int8_t)((ql[48] & 0xF) | (((qh[16] >> 2) & 3) << 4)) - 32)
           + y[ 80] * d * sc[5] * ((int8_t)((ql[16]  >> 4) | (((qh[16] >> 4) & 3) << 4)) - 32)
           + y[112] * d * sc[7] * ((int8_t)((ql[48]  >> 4) | (((qh[16] >> 6) & 3) << 4)) - 32);

}

static __device__ void convert_f16(const void * vx, const int ib, const int iqs, float & v0, float & v1){
    const half * x = (const half *) vx;

    v0 = __half2float(x[ib + 0]);
    v1 = __half2float(x[ib + 1]);
}

template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
static __global__ void dequantize_block(const void * vx, float * y, const int k) {
    const int i = blockDim.x*blockIdx.x + 2*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
    float & v0 = y[iybs + iqs + 0];
    float & v1 = y[iybs + iqs + y_offset];
    dequantize_kernel(vx, ib, iqs, v0, v1);
}

template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, float * dst, const int ncols) {
    // 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;
    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;

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

    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
            float v0, v1;
            dequantize_kernel(vx, ib, iqs + j/qr, v0, v1);
            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val

            // matrix multiplication
            tmp += v0 * y[iybs + iqs + j/qr + 0];
            tmp += v1 * y[iybs + iqs + j/qr + y_offset];
            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
        }
    }

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

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

template <int n_thread, dot_kernel_k_t dot_kernel>
static __global__ void dequantize_mul_mat_vec_k(const void * vx, const float * y, float * dst, const int ncols) {
    const int row = blockIdx.x*blockDim.y + threadIdx.y;
    const int tid = threadIdx.x;

    const int iter_stride = QK_K;
    const int vals_per_iter = iter_stride / n_thread;
    const int num_blocks_per_row = ncols / QK_K;
    const int ib0 = row*num_blocks_per_row;

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

    for (int i = 0; i < ncols; i += iter_stride) {
        const int col = i + vals_per_iter*tid;
        const int ib = ib0 + col/QK_K; // x block index
        const int iqs = col%QK_K; // x quant index
        const int iybs = col - col%QK_K; // y block start index

        float v;
        dot_kernel(vx, ib, iqs, y + iybs, v);
        tmp += v;
    }

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

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

static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
    const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
    mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
}

static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
    dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

static void dequantize_row_q4_1_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
    dequantize_block<QK4_1, QR4_1, dequantize_q4_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

static void dequantize_row_q5_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
    dequantize_block<QK5_0, QR5_0, dequantize_q5_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

static void dequantize_row_q5_1_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
    dequantize_block<QK5_1, QR5_1, dequantize_q5_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
    dequantize_block<QK8_0, QR8_0, dequantize_q8_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

static void dequantize_row_q2_k_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q2_k<<<nb, 64, 0, stream>>>(vx, y);
}

static void dequantize_row_q3_k_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q3_k<<<nb, 64, 0, stream>>>(vx, y);
}

static void dequantize_row_q4_k_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q4_k<<<nb, 32, 0, stream>>>(vx, y);
}

static void dequantize_row_q5_k_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q5_k<<<nb, 64, 0, stream>>>(vx, y);
}

static void dequantize_row_q6_k_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int nb = k / QK_K;
    dequantize_block_q6_k<<<nb, 64, 0, stream>>>(vx, y);
}

static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
    GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
        <<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
    GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
        <<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
    GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
        <<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
    GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
        <<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
    GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
        <<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q2_k_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int ny = 2;
    const dim3 block_dims(32, ny, 1);
    dequantize_mul_mat_vec_k<32, vec_dot_q2_k><<<(nrows + ny - 1)/ny, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q3_k_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const dim3 block_dims(32, 2, 1);
    dequantize_mul_mat_vec_k<32, vec_dot_q3_k><<<nrows/2, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q4_k_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const dim3 block_dims(32, 2, 1);
    dequantize_mul_mat_vec_k<32, vec_dot_q4_k><<<nrows/2, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q5_k_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const dim3 block_dims(32, 2, 1);
    dequantize_mul_mat_vec_k<32, vec_dot_q5_k><<<nrows/2, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void dequantize_mul_mat_vec_q6_k_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const dim3 block_dims(32, 2, 1);
    dequantize_mul_mat_vec_k<32, vec_dot_q6_k><<<nrows/2, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
    dequantize_block<32, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}

