Q8_K_R8: Fastest quantized matrix multiplications (#141)

* q8_k_r8: fastest matrix multiplication known to human kind

We get PP-512(LLaMA-3.1-8B) = 370 t/s on a Ryzen-7950X!

* q8_k_r8: AVX2

I was worried that we don't have enough vector registrers on
AVX2, but it looks like it handles it just fine. We get
PP-512(LLaMA-3.1-8B) = 354 t/s on a Ryzen-5975WX.
Slightly slower than the Zen4 version with double the threads,
but still a huge upgrade compared to Q8_0_R4.

* q8_k_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 159.2 t/s.
Compare this to the 128 t/s we have fr Q8_0_R4.

* q8_k_r4: go to signed ints

Why?
* On AVX2 _mm256_maddubs_epi16() may overflow, so we need to
  stay within the signed int range and use _mm256_sign_epi8.
  Not yet tested on the AVX2 comp, vut expect major slowdown.
* It is almost 10% faster on ARM_NEON. Somehow the veorrq_u8()
  needed tto convert from unsigned to signed seems to be extremely
  slow on the M2-Max
* We only lose ~0.5% in oerformance on Zen4 (there the exclusive
  or that we now use to convert fro signed to unsigned seems to be
  much faster than on M2-Max)

* Shutup useless compiler warnings

---------

Co-authored-by: Iwan Kawrakow <[email protected]>

Author: ikawrakow

examples/quantize/quantize.cpp

@@ -70,6 +70,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_K_M",   LLAMA_FTYPE_MOSTLY_Q5_K_M,   " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
     { "Q6_K",     LLAMA_FTYPE_MOSTLY_Q6_K,     " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", },
     { "Q6_K_R4",  LLAMA_FTYPE_MOSTLY_Q6_K_R4,  "Q6_K repacked", },
+    { "Q8_K_R8",  LLAMA_FTYPE_MOSTLY_Q8_K_R8,  "Q8_K repacked", },
     { "Q8_0",     LLAMA_FTYPE_MOSTLY_Q8_0,     " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
     { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
     { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },

ggml/include/ggml.h

@@ -408,6 +408,7 @@ extern "C" {
         GGML_TYPE_IQ4_KSS = 146,
         GGML_TYPE_Q8_K16  = 147,
         GGML_TYPE_Q8_K32  = 148,
+        GGML_TYPE_Q8_KR8  = 149,
 
         GGML_TYPE_Q4_0_R4   = 202,
         GGML_TYPE_Q5_0_R4   = 206,
@@ -422,6 +423,7 @@ extern "C" {
         GGML_TYPE_Q6_0_R4   = 233,
         GGML_TYPE_IQ2_BN_R4 = 335,
         GGML_TYPE_IQ4_K_R4  = 339,
+        GGML_TYPE_Q8_K_R8   = 399,
         GGML_TYPE_COUNT,
     };
 
@@ -494,6 +496,7 @@ extern "C" {
         GGML_FTYPE_MOSTLY_Q6_0_R4   = 227, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_BN_R4 = 329, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ4_K_R4  = 332, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q8_K_R8   = 399, // except 1d tensors
     };
 
     // available tensor operations:

ggml/src/ggml-common.h

@@ -382,6 +382,12 @@ typedef struct {
 } block_q8_K128;
 static_assert(sizeof(block_q8_K128) == sizeof(float) + 128, "wrong q8_K128 block size/padding");
 
+typedef struct {
+    ggml_half d[8];         // delta
+    int8_t    qs[8*QK_K];   // quants, stored as unsigned ints
+} block_q8_k_r8;
+static_assert(sizeof(block_q8_k_r8) == 8*sizeof(ggml_half) + 8*QK_K, "wrong q8_k_r8 block size/padding");
+
 // (Almost) "true" 2-bit quantization.
 // Due to the need to use blocks as per ggml design, it ends up using
 // 2.0625 bpw because of the 16-bit scale for each block of 256.

