From 39d8bc71edcb8b6f99d46fa4216af7a15232e218 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 30 Dec 2023 13:52:01 +0100
Subject: [PATCH 01/60] CUDA: fixed tensor cores not being used on RDNA3
 (#4697)

---
 ggml-cuda.cu | 47 ++++++++++++++++++++++++-----------------------
 1 file changed, 24 insertions(+), 23 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 71a64ca09..8c2712308 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -119,10 +119,29 @@
 #define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
 #define CC_VOLTA      700
 #define CC_OFFSET_AMD 1000000
+#define CC_RDNA1      (CC_OFFSET_AMD + 1010)
 #define CC_RDNA2      (CC_OFFSET_AMD + 1030)
+#define CC_RDNA3      (CC_OFFSET_AMD + 1100)
 
 #define GGML_CUDA_MAX_NODES 8192
 
+// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
+// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
+// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
+// -  7B quantum model: +100-200 MB
+// - 13B quantum model: +200-400 MB
+//
+//#define GGML_CUDA_FORCE_MMQ
+
+// TODO: improve this to be correct for more hardware
+//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
+#if !defined(GGML_CUDA_FORCE_MMQ)
+#define CUDA_USE_TENSOR_CORES
+#endif
+
+// max batch size to use MMQ kernels when tensor cores are available
+#define MMQ_MAX_BATCH_SIZE 32
+
 #if defined(GGML_USE_HIPBLAS)
 #define __CUDA_ARCH__ 1300
 
@@ -189,23 +208,6 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
 }
 #endif // defined(GGML_USE_HIPBLAS)
 
-// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
-// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
-// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
-// -  7B quantum model: +100-200 MB
-// - 13B quantum model: +200-400 MB
-//
-//#define GGML_CUDA_FORCE_MMQ
-
-// TODO: improve this to be correct for more hardware
-//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
-#if !defined(GGML_CUDA_FORCE_MMQ) && (!defined(GGML_USE_HIPBLAS) || defined(RDNA3))
-#define CUDA_USE_TENSOR_CORES
-#endif
-
-// max batch size to use MMQ kernels when tensor cores are available
-#define MMQ_MAX_BATCH_SIZE 32
-
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
@@ -8661,13 +8663,12 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     }
 
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    const bool fp16_performance_good = true;
 
-#ifdef RDNA3
-    const bool use_mul_mat_q = false;
-#else
-    const bool use_mul_mat_q = true;
-#endif // RDNA3
+    const bool fp16_performance_good = min_compute_capability >= CC_RDNA1;
+    bool               use_mul_mat_q = ggml_is_quantized(src0->type);
+#ifdef CUDA_USE_TENSOR_CORES
+    use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3;
+#endif // CUDA_USE_TENSOR_CORES
 
 #else
 

From 9fbda719de18a9400a064c28759c39d55d687d3e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 30 Dec 2023 23:24:42 +0200
Subject: [PATCH 02/60] clip : refactor + bug fixes (#4696)

* clip : refactor + bug fixes

ggml-ci

* server : add log message
---
 examples/llava/clip.cpp    | 241 +++++++++++++++++++++----------------
 examples/llava/clip.h      |  48 +++-----
 examples/llava/llava.cpp   |   4 +-
 examples/server/server.cpp |  38 +++---
 4 files changed, 169 insertions(+), 162 deletions(-)

diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 6a731eeec..cfb79e789 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -146,6 +146,27 @@ static std::string get_ftype(int ftype) {
     }
 }
 
+//
+// image data
+//
+
+// RGB uint8 image
+struct clip_image_u8 {
+    int nx;
+    int ny;
+
+    std::vector<uint8_t> buf;
+};
+
+// RGB float32 image (NHWC)
+// Memory layout: RGBRGBRGB...
+struct clip_image_f32 {
+    int nx;
+    int ny;
+
+    std::vector<float> buf;
+};
+
 //
 // clip layers
 //
@@ -204,16 +225,21 @@ struct clip_vision_model {
 };
 
 struct clip_ctx {
-    bool has_text_encoder = false;
-    bool has_vision_encoder = false;
+    bool has_text_encoder    = false;
+    bool has_vision_encoder  = false;
     bool has_llava_projector = false;
+
     struct clip_vision_model vision_model;
+
     float image_mean[3];
     float image_std[3];
     bool use_gelu = false;
     int32_t ftype = 1;
-    struct ggml_context * ctx;
+
     struct gguf_context * ctx_gguf;
+    struct ggml_context * ctx_data;
+
+    std::vector<uint8_t> buf_compute_meta;
 
     // memory buffers to evaluate the model
     ggml_backend_buffer_t params_buffer = NULL;
@@ -222,7 +248,7 @@ struct clip_ctx {
     ggml_allocr * compute_alloc = NULL;
 };
 
-static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) {
+static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
         return nullptr;
@@ -243,13 +269,14 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
     //const int projection_dim = hparams.projection_dim;
     const float eps = hparams.eps;
     int batch_size = imgs->size;
-    if(ctx->has_llava_projector) {
+    if (ctx->has_llava_projector) {
         GGML_ASSERT(batch_size == 1);
     }
+
     struct ggml_init_params params = {
-        /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead(),
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc =*/ true,
+        /*.mem_size   =*/ ctx->buf_compute_meta.size(),
+        /*.mem_buffer =*/ ctx->buf_compute_meta.data(),
+        /*.no_alloc   =*/ true,
     };
 
     struct ggml_context * ctx0 = ggml_init(params);
@@ -272,7 +299,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
                 for (int k = 0; k < 3; k++) {
                     for (int y = 0; y < ny; y++) {
                         for (int x = 0; x < nx; x++) {
-                            data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].data[3 * (y * nx + x) + k];
+                            data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
                         }
                     }
                 }
@@ -413,7 +440,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
         ggml_allocr_alloc(ctx->compute_alloc, patches);
         if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
             int* patches_data = (int*)malloc(ggml_nbytes(patches));
-            for (int i = 0; i < num_positions; i++) {
+            for (int i = 0; i < num_patches; i++) {
                 patches_data[i] = i + 1;
             }
             ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
@@ -561,8 +588,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             /*.no_alloc =*/ true,
         };
 
-        new_clip->ctx = ggml_init(params);
-        if (!new_clip->ctx) {
+        new_clip->ctx_data = ggml_init(params);
+        if (!new_clip->ctx_data) {
             fprintf(stderr, "%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
             return nullptr;
@@ -579,7 +606,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         for (int i = 0; i < n_tensors; ++i) {
             const char * name = gguf_get_tensor_name(ctx, i);
             struct ggml_tensor * t = ggml_get_tensor(meta, name);
-            struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t);
+            struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t);
             ggml_set_name(cur, name);
         }
 
@@ -588,7 +615,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer);
         for (int i = 0; i < n_tensors; ++i) {
             const char * name = gguf_get_tensor_name(ctx, i);
-            struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx, name);
+            struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
             ggml_allocr_alloc(alloc, cur);
             const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
             fin.seekg(offset, std::ios::beg);
@@ -617,20 +644,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         // load vision model
         auto & vision_model = new_clip->vision_model;
         auto & hparams = vision_model.hparams;
-        hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision"));
-        hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision"));
+        hparams.hidden_size    = get_u32(ctx, format(KEY_N_EMBD, "vision"));
+        hparams.n_head         = get_u32(ctx, format(KEY_N_HEAD, "vision"));
         hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision"));
-        hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision"));
-        hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE);
-        hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE);
+        hparams.n_layer        = get_u32(ctx, format(KEY_N_BLOCK, "vision"));
+        hparams.image_size     = get_u32(ctx, KEY_IMAGE_SIZE);
+        hparams.patch_size     = get_u32(ctx, KEY_PATCH_SIZE);
         hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision"));
-        hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));
+        hparams.eps            = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));
 
         int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
-        int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);
+        int idx_std  = get_key_idx(ctx, KEY_IMAGE_STD);
         for (int i = 0; i < 3; ++i) {
             new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean));
-            new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std));
+            new_clip->image_std[i]  = *((const float *)gguf_get_arr_data(ctx, idx_std));
         }
 
         if (verbosity >= 2) {
@@ -644,35 +671,35 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             printf("v_n_layer          %d\n", hparams.n_layer);
         }
 
-        vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD);
-        vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD);
-        vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "v"));
-        vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "weight"));
-        vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "bias"));
-        vision_model.mm_0_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "weight"));
-        vision_model.mm_0_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "bias"));
-        vision_model.mm_2_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "weight"));
-        vision_model.mm_2_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "bias"));
+        vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+        vision_model.class_embedding     = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
+        vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
+        vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
+        vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
+        vision_model.mm_0_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
+        vision_model.mm_0_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
+        vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
+        vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
 
         vision_model.layers.resize(hparams.n_layer);
         for (int il = 0; il < hparams.n_layer; ++il) {
             auto & layer = vision_model.layers[il];
-            layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "weight"));
-            layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "weight"));
-            layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "weight"));
-            layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "weight"));
-            layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "weight"));
-            layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "weight"));
-            layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "weight"));
-            layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "weight"));
-            layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "bias"));
-            layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "bias"));
-            layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "bias"));
-            layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "bias"));
-            layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "bias"));
-            layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "bias"));
-            layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "bias"));
-            layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "bias"));
+            layer.k_w    = get_tensor(new_clip->ctx_data, format(TN_ATTN_K,      "v", il, "weight"));
+            layer.q_w    = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q,      "v", il, "weight"));
+            layer.v_w    = get_tensor(new_clip->ctx_data, format(TN_ATTN_V,      "v", il, "weight"));
+            layer.o_w    = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight"));
+            layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1,        "v", il, "weight"));
+            layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2,        "v", il, "weight"));
+            layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN,    "v", il, "weight"));
+            layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP,      "v", il, "weight"));
+            layer.k_b    = get_tensor(new_clip->ctx_data, format(TN_ATTN_K,      "v", il, "bias"));
+            layer.q_b    = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q,      "v", il, "bias"));
+            layer.v_b    = get_tensor(new_clip->ctx_data, format(TN_ATTN_V,      "v", il, "bias"));
+            layer.o_b    = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias"));
+            layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1,        "v", il, "bias"));
+            layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2,        "v", il, "bias"));
+            layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN,    "v", il, "bias"));
+            layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP,      "v", il, "bias"));
         }
     }
 
@@ -680,8 +707,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
     new_clip->ctx_gguf = ctx;
 
-// measure mem requirement and allocate
+    // measure mem requirement and allocate
     {
+        new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
         new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend);
         clip_image_f32_batch batch;
         batch.size = 1;
@@ -697,26 +725,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     return new_clip;
 }
 
-clip_image_u8 * make_clip_image_u8() {
-    auto img = new clip_image_u8();
-    return img;
+struct clip_image_u8 * clip_image_u8_init() {
+    return new clip_image_u8();
 }
-clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); }
 
-void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; }
-void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; }
+struct clip_image_f32 * clip_image_f32_init() {
+    return new clip_image_f32();
+}
+
+void clip_image_u8_free (struct clip_image_u8  * img) { delete img; }
+void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
 
 static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
     img->nx = nx;
     img->ny = ny;
-    img->size = nx * ny * 3;
-    img->data = new uint8_t[img->size]();
-    memcpy(img->data, data, img->size);
+    img->buf.resize(3 * nx * ny);
+    memcpy(img->buf.data(), data, img->buf.size());
 }
 
 bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
     int nx, ny, nc;
-    auto data = stbi_load(fname, &nx, &ny, &nc, 3);
+    auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
     if (!data) {
         fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
         return false;
@@ -728,7 +757,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
 
 bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {
     int nx, ny, nc;
-    auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
+    auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
     if (!data) {
         fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
         return false;
@@ -740,7 +769,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
 
 // normalize: x = (x - mean) / std
 // TODO: implement bicubic interpolation instead of linear.
-bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
+bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
         return false;
@@ -749,18 +778,17 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
 
-    clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily
+    clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
     if (pad2square && img->nx != img->ny) {
         int longer_side = std::max(img->nx, img->ny);
         temp->nx = longer_side;
         temp->ny = longer_side;
-        temp->size = 3 * longer_side * longer_side;
-        temp->data = new uint8_t[temp->size]();
-        uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
+        temp->buf.resize(3 * longer_side * longer_side);
+        const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
 
         // fill with background color
-        for (size_t i = 0; i < temp->size; i++) {
-            temp->data[i] = bc[i % 3];
+        for (size_t i = 0; i < temp->buf.size(); i++) {
+            temp->buf[i] = bc[i % 3];
         }
 
         // copy from the input image
@@ -768,17 +796,16 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
             for (int x = 0; x < img->nx; x++) {
                 const int i = 3 * (y * img->nx + x);
                 const int j = 3 * (y * temp->nx + x);
-                temp->data[j] = img->data[i];
-                temp->data[j+1] = img->data[i+1];
-                temp->data[j+2] = img->data[i+2];
+                temp->buf[j]   = img->buf[i];
+                temp->buf[j+1] = img->buf[i+1];
+                temp->buf[j+2] = img->buf[i+2];
             }
         }
     } else {
-        temp->nx   = img->nx;
-        temp->ny   = img->ny;
-        temp->size = img->size;
-        temp->data = new uint8_t[temp->size]();
-        memcpy(&temp->data[0], &img->data[0], temp->size); // copy
+        temp->nx = img->nx;
+        temp->ny = img->ny;
+        temp->buf.resize(img->buf.size());
+        memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
     }
 
     const int nx = temp->nx;
@@ -789,8 +816,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
 
     res->nx = nx2;
     res->ny = ny2;
-    res->size = 3 * nx2 * ny2;
-    res->data = new float[res->size]();
+    res->buf.resize(3 * nx2 * ny2);
 
     const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;
 
@@ -821,10 +847,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
                 const int j10 = 3 * (y1 * nx + x0) + c;
                 const int j11 = 3 * (y1 * nx + x1) + c;
 
-                const float v00 = temp->data[j00];
-                const float v01 = temp->data[j01];
-                const float v10 = temp->data[j10];
-                const float v11 = temp->data[j11];
+                const float v00 = temp->buf[j00];
+                const float v01 = temp->buf[j01];
+                const float v10 = temp->buf[j10];
+                const float v11 = temp->buf[j11];
 
                 const float v0 = v00 * (1.0f - dx) + v01 * dx;
                 const float v1 = v10 * (1.0f - dx) + v11 * dx;
@@ -835,7 +861,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
 
                 const int i = 3 * (y * nx3 + x) + c;
 
-                res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
+                res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
             }
         }
     }
@@ -845,12 +871,13 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
 }
 
 void clip_free(clip_ctx * ctx) {
-    ggml_free(ctx->ctx);
+    ggml_free(ctx->ctx_data);
     gguf_free(ctx->ctx_gguf);
+
     delete ctx;
 }
 
-bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
+bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
         return false;
@@ -862,8 +889,7 @@ bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32
     return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
 }
 
-bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
-
+bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
         return false;
@@ -906,31 +932,32 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     ggml_type type = GGML_TYPE_Q4_1;
 
     switch (itype) {
-    case 2:
-        type = GGML_TYPE_Q4_0;
-        break;
-    case 3:
-        type = GGML_TYPE_Q4_1;
-        break;
-    case 6:
-        type = GGML_TYPE_Q5_0;
-        break;
-    case 7:
-        type = GGML_TYPE_Q5_1;
-        break;
-    case 8:
-        type = GGML_TYPE_Q8_0;
-        break;
-    default:
-        fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype);
-        return false;
+        case 2:
+            type = GGML_TYPE_Q4_0;
+            break;
+        case 3:
+            type = GGML_TYPE_Q4_1;
+            break;
+        case 6:
+            type = GGML_TYPE_Q5_0;
+            break;
+        case 7:
+            type = GGML_TYPE_Q5_1;
+            break;
+        case 8:
+            type = GGML_TYPE_Q8_0;
+            break;
+        default:
+            fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype);
+            return false;
     };
 
-    auto ctx_clip = clip_model_load(fname_inp, 2);
-    const auto & ctx_src = ctx_clip->ctx_gguf;
-    const auto & ctx_data = ctx_clip->ctx;
+    auto * ctx_clip = clip_model_load(fname_inp, 2);
 
-    auto ctx_out = gguf_init_empty();
+    const auto & ctx_src = ctx_clip->ctx_gguf;
+    const auto & ctx_data = ctx_clip->ctx_data;
+
+    auto * ctx_out = gguf_init_empty();
     gguf_set_kv(ctx_out, ctx_src);
     gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
     gguf_set_val_u32(ctx_out, "general.file_type", itype);
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index f11df85de..458a256a1 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -35,31 +35,14 @@ struct clip_vision_hparams {
     float eps;
 };
 
