NEON implementation for trellis quants (#471)

* iq2_kt: NEON implementation

* iq3_kt: NEON implementation

* iq4_kt: not working NEON implementation

* iq4_kt: NEON implementation

Have to use f32 arithmetic else I get gibberish?
Correspondigly ridiculously slow.

* Cleanup

* iq4_kt: slightly faster TG on NEON

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

1 files changed, 388 insertions, 28 deletions

diff --git a/ggml/src/iqk/iqk_gemm_ktquants.cpp b/ggml/src/iqk/iqk_gemm_ktquants.cpp
index 6604480d..38e76e1e 100644
--- a/ggml/src/iqk/iqk_gemm_ktquants.cpp
+++ b/ggml/src/iqk/iqk_gemm_ktquants.cpp
@@ -1,3 +1,4 @@
+#include "iqk_common.h"
 #include "iqk_gemm_ktquants.h"
 #include "ggml.h"
 
@@ -316,37 +317,13 @@ bool iqk_set_kernels_ktquants(int ne00, int typeA, int typeB, std::array<mul_mat
 
     switch (typeA) {
         case GGML_TYPE_IQ2_KT:
-            assert (ne00 % QK_K == 0);
-            kernels[0] = mul_mat_iq2_kt_F32_T<1>;
-            kernels[1] = mul_mat_iq2_kt_F32_T<2>;
-            kernels[2] = mul_mat_iq2_kt_F32_T<3>;
-            kernels[3] = mul_mat_iq2_kt_F32_T<4>;
-            kernels[4] = mul_mat_iq2_kt_F32_T<5>;
-            kernels[5] = mul_mat_iq2_kt_F32_T<6>;
-            kernels[6] = mul_mat_iq2_kt_F32_T<7>;
-            kernels[7] = mul_mat_iq2_kt_F32_T<8>;
+            IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq2_kt_F32_T, kernels);
             break;
         case GGML_TYPE_IQ3_KT:
-            assert (ne00 % QK_K == 0);
-            kernels[0] = mul_mat_iq3_kt_F32_T<1>;
-            kernels[1] = mul_mat_iq3_kt_F32_T<2>;
-            kernels[2] = mul_mat_iq3_kt_F32_T<3>;
-            kernels[3] = mul_mat_iq3_kt_F32_T<4>;
-            kernels[4] = mul_mat_iq3_kt_F32_T<5>;
-            kernels[5] = mul_mat_iq3_kt_F32_T<6>;
-            kernels[6] = mul_mat_iq3_kt_F32_T<7>;
-            kernels[7] = mul_mat_iq3_kt_F32_T<8>;
+            IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq3_kt_F32_T, kernels);
             break;
         case GGML_TYPE_IQ4_KT:
-            assert (ne00 % QK_K == 0);
-            kernels[0] = mul_mat_iq4_kt_F32_T<1>;
-            kernels[1] = mul_mat_iq4_kt_F32_T<2>;
-            kernels[2] = mul_mat_iq4_kt_F32_T<3>;
-            kernels[3] = mul_mat_iq4_kt_F32_T<4>;
-            kernels[4] = mul_mat_iq4_kt_F32_T<5>;
-            kernels[5] = mul_mat_iq4_kt_F32_T<6>;
-            kernels[6] = mul_mat_iq4_kt_F32_T<7>;
-            kernels[7] = mul_mat_iq4_kt_F32_T<8>;
+            IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq4_kt_F32_T, kernels);
             break;
         default:
             return false;
@@ -358,8 +335,391 @@ bool iqk_set_kernels_ktquants(int ne00, int typeA, int typeB, std::array<mul_mat
 