ggml/src/ggml-quants.c

@@ -15208,6 +15208,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_Q5_K_R4: break;
         case GGML_TYPE_Q6_K_R4: break;
         case GGML_TYPE_IQ4_K_R4: break;
+        case GGML_TYPE_Q8_K_R8: break;
         case GGML_TYPE_Q4_0_4_4:
         case GGML_TYPE_Q4_0_4_8:
             {

ggml/src/ggml.c

@@ -979,6 +979,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .nrows                    = 1,
         .row_meta_size            = 0,
     },
+    [GGML_TYPE_Q8_K_R8] = {
+        .type_name                = "q8_k_r8",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q8_k_r8)/8,
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q8_k_r8,
+        .from_float               = quantize_row_q8_k_r8,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q8_k_r8_ref,
+        .vec_dot                  = vec_dot_q8_k_r8_q8_k,
+        .vec_dot_type             = GGML_TYPE_Q8_KR8,
+        .nrows                    = 1,
+        .row_meta_size            = 0,
+    },
     [GGML_TYPE_IQ2_XXS] = {
         .type_name                = "iq2_xxs",
         .blck_size                = QK_K,
@@ -1197,6 +1210,14 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float               = quantize_row_q8_K32,
         .row_meta_size            = 0,
     },
+    [GGML_TYPE_Q8_KR8] = {
+        .type_name                = "q8_KR8",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q8_K),
+        .is_quantized             = true,
+        .from_float               = quantize_row_q8_KR8,
+        .row_meta_size            = 0,
+    },
     [GGML_TYPE_BF16] = {
         .type_name                = "bf16",
         .blck_size                = 1,
@@ -4105,6 +4126,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_Q5_K_R4:       wtype = GGML_TYPE_Q5_K_R4;  break;
         case GGML_FTYPE_MOSTLY_Q6_K:          wtype = GGML_TYPE_Q6_K;     break;
         case GGML_FTYPE_MOSTLY_Q6_K_R4:       wtype = GGML_TYPE_Q6_K_R4;  break;
+        case GGML_FTYPE_MOSTLY_Q8_K_R8:       wtype = GGML_TYPE_Q8_K_R8;  break;
         case GGML_FTYPE_MOSTLY_IQ2_XXS:       wtype = GGML_TYPE_IQ2_XXS;  break;
         case GGML_FTYPE_MOSTLY_IQ2_XS:        wtype = GGML_TYPE_IQ2_XS;   break;
         case GGML_FTYPE_MOSTLY_IQ3_XXS:       wtype = GGML_TYPE_IQ3_XXS;  break;
@@ -10641,6 +10663,7 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -11096,6 +11119,7 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -11248,6 +11272,7 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -14446,6 +14471,7 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -14838,6 +14864,7 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -15124,6 +15151,7 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -15737,6 +15765,8 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_Q5_K_R4:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_Q6_K_R4:
+        case GGML_TYPE_Q8_K_R8:
+        case GGML_TYPE_Q8_KR8:
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
@@ -22578,6 +22608,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_Q5_K_R4: result = quantize_q5_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q6_K:    result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q6_K_R4: result = quantize_q6_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q8_K_R8: result = quantize_q8_k_r8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_XS:  result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

ggml/src/iqk/iqk_mul_mat.cpp

@@ -92,6 +92,9 @@ struct DataInfo {
     inline void store(int ix, int iy, __m128 result) const {
         _mm_storeu_ps(dst_row(iy) + ix, result);
     }
+    inline void store(int ix, int iy, __m256 result) const {
+        _mm256_storeu_ps(dst_row(iy) + ix, result);
+    }
 #endif
 #ifdef __ARM_NEON
     inline void store(int ix, int iy, float32x4_t result) const {
@@ -175,6 +178,7 @@ struct MulMat {
             case GGML_TYPE_IQ4_NL_R4:
             case GGML_TYPE_IQ4_XS_R4:
             case GGML_TYPE_IQ2_BN_R4: return 4;
+            case GGML_TYPE_Q8_K_R8: return 8;
             default: return 1;
         }
     }
@@ -3802,6 +3806,76 @@ static void mul_mat_q6_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn
     }
 }
 