-/** load mmproj model */
-CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity);
-/** free mmproj model */
+CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
+
 CLIP_API void clip_free(struct clip_ctx * ctx);
 
-size_t clip_embd_nbytes(const struct clip_ctx * ctx);
-int clip_n_patches(const struct clip_ctx * ctx);
-int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
 
-// RGB uint8 image
-struct clip_image_u8 {
-    int nx;
-    int ny;
-    uint8_t * data = NULL;
-    size_t size;
-};
-
-// RGB float32 image (NHWC)
-// Memory layout: RGBRGBRGB...
-struct clip_image_f32 {
-    int nx;
-    int ny;
-    float * data = NULL;
-    size_t size;
-};
+CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
+CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
 
 struct clip_image_u8_batch {
     struct clip_image_u8 * data;
@@ -71,21 +54,22 @@ struct clip_image_f32_batch {
     size_t size;
 };
 
-struct clip_image_u8 * make_clip_image_u8();
-struct clip_image_f32 * make_clip_image_f32();
-CLIP_API void clip_image_u8_free(clip_image_u8 * img);
-CLIP_API void clip_image_f32_free(clip_image_f32 * img);
+CLIP_API struct clip_image_u8  * clip_image_u8_init ();
+CLIP_API struct clip_image_f32 * clip_image_f32_init();
+
+CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
+CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
+
 CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
+
 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */
 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
-bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square);
-bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec);
+CLIP_API bool clip_image_preprocess  (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square);
+CLIP_API bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
+CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
 
-bool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs,
-                             float * vec);
-
-bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype);
+CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
 
 #ifdef __cplusplus
 }
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 0cae8c4b1..d42e7582e 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -10,7 +10,7 @@
 #include "base64.hpp"
 
 static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
-    clip_image_f32 * img_res = make_clip_image_f32();
+    clip_image_f32 * img_res = clip_image_f32_init();
     if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
         fprintf(stderr, "%s: unable to preprocess image\n", __func__);
         clip_image_f32_free(img_res);
@@ -86,7 +86,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
 }
 
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
-    clip_image_u8 * img = make_clip_image_u8();
+    clip_image_u8 * img = clip_image_u8_init();
     if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
         clip_image_u8_free(img);
         fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 0aada8e28..52d9b9768 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -82,7 +82,7 @@ static inline bool is_base64(uint8_t c)
     return (isalnum(c) || (c == '+') || (c == '/'));
 }
 
-static std::vector<uint8_t> base64_decode(std::string const &encoded_string)
+static std::vector<uint8_t> base64_decode(const std::string & encoded_string)
 {
     int i = 0;
     int j = 0;
@@ -209,10 +209,10 @@ struct slot_image
     int32_t id;
 
     bool request_encode_image = false;
-    float* image_embedding = nullptr;
+    float * image_embedding = nullptr;
     int32_t image_tokens = 0;
 
-    clip_image_u8 img_data;
+    clip_image_u8 * img_data;
 
     std::string prefix_prompt; // before of this image
 };
@@ -434,10 +434,12 @@ struct llama_client_slot
 
         generated_token_probs.clear();
 
-        for (slot_image &img : images)
+        for (slot_image & img : images)
         {
             free(img.image_embedding);
-            delete[] img.img_data.data;
+            if (img.img_data) {
+                clip_image_u8_free(img.img_data);
+            }
             img.prefix_prompt = "";
         }
 
@@ -851,24 +853,17 @@ struct llama_server_context
             {
                 for (const auto &img : *images_data)
                 {
-                    std::string data_b64 = img["data"].get<std::string>();
+                    const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
+
                     slot_image img_sl;
                     img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
-                    int width, height, channels;
-                    std::vector<uint8_t> image_buffer = base64_decode(data_b64);
-                    data_b64.clear();
-                    auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3);
-                    if (!data) {
+                    img_sl.img_data = clip_image_u8_init();
+                    if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
+                    {
                         LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
                         return false;
                     }
-                    LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height);
-                    img_sl.img_data.nx = width;
-                    img_sl.img_data.ny = height;
-                    img_sl.img_data.size = width * height * 3;
-                    img_sl.img_data.data = new uint8_t[width * height * 3]();
-                    memcpy(img_sl.img_data.data, data, width * height * 3);
-                    stbi_image_free(data);
+                    LOG_TEE("slot %i - loaded image\n", slot->id);
                     img_sl.request_encode_image = true;
                     slot->images.push_back(img_sl);
                 }
@@ -1143,8 +1138,8 @@ struct llama_server_context
             {
                 continue;
             }
-            clip_image_f32 img_res;
-            if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true))
+            clip_image_f32 * img_res = clip_image_f32_init();
+            if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
             {
                 LOG_TEE("Error processing the given image");
                 clip_free(clp_ctx);
@@ -1159,11 +1154,12 @@ struct llama_server_context
                 return false;
             }
             LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
-            if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding))
+            if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
             {
                 LOG_TEE("Unable to encode image\n");
                 return false;
             }
+            clip_image_f32_free(img_res);
             img.request_encode_image = false;
         }
 

From e39106c0554cbd0e9310e08fb3b2a577ea4b6273 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 31 Dec 2023 11:43:31 +0200
Subject: [PATCH 03/60] ggml : add ggml_vdotq_s32 alias (#4715)

ggml-ci
---
 ggml-quants.c | 118 ++++++++++++++++++++++++++------------------------
 1 file changed, 61 insertions(+), 57 deletions(-)

diff --git a/ggml-quants.c b/ggml-quants.c
index 05ef8f9b7..55a9496d1 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -410,13 +410,17 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
 
 #if !defined(__ARM_FEATURE_DOTPROD)
 
-inline static int32x4_t vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
+inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
     const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
     const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
 
     return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
 }
 
+#else
+
+#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
+
 #endif
 
 #endif
@@ -2481,8 +2485,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
 
         // dot product into int32x4_t
-        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
-        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
+        const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
+        const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
@@ -2769,8 +2773,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
 
         // dot product into int32x4_t
-        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
-        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
+        const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
+        const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
@@ -2936,11 +2940,11 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
         const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
@@ -3228,11 +3232,11 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
         const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
@@ -3483,12 +3487,12 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
         const int8x16_t y1_1 = vld1q_s8(y1->qs + 16);
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
-                        vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+                        ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
+                        ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
 
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
-                        vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+                        ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
+                        ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
@@ -3598,8 +3602,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 // We use this macro instead of a function call because for some reason
 // the code runs 2-3% slower, even if the function is declared inline
 #define MULTIPLY_ACCUM_WITH_SCALE(index)\
-        isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\
-        isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)];
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)];
 
 #define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\
         q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\
@@ -3973,10 +3977,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3));
         q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3));
 
-        isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0];
-        isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1];
-        isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2];
-        isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3];
+        isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0];
+        isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1];
+        isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2];
+        isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3];
 
         sum += d * (isum1 + isum2);
     }
@@ -4256,10 +4260,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
             q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
             q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
 
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0];
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1];
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2];
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3];
 
             scale += 4;
 
@@ -4273,10 +4277,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
             q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
             q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
 
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0];
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1];
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2];
-            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3];
 
             scale += 4;
 
@@ -4757,10 +4761,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
         q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2]));
         q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6),                q3h.val[3]));
 
-        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0];
-        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2];
-        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1];
-        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3];
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0];
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2];
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1];
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3];
 
         sum += d * isum;
 
@@ -5109,14 +5113,14 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
             q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
             q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
 
-            const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
+            const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
             sumi1 += vaddvq_s32(p1) * scales[2*j+0];
 
             q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
             q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
             q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
 
-            const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
+            const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
 
             sumi2 += vaddvq_s32(p2) * scales[2*j+1];
         }
@@ -5449,13 +5453,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
         q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
         q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
 
-        const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
+        const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
         const int32_t sumi1 = vaddvq_s32(p1) * scales[0];
 
         q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
         q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
 
-        const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]);
+        const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]);
         const int32_t sumi2 = vaddvq_s32(p2) * scales[1];
 
         sumf += d * (sumi1 + sumi2);
@@ -5722,8 +5726,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
             q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2]));
             q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3]));
 
-            sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++;
-            sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++;
+            sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++;
+            sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++;
         }
 
         sumf += d * sumi - dmin * sumi_mins;
@@ -6112,10 +6116,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2]));
         q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3]));
 
-        int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]));
-        int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1]));
-        int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]));
-        int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3]));
+        int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]));
+        int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1]));
+        int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]));
+        int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3]));
 
         sumf += d * (sumi1 + sumi2 + sumi3 + sumi4);
     }
@@ -6399,10 +6403,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
             q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2]));
             q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3]));
 
-            isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
-                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
-                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
-                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
+                    vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
+                    vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
+                    vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
 
             scale += 4;
 
@@ -6426,10 +6430,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
             q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2]));
             q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3]));
 
-            isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
-                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
-                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
-                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
+                    vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
+                    vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
+                    vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
             scale += 4;
         }
         //sum += isum * d_all * y[i].d;
@@ -6816,10 +6820,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
         q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s);
         q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s);
 
-        isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
-                vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
-                vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
-                vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
+        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
+                vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
+                vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
+                vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
 
         sum += isum * d_all * y[i].d;
 

From 1e3900ebacb3a0b385271389686403c97ad76d88 Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Fri, 29 Dec 2023 16:15:37 +0000
Subject: [PATCH 04/60] flake.nix: expose full scope in legacyPackages

---
 .devops/nix/jetson-support.nix | 19 +++++++++++++------
 flake.nix                      | 20 +++++++++++++++++---
 2 files changed, 30 insertions(+), 9 deletions(-)

diff --git a/.devops/nix/jetson-support.nix b/.devops/nix/jetson-support.nix
index 08426d2ab..78e2e40e0 100644
--- a/.devops/nix/jetson-support.nix
+++ b/.devops/nix/jetson-support.nix
@@ -8,12 +8,13 @@
       pkgsCuda,
       ...
     }:
-    lib.optionalAttrs (system == "aarch64-linux") {
-      packages =
+    {
+      legacyPackages =
         let
-          caps.jetson-xavier = "7.2";
-          caps.jetson-orin = "8.7";
-          caps.jetson-nano = "5.3";
+          caps.llamaPackagesXavier = "7.2";
+          caps.llamaPackagesOrin = "8.7";
+          caps.llamaPackagesTX2 = "6.2";
+          caps.llamaPackagesNano = "5.3";
 
           pkgsFor =
             cap:
@@ -27,6 +28,12 @@
               };
             };
         in
-        builtins.mapAttrs (name: cap: ((pkgsFor cap).callPackage ./scope.nix { }).llama-cpp) caps;
+        builtins.mapAttrs (name: cap: (pkgsFor cap).callPackage ./scope.nix { }) caps;
+
+      packages = lib.optionalAttrs (system == "aarch64-linux") {
+        jetson-xavier = config.legacyPackages.llamaPackagesXavier.llama-cpp;
+        jetson-orin = config.legacyPackages.llamaPackagesOrin.llama-cpp;
+        jetson-nano = config.legacyPackages.llamaPackagesNano.llama-cpp;
+      };
     };
 }
diff --git a/flake.nix b/flake.nix
index 2209070aa..6785b52f4 100644
--- a/flake.nix
+++ b/flake.nix
@@ -80,16 +80,30 @@
             ...
           }:
           {
+            # Unlike `.#packages`, legacyPackages may contain values of
+            # arbitrary types (including nested attrsets) and may even throw
+            # exceptions. This attribute isn't recursed into by `nix flake
+            # show` either.
+            #
+            # You can add arbitrary scripts to `.devops/nix/scope.nix` and
+            # access them as `nix build .#llamaPackages.${scriptName}` using
+            # the same path you would with an overlay.
+            legacyPackages = {
+              llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
+              llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
+              llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
+            };
+
             # We don't use the overlay here so as to avoid making too many instances of nixpkgs,
             # cf. https://zimbatm.com/notes/1000-instances-of-nixpkgs
             packages =
               {
-                default = (pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp;
+                default = config.legacyPackages.llamaPackages.llama-cpp;
               }
               // lib.optionalAttrs pkgs.stdenv.isLinux {
                 opencl = config.packages.default.override { useOpenCL = true; };
-                cuda = (pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp;
-                rocm = (pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp;
+                cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp;
+                rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp;
 
                 mpi-cpu = config.packages.default.override { useMpi = true; };
                 mpi-cuda = config.packages.default.override { useMpi = true; };

From a5c088d8c698299b973d2709153e5d95295606d9 Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Tue, 26 Dec 2023 23:34:40 +0000
Subject: [PATCH 05/60] flake.nix: rocm not yet supported on aarch64, so hide
 the output

---
 flake.nix | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/flake.nix b/flake.nix
index 6785b52f4..920a79906 100644
--- a/flake.nix
+++ b/flake.nix
@@ -74,6 +74,7 @@
           {
             config,
             lib,
+            system,
             pkgs,
             pkgsCuda,
             pkgsRocm,
@@ -103,10 +104,12 @@
               // lib.optionalAttrs pkgs.stdenv.isLinux {
                 opencl = config.packages.default.override { useOpenCL = true; };
                 cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp;
-                rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp;
 
                 mpi-cpu = config.packages.default.override { useMpi = true; };
                 mpi-cuda = config.packages.default.override { useMpi = true; };
+              }
+              // lib.optionalAttrs (system == "x86_64-linux") {
+                rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp;
               };
           };
       };

From 356ea17e0f92bfbbf28a4f69261bed48eff68d9c Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Fri, 29 Dec 2023 16:21:50 +0000
Subject: [PATCH 06/60] flake.nix: expose checks

---
 flake.nix | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/flake.nix b/flake.nix
index 920a79906..8d0f095d7 100644
--- a/flake.nix
+++ b/flake.nix
@@ -111,6 +111,11 @@
               // lib.optionalAttrs (system == "x86_64-linux") {
                 rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp;
               };
+
+            # Packages exposed in `.#checks` will be built by the CI and by
+            # `nix flake check`. Currently we expose all packages, but we could
+            # make more granular choices
+            checks = config.packages;
           };
       };
 }

From 7adedecbe39bd552bc14142f496246d55a43ac4e Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Tue, 26 Dec 2023 19:17:26 +0000
Subject: [PATCH 07/60] workflows: nix-ci: init; build flake outputs

---
 .github/workflows/build.yml  |  1 -
 .github/workflows/nix-ci.yml | 44 ++++++++++++++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/nix-ci.yml

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index a5090e398..0a28a1111 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -515,7 +515,6 @@ jobs:
       - name: Build Xcode project
         run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
 
-
 #  freeBSD-latest:
 #    runs-on: macos-12
 #    steps:
diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml
new file mode 100644
index 000000000..f82b2cb3d
--- /dev/null
+++ b/.github/workflows/nix-ci.yml
@@ -0,0 +1,44 @@
+name: Nix CI
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
+
+jobs:
+  nix-build:
+    if: ${{ vars.CACHIX_NAME != '' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest, macos-latest ]
+    runs-on: ${{ matrix.os }}
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: Set-up cachix to push the results to
+      uses: cachix/cachix-action@v13
+      with:
+        authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
+        name: ${{ vars.CACHIX_NAME }}
+    - name: Build
+      run: >
+          nix run github:Mic92/nix-fast-build
+          -- --skip-cached --no-nom
+          --flake
+          ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)"

From 1e9ae54cf24d27afe3900d1250634a2a33423db1 Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Sat, 30 Dec 2023 17:19:11 +0000
Subject: [PATCH 08/60] workflows: nix-ci: add a job for eval

---
 .github/workflows/nix-ci.yml | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml
index f82b2cb3d..845b93bfb 100644
--- a/.github/workflows/nix-ci.yml
+++ b/.github/workflows/nix-ci.yml
@@ -11,6 +11,33 @@ on:
     paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
 
 jobs:
+  nix-eval:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest, macos-latest ]
+    runs-on: ${{ matrix.os }}
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: List all flake outputs
+      run: nix flake show --all-systems
+    - name: Show all output paths
+      run: >
+          nix run github:nix-community/nix-eval-jobs
+          -- --gc-roots-dir gcroot
+          --flake
+          ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)"
   nix-build:
     if: ${{ vars.CACHIX_NAME != '' }}
     strategy:

From c5239944bab0ff71915df8f2dc7e42fc2c138ff6 Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Sat, 30 Dec 2023 16:38:36 +0000
Subject: [PATCH 09/60] workflows: weekly `nix flake update`

