Q6_0_R4 (#122)

* Adding q6_0_r4

We get PP-512(LLaMA-3.1-8B) = 257 t/s on a Ryzen-7950X.

* q6_0_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 95 t/s on M2-Max.
In terms of ops, q6_0_r4 is identical to q5_0_r4
except for loading the high bits being
vld1q_u8_x2 instead of vld1q_u8. It is strange that
this can make a 5% difference in performance, especially
considering that this is amortized (re-used) over 8 columns
in the right matrix. Or am I running out of vector registers?

* Fix AVX2

---------

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

5 files changed, 324 insertions, 0 deletions

diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 1953fb7e..94950a36 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -15199,6 +15199,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_IQ4_NL_X4: break;
         case GGML_TYPE_Q4_0_R4: break;
         case GGML_TYPE_Q5_0_R4: break;
+        case GGML_TYPE_Q6_0_R4: break;
         case GGML_TYPE_Q8_0_R4: break;
         case GGML_TYPE_Q4_0_4_4:
         case GGML_TYPE_Q4_0_4_8:
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 0eb76a07..203b1b57 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1313,6 +1313,23 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .nrows                    = 1,
         .row_meta_size            = 0,
     },
+    [GGML_TYPE_Q6_0_R4] = {
+        .type_name                = "q6_0_r4",
+        .blck_size                = QK6_0,
+        .type_size                = sizeof(block_q6_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q6_0_r4,
+        .from_float               = quantize_row_q6_0_r4,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_q6_0_r4_ref,
+        .vec_dot                  = vec_dot_q6_0_r4_q8_0,
+#if GGML_USE_IQK_MULMAT && defined __AVX2__
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+#else
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+#endif
+        .nrows                    = 1,
+        .row_meta_size            = 0,
+    },
 };
 
 // For internal test use
@@ -3974,6 +3991,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_IQ4_NL_X4:     wtype = GGML_TYPE_IQ4_NL_X4;break;
         case GGML_FTYPE_MOSTLY_Q4_0_R4:       wtype = GGML_TYPE_Q4_0_R4;  break;
         case GGML_FTYPE_MOSTLY_Q5_0_R4:       wtype = GGML_TYPE_Q5_0_R4;  break;
+        case GGML_FTYPE_MOSTLY_Q6_0_R4:       wtype = GGML_TYPE_Q6_0_R4;  break;
         case GGML_FTYPE_MOSTLY_Q8_0_R4:       wtype = GGML_TYPE_Q8_0_R4;  break;
         case GGML_FTYPE_MOSTLY_IQ4_XS:        wtype = GGML_TYPE_IQ4_XS;   break;
         case GGML_FTYPE_MOSTLY_IQ4_KS:        wtype = GGML_TYPE_IQ4_KS;   break;
@@ -10501,6 +10519,7 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -10947,6 +10966,7 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -11090,6 +11110,7 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -14279,6 +14300,7 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -14662,6 +14684,7 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -14939,6 +14962,7 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -15543,6 +15567,7 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_Q4_0_R4:
         case GGML_TYPE_Q5_0_R4:
+        case GGML_TYPE_Q6_0_R4:
         case GGML_TYPE_Q8_0_R4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
@@ -22373,6 +22398,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_IQ4_NL_X4: result = quantize_iq4_nl_x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q4_0_R4: result = quantize_q4_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q5_0_R4: result = quantize_q5_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q6_0_R4: result = quantize_q6_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q8_0_R4: result = quantize_q8_0_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_KS:  result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp
index 4cdc1a08..f827e460 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -2400,6 +2400,128 @@ static void mul_mat_q5_0_r4_q8_1(int n, const void * vx, size_t bx, const DataIn
 }
 #endif
 