 #else // !__x86_64__
 
+namespace {
+
+struct Trellis1 {
+    constexpr static uint32_t kmask = 0x8fff8fff;
+    constexpr static uint32_t km32 = 0x3b603b60;
+    constexpr static uint32_t ka = 89226354;
+    constexpr static uint32_t kb = 64248484;
+    constexpr static uint32_t ka1 = ka*ka;
+    constexpr static uint32_t kb1 = kb*ka+kb;
+    constexpr static uint32_t ka2 = ka1*ka;
+    constexpr static uint32_t kb2 = kb1*ka+kb;
+    constexpr static uint32_t ka3 = ka2*ka;
+    constexpr static uint32_t kb3 = kb2*ka+kb;
+    constexpr static uint32_t ka4 = ka3*ka;
+    constexpr static uint32_t kb4 = kb3*ka+kb;
+    constexpr static uint32_t ka5 = ka4*ka;
+    constexpr static uint32_t kb5 = kb4*ka+kb;
+    constexpr static uint32_t ka6 = ka5*ka;
+    constexpr static uint32_t kb6 = kb5*ka+kb;
+    constexpr static uint32_t ka7 = ka6*ka;
+    constexpr static uint32_t kb7 = kb6*ka+kb;
+    const uint32x4x2_t mka = {uint32x4_t{ka, ka1, ka2, ka3}, uint32x4_t{ka4, ka5, ka6, ka7}};
+    const uint32x4x2_t mkb = {uint32x4_t{kb, kb1, kb2, kb3}, uint32x4_t{kb4, kb5, kb6, kb7}};
+    const uint32x4_t mask1 = vdupq_n_u32(kmask);
+    const uint32x4_t mask2 = vdupq_n_u32(km32);
+
+    inline uint32x4x2_t next8(uint32_t val) const {
+        auto mval = vdupq_n_u32(val);
+        uint32x4x2_t mres;
+        // This does not seem to be faster
+        //mres.val[0] = vmlaq_u32(mkb.val[0], mka.val[0], mval);
+        //mres.val[1] = vmlaq_u32(mkb.val[1], mka.val[1], mval);
+        mres.val[0] = vaddq_u32(vmulq_u32(mval, mka.val[0]), mkb.val[0]);
+        mres.val[1] = vaddq_u32(vmulq_u32(mval, mka.val[1]), mkb.val[1]);
+        mres.val[0] = veorq_u32(vandq_u32(mres.val[0], mask1), mask2);
+        mres.val[1] = veorq_u32(vandq_u32(mres.val[1], mask1), mask2);
+        return mres;
+    }
+    inline uint32x4x2_t next8(uint32_t val1, uint32_t val2) const {
+        auto mval1 = vdupq_n_u32(val1);
+        auto mval2 = vdupq_n_u32(val2);
+        uint32x4x2_t mres;
+        // This does not seem to be faster
+        //mres.val[0] = vmlaq_u32(mkb.val[0], mka.val[0], mval1);
+        //mres.val[1] = vmlaq_u32(mkb.val[0], mka.val[0], mval2);
+        mres.val[0] = vaddq_u32(vmulq_u32(mval1, mka.val[0]), mkb.val[0]);
+        mres.val[1] = vaddq_u32(vmulq_u32(mval2, mka.val[0]), mkb.val[0]);
+        mres.val[0] = veorq_u32(vandq_u32(mres.val[0], mask1), mask2);
+        mres.val[1] = veorq_u32(vandq_u32(mres.val[1], mask1), mask2);
+        return mres;
+    }
+    static inline float16x8_t gen8(const uint32x4x2_t& i8) {
+        auto fv1 = vreinterpretq_f16_u32(i8.val[0]);
+        auto fv2 = vreinterpretq_f16_u32(i8.val[1]);
+        return vpaddq_f16(fv1, fv2);
+    }
+    inline float16x8_t gen8(uint32_t val) const { return gen8(next8(val)); }
+    inline float16x8_t gen8(uint32_t val1, uint32_t val2) const { return gen8(next8(val1, val2)); }
+    inline float32x4x2_t gen8_f32(uint32_t val1, uint32_t val2) const {
+        auto x16 = gen8(val1, val2);
