Convert models to row-interleaved quants using the quantize tool (#272)

* Repack a model with the quantize tool

* WIP

* Fixed various issues

As we don't have a way to tell if a repacked quant has been modified,
I had to remove the modification at the expense of a slight decrease
in performance. This affects q8_0_r8, q8_KV_r8, q8_k_r8 on Zen4, and
q4_0_r8 on ARM.

* Create wk_b and wv_b as Q8_0_R8 if the wkv_b type is interleaved

* Fix GCC 13.3 compilation error

* Another one

* Add missing include

---------

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

7 files changed, 246 insertions, 58 deletions

diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 89de794b..84ea38d4 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -145,6 +145,7 @@ static void usage(const char * executable) {
     printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor.\n");
     printf("  --token-embedding-type ggml_type: use this ggml_type for the token_embd.weight tensor.\n\n");
     printf("  --custom-q regex1=type1,regex2=type2...: use this to specify custom quantization type rules.\n\n");
+    printf("  --repack Repack all tensors to the corresponding _r4/8 variant if available.\n\n");
     printf("Additional specific tensor quantization types used in the custom quant scheme 'CQS (default is Q2_K):\n");
     printf("      --attn-q-type ggml_type: use this ggml_type for the attn_q.weight tensor.\n");
     printf("      --attn-k-type ggml_type: use this ggml_type for the attn_k.weight tensor.\n");
@@ -331,6 +332,8 @@ int main(int argc, char ** argv) {
             params.quantize_output_tensor = false;
         } else if (strcmp(argv[arg_idx], "--ignore-imatrix-rules") == 0) {
             params.ignore_imatrix_rules = true;
+        } else if (strcmp(argv[arg_idx], "--repack") == 0) {
+            params.only_repack = true;
         } else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) {
             if (arg_idx < argc-1) {
                 params.output_tensor_type = parse_ggml_type(argv[++arg_idx]);
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index faf1902d..a2bdc156 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1756,6 +1756,15 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
     return type_traits[type];
 }
 
+static inline int ggml_packed_rows(enum ggml_type type) {
+    return type == GGML_TYPE_BF16_R16 ? 16
+         : type == GGML_TYPE_Q8_K_R8 || type == GGML_TYPE_Q8_KV_R8 ||
+           type == GGML_TYPE_Q8_0_R8 || type == GGML_TYPE_Q4_0_R8 ||
+           type == GGML_TYPE_IQ4_XS_R8 ? 8
+           : type >= GGML_TYPE_Q4_0_R8 && type <= GGML_TYPE_Q8_K_R8 ? 4
+         : 1;
+}
+
 //
 // simd mappings
 //
@@ -10119,9 +10128,11 @@ static void ggml_compute_forward_dup_f32(
     }
 
     // parallelize by rows
+    int n_packed = ggml_packed_rows(dst->type);
+    GGML_ASSERT(dst->ne[1] % n_packed == 0);
     const int nr = ne01;
     // number of rows per thread
-    const int dr = (nr + nth - 1) / nth;
+    const int dr = n_packed*((nr/n_packed + nth - 1) / nth);
     // row range for this thread
     const int ir0 = dr * ith;
     const int ir1 = MIN(ir0 + dr, nr);
@@ -10173,10 +10184,10 @@ static void ggml_compute_forward_dup_f32(
                 for (int i03 = 0; i03 < ne03; i03++) {
                     for (int i02 = 0; i02 < ne02; i02++) {
                         id += rs * ir0;
-                        for (int i01 = ir0; i01 < ir1; i01++) {
+                        for (int i01 = ir0; i01 < ir1; i01 += n_packed) {
                             const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
-                            quantize_row_q(src0_ptr, dst_ptr + id, ne00);
-                            id += rs;
+                            quantize_row_q(src0_ptr, dst_ptr + id, ne00*n_packed);
+                            id += rs*n_packed;
                         }
                         id += rs * (ne01 - ir1);
                     }
@@ -10441,10 +10452,15 @@ static void ggml_compute_forward_dup_bytes(
 