---
 .github/workflows/nix-flake-update.yml | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 .github/workflows/nix-flake-update.yml

diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml
new file mode 100644
index 000000000..fa9360841
--- /dev/null
+++ b/.github/workflows/nix-flake-update.yml
@@ -0,0 +1,22 @@
+name: update-flake-lock
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00
+
+jobs:
+  lockfile:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - name: Install Nix
+        uses: DeterminateSystems/nix-installer-action@main
+      - name: Update flake.lock
+        uses: DeterminateSystems/update-flake-lock@main
+        with:
+          pr-title: "nix: update flake.lock"
+          pr-labels: |
+            nix
+          pr-reviewers: philiptaron,SomeoneSerge
+          token: ${{ secrets.GITHUB_TOKEN }}

From 06f2a5d1909a1385b1a16dab4ade68377e121bdd Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Sat, 30 Dec 2023 17:36:08 +0000
Subject: [PATCH 10/60] workflows: nix-flakestry: drop tag filters

...and add a job for flakehub.com
---
 .github/workflows/nix-flakestry.yml     | 23 ----------------
 .github/workflows/nix-publish-flake.yml | 36 +++++++++++++++++++++++++
 2 files changed, 36 insertions(+), 23 deletions(-)
 delete mode 100644 .github/workflows/nix-flakestry.yml
 create mode 100644 .github/workflows/nix-publish-flake.yml

diff --git a/.github/workflows/nix-flakestry.yml b/.github/workflows/nix-flakestry.yml
deleted file mode 100644
index 3abfb3509..000000000
--- a/.github/workflows/nix-flakestry.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-# Make the flake discoverable on https://flakestry.dev
-name: "Publish a flake to flakestry"
-on:
-    push:
-        tags:
-        - "v?[0-9]+.[0-9]+.[0-9]+"
-        - "v?[0-9]+.[0-9]+"
-    workflow_dispatch:
-        inputs:
-            tag:
-                description: "The existing tag to publish"
-                type: "string"
-                required: true
-jobs:
-    publish-flake:
-        runs-on: ubuntu-latest
-        permissions:
-            id-token: "write"
-            contents: "read"
-        steps:
-            - uses: flakestry/flakestry-publish@main
-              with:
-                version: "${{ inputs.tag || github.ref_name }}"
diff --git a/.github/workflows/nix-publish-flake.yml b/.github/workflows/nix-publish-flake.yml
new file mode 100644
index 000000000..2c3c1ebda
--- /dev/null
+++ b/.github/workflows/nix-publish-flake.yml
@@ -0,0 +1,36 @@
+# Make the flake discoverable on https://flakestry.dev and https://flakehub.com/flakes
+name: "Publish a flake to flakestry & flakehub"
+on:
+    push:
+        tags:
+        - "*"
+    workflow_dispatch:
+        inputs:
+            tag:
+                description: "The existing tag to publish"
+                type: "string"
+                required: true
+jobs:
+    flakestry-publish:
+        runs-on: ubuntu-latest
+        permissions:
+            id-token: "write"
+            contents: "read"
+        steps:
+            - uses: flakestry/flakestry-publish@main
+              with:
+                version: "${{ inputs.tag || github.ref_name }}"
+    flakehub-publish:
+      runs-on: "ubuntu-latest"
+      permissions:
+        id-token: "write"
+        contents: "read"
+      steps:
+        - uses: "actions/checkout@v4"
+          with:
+            ref: "${{ (inputs.tag != null) && format('refs/tags/{0}', inputs.tag) || '' }}"
+        - uses: "DeterminateSystems/nix-installer-action@main"
+        - uses: "DeterminateSystems/flakehub-push@main"
+          with:
+            visibility: "public"
+            tag: "${{ inputs.tag }}"

From d8361747317c5cb2e00e7fb3b59ff4dce5a176a5 Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Sat, 30 Dec 2023 18:01:07 +0000
Subject: [PATCH 11/60] workflows: nix-ci: add a qemu job for jetsons

---
 .github/workflows/nix-ci.yml | 41 ++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)

diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml
index 845b93bfb..a38c6ead4 100644
--- a/.github/workflows/nix-ci.yml
+++ b/.github/workflows/nix-ci.yml
@@ -69,3 +69,44 @@ jobs:
           -- --skip-cached --no-nom
           --flake
           ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)"
+  nix-build-aarch64:
+    if: ${{ vars.CACHIX_NAME != '' }}
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install QEMU
+      # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654
+      run: |
+        sudo apt-get install -y qemu-user-static qemu-system-aarch64
+        sudo usermod -a -G kvm $USER
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-platforms = aarch64-linux
+          extra-system-features = nixos-test kvm
+          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: Set-up cachix to push the results to
+      uses: cachix/cachix-action@v13
+      with:
+        authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
+        name: ${{ vars.CACHIX_NAME }}
+    - name: Show all output paths
+      run: >
+          nix run github:nix-community/nix-eval-jobs
+          -- --gc-roots-dir gcroot
+          --flake
+          ".#packages.aarch64-linux"
+    - name: Build
+      run: >
+          nix run github:Mic92/nix-fast-build
+          -- --skip-cached --no-nom
+          --systems aarch64-linux
+          --flake
+          ".#checks.aarch64-linux"

From 198ed7ebfc89b8f2b35a8b1655d57bfb57530c1a Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Sat, 30 Dec 2023 18:25:25 +0000
Subject: [PATCH 12/60] flake.nix: suggest the binary caches

---
 flake.nix | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/flake.nix b/flake.nix
index 8d0f095d7..488ed6c59 100644
--- a/flake.nix
+++ b/flake.nix
@@ -6,6 +6,29 @@
     flake-parts.url = "github:hercules-ci/flake-parts";
   };
 
+  # Optional binary cache
+  nixConfig = {
+    extra-substituters = [
+      # Populated by the CI in ggerganov/llama.cpp
+      "https://llama-cpp.cachix.org"
+
+      # A development cache for nixpkgs imported with `config.cudaSupport = true`.
+      # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
+      # This lets one skip building e.g. the CUDA-enabled openmpi.
+      # TODO: Replace once nix-community obtains an official one.
+      "https://cuda-maintainers.cachix.org"
+    ];
+
+    # Verify these are the same keys as published on
+    # - https://app.cachix.org/cache/llama-cpp
+    # - https://app.cachix.org/cache/cuda-maintainers
+    extra-trusted-public-keys = [
+      "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
+      "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
+    ];
+  };
+
+
   # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl:
   #
   # ```bash

From edd1ab7bc34c10a780ee7f9a4499f7689cdad36d Mon Sep 17 00:00:00 2001
From: Someone Serge <sergei.kozlukov@aalto.fi>
Date: Sun, 31 Dec 2023 17:42:22 +0000
Subject: [PATCH 13/60] flake.lock: update

to a commit recently cached by nixpkgs-cuda-ci
---
 flake.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/flake.lock b/flake.lock
index 3fcd1f45d..15a0a1a8e 100644
--- a/flake.lock
+++ b/flake.lock
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1703559957,
-        "narHash": "sha256-x9PUuMEPGUOMB51zNxrDr2QoHbYWlCS2xhFedm9MC5Q=",
+        "lastModified": 1703637592,
+        "narHash": "sha256-8MXjxU0RfFfzl57Zy3OfXCITS0qWDNLzlBAdwxGZwfY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "75dd68c36f458c6593c5bbb48abfd3e59bfed380",
+        "rev": "cfc3698c31b1fb9cdcf10f36c9643460264d0ca8",
         "type": "github"
       },
       "original": {

From 58ba655af054715c0516ee270ad028ad9e74f357 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 2 Jan 2024 10:57:44 +0200
Subject: [PATCH 14/60] metal : enable shader debugging (cmake option) (#4705)

* ggml : disable fast-math for Metal (cmake build only)

ggml-ci

* metal : fix Metal API debug warnings

* cmake : add -fno-inline for Metal build (#4545)

* metal : fix API debug warnings

* metal : fix compile warnings

* metal : use uint64_t for strides

* cmake : rename option to LLAMA_METAL_SHADER_DEBUG

* metal : fix mat-vec Q8_0 kernel for BS > 1

* metal : normalize mat-vec kernel signatures

* cmake : respect LLAMA_QKK_64 option

* metal : fix mat-vec Q4_K kernel for QK_K == 64

ggml-ci
---
 CMakeLists.txt             |  34 ++-
 ci/run.sh                  |  14 +-
 ggml-metal.m               |  28 ++-
 ggml-metal.metal           | 475 +++++++++++++++++++++----------------
 tests/test-backend-ops.cpp |   8 +-
 5 files changed, 329 insertions(+), 230 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 545aab267..57ae4c2df 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -95,6 +95,7 @@ option(LLAMA_HIP_UMA                         "llama: use HIP unified memory arch
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
 option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
+option(LLAMA_METAL_SHADER_DEBUG              "llama: compile Metal with -fno-fast-math"         OFF)
 option(LLAMA_MPI                             "llama: use MPI"                                   OFF)
 option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
 
@@ -154,9 +155,9 @@ if (APPLE AND LLAMA_ACCELERATE)
 endif()
 
 if (LLAMA_METAL)
-    find_library(FOUNDATION_LIBRARY         Foundation              REQUIRED)
-    find_library(METAL_FRAMEWORK            Metal                   REQUIRED)
-    find_library(METALKIT_FRAMEWORK         MetalKit                REQUIRED)
+    find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
+    find_library(METAL_FRAMEWORK    Metal      REQUIRED)
+    find_library(METALKIT_FRAMEWORK MetalKit   REQUIRED)
 
     message(STATUS "Metal framework found")
     set(GGML_HEADERS_METAL ggml-metal.h)
@@ -173,6 +174,33 @@ if (LLAMA_METAL)
     # copy ggml-metal.metal to bin directory
     configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
 
+    if (LLAMA_METAL_SHADER_DEBUG)
+        # custom command to do the following:
+        #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o ggml.metallib
+        #
+        # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
+        #       disabling fast math is needed in order to pass tests/test-backend-ops
+        # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+        set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        if (LLAMA_QKK_64)
+            set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
+        endif()
+
+        add_custom_command(
+            OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
+            COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+            COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
+            DEPENDS ggml-metal.metal
+            COMMENT "Compiling Metal kernels"
+        )
+
+        add_custom_target(
+            ggml-metal ALL
+            DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
+        )
+    endif()
+
     set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
         ${FOUNDATION_LIBRARY}
         ${METAL_FRAMEWORK}
diff --git a/ci/run.sh b/ci/run.sh
index 2e3343831..47a254f4c 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -30,6 +30,12 @@ sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
+CMAKE_EXTRA=""
+
+if [ ! -z ${GG_BUILD_METAL} ]; then
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON"
+fi
+
 ## helpers
 
 # download a file if it does not exist or if it is outdated
@@ -81,8 +87,8 @@ function gg_run_ctest_debug {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Debug ..     ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                               ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                          ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
 
@@ -109,8 +115,8 @@ function gg_run_ctest_release {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release ..   ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                               ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     if [ -z ${GG_BUILD_LOW_PERF} ]; then
         (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log
diff --git a/ggml-metal.m b/ggml-metal.m
index 51a72ae33..cd9d00456 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -257,13 +257,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
         NSError * error = nil;
-        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
+        NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"];
         if (libPath != nil) {
+            // pre-compiled library found
             NSURL * libURL = [NSURL fileURLWithPath:libPath];
             GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
             ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
         } else {
-            GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
+            GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__);
 
             NSString * sourcePath;
             NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
@@ -291,6 +292,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
             options = [MTLCompileOptions new];
             options.preprocessorMacros = @{ @"QK_K" : @(64) };
 #endif
+            // try to disable fast-math
+            // NOTE: this seems to have no effect whatsoever
+            //       instead, in order to disable fast-math, we have to build ggml.metallib from the command line
+            //       using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+            //       and go through the "pre-compiled library found" path above
+            //[options setFastMathEnabled:false];
+
             ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error];
         }
 
@@ -1230,7 +1238,7 @@ void ggml_metal_graph_compute(
                                 // not sure how to avoid this
                                 // TODO: make a simpler cpy_bytes kernel
 
-                                const int nth = MIN(1024, ne00);
+                                const int nth = MIN((int) ctx->pipeline_cpy_f32_f32.maxTotalThreadsPerThreadgroup, ne00);
 
                                 [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32];
                                 [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
@@ -1285,7 +1293,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26];
                             [encoder setBytes:&offs length:sizeof(offs) atIndex:27];
 
-                            const int nth = MIN(1024, ne0);
+                            const int nth = MIN((int) ctx->pipeline_add.maxTotalThreadsPerThreadgroup, ne00);
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
@@ -1785,8 +1793,9 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&r3      length:sizeof(r3)   atIndex:17];
                                 [encoder setBytes:&idx     length:sizeof(idx)  atIndex:18];
                                 // TODO: how to make this an array? read Metal docs
-                                for (int j = 0; j < n_as; ++j) {
-                                    struct ggml_tensor * src_cur = dst->src[2 + j];
+                                for (int j = 0; j < 8; ++j) {
+                                    // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
+                                    struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
 
                                     size_t offs_src_cur = 0;
                                     id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
@@ -1909,8 +1918,9 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&r3   length:sizeof(r3)   atIndex:21];
                                 [encoder setBytes:&idx  length:sizeof(idx)  atIndex:22];
                                 // TODO: how to make this an array? read Metal docs
-                                for (int j = 0; j < n_as; ++j) {
-                                    struct ggml_tensor * src_cur = dst->src[2 + j];
+                                for (int j = 0; j < 8; ++j) {
+                                    // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
+                                    struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
 
                                     size_t offs_src_cur = 0;
                                     id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
@@ -2229,7 +2239,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
                             [encoder setBytes:&sf   length:sizeof(sf)   atIndex:18];
 
-                            const int nth = MIN(1024, ne0);
+                            const int nth = MIN((int) ctx->pipeline_upscale_f32.maxTotalThreadsPerThreadgroup, ne0);
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
diff --git a/ggml-metal.metal b/ggml-metal.metal
index d5b54e112..1d5b8f6f4 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -59,26 +59,26 @@ kernel void kernel_add(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         constant  int64_t & offs,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
@@ -109,26 +109,26 @@ kernel void kernel_mul(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
         uint3   ntg[[threads_per_threadgroup]]) {
@@ -158,26 +158,26 @@ kernel void kernel_div(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
         uint3   ntg[[threads_per_threadgroup]]) {
@@ -205,7 +205,7 @@ kernel void kernel_add_row(
         device const float4 * src0,
         device const float4 * src1,
         device       float4 * dst,
-        constant    int64_t & nb [[buffer(28)]],
+        constant   uint64_t & nb [[buffer(28)]],
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] + src1[tpig % nb];
 }
@@ -214,7 +214,7 @@ kernel void kernel_mul_row(
         device const float4 * src0,
         device const float4 * src1,
         device       float4 * dst,
-        constant    int64_t & nb  [[buffer(28)]],
+        constant   uint64_t & nb  [[buffer(28)]],
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] * src1[tpig % nb];
 }
@@ -223,7 +223,7 @@ kernel void kernel_div_row(
         device const float4 * src0,
         device const float4 * src1,
         device       float4 * dst,
-        constant    int64_t & nb  [[buffer(28)]],
+        constant   uint64_t & nb  [[buffer(28)]],
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] / src1[tpig % nb];
 }
@@ -307,26 +307,26 @@ kernel void kernel_sum_rows(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         uint3 tpig[[thread_position_in_grid]]) {
     int64_t i3 = tpig.z;
     int64_t i2 = tpig.y;
@@ -920,14 +920,21 @@ kernel void kernel_mul_mv_q4_0_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -939,14 +946,21 @@ kernel void kernel_mul_mv_q4_1_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -958,14 +972,21 @@ kernel void kernel_mul_mv_q5_0_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -977,14 +998,21 @@ kernel void kernel_mul_mv_q5_1_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -1071,12 +1099,19 @@ kernel void kernel_mul_mv_q8_0_f32(
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
         constant   int64_t & ne10,
+        constant   int64_t & ne11,
         constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -1182,8 +1217,8 @@ kernel void kernel_mul_mv_f32_f32(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
     kernel_mul_mv_f32_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
@@ -1209,8 +1244,8 @@ kernel void kernel_mul_mv_f16_f16(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
 
@@ -1346,8 +1381,8 @@ kernel void kernel_mul_mv_f16_f32_1row(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
     kernel_mul_mv_f16_f32_1row_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
@@ -1452,8 +1487,8 @@ kernel void kernel_mul_mv_f16_f32(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]]) {
     kernel_mul_mv_f16_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
@@ -1478,8 +1513,8 @@ kernel void kernel_mul_mv_f16_f32_l4(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]]) {
 
@@ -1543,7 +1578,8 @@ kernel void kernel_alibi_f32(
     const int64_t i3 = n / (ne2*ne1*ne0);
     const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
     const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+  //const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
     const int64_t k = i3*ne3 + i2;
 
     float m_k;
@@ -2410,22 +2446,6 @@ typedef struct {
 } block_q6_K;
 // 210 bytes / block
 