+        return { vcvt_f32_f16(vget_low_f16(x16)), vcvt_f32_f16(vget_high_f16(x16)) };
+    }
+    inline float32x4x2_t gen8_f32(uint32_t val1, uint32_t val2, float16x8_t scale) const {
+        auto x16 = vmulq_f16(gen8(val1, val2), scale);
+        return { vcvt_f32_f16(vget_low_f16(x16)), vcvt_f32_f16(vget_high_f16(x16)) };
+    }
+};
+
+template <int nrc_y>
+static void mul_mat_iq2_kt_F16_T(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    assert(n%QK_K == 0);
+    const int nb = n/QK_K;
+
+    Trellis1 trellis;
+
+    auto values = vld1q_s8(iq4k_values);
+
+    union { float16x8_t vec; float16_t val[8]; } s_helper;
+
+    constexpr int k_acc = nrc_y == 1 ? 2 : nrc_y;
+    float16x8_t accd[k_acc];
+    const float16_t * y[nrc_y];
+    for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const float16_t *)info.src1_row(iy);
+
+    for (int ix = 0; ix < nrc_x; ++ix) {
+        const float * dptr = (const float *)((const char*)vx + ix*bx);
+        const float d = *dptr * 31.75f * 1.05f;
+        const block_iq2_kt * x = (const block_iq2_kt *)(dptr + 1);
+
+        for (int iy = 0; iy < k_acc; ++iy) accd[iy] = vdupq_n_f16(0);
+
+        for (int i = 0; i < nb; ++i) {
+            const uint16_t * ql = (const uint16_t *)x[i].ql;
+            auto u32 = *(const uint32_t *)x[i].scales;
+            auto s8_u32 = uint32x2_t{u32, u32 >> 4};
+            s8_u32 = vand_u8(s8_u32, vdup_n_u32(0x0f0f0f0f));
+            auto s8 = vqtbl1_s8(values, vreinterpret_u8_u32(s8_u32));
+            auto s16 = vmovl_s8(s8);
+            s_helper.vec = vcvtq_f16_s16(s16);
+            for (int ib = 0; ib < QK_K/64; ++ib) {
+                auto scale1 = vdupq_n_f16(s_helper.val[2*ib+0]);
+                auto scale2 = vdupq_n_f16(s_helper.val[2*ib+1]);
+                for (int j = 0; j < 4; ++j) {
+                    auto xval1 = vmulq_f16(scale1, trellis.gen8(ql[8*ib+j+0]+4096));
+                    auto xval2 = vmulq_f16(scale2, trellis.gen8(ql[8*ib+j+4]+4096));
+                    if constexpr (nrc_y == 1) {
+                        accd[0] = vfmaq_f16(accd[0], xval1, vld1q_f16(y[0] + i*QK_K + 64*ib + 8*j +  0));
+                        accd[1] = vfmaq_f16(accd[1], xval2, vld1q_f16(y[0] + i*QK_K + 64*ib + 8*j + 32));
+                    } else {
+                        for (int iy = 0; iy < nrc_y; ++iy) {
+                            accd[iy] = vfmaq_f16(accd[iy], xval1, vld1q_f16(y[iy] + i*QK_K + 64*ib + 8*j +  0));
+                            accd[iy] = vfmaq_f16(accd[iy], xval2, vld1q_f16(y[iy] + i*QK_K + 64*ib + 8*j + 32));
+                        }
+                    }
+                }
+            }
+        }
+
+        if constexpr (nrc_y == 1) {
+            auto res16 = vpaddq_f16(accd[0], accd[1]);
+            auto res = vaddq_f32(vcvt_f32_f16(vget_low_f16(res16)), vcvt_f32_f16(vget_high_f16(res16)));
+            info.store(ix, 0, vaddvq_f32(res)*d);
+        } else {