     // parallelize by rows
     const int nr = ne01;
+    const int n_packed = ggml_packed_rows(dst->type);
+    GGML_ASSERT(nr%n_packed == 0);
+    const int nrp = nr/n_packed;
     // number of rows per thread
-    const int dr = (nr + nth - 1) / nth;
+    const int drp = (nrp + nth - 1) / nth;
+    const int dr  = drp*n_packed;
     // row range for this thread
     const int ir0 = dr * ith;
+    if (ir0 >= nr) return;
     const int ir1 = MIN(ir0 + dr, nr);
 
     if (src0->type == dst->type &&
@@ -10569,10 +10585,19 @@ static void ggml_compute_forward_dup_q(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
 
+    GGML_ASSERT(ggml_is_quantized(dst->src[0]->type));
+
     int64_t nrows = ggml_nrows(dst);
     int ith = params->ith;
     int nth = params->nth;
 
+    if (dst->src[0]->type == dst->type &&
+        dst->src[0]->nb[0] == ggml_type_size(dst->type) &&
+        dst->nb[0] == ggml_type_size(dst->type)) {
+        ggml_compute_forward_dup_bytes(params, dst);
+        return;
+    }
+
     if (dst->type == GGML_TYPE_Q8_0 && dst->src[0]->type == GGML_TYPE_Q8_0 &&
             ggml_are_same_shape(dst, dst->src[0])) {
 
@@ -10626,6 +10651,10 @@ static void ggml_compute_forward_dup_q(
         return;
     }
 
+    if (dst->type != GGML_TYPE_F32) {
+        printf("%s: %s -> %s is of type %s\n", __func__, dst->src[0]->name, dst->name, ggml_type_name(dst->type));
+        GGML_ABORT("fatal error");
+    }
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
     struct ggml_tensor * src0 = dst->src[0];
     GGML_ASSERT(src0->ne[0] == dst->ne[0] && src0->nb[0] == ggml_type_size(src0->type));
@@ -10633,12 +10662,15 @@ static void ggml_compute_forward_dup_q(
     ggml_to_float_t to_float = type_traits[src0->type].to_float;
     GGML_ASSERT(to_float != NULL);
 
-    int64_t n_per_thread = (nrows + nth - 1)/nth;
+    int n_packed = ggml_packed_rows(src0->type);
+    GGML_ASSERT(src0->ne[1] % n_packed == 0);
+
+    int64_t n_per_thread = n_packed*((nrows/n_packed + nth - 1)/nth);
     int64_t first_row = ith*n_per_thread;
     if (first_row >= nrows) return;
     int64_t last_row = MIN(first_row + n_per_thread, nrows);
 
-    for (int64_t ir = first_row; ir < last_row; ++ir) {
+    for (int64_t ir = first_row; ir < last_row; ir += n_packed) {
         int64_t i03 = ir/(src0->ne[1]*src0->ne[2]);
         int64_t i02 = (ir - i03*src0->ne[1]*src0->ne[2])/src0->ne[1];
         int64_t i01 = ir - i03*src0->ne[1]*src0->ne[2] - i02*src0->ne[1];
@@ -10649,7 +10681,7 @@ static void ggml_compute_forward_dup_q(
         const char * q = (const char *)src0->data + i03*src0->nb[3] + i02*src0->nb[2] + i01*src0->nb[1];
               char * f = (      char *)dst->data  +  i3* dst->nb[3] +  i2* dst->nb[2] +  i1* dst->nb[1];
 
-        to_float((const void *)q, (float *)f, src0->ne[0]);
+        to_float((const void *)q, (float *)f, src0->ne[0]*n_packed);
     }
 