-static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
-    uchar4 r;
-    if (j < 4) {
-        r[0] = q[j+0] & 63;
-        r[2] = q[j+1] & 63;
-        r[1] = q[j+4] & 63;
-        r[3] = q[j+5] & 63;
-    } else {
-        r[0] = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        r[2] = (q[j+5] & 0xF) | ((q[j-3] >> 6) << 4);
-        r[1] = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
-        r[3] = (q[j+5] >>  4) | ((q[j+1] >> 6) << 4);
-    }
-    return r;
-}
-
 //====================================== dot products =========================
 
 void kernel_mul_mv_q2_K_f32_impl(
@@ -2584,14 +2604,21 @@ kernel void kernel_mul_mv_q2_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -2841,14 +2868,21 @@ kernel void kernel_mul_mv_q3_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -2984,8 +3018,8 @@ void kernel_mul_mv_q4_K_f32_impl(
         constant   uint    & r2,
         constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
-        uint tiisg[[thread_index_in_simdgroup]],
-        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
     const int ix = tiisg/4;  // 0...7
     const int it = tiisg%4;  // 0...3
@@ -2994,7 +3028,7 @@ void kernel_mul_mv_q4_K_f32_impl(
     const int r0 = tgpig.x;
     const int r1 = tgpig.y;
     const int im = tgpig.z;
-    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int first_row = r0 * N_DST;
     const int ib_row = first_row * nb;
 
     const uint i12 = im%ne12;
@@ -3060,7 +3094,7 @@ void kernel_mul_mv_q4_K_f32_impl(
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
-            dst[r1*ne0+ im*ne0*ne1 + first_row + row] = all_sum;
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
         }
     }
 }
@@ -3072,14 +3106,21 @@ kernel void kernel_mul_mv_q4_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3271,14 +3312,21 @@ kernel void kernel_mul_mv_q5_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3398,14 +3446,21 @@ kernel void kernel_mul_mv_q6_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3523,7 +3578,7 @@ void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg
     device const int8_t * qs = ((device const int8_t *)xb->qs);
     const half d = xb->d;
 
-    for (int i=0;i<16;i++) {
+    for (int i = 0; i < 16; i++) {
         reg[i/4][i%4] = (qs[i + 16*il] * d);
     }
 }
@@ -3792,12 +3847,12 @@ void kernel_mul_mm_impl(device const  uchar * src0,
                         device        float * dst,
                         constant    int64_t & ne00,
                         constant    int64_t & ne02,
-                        constant    int64_t & nb01,
-                        constant    int64_t & nb02,
+                        constant   uint64_t & nb01,
+                        constant   uint64_t & nb02,
                         constant    int64_t & ne12,
-                        constant    int64_t & nb10,
-                        constant    int64_t & nb11,
-                        constant    int64_t & nb12,
+                        constant   uint64_t & nb10,
+                        constant   uint64_t & nb11,
+                        constant   uint64_t & nb12,
                         constant    int64_t & ne0,
                         constant    int64_t & ne1,
                         constant       uint & r2,
@@ -3924,12 +3979,12 @@ kernel void kernel_mul_mm(device const  uchar * src0,
                           device        float * dst,
                           constant    int64_t & ne00,
                           constant    int64_t & ne02,
-                          constant    int64_t & nb01,
-                          constant    int64_t & nb02,
+                          constant   uint64_t & nb01,
+                          constant   uint64_t & nb02,
                           constant    int64_t & ne12,
-                          constant    int64_t & nb10,
-                          constant    int64_t & nb11,
-                          constant    int64_t & nb12,
+                          constant   uint64_t & nb10,
+                          constant   uint64_t & nb11,
+                          constant   uint64_t & nb12,
                           constant    int64_t & ne0,
                           constant    int64_t & ne1,
                           constant       uint & r2,
@@ -3965,19 +4020,19 @@ kernel void kernel_mul_mm_id(
         device const   uchar * ids,
         device const   uchar * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
-        constant     int64_t & nb01,
-        constant     int64_t & nb02,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
         constant     int64_t & ne12,
         constant     int64_t & ne13,
-        constant     int64_t & nb10,
-        constant     int64_t & nb11,
-        constant     int64_t & nb12,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4070,12 +4125,12 @@ typedef void (mat_mm_t)(
         device        float * dst,
         constant    int64_t & ne00,
         constant    int64_t & ne02,
-        constant    int64_t & nb01,
-        constant    int64_t & nb02,
+        constant   uint64_t & nb01,
+        constant   uint64_t & nb02,
         constant    int64_t & ne12,
-        constant    int64_t & nb10,
-        constant    int64_t & nb11,
-        constant    int64_t & nb12,
+        constant   uint64_t & nb10,
+        constant   uint64_t & nb11,
+        constant   uint64_t & nb12,
         constant    int64_t & ne0,
         constant    int64_t & ne1,
         constant       uint & r2,
@@ -4104,19 +4159,19 @@ typedef void (mat_mm_id_t)(
         device const   uchar * ids,
         device const   uchar * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
-        constant     int64_t & nb01,
-        constant     int64_t & nb02,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
         constant     int64_t & ne12,
         constant     int64_t & ne13,
-        constant     int64_t & nb10,
-        constant     int64_t & nb11,
-        constant     int64_t & nb12,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4153,7 +4208,7 @@ kernel void kernel_mul_mv_id_f32_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4169,7 +4224,7 @@ kernel void kernel_mul_mv_id_f32_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4222,7 +4277,7 @@ kernel void kernel_mul_mv_id_f16_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4238,7 +4293,7 @@ kernel void kernel_mul_mv_id_f16_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4291,7 +4346,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4307,7 +4362,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4354,7 +4409,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4370,7 +4425,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4417,7 +4472,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4433,7 +4488,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4480,7 +4535,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4496,7 +4551,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4543,7 +4598,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4559,7 +4614,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4606,7 +4661,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4622,7 +4677,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4669,7 +4724,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4685,7 +4740,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4732,7 +4787,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4748,7 +4803,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4795,7 +4850,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4811,7 +4866,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4858,7 +4913,7 @@ kernel void kernel_mul_mv_id_q6_K_f32(
         device const    char * ids,
         device const    char * src1,
         device         uchar * dst,
-        constant     int64_t & nbi1,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4874,7 +4929,7 @@ kernel void kernel_mul_mv_id_q6_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index b115299c0..eff063b2d 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -15,19 +15,18 @@
 #include <thread>
 #include <vector>
 
-
 static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
     size_t size = ggml_nelements(tensor);
     std::vector<float> data(size);
 
 #if 0
-    std::default_random_engine generator(rd());
+    static std::default_random_engine generator(1234);
     std::uniform_real_distribution<float> distribution(min, max);
 
     for (size_t i = 0; i < size; i++) {
         data[i] = distribution(generator);
     }
-#endif
+#else
     auto init_thread = [&](size_t start, size_t end) {
         std::random_device rd;
         std::default_random_engine generator(rd());
@@ -49,6 +48,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
     for (auto & t : threads) {
         t.join();
     }
+#endif
 
     if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
         ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
@@ -437,7 +437,7 @@ struct test_case {
             double err = nmse(f1.data(), f2.data(), f1.size());
             if (err > ud->max_err) {
                 printf("[%s] NMSE = %f ", ggml_op_desc(t1), err);
-                //for (int i = 0; i < f1.size(); i++) {
+                //for (int i = 0; i < (int) f1.size(); i++) {
                 //    printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
                 //}
                 //printf("\n");

From 775ac8712a7b42cfead2585f42cec0dfd56644ab Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 2 Jan 2024 10:16:55 +0100
Subject: [PATCH 15/60] finetune: fix typo in README.md (#4733)