+template <int nrc_y>
+static void mul_mat_q6_0_r4_q8_1_avx2(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%8 == 0);
+    Q8<nrc_y, block_q8_1_x4> q8(info);
+    auto m4 = _mm256_set1_epi8(0xf);
+    auto m6 = _mm256_set1_epi8(0x30);
+#ifndef HAVE_FANCY_SIMD
+    auto m1 = _mm256_set1_epi16(1);
+#endif
+    int nb = n / QK6_0;
+    GGML_ASSERT(nb%4 == 0);
+    __m256 acc[nrc_y] = {};
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_q6_0_r4 * iq6 = (const block_q6_0_r4 *)((const char *)vx + ix*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            for (int k = 0; k < 4; ++k) {
+                auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq6[4*ib4+k].d));
+                auto scales = _mm256_set_m128(scales128, scales128);
+                auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-16.f));
+                auto bits1 = _mm256_loadu_si256((const __m256i *)iq6[4*ib4+k].qs+0);
+                auto bits2 = _mm256_loadu_si256((const __m256i *)iq6[4*ib4+k].qs+1);
+                auto hbits = _mm256_loadu_si256((const __m256i *)iq6[4*ib4+k].qh);
+                auto q1 = _mm256_and_si256(bits1, m4) | _mm256_and_si256(_mm256_slli_epi16(hbits, 4), m6);
+                auto q2 = _mm256_and_si256(bits2, m4) | _mm256_and_si256(_mm256_slli_epi16(hbits, 2), m6);
+                auto q3 = _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4) | _mm256_and_si256(hbits, m6);
+                auto q4 = _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4) | _mm256_and_si256(_mm256_srli_epi16(hbits, 2), m6);;
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
+#ifdef HAVE_FANCY_SIMD
+                    auto sumi = _mm256_dpbusd_epi32(_mm256_setzero_si256(), q1, _mm256_shuffle_epi32(y, 0x00));
+                    sumi = _mm256_dpbusd_epi32(sumi, q2, _mm256_shuffle_epi32(y, 0x55));
+                    sumi = _mm256_dpbusd_epi32(sumi, q3, _mm256_shuffle_epi32(y, 0xaa));
+                    sumi = _mm256_dpbusd_epi32(sumi, q4, _mm256_shuffle_epi32(y, 0xff));
+#else
+                    auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(q1, _mm256_shuffle_epi32(y, 0x00)),
+                                                  _mm256_maddubs_epi16(q2, _mm256_shuffle_epi32(y, 0x55)));
+                    auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(q3, _mm256_shuffle_epi32(y, 0xaa)),
+                                                  _mm256_maddubs_epi16(q4, _mm256_shuffle_epi32(y, 0xff)));
+                    auto sumi = _mm256_add_epi32(_mm256_madd_epi16(m1, sumi1), _mm256_madd_epi16(m1, sumi2));
+#endif
+                    auto d4d8 = _mm256_mul_ps(scales, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
+                    acc[iy] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(sumi), acc[iy]);
+                    acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[iy]);
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            auto sum = _mm_add_ps(_mm256_castps256_ps128(acc[iy]), _mm256_extractf128_ps(acc[iy], 1));
+            info.store(ix, iy, sum);
+            acc[iy] = _mm256_setzero_ps();
+        }
+    }
+}
+
+#ifdef HAVE_FANCY_SIMD
+template <int nrc_y>
+static void mul_mat_q6_0_r4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    if constexpr (nrc_y == 1) {
+        mul_mat_q6_0_r4_q8_1_avx2<1>(n, vx, bx, info, nrc_x);
+    } else {
+    GGML_ASSERT(nrc_x%8 == 0);
+    Q8<nrc_y, block_q8_1_x4> q8(info);
+    auto m4 = _mm512_set1_epi8(0xf);
+    auto m6 = _mm512_set1_epi8(0x30);
+    int nb = n / QK6_0;
+    GGML_ASSERT(nb%4 == 0);
+    __m512  acc[2*nrc_y] = {};
+    __m512i qx[4];
+    for (int ix = 0; ix < nrc_x; ix += 8) {
+        const block_q6_0_r4 * iq6l = (const block_q6_0_r4 *)((const char *)vx + (ix+0)*bx);
+        const block_q6_0_r4 * iq6h = (const block_q6_0_r4 *)((const char *)vx + (ix+4)*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            for (int k = 0; k < 4; ++k) {
+                auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq6l[4*ib4+k].d));
+                auto scales1 = _mm256_set_m128(scales128, scales128);
+                scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq6h[4*ib4+k].d));
+                auto scales2 = _mm256_set_m128(scales128, scales128);