 }
diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp
index 14cc64db..8b6d6b1c 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -4434,14 +4434,17 @@ inline __m256i qx_r8_q8_dot_product(const __m256i * qx, const int8_t * y) {
     return sumi;
 }
 inline __m256i q8_0_r8_dot_product(const uint8_t * x, const int8_t * y, __m256i * qx) {
-    qx[0] = _mm256_loadu_si256((const __m256i *)x+0);
-    qx[1] = _mm256_loadu_si256((const __m256i *)x+1);
-    qx[2] = _mm256_loadu_si256((const __m256i *)x+2);
-    qx[3] = _mm256_loadu_si256((const __m256i *)x+3);
-    qx[4] = _mm256_loadu_si256((const __m256i *)x+4);
-    qx[5] = _mm256_loadu_si256((const __m256i *)x+5);
-    qx[6] = _mm256_loadu_si256((const __m256i *)x+6);
-    qx[7] = _mm256_loadu_si256((const __m256i *)x+7);
+    for (int i = 0; i < 8; ++i) {
+        qx[i] = _mm256_add_epi8(_mm256_loadu_si256((const __m256i *)x+i), _mm256_set1_epi8(127));
+    }
+    //qx[0] = _mm256_loadu_si256((const __m256i *)x+0);
+    //qx[1] = _mm256_loadu_si256((const __m256i *)x+1);
+    //qx[2] = _mm256_loadu_si256((const __m256i *)x+2);
+    //qx[3] = _mm256_loadu_si256((const __m256i *)x+3);
+    //qx[4] = _mm256_loadu_si256((const __m256i *)x+4);
+    //qx[5] = _mm256_loadu_si256((const __m256i *)x+5);
+    //qx[6] = _mm256_loadu_si256((const __m256i *)x+6);
+    //qx[7] = _mm256_loadu_si256((const __m256i *)x+7);
     return qx_r8_q8_dot_product(qx, y);
 }
 template <int nrc_y>
@@ -4496,6 +4499,7 @@ static void mul_mat_q8_0_r8_q8_1(int n, const void * vx, size_t bx, const DataIn
                     for (int j = 0; j < 8; ++j) {
                         qx[j] = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)q8l[4*ib4+k].qs+j)),
                                                                           _mm256_loadu_si256((const __m256i *)q8h[4*ib4+k].qs+j), 1);
+                        qx[j] = _mm512_add_epi8(qx[j], _mm512_set1_epi8(127));
                     }
                     for (int iy = 0; iy < nrc_y; ++iy) {
                         auto sumi = qx_r8_q8_dot_product(qx, q8.y[iy][ib4].qs+32*k);
@@ -4512,6 +4516,7 @@ static void mul_mat_q8_0_r8_q8_1(int n, const void * vx, size_t bx, const DataIn
                 for (int j = 0; j < 8; ++j) {
                     qx[j] = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)q8l[ib].qs+j)),
                                                                       _mm256_loadu_si256((const __m256i *)q8h[ib].qs+j), 1);
+                    qx[j] = _mm512_add_epi8(qx[j], _mm512_set1_epi8(127));
                 }
                 for (int iy = 0; iy < nrc_y; ++iy) {
                     auto qy = (const block_q8_1 *)q8.y[iy];
@@ -6347,6 +6352,11 @@ static void mul_mat_q8_k_r8_q8_k(int n, const void * vx, size_t bx, const DataIn
                 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]);
+#else
+                qx[0] = _mm256_add_epi8(qx[0], _mm256_set1_epi8(127));
+                qx[1] = _mm256_add_epi8(qx[1], _mm256_set1_epi8(127));
+                qx[2] = _mm256_add_epi8(qx[2], _mm256_set1_epi8(127));
+                qx[3] = _mm256_add_epi8(qx[3], _mm256_set1_epi8(127));
 #endif
                 for (int iy = 0; iy < nrc_y; ++iy) {
                     auto y128 = _mm_loadu_si128((const __m128i*)q8.y[iy][ibl].qs+ib);
@@ -6425,6 +6435,11 @@ static void mul_mat_q8_KV_r8_q8_KV(int n, const void * vx, size_t bx, const Data
             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]);
+#else
+            qx[0] = _mm256_add_epi8(qx[0], _mm256_set1_epi8(127));
+            qx[1] = _mm256_add_epi8(qx[1], _mm256_set1_epi8(127));
+            qx[2] = _mm256_add_epi8(qx[2], _mm256_set1_epi8(127));
+            qx[3] = _mm256_add_epi8(qx[3], _mm256_set1_epi8(127));
 #endif
             for (int iy = 0; iy < nrc_y; ++iy) {
                 auto y128 = _mm_loadu_si128((const __m128i*)q8y[iy]+ib);
@@ -14305,8 +14320,8 @@ struct Q4_0_R8_Dequantizer {
         float32x4x2_t scales = { vcvt_f32_f16(vget_low_f16(scales16)), vcvt_f32_f16(vget_high_f16(scales16)) };
         for (int j = 0; j < 4; ++j) {
             auto bits = vld1q_u8_x2(iq4[4*ib4+k].qs + 32*j);
-            //bits.val[0] = veorq_u8(m88, bits.val[0]);
-            //bits.val[1] = veorq_u8(m88, bits.val[1]);
+            bits.val[0] = veorq_u8(m88, bits.val[0]);
+            bits.val[1] = veorq_u8(m88, bits.val[1]);
             qx[2*j+0] = vshlq_n_u8(bits.val[0], 4);