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---
 examples/finetune/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/finetune/README.md b/examples/finetune/README.md
index a2a2c1281..a884706c5 100644
--- a/examples/finetune/README.md
+++ b/examples/finetune/README.md
@@ -61,7 +61,7 @@ For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' L
   --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
 ```
 
-The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.
+The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values.
 
 Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
 If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.

From 26f3071d714f0b27ad7f021a46a66a1085480258 Mon Sep 17 00:00:00 2001
From: "Nam D. Tran" <42194884+namtranase@users.noreply.github.com>
Date: Tue, 2 Jan 2024 16:23:38 +0700
Subject: [PATCH 16/60] py : re-enable mmap in convert hf (#4732)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* update: awq support llama-7b model

* update: change order

* update: benchmark results for llama2-7b

* update: mistral 7b v1 benchmark

* update: support 4 models

* fix: Readme

* update: ready for PR

* update: readme

* fix: readme

* update: change order import

* black

* format code

* update: work for bot mpt and awqmpt

* update: readme

* Rename to llm_build_ffn_mpt_awq

* Formatted other files

* Fixed params count

* fix: remove code

* update: more detail for mpt

* fix: readme

* fix: readme

* update: change folder architecture

* fix: common.cpp

* fix: readme

* fix: remove ggml_repeat

* update: cicd

* update: cicd

* uppdate: remove use_awq arg

* update: readme

* llama : adapt plamo to new ffn

ggml-ci

* fix: update torch version

---------

Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io>
Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 awq-py/requirements.txt | 2 +-
 convert-hf-to-gguf.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt
index 5fe604329..991896116 100644
--- a/awq-py/requirements.txt
+++ b/awq-py/requirements.txt
@@ -1,2 +1,2 @@
-torch>=2.0.0
+torch>=2.1.1
 transformers>=4.32.0
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 51724c0df..203eaf64b 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -59,7 +59,7 @@ class Model:
                 from safetensors import safe_open
                 ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
             else:
-                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", weights_only=True))
+                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
 
             with ctx as model_part:
                 for name in model_part.keys():

From 5d7002d4372ebf107cfaf46fcd90df27b204f330 Mon Sep 17 00:00:00 2001
From: minarchist <minarchist@users.noreply.github.com>
Date: Tue, 2 Jan 2024 04:38:15 -0600
Subject: [PATCH 17/60] server : add --override-kv parameter (#4710)

* Changes to server to allow metadata override

* documentation

* flake.nix: expose full scope in legacyPackages

* flake.nix: rocm not yet supported on aarch64, so hide the output

* flake.nix: expose checks

* workflows: nix-ci: init; build flake outputs

* workflows: nix-ci: add a job for eval

* workflows: weekly `nix flake update`

* workflows: nix-flakestry: drop tag filters

...and add a job for flakehub.com

* workflows: nix-ci: add a qemu job for jetsons

* flake.nix: suggest the binary caches

* flake.lock: update

to a commit recently cached by nixpkgs-cuda-ci

---------

Co-authored-by: John <john@jLap.lan>
Co-authored-by: Someone Serge <sergei.kozlukov@aalto.fi>
---
 examples/server/server.cpp | 51 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 52d9b9768..b77d3f079 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -2016,6 +2016,10 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA.\n");
     printf("  --log-disable         disables logging to a file.\n");
     printf("\n");
+    printf("  --override-kv KEY=TYPE:VALUE\n");
+    printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
+    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("\n");
 }
 
 static void server_params_parse(int argc, char **argv, server_params &sparams,
@@ -2379,6 +2383,49 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             log_set_target(stdout);
             LOG_INFO("logging to file is disabled.", {});
         }
+	else if (arg == "--override-kv") 
+	{
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            char * sep = strchr(argv[i], '=');
+            if (sep == nullptr || sep - argv[i] >= 128) {
+                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
+                invalid_param = true;
+                break;
+            }
+            struct llama_model_kv_override kvo;
+            std::strncpy(kvo.key, argv[i], sep - argv[i]);
+            kvo.key[sep - argv[i]] = 0;
+            sep++;
+            if (strncmp(sep, "int:", 4) == 0) {
+                sep += 4;
+                kvo.tag = LLAMA_KV_OVERRIDE_INT;
+                kvo.int_value = std::atol(sep);
+            } else if (strncmp(sep, "float:", 6) == 0) {
+                sep += 6;
+                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
+                kvo.float_value = std::atof(sep);
+            } else if (strncmp(sep, "bool:", 5) == 0) {
+                sep += 5;
+                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
+                if (std::strcmp(sep, "true") == 0) {
+                    kvo.bool_value = true;
+                } else if (std::strcmp(sep, "false") == 0) {
+                    kvo.bool_value = false;
+                } else {
+                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
+                    invalid_param = true;
+                    break;
+                }
+            } else {
+                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
+                invalid_param = true;
+                break;
+            }
+            params.kv_overrides.push_back(kvo);
+        }
         else
         {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@@ -2386,6 +2433,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             exit(1);
         }
     }
+    if (!params.kv_overrides.empty()) {
+        params.kv_overrides.emplace_back(llama_model_kv_override());
+        params.kv_overrides.back().key[0] = 0;
+    }
 
     if (invalid_param)
     {

From 32866c5edde402f42ff4233bb89dcfcede34fd22 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 2 Jan 2024 13:28:15 +0200
Subject: [PATCH 18/60] editorconfig : fix whitespace and indentation #4710

---
 examples/server/server.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index b77d3f079..e45ea809a 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -2383,8 +2383,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             log_set_target(stdout);
             LOG_INFO("logging to file is disabled.", {});
         }
-	else if (arg == "--override-kv") 
-	{
+        else if (arg == "--override-kv")
+        {
             if (++i >= argc) {
                 invalid_param = true;
                 break;

From 83e633c27efdf0eb0ba54249e784b0ea760b1007 Mon Sep 17 00:00:00 2001
From: postmasters <namnguyen@google.com>
Date: Tue, 2 Jan 2024 03:51:28 -0800
Subject: [PATCH 19/60] llama : differentiate the KV dims in the attention
 (#4657)

* Add n_key_dim and n_value_dim

Some models use values that are not derived from `n_embd`.
Also remove `n_embd_head` and `n_embd_gqa` because it is not clear
which "head" is referred to (key or value).

Fix issue #4648.

* Fix `llm_build_kqv` to use `n_value_gqa`

* Rebase

* Rename variables

* Fix llm_build_kqv to be more generic wrt n_embd_head_k

* Update default values for n_embd_head_k and n_embd_head_v

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Fix llm_load_tensors: the asserts were not backcompat

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 gguf-py/gguf/constants.py   |   2 +
 gguf-py/gguf/gguf_writer.py |   6 +
 llama.cpp                   | 271 +++++++++++++++++++++++++-----------
 3 files changed, 201 insertions(+), 78 deletions(-)

diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index ae62cc575..f0a1c51f8 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -46,6 +46,8 @@ class Keys:
         HEAD_COUNT_KV     = "{arch}.attention.head_count_kv"
         MAX_ALIBI_BIAS    = "{arch}.attention.max_alibi_bias"
         CLAMP_KQV         = "{arch}.attention.clamp_kqv"
+        KEY_LENGTH        = "{arch}.attention.key_length"
+        VALUE_LENGTH      = "{arch}.attention.value_length"
         LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
         LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
 
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 73e021607..d93aaa877 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -333,6 +333,12 @@ class GGUFWriter:
     def add_head_count_kv(self, count: int) -> None:
         self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
 
+    def add_key_length(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length)
+
+    def add_value_length(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length)
+
     def add_max_alibi_bias(self, bias: float) -> None:
         self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
 
diff --git a/llama.cpp b/llama.cpp
index a833d4c15..704464039 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -245,6 +245,8 @@ enum llm_kv {
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
     LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
     LLM_KV_ATTENTION_CLAMP_KQV,
+    LLM_KV_ATTENTION_KEY_LENGTH,
+    LLM_KV_ATTENTION_VALUE_LENGTH,
     LLM_KV_ATTENTION_LAYERNORM_EPS,
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
 
@@ -297,6 +299,8 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
     { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,      "%s.attention.max_alibi_bias"         },
     { LLM_KV_ATTENTION_CLAMP_KQV,           "%s.attention.clamp_kqv"              },
+    { LLM_KV_ATTENTION_KEY_LENGTH,          "%s.attention.key_length"             },
+    { LLM_KV_ATTENTION_VALUE_LENGTH,        "%s.attention.value_length"           },
     { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
 
@@ -1284,6 +1288,8 @@ struct llama_hparams {
     uint32_t n_head_kv;
     uint32_t n_layer;
     uint32_t n_rot;
+    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
+    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
     uint32_t n_ff;
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
@@ -1310,6 +1316,8 @@ struct llama_hparams {
         if (this->n_head_kv     != other.n_head_kv)     return true;
         if (this->n_layer       != other.n_layer)       return true;
         if (this->n_rot         != other.n_rot)         return true;
+        if (this->n_embd_head_k != other.n_embd_head_k) return true;
+        if (this->n_embd_head_v != other.n_embd_head_v) return true;
         if (this->n_ff          != other.n_ff)          return true;
         if (this->n_expert      != other.n_expert)      return true;
         if (this->n_expert_used != other.n_expert_used) return true;
@@ -1331,12 +1339,12 @@ struct llama_hparams {
         return n_head/n_head_kv;
     }
 
-    uint32_t n_embd_head() const {
-        return n_embd/n_head;
+    uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads
+        return n_embd_head_k * n_head_kv;
     }
 
-    uint32_t n_embd_gqa() const {
-        return n_embd/n_gqa();
+    uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
+        return n_embd_head_v * n_head_kv;
     }
 };
 
@@ -1645,8 +1653,9 @@ static bool llama_kv_cache_init(
                           uint32_t   n_ctx,
                                int   n_gpu_layers,
                               bool   offload) {
-    const uint32_t n_embd  = hparams.n_embd_gqa();
-    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+    const uint32_t n_layer      = hparams.n_layer;
 
     cache.has_shift = false;
 
@@ -1677,8 +1686,8 @@ static bool llama_kv_cache_init(
     const int i_gpu_start = (int) n_layer - n_gpu_layers;
 
     for (int i = 0; i < (int) n_layer; i++) {
-        ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd*n_ctx);
-        ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd*n_ctx);
+        ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx);
+        ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx);
         ggml_format_name(k, "cache_k_l%d", i);
         ggml_format_name(v, "cache_v_l%d", i);
         cache.k_l.push_back(k);
@@ -2672,6 +2681,12 @@ static void llm_load_hparams(
         // gpt-j n_rot = rotary_dim
     }
 
+    hparams.n_embd_head_k = hparams.n_embd / hparams.n_head;
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+
+    hparams.n_embd_head_v = hparams.n_embd / hparams.n_head;
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+
     // arch-specific KVs
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
@@ -3082,8 +3097,12 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
     LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
     LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
-    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
+    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
+    LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
+    LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
     LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
+    LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %u\n",     __func__, hparams.n_embd_k_gqa());
+    LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %u\n",     __func__, hparams.n_embd_v_gqa());
     LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
     LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
     LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
@@ -3173,10 +3192,11 @@ static bool llm_load_tensors(
 
     // create tensors for the weights
     {
-        const int64_t n_embd     = hparams.n_embd;
-        const int64_t n_embd_gqa = hparams.n_embd_gqa();
-        const int64_t n_layer    = hparams.n_layer;
-        const int64_t n_vocab    = hparams.n_vocab;
+        const int64_t n_embd       = hparams.n_embd;
+        const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+        const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const int64_t n_layer      = hparams.n_layer;
+        const int64_t n_vocab      = hparams.n_vocab;
 
         const auto tn = LLM_TN(model.arch);
         switch (model.arch) {
@@ -3202,7 +3222,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3270,7 +3293,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3318,7 +3344,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3368,7 +3397,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3420,7 +3452,11 @@ static bool llm_load_tensors(
                         model.output         = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
+
                     const int i_gpu_start = n_layer - n_gpu_layers;
                     model.layers.resize(n_layer);
                     for (uint32_t i = 0; i < n_layer; ++i) {
@@ -3469,7 +3505,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3520,7 +3559,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3567,7 +3609,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3665,7 +3710,10 @@ static bool llm_load_tensors(
                         model.output_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab},         backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3714,7 +3762,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3761,7 +3812,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -4000,8 +4054,8 @@ static struct ggml_tensor * llm_build_inp_embd(
     return inpL;
 }
 
-// Persimmon: n_rot = n_embd_head/2
-// Other:     n_rot = n_embd_head
+// Persimmon: n_rot = n_embd_head_k/2
+// Other:     n_rot = n_embd_head_k
 static void llm_build_k_shift(
       struct ggml_context * ctx,
       const llama_hparams & hparams,
@@ -4014,17 +4068,17 @@ static void llm_build_k_shift(
                   float     freq_base,
                   float     freq_scale,
        const llm_build_cb & cb) {
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
-    const float   ext_factor  = cparams.yarn_ext_factor;
-    const float   attn_factor = cparams.yarn_attn_factor;
-    const float   beta_fast   = cparams.yarn_beta_fast;
-    const float   beta_slow   = cparams.yarn_beta_slow;
+    const int64_t n_layer       = hparams.n_layer;
+    const int64_t n_head_kv     = hparams.n_head_kv;
+    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+    const int32_t n_orig_ctx    = cparams.n_yarn_orig_ctx;
+    const float   ext_factor    = cparams.yarn_ext_factor;
+    const float   attn_factor   = cparams.yarn_attn_factor;
+    const float   beta_fast     = cparams.yarn_beta_fast;
+    const float   beta_slow     = cparams.yarn_beta_slow;
 
-    GGML_ASSERT(n_embd_head % n_rot == 0);
+    GGML_ASSERT(n_embd_head_k % n_rot == 0);
 
     struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
     cb(K_shift, "K_shift", -1);
@@ -4042,9 +4096,9 @@ static void llm_build_k_shift(
             // we rotate only the first n_rot dimensions
             ggml_rope_custom_inplace(ctx,
                     ggml_view_3d(ctx, kv.k_l[il],
-                        n_embd_head, n_head_kv, n_ctx,
-                        ggml_row_size(kv.k_l[il]->type, n_embd_head),
-                        ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
+                        n_embd_head_k, n_head_kv, n_ctx,
+                        ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
+                        ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
                         0),
                     K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow);
@@ -4065,18 +4119,19 @@ static void llm_build_kv_store(
                     int32_t   kv_head,
          const llm_build_cb & cb,
                     int64_t   il) {
-    const int64_t n_embd_gqa = hparams.n_embd_gqa();
+    const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
     // compute the transposed [n_tokens, n_embd] V matrix
-    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens));
+    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens));
     //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
     cb(v_cur_t, "v_cur_t", il);
 
-    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_gqa,
-            (ggml_row_size(kv.k_l[il]->type, n_embd_gqa))*kv_head);
+    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
+            (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
     cb(k_cache_view, "k_cache_view", il);
 
-    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_gqa,
+    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
             (  n_ctx)*ggml_element_size(kv.v_l[il]),
             (kv_head)*ggml_element_size(kv.v_l[il]));
     cb(v_cache_view, "v_cache_view", il);
@@ -4226,20 +4281,20 @@ static struct ggml_tensor * llm_build_kqv(
                     float     kq_scale,
          const llm_build_cb & cb,
                     int       il) {
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+    const int64_t n_head        = hparams.n_head;
+    const int64_t n_head_kv     = hparams.n_head_kv;
+    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+    const int64_t n_embd_head_v = hparams.n_embd_head_v;
 
     struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
     cb(q, "q", il);
 
     struct ggml_tensor * k =
         ggml_view_3d(ctx, kv.k_l[il],
-                n_embd_head, n_kv, n_head_kv,
-                ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
-                ggml_row_size(kv.k_l[il]->type, n_embd_head),
+                n_embd_head_k, n_kv, n_head_kv,
+                ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
+                ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
                 0);
     cb(k, "k", il);
 
@@ -4278,9 +4333,9 @@ static struct ggml_tensor * llm_build_kqv(
     // split cached v into n_head heads
     struct ggml_tensor * v =
         ggml_view_3d(ctx, kv.v_l[il],
-                n_kv, n_embd_head, n_head_kv,
+                n_kv, n_embd_head_v, n_head_kv,
                 ggml_element_size(kv.v_l[il])*n_ctx,
-                ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head,
+                ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
                 0);
     cb(v, "v", il);
 
@@ -4290,7 +4345,7 @@ static struct ggml_tensor * llm_build_kqv(
     struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
     cb(kqv_merged, "kqv_merged", il);
 
-    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens);
+    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
     cb(cur, "kqv_merged_cont", il);
 
     cur = ggml_mul_mat(ctx, wo, cur);
@@ -4317,8 +4372,10 @@ struct llm_build_context {
     const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
     const int64_t n_head;
     const int64_t n_head_kv;
-    const int64_t n_embd_head;
-    const int64_t n_embd_gqa;
+    const int64_t n_embd_head_k;
+    const int64_t n_embd_k_gqa;
+    const int64_t n_embd_head_v;
+    const int64_t n_embd_v_gqa;
     const int64_t n_expert;
     const int64_t n_expert_used;
 
@@ -4360,8 +4417,10 @@ struct llm_build_context {
         n_ctx            (cparams.n_ctx),
         n_head           (hparams.n_head),
         n_head_kv        (hparams.n_head_kv),
-        n_embd_head      (hparams.n_embd_head()),
-        n_embd_gqa       (hparams.n_embd_gqa()),
+        n_embd_head_k    (hparams.n_embd_head_k),
+        n_embd_k_gqa     (hparams.n_embd_k_gqa()),
+        n_embd_head_v    (hparams.n_embd_head_v),
+        n_embd_v_gqa     (hparams.n_embd_v_gqa()),
         n_expert         (hparams.n_expert),
         n_expert_used    (hparams.n_expert_used),
         freq_base        (cparams.rope_freq_base),
@@ -4404,6 +4463,8 @@ struct llm_build_context {
     struct ggml_cgraph * build_llama() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
         GGML_ASSERT(n_embd_head == hparams.n_rot);
 
         struct ggml_tensor * cur;
@@ -4588,6 +4649,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_baichuan() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -4705,6 +4769,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_falcon() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -4824,6 +4893,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_starcoder() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
@@ -4920,7 +4994,12 @@ struct llm_build_context {
     struct ggml_cgraph * build_persimmon() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
-        const int64_t n_rot = n_embd_head / 2;
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
+        const int64_t n_rot = n_embd_head_k / 2;
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -5129,6 +5208,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_refact() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5217,6 +5301,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_bloom() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5308,6 +5397,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_mpt() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5403,6 +5497,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_stablelm() {
         struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5513,6 +5610,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_qwen() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5624,6 +5724,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_phi2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * attn_norm_output;
         struct ggml_tensor * ffn_output;
@@ -5736,6 +5841,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_plamo() {
         struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5840,6 +5948,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_gpt2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
@@ -9627,8 +9740,8 @@ struct llama_context * llama_new_context_with_model(
     const ggml_type type_k = params.type_k;
     const ggml_type type_v = params.type_v;
 
-    GGML_ASSERT(hparams.n_embd_head() % ggml_blck_size(type_k) == 0);
-    GGML_ASSERT(hparams.n_embd_head() % ggml_blck_size(type_v) == 0);
+    GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