static void convert_mul_mat_vec_f16_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
    GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
    dequantize_mul_mat_vec<1, 1, convert_f16>
        <<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
}

static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q4_0:
            return dequantize_row_q4_0_cuda;
        case GGML_TYPE_Q4_1:
            return dequantize_row_q4_1_cuda;
        case GGML_TYPE_Q5_0:
            return dequantize_row_q5_0_cuda;
        case GGML_TYPE_Q5_1:
            return dequantize_row_q5_1_cuda;
        case GGML_TYPE_Q8_0:
            return dequantize_row_q8_0_cuda;
        case GGML_TYPE_Q2_K:
            return dequantize_row_q2_k_cuda;
        case GGML_TYPE_Q3_K:
            return dequantize_row_q3_k_cuda;
        case GGML_TYPE_Q4_K:
            return dequantize_row_q4_k_cuda;
        case GGML_TYPE_Q5_K:
            return dequantize_row_q5_k_cuda;
        case GGML_TYPE_Q6_K:
            return dequantize_row_q6_k_cuda;
        case GGML_TYPE_F16:
            return convert_fp16_to_fp32_cuda;
        default:
            return nullptr;
    }
}

static dequantize_mul_mat_vec_cuda_t ggml_get_dequantize_mul_mat_vec_cuda(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q4_0:
            return dequantize_mul_mat_vec_q4_0_cuda;
        case GGML_TYPE_Q4_1:
            return dequantize_mul_mat_vec_q4_1_cuda;
        case GGML_TYPE_Q5_0:
            return dequantize_mul_mat_vec_q5_0_cuda;
        case GGML_TYPE_Q5_1:
            return dequantize_mul_mat_vec_q5_1_cuda;
        case GGML_TYPE_Q8_0:
            return dequantize_mul_mat_vec_q8_0_cuda;
        case GGML_TYPE_Q2_K:
            return dequantize_mul_mat_vec_q2_k_cuda;
        case GGML_TYPE_Q3_K:
            return dequantize_mul_mat_vec_q3_k_cuda;
        case GGML_TYPE_Q4_K:
            return dequantize_mul_mat_vec_q4_k_cuda;
        case GGML_TYPE_Q5_K:
            return dequantize_mul_mat_vec_q5_k_cuda;
        case GGML_TYPE_Q6_K:
            return dequantize_mul_mat_vec_q6_k_cuda;
        case GGML_TYPE_F16:
            return convert_mul_mat_vec_f16_cuda;
        default:
            return nullptr;
    }
}

// buffer pool for cuda
#define MAX_CUDA_BUFFERS 256

struct scoped_spin_lock {
    std::atomic_flag& lock;
    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
        while (lock.test_and_set(std::memory_order_acquire)) {
            ; // spin
        }
    }
    ~scoped_spin_lock() {
        lock.clear(std::memory_order_release);
    }
    scoped_spin_lock(const scoped_spin_lock&) = delete;
    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
};

struct cuda_buffer {
    void * ptr = nullptr;
    size_t size = 0;
};

static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS];
static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;

static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
    scoped_spin_lock lock(g_cuda_pool_lock);

    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
        cuda_buffer& b = g_cuda_buffer_pool[i];
        if (b.size >= size && b.ptr != nullptr) {
            void * ptr = b.ptr;
            *actual_size = b.size;
            b.ptr = nullptr;
            b.size = 0;
            return ptr;
        }
    }
    void * ptr;
    CUDA_CHECK(cudaMalloc((void **) &ptr, size));
    *actual_size = size;
    return ptr;
}

static void ggml_cuda_pool_free(void * ptr, size_t size) {
    scoped_spin_lock lock(g_cuda_pool_lock);

    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
        cuda_buffer& b = g_cuda_buffer_pool[i];
        if (b.ptr == nullptr) {
            b.ptr = ptr;
            b.size = size;
            return;
        }
    }
    fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
    CUDA_CHECK(cudaFree(ptr));
}