+// The HAVE_FANCY_SIMD should only be #if defined(__AVX512_VNNI__ && defined(__AVX512VL__)
+template <int nrc_y>
+static void mul_mat_q8_k_r8_q8_k(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_K> q8(info);
+#ifndef HAVE_FANCY_SIMD
+    auto m1 = _mm256_set1_epi16(1);
+#endif
+    int nbl = n / QK_K;
+    __m256  acc[nrc_y] = {};
+    __m256i isum[nrc_y] = {};
+    __m256i qx[4];
+    for (int ix = 0; ix < nrc_x; ix += 8) {
+        const block_q8_k_r8 * iq8 = (const block_q8_k_r8 *)((const char *)vx + (ix+0)*bx);
+        for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
+            auto d4 = _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)iq8[ibl].d));
+            for (int ib = 0; ib < QK_K/16; ++ib) {
+                qx[0] = _mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+0);
+                qx[1] = _mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+1);
+                qx[2] = _mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+2);
+                qx[3] = _mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+3);
+#ifdef HAVE_FANCY_SIMD
+                qx[0] = _mm256_xor_si256(_mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+0), _mm256_set1_epi8(-128));
+                qx[1] = _mm256_xor_si256(_mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+1), _mm256_set1_epi8(-128));
+                qx[2] = _mm256_xor_si256(_mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+2), _mm256_set1_epi8(-128));
+                qx[3] = _mm256_xor_si256(_mm256_loadu_si256((const __m256i *)iq8[ibl].qs+4*ib+3), _mm256_set1_epi8(-128));
+#else
+                auto s0 = _mm256_sign_epi8(qx[0], qx[0]);
+                auto s1 = _mm256_sign_epi8(qx[1], qx[1]);
+                auto s2 = _mm256_sign_epi8(qx[2], qx[2]);
+                auto s3 = _mm256_sign_epi8(qx[3], qx[3]);
+#endif
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y128 = _mm_loadu_si128((const __m128i*)q8.y[iy][ibl].qs+ib);
+                    auto y = MM256_SET_M128I(y128, y128);
+#ifdef HAVE_FANCY_SIMD
+                    isum[iy] = _mm256_dpbusd_epi32(isum[iy], qx[0], _mm256_shuffle_epi32(y, 0x00));
+                    isum[iy] = _mm256_dpbusd_epi32(isum[iy], qx[1], _mm256_shuffle_epi32(y, 0x55));
+                    isum[iy] = _mm256_dpbusd_epi32(isum[iy], qx[2], _mm256_shuffle_epi32(y, 0xaa));
+                    isum[iy] = _mm256_dpbusd_epi32(isum[iy], qx[3], _mm256_shuffle_epi32(y, 0xff));
+#else
+                    auto sumi1 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(s0, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x00), qx[0])));
+                    auto sumi2 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(s1, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0x55), qx[1])));
+                    auto sumi3 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(s2, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xaa), qx[2])));
+                    auto sumi4 = _mm256_madd_epi16(m1, _mm256_maddubs_epi16(s3, _mm256_sign_epi8(_mm256_shuffle_epi32(y, 0xff), qx[3])));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(sumi1, sumi2));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(sumi3, sumi4));
+#endif
+                }
+            }
+#ifdef HAVE_FANCY_SIMD
+            auto m4 = _mm256_mul_ps(d4, _mm256_set1_ps(-128.f));
+#endif
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                auto d4y = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl)));
+                acc[iy] = _mm256_fmadd_ps(d4y, _mm256_cvtepi32_ps(isum[iy]), acc[iy]);
+#ifdef HAVE_FANCY_SIMD
+                auto bsums = (const float *)q8.y[iy][ibl].bsums;
+                acc[iy] = _mm256_fmadd_ps(m4, _mm256_set1_ps(bsums[0]), acc[iy]);
+#endif
+                isum[iy] = _mm256_setzero_si256();
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            info.store(ix, iy, acc[iy]);
+            acc[iy] = _mm256_setzero_ps();
+        }
+    }
+}
+
 template <int nrc_y>
 static void mul_mat_iq4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
     GGML_ASSERT(nrc_x%4 == 0);
@@ -5976,6 +6050,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
             mm.funcs[7] = mul_mat_q6_k_r4_q8_k<8>;
             expected_typeB = GGML_TYPE_Q8_K;
             break;
+        case GGML_TYPE_Q8_K_R8:
+            assert (ne00 % QK_K == 0);
+            mm.funcs[0] = mul_mat_q8_k_r8_q8_k<1>;
+            mm.funcs[1] = mul_mat_q8_k_r8_q8_k<2>;
+            mm.funcs[2] = mul_mat_q8_k_r8_q8_k<3>;
+            mm.funcs[3] = mul_mat_q8_k_r8_q8_k<4>;
+            mm.funcs[4] = mul_mat_q8_k_r8_q8_k<5>;
+            mm.funcs[5] = mul_mat_q8_k_r8_q8_k<6>;
+            mm.funcs[6] = mul_mat_q8_k_r8_q8_k<7>;
+            mm.funcs[7] = mul_mat_q8_k_r8_q8_k<8>;
+            expected_typeB = GGML_TYPE_Q8_KR8;
+            break;
         case GGML_TYPE_IQ4_K_R4:
             assert (ne00 % QK_K == 0);
             mm.funcs[0] = mul_mat_iq4_k_r4_q8_k<1>;
@@ -9158,6 +9244,55 @@ void mul_mat_q6_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& inf
     }
 }
 