+    GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
 
     // reserve memory for context buffers
     if (!hparams.vocab_only) {
@@ -10172,9 +10285,10 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         const auto & hparams = ctx->model.hparams;
         const auto & cparams = ctx->cparams;
 
-        const auto   n_layer = hparams.n_layer;
-        const auto   n_embd  = hparams.n_embd_gqa();
-        const auto   n_ctx   = cparams.n_ctx;
+        const auto   n_layer      = hparams.n_layer;
+        const auto   n_embd_k_gqa = hparams.n_embd_k_gqa();
+        const auto   n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const auto   n_ctx        = cparams.n_ctx;
 
         const size_t   kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf);
         const uint32_t kv_head     = kv_self.head;
@@ -10196,15 +10310,15 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
             std::vector<struct ggml_tensor *> vout2d(n_layer);
 
             for (int il = 0; il < (int) n_layer; ++il) {
-                kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head);
-                vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd);
+                kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
+                vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
 
                 ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
-                        n_embd, kv_head,
-                        elt_size*n_embd, 0);
+                        n_embd_k_gqa, kv_head,
+                        elt_size*n_embd_k_gqa, 0);
 
                 ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
-                        kv_head, n_embd,
+                        kv_head, n_embd_v_gqa,
                         elt_size*n_ctx, 0);
 
                 ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il]));
@@ -10311,9 +10425,10 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
         const auto & hparams = ctx->model.hparams;
         const auto & cparams = ctx->cparams;
 
-        const int    n_layer = hparams.n_layer;
-        const int    n_embd  = hparams.n_embd_gqa();
-        const int    n_ctx   = cparams.n_ctx;
+        const int    n_layer      = hparams.n_layer;
+        const int    n_embd_k_gqa = hparams.n_embd_k_gqa();
+        const int    n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const int    n_ctx        = cparams.n_ctx;
 
         size_t   kv_buf_size;
         uint32_t kv_head;
@@ -10337,15 +10452,15 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
             std::vector<struct ggml_tensor *> vin2d(n_layer);
 
             for (int il = 0; il < n_layer; ++il) {
-                kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head);
-                vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd);
+                kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
+                vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
 
                 ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
-                    n_embd, kv_head,
-                    elt_size*n_embd, 0);
+                    n_embd_k_gqa, kv_head,
+                    elt_size*n_embd_k_gqa, 0);
 
                 ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
-                    kv_head, n_embd,
+                    kv_head, n_embd_v_gqa,
                     elt_size*n_ctx, 0);
 
                 ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d));

From 0040d42eeb237197054cc7790df5776eacfa608e Mon Sep 17 00:00:00 2001
From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
Date: Tue, 2 Jan 2024 06:15:16 -0800
Subject: [PATCH 20/60] llama : replace all API facing `int`'s with `int32_t`
 (#4577)

* replaced all API facing `int`'s with `int32_t`

* formatting and missed `int` in `llama_token_to_piece`
---
 llama.cpp | 50 +++++++++++++++++++++----------------------
 llama.h   | 63 +++++++++++++++++++++++++++----------------------------
 2 files changed, 56 insertions(+), 57 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 704464039..2e34cb395 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -8030,7 +8030,7 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c
     }
 }
 
-void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
+void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
     const int64_t t_start_sample_us = ggml_time_us();
 
     k = std::max(k, (int) min_keep);
@@ -8390,7 +8390,7 @@ void llama_sample_classifier_free_guidance(
     }
 }
 
-llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
+llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
     GGML_ASSERT(ctx);
 
     auto N = float(llama_n_vocab(llama_get_model(ctx)));
@@ -9598,7 +9598,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
     return result;
 }
 
-int llama_max_devices(void) {
+int32_t llama_max_devices(void) {
     return LLAMA_MAX_DEVICES;
 }
 
@@ -9909,15 +9909,15 @@ enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
     return model->vocab.type;
 }
 
-int llama_n_vocab(const struct llama_model * model) {
+int32_t llama_n_vocab(const struct llama_model * model) {
     return model->vocab.id_to_token.size();
 }
 
-int llama_n_ctx_train(const struct llama_model * model) {
+int32_t llama_n_ctx_train(const struct llama_model * model) {
     return model->hparams.n_ctx_train;
 }
 
-int llama_n_embd(const struct llama_model * model) {
+int32_t llama_n_embd(const struct llama_model * model) {
     return model->hparams.n_embd;
 }
 
@@ -9925,7 +9925,7 @@ float llama_rope_freq_scale_train(const struct llama_model * model) {
     return model->hparams.rope_freq_scale_train;
 }
 
-int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
+int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
     const auto & it = model->gguf_kv.find(key);
     if (it == model->gguf_kv.end()) {
         if (buf_size > 0) {
@@ -9936,11 +9936,11 @@ int llama_model_meta_val_str(const struct llama_model * model, const char * key,
     return snprintf(buf, buf_size, "%s", it->second.c_str());
 }
 
-int llama_model_meta_count(const struct llama_model * model) {
+int32_t llama_model_meta_count(const struct llama_model * model) {
     return (int)model->gguf_kv.size();
 }
 
-int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
+int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
     if (i < 0 || i >= (int)model->gguf_kv.size()) {
         if (buf_size > 0) {
             buf[0] = '\0';
@@ -9952,7 +9952,7 @@ int llama_model_meta_key_by_index(const struct llama_model * model, int i, char
     return snprintf(buf, buf_size, "%s", it->first.c_str());
 }
 
-int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
+int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
     if (i < 0 || i >= (int)model->gguf_kv.size()) {
         if (buf_size > 0) {
             buf[0] = '\0';
@@ -9964,7 +9964,7 @@ int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, c
     return snprintf(buf, buf_size, "%s", it->second.c_str());
 }
 
-int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
+int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
     return snprintf(buf, buf_size, "%s %s %s",
             llama_model_arch_name(model->arch).c_str(),
             llama_model_type_name(model->type),
@@ -9991,7 +9991,7 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch
     return ggml_get_tensor(model->ctx, name);
 }
 
-int llama_model_quantize(
+uint32_t llama_model_quantize(
         const char * fname_inp,
         const char * fname_out,
         const llama_model_quantize_params * params) {
@@ -10004,7 +10004,7 @@ int llama_model_quantize(
     }
 }
 
-int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
+int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
     try {
         return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
     } catch (const std::exception & err) {
@@ -10013,7 +10013,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
     }
 }
 
-int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
+int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
     try {
         return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
     } catch (const std::exception & err) {
@@ -10111,7 +10111,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
     }
 }
 
-int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
+int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
     int result = 0;
 
     for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
@@ -10121,7 +10121,7 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
     return result;
 }
 
-int llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
+int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
     return ctx->kv_self.used;
 }
 
@@ -10603,7 +10603,7 @@ int llama_eval(
         struct llama_context * ctx,
                  llama_token * tokens,
                      int32_t   n_tokens,
-                         int   n_past) {
+                     int32_t   n_past) {
     llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
 
     const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
@@ -10618,7 +10618,7 @@ int llama_eval_embd(
             struct llama_context * ctx,
                            float * embd,
                          int32_t   n_tokens,
-                             int   n_past) {
+                         int32_t   n_past) {
     llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
 
     llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
@@ -10689,7 +10689,7 @@ void llama_batch_free(struct llama_batch batch) {
     if (batch.logits)   free(batch.logits);
 }
 
-int llama_decode(
+int32_t llama_decode(
         struct llama_context * ctx,
           struct llama_batch   batch) {
     const int ret = llama_decode_internal(*ctx, batch);
@@ -10737,11 +10737,11 @@ llama_token llama_token_nl(const struct llama_model * model) {
     return model->vocab.linefeed_id;
 }
 
-int llama_add_bos_token(const struct llama_model * model) {
+int32_t llama_add_bos_token(const struct llama_model * model) {
     return model->vocab.special_add_bos;
 }
 
-int llama_add_eos_token(const struct llama_model * model) {
+int32_t llama_add_eos_token(const struct llama_model * model) {
     return model->vocab.special_add_eos;
 }
 
@@ -10761,12 +10761,12 @@ llama_token llama_token_eot(const struct llama_model * model) {
     return model->vocab.special_eot_id;
 }
 
-int llama_tokenize(
+int32_t llama_tokenize(
     const struct llama_model * model,
                   const char * text,
-                         int   text_len,
+                     int32_t   text_len,
                  llama_token * tokens,
-                         int   n_max_tokens,
+                     int32_t   n_max_tokens,
                         bool   add_bos,
                         bool   special) {
     auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
@@ -10794,7 +10794,7 @@ static std::string llama_decode_text(const std::string & text) {
 }
 
 // does not write null-terminator to buf
-int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) {
+int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
         case LLAMA_VOCAB_TYPE_SPM: {
diff --git a/llama.h b/llama.h
index af76bae2d..461d4604a 100644
--- a/llama.h
+++ b/llama.h
@@ -226,7 +226,7 @@ extern "C" {
 
     // model quantization parameters
     typedef struct llama_model_quantize_params {
-        int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        int32_t nthread;             // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
         enum llama_ftype ftype;      // quantize to this llama_ftype
         bool allow_requantize;       // allow quantizing non-f32/f16 tensors
         bool quantize_output_tensor; // quantize output.weight
@@ -310,21 +310,20 @@ extern "C" {
 
     LLAMA_API int64_t llama_time_us(void);
 
-    LLAMA_API int  llama_max_devices    (void);
+    LLAMA_API int32_t  llama_max_devices(void);
     LLAMA_API bool llama_mmap_supported (void);
     LLAMA_API bool llama_mlock_supported(void);
 
     LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
 
-    // TODO: become more consistent with returned int types across the API
     LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
 
     LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
 
-    LLAMA_API int llama_n_vocab    (const struct llama_model * model);
-    LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
-    LLAMA_API int llama_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
+    LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@@ -335,19 +334,19 @@ extern "C" {
     // - GGUF array values are not supported by these functions
 
     // Get metadata value as a string by key name
-    LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
+    LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
 
     // Get the number of metadata key/value pairs
-    LLAMA_API int llama_model_meta_count(const struct llama_model * model);
+    LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
 
     // Get metadata key name by index
-    LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
+    LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
 
     // Get metadata value as a string by index
-    LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
+    LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
 
     // Get a string describing the model type
-    LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
+    LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
 
     // Returns the total size of all the tensors in the model in bytes
     LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
@@ -359,7 +358,7 @@ extern "C" {
     LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
 
     // Returns 0 on success
-    LLAMA_API int llama_model_quantize(
+    LLAMA_API uint32_t llama_model_quantize(
             const char * fname_inp,
             const char * fname_out,
             const llama_model_quantize_params * params);
@@ -370,20 +369,20 @@ extern "C" {
     // The model needs to be reloaded before applying a new adapter, otherwise the adapter
     // will be applied on top of the previous one
     // Returns 0 on success
-    LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
+    LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
             struct llama_context * ctx,
                       const char * path_lora,
                            float   scale,
                       const char * path_base_model,
-                             int   n_threads),
+                         int32_t   n_threads),
             "use llama_model_apply_lora_from_file instead");
 
-    LLAMA_API int llama_model_apply_lora_from_file(
+    LLAMA_API int32_t llama_model_apply_lora_from_file(
             const struct llama_model * model,
                       const char * path_lora,
                            float   scale,
                       const char * path_base_model,
-                             int   n_threads);
+                         int32_t   n_threads);
 
     //
     // KV cache
@@ -439,10 +438,10 @@ extern "C" {
 
     // Returns the number of tokens in the KV cache (slow, use only for debug)
     // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
-    LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
+    LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
 
     // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
-    LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx);
+    LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
 
     // Clear the KV cache
     LLAMA_API void llama_kv_cache_clear(
@@ -533,7 +532,7 @@ extern "C" {
             struct llama_context * ctx,
                      llama_token * tokens,
                          int32_t   n_tokens,
-                             int   n_past),
+                         int32_t   n_past),
             "use llama_decode() instead");
 
     // Same as llama_eval, but use float matrix input directly.
@@ -542,7 +541,7 @@ extern "C" {
             struct llama_context * ctx,
                            float * embd,
                          int32_t   n_tokens,
-                             int   n_past),
+                         int32_t   n_past),
             "use llama_decode() instead");
 
     // Return batch for single sequence of tokens starting at pos_0
@@ -574,7 +573,7 @@ extern "C" {
     //   0 - success
     //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
     // < 0 - error
-    LLAMA_API int llama_decode(
+    LLAMA_API int32_t llama_decode(
             struct llama_context * ctx,
               struct llama_batch   batch);
 
@@ -614,10 +613,10 @@ extern "C" {
     LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
 
     // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int         llama_add_bos_token(const struct llama_model * model);
+    LLAMA_API int32_t         llama_add_bos_token(const struct llama_model * model);
 
     // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int         llama_add_eos_token(const struct llama_model * model);
+    LLAMA_API int32_t         llama_add_eos_token(const struct llama_model * model);
 
     // codellama infill tokens
     LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
@@ -635,12 +634,12 @@ extern "C" {
     /// @return Returns a negative number on failure - the number of tokens that would have been returned
     /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
     ///                Does not insert a leading space.
-    LLAMA_API int llama_tokenize(
+    LLAMA_API int32_t llama_tokenize(
         const struct llama_model * model,
                       const char * text,
-                             int   text_len,
+                         int32_t   text_len,
                      llama_token * tokens,
-                             int   n_max_tokens,
+                         int32_t   n_max_tokens,
                             bool   add_bos,
                             bool   special);
 
@@ -648,11 +647,11 @@ extern "C" {
     // Uses the vocabulary in the provided context.
     // Does not write null terminator to the buffer.
     // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
-    LLAMA_API int llama_token_to_piece(
+    LLAMA_API int32_t llama_token_to_piece(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
-                                  int    length);
+                               int32_t   length);
 
     //
     // Grammar
@@ -704,7 +703,7 @@ extern "C" {
     LLAMA_API void llama_sample_top_k(
             struct llama_context * ctx,
           llama_token_data_array * candidates,
-                             int   k,
+                         int32_t   k,
                           size_t   min_keep);
 
     /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
@@ -763,7 +762,7 @@ extern "C" {
           llama_token_data_array * candidates,
                            float   tau,
                            float   eta,
-                             int   m,
+                         int32_t   m,
                            float * mu);
 
     /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
@@ -836,8 +835,8 @@ extern "C" {
         llama_beam_search_callback_fn_t   callback,
                                    void * callback_data,
                                  size_t   n_beams,
-                                    int   n_past,
-                                    int   n_predict);
+                                int32_t   n_past,
+                                int32_t   n_predict);
 
     // Performance information
     LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);

From 540938f8904707dd74cb3be18495f853b312e72f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 2 Jan 2024 16:26:45 +0200
Subject: [PATCH 21/60] llama : llama_model_desc print number of experts

---
 llama.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/llama.cpp b/llama.cpp
index 2e34cb395..3bb056dba 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -9965,8 +9965,9 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
 }
 
 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
-    return snprintf(buf, buf_size, "%s %s %s",
+    return snprintf(buf, buf_size, "%s %s%s %s",
             llama_model_arch_name(model->arch).c_str(),
+            model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "",
             llama_model_type_name(model->type),
             llama_model_ftype_name(model->ftype).c_str());
 }

From 0ef3ca2ac62016c0c545de1c89dc2e3e130f4a99 Mon Sep 17 00:00:00 2001
From: Phil H <5756783+phiharri@users.noreply.github.com>
Date: Tue, 2 Jan 2024 15:48:49 +0000
Subject: [PATCH 22/60] server : add token counts to html footer (#4738)

* server: add token counts to stats

* server: generate hpp

---------

Co-authored-by: phiharri <ph@got-root.co.uk>
---
 examples/server/completion.js.hpp |  693 ++---
 examples/server/index.html.hpp    | 4591 +++++++++++++++--------------
 examples/server/index.js.hpp      | 3693 +++++++++++------------
 examples/server/public/index.html |    4 +-
 4 files changed, 4529 insertions(+), 4452 deletions(-)

diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
index f0a071a69..fe5f81228 100644
--- a/examples/server/completion.js.hpp
+++ b/examples/server/completion.js.hpp
@@ -74,355 +74,376 @@ unsigned char completion_js[] = {
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 };
-unsigned int completion_js_len = 5099;
+unsigned int completion_js_len = 5346;
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index f22b77e7f..20551520e 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -383,2380 +383,2409 @@ unsigned char index_html[] = {
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 };
-unsigned int index_html_len = 33103;
+unsigned int index_html_len = 33456;
diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp
index c9dc078b7..e09b3c8c5 100644
--- a/examples/server/index.js.hpp
+++ b/examples/server/index.js.hpp
@@ -2,1875 +2,1902 @@ unsigned char index_js[] = {
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+  0x22, 0x22, 0x3a, 0x69, 0x2b, 0x3d, 0x6e, 0x3a, 0x27, 0x22, 0x27, 0x3d,
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+  0x3f, 0x6f, 0x3d, 0x6e, 0x3a, 0x22, 0x3e, 0x22, 0x3d, 0x3d, 0x3d, 0x6e,
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+  0x5f, 0x3c, 0x35, 0x7c, 0x7c, 0x22, 0x3e, 0x22, 0x3d, 0x3d, 0x3d, 0x74,
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+  0x74, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x5c, 0x6e, 0x22,
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+  0x73, 0x20, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x43, 0x6f, 0x6e, 0x74,
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+  0x61, 0x74, 0x65, 0x45, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x52,
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+  0x61, 0x73, 0x20, 0x69, 0x73, 0x56, 0x61, 0x6c, 0x69, 0x64, 0x45, 0x6c,
+  0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x43, 0x20, 0x61, 0x73, 0x20, 0x6f,
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+  0x20, 0x74, 0x6f, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x41, 0x72, 0x72, 0x61,
+  0x79, 0x2c, 0x75, 0x20, 0x61, 0x73, 0x20, 0x75, 0x6e, 0x74, 0x72, 0x61,
+  0x63, 0x6b, 0x65, 0x64, 0x2c, 0x54, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x43, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x2c, 0x59,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x43, 0x6f, 0x6d, 0x70,
+  0x75, 0x74, 0x65, 0x64, 0x2c, 0x56, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x2c, 0x41, 0x74,
+  0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x44, 0x65, 0x62, 0x75, 0x67,
+  0x56, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x48, 0x74, 0x20, 0x61, 0x73, 0x20,
+  0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x46, 0x74,
+  0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x45, 0x72, 0x72, 0x6f, 0x72,
+  0x42, 0x6f, 0x75, 0x6e, 0x64, 0x61, 0x72, 0x79, 0x2c, 0x4d, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x49, 0x64, 0x2c, 0x24, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x49, 0x6d, 0x70, 0x65, 0x72, 0x61,
+  0x74, 0x69, 0x76, 0x65, 0x48, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x2c, 0x50,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x4c, 0x61, 0x79, 0x6f,