+                auto scales = _mm512_insertf32x8(_mm512_castps256_ps512(scales1), scales2, 1);
+                auto scales_m = _mm512_mul_ps(scales, _mm512_set1_ps(-16.f));
+                auto bits1 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq6l[4*ib4+k].qs+0)),
+                                                                       _mm256_loadu_si256((const __m256i *)iq6h[4*ib4+k].qs+0), 1);
+                auto bits2 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq6l[4*ib4+k].qs+1)),
+                                                                       _mm256_loadu_si256((const __m256i *)iq6h[4*ib4+k].qs+1), 1);
+                auto hbits1 = _mm256_loadu_si256((const __m256i *)iq6l[4*ib4+k].qh);
+                auto hbits2 = _mm256_loadu_si256((const __m256i *)iq6h[4*ib4+k].qh);
+                auto hb = _mm512_inserti32x8(_mm512_castsi256_si512(hbits1), hbits2, 1);
+                qx[0] = _mm512_and_si512(bits1, m4) | _mm512_and_si512(_mm512_slli_epi16(hb, 4), m6);
+                qx[1] = _mm512_and_si512(bits2, m4) | _mm512_and_si512(_mm512_slli_epi16(hb, 2), m6);;
+                qx[2] = _mm512_and_si512(_mm512_srli_epi16(bits1, 4), m4) | _mm512_and_si512(hb, m6);
+                qx[3] = _mm512_and_si512(_mm512_srli_epi16(bits2, 4), m4) | _mm512_and_si512(_mm512_srli_epi16(hb, 2), m6);
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y8 = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
+                    auto y = _mm512_inserti32x8(_mm512_castsi256_si512(y8), y8, 1);
+                    auto sumi = _mm512_setzero_si512();
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[0], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x00)));
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[1], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x55)));
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[2], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xaa)));
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[3], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xff)));
+                    auto dy = _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k]));
+                    acc[2*iy+0] = _mm512_fmadd_ps(_mm512_mul_ps(scales, dy), _mm512_cvtepi32_ps(sumi), acc[2*iy+0]);
+                    acc[2*iy+1] = _mm512_fmadd_ps(scales_m, _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[2*iy+1]);
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            auto sum512 = _mm512_add_ps(acc[2*iy+0], acc[2*iy+1]);
+            acc[2*iy+0] = acc[2*iy+1] = _mm512_setzero_ps();
+            auto sum1 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 0), _mm512_extractf32x4_ps(sum512, 1));
+            auto sum2 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 2), _mm512_extractf32x4_ps(sum512, 3));
+            info.store(ix+0, iy, sum1);
+            info.store(ix+4, iy, sum2);
+        }
+    }
+    }
+}
+#else
+template <int nrc_y>
+static void mul_mat_q6_0_r4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    mul_mat_q6_0_r4_q8_1_avx2<nrc_y>(n, vx, bx, info, nrc_x);
+}
+#endif
+
 #ifdef HAVE_FANCY_SIMD
 template <int nrc_y>
 static void mul_mat_q8_0_r4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
@@ -4527,6 +4649,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
             mm.funcs[7] = mul_mat_q5_0_r4_q8_1<8>;
             expected_typeB = GGML_TYPE_Q8_1;
             break;
+        case GGML_TYPE_Q6_0_R4:
+            assert (ne00 % QK4_NL == 0);
+            mm.funcs[0] = mul_mat_q6_0_r4_q8_1<1>;
+            mm.funcs[1] = mul_mat_q6_0_r4_q8_1<2>;
+            mm.funcs[2] = mul_mat_q6_0_r4_q8_1<3>;
+            mm.funcs[3] = mul_mat_q6_0_r4_q8_1<4>;
+            mm.funcs[4] = mul_mat_q6_0_r4_q8_1<5>;
+            mm.funcs[5] = mul_mat_q6_0_r4_q8_1<6>;
+            mm.funcs[6] = mul_mat_q6_0_r4_q8_1<7>;
+            mm.funcs[7] = mul_mat_q6_0_r4_q8_1<8>;
+            expected_typeB = GGML_TYPE_Q8_1;
+            break;
         case GGML_TYPE_Q8_0_R4:
             assert (ne00 % QK4_NL == 0);
             mm.funcs[0] = mul_mat_q8_0_r4_q8_1<1>;
@@ -7130,6 +7264,55 @@ void mul_mat_q5_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& inf
 }
 