             qx[2*j+1] = vandq_u8(bits.val[0], m4);
             qx[2*j+8] = vshlq_n_u8(bits.val[1], 4);
@@ -15305,12 +15320,12 @@ struct HelperQ80R8 : public BaseHelper<step> {
                 m1 = _mm256_unpackhi_epi64(t0, t1);
                 m2 = _mm256_unpacklo_epi64(t2, t3);
                 m3 = _mm256_unpackhi_epi64(t2, t3);
-#ifdef HAVE_FANCY_SIMD
-                m0 = _mm256_add_epi8(m0, _mm256_set1_epi8(127));
-                m1 = _mm256_add_epi8(m1, _mm256_set1_epi8(127));
-                m2 = _mm256_add_epi8(m2, _mm256_set1_epi8(127));
-                m3 = _mm256_add_epi8(m3, _mm256_set1_epi8(127));
-#endif
+//#ifdef HAVE_FANCY_SIMD
+//                m0 = _mm256_add_epi8(m0, _mm256_set1_epi8(127));
+//                m1 = _mm256_add_epi8(m1, _mm256_set1_epi8(127));
+//                m2 = _mm256_add_epi8(m2, _mm256_set1_epi8(127));
+//                m3 = _mm256_add_epi8(m3, _mm256_set1_epi8(127));
+//#endif
                 _mm256_storeu_si256((__m256i *)y[ib].qs + 0, m0);
                 _mm256_storeu_si256((__m256i *)y[ib].qs + 1, m1);
                 _mm256_storeu_si256((__m256i *)y[ib].qs + 2, m2);
@@ -15327,12 +15342,12 @@ struct HelperQ80R8 : public BaseHelper<step> {
                 m1 = _mm256_unpackhi_epi64(t0, t1);
                 m2 = _mm256_unpacklo_epi64(t2, t3);
                 m3 = _mm256_unpackhi_epi64(t2, t3);
-#ifdef HAVE_FANCY_SIMD
-                m0 = _mm256_add_epi8(m0, _mm256_set1_epi8(127));
-                m1 = _mm256_add_epi8(m1, _mm256_set1_epi8(127));
-                m2 = _mm256_add_epi8(m2, _mm256_set1_epi8(127));
-                m3 = _mm256_add_epi8(m3, _mm256_set1_epi8(127));
-#endif
+//#ifdef HAVE_FANCY_SIMD
+//                m0 = _mm256_add_epi8(m0, _mm256_set1_epi8(127));
+//                m1 = _mm256_add_epi8(m1, _mm256_set1_epi8(127));
+//                m2 = _mm256_add_epi8(m2, _mm256_set1_epi8(127));
+//                m3 = _mm256_add_epi8(m3, _mm256_set1_epi8(127));
+//#endif
                 _mm256_storeu_si256((__m256i *)y[ib].qs + 4, m0);
                 _mm256_storeu_si256((__m256i *)y[ib].qs + 5, m1);
                 _mm256_storeu_si256((__m256i *)y[ib].qs + 6, m2);
@@ -15424,12 +15439,12 @@ struct HelperQ8KVR8 : public BaseHelper<step> {
                 m1 = _mm256_unpackhi_epi64(t0, t1);
                 m2 = _mm256_unpacklo_epi64(t2, t3);
                 m3 = _mm256_unpackhi_epi64(t2, t3);
-#ifdef HAVE_FANCY_SIMD
-                m0 = _mm256_add_epi8(m0, _mm256_set1_epi8(127));
-                m1 = _mm256_add_epi8(m1, _mm256_set1_epi8(127));
-                m2 = _mm256_add_epi8(m2, _mm256_set1_epi8(127));
-                m3 = _mm256_add_epi8(m3, _mm256_set1_epi8(127));
-#endif
+//#ifdef HAVE_FANCY_SIMD
+//                m0 = _mm256_add_epi8(m0, _mm256_set1_epi8(127));
+//                m1 = _mm256_add_epi8(m1, _mm256_set1_epi8(127));
+//                m2 = _mm256_add_epi8(m2, _mm256_set1_epi8(127));
+//                m3 = _mm256_add_epi8(m3, _mm256_set1_epi8(127));
+//#endif
                 _mm256_storeu_si256((__m256i *)y[ix].qs + 4*ib+0, m0);
                 _mm256_storeu_si256((__m256i *)y[ix].qs + 4*ib+1, m1);
                 _mm256_storeu_si256((__m256i *)y[ix].qs + 4*ib+2, m2);
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index fb6a5db4..5e657f4a 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -25,6 +25,7 @@
 #include <thread>
 #include <atomic>
 #include <unordered_map>
+#include <string>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -6766,9 +6767,7 @@ struct Modify {
     modify_func_t  mod_func;
     int            nrows;
 };
-}
-
-bool iqk_modify_tensor(struct ggml_tensor * tensor) {
+const Modify * get_modify_info(ggml_type type) {
     static const std::unordered_map<ggml_type, Modify> k_mod_map = {
 #ifdef __ARM_NEON
         { GGML_TYPE_Q4_0_R8, {modify_q4_0_r8, 8} },
@@ -6779,10 +6778,31 @@ bool iqk_modify_tensor(struct ggml_tensor * tensor) {
         { GGML_TYPE_Q8_KV_R8, {modify_q8_KV_r8, 8} },
 #endif
     };
-    auto it = k_mod_map.find(tensor->type);
-    if (it == k_mod_map.end()) return false;
+    auto it = k_mod_map.find(type);
+    return it != k_mod_map.end() ? &it->second : nullptr;
+}
+bool is_forbidden_tensor(const std::string& name) {
+    static const std::string kTokenEmbd{"token_embd.weight"};
+    if (name == kTokenEmbd) return true;
+    //if (auto pos = name.find("attn_kv_b.weight"); pos != std::string::npos) return true;
+    return false;
+}
+}
 