#define GGML_CUDA_MAX_STREAMS 8 // Set this to 1 for reproducible matrix multiplication.
#define GGML_CUDA_MAX_EVENTS 64
static cublasHandle_t g_cublasH = nullptr;
static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaStream_t g_cudaStreams2[GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaEvent_t g_cudaEvents[GGML_CUDA_MAX_EVENTS] = { nullptr };

void ggml_init_cublas() {
    if (g_cublasH == nullptr) {
        // create streams
        for (int i = 0; i < GGML_CUDA_MAX_STREAMS; ++i) {
            CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[i], cudaStreamNonBlocking));
            CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams2[i], cudaStreamNonBlocking));
        }
        // create events
        for (int i = 0; i < GGML_CUDA_MAX_EVENTS; ++i) {
            CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvents[i], cudaEventDisableTiming));
        }

        // create cublas handle
        CUBLAS_CHECK(cublasCreate(&g_cublasH));
        CUBLAS_CHECK(cublasSetMathMode(g_cublasH, CUBLAS_TF32_TENSOR_OP_MATH));

        // configure logging to stdout
        // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
    }
}

void * ggml_cuda_host_malloc(size_t size) {
    if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
        return nullptr;
    }

    void * ptr = nullptr;
    cudaError_t err = cudaMallocHost((void **) &ptr, size);
    if (err != cudaSuccess) {
        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
            size/1024.0/1024.0, cudaGetErrorString(err));
        return nullptr;
    }

    return ptr;
}

void ggml_cuda_host_free(void * ptr) {
    CUDA_CHECK(cudaFreeHost(ptr));
}

static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
    const uint64_t ne0 = src->ne[0];
    const uint64_t ne1 = src->ne[1];
    const uint64_t nb0 = src->nb[0];
    const uint64_t nb1 = src->nb[1];
    const uint64_t nb2 = src->nb[2];
    const uint64_t nb3 = src->nb[3];
    const enum ggml_type type = src->type;
    const size_t ts = ggml_type_size(type);
    const size_t bs = ggml_blck_size(type);

    const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
    if (nb0 == ts && nb1 == ts*ne0/bs) {
        return cudaMemcpyAsync(dst, x, ne1*nb1, cudaMemcpyHostToDevice, stream);
    } else if (nb0 == ts) {
        return cudaMemcpy2DAsync(dst, ts*ne0/bs, x, nb1, ts*ne0/bs, ne1, cudaMemcpyHostToDevice, stream);
    } else {
        for (uint64_t i1 = 0; i1 < ne1; i1++) {
            const void * rx = (const void *) ((const char *) x + i1*nb1);
            void * rd = (void *) ((char *) dst + i1*ts*ne0/bs);
            // pretend the row is a matrix with cols=1
            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyHostToDevice, stream);
            if (r != cudaSuccess) return r;
        }
        return cudaSuccess;
    }
}

static void ggml_cuda_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src1->backend == GGML_BACKEND_CUDA);
    const int64_t ne00 = src0->ne[0];
    const int64_t ne01 = src0->ne[1];
    const int64_t ne02 = src0->ne[2];
    const int64_t ne03 = src0->ne[2];
    const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
    const int64_t ne10 = src1->ne[0];
    const int64_t ne11 = src1->ne[1];
    const int64_t ne12 = src1->ne[2];
    const int64_t ne13 = src1->ne[3];
    const int nb2  = dst->nb[2];
    const int nb3  = dst->nb[3];
    size_t x_size, d_size;

    float * d_X = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &x_size); // src0
    float * d_Y = (float *) src1->data; // src1 is already on device, broadcasted.
    float * d_D = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &d_size); // dst

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            const int i0 = i03*ne02 + i02;
            float * c_X2 = d_X + i0*ne01*ne00;
            float * c_D2 = d_D + i0*ne01*ne00;

            cudaStream_t cudaStream = g_cudaStreams[i0 % GGML_CUDA_MAX_STREAMS];
            cudaStream_t cudaStream2 = g_cudaStreams2[i0 % GGML_CUDA_MAX_STREAMS];
            cudaEvent_t  cudaEvent = g_cudaEvents[i0 % GGML_CUDA_MAX_EVENTS];

            // copy src0 to device
            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X2, src0, i03, i02, cudaStream2));
            CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));