 template <int nrc_y>
+void mul_mat_q6_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%4 == 0);
+    Q8<nrc_y, block_q8_0_x4> q8(info);
+    auto m4 = vdupq_n_u8(0x0f);
+    auto m6 = vdupq_n_u8(0x30);
+    auto m32 = vdupq_n_s8(-32);
+    int nb = n / QK6_0;
+    GGML_ASSERT(nb%4 == 0);
+    int8x16_t qx[8];
+    float32x4_t acc[nrc_y] = {};
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_q6_0_r4 * iq6 = (const block_q6_0_r4 *)((const char *)vx + ix*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            for (int k = 0; k < 4; ++k) {
+                auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq6[4*ib4+k].d));
+                auto lbits   = vld1q_u8_x4(iq6[4*ib4+k].qs);
+                auto hbits   = vld1q_u8_x2(iq6[4*ib4+k].qh);
+                qx[0] = vaddq_s8(vandq_u8(lbits.val[0], m4) | vandq_u8(vshlq_n_u8(hbits.val[0], 4), m6), m32); //  0...3
+                qx[1] = vaddq_s8(vandq_u8(lbits.val[1], m4) | vandq_u8(vshlq_n_u8(hbits.val[1], 4), m6), m32); // 16..19
+                qx[2] = vaddq_s8(vandq_u8(lbits.val[2], m4) | vandq_u8(vshlq_n_u8(hbits.val[0], 2), m6), m32); //  4...7
+                qx[3] = vaddq_s8(vandq_u8(lbits.val[3], m4) | vandq_u8(vshlq_n_u8(hbits.val[1], 2), m6), m32); // 20..23
+                qx[4] = vaddq_s8(vshrq_n_u8(lbits.val[0], 4)| vandq_u8(hbits.val[0], m6), m32);                //  8..11
+                qx[5] = vaddq_s8(vshrq_n_u8(lbits.val[1], 4)| vandq_u8(hbits.val[1], m6), m32);                // 24..27
+                qx[6] = vaddq_s8(vshrq_n_u8(lbits.val[2], 4)| vandq_u8(vshrq_n_u8(hbits.val[0], 2), m6), m32); // 12..15
+                qx[7] = vaddq_s8(vshrq_n_u8(lbits.val[3], 4)| vandq_u8(vshrq_n_u8(hbits.val[1], 2), m6), m32); // 28..31
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = vld1q_s8_x2(q8.y[iy][ib4].qs+32*k);
+                    auto sumi = vdupq_n_s32(0);
+                    sumi = vdotq_laneq_s32(sumi, qx[0], y.val[0], 0);
+                    sumi = vdotq_laneq_s32(sumi, qx[1], y.val[1], 0);
+                    sumi = vdotq_laneq_s32(sumi, qx[2], y.val[0], 1);
+                    sumi = vdotq_laneq_s32(sumi, qx[3], y.val[1], 1);
+                    sumi = vdotq_laneq_s32(sumi, qx[4], y.val[0], 2);
+                    sumi = vdotq_laneq_s32(sumi, qx[5], y.val[1], 2);
+                    sumi = vdotq_laneq_s32(sumi, qx[6], y.val[0], 3);
+                    sumi = vdotq_laneq_s32(sumi, qx[7], y.val[1], 3);
+                    auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
+                    acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi));
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            info.store(ix, iy, acc[iy]);
+            acc[iy] = vdupq_n_f32(0.f);
+        }
+    }
+}
+
+template <int nrc_y>
 void mul_mat_q8_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
