/src/llama.cpp/ggml/src/ggml-cpu/repack.cpp

Source
#define GGML_COMMON_IMPL_CPP
#define GGML_COMMON_DECL_CPP
#include "ggml-common.h"
#include "ggml-backend-impl.h"

#include "ggml-impl.h"
#include "ggml-cpu.h"
#include "ggml-cpu-impl.h"
#include "simd-mappings.h"
#include "traits.h"

#include "arch-fallback.h"

#include <cmath>
#include <cstring>
#include <cassert>
#include <cstdio>  // for GGML_ASSERT

#include "repack.h"

#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Woverlength-strings"
#endif

#define UNUSED GGML_UNUSED

static inline int nearest_int(float fval) {
    assert(fabsf(fval) <= 4194303.f);
    float val = fval + 12582912.f;
    int i; memcpy(&i, &val, sizeof(int));
    return (i & 0x007fffff) - 0x00400000;
}

// Functions to create the interleaved data layout formats

// interleave 4 block_q4_0s in blocks of blck_size_interleave
// returns an interleaved block_q4_0x4
// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks
// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave
//
// - in                  : an array of block_q4_0 pointers
// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of
//                         blck_size_interleave bytes
// - xor_mask            : the mask to convert the nibbles in block_q4_0 quants bytes
//                         from bias offset form to pure sign form (this saves subtract
//                         operations durin unpacking)
//

extern "C" {

void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
    assert(QK8_0 == 32);
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

    block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy;

    // scalar
    const int blck_size_interleave = 4;
    float srcv[4][QK8_0];
    float id[4];

    for (int i = 0; i < nb; i++) {
        for (int row_iter = 0; row_iter < 4; row_iter++) {
            float amax = 0.0f; // absolute max

            for (int j = 0; j < QK8_0; j++) {
                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
                amax = MAX(amax, fabsf(srcv[row_iter][j]));
            }

            const float d = amax / ((1 << 7) - 1);
            id[row_iter] = d ? 1.0f / d : 0.0f;

            y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d);
        }

        for (int j = 0; j < QK8_0 * 4; j++) {
            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
            src_offset += (j % blck_size_interleave);

            float x0 = srcv[src_id][src_offset] * id[src_id];
            y[i].qs[j] = roundf(x0);
        }
    }
}

void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
    assert(QK8_0 == 32);
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

    block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy;

    // scalar
    const int blck_size_interleave = 8;
    float srcv[4][QK8_0];
    float id[4];

    for (int i = 0; i < nb; i++) {
        for (int row_iter = 0; row_iter < 4; row_iter++) {
            float amax = 0.0f; // absolute max

            for (int j = 0; j < QK8_0; j++) {
                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
                amax = MAX(amax, fabsf(srcv[row_iter][j]));
            }

            const float d = amax / ((1 << 7) - 1);
            id[row_iter] = d ? 1.0f / d : 0.0f;

            y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d);
        }

        for (int j = 0; j < QK8_0 * 4; j++) {
            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
            src_offset += (j % blck_size_interleave);

            float x0 = srcv[src_id][src_offset] * id[src_id];
            y[i].qs[j] = roundf(x0);
        }
    }
}

void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
    assert(QK_K == 256);
    assert(k % QK_K == 0);
    const int nb = k / QK_K;

    block_q8_Kx4 * GGML_RESTRICT y = (block_q8_Kx4 *) vy;

    // scalar
    const int blck_size_interleave = 8;
    float srcv[4][QK_K];
    float iscale[4];

    for (int i = 0; i < nb; i++) {
        for (int row_iter = 0; row_iter < 4; row_iter++) {
            float amax = 0.0f; // absolute max
            float max = 0;

            for (int j = 0; j < QK_K; j++) {
                srcv[row_iter][j] = x[row_iter * k + i * QK_K + j];
                // Update the maximum value of the corresponding super block
                if(amax < fabsf(srcv[row_iter][j])) {
                    amax = fabsf(srcv[row_iter][j]);
                    max = srcv[row_iter][j];
                }
            }

            iscale[row_iter] = amax ? -127.f/max : 0;

            y[i].d[row_iter] = amax ? 1/iscale[row_iter] : 0;
        }

        for (int j = 0; j < QK_K / 4; j++) {
            y[i].bsums[j] = 0;
        }

        // Quants values are interleaved in sequence of eight bytes from corresponding super blocks
        // Bsums values are interleaved in sequence of four bsums from each super block taken for interleaving
        // i.e first four bsums from the first super block, followed by first four bsums from second super block and so on
        for (int j = 0; j < QK_K * 4; j++) {
            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
            int src_id     = (j % (4 * blck_size_interleave)) / blck_size_interleave;
            src_offset += (j % blck_size_interleave);
            int index = (((j & 31) >> 3) << 2) + ((j >> 8) << 4) + ((j >> 6) & 3);

            float x0 = srcv[src_id][src_offset] * iscale[src_id];
            y[i].qs[j] = nearest_int(x0);
            y[i].bsums[index] += y[i].qs[j];
        }
    }
}

} // extern "C"

template <int64_t INTER_SIZE, ggml_type PARAM_TYPE>
void ggml_quantize_mat_t(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row);

template <> void ggml_quantize_mat_t<4, GGML_TYPE_Q8_0>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
    assert(nrow == 4);
    UNUSED(nrow);
    ggml_quantize_mat_q8_0_4x4(x, vy, n_per_row);
}

template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_0>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
    assert(nrow == 4);
    UNUSED(nrow);
    ggml_quantize_mat_q8_0_4x8(x, vy, n_per_row);
}

template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
    assert(nrow == 4);
    UNUSED(nrow);
    ggml_quantize_mat_q8_K_4x8(x, vy, n_per_row);
}

extern "C" {

void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
    const int blocklen = 4;

    assert(nr == 1);
    assert(n % qk == 0);
    assert(nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[4];
    int sumi;

    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
    for (int x = 0; x < nc / ncols_interleaved; x++) {
        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);

        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
        for (int l = 0; l < nb; l++) {
            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi = 0;
                    for (int i = 0; i < blocklen; ++i) {
                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
                    }
                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
    }
}

void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
    const int blocklen = 8;

    assert (n % qk == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[4];
    int sumi;

    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
    for (int x = 0; x < nc / ncols_interleaved; x++) {
        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);

        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
        for (int l = 0; l < nb; l++) {
            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi = 0;
                    for (int i = 0; i < blocklen; ++i) {
                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
                    }
                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
    }
}

void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;

    assert (n % qk == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[8];
    int sumi;

    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
    for (int x = 0; x < nc / ncols_interleaved; x++) {
        const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);