-    auto& m = it->second;
+bool iqk_should_modify_tensor([[maybe_unused]] const struct ggml_tensor * tensor) {
+    return false;
+    //if (is_forbidden_tensor(tensor->name)) return false;
+    //auto mptr = get_modify_info(tensor->type);
+    //return mptr ? true : false;
+}
+
+bool iqk_modify_tensor(struct ggml_tensor * tensor) {
+    return false;
+    auto mptr = get_modify_info(tensor->type);
+    if (!mptr) return false;
+    if (is_forbidden_tensor(std::string{tensor->name})) return false;
+
+    auto& m = *mptr;
     int nrows = ggml_nrows(tensor);
     int nchunks = nrows/m.nrows;
     int max_thread = std::max(1, int(std::thread::hardware_concurrency()/2));
@@ -6805,12 +6825,8 @@ bool iqk_modify_tensor(struct ggml_tensor * tensor) {
     return true;
 }
 
-void iqk_repack_tensor(struct ggml_tensor * tensor) {
-    constexpr int kChunk = 8;
-    if (!tensor) return;
-    if (!ggml_is_contiguous(tensor)) return;
-    if (strncmp(tensor->name, "token_embd.weight", GGML_MAX_NAME) == 0) return;
-    if (tensor->ne[1] % 4) return;
+namespace {
+const Repack * get_repack_info(ggml_type type) {
     static const std::unordered_map<ggml_type, Repack> k_map = {
         { GGML_TYPE_IQ2_K,  { GGML_TYPE_IQ2_K_R4,  4,  (Repack::repack_func)repack_iq2_k}   },
         { GGML_TYPE_IQ3_K,  { GGML_TYPE_IQ3_K_R4,  4,  (Repack::repack_func)repack_iq3_k}   },
@@ -6841,12 +6857,30 @@ void iqk_repack_tensor(struct ggml_tensor * tensor) {
         { GGML_TYPE_F16,    { GGML_TYPE_BF16_R16, 16,  (Repack::repack_func)repack_bf16<ggml_half>}  },
 #endif
     };
+    auto it = k_map.find(type);
+    return it != k_map.end() ? &it->second : nullptr;
+}
+}
+
+int iqk_repacked_type(const struct ggml_tensor * tensor) {
+    if (!ggml_is_contiguous(tensor)) return (int)tensor->type;
+    if (is_forbidden_tensor(tensor->name)) return (int)tensor->type;
+    auto rptr = get_repack_info(tensor->type);
+    return rptr && tensor->ne[1] % rptr->num_rows == 0 ? (int)rptr->new_type : (int)tensor->type;
+}
+
+void iqk_repack_tensor(struct ggml_tensor * tensor) {
+    constexpr int kChunk = 8;
+    if (!tensor) return;
+    if (!ggml_is_contiguous(tensor)) return;
+    if (is_forbidden_tensor(tensor->name)) return;
+    if (tensor->ne[1] % 4) return;
 