     GGML_ASSERT(nrc_x%4 == 0);
     Q8<nrc_y, block_q8_0_x4> q8(info);
@@ -7368,6 +7551,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
             m.funcs[7] = mul_mat_q5_0_r4_q8_0<8>;
             expected_Btype = GGML_TYPE_Q8_0;
             break;
+        case GGML_TYPE_Q6_0_R4:
+            m.funcs[0] = mul_mat_q6_0_r4_q8_0<1>;
+            m.funcs[1] = mul_mat_q6_0_r4_q8_0<2>;
+            m.funcs[2] = mul_mat_q6_0_r4_q8_0<3>;
+            m.funcs[3] = mul_mat_q6_0_r4_q8_0<4>;
+            m.funcs[4] = mul_mat_q6_0_r4_q8_0<5>;
+            m.funcs[5] = mul_mat_q6_0_r4_q8_0<6>;
+            m.funcs[6] = mul_mat_q6_0_r4_q8_0<7>;
+            m.funcs[7] = mul_mat_q6_0_r4_q8_0<8>;
+            expected_Btype = GGML_TYPE_Q8_0;
+            break;
         case GGML_TYPE_Q8_0_R4:
             m.funcs[0] = mul_mat_q8_0_r4_q8_0<1>;
             m.funcs[1] = mul_mat_q8_0_r4_q8_0<2>;
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index eafb2887..f2e6a45e 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -3475,3 +3475,100 @@ void vec_dot_q5_0_r4_q8_0(int n, float * s, size_t bs, const void * vx, size_t b
     GGML_UNUSED(bx);
     GGML_UNUSED(by);
 }
+
+//
+// ========================================= q6_0_r4
+//
+void quantize_row_q6_0_r4_ref(const float * x, block_q6_0_r4  * y, int64_t k) {
+    // we assume we are called with 4 rows
+    quantize_q6_0_r4(x, (void *)y, 4, k/4, nullptr);
+}
+
+void quantize_row_q6_0_r4(const float * x, void * y, int64_t k) {
+    // we assume we are called with 4 rows
+    quantize_q6_0_r4(x, y, 4, k/4, nullptr);
+}
+
+static inline void convert_q6_0(const block_q6_0& x, uint8_t * L) {
+
+    for (int j = 0; j < QK6_0/2; ++j) {
+        const uint8_t h = x.qh[j%(QK6_0/4)] >> 4*(j/(QK6_0/4));
+        L[j          ] = (x.qs[j] & 0x0F) | ((h << 4) & 0x30);
+        L[j + QK6_0/2] = (x.qs[j] >>   4) | ((h << 2) & 0x30);
+    }
+}
+
+static void repack_q6_0(int nrows, int n_per_row, const block_q6_0 * x, block_q6_0_r4 * y) {
+    GGML_ASSERT(nrows%4 == 0);
+    GGML_ASSERT(n_per_row%QK5_0 == 0);
+    int nblock = n_per_row/QK6_0;
+    const block_q6_0 * x4[4];
+    uint8_t L[QK6_0];
+    for (int row = 0; row < nrows; row += 4) {
+        for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
+        for (int ib = 0; ib < nblock; ++ib) {
+            std::memset(y[ib].qh, 0, QK6_0);
+            for (int k = 0; k < 4; ++k) {
+                y[ib].d[k] = x4[k][ib].d;
+                convert_q6_0(x4[k][ib], L);
+                for (int l = 0; l < 4; ++l) {
+                    int l1 = 4*(l/2) + 16*(l%2), l2 = l1 + 8;
+                    for (int i = 0; i < 4; ++i) {
+                        y[ib].qs[4*k+i+16*l] = (L[i + l1] & 0xf) | ((L[i + l2] & 0xf) << 4);
+                        y[ib].qh[4*k+i+16*(l%2)] |= ((L[i + l1] >> 4) | ((L[i + l2] >> 4) << 4)) << 2*(l/2);