        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
        for (int l = 0; l < nb; l++) {
            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi = 0;
                    for (int i = 0; i < blocklen; ++i) {
                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
                    }
                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
    }
}

void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK_K;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;
    static const uint32_t kmask1 = 0x3f3f3f3f;
    static const uint32_t kmask2 = 0x0f0f0f0f;
    static const uint32_t kmask3 = 0x03030303;

    assert (n % qk == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[8];
    float sum_minf[8];
    uint32_t utmp[32];
    int sumi1;
    int sumi2;
    int sumi;

    const block_q8_K * a_ptr = (const block_q8_K *) vy;
    for (int x = 0; x < nc / ncols_interleaved; x++) {
        const block_q4_Kx8 * b_ptr = (const block_q4_Kx8 *) vx + (x * nb);

        for (int j = 0; j < ncols_interleaved; j++) {
            sumf[j] = 0.0;
            sum_minf[j] = 0.0;
        }
        for (int l = 0; l < nb; l++) {
            for (int sb = 0; sb < 8; sb++) {
                memcpy(utmp + sb * 4, b_ptr[l].scales + sb * 12, 12);
                utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4);
                const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1;
                utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4);
                utmp[sb * 4 + 2] = uaux_0;
                utmp[sb * 4 + 0] &= kmask1;
            }
            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                uint8_t *scales_0 = (uint8_t*) utmp + (k / 4) * 32;
                uint8_t *scales_1 = (uint8_t*) utmp + (k / 4) * 32 + 16;
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi1 = 0;
                    sumi2 = 0;
                    sumi = 0;
                    for (int i = 0; i < blocklen; ++i) {
                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF);
                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4);
                        sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 64 + (k % 4) * blocklen + i]);
                        sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 64 + (k % 4) * blocklen + i + 32]);
                        sumi1 = sumi1 * scales_0[j];
                        sumi2 = sumi2 * scales_1[j];
                        sumi += sumi1 + sumi2;
                    }
                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
                }
            }
            for (int sb = 0; sb < 8; sb++) {
                uint8_t *mins = (uint8_t*) utmp + 8 + sb * 16;
                for (int j = 0; j < ncols_interleaved; j++) {
                    sum_minf[j] += mins[j] * (a_ptr[l].bsums[sb * 2] + a_ptr[l].bsums[sb * 2 + 1]) * GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d;
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) {
            s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j];
        }
    }
}

void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK_K;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;

    assert (n % qk == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[8];
    float sum_minf[8];
    int sumi1,sumi2,sumi3,sumi4;
    int sumi;

    const block_q8_K * a_ptr = (const block_q8_K *)vy;
    for(int x = 0; x < nc / ncols_interleaved; x++) {
        const block_q2_Kx8 * b_ptr = (const block_q2_Kx8 *) vx + (x * nb);
        for (int j = 0; j < ncols_interleaved; j++) {
            sumf[j] = 0.0;
            sum_minf[j] = 0.0;
        }
        for (int l = 0; l < nb; l++) {
            for (int k = 0; k < (qk / (4 * blocklen)); k++) {
                const uint8_t *scales_0 = b_ptr[l].scales + (k / 4) * 64 ;
                const uint8_t *scales_1 = b_ptr[l].scales + (k / 4) * 64 + 16;
                const uint8_t *scales_2 = b_ptr[l].scales + (k / 4) * 64 + 32;
                const uint8_t *scales_3 = b_ptr[l].scales + (k / 4) * 64 + 48;
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi1 = 0;
                    sumi2 = 0;
                    sumi3 = 0;
                    sumi4 = 0;
                    sumi = 0;
                    int offset = ((k / 2) % 2) + j * 2;
                    for (int i = 0; i < blocklen; ++i){
                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 3);
                        const int v1 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 2 ) & 3);
                        const int v2 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4 ) & 3);
                        const int v3 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 6 ) & 3);
                        sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i]);
                        sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 32]);
                        sumi3 = (v2 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 64]);
                        sumi4 = (v3 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 96]);

                        sumi1 = sumi1 * (scales_0[offset] & 0xF);
                        sumi2 = sumi2 * (scales_1[offset] & 0xF);
                        sumi3 = sumi3 * (scales_2[offset] & 0xF);
                        sumi4 = sumi4 * (scales_3[offset] & 0xF);
                        sumi += sumi1 + sumi2 + sumi3 + sumi4;
                    }
                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
                }
            }
            for(int sb = 0; sb < 8; sb++) {
                const uint8_t *mins = b_ptr[l].scales + sb * 16;
                for(int j = 0; j < ncols_interleaved; j++){
                    sum_minf[j] += ((mins[j * 2] >> 4) * a_ptr[l].bsums[sb * 2] + (mins[(j * 2)+ 1] >> 4) * a_ptr[l].bsums[sb * 2 + 1]) * GGML_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d;
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) {
            s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j];
        }
    }
}

void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
    const int blocklen = 4;

    assert(nr == 1);
    assert(n % qk == 0);
    assert(nc % ncols_interleaved == 0);

    UNUSED(bs);
    UNUSED(nr);

    float sumf[4];
    int sumi;

    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
    for (int x = 0; x < nc / ncols_interleaved; x++) {
        const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);

        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
        for (int l = 0; l < nb; l++) {
            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi = 0;
                    for (int i = 0; i < blocklen; ++i) {
                        const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
                        const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
                    }
                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
    }
}

void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;

    assert(nr == 1);
    assert(n % qk == 0);
    assert(nc % ncols_interleaved == 0);

    UNUSED(bs);
    UNUSED(nr);

    float sumf[8];
    int sumi;

    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
    for (int x = 0; x < nc / ncols_interleaved; x++) {
        const block_iq4_nlx8 * b_ptr = (const block_iq4_nlx8 *) vx + (x * nb);

        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
        for (int l = 0; l < nb; l++) {
            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumi = 0;
                    for (int i = 0; i < blocklen; ++i) {
                        const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
                        const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
                    }
                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                }
            }
        }
        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
    }
}

void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
    const int blocklen = 4;

    assert (n % qk == 0);
    assert (nr % 4 == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    {
        float sumf[4][4];
        int sumi;

        for (int y = 0; y < nr / 4; y++) {
            const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
            for (int x = 0; x < nc / ncols_interleaved; x++) {
                const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
                for (int m = 0; m < 4; m++) {
                    for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
                }
                for (int l = 0; l < nb; l++) {
                    for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                        for (int m = 0; m < 4; m++) {
                            for (int j = 0; j < ncols_interleaved; j++) {
                                sumi = 0;
                                for (int i = 0; i < blocklen; ++i) {
                                    const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
                                    const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
                                    sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
                                            (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
                                }
                                sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
                            }
                        }
                    }
                }
                for (int m = 0; m < 4; m++) {
                    for (int j = 0; j < ncols_interleaved; j++)
                        s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
                }
            }
        }
    }
}

void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
    const int blocklen = 8;

    assert (n % qk == 0);
    assert (nr % 4 == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[4][4];
    int sumi;

    for (int y = 0; y < nr / 4; y++) {
        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
        for (int x = 0; x < nc / ncols_interleaved; x++) {
            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
            }
            for (int l = 0; l < nb; l++) {
                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                    for (int m = 0; m < 4; m++) {
                        for (int j = 0; j < ncols_interleaved; j++) {
                            sumi = 0;
                            for (int i = 0; i < blocklen; ++i) {
                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
                                        (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
                            }
                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
                        }
                    }
                }
            }
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++)
                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
            }
        }
    }
}

void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;

    assert (n % qk == 0);
    assert (nr % 4 == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[4][8];
    int sumi;

    for (int y = 0; y < nr / 4; y++) {
        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
        for (int x = 0; x < nc / ncols_interleaved; x++) {
            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
            }
            for (int l = 0; l < nb; l++) {
                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                    for (int m = 0; m < 4; m++) {
                        for (int j = 0; j < ncols_interleaved; j++) {
                            sumi = 0;
                            for (int i = 0; i < blocklen; ++i) {
                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
                            }
                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
                        }
                    }
                }
            }
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++)
                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
            }
        }
    }
}