+  0x75, 0x74, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x44, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x4d, 0x65, 0x6d, 0x6f, 0x2c, 0x55,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x64, 0x75,
+  0x63, 0x65, 0x72, 0x2c, 0x4e, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73,
+  0x65, 0x52, 0x65, 0x66, 0x2c, 0x58, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x5a, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c,
+  0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x45, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
 };
-unsigned int index_js_len = 22472;
+unsigned int index_js_len = 22800;
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 07d779d20..b059c75f2 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -427,7 +427,7 @@
         }
 
         if (data.timings) {
-          llamaStats.value = data.timings;
+          llamaStats.value = data;
         }
       }
 
@@ -880,7 +880,7 @@
       }
       return html`
         <span>
-          ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
+          ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
         </span>
       `
     }

From f3f62f0d835d559e80714bbeb05d03125574e3dd Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 2 Jan 2024 21:07:47 +0200
Subject: [PATCH 23/60] metal : optimize ggml_mul_mat_id (faster Mixtral PP)
 (#4725)

* ggml : disable fast-math for Metal (cmake build only)

ggml-ci

* metal : fix Metal API debug warnings

* cmake : add -fno-inline for Metal build (#4545)

* metal : fix API debug warnings

* metal : fix compile warnings

* metal : use uint64_t for strides

* cmake : rename option to LLAMA_METAL_SHADER_DEBUG

* metal : fix mat-vec Q8_0 kernel for BS > 1

* metal : normalize mat-vec kernel signatures

* cmake : respect LLAMA_QKK_64 option

* metal : fix mat-vec Q4_K kernel for QK_K == 64

* metal : optimizing ggml_mul_mat_id (wip)

* metal : minor fix

* metal : opt mul_mm_id
---
 ggml-metal.m     |  31 ++++---
 ggml-metal.metal | 205 +++++++++++++++++++++++++++++++++++++++--------
 2 files changed, 190 insertions(+), 46 deletions(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index cd9d00456..7a369b55e 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1657,6 +1657,10 @@ void ggml_metal_graph_compute(
                                         }
                                 };
 
+                                if (ggml_is_quantized(src0t)) {
+                                    GGML_ASSERT(ne00 >= nth0*nth1);
+                                }
+
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
@@ -1715,6 +1719,9 @@ void ggml_metal_graph_compute(
                             // TODO: make this more general
                             GGML_ASSERT(n_as <= 8);
 
+                            // max size of the src1ids array in the kernel stack
+                            GGML_ASSERT(ne11 <= 512);
+
                             struct ggml_tensor * src2 = gf->nodes[i]->src[2];
 
                             const int64_t  ne20 = src2 ? src2->ne[0] : 0;
@@ -1732,9 +1739,6 @@ void ggml_metal_graph_compute(
                             GGML_ASSERT(!ggml_is_transposed(src2));
                             GGML_ASSERT(!ggml_is_transposed(src1));
 
-                            GGML_ASSERT(ne20 % 32 == 0);
-                            // !!!!!!!!! TODO: this assert is probably required but not sure!
-                            //GGML_ASSERT(ne20 >= 64);
                             GGML_ASSERT(src1t == GGML_TYPE_F32);
 
                             const uint r2 = ne12/ne22;
@@ -1742,22 +1746,22 @@ void ggml_metal_graph_compute(
 
                             // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                             // to the matrix-vector kernel
-                            int ne11_mm_min = 1;
+                            int ne11_mm_min = n_as;
 
                             const int idx = ((int32_t *) dst->op_params)[0];
 
                             // batch size
                             GGML_ASSERT(ne01 == ne11);
 
-                            const int64_t _ne1 = 1; // kernel_mul_mm_impl needs a reference in constant memory
-
                             // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                             // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
                             // !!!
                             // TODO: for now, always use mat-vec kernels until we figure out how to improve the
                             //       indirect matrix multiplication
                             // !!!
-                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && _ne1 > ne11_mm_min) {
+                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
+                                ne20 % 32 == 0 && ne20 >= 64 &&
+                                ne11 > ne11_mm_min) {
                                 switch (src2->type) {
                                     case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32];  break;
                                     case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32];  break;
@@ -1787,7 +1791,7 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:11];
                                 [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:12];
                                 [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:13];
-                                [encoder setBytes:&_ne1    length:sizeof(_ne1) atIndex:14];
+                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:14];
                                 [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:15];
                                 [encoder setBytes:&r2      length:sizeof(r2)   atIndex:16];
                                 [encoder setBytes:&r3      length:sizeof(r3)   atIndex:17];
@@ -1805,8 +1809,7 @@ void ggml_metal_graph_compute(
 
                                 [encoder setThreadgroupMemoryLength:8192 atIndex:0];
 
-                                // TODO: processing one row at a time (ne11 -> 1) is not efficient
-                                [encoder dispatchThreadgroups:MTLSizeMake( (_ne1 + 31)/32, (ne21 + 63)/64, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                             } else {
                                 int nth0 = 32;
                                 int nth1 = 1;
@@ -1889,11 +1892,17 @@ void ggml_metal_graph_compute(
                                         } break;
                                     default:
                                         {
-                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
+                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
                                             GGML_ASSERT(false && "not implemented");
                                         }
                                 };
 
+                                if (ggml_is_quantized(src2t)) {
+                                    GGML_ASSERT(ne20 >= nth0*nth1);
+                                }
+
+                                const int64_t _ne1 = 1; // kernels needs a reference in constant memory
+
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 1d5b8f6f4..9aa7b502a 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -846,7 +846,7 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre
 #define N_SIMDGROUP 2  // number of SIMD groups in a thread group
 //Note: This is a template, but strictly speaking it only applies to
 //      quantizations where the block size is 32. It also does not
-//      giard against the number of rows not being divisible by
+//      guard against the number of rows not being divisible by
 //      N_DST, so this is another explicit assumption of the implementation.
 template<typename block_q_type, int nr, int nsg, int nw>
 void mul_vec_q_n_f32_impl(
@@ -3973,6 +3973,131 @@ void kernel_mul_mm_impl(device const  uchar * src0,
     }
 }
 
+// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in src1ids
+template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
+void kernel_mul_mm_id_impl(
+        device const  uchar * src0,
+        device const  uchar * src1,
+        thread        short * src1ids,
+        device        float * dst,
+        constant    int64_t & ne00,
+        constant    int64_t & ne02,
+        constant   uint64_t & nb01,
+        constant   uint64_t & nb02,
+        constant    int64_t & ne12,
+        constant   uint64_t & nb10,
+        constant   uint64_t & nb11,
+        constant   uint64_t & nb12,
+        constant    int64_t & ne0,
+                    int64_t   ne1,
+        constant       uint & r2,
+        constant       uint & r3,
+        threadgroup   uchar * shared_memory,
+        uint3                 tgpig[[threadgroup_position_in_grid]],
+        uint                  tiitg[[thread_index_in_threadgroup]],
+        uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    threadgroup half  * sa = (threadgroup half  *)(shared_memory);
+    threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
+
+    const uint r0 = tgpig.y;
+    const uint r1 = tgpig.x;
+    const uint im = tgpig.z;
+
+    if (r1 * BLOCK_SIZE_N >= ne1) return;
+
+    // if this block is of 64x32 shape or smaller
+    short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M;
+    short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N;
+
+    // a thread shouldn't load data outside of the matrix
+    short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
+    short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
+
+    simdgroup_half8x8  ma[4];
+    simdgroup_float8x8 mb[2];
+    simdgroup_float8x8 c_res[8];
+    for (int i = 0; i < 8; i++){
+        c_res[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
+    }
+
+    short il = (tiitg % THREAD_PER_ROW);
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    uint   offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02);
+    ushort offset1 = il/nl;
+
+    device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1;
+    device const float   * y = (device const float   *)(src1
+        + nb12 * im
+        + nb11 * src1ids[r1 * BLOCK_SIZE_N + thread_col]
+        + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
+
+    for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
+        // load data and store to threadgroup memory
+        half4x4 temp_a;
+        dequantize_func(x, il, temp_a);
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        for (int i = 0; i < 16; i++) {
+            *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \
+            +                     (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \
+            +                     (tiitg / THREAD_PER_ROW) % 8  + (i & 7) * 8) = temp_a[i/4][i%4];
+        }
+
+        *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y);
+
+        il = (il + 2 < nl) ? il + 2 : il % 2;
+        x  = (il < 2) ? x + (2+nl-1)/nl : x;
+        y += BLOCK_SIZE_K;
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // load matrices from threadgroup memory and conduct outer products
+        threadgroup half  * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
+        threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
+
+        for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) {
+            for (int i = 0; i < 4; i++) {
+                simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i);
+            }
+            simdgroup_barrier(mem_flags::mem_none);
+            for (int i = 0; i < 2; i++) {
+                simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i);
+            }
+
+            lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE;
+            lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE;
+
+            for (int i = 0; i < 8; i++){
+                simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]);
+            }
+        }
+    }
+
+    {
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+        threadgroup float * temp_str = ((threadgroup float *)shared_memory) \
+                                      + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
+        for (int i = 0; i < 8; i++) {
+            simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        device float * C = dst + (BLOCK_SIZE_M * r0) + im*ne1*ne0;
+        if (sgitg == 0) {
+            for (int i = 0; i < n_rows; i++) {
+                for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
+                    *(C + i + src1ids[j + r1*BLOCK_SIZE_N] * ne0) = *(temp_str + i + j * BLOCK_SIZE_M);
+                }
+            }
+        }
+    }
+}
+
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm(device const  uchar * src0,
                           device const  uchar * src1,
@@ -4019,7 +4144,7 @@ template<typename block_q, short nl, void (*dequantize_func)(device const block_
 kernel void kernel_mul_mm_id(
         device const   uchar * ids,
         device const   uchar * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
@@ -4048,18 +4173,28 @@ kernel void kernel_mul_mm_id(
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const uchar * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
+    device const uchar * src0s[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
 
-    const int64_t bid = tgpig.z/(ne12*ne13);
+    // expert id
+    const int32_t id = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    // row indices of src1 for expert id
+    int64_t _ne1 = 0;
+    short src1ids[512];
 
-    kernel_mul_mm_impl<block_q, nl, dequantize_func>(
-        src0[id],
-        src1 + bid*nb11,
-        (device float *) (dst + bid*nb1),
+    for (int64_t i1 = 0; i1 < ne1; i1++) {
+        if (((device int32_t *) (ids + i1*nbi1))[idx] == id) {
+            src1ids[_ne1++] = i1;
+        }
+    }
+
+    kernel_mul_mm_id_impl<block_q, nl, dequantize_func>(
+        src0s[id],
+        src1,
+        src1ids,
+        dst,
         ne00,
         ne02,
         nb01,
@@ -4069,7 +4204,7 @@ kernel void kernel_mul_mm_id(
         nb11,
         nb12,
         ne0,
-        ne1,
+        _ne1,
         r2,
         r3,
         shared_memory,
@@ -4158,7 +4293,7 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<b
 typedef void (mat_mm_id_t)(
         device const   uchar * ids,
         device const   uchar * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
@@ -4207,7 +4342,7 @@ template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mu
 kernel void kernel_mul_mv_id_f32_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4251,7 +4386,7 @@ kernel void kernel_mul_mv_id_f32_f32(
     kernel_mul_mv_f32_f32_impl(
         src0[id],
         src1 + bid*nb11,
-        (device float *) (dst + bid*nb1),
+        dst  + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4276,7 +4411,7 @@ kernel void kernel_mul_mv_id_f32_f32(
 kernel void kernel_mul_mv_id_f16_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4320,7 +4455,7 @@ kernel void kernel_mul_mv_id_f16_f32(
     kernel_mul_mv_f16_f32_impl(
         src0[id],
         src1 + bid*nb11,
-        (device float *) (dst + bid*nb1),
+        dst  + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4345,7 +4480,7 @@ kernel void kernel_mul_mv_id_f16_f32(
 kernel void kernel_mul_mv_id_q8_0_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4389,7 +4524,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
     kernel_mul_mv_q8_0_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4408,7 +4543,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
 kernel void kernel_mul_mv_id_q4_0_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4452,7 +4587,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
     mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4471,7 +4606,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
 kernel void kernel_mul_mv_id_q4_1_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4515,7 +4650,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
     mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4534,7 +4669,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
 kernel void kernel_mul_mv_id_q5_0_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4578,7 +4713,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
     mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4597,7 +4732,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
 kernel void kernel_mul_mv_id_q5_1_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4641,7 +4776,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
     mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4660,7 +4795,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
 kernel void kernel_mul_mv_id_q2_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4704,7 +4839,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
     kernel_mul_mv_q2_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4723,7 +4858,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
 kernel void kernel_mul_mv_id_q3_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4767,7 +4902,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
     kernel_mul_mv_q3_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4786,7 +4921,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
 kernel void kernel_mul_mv_id_q4_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4830,7 +4965,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
     kernel_mul_mv_q4_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4849,7 +4984,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
 kernel void kernel_mul_mv_id_q5_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4893,7 +5028,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
     kernel_mul_mv_q5_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4912,7 +5047,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
 kernel void kernel_mul_mv_id_q6_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
+        device         float * dst,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4956,7 +5091,7 @@ kernel void kernel_mul_mv_id_q6_K_f32(
     kernel_mul_mv_q6_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,

From f2eb19bd8bc9f5730d6e05d7a52a9e19bf5ac099 Mon Sep 17 00:00:00 2001
From: Justin Parker <jparkerweb@gmail.com>
Date: Wed, 3 Jan 2024 03:43:19 -0500
Subject: [PATCH 24/60] server : throw an error when `slot unavailable` (#4741)

---
 examples/server/public/completion.js | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index 6e2b99565..baaec1d60 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -95,6 +95,15 @@ export async function* llama(prompt, params = {}, config = {}) {
               break;
             }
           }
+          if (result.error) {
+            result.error = JSON.parse(result.error);
+            if (result.error.content.includes('slot unavailable')) {
+              // Throw an error to be caught by upstream callers
+              throw new Error('slot unavailable');
+            } else {
+              console.error(`llama.cpp error: ${result.error.content}`);
+            }
+          }
           if (result.error) {
             result.error = JSON.parse(result.error);
             console.error(`llama.cpp error: ${result.error.content}`);

From 5f66ebca9c41a17385341da4b553a8eb5f07edee Mon Sep 17 00:00:00 2001
From: Guillaume Wenzek <gwenzek@users.noreply.github.com>
Date: Fri, 29 Dec 2023 18:07:03 +0100
Subject: [PATCH 25/60] ggml : extend ggml_get_rows, ggml_repeat, ggml_concat
 (ggml/639)

* add more int ops

* ggml_compute_forward_dup_bytes

* add tests

* PR comments

* tests : minor indentations

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml.c                     | 166 ++++++++++++++++++++++++++++++++++++-
 tests/test-backend-ops.cpp |  42 ++++++++--
 2 files changed, 198 insertions(+), 10 deletions(-)

diff --git a/ggml.c b/ggml.c
index bcec200f6..b124f14cc 100644
--- a/ggml.c
+++ b/ggml.c
@@ -4766,8 +4766,11 @@ struct ggml_tensor * ggml_get_rows(
     }
 
     // TODO: implement non F32 return
-    //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
-    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
+    enum ggml_type type = GGML_TYPE_F32;
+    if (a->type == GGML_TYPE_I32) {
+        type = a->type;
+    }
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
 
     result->op   = GGML_OP_GET_ROWS;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6938,14 +6941,165 @@ static void ggml_compute_forward_dup_f32(
     }
 }
 
+// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy.
+static void ggml_compute_forward_dup_bytes(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+    GGML_ASSERT(src0->type == dst->type);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
+        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS;
+
+    const size_t type_size = ggml_type_size(src0->type);
+    const int ith = params->ith; // thread index
+    const int nth = params->nth; // number of threads
+
+
+    // parallelize by rows
+    const int nr = ne01;
+    // number of rows per thread
+    const int dr = (nr + nth - 1) / nth;
+    // row range for this thread
+    const int ir0 = dr * ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (src0->type == dst->type &&
+        ne00 == ne0 &&
+        nb00 == type_size && nb0 == type_size) {
+        // copy by rows
+        const size_t rs = ne00 * type_size;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
+                }
+            }
+        }
+        return;
+    }
+
+    if (ggml_is_contiguous(dst)) {
+        size_t id = 0;
+        char * dst_ptr = (char *) dst->data;
+        const size_t rs = ne00 * type_size;
+
+        if (nb00 == type_size) {
+            // src0 is contigous on first dimension, copy by rows
+            for (int64_t i03 = 0; i03 < ne03; i03++) {
+                for (int64_t i02 = 0; i02 < ne02; i02++) {
+                    id += rs * ir0;
+                    for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                        const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                        memcpy(dst_ptr + id, src0_ptr, rs);
+                        id += rs;
+                    }
+                    id += rs * (ne01 - ir1);
+                }
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            for (int64_t i03 = 0; i03 < ne03; i03++) {
+                for (int64_t i02 = 0; i02 < ne02; i02++) {
+                    id += rs * ir0;
+                    for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                        for (int64_t i00 = 0; i00 < ne00; i00++) {
+                            const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03;
+                            memcpy(dst_ptr + id, src0_ptr, type_size);
+
+                            id += type_size;
+                        }
+                    }
+                    id += rs * (ne01 - ir1);
+                }
+            }
+        }
+
+        return;
+    }
+
+    // dst counters
+
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            i10 += ne00 * ir0;
+            while (i10 >= ne0) {
+                i10 -= ne0;
+                if (++i11 == ne1) {
+                    i11 = 0;
+                    if (++i12 == ne2) {
+                        i12 = 0;
+                        if (++i13 == ne3) {
+                            i13 = 0;
+                        }
+                    }
+                }
+            }
+            for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                for (int64_t i00 = 0; i00 < ne00; i00++) {
+                    const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                          char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                    memcpy(dst_ptr, src0_ptr, type_size);
+
+                    if (++i10 == ne0) {
+                        i10 = 0;
+                        if (++i11 == ne1) {
+                            i11 = 0;
+                            if (++i12 == ne2) {
+                                i12 = 0;
+                                if (++i13 == ne3) {
+                                    i13 = 0;
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            i10 += ne00 * (ne01 - ir1);
+            while (i10 >= ne0) {
+                i10 -= ne0;
+                if (++i11 == ne1) {
+                    i11 = 0;
+                    if (++i12 == ne2) {
+                        i12 = 0;