            // wait for data
            CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));

            for (int64_t i01 = 0; i01 < ne01; i01++) {
                const int64_t i13 = i03%ne13;
                const int64_t i12 = i02%ne12;
                const int64_t i11 = i01%ne11;
                const int i1 = i13*ne12*ne11 + i12*ne11 + i11;

                float * c_X1 = c_X2 + i01*ne00;
                float * c_Y = d_Y + i1*ne10;
                float * c_D1 = c_D2 + i01*ne00;

                // compute
                mul_f32_cuda(c_X1, c_Y, c_D1, ne00, ne10, cudaStream);
                CUDA_CHECK(cudaGetLastError());
            }

            // copy dst to host
            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
            CUDA_CHECK(cudaMemcpyAsync(d, c_D2, sizeof(float)*ne00*ne01, cudaMemcpyDeviceToHost, cudaStream));
        }
    }
    CUDA_CHECK(cudaDeviceSynchronize());
    ggml_cuda_pool_free(d_X, x_size);
    ggml_cuda_pool_free(d_D, d_size);
}

static void ggml_cuda_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const int64_t ne00 = src0->ne[0];
    const int64_t ne01 = src0->ne[1];
    const int64_t ne02 = src0->ne[2];
    const int64_t ne03 = src0->ne[3];

    const int64_t ne10 = src1->ne[0];
    const int64_t ne11 = src1->ne[1];

    const int nb2  = dst->nb[2];
    const int nb3  = dst->nb[3];

    const float alpha = 1.0f;
    const float beta = 0.0f;
    const int x_ne = ne01 * ne00;
    const int y_ne = ne11 * ne10;
    const int d_ne = ne11 * ne01;
    const int n_mm = ne03 * ne02;

    size_t x_size, y_size, d_size;
    float * d_X = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
    float * d_Y = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
    float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            int i = i03*ne02 + i02;
            cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];

            float * c_X = d_X + i * x_ne;
            float * c_Y = d_Y + i * y_ne;
            float * c_D = d_D + i * d_ne;

            // copy data to device
            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));

            // compute
            CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
            CUBLAS_CHECK(
                cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
                        ne01, ne11, ne10,
                        &alpha, c_X, ne00,
                                c_Y, ne10,
                        &beta,  c_D, ne01));

            // copy dst to host
            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
            CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
        }
    }

    CUDA_CHECK(cudaDeviceSynchronize());
    ggml_cuda_pool_free(d_X, x_size);
    ggml_cuda_pool_free(d_Y, y_size);
    ggml_cuda_pool_free(d_D, d_size);
}

static void ggml_cuda_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t /* wsize */) {
    const int64_t ne00 = src0->ne[0];
    const int64_t ne01 = src0->ne[1];
    const int64_t ne02 = src0->ne[2];
    const int64_t ne03 = src0->ne[3];

    const int64_t ne10 = src1->ne[0];
    const int64_t ne11 = src1->ne[1];

    const int nb10 = src1->nb[0];
    const int nb11 = src1->nb[1];
    const int nb12 = src1->nb[2];
    const int nb13 = src1->nb[3];

    const int nb2  = dst->nb[2];
    const int nb3  = dst->nb[3];

    const float alpha = 1.0f;
    const float beta = 0.0f;
    const int x_ne = ne01 * ne00;
    const int y_ne = ne11 * ne10;
    const int d_ne = ne11 * ne01;
    const int n_mm = ne03 * ne02;

    size_t x_size, y_size, d_size;
    half  * d_X =  (half *) ggml_cuda_pool_malloc(n_mm * sizeof(half) * x_ne, &x_size);
    half  * d_Y =  (half *) ggml_cuda_pool_malloc(n_mm * sizeof(half) * y_ne, &y_size);
    float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);

    bool src1_cont_rows = nb10 == sizeof(float);
    bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            int i = i03*ne02 + i02;
            cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];

            half  * c_X = d_X + i * x_ne;
            half  * c_Y = d_Y + i * y_ne;
            float * c_D = d_D + i * d_ne;

            // copy src0 to device
            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));