void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK_K;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;
    static const uint32_t kmask1 = 0x3f3f3f3f;
    static const uint32_t kmask2 = 0x0f0f0f0f;
    static const uint32_t kmask3 = 0x03030303;

    assert (n % qk == 0);
    assert (nr % 4 == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[4][8];
    float sum_minf[4][8];
    uint32_t utmp[32];
    int sumi1;
    int sumi2;
    int sumi;

    for (int y = 0; y < nr / 4; y++) {
        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
        for (int x = 0; x < nc / ncols_interleaved; x++) {
            const block_q4_Kx8 * b_ptr = (const block_q4_Kx8 *) vx + (x * nb);
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumf[m][j] = 0.0;
                    sum_minf[m][j] = 0.0;
                }
            }
            for (int l = 0; l < nb; l++) {
                for (int sb = 0; sb < 8; sb++) {
                    memcpy(utmp + sb * 4, b_ptr[l].scales + sb * 12, 12);
                    utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4);
                    const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1;
                    utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4);
                    utmp[sb * 4 + 2] = uaux_0;
                    utmp[sb * 4 + 0] &= kmask1;
                }
                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                    uint8_t *scales_0 = (uint8_t*) utmp + (k / 4) * 32;
                    uint8_t *scales_1 = (uint8_t*) utmp + (k / 4) * 32 + 16;
                    for (int m = 0; m < 4; m++) {
                        for (int j = 0; j < ncols_interleaved; j++) {
                            sumi1 = 0;
                            sumi2 = 0;
                            sumi = 0;
                            for (int i = 0; i < blocklen; ++i) {
                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF);
                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4);
                                sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 256 + (k % 4) * 4 * blocklen + m * blocklen + i]);
                                sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 256 + (k % 4) * 4 * blocklen + m * blocklen + i + 128]);
                                sumi1 = sumi1 * scales_0[j];
                                sumi2 = sumi2 * scales_1[j];
                                sumi += sumi1 + sumi2;
                            }
                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m];
                        }
                    }
                }
                for (int sb = 0; sb < 8; sb++) {
                    uint8_t *mins = (uint8_t*) utmp + 8 + sb * 16;
                    for(int m = 0; m < 4; m++) {
                        const int16_t *bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) * 6);
                        for(int j = 0; j < ncols_interleaved; j++) {
                            sum_minf[m][j] += mins[j] * (bsums[0] + bsums[1]) * GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m];
                        }
                    }
                }
            }
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j];
                }
            }
        }
    }
}

void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK_K;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;

    assert (n % qk == 0);
    assert (nr % 4 == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    float sumf[4][8];
    float sum_minf[4][8];
    int sumi1, sumi2, sumi3, sumi4;
    int sumi;

    for (int y = 0; y < nr / 4; y++) {
        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
        for (int x = 0; x < nc / ncols_interleaved; x++) {
            const block_q2_Kx8 * b_ptr = (const block_q2_Kx8 *) vx + (x * nb);
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    sumf[m][j] = 0.0;
                    sum_minf[m][j] = 0.0;
                }
            }
            for (int l = 0; l < nb; l++) {
                for (int k = 0; k < (qk / (4 * blocklen)); k++) {

                    const uint8_t *scales_0 = b_ptr[l].scales + (k / 4) * 64 ;
                    const uint8_t *scales_1 = b_ptr[l].scales + (k / 4) * 64 + 16;
                    const uint8_t *scales_2 = b_ptr[l].scales + (k / 4) * 64 + 32;
                    const uint8_t *scales_3 = b_ptr[l].scales + (k / 4) * 64 + 48;
                    for (int m = 0; m < 4; m++) {
                        for (int j = 0; j < ncols_interleaved; j++) {
                            sumi1 = 0;
                            sumi2 = 0;
                            sumi3 = 0;
                            sumi4 = 0;
                            sumi = 0;
                            int offset = ((k / 2) % 2) + j * 2;
                            for (int i = 0; i < blocklen; ++i){
                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 3);
                                const int v1 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 2 ) & 3);
                                const int v2 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4 ) & 3);
                                const int v3 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 6 ) & 3);
                                sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 512 + (k % 4) * 4 * blocklen + m * blocklen + i]);
                                sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 128]);
                                sumi3 = (v2 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 256]);
                                sumi4 = (v3 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 384]);
                                sumi1 = sumi1 * (scales_0[offset] & 0xF);
                                sumi2 = sumi2 * (scales_1[offset] & 0xF);
                                sumi3 = sumi3 * (scales_2[offset] & 0xF);
                                sumi4 = sumi4 * (scales_3[offset] & 0xF);
                                sumi += sumi1 + sumi2 + sumi3 + sumi4;
                            }
                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m];
                        }
                    }
                }
                for(int sb = 0; sb < 8; sb++) {
                    const uint8_t *mins = b_ptr[l].scales + sb * 16;
                    for(int m = 0; m < 4; m++) {
                        const int16_t *bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) *  6);
                        for(int j = 0; j < ncols_interleaved; j++) {
                            int mins_prod = ((mins[j * 2] >> 4) * bsums[0] + (mins[(j * 2)+ 1] >> 4) * bsums[1]);
                            sum_minf[m][j] += (mins_prod) * GGML_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m];
                        }
                    }
                }
            }

            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) {
                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j];
                }
            }
        }
    }
}


void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
    const int blocklen = 4;

    assert (n % qk == 0);
    assert (nr % 4 == 0);
    assert (nc % ncols_interleaved == 0);

    UNUSED(s);
    UNUSED(bs);
    UNUSED(vx);
    UNUSED(vy);
    UNUSED(nr);
    UNUSED(nc);
    UNUSED(nb);
    UNUSED(ncols_interleaved);
    UNUSED(blocklen);

    {
        float sumf[4][4];
        int sumi;

        for (int y = 0; y < nr / 4; y++) {
            const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
            for (int x = 0; x < nc / ncols_interleaved; x++) {
                const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
                for (int m = 0; m < 4; m++) {
                    for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
                }
                for (int l = 0; l < nb; l++) {
                    for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                        for (int m = 0; m < 4; m++) {
                            for (int j = 0; j < ncols_interleaved; j++) {
                                sumi = 0;
                                for (int i = 0; i < blocklen; ++i) {
                                    const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
                                    const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
                                    sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
                                            (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
                                }
                                sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
                            }
                        }
                    }
                }
                for (int m = 0; m < 4; m++) {
                    for (int j = 0; j < ncols_interleaved; j++)
                        s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
                }
            }
        }
    }
}

void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
    const int blocklen = 8;

    assert(n % qk == 0);
    assert(nr % 4 == 0);
    assert(nc % ncols_interleaved == 0);

    float sumf[4][8];
    int sumi;

    for (int y = 0; y < nr / 4; y++) {
        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
        for (int x = 0; x < nc / ncols_interleaved; x++) {
            const block_iq4_nlx8 * b_ptr = (const block_iq4_nlx8 *) vx + (x * nb);
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
            }
            for (int l = 0; l < nb; l++) {
                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
                    for (int m = 0; m < 4; m++) {
                        for (int j = 0; j < ncols_interleaved; j++) {
                            sumi = 0;
                            for (int i = 0; i < blocklen; ++i) {
                                const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
                                const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
                            }
                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
                        }
                    }
                }
            }
            for (int m = 0; m < 4; m++) {
                for (int j = 0; j < ncols_interleaved; j++)
                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
            }
        }
    }
}