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                auto res = vaddq_f32(vcvt_f32_f16(vget_low_f16(accd[iy])), vcvt_f32_f16(vget_high_f16(accd[iy])));
+                info.store(ix, iy, vaddvq_f32(res)*d);
+            }
+        }
+    }
+}
+
+template <int nrc_y>
+static void mul_mat_iq3_kt_F16_T(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    assert(n%QK_K == 0);
+    const int nb = n/QK_K;
+
+    Trellis1 trellis;
+
+    union { float16x8_t vec; float16_t val[8]; } s_helper;
+
+    uint16x8_t all_signs[4];
+    auto mask1 = vdupq_n_u16(0x01);
+    auto mask2 = vdupq_n_u16(0x10);
+
+    float16x8_t accd[nrc_y];
+    const float16_t * y[nrc_y];
+    for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const float16_t *)info.src1_row(iy);
+
+    for (int ix = 0; ix < nrc_x; ++ix) {
+        const float * dptr = (const float *)((const char*)vx + ix*bx);
+        const float d = *dptr * 31.75f * 1.015f;
+        const block_iq3_kt * x = (const block_iq3_kt *)(dptr + 1);
+
+        for (int iy = 0; iy < nrc_y; ++iy) accd[iy] = vdupq_n_f16(0);
+
+        for (int i = 0; i < nb; ++i) {
+            const uint16_t * ql = (const uint16_t *)x[i].ql;
+            const uint8_t * qh = x[i].qh;
+            auto u32 = *(const uint32_t *)x[i].scales;
+            auto s8_u32 = uint32x2_t{u32, u32 >> 4};
+            s8_u32 = vand_u8(s8_u32, vdup_n_u32(0x0f0f0f0f));
+            auto s16 = vmovl_s8(vreinterpret_s8_u32(s8_u32));
+            s_helper.vec = vcvtq_f16_s16(s16);
+            for (int j = 0; j < 4; ++j) all_signs[j] = vmovl_u8(vld1_u8(qh + 8*j));
+            for (int ib = 0; ib < 4; ++ib) {
+                auto scale1 = vdupq_n_f16(s_helper.val[ib+0]);
+                auto scale2 = vdupq_n_f16(s_helper.val[ib+4]);
+                for (int j = 0; j < 4; ++j) {
+                    uint32_t val1 = ql[4*ib+j   ] + 4096;
+                    uint32_t val2 = ql[4*ib+j+16] + 4096;
+                    auto sign1 = vshlq_n_u16(vandq_u16(all_signs[j], mask1), 15);
+                    auto sign2 = vshlq_n_u16(vandq_u16(all_signs[j], mask2), 11);
+                    all_signs[j] = vshrq_n_u16(all_signs[j], 1);
+                    auto x_val1 = vabsq_f16(trellis.gen8(val1));
+                    auto x_val2 = vabsq_f16(trellis.gen8(val2));
+                    x_val1 = vmulq_f16(scale1, vreinterpretq_f16_u16(vorrq_u16(vreinterpretq_u16_f16(x_val1), sign1)));
+                    x_val2 = vmulq_f16(scale2, vreinterpretq_f16_u16(vorrq_u16(vreinterpretq_u16_f16(x_val2), sign2)));
+                    for (int iy = 0; iy < nrc_y; ++iy) {
+                        accd[iy] = vfmaq_f16(accd[iy], x_val1, vld1q_f16(y[iy] + i*QK_K+32*ib+8*j    ));
+                        accd[iy] = vfmaq_f16(accd[iy], x_val2, vld1q_f16(y[iy] + i*QK_K+32*ib+8*j+128));
+                    }
+                }
+            }
+        }
+
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            auto res = vaddq_f32(vcvt_f32_f16(vget_low_f16(accd[iy])), vcvt_f32_f16(vget_high_f16(accd[iy])));