-    auto it = k_map.find(tensor->type);
-    if (it == k_map.end()) return;
-    if (tensor->ne[1] % it->second.num_rows) return;
+    auto rptr = get_repack_info(tensor->type);
+    if (!rptr) return;
+    if (tensor->ne[1] % rptr->num_rows) return;
 
-    auto& r = it->second;
+    auto& r = *rptr;
 
     auto nrows = ggml_nrows(tensor);
 
@@ -6871,7 +6905,8 @@ void iqk_repack_tensor(struct ggml_tensor * tensor) {
             int last_row = std::min(first_row + chunkSize*r.num_rows, nrows);
             for (int row = first_row; row < last_row; row += r.num_rows) {
                 std::memcpy(qtmp.data(), data + row*row_size, r.num_rows*row_size);
-                r.repack(r.num_rows, n_per_row, qtmp.data(), data + row*row_size, true);
+                //r.repack(r.num_rows, n_per_row, qtmp.data(), data + row*row_size, true);
+                r.repack(r.num_rows, n_per_row, qtmp.data(), data + row*row_size, false);
             }
         }
     };
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index d447705b..dd148f2e 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -245,6 +245,9 @@ void repack_bf16_bf16_r16(const void * GGML_RESTRICT src, void * GGML_RESTRICT d
 void iqk_repack_tensor(struct ggml_tensor * tensor);
 bool iqk_modify_tensor(struct ggml_tensor * tensor);
 
+int iqk_repacked_type(const struct ggml_tensor * tensor); // int instead of ggml_type so we don't need to include ggml.h
+bool iqk_should_modify_tensor(const struct ggml_tensor * tensor);
+
 // So we can re-pack Microsoft's BitNet I2_S quants
 void dequantize_row_ms_i2s(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 
diff --git a/include/llama.h b/include/llama.h
index 5e86cb68..66e9af02 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -416,6 +416,7 @@ extern "C" {
         bool pure;                           // quantize all tensors to the default type
         bool keep_split;                     // quantize to the same number of shards
         bool ignore_imatrix_rules;           // If set to true, the built-in rules for refusing to quantize into certain quants without imatrix are ignored
+        bool only_repack;                    // Only repack tensors
         void * imatrix;                      // pointer to importance matrix data
         void * kv_overrides;                 // pointer to vector containing overrides
         void * custom_quants;                // pointer to vector containing custom quantization rules
diff --git a/src/llama.cpp b/src/llama.cpp
index 03139e41..a459cb00 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8194,7 +8194,8 @@ static bool llm_load_tensors(
                 auto wk_b_f32_t = ggml_cont(ctx, wk_b_f32_tview);
                 wk_b_f32_t->data = (char *)wk_b_f32->data + ggml_nbytes(wk_b_f32);
 
-                auto new_type = ggml_is_quantized(wkv_b.type) ? GGML_TYPE_Q8_0 : wkv_b.type;
+                auto new_type = ggml_is_quantized(wkv_b.type) ?
+                    wkv_b.type >= GGML_TYPE_Q4_0_R8 && wkv_b.type <= GGML_TYPE_Q8_K_R8 ? GGML_TYPE_Q8_0_R8 : GGML_TYPE_Q8_0 : wkv_b.type;
                 auto wk_b = ggml_cast(ctx, wk_b_f32_t, new_type);
                 wk_b->data = (char *)wk_b_f32_t->data + ggml_nbytes(wk_b_f32_t);
 
@@ -8218,6 +8219,9 @@ static bool llm_load_tensors(
                 ggml_set_name(l.computed_wk_b.get(), name.c_str());
                 ggml_backend_buffer_set_usage(l.computed_wk_b->buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
                 ggml_backend_tensor_set(l.computed_wk_b.get(), wk_b->data, 0, ggml_nbytes(wk_b));
+                if (ggml_backend_buffer_is_host(l.computed_wk_b->buffer)) {
+                    iqk_modify_tensor(l.computed_wk_b.get());
+                }
 
                 l.wk_b = l.computed_wk_b.get();
 