} // extern "C"

static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
    block_q4_0x4 out;

    for (int i = 0; i < 4; i++) {
        out.d[i] = in[i].d;
    }

    const int end = QK4_0 * 2 / blck_size_interleave;

    if (blck_size_interleave == 8) {
        const uint64_t xor_mask = 0x8888888888888888ULL;
        for (int i = 0; i < end; ++i) {
            int src_id = i % 4;
            int src_offset = (i / 4) * blck_size_interleave;
            int dst_offset = i * blck_size_interleave;

            uint64_t elems;
            // Using memcpy to avoid unaligned memory accesses
            memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
            elems ^= xor_mask;
            memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
        }
    } else if (blck_size_interleave == 4) {
        const uint32_t xor_mask = 0x88888888;
        for (int i = 0; i < end; ++i) {
            int src_id = i % 4;
            int src_offset = (i / 4) * blck_size_interleave;
            int dst_offset = i * blck_size_interleave;

            uint32_t elems;
            memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t));
            elems ^= xor_mask;
            memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t));
        }
    } else {
        GGML_ASSERT(false);
    }

    return out;
}

// interleave 8 block_q4_0s in blocks of blck_size_interleave
// returns an interleaved block_q4_0x8
// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave
static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) {
    block_q4_0x8 out;

    for (int i = 0; i < 8; i++) {
        out.d[i] = in[i].d;
    }

    const int end = QK4_0 * 4 / blck_size_interleave;
    const uint64_t xor_mask = 0x8888888888888888ULL;

    for (int i = 0; i < end; ++i) {
        int src_id = i % 8;
        int src_offset = (i / 8) * blck_size_interleave;
        int dst_offset = i * blck_size_interleave;

        uint64_t elems;
        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
        elems ^= xor_mask;
        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
    }

    return out;
}

static block_q4_Kx8 make_block_q4_Kx8(block_q4_K * in, unsigned int blck_size_interleave) {
    block_q4_Kx8 out;
    //Delta(scale) and dmin values of the eight Q4_K structures are copied onto the output interleaved structure
    for (int i = 0; i < 8; i++) {
        out.d[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d;
    }

    for (int i = 0; i < 8; i++) {
        out.dmin[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin;
    }

    const int end = QK_K * 4 / blck_size_interleave;

    // Interleave Q4_K quants by taking 8 bytes at a time
    for (int i = 0; i < end; ++i) {
        int src_id = i % 8;
        int src_offset = (i / 8) * blck_size_interleave;
        int dst_offset = i * blck_size_interleave;

        uint64_t elems;
        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
    }

    // The below logic is designed so as to unpack and rearrange scales and mins values in Q4_K
    // Currently the Q4_K structure has 8 scales and 8 mins packed in 12 bytes ( 6 bits for each value)
    // The output Q4_Kx8 structure has 96 bytes
    // Every 12 byte is packed such that it contains scales and mins for corresponding sub blocks from Q4_K structure
    // For eg - First 12 bytes contains 8 scales and 8 mins - each of first sub block from different Q4_K structures
    uint8_t s[8], m[8];

    for (int i = 0; i < 4; i++) {
        for (int j = 0; j < 8; j++) {
            s[j] = in[j].scales[i] & 63;
            m[j] = in[j].scales[i + 4] & 63;
        }

        out.scales[i * 12]      = (s[0] & 63) + ((s[4] & 48) << 2);
        out.scales[i * 12 + 1]  = (s[1] & 63) + ((s[5] & 48) << 2);
        out.scales[i * 12 + 2]  = (s[2] & 63) + ((s[6] & 48) << 2);
        out.scales[i * 12 + 3]  = (s[3] & 63) + ((s[7] & 48) << 2);
        out.scales[i * 12 + 4]  = (m[0] & 63) + ((m[4] & 48) << 2);
        out.scales[i * 12 + 5]  = (m[1] & 63) + ((m[5] & 48) << 2);
        out.scales[i * 12 + 6]  = (m[2] & 63) + ((m[6] & 48) << 2);
        out.scales[i * 12 + 7]  = (m[3] & 63) + ((m[7] & 48) << 2);
        out.scales[i * 12 + 8]  = (s[4] & 15) + ((m[4] & 15) << 4);
        out.scales[i * 12 + 9]  = (s[5] & 15) + ((m[5] & 15) << 4);
        out.scales[i * 12 + 10] = (s[6] & 15) + ((m[6] & 15) << 4);
        out.scales[i * 12 + 11] = (s[7] & 15) + ((m[7] & 15) << 4);

    }

    for (int i = 0; i < 4; i++) {
        for (int j = 0; j < 8; j++) {
            s[j] = ((in[j].scales[i] & 192) >> 2) | (in[j].scales[i+8] & 15);
            m[j] = ((in[j].scales[i + 4] & 192) >> 2) | ((in[j].scales[i+8] & 240) >> 4);
        }

        out.scales[i * 12 + 48] = (s[0] & 63) + ((s[4] & 48) << 2);
        out.scales[i * 12 + 49] = (s[1] & 63) + ((s[5] & 48) << 2);
        out.scales[i * 12 + 50] = (s[2] & 63) + ((s[6] & 48) << 2);
        out.scales[i * 12 + 51] = (s[3] & 63) + ((s[7] & 48) << 2);
        out.scales[i * 12 + 52] = (m[0] & 63) + ((m[4] & 48) << 2);
        out.scales[i * 12 + 53] = (m[1] & 63) + ((m[5] & 48) << 2);
        out.scales[i * 12 + 54] = (m[2] & 63) + ((m[6] & 48) << 2);
        out.scales[i * 12 + 55] = (m[3] & 63) + ((m[7] & 48) << 2);
        out.scales[i * 12 + 56] = (s[4] & 15) + ((m[4] & 15) << 4);
        out.scales[i * 12 + 57] = (s[5] & 15) + ((m[5] & 15) << 4);
        out.scales[i * 12 + 58] = (s[6] & 15) + ((m[6] & 15) << 4);
        out.scales[i * 12 + 59] = (s[7] & 15) + ((m[7] & 15) << 4);

    }

    return out;
}

static block_q2_Kx8 make_block_q2_Kx8(block_q2_K * in, unsigned int blck_size_interleave) {
    block_q2_Kx8 out;

    // Delta(scale) and dmin values of the eight Q2_K structures are copied onto the output interleaved structure
    for (int i = 0; i < 8; i++) {
        out.d[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d;
    }

    for (int i = 0; i < 8; i++) {
        out.dmin[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin;
    }

    const int end = QK_K * 2 / blck_size_interleave;

    // Interleave Q2_K quants by taking 8 bytes at a time
    for (int i = 0; i < end; ++i) {
        int src_id = i % 8;
        int src_offset = (i / 8) * blck_size_interleave;
        int dst_offset = i * blck_size_interleave;

        uint64_t elems;
        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
    }

