ollama/llama/ggml-cuda/dmmv.cu

/**
 * llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
 *
 * MIT License
 *
 * Copyright (c) 2023-2024 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

#include "dmmv.cuh"
#include "dequantize.cuh"
#include "convert.cuh"

#ifndef K_QUANTS_PER_ITERATION
#define K_QUANTS_PER_ITERATION 2
#else
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
#endif

static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __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 float   * y = yy + i * QK_K + y_offset;
        const uint8_t * q = x[i].qs + q_offset;

        const float dall = __low2half(x[i].dm);
        const float dmin = __high2half(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 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];

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

    }

    // sum up partial sums and write back result
    tmp = warp_reduce_sum(tmp);

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

static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __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 float   * 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 = x[i].d;

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

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

static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __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 float   * y1 = yy + i*QK_K + y_offset;
        const float   * y2 = y1 + 128;

        const float dall = __low2half(x[i].dm);
        const float dmin = __high2half(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 += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
            s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + 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 += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + 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
    tmp = warp_reduce_sum(tmp);

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

static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __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 float   * y1  = yy + i*QK_K + y_offset;
        const float   * y2  = y1 + 128;

        const float dall = __low2half(x[i].dm);
        const float dmin = __high2half(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 += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
                   + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
            sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
                   + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
            sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
                   + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
            sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
                   + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + 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
    tmp = warp_reduce_sum(tmp);

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

static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __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 float   * 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 = x[i].d;

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

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

static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
    const half * x = (const half *) vx;

    // automatic half -> float type cast if dfloat == float
    v.x = x[ib + iqs + 0];
    v.y = x[ib + iqs + 1];
}

static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) {
    return type == GGML_TYPE_Q4_0 ? dequantize_q4_0 :
        type == GGML_TYPE_Q4_1 ? dequantize_q4_1 :
        type == GGML_TYPE_Q5_0 ? dequantize_q5_0 :
        type == GGML_TYPE_Q5_1 ? dequantize_q5_1 :
        type == GGML_TYPE_Q8_0 ? dequantize_q8_0 :
        type == GGML_TYPE_F16 ? convert_f16 :
        nullptr;
}

template <ggml_type type>
static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
    constexpr int qk = ggml_cuda_type_traits<type>::qk; // quantized weights per x block
    constexpr int qr = ggml_cuda_type_traits<type>::qr; // number of quantized weights per data value in x block
    constexpr dequantize_kernel_t dequantize_kernel = get_dequantize_kernel(type);

    const int64_t row = (int64_t)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;

// partial sum for each thread
#ifdef GGML_CUDA_F16
    half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
#else
    float tmp = 0.0f;
#endif // GGML_CUDA_F16

    for (int i = 0; i < ncols; i += iter_stride) {
        const int col = i + vals_per_iter*tid;
        const int64_t ib = ((int64_t)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
#ifdef GGML_CUDA_F16
            tmp += __hmul2(v, {
                y[iybs + iqs + j/qr + 0],
                y[iybs + iqs + j/qr + y_offset]
            });
#else
            tmp += v.x * y[iybs + iqs + j/qr + 0];
            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
#endif // GGML_CUDA_F16
        }
    }

    // sum up partial sums and write back result
    tmp = warp_reduce_sum(tmp);

    if (tid == 0) {
#ifdef GGML_CUDA_F16
        dst[row] = tmp.x + tmp.y;
#else
        dst[row] = tmp;
#endif // GGML_CUDA_F16
    }
}

static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    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<GGML_TYPE_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, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    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<GGML_TYPE_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, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    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<GGML_TYPE_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, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    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<GGML_TYPE_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, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    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<GGML_TYPE_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 float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    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 float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    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 float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    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 float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    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 float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    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 convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    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<GGML_TYPE_F16>
        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}

void ggml_cuda_op_dequantize_mul_mat_vec(
    ggml_backend_cuda_context & ctx,
    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
    const int64_t src1_padded_row_size, cudaStream_t stream) {
    GGML_UNUSED(ctx);
    const int64_t ne00 = src0->ne[0];
    const int64_t row_diff = row_high - row_low;

    GGML_ASSERT(src1->type == GGML_TYPE_F32);

    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
#ifdef GGML_CUDA_F16
    ggml_cuda_pool_alloc<half> src1_dfloat_a(ctx.pool());
    half * src1_dfloat = nullptr; // dfloat == half

    bool src1_convert_f16 =
        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;

    if (src1_convert_f16) {
        src1_dfloat = src1_dfloat_a.alloc(ne00);
        const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
        GGML_ASSERT(to_fp16_cuda != nullptr);
        to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream);
    }
#else
    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
#endif // GGML_CUDA_F16

    switch (src0->type) {
        case GGML_TYPE_Q4_0:
            dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q4_1:
            dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q5_0:
            dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q5_1:
            dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q8_0:
            dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q2_K:
            dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q3_K:
            dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q4_K:
            dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q5_K:
            dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q6_K:
            dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
            break;
        case GGML_TYPE_F16:
            convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
            break;
        default:
            GGML_ABORT("fatal error");
            break;
    }

    GGML_UNUSED(src1);
    GGML_UNUSED(dst);
    GGML_UNUSED(src1_ddq_i);
    GGML_UNUSED(src1_ncols);
    GGML_UNUSED(src1_padded_row_size);
}

bool ggml_cuda_dmmv_type_supported(ggml_type src0_type) {
    return src0_type == GGML_TYPE_Q4_0 || src0_type == GGML_TYPE_Q4_1 ||
        src0_type == GGML_TYPE_Q5_0 || src0_type == GGML_TYPE_Q5_1 ||
        src0_type == GGML_TYPE_Q8_0 || src0_type == GGML_TYPE_Q2_K ||
        src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q4_K ||
        src0_type == GGML_TYPE_Q5_K || src0_type == GGML_TYPE_Q6_K ||
        src0_type == GGML_TYPE_F16;
}