+template <int nrc_y>
+void mul_mat_q8_k_r8_q8_k(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_K> q8(info);
+    int nbl = n / QK_K;
+    float32x4_t acc[2*nrc_y] = {};
+    for (int ix = 0; ix < nrc_x; ix += 8) {
+        const block_q8_k_r8 * iq8 = (const block_q8_k_r8 *)((const char *)vx + ix*bx);
+        for (int ibl = 0; ibl < nbl; ++ibl) {
+            auto d4l = vcvt_f32_f16(vld1_f16((const float16_t *)iq8[ibl].d+0));
+            auto d4h = vcvt_f32_f16(vld1_f16((const float16_t *)iq8[ibl].d+4));
+            int32x4_t isum[2*nrc_y] = {};
+            for (int ib = 0; ib < QK_K/16; ++ib) {
+                auto q1 = vld1q_u8_x4(iq8[ibl].qs + 128*ib +  0);
+                auto q2 = vld1q_u8_x4(iq8[ibl].qs + 128*ib + 64);
+                for (int k = 0; k < 4; ++k) {
+                    q1.val[k] = veorq_u8(q1.val[k], vdupq_n_u8(0x80));
+                    q2.val[k] = veorq_u8(q2.val[k], vdupq_n_u8(0x80));
+                }
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = vld1q_s8(q8.y[iy][ibl].qs+16*ib);
+                    isum[2*iy+0] = vdotq_laneq_s32(isum[2*iy+0], q1.val[0], y, 0);
+                    isum[2*iy+1] = vdotq_laneq_s32(isum[2*iy+1], q1.val[1], y, 0);
+                    isum[2*iy+0] = vdotq_laneq_s32(isum[2*iy+0], q1.val[2], y, 1);
+                    isum[2*iy+1] = vdotq_laneq_s32(isum[2*iy+1], q1.val[3], y, 1);
+                    isum[2*iy+0] = vdotq_laneq_s32(isum[2*iy+0], q2.val[0], y, 2);
+                    isum[2*iy+1] = vdotq_laneq_s32(isum[2*iy+1], q2.val[1], y, 2);
+                    isum[2*iy+0] = vdotq_laneq_s32(isum[2*iy+0], q2.val[2], y, 3);
+                    isum[2*iy+1] = vdotq_laneq_s32(isum[2*iy+1], q2.val[3], y, 3);
+                }
+            }
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                auto d8 = vdupq_n_f32(q8.scale(iy, ibl));
+                const float * bsum = (const float *)q8.y[iy][ibl].bsums;
+                auto m8 = vdupq_n_f32(-128.f*bsum[0]);
+                acc[2*iy+0] = vfmaq_f32(acc[2*iy+0], vmulq_f32(d4l, d8), vcvtq_f32_s32(isum[2*iy+0]));
+                acc[2*iy+1] = vfmaq_f32(acc[2*iy+1], vmulq_f32(d4h, d8), vcvtq_f32_s32(isum[2*iy+1]));
+                acc[2*iy+0] = vfmaq_f32(acc[2*iy+0], d4l, m8);
+                acc[2*iy+1] = vfmaq_f32(acc[2*iy+1], d4l, m8);
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            info.store(ix+0, iy, acc[2*iy+0]);
+            info.store(ix+4, iy, acc[2*iy+1]);
+            acc[2*iy+0] = acc[2*iy+1] = vdupq_n_f32(0.f);
+        }
+    }
+}
+
 void mul_mat_iq4_nl_r4_q8_0_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
     GGML_ASSERT(nrc_x%4 == 0);
     Q8<1, block_q8_0_x4> q8(info);
@@ -9575,6 +9710,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
             SET_MUL_MAT_FUNCTIONS(m, mul_mat_q6_k_r4_q8_k);
             expected_Btype = GGML_TYPE_Q8_K;
             break;
+        case GGML_TYPE_Q8_K_R8:
+            SET_MUL_MAT_FUNCTIONS(m, mul_mat_q8_k_r8_q8_k);
+            expected_Btype = GGML_TYPE_Q8_KR8;
+            break;
         case GGML_TYPE_IQ4_K_R4:
             SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq4_k_r4_q8_k);
             expected_Btype = GGML_TYPE_Q8_K;