    // The below logic is designed so as to unpack and rearrange scales and mins values in Q2_K
    // Currently the Q2_K structure has 16 scales and 16 mins packed in 16 bytes ( 4 bits for each value)
    // The output Q2_Kx8 structure has 128 bytes for storing scales and mins
    // Every 16 byte is packed such that it contains scales and mins for corresponding sub blocks from Q2_K structure
    // For eg - First 16 bytes contains 16 scales and 16 mins - each of first and second sub blocks from different Q2_K structures

    for(int i = 0; i < 128; i++){

        // Index for selecting which q2k super block
        int src1 = (i % 16) / 2;
        // Index for selecting scale
        int src2 = ((i / 16) * 2) + (i % 2);

        out.scales[i] = in[src1].scales[src2];
    }
    return out;

}

static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
    GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
    constexpr int nrows_interleaved = 4;

    block_q4_0x4 * dst = (block_q4_0x4 *)t->data;
    const block_q4_0 * src = (const block_q4_0 *)data;
    block_q4_0 dst_tmp[4];
    int nrow = ggml_nrows(t);
    int nblocks = t->ne[0] / QK4_0;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));

    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

    for (int b = 0; b < nrow; b += nrows_interleaved) {
        for (int64_t x = 0; x < nblocks; x++) {
            for (int i = 0; i < nrows_interleaved; i++) {
                dst_tmp[i] = src[x + i * nblocks];
            }
            *dst++ = make_block_q4_0x4(dst_tmp, interleave_block);
        }
        src += nrows_interleaved * nblocks;
    }
    return 0;

    GGML_UNUSED(data_size);
}
static int repack_q4_K_to_q4_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_Q4_K);
    GGML_ASSERT(interleave_block == 8);
    constexpr int nrows_interleaved = 8;

    block_q4_Kx8 * dst = (block_q4_Kx8*)t->data;
    const block_q4_K * src = (const block_q4_K*) data;
    block_q4_K dst_tmp[8];
    int nrow = ggml_nrows(t);
    int nblocks = t->ne[0] / QK_K;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_K));

    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

    for (int b = 0; b < nrow; b += nrows_interleaved) {
        for (int64_t x = 0; x < nblocks; x++) {
            for (int i  = 0; i < nrows_interleaved; i++ ) {
                dst_tmp[i] = src[x + i * nblocks];
            }
            *dst++ = make_block_q4_Kx8(dst_tmp, interleave_block);
        }
        src += nrows_interleaved * nblocks;
    }
    return 0;

    GGML_UNUSED(data_size);
}

static int repack_q2_K_to_q2_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_Q2_K);
    GGML_ASSERT(interleave_block == 8);
    constexpr int nrows_interleaved = 8;

    block_q2_Kx8 * dst = (block_q2_Kx8*)t->data;
    const block_q2_K * src = (const block_q2_K*) data;
    block_q2_K dst_tmp[8];
    int nrow = ggml_nrows(t);
    int nblocks = t->ne[0] / QK_K;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q2_K));

    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

    for (int b = 0; b < nrow; b += nrows_interleaved) {
        for (int64_t x = 0; x < nblocks; x++) {
            for (int i  = 0; i < nrows_interleaved; i++ ) {
                dst_tmp[i] = src[x + i * nblocks];
            }
            *dst++ = make_block_q2_Kx8(dst_tmp, interleave_block);
        }
        src += nrows_interleaved * nblocks;
    }
    return 0;

    GGML_UNUSED(data_size);
}

static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
    GGML_ASSERT(interleave_block == 8);
    constexpr int nrows_interleaved = 8;

    block_q4_0x8 * dst = (block_q4_0x8*)t->data;
    const block_q4_0 * src = (const block_q4_0*) data;
    block_q4_0 dst_tmp[8];
    int nrow = ggml_nrows(t);
    int nblocks = t->ne[0] / QK4_0;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));

    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

    for (int b = 0; b < nrow; b += nrows_interleaved) {
        for (int64_t x = 0; x < nblocks; x++) {
            for (int i  = 0; i < nrows_interleaved; i++ ) {
                dst_tmp[i] = src[x + i * nblocks];
            }
            *dst++ = make_block_q4_0x8(dst_tmp, interleave_block);
        }
        src += nrows_interleaved * nblocks;
    }
    return 0;

    GGML_UNUSED(data_size);
}

static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_size_interleave) {
    block_iq4_nlx4 out;

    for (int i = 0; i < 4; i++) {
        out.d[i] = in[i].d;
    }

    const int end = QK4_NL * 2 / blck_size_interleave;

    // TODO: this branch seems wrong
    //if (blck_size_interleave == 8) {
    //    for (int i = 0; i < end; ++i) {
    //        int src_id = i % 4;
    //        int src_offset = (i / 4) * blck_size_interleave;
    //        int dst_offset = i * blck_size_interleave;

    //        // Using memcpy to avoid unaligned memory accesses
    //        memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t));
    //    }
    //} else
    if (blck_size_interleave == 4) {
        for (int i = 0; i < end; ++i) {
            int src_id = i % 4;
            int src_offset = (i / 4) * blck_size_interleave;
            int dst_offset = i * blck_size_interleave;

            memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint32_t));
        }
    } else {
        GGML_ASSERT(false);
    }

    return out;
}

static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL);
    GGML_ASSERT(interleave_block == 4);

    const block_iq4_nl   * src = (const block_iq4_nl   *)data;
          block_iq4_nlx4 * dst = (      block_iq4_nlx4 *)t->data;

    block_iq4_nl dst_tmp[4];

    int nrow = ggml_nrows(t);
    int nrows_interleaved = 4;
    int nblocks = t->ne[0] / QK4_NL;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl));

    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

    for (int b = 0; b < nrow; b += nrows_interleaved) {
        for (int64_t x = 0; x < nblocks; x++) {
            for (int i = 0; i < nrows_interleaved; i++) {
                dst_tmp[i] = src[x + i * nblocks];
            }
            *dst++ = make_block_iq4_nlx4(dst_tmp, interleave_block);
        }
        src += nrows_interleaved * nblocks;
    }
    return 0;

    GGML_UNUSED(data_size);
}

static block_iq4_nlx8 make_block_iq4_nlx8(block_iq4_nl * in, unsigned int blck_size_interleave) {
    block_iq4_nlx8 out;

    for (int i = 0; i < 8; i++) {
        out.d[i] = in[i].d;
    }

    const int end = QK4_NL * 4 / blck_size_interleave;

    if (blck_size_interleave == 8) {
        for (int i = 0; i < end; ++i) {
            int src_id = i % 8;
            int src_offset = (i / 8) * blck_size_interleave;
            int dst_offset = i * blck_size_interleave;

            memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t));
        }
    } else {
        GGML_ASSERT(false);
    }

    return out;
}

static int repack_iq4_nl_to_iq4_nl_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL);
    GGML_ASSERT(interleave_block == 8);

    const block_iq4_nl   * src = (const block_iq4_nl   *)data;
          block_iq4_nlx8 * dst = (      block_iq4_nlx8 *)t->data;

    block_iq4_nl dst_tmp[8];

    int nrow = ggml_nrows(t);
    int nrows_interleaved = 8;
    int nblocks = t->ne[0] / QK4_NL;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl));

    if (t->ne[1] % nrows_interleaved != 0) {
        return -1;
    }

    for (int b = 0; b < nrow; b += nrows_interleaved) {
        for (int64_t x = 0; x < nblocks; x++) {
            for (int i = 0; i < nrows_interleaved; i++) {
                dst_tmp[i] = src[x + i * nblocks];
            }
            *dst++ = make_block_iq4_nlx8(dst_tmp, interleave_block);
        }
        src += nrows_interleaved * nblocks;
    }
    return 0;