+            info.store(ix, iy, d*vaddvq_f32(res));
+        }
+    }
+}
+
+template <int nrc_y>
+static void mul_mat_iq4_kt_F16_T(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    assert(n%QK_K == 0);
+    const int nb = n/QK_K;
+    constexpr int kNumGroups = 64;
+
+    Trellis1 trellis;
+
+    union { float16x8_t vec; float16_t val[8]; } s_helper;
+    union { uint16x8_t  vec; uint16_t  val[8]; } o_helper;
+
+    constexpr int k_acc = nrc_y == 1 ? 2 : nrc_y;
+
+    float16x8_t accd[k_acc];
+    const float16_t * y[nrc_y];
+    float row_sum[nrc_y];
+    for (int iy = 0; iy < nrc_y; ++iy) {
+        y[iy] = (const float16_t *)info.src1_row(iy);
+        auto sum = vdupq_n_f16(0);
+        for (int i = 0; i < n/8; ++i) sum = vaddq_f16(sum, vld1q_f16(y[iy] + 8*i));
+        auto sum32 = vaddq_f32(vcvt_f32_f16(vget_low_f16(sum)), vcvt_f32_f16(vget_high_f16(sum)));
+        //auto sum32 = vdupq_n_f32(0);
+        //for (int i = 0; i < n/4; ++i) sum32 = vaddq_f32(sum32, vcvt_f32_f16(vld1_f16(y[iy] + 4*i)));
+        row_sum[iy] = vaddvq_f32(sum32);
+    }
+
+    for (int ix = 0; ix < nrc_x; ++ix) {
+        const float * dptr = (const float *)((const char*)vx + ix*bx);
+        auto d = dptr[0] * 31.75f * 1.01f;
+        auto dav = dptr[1];
+        const block_iq4_kt * x = (const block_iq4_kt *)(dptr + 2);
+
+        for (int iy = 0; iy < k_acc; ++iy) accd[iy] = vdupq_n_f16(0);
+
+        for (int i = 0; i < nb; ++i) {
+            const uint32_t * shb = x[i].qs;
+            auto vshb = vld1q_u32_x2(shb);
+            auto vshb16 = vcombine_u16(vmovn_u32(vandq_u32(vshb.val[0], vdupq_n_u32(0xff))), vmovn_u32(vandq_u32(vshb.val[1], vdupq_n_u32(0xff))));
+            const uint8_t * ql = (const uint8_t *)(shb + 8);
+            const uint8_t * qh = ql + kNumGroups;
+            auto iscales = vsubq_s16(vreinterpretq_s16_u16(vshrq_n_u16(vshb16, 1)), vdupq_n_s16(64));
+            s_helper.vec = vcvtq_f16_s16(iscales);
+            o_helper.vec = vaddq_u16(vshlq_n_u16(vandq_u16(vshb16, vdupq_n_u16(1)), 15), vdupq_n_u16(4096));
+            for (int ib = 0; ib < 4; ++ib) {
+                auto scale1 = vdupq_n_f16(s_helper.val[ib+0]);
+                auto scale2 = vdupq_n_f16(s_helper.val[ib+4]);
+                for (int j = 0; j < 4; ++j) {
+                    const uint32_t sh1 = shb[ib+0] >> (8 + 6*j);
+                    const uint32_t sh2 = shb[ib+4] >> (8 + 6*j);
+                    uint32_t val1 = ql[8*ib+2*j+ 0] + ((qh[8*ib+2*j+0] << 8) & 0xf00) + ((sh1 & 7) << 12) + o_helper.val[ib+0];
+                    uint32_t val2 = ql[8*ib+2*j+32] + ((qh[8*ib+2*j+0] << 4) & 0xf00) + ((sh2 & 7) << 12) + o_helper.val[ib+4];
+                    uint32_t val3 = ql[8*ib+2*j+ 1] + ((qh[8*ib+2*j+1] << 8) & 0xf00) + ((sh1 & 56) << 9) + o_helper.val[ib+0];
+                    uint32_t val4 = ql[8*ib+2*j+33] + ((qh[8*ib+2*j+1] << 4) & 0xf00) + ((sh2 & 56) << 9) + o_helper.val[ib+4];