@@ -8243,6 +8247,9 @@ static bool llm_load_tensors(
                 ggml_set_name(l.computed_wv_b.get(), name.c_str());
                 ggml_backend_buffer_set_usage(l.computed_wv_b->buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
                 ggml_backend_tensor_set(l.computed_wv_b.get(), wv_b->data, 0, ggml_nbytes(wv_b));
+                if (ggml_backend_buffer_is_host(l.computed_wv_b->buffer)) {
+                    iqk_modify_tensor(l.computed_wv_b.get());
+                }
 
                 l.wv_b = l.computed_wv_b.get();
 
@@ -17140,11 +17147,48 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
     return new_size;
 }
 
+static llama_ftype repacked_ftype(llama_ftype ftype) {
+    static std::unordered_map<llama_ftype, llama_ftype> k_map = {
+        { LLAMA_FTYPE_MOSTLY_Q4_0,    LLAMA_FTYPE_MOSTLY_Q4_0_R8    },
+        { LLAMA_FTYPE_MOSTLY_Q8_0,    LLAMA_FTYPE_MOSTLY_Q8_0_R8    },
+        { LLAMA_FTYPE_MOSTLY_Q5_0,    LLAMA_FTYPE_MOSTLY_Q5_0_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q2_K,    LLAMA_FTYPE_MOSTLY_Q2_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q3_K_S,  LLAMA_FTYPE_MOSTLY_Q3_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q3_K_M,  LLAMA_FTYPE_MOSTLY_Q3_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q3_K_L,  LLAMA_FTYPE_MOSTLY_Q3_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q4_K_S,  LLAMA_FTYPE_MOSTLY_Q4_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q4_K_M,  LLAMA_FTYPE_MOSTLY_Q4_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q5_K_S,  LLAMA_FTYPE_MOSTLY_Q5_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q5_K_M,  LLAMA_FTYPE_MOSTLY_Q5_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_Q6_K,    LLAMA_FTYPE_MOSTLY_Q6_K_R4    },
+        { LLAMA_FTYPE_MOSTLY_IQ2_XXS, LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 },
+        { LLAMA_FTYPE_MOSTLY_IQ2_XS,  LLAMA_FTYPE_MOSTLY_IQ2_XS_R4  },
+        { LLAMA_FTYPE_MOSTLY_IQ3_XXS, LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 },
+        { LLAMA_FTYPE_MOSTLY_IQ1_S,   LLAMA_FTYPE_MOSTLY_IQ1_S_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ4_NL,  LLAMA_FTYPE_MOSTLY_IQ4_NL_R4  },
+        { LLAMA_FTYPE_MOSTLY_IQ3_S,   LLAMA_FTYPE_MOSTLY_IQ3_S_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ2_M,   LLAMA_FTYPE_MOSTLY_IQ2_M_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ4_XS,  LLAMA_FTYPE_MOSTLY_IQ4_XS_R8  },
+        { LLAMA_FTYPE_MOSTLY_IQ1_M,   LLAMA_FTYPE_MOSTLY_IQ1_M_R4   },
+        { LLAMA_FTYPE_MOSTLY_Q6_0,    LLAMA_FTYPE_MOSTLY_Q6_0_R4    },
+        { LLAMA_FTYPE_MOSTLY_BF16,    LLAMA_FTYPE_MOSTLY_BF16_R16   },
+        { LLAMA_FTYPE_MOSTLY_IQ2_BN,  LLAMA_FTYPE_MOSTLY_IQ2_BN_R4  },
+        { LLAMA_FTYPE_MOSTLY_IQ2_K,   LLAMA_FTYPE_MOSTLY_IQ2_K_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ3_K,   LLAMA_FTYPE_MOSTLY_IQ3_K_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ4_K,   LLAMA_FTYPE_MOSTLY_IQ4_K_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ5_K,   LLAMA_FTYPE_MOSTLY_IQ5_K_R4   },
+        { LLAMA_FTYPE_MOSTLY_IQ4_KS,  LLAMA_FTYPE_MOSTLY_IQ4_KS_R4  },
+        { LLAMA_FTYPE_MOSTLY_Q8_KV,   LLAMA_FTYPE_MOSTLY_Q8_KV_R8   },
+    };
+    if (auto it = k_map.find(ftype); it != k_map.end()) return it->second;
+    return ftype;
+}
+
 static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
     ggml_type default_type;
     llama_ftype ftype = params->ftype;
 