    GGML_UNUSED(data_size);
}

namespace ggml::cpu::repack {
// repack
template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
int repack(struct ggml_tensor *, const void *, size_t);

// TODO: generalise.
template <> int repack<block_q4_0, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_q4_0_to_q4_0_4_bl(t, 4, data, data_size);
}

template <> int repack<block_q4_0, 8, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_q4_0_to_q4_0_4_bl(t, 8, data, data_size);
}

template <> int repack<block_q4_0, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_q4_0_to_q4_0_8_bl(t, 8, data, data_size);
}

template <> int repack<block_q4_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_q4_K_to_q4_K_8_bl(t, 8, data, data_size);
}

template <> int repack<block_q2_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_q2_K_to_q2_K_8_bl(t, 8, data, data_size);
}

template <> int repack<block_iq4_nl, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_iq4_nl_to_iq4_nl_4_bl(t, 4, data, data_size);
}

// TODO: needs to be revisited
//template <> int repack<block_iq4_nl, 8, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
//    return repack_iq4_nl_to_iq4_nl_4_bl(t, 8, data, data_size);
//}

template <> int repack<block_iq4_nl, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_iq4_nl_to_iq4_nl_8_bl(t, 8, data, data_size);
}

// gemv
template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
void gemv(int, float *, size_t, const void *, const void *, int, int);

template <> void gemv<block_q4_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_q4_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemv<block_q4_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemv<block_q4_0, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemv<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
}

template <> void gemv<block_q2_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
}

template <> void gemv<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemv<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
}

// gemm
template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
void gemm(int, float *, size_t, const void *, const void *, int, int);

template <> void gemm<block_q4_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_q4_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemm<block_q4_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemm<block_q4_0, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemm<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
}

template <> void gemm<block_q2_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
}

template <> void gemm<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
}

template <> void gemm<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
}

class tensor_traits_base : public ggml::cpu::tensor_traits {
  public:
    virtual int repack(struct ggml_tensor * t, const void * data, size_t data_size) = 0;
};

template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE> class tensor_traits : public tensor_traits_base {

    bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
        // not realy a GGML_TYPE_Q8_0 but same size.
        switch (op->op) {
            case GGML_OP_MUL_MAT:
                {
                    size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
                    return true;
                }
            case GGML_OP_MUL_MAT_ID:
                {
                    size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
                    size = GGML_PAD(size, sizeof(int64_t)); // + padding for next bloc.

                    const int64_t ne02 = op->src[0]->ne[2]; // n_as, n_expert
                    const int64_t ne12 = op->src[1]->ne[2]; // n_tokens

                    const size_t sizeof_mmid_row_mapping = sizeof(int64_t);

                    size += sizeof_mmid_row_mapping*ne02*(ne12 + 1);

                    return true;
                }
            default:
                // GGML_ABORT("fatal error");
                break;
        }
        return false;
    }

    bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
        switch (op->op) {
            case GGML_OP_MUL_MAT:
                forward_mul_mat(params, op);
                return true;
            case GGML_OP_MUL_MAT_ID:
                forward_mul_mat_id(params, op);
                return true;
            default:
                // GGML_ABORT("fatal error");
                break;
        }
        return false;
    }

    void forward_mul_mat_one_chunk(ggml_compute_params * params,
                                   ggml_tensor *         op,
                                   int64_t               src0_start,
                                   int64_t               src0_end,
                                   int64_t               src1_start,
                                   int64_t               src1_end) {
        const ggml_tensor * src0 = op->src[0];
        const ggml_tensor * src1 = op->src[1];
        ggml_tensor *       dst  = op;

        GGML_TENSOR_BINARY_OP_LOCALS

        const size_t src1_col_stride = ggml_row_size(PARAM_TYPE, ne10);

        GGML_ASSERT(ne03 == 1 && ne13 == 1);
        GGML_ASSERT(ne12 % ne02 == 0);
        const int64_t r2 = ne12 / ne02;

        const int64_t i12 = src1_start / ne1;
        const int64_t i11 = src1_start - i12 * ne1;

        // Determine batch index
        const int64_t i02 = i12 / r2;

        const int64_t i1 = i11;
        const int64_t i2 = i12;

        const char * src0_ptr = (const char *) src0->data + i02 * nb02;
        const char * src1_ptr = (const char *) params->wdata + (i11 + i12 * ne11) * src1_col_stride;
        char *       dst_ptr  = ((char *) dst->data + (i1 * nb1 + i2 * nb2));

        const int64_t nrows = src1_end - src1_start;
        const int64_t ncols = src0_end - src0_start;

        GGML_ASSERT(src1_ptr + src1_col_stride * nrows <= (const char *) params->wdata + params->wsize);

        // If there are more than three rows in src1, use gemm; otherwise, use gemv.
        if (nrows > 3) {
            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00, (float *) (dst_ptr) + src0_start, nb1 / nb0,
                                                             src0_ptr + src0_start * nb01, src1_ptr,
                                                             nrows - (nrows % 4), ncols);
        }
        for (int iter = nrows - (nrows % 4); iter < nrows; iter++) {
            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00, (float *) (dst_ptr + (iter * nb1)) + src0_start,
                                                             ne01, src0_ptr + src0_start * nb01,
                                                             src1_ptr + (src1_col_stride * iter), 1 /* nrows */, ncols);
        }
    }

    void forward_mul_mat(ggml_compute_params * params, ggml_tensor * op) {
        const ggml_tensor * src0 = op->src[0];
        const ggml_tensor * src1 = op->src[1];
        ggml_tensor *       dst  = op;

        GGML_TENSOR_BINARY_OP_LOCALS

        const int ith = params->ith;
        const int nth = params->nth;

        GGML_ASSERT(ne0 == ne01);
        GGML_ASSERT(ne1 == ne11);
        GGML_ASSERT(ne2 == ne12);
        GGML_ASSERT(ne3 == ne13);

        // dst cannot be transposed or permuted
        GGML_ASSERT(nb0 == sizeof(float));
        GGML_ASSERT(nb0 <= nb1);
        GGML_ASSERT(nb1 <= nb2);
        GGML_ASSERT(nb2 <= nb3);

        // TODO: General batched mul mat for 4D tensors
        // Currently only supports 3D tensors
        GGML_ASSERT(ne03 == 1);
        GGML_ASSERT(ne13 == 1);
        GGML_ASSERT(ne3 == 1);

        GGML_ASSERT(src1->type == GGML_TYPE_F32);

        GGML_ASSERT(ggml_n_dims(op->src[0]) == 2);
        // GGML_ASSERT(ggml_n_dims(op->src[1]) == 2);

        char *       wdata = static_cast<char *>(params->wdata);
        const size_t nbw1  = ggml_row_size(PARAM_TYPE, ne10);
        const size_t nbw2  = nbw1 * ne11;

        assert(params->wsize >= nbw2 * ne12);

        const ggml_from_float_t from_float = ggml_get_type_traits_cpu(PARAM_TYPE)->from_float;