+                    auto x_val1 = vmulq_f16(scale1, trellis.gen8(val1, val3));
+                    auto x_val2 = vmulq_f16(scale2, trellis.gen8(val2, val4));
+                    if constexpr (nrc_y == 1) {
+                        auto y1 = vld1q_f16(y[0] + i*QK_K+32*ib+8*j+  0);
+                        auto y2 = vld1q_f16(y[0] + i*QK_K+32*ib+8*j+128);
+                        accd[0] = vfmaq_f16(accd[0], y1, x_val1);
+                        accd[1] = vfmaq_f16(accd[1], y2, x_val2);
+                    } else {
+                        for (int iy = 0; iy < nrc_y; ++iy) {
+                            auto y1 = vld1q_f16(y[iy] + i*QK_K+32*ib+8*j+  0);
+                            auto y2 = vld1q_f16(y[iy] + i*QK_K+32*ib+8*j+128);
+                            accd[iy] = vfmaq_f16(accd[iy], y1, x_val1);
+                            accd[iy] = vfmaq_f16(accd[iy], y2, x_val2);
+                        }
+                    }
+                }
+            }
+        }
+
+        if constexpr (nrc_y == 1) {
+            auto sum16 = vaddq_f16(accd[0], accd[1]);
+            auto sum = vaddq_f32(vcvt_f32_f16(vget_low_f16(sum16)), vcvt_f32_f16(vget_high_f16(sum16)));
+            info.store(ix, 0, d*vaddvq_f32(sum) + dav*row_sum[0]);
+        } else {
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                auto sum = vaddq_f32(vcvt_f32_f16(vget_low_f16(accd[iy])), vcvt_f32_f16(vget_high_f16(accd[iy])));
+                info.store(ix, iy, d*vaddvq_f32(sum) + dav*row_sum[iy]);
+            }
+        }
+    }
+}
+
+template <int nrc_y>
+static void mul_mat_iq4_kt_F32_T(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    assert(n%QK_K == 0);
+    const int nb = n/QK_K;
+    constexpr int kNumGroups = 64;
+
+    Trellis1 trellis;
+
+    float32x4_t accd[nrc_y * 2];
+    const float * y[nrc_y];
+    float row_sum[nrc_y];
+    for (int iy = 0; iy < nrc_y; ++iy) {
+        y[iy] = (const float *)info.src1_row(iy);
+        auto sum = vdupq_n_f32(0);
+        for (int i = 0; i < n/4; ++i) sum = vaddq_f32(sum, vld1q_f32(y[iy] + 4*i));
+        row_sum[iy] = vaddvq_f32(sum);
+    }
+
+    for (int ix = 0; ix < nrc_x; ++ix) {
+        const float * dptr = (const float *)((const char*)vx + ix*bx);
+        const float d = dptr[0] * 31.75f * 1.01f;
+        const float row_av = dptr[1];
+        const block_iq4_kt * x = (const block_iq4_kt *)(dptr + 2);
+
+        for (int iy = 0; iy < nrc_y * 2; ++iy) accd[iy] = vdupq_n_f32(0.0f);
+
+        for (int i = 0; i < nb; ++i) {
+            const uint32_t * shb = x[i].qs;
+            const uint8_t * ql = (const uint8_t *)(shb + 8);
+            const uint8_t * qh = ql + kNumGroups;
+
+            for (int ib = 0; ib < 4; ++ib) {
+                const uint16_t x_scale1 = (int16_t)((shb[ib+0] & 0xff) >> 1) - 64;
+                const uint16_t x_scale2 = (int16_t)((shb[ib+4] & 0xff) >> 1) - 64;
+                const float16x8_t scale1 = vcvtq_f16_s16(vdupq_n_s16(x_scale1));