-    switch (params->ftype) {
+    switch (ftype) {
         case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
         case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
         case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
@@ -17256,7 +17300,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         ftype = model.ftype;
     }
     const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
-    if (params->imatrix) {
+    if (!params->only_repack && params->imatrix) {
         imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
         if (imatrix_data) {
             LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
@@ -17278,7 +17322,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     // copy the KV pairs from the input file
     gguf_set_kv     (ctx_out, ml.meta);
     gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
-    gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV
 
     // Remove split metadata
     gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
@@ -17303,9 +17346,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         }
     }
 
+    bool is_repacked = ml.ftype >= LLAMA_FTYPE_MOSTLY_Q4_0_R8 && ml.ftype <= LLAMA_FTYPE_MOSTLY_Q8_K_R8;
+    int n_to_repack = 0, n_to_modify = 0;
     for (int i = 0; i < ml.n_tensors; ++i) {
         const struct ggml_tensor * meta = ml.get_tensor_meta(i);
 
+        if (params->only_repack) {
+            auto repacked_type = (ggml_type)iqk_repacked_type(meta);
+            if (repacked_type != meta->type) {
+                ++n_to_repack;
+            } else if (!is_repacked) {
+                if (iqk_should_modify_tensor(meta)) ++n_to_modify;
+            }
+        }
+
         const std::string name = ggml_get_name(meta);
 
         // TODO: avoid hardcoded tensor names - use the TN_* constants
@@ -17317,6 +17371,18 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         }
     }
 
+    if (params->only_repack) {
+        if (n_to_repack == 0 && n_to_modify == 0) {
+            printf("=========================== %s: nothing to do for only_repack option\n", __func__);
+            return;
+        }
+        ftype = repacked_ftype(model.ftype);
+        printf("===================== Model ftype: %s: Repacked ftype: %s\n", llama_model_ftype_name(model.ftype).c_str(),
+                llama_model_ftype_name(ftype).c_str());
+    }
+
+    gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV
+
     qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
 
     // sanity checks
@@ -17457,6 +17523,36 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         void * new_data;
         size_t new_size;
 
+        if (params->only_repack) {
+            ggml_type repacked_type = (ggml_type)iqk_repacked_type(tensor);
+            bool modify = !is_repacked && iqk_should_modify_tensor(tensor);
+            if (modify || repacked_type != tensor->type) {
+                new_type = repacked_type;
+                new_size = ggml_nbytes(tensor);
+                if ((int)work.size() < new_size) work.resize(new_size);
+                new_data = work.data();
+
+                auto aux_tensor = *tensor;
+                aux_tensor.data = work.data();
+                std::memcpy(aux_tensor.data, tensor->data, new_size);
+
+                if (repacked_type != tensor->type) {
+                    iqk_repack_tensor(&aux_tensor);
+                    GGML_ASSERT(aux_tensor.type == repacked_type);
+                } else {
+                    bool did_modify = iqk_modify_tensor(&aux_tensor);
+                    GGML_ASSERT(did_modify);
+                }
+            }
+            else {
+                new_type = tensor->type;
+                new_size = ggml_nbytes(tensor);
+                new_data = tensor->data;
+            }
+            LLAMA_LOG_INFO("size = %8.3f MB, type = %s\n", new_size/1024.0/1024.0, ggml_type_name(new_type));
+            goto QuantizationDone;
+        }
+
         if (quantize) {
             new_type = default_type;
             if (new_type == GGML_TYPE_BF16_R16 && strcmp(tensor->name, "token_embd.weight") == 0) {
@@ -17562,7 +17658,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                  new_type == GGML_TYPE_IQ1_S_R4||
                  new_type == GGML_TYPE_IQ1_M_R4||
                 (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight"))  ||
-                (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0))) {
+                (new_type == GGML_TYPE_Q2_K && ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0))) {
                 LLAMA_LOG_ERROR("\n\n============================================================\n");
                 LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
                 LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
@@ -17727,6 +17823,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             }
             LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
         }
+
+QuantizationDone:;
         total_size_org += ggml_nbytes(tensor);
         total_size_new += new_size;
 
@@ -18051,6 +18149,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
         /*.pure                        =*/ false,
         /*.keep_split                  =*/ false,
         /*.ignore_imatrix_rules        =*/ false,
+        /*.only_repack                 =*/ false,
         /*.imatrix                     =*/ nullptr,
         /*.kv_overrides                =*/ nullptr,
         /*.custom_quants               =*/ nullptr,