        // INFO: Quantization is done in planes to avoid extra complexity in chunking.
        // Flattening dimensions not multiple of INTER_SIZE would require extra handling depending on how
        // the planes are broadcast.
        for (int64_t i12 = 0; i12 < ne12; i12++) {
            char * data_ptr  = (char *) src1->data + i12 * nb12;
            char * wdata_ptr = wdata + i12 * nbw2;

            for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
                ggml_quantize_mat_t<INTER_SIZE, PARAM_TYPE>((float *) (data_ptr + i11 * nb11),
                                                            (void *) (wdata_ptr + i11 * nbw1), 4, ne10);
            }

            const int64_t i11_processed = ne11 - ne11 % 4;
            for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
                from_float((float *) (data_ptr + i11 * nb11), (void *) (wdata_ptr + i11 * nbw1), ne10);
            }
        }

        // disable for NUMA
        const bool disable_chunking = ggml_is_numa();

        // 4x chunks per thread
        const int64_t nr0 = ggml_nrows(op->src[0]);

        int     nth_scaled  = nth * 4;
        int64_t chunk_size0 = (nr0 + nth_scaled - 1) / nth_scaled;
        int64_t nchunk0     = (nr0 + chunk_size0 - 1) / chunk_size0;

        // src1 is chunked only by full planes.
        // When we flatten we need to address dimensions not multiple of the q8 INTER_SIZE
        // to route them thorugh GEMV.
        // nchunk1 = ne12 also avoids messing the chunking for models with no 3d tensors
        // to avoid affecting their performance
        int64_t nchunk1 = ne12;

        // Ensure minimum chunk size to avoid alignment issues with high thread counts
        // Minimum chunk size should be at least NB_COLS to prevent overlapping chunks after alignment
        const int64_t min_chunk_size = NB_COLS;
        if (nchunk0 > 0 && (nr0 / nchunk0) < min_chunk_size && nr0 >= min_chunk_size) {
            nchunk0 = (nr0 + min_chunk_size - 1) / min_chunk_size;
        }

        if (nth == 1 || nchunk0 < nth || disable_chunking) {
            nchunk0 = nth;
        }

        const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;

        // Ensure nchunk doesn't exceed the number of rows divided by minimum chunk size
        // This prevents creating too many tiny chunks that could overlap after alignment
        const int64_t max_nchunk = (nr0 + min_chunk_size - 1) / min_chunk_size;
        nchunk0                  = MIN(nchunk0, max_nchunk);

        if (ith == 0) {
            // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
            ggml_threadpool_chunk_set(params->threadpool, nth);
        }

        ggml_barrier(params->threadpool);

        // The first chunk comes from our thread_id, the rest will get auto-assigned.
        int current_chunk = ith;

        while (current_chunk < nchunk0 * nchunk1) {
            const int64_t ith0 = current_chunk % nchunk0;
            const int64_t ith1 = current_chunk / nchunk0;

            int64_t src0_start = dr0 * ith0;
            int64_t src0_end   = MIN(src0_start + dr0, nr0);

            // full-plane range for src1
            int64_t src1_start = ith1 * ne11;
            int64_t src1_end = (ith1 + 1) * ne11;

            // Align boundaries to NB_COLS - round up to ensure all data is included
            // The chunk size limiting above ensures chunks are large enough to prevent overlaps
            src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
            src0_end   = (src0_end % NB_COLS) ? src0_end + NB_COLS - (src0_end % NB_COLS) : src0_end;
            src0_end   = MIN(src0_end, ne01);

            // Make sure current plane is the last one before exiting
            if (src0_start >= src0_end) {
                current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
                continue;
            }

            forward_mul_mat_one_chunk(params, dst, src0_start, src0_end, src1_start, src1_end);

            current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
        }
    }

    void forward_mul_mat_id(ggml_compute_params * params, ggml_tensor * op) {
        const ggml_tensor * src0 = op->src[0];
        const ggml_tensor * src1 = op->src[1];
        const ggml_tensor * ids  = op->src[2];
        ggml_tensor *       dst  = op;

        GGML_TENSOR_BINARY_OP_LOCALS

        const int ith = params->ith;
        const int nth = params->nth;

        const ggml_from_float_t from_float = ggml_get_type_traits_cpu(PARAM_TYPE)->from_float;

        // we don't support permuted src0 or src1
        GGML_ASSERT(nb00 == ggml_type_size(src0->type));
        GGML_ASSERT(nb10 == ggml_type_size(src1->type));

        // dst cannot be transposed or permuted
        GGML_ASSERT(nb0 == sizeof(float));
        GGML_ASSERT(nb0 <= nb1);
        GGML_ASSERT(nb1 <= nb2);
        GGML_ASSERT(nb2 <= nb3);

        GGML_ASSERT(ne03 == 1);
        GGML_ASSERT(ne13 == 1);
        GGML_ASSERT(ne3  == 1);

        GGML_ASSERT(src1->type == GGML_TYPE_F32);

        // row groups
        const int n_ids = ids->ne[0]; // n_expert_used
        const int n_as  = ne02;       // n_expert

        const size_t nbw1 = ggml_row_size(PARAM_TYPE, ne10);
        const size_t nbw2 = nbw1*ne11;
        const size_t nbw3 = nbw2*ne12;

        struct mmid_row_mapping {
            int32_t i1;
            int32_t i2;
        };

        GGML_ASSERT(params->wsize >=
                (GGML_PAD(nbw3, sizeof(int64_t)) +
                 n_as*(ne12 + 1)*sizeof(mmid_row_mapping))
                );

        auto * wdata          = (char *)params->wdata;
        auto * wdata_src1_end = (char *)wdata + GGML_PAD(nbw3, sizeof(int64_t));

        // total of [n_as][ne12 + 1] elemets of type mmid_row_mapping (2*int32_t = int64_t)
        auto * matrix_row_counts = (int64_t *) (wdata_src1_end);                                        // [n_as]
        struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as); // [n_as][ne12]

        // src1: float32 => param type
        for (int64_t i12 = 0; i12 < ne12; ++i12) {
            for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
                from_float((float *)((char *) src1->data + i12 * nb12 + i11 * nb11),
                           (void *)               (wdata + i12 * nbw2 + i11 * nbw1),
                           ne10);
            }
        }

#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * ne12 + (i1)]

        if (ith == 0) {
            // initialize matrix_row_counts
            memset(matrix_row_counts, 0, n_as * sizeof(int64_t));

            // group rows by src0 matrix
            for (int32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
                for (int32_t id = 0; id < n_ids; ++id) {
                    const int32_t i02 =
                        *(const int32_t *) ((const char *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);

                    GGML_ASSERT(i02 >= 0 && i02 < n_as);

                    MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = { id, iid1 };
                    matrix_row_counts[i02] += 1;
                }
            }
        }

        ggml_barrier(params->threadpool);

        // compute each matrix multiplication in sequence
        for (int cur_a = 0; cur_a < n_as; ++cur_a) {
            const int64_t cne1 = matrix_row_counts[cur_a];

            if (cne1 == 0) {
                continue;
            }

            const auto * src0_cur = (const char *) src0->data + cur_a*nb02;

            //const int64_t nr0 = ne01; // src0 rows
            const int64_t nr1 = cne1; // src1 rows

            int64_t src0_cur_start = (ith * ne01) / nth;
            int64_t src0_cur_end   = ((ith + 1) * ne01) / nth;