            // convert src1 to fp16
            // TODO: use multiple threads
            ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i03 * ne02 + i02);
            char * src1i = (char *) src1->data + i03*nb13 + i02*nb12;
            if (src1_cont_rows) {
                if (src1_cont_cols) {
                    ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
                }
                else {
                    for (int64_t i01 = 0; i01 < ne11; i01++) {
                        ggml_fp32_to_fp16_row((float *) (src1i + i01*nb11), tmp + i01*ne10, ne10);
                    }
                }
            }
            else {
                for (int64_t i01 = 0; i01 < ne11; i01++) {
                    for (int64_t i00 = 0; i00 < ne10; i00++) {
                        // very slow due to no inlining
                        tmp[i01*ne10 + i00] = ggml_fp32_to_fp16(*(float *) (src1i + i01*nb11 + i00*nb10));
                    }
                }
            }

            // copy src1 to device
            CUDA_CHECK(cudaMemcpyAsync(c_Y, tmp, sizeof(half) * y_ne, cudaMemcpyHostToDevice, cudaStream));

            // compute
            CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
            CUBLAS_CHECK(
                cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
                        ne01, ne11, ne10,
                        &alpha, c_X, CUDA_R_16F, ne00,
                                c_Y, CUDA_R_16F, ne10,
                        &beta,  c_D, CUDA_R_32F, ne01,
                        CUBLAS_COMPUTE_32F_FAST_16F,
                        CUBLAS_GEMM_DEFAULT));

            // copy dst to host
            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
            CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
        }
    }

    CUDA_CHECK(cudaDeviceSynchronize());
    ggml_cuda_pool_free(d_X, x_size);
    ggml_cuda_pool_free(d_Y, y_size);
    ggml_cuda_pool_free(d_D, d_size);
}

static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const int64_t ne00 = src0->ne[0];
    const int64_t ne01 = src0->ne[1];
    const int64_t ne02 = src0->ne[2];
    const int64_t ne03 = src0->ne[3];

    const int64_t ne10 = src1->ne[0];
    const int64_t ne11 = src1->ne[1];

    const int nb2  = dst->nb[2];
    const int nb3  = dst->nb[3];
    const ggml_type type = src0->type;
    const bool mul_mat_vec = ne11 == 1;

    const float alpha = 1.0f;
    const float beta = 0.0f;
    const int x_ne = ne01 * ne00;
    const int y_ne = ne11 * ne10;
    const int d_ne = ne11 * ne01;
    const int n_mm = ne03 * ne02;
    const size_t q_sz = ggml_type_size(type) * x_ne / ggml_blck_size(type);

    size_t x_size, y_size, d_size, q_size;
    float * d_X = nullptr;
    if (!mul_mat_vec) {
        d_X = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
    }
    float * d_Y = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
    float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
    char  * d_Q = (char  *) ggml_cuda_pool_malloc(n_mm * q_sz, &q_size);

    const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(type);
    dequantize_mul_mat_vec_cuda_t dmmv = ggml_get_dequantize_mul_mat_vec_cuda(type);
    GGML_ASSERT(to_fp32_cuda != nullptr);

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            int i = i03*ne02 + i02;
            cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
            cudaStream_t cudaStream2 = g_cudaStreams2[i % GGML_CUDA_MAX_STREAMS];
            cudaEvent_t  cudaEvent = g_cudaEvents[i % GGML_CUDA_MAX_EVENTS];

            float * c_Y = d_Y + i * y_ne;
            float * c_D = d_D + i * d_ne;
            char  * c_Q = d_Q + i * q_sz;

            // copy src0 to device if necessary
            if (src0->backend == GGML_BACKEND_CPU) {
                CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Q, src0, i03, i02, cudaStream2));
            } else if (src0->backend == GGML_BACKEND_CUDA) {
                c_Q = ((char *) src0->data) + i * q_sz;
            } else {
                GGML_ASSERT(false);
            }
            if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
                CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));

                // copy src1 to device
                CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));

                // wait for data
                CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));

                // compute
                //printf("Calling dmmv\n");
                dmmv(c_Q, c_Y, c_D, ne00, ne01, cudaStream);
                CUDA_CHECK(cudaGetLastError());

            } else { // general dequantization kernel + cuBLAS matrix matrix multiplication
                float * c_X = d_X + i * x_ne;

//typedef void (*to_fp32_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
                // convert src0 to fp32 on device
                to_fp32_cuda(c_Q, c_X, x_ne, cudaStream2);
                CUDA_CHECK(cudaGetLastError());
                CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));

                // copy src1 to device
                CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));

                // wait for conversion
                CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));