+                const float16x8_t scale2 = vcvtq_f16_s16(vdupq_n_s16(x_scale2));
+                const uint32_t offset1 = 4096 + ((shb[ib+0] & 1) << 15);
+                const uint32_t offset2 = 4096 + ((shb[ib+4] & 1) << 15);
+
+                uint32_t sh1 = shb[ib+0] >> 8;
+                uint32_t sh2 = shb[ib+4] >> 8;
+
+                for (int j = 0; j < 4; ++j) {
+
+                    uint32_t val1 = ql[8*ib+2*j+ 0] + ((qh[8*ib+2*j+0] << 8) & 0xf00) + ((sh1 & 7) << 12) + offset1;
+                    uint32_t val2 = ql[8*ib+2*j+32] + ((qh[8*ib+2*j+0] << 4) & 0xf00) + ((sh2 & 7) << 12) + offset2;
+                    uint32_t val3 = ql[8*ib+2*j+ 1] + ((qh[8*ib+2*j+1] << 8) & 0xf00) + ((sh1 & 56) << 9) + offset1;
+                    uint32_t val4 = ql[8*ib+2*j+33] + ((qh[8*ib+2*j+1] << 4) & 0xf00) + ((sh2 & 56) << 9) + offset2;
+
+                    sh1 >>= 6;
+                    sh2 >>= 6;
+
+                    auto x1 = trellis.gen8_f32(val1, val3, scale1);
+                    auto x2 = trellis.gen8_f32(val2, val4, scale2);
+
+                    for (int iy = 0; iy < nrc_y; ++iy) {
+                        auto y1 = vld1q_f32_x2(y[iy] + i*QK_K + 32*ib + 8*j);
+                        auto y2 = vld1q_f32_x2(y[iy] + i*QK_K + 32*ib + 8*j + 128);
+
+                        accd[iy*2 + 0] = vfmaq_f32(accd[iy*2 + 0], y1.val[0], x1.val[0]);
+                        accd[iy*2 + 1] = vfmaq_f32(accd[iy*2 + 1], y1.val[1], x1.val[1]);
+                        accd[iy*2 + 0] = vfmaq_f32(accd[iy*2 + 0], y2.val[0], x2.val[0]);
+                        accd[iy*2 + 1] = vfmaq_f32(accd[iy*2 + 1], y2.val[1], x2.val[1]);
+                    }
+                }
+
+            }
+        }
+
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            float32x4_t sum1 = vaddq_f32(accd[iy*2], accd[iy*2 + 1]);
+            float result = d*vaddvq_f32(sum1) + row_av*row_sum[iy];
+            info.store(ix, iy, result);
+        }
+    }
+}
+
+}
+
 bool iqk_set_kernels_ktquants(int ne00, int typeA, int typeB, std::array<mul_mat_t, IQK_MAX_NY>& kernels, mul_mat_t& func16) {
-    return false;
+
+    if (ne00%QK_K == 0 && ggml_type(typeB) == GGML_TYPE_F32 && ggml_type(typeA) == GGML_TYPE_IQ4_KT) {
+        IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq4_kt_F32_T, kernels);
+        func16 = nullptr;
+        return true;
+    }
+
+    if (ne00%QK_K != 0 || ggml_type(typeB) != GGML_TYPE_F16) {
+        return false;
+    }
+
+    func16 = nullptr;
+
+    switch (typeA) {
+        case GGML_TYPE_IQ2_KT:
+            IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq2_kt_F16_T, kernels);
+            break;
+        case GGML_TYPE_IQ3_KT:
+            IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq3_kt_F16_T, kernels);
+            break;
+        case GGML_TYPE_IQ4_KT:
+            IQK_SET_MUL_MAT_FUNCTIONS(mul_mat_iq4_kt_F16_T, kernels);
+            break;
+        default:
+            return false;
+    }
+
+    return true;
 }
 
 #endif