            // Align boundaries to NB_COLS - round up to ensure all data is included
            src0_cur_start = (src0_cur_start % NB_COLS) ? src0_cur_start + NB_COLS - (src0_cur_start % NB_COLS) : src0_cur_start;
            src0_cur_end   = (src0_cur_end   % NB_COLS) ? src0_cur_end   + NB_COLS - (src0_cur_end   % NB_COLS) : src0_cur_end;
            if (src0_cur_end > ne01) {
                src0_cur_end = ne01;
            }

            if (src0_cur_start >= src0_cur_end) {
                return;
            }

            for (int ir1 = 0; ir1 < nr1; ir1++) {
                struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);

                const int id = row_mapping.i1; // selected expert index

                const int64_t i11 = id % ne11;
                const int64_t i12 = row_mapping.i2; // row index in src1

                const int64_t i1 = id;  // selected expert index
                const int64_t i2 = i12; // row

                const auto * src1_col = (const char *) wdata + (i11 * nbw1 + i12 * nbw2);

                gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
                        (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01,
                        src0_cur + src0_cur_start * nb01,
                        src1_col, 1, src0_cur_end - src0_cur_start);
            }
        }
#undef MMID_MATRIX_ROW
    }

    int repack(struct ggml_tensor * t, const void * data, size_t data_size) override {
        GGML_LOG_DEBUG("%s: repack tensor %s with %s_%dx%d\n", __func__, t->name, ggml_type_name(t->type),
                       (int) NB_COLS, (int) INTER_SIZE);
        return ggml::cpu::repack::repack<BLOC_TYPE, INTER_SIZE, NB_COLS>(t, data, data_size);
    }
};

}  // namespace ggml::cpu::repack

static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(const struct ggml_tensor * cur) {

    // instance for Q4
    static const ggml::cpu::repack::tensor_traits<block_q4_0, 4, 4, GGML_TYPE_Q8_0> q4_0_4x4_q8_0;
    static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 4, GGML_TYPE_Q8_0> q4_0_4x8_q8_0;
    static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
    static const ggml::cpu::repack::tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;

    // instance for Q2
    static const ggml::cpu::repack::tensor_traits<block_q2_K, 8, 8, GGML_TYPE_Q8_K> q2_K_8x8_q8_K;

    // instance for IQ4
    static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
    static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0> iq4_nl_8x8_q8_0;

    if (cur->type == GGML_TYPE_Q4_0) {
        if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
            if (cur->ne[1] % 8 == 0) {
                return &q4_0_8x8_q8_0;
            }
        }
        if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
            if (cur->ne[1] % 4 == 0) {
                return &q4_0_4x8_q8_0;
            }
        }
        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
            if (cur->ne[1] % 4 == 0) {
                return &q4_0_4x4_q8_0;
            }
        }
    } else if (cur->type == GGML_TYPE_Q4_K) {
        if (ggml_cpu_has_avx2()) {
            if (cur->ne[1] % 8 == 0) {
                return &q4_K_8x8_q8_K;
            }
        }
    } else if (cur->type == GGML_TYPE_Q2_K) {
        if (ggml_cpu_has_avx512()) {
            if (cur->ne[1] % 8 == 0) {
                return &q2_K_8x8_q8_K;
            }
        }
    } else if (cur->type == GGML_TYPE_IQ4_NL) {
        if (ggml_cpu_has_avx2()) {
            if (cur->ne[1] % 8 == 0) {
                return &iq4_nl_8x8_q8_0;
            }
        }
        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
            if (cur->ne[1] % 4 == 0) {
                return &iq4_nl_4x4_q8_0;
            }
        }
    }

    return nullptr;
}

static enum ggml_status ggml_backend_cpu_repack_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
    tensor->extra = (void *) const_cast<ggml::cpu::tensor_traits *>(ggml_repack_get_optimal_repack_type(tensor));

    GGML_UNUSED(buffer);
    return GGML_STATUS_SUCCESS;
}

static void ggml_backend_cpu_repack_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
                                                       const void * data, size_t offset, size_t size) {
    GGML_ASSERT(offset == 0);
    GGML_ASSERT(size == ggml_nbytes(tensor));

    auto tensor_traits = (ggml::cpu::repack::tensor_traits_base *) tensor->extra;
    auto OK            = tensor_traits->repack(tensor, data, size);

    GGML_ASSERT(OK == 0);
    GGML_UNUSED(buffer);
}

static const char * ggml_backend_cpu_repack_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
    return "CPU_REPACK";

    GGML_UNUSED(buft);
}

static ggml_backend_buffer_t ggml_backend_cpu_repack_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);

    if (buffer == nullptr) {
        return nullptr;
    }

    buffer->buft              = buft;
    buffer->iface.init_tensor = ggml_backend_cpu_repack_buffer_init_tensor;
    buffer->iface.set_tensor  = ggml_backend_cpu_repack_buffer_set_tensor;
    buffer->iface.get_tensor  = nullptr;
    buffer->iface.cpy_tensor  = nullptr;
    return buffer;
}

static size_t ggml_backend_cpu_repack_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
    return TENSOR_ALIGNMENT;

    GGML_UNUSED(buft);
}

namespace ggml::cpu::repack {
class extra_buffer_type : ggml::cpu::extra_buffer_type {
    bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
        if (    op->op == GGML_OP_MUL_MAT &&
                op->src[0]->buffer &&
                (ggml_n_dims(op->src[0]) == 2) &&
                op->src[0]->buffer->buft == ggml_backend_cpu_repack_buffer_type() &&
                ggml_repack_get_optimal_repack_type(op->src[0])
                ) {
            if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
                return false;
            }
            if (op->src[1]->type == GGML_TYPE_F32) {
                return true;
            }
            //if (op->src[1]->type == GGML_TYPE_Q8_0) {
            //    return true;
            //}
            // may be possible if Q8_0 packed...
        } else if (op->op == GGML_OP_MUL_MAT_ID
                && op->src[0]->buffer
                && (ggml_n_dims(op->src[0]) == 3)
                && op->src[0]->buffer->buft == ggml_backend_cpu_repack_buffer_type()
                && ggml_repack_get_optimal_repack_type(op->src[0])
                ) {
            if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
                return false;
            }
            if (op->src[1]->type == GGML_TYPE_F32) {
                return true;
            }
            //if (op->src[1]->type == GGML_TYPE_Q8_0) {
            //    return true;
            //}
        }
        return false;
    }

    ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
        if (op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_MUL_MAT_ID) {
            if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_repack_buffer_type()) {
                return (ggml::cpu::tensor_traits *) op->src[0]->extra;
            }
        }
        return nullptr;
    }
};
}  // namespace ggml::cpu::repack

ggml_backend_buffer_type_t ggml_backend_cpu_repack_buffer_type(void) {
    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_repack = {
        /* .iface    = */ {
                           /* .get_name         = */ ggml_backend_cpu_repack_buffer_type_get_name,
                           /* .alloc_buffer     = */ ggml_backend_cpu_repack_buffer_type_alloc_buffer,
                           /* .get_alignment    = */ ggml_backend_cpu_repack_buffer_type_get_alignment,
                           /* .get_max_size     = */ nullptr,  // defaults to SIZE_MAX
                           /* .get_alloc_size   = */ nullptr,  // defaults to ggml_nbytes
                           /* .is_host          = */ nullptr,
                           },
        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
        /* .context = */ new ggml::cpu::repack::extra_buffer_type(),
    };

    return &ggml_backend_cpu_buffer_type_repack;
}

Coverage Report

Created: 2025-11-24 06:10