                // compute
                CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
                CUBLAS_CHECK(
                    cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
                            ne01, ne11, ne10,
                            &alpha, c_X, ne00,
                                    c_Y, ne10,
                            &beta,  c_D, ne01));
            }

            // copy dst to host
            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
            CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
        }
    }

    CUDA_CHECK(cudaDeviceSynchronize());
    if (!mul_mat_vec) {
        ggml_cuda_pool_free(d_X, x_size);
    }
    ggml_cuda_pool_free(d_Y, y_size);
    ggml_cuda_pool_free(d_D, d_size);
    ggml_cuda_pool_free(d_Q, q_size);
}

void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
    ggml_cuda_mul_f32(src0, src1, dst);
}

bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    const int64_t ne10 = src1->ne[0];

    const int64_t ne0 = dst->ne[0];
    const int64_t ne1 = dst->ne[1];

    // TODO: find the optimal values for these
    if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
        src1->type == GGML_TYPE_F32 &&
        dst->type == GGML_TYPE_F32 &&
        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_CUDA)) {
        return true;
    }

    return false;
}

bool ggml_cuda_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
    size_t src0_sz = ggml_nbytes(src0);
    size_t src1_sz = ggml_nbytes(src1);

    // mul_mat_q: src0 is converted to fp32 on device
    size_t mul_mat_q_transfer = src0_sz + src1_sz;

    // mul_mat_f16: src1 is converted to fp16 on cpu
    size_t mul_mat_f16_transfer = src0_sz + sizeof(half) * ggml_nelements(src1);

    // choose the smaller one to transfer to the device
    // TODO: this is not always the best choice due to the overhead of converting to fp16
    return mul_mat_f16_transfer < mul_mat_q_transfer;
}

void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
    GGML_ASSERT(ggml_cuda_can_mul_mat(src0, src1, dst));

    if (src0->type == GGML_TYPE_F32) {
        ggml_cuda_mul_mat_f32(src0, src1, dst);
    }
    else if (src0->type == GGML_TYPE_F16) {
        if (ggml_cuda_mul_mat_use_f16(src0, src1, dst)) {
            ggml_cuda_mul_mat_f16(src0, src1, dst, wdata, wsize);
        }
        else {
            ggml_cuda_mul_mat_q_f32(src0, src1, dst);
        }
    }
    else if (ggml_is_quantized(src0->type)) {
        ggml_cuda_mul_mat_q_f32(src0, src1, dst);
    }
    else {
        GGML_ASSERT(false);
    }
}

size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    if (ggml_cuda_mul_mat_use_f16(src0, src1, dst)) {
        return ggml_nelements(src1) * sizeof(ggml_fp16_t);
    }
    else {
        return 0;
    }
}

void ggml_cuda_transform_tensor(ggml_tensor * tensor) {
    const int64_t ne0 = tensor->ne[0];
    const int64_t ne1 = tensor->ne[1];
    const int64_t ne2 = tensor->ne[2];
    const int64_t ne3 = tensor->ne[3];

    const ggml_type type = tensor->type;
    const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);

    size_t q_size;
    char * dst = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);

    cudaStream_t cudaStream2 = g_cudaStreams2[0];

    // copy tensor to device
    for (int64_t i3 = 0; i3 < ne3; i3++) {
        for (int64_t i2 = 0; i2 < ne2; i2++) {
            int i = i3*ne2 + i2;
            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(dst + i*ne0*ne1, tensor, i3, i2, cudaStream2));
        }
    }

    tensor->data = dst;
    tensor->backend = GGML_BACKEND_CUDA;
}

void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
    FILE * fp = fopen(fname, "rb");

    const size_t size = ggml_nbytes(tensor);

    void * buf;
    CUDA_CHECK(cudaMalloc(&buf, size));
    void * buf_host = malloc(size);

#ifdef _WIN32
    int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
#else
    int ret = fseek(fp, (long) offset, SEEK_SET);
#endif
    GGML_ASSERT(ret == 0); // same

    size_t ret2 = fread(buf_host, size, 1, fp);
    if (ret2 != 1) {
        fprintf(stderr, "unexpectedly reached end of file");
        exit(1);
    }

    cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
    cudaDeviceSynchronize();

    tensor->data = buf;
    free(buf_host);
    fclose(fp);
}