+                    }
+                }
+            }
+        }
+        x += 4*nblock;
+        y += nblock;
+    }
+}
+
+size_t quantize_q6_0_r4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
+    GGML_ASSERT(nrows%4 == 0);
+    auto row_size_0 = ggml_row_size(GGML_TYPE_Q6_0, n_per_row);
+    std::vector<char> qtmp(4*row_size_0);
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrows; row += 4) {
+        quantize_q6_0(src, qtmp.data(), 4, n_per_row, imatrix);
+        repack_q6_0(4, n_per_row, (const block_q6_0 *)qtmp.data(), (block_q6_0_r4 *)qrow);
+        src += 4*n_per_row;
+        qrow += 4*row_size_0;
+    }
+    return nrows*row_size_0;
+}
+
+void dequantize_row_q6_0_r4(const block_q6_0_r4 * x, float * y, int64_t k) {
+    // we assume we are called with 4 rows
+    int n_per_row = k/4;
+    int nb = n_per_row/QK6_0;
+    float * yk[4];
+    for (int k = 0; k < 4; ++k) yk[k] = y + k*n_per_row;
+    for (int ib = 0; ib < nb; ++ib) {
+        for (int k = 0; k < 4; ++k) {
+            float d = GGML_FP16_TO_FP32(x[ib].d[k]);
+            float m = -32*d;
+            for (int l = 0; l < 4; ++l) {
+                int ll = 16*(l%2) + 4*(l/2);
+                for (int i = 0; i < 4; ++i) {
+                    yk[k][QK4_0*ib+i+ll+0] = d * ((x[ib].qs[4*k+i+16*l] & 0xf) | (((x[ib].qh[4*k+i+16*(l%2)] >> (2*(l/2)+0)) & 3) << 4)) + m;
+                    yk[k][QK4_0*ib+i+ll+8] = d * ((x[ib].qs[4*k+i+16*l] >>  4) | (((x[ib].qh[4*k+i+16*(l%2)] >> (2*(l/2)+4)) & 3) << 4)) + m;
+                }
+            }
+        }
+    }
+}
+
+void vec_dot_q6_0_r4_q8_0(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
+#if GGML_USE_IQK_MULMAT
+    if (iqk_mul_mat(1, 1, n, GGML_TYPE_Q6_0_R4, vx, 0, GGML_TYPE_Q8_0, vy, 0, s, 0, 0, 1)) {
+        return;
+    }
+#endif
+    GGML_ASSERT(n%QK4_NL == 0);
+    GGML_ASSERT(nrc == 1);
+    GGML_UNUSED(bs);
+    GGML_UNUSED(bx);
+    GGML_UNUSED(by);
+}
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index 24c241a2..3349c675 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -87,6 +87,12 @@ size_t quantize_q5_0_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
 void   dequantize_row_q5_0_r4(const block_q5_0_r4  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 void   vec_dot_q5_0_r4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
+void   quantize_row_q6_0_r4_ref(const float * GGML_RESTRICT x, block_q6_0_r4  * GGML_RESTRICT y, int64_t k);
+void   quantize_row_q6_0_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_q6_0_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+void   dequantize_row_q6_0_r4(const block_q6_0_r4  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void   vec_dot_q6_0_r4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 #ifdef __cplusplus
 }
 #endif