+                        if (++i13 == ne3) {
+                            i13 = 0;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
 static void ggml_compute_forward_dup(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+    if (src0->type == dst->type) {
+        ggml_compute_forward_dup_bytes(params, src0, dst);
         return;
     }
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
@@ -8404,10 +8558,12 @@ static void ggml_compute_forward_repeat(
         struct ggml_tensor * dst) {
     switch (src0->type) {
         case GGML_TYPE_F16:
+        case GGML_TYPE_I16:
             {
                 ggml_compute_forward_repeat_f16(params, src0, dst);
             } break;
         case GGML_TYPE_F32:
+        case GGML_TYPE_I32:
             {
                 ggml_compute_forward_repeat_f32(params, src0, dst);
             } break;
@@ -8550,6 +8706,7 @@ static void ggml_compute_forward_concat(
     struct ggml_tensor* dst) {
     switch (src0->type) {
         case GGML_TYPE_F32:
+        case GGML_TYPE_I32:
             {
                 ggml_compute_forward_concat_f32(params, src0, src1, dst);
             } break;
@@ -10674,6 +10831,7 @@ static void ggml_compute_forward_get_rows(
                 ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
             } break;
         case GGML_TYPE_F32:
+        case GGML_TYPE_I32:
             {
                 ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
             } break;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index eff063b2d..44412cb94 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -58,6 +58,9 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
         int64_t hist[16];
         ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
+    } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
+        // This is going to create some weird integers though.
+        ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
     } else {
         GGML_ASSERT(false);
     }
@@ -87,8 +90,13 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                         tv.push_back(*(float *) &buf[i]);
                     } else if (t->type == GGML_TYPE_I32) {
                         tv.push_back((float)*(int32_t *) &buf[i]);
+                    } else if (t->type == GGML_TYPE_I16) {
+                        tv.push_back((float)*(int16_t *) &buf[i]);
+                    } else if (t->type == GGML_TYPE_I8) {
+                        tv.push_back((float)*(int8_t *) &buf[i]);
                     } else if (quantized) {
-                        tt.to_float(&buf[i], vq.data(), bs);
+                        std::vector<float> vq(ggml_blck_size(t->type));
+                        tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
                         tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {
                         GGML_ASSERT(false);
@@ -661,17 +669,26 @@ struct test_repeat : public test_case {
 struct test_dup : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> permute;
+    bool _use_permute;
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        std::string v = VARS_TO_STR2(type, ne);
+        if (_use_permute) v += "," + VAR_TO_STR(permute);
+        return v;
     }
 
     test_dup(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1})
-        : type(type), ne(ne) {}
+            std::array<int64_t, 4> ne = {10, 10, 10, 1},
+            std::array<int64_t, 4> permute = {0, 0, 0, 0})
+        : type(type), ne(ne), permute(permute),
+            _use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
+        if (_use_permute) {
+            src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
+        }
         ggml_tensor * out = ggml_dup(ctx, src);
         return out;
     }
@@ -1450,14 +1467,26 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
             }
         }
     }
+    for (int b : {1, 7}) {
+        for (bool v : {false, true}) {
+            test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v));
+        }
+    }
 
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
+    test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1}));
+    test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2}));
 
-    test_cases.emplace_back(new test_dup());
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
 
     for (ggml_type type : all_types) {
        test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
@@ -1565,7 +1594,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 
     test_cases.emplace_back(new test_alibi());
     test_cases.emplace_back(new test_im2col());
-    test_cases.emplace_back(new test_concat());
+    test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
+    test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
 
     for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));

From ab62fc3e5520f5a143c36cb23c269f11aa4dafd6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 3 Jan 2024 11:25:54 +0200
Subject: [PATCH 26/60] scripts : fix sync order + metal sed

---
 scripts/sync-ggml-am.sh | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index 91478f177..248cf1023 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -27,7 +27,7 @@ echo "Syncing ggml changes since commit $lc"
 cd $SRC_GGML
 
 git log --oneline $lc..HEAD
-git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits
+git log --oneline $lc..HEAD --reverse | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits
 
 if [ ! -s $SRC_LLAMA/ggml-commits ]; then
     rm -v $SRC_LLAMA/ggml-commits
@@ -87,7 +87,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-impl.h             -> ggml-impl.h
     # src/ggml-metal.h            -> ggml-metal.h
     # src/ggml-metal.m            -> ggml-metal.m
-    # src/ggml-metal.metal        -> ggml-metal.metal
     # src/ggml-mpi.h              -> ggml-mpi.h
     # src/ggml-mpi.c              -> ggml-mpi.c
     # src/ggml-opencl.cpp         -> ggml-opencl.cpp
@@ -114,7 +113,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \
         -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
         -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
-        -e 's/src\/ggml-metal\.metal/ggml-metal.metal/g' \
         -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \
         -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \
         -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \

From 289313716ff7ccf6aee284f686a0fe8cbc7714af Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 3 Jan 2024 11:35:46 +0200
Subject: [PATCH 27/60] metal : add kernel_get_rows_i32

ggml-ci
---
 ggml-metal.m     |  4 ++++
 ggml-metal.metal | 29 +++++++++++++++++++++++++++++
 2 files changed, 33 insertions(+)

diff --git a/ggml-metal.m b/ggml-metal.m
index 7a369b55e..7aa92c14c 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -87,6 +87,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(get_rows_q4_K);
     GGML_METAL_DECL_KERNEL(get_rows_q5_K);
     GGML_METAL_DECL_KERNEL(get_rows_q6_K);
+    GGML_METAL_DECL_KERNEL(get_rows_i32);
     GGML_METAL_DECL_KERNEL(rms_norm);
     GGML_METAL_DECL_KERNEL(group_norm);
     GGML_METAL_DECL_KERNEL(norm);
@@ -377,6 +378,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(get_rows_q4_K);
         GGML_METAL_ADD_KERNEL(get_rows_q5_K);
         GGML_METAL_ADD_KERNEL(get_rows_q6_K);
+        GGML_METAL_ADD_KERNEL(get_rows_i32);
         GGML_METAL_ADD_KERNEL(rms_norm);
         GGML_METAL_ADD_KERNEL(group_norm);
         GGML_METAL_ADD_KERNEL(norm);
@@ -499,6 +501,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(get_rows_q4_K);
     GGML_METAL_DEL_KERNEL(get_rows_q5_K);
     GGML_METAL_DEL_KERNEL(get_rows_q6_K);
+    GGML_METAL_DEL_KERNEL(get_rows_i32);
     GGML_METAL_DEL_KERNEL(rms_norm);
     GGML_METAL_DEL_KERNEL(group_norm);
     GGML_METAL_DEL_KERNEL(norm);
@@ -1978,6 +1981,7 @@ void ggml_metal_graph_compute(
                                 case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
                                 case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break;
                                 case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break;
+                                case GGML_TYPE_I32:  [encoder setComputePipelineState:ctx->pipeline_get_rows_i32];  break;
                                 default: GGML_ASSERT(false && "not implemented");
                             }
 
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 9aa7b502a..a7d3f9efa 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -3829,6 +3829,35 @@ kernel void kernel_get_rows_f16(
     }
 }
 
+kernel void kernel_get_rows_i32(
+        device const  void * src0,
+        device const  char * src1,
+        device     int32_t * dst,
+        constant   int64_t & ne00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        uint3                tgpig[[threadgroup_position_in_grid]],
+        uint                 tiitg[[thread_index_in_threadgroup]],
+        uint3                tptg [[threads_per_threadgroup]]) {
+    const int64_t i10 = tgpig.x;
+    const int64_t i11 = tgpig.y;
+
+    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    const int64_t i02 = i11;
+
+    for (int ind = tiitg; ind < ne00; ind += tptg.x) {
+        ((device int32_t *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
+            ((device int32_t *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
+    }
+}
+
+
 #define BLOCK_SIZE_M 64 // 8 simdgroup matrices from matrix A
 #define BLOCK_SIZE_N 32 // 4 simdgroup matrices from matrix B
 #define BLOCK_SIZE_K 32

From 75e3fd85814c367b55aea11e7bb38cb7b82c6aa0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 3 Jan 2024 11:37:44 +0200
Subject: [PATCH 28/60] sync : ggml

ggml-ci
---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 5b6a440f7..2105a8df2 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-df098ea908764cba4a4889a1cbe7b026b2d31a14
+5b6f3aeba051be8926cb921b8ba529ff990608bf

From d55356d3baa58a6c3a9171cb67a67094b9aa9dff Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 3 Jan 2024 13:01:44 +0200
Subject: [PATCH 29/60] cuda : mark I16 and I32 ops as unsupported

ggml-ci
---
 ggml-cuda.cu | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 8c2712308..2e759d43e 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -10039,14 +10039,22 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
                 }
                 return false;
             } break;
+        case GGML_OP_DUP:
+        case GGML_OP_REPEAT:
+        case GGML_OP_CONCAT:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                if (src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16) {
+                    return true;
+                }
+                return false;
+            } break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
         case GGML_OP_NORM:
-        case GGML_OP_REPEAT:
-        case GGML_OP_DUP:
         case GGML_OP_ADD:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
@@ -10063,7 +10071,6 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
-        case GGML_OP_CONCAT:
         case GGML_OP_GROUP_NORM:
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:

From 7bed7eba359b0fa8e575345dc5467a46b4ba509f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 3 Jan 2024 14:18:46 +0200
Subject: [PATCH 30/60] cuda : simplify expression

Co-authored-by: slaren <slarengh@gmail.com>
---
 ggml-cuda.cu           | 5 +----
 scripts/sync-ggml.last | 2 +-
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 2e759d43e..52d3cc6a6 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -10044,10 +10044,7 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
         case GGML_OP_CONCAT:
             {
                 ggml_type src0_type = op->src[0]->type;
-                if (src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16) {
-                    return true;
-                }
-                return false;
+                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
             } break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 2105a8df2..354246a26 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-5b6f3aeba051be8926cb921b8ba529ff990608bf
+3fd01e00e40583ccd4b393a7c6502d6a4455a1d5

From ece9a45e8ffb73ad461c792720c2fec28b0137bc Mon Sep 17 00:00:00 2001
From: Ashraful Islam <ashraful.meche@gmail.com>
Date: Wed, 3 Jan 2024 11:30:02 -0600
Subject: [PATCH 31/60] swift : update Package.swift to use ggml as dependency
 (#4691)

* updates the package.swift to use ggml as dependency

* changes the ggml package url src to ggerganov
---
 Package.swift | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/Package.swift b/Package.swift
index 18d610d69..e33a4ff46 100644
--- a/Package.swift
+++ b/Package.swift
@@ -13,21 +13,17 @@ let package = Package(
     products: [
         .library(name: "llama", targets: ["llama"]),
     ],
+    dependencies: [
+        .package(url: "https://github.com/ggerganov/ggml.git", .branch("master"))
+    ],
     targets: [
         .target(
             name: "llama",
+            dependencies: ["ggml"],
             path: ".",
             exclude: [],
             sources: [
-                "ggml.c",
                 "llama.cpp",
-                "ggml-alloc.c",
-                "ggml-backend.c",
-                "ggml-quants.c",
-                "ggml-metal.m",
-            ],
-            resources: [
-                .process("ggml-metal.metal")
             ],
             publicHeadersPath: "spm-headers",
             cSettings: [

From cb1e2818e0e12ec99f7236ec5d4f3ffd8bcc2f4a Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 3 Jan 2024 18:53:40 +0100
Subject: [PATCH 32/60] train : fix typo in overlapping-samples help msg
 (#4758)

This commit fixes a typo in the help message for the
--overlapping-samples option.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---
 common/train.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/train.cpp b/common/train.cpp
index dcf9614e4..e6f2f7a2f 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -1107,7 +1107,7 @@ void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train
     fprintf(stderr, "  --sample-start STR         Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\n", params->sample_start.c_str());
     fprintf(stderr, "  --include-sample-start     Include the sample start in the samples. (default off)\n");
     fprintf(stderr, "  --escape                   process sample start escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
-    fprintf(stderr, "  --overlapping-samples      Samples my overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n");
+    fprintf(stderr, "  --overlapping-samples      Samples may overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n");
     fprintf(stderr, "  --fill-with-next-samples   Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\n");
     fprintf(stderr, "  --separate-with-eos        When fill-with-next-samples, insert end-of-sequence token between samples.%s\n", params->separate_with_eos ? " (default)" : "");
     fprintf(stderr, "  --separate-with-bos        When fill-with-next-samples, insert begin-of-sequence token between samples.%s\n", params->separate_with_bos ? " (default)" : "");

From 46cea79e1f32499bb24b9fab12123cd386e96728 Mon Sep 17 00:00:00 2001
From: singularity <12184989+singularity-s0@users.noreply.github.com>
Date: Thu, 4 Jan 2024 15:58:16 +0800
Subject: [PATCH 33/60] llama.swiftui : fix build of ggml.metallib (#4754)

* metal: fix metal backend init failure in swiftui

* metal: build ggml.metallib instead of copy src

* llama.swift : remove debug flags from metallib build

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 .../llama.swiftui.xcodeproj/project.pbxproj   | 21 +++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
index 2e6159928..7bf4489a2 100644
--- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
+++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
@@ -9,7 +9,6 @@
 /* Begin PBXBuildFile section */
 		542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
 		5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
-		542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
 		542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
 		542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
 		542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
@@ -24,8 +23,25 @@
 		8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
 		8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
 		8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
+		F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
 /* End PBXBuildFile section */
 
+/* Begin PBXBuildRule section */
+		F1FE20DB2B465C2100B45541 /* PBXBuildRule */ = {
+			isa = PBXBuildRule;
+			compilerSpec = com.apple.compilers.proxy.script;
+			fileType = sourcecode.metal;
+			inputFiles = (
+			);
+			isEditable = 1;
+			outputFiles = (
+				"${DERIVED_FILES_DIR}/ggml-metal.air",
+				"${DERIVED_FILES_DIR}/ggml.metallib",
+			);
+			script = "# metal\nxcrun metal    -c \"${INPUT_FILE_PATH}\" -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\nxcrun metallib -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE%-metal}.metallib\" \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\n";
+		};
+/* End PBXBuildRule section */
+
 /* Begin PBXFileReference section */
 		542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
 		542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
@@ -190,6 +206,7 @@
 				8A1C83712AC328BD0096AF73 /* Resources */,
 			);
 			buildRules = (
+				F1FE20DB2B465C2100B45541 /* PBXBuildRule */,
 			);
 			dependencies = (
 			);
@@ -241,7 +258,7 @@
 			isa = PBXResourcesBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
-				542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */,
+				F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */,
 				8A3F84242AC4C891005E2EE8 /* models in Resources */,
 				8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
 				8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,

From dc891b7f7a23158d54f9383790b92c79cc5906c1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 4 Jan 2024 10:12:26 +0200
Subject: [PATCH 34/60] ggml : include stdlib.h before intrin.h (#4736)

---
 ggml-impl.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ggml-impl.h b/ggml-impl.h
index 1f5610a86..2faced080 100644
--- a/ggml-impl.h
+++ b/ggml-impl.h
@@ -5,6 +5,7 @@
 // GGML internal header
 
 #include <assert.h>
+#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
 #include <stddef.h>
 #include <stdbool.h>
 #include <string.h> // memcpy

From e5804313a1edaf00726ed0b96ecced07accbf50c Mon Sep 17 00:00:00 2001
From: Michael Coppola <m18coppola@gmail.com>
Date: Thu, 4 Jan 2024 03:17:09 -0500
Subject: [PATCH 35/60] server : fix options in README.md (#4765)

* fix examples/server/README.md

* minor : fix whitespace

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 examples/server/README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 718a7e064..243e66991 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -168,6 +168,12 @@ node index.js
 
     `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
+    `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
+
+    `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
+
+    `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
+
     *Result JSON:*
 
     Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
@@ -198,12 +204,6 @@ node index.js
 
     `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
 
-    `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
-
-    `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
-
-    `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
-
 -   **POST** `/tokenize`: Tokenize a given text.
 
     *Options:*

From 3c0b585561d74a56977cf3a3844535ecc9e37972 Mon Sep 17 00:00:00 2001
From: singularity <12184989+singularity-s0@users.noreply.github.com>
Date: Thu, 4 Jan 2024 16:22:38 +0800
Subject: [PATCH 36/60] llama.swiftui : support loading custom model from file
 picker (#4767)

* swiftui: support load model from file picker

* swiftui: remove trailing whitespace
---
 .../llama.swiftui.xcodeproj/project.pbxproj   |  4 ++
 .../llama.swiftui/UI/ContentView.swift        |  2 +
 .../llama.swiftui/UI/LoadCustomButton.swift   | 44 +++++++++++++++++++
 3 files changed, 50 insertions(+)
 create mode 100644 examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift

diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
index 7bf4489a2..a70750a22 100644
--- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
+++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
@@ -23,6 +23,7 @@
 		8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
 		8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
 		8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
+		F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
 		F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
 /* End PBXBuildFile section */
 
@@ -68,6 +69,7 @@
 		8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = "<group>"; };
 		8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = "<group>"; };
 		8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = "<group>"; };
+		F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = "<group>"; };
 /* End PBXFileReference section */
 
 /* Begin PBXFrameworksBuildPhase section */
@@ -182,6 +184,7 @@
 			children = (
 				7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */,
 				8A1C83782AC328BD0096AF73 /* ContentView.swift */,
+				F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */,
 			);
 			path = UI;
 			sourceTree = "<group>";
@@ -274,6 +277,7 @@
 			files = (
 				542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */,
 				549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */,
+				F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */,
 				542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */,
 				8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
 				542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
index 147e0c63b..7c81ea256 100644
--- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
+++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
@@ -103,6 +103,8 @@ struct ContentView: View {
                     ContentView.cleanupModelCaches()
                     llamaState.cacheCleared = true
                 }
+
+                LoadCustomButton(llamaState: llamaState)
             }
             .padding(.top, 4)
             .font(.system(size: 12))
diff --git a/examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift b/examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift
new file mode 100644
index 000000000..4315dbe4f
--- /dev/null
+++ b/examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift
@@ -0,0 +1,44 @@
+import SwiftUI
+import UniformTypeIdentifiers
+
+struct LoadCustomButton: View {
+    @ObservedObject private var llamaState: LlamaState
+    @State private var showFileImporter = false
+
+    init(llamaState: LlamaState) {
+        self.llamaState = llamaState
+    }
+
+    var body: some View {
+        VStack {
+            Button(action: {
+                showFileImporter = true
+            }) {
+                Text("Load Custom Model")
+            }
+        }
+        .fileImporter(
+            isPresented: $showFileImporter,
+            allowedContentTypes: [UTType(filenameExtension: "gguf", conformingTo: .data)!],
+            allowsMultipleSelection: false
+        ) { result in
+            switch result {
+            case .success(let files):
+                files.forEach { file in
+                    let gotAccess = file.startAccessingSecurityScopedResource()
+                    if !gotAccess { return }
+
+                    do {
+                        try llamaState.loadModel(modelUrl: file.absoluteURL)
+                    } catch let err {
+                        print("Error: \(err.localizedDescription)")
+                    }
+
+                    file.stopAccessingSecurityScopedResource()
+                }
+            case .failure(let error):
+                print(error)
+            }
+        }
+    }
+}

From a91928014fcf51fe297823fcff0788de4f14206e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 4 Jan 2024 09:43:23 +0100
Subject: [PATCH 37/60] Print backend name on test-backend-ops failure (#4751)

---
 tests/test-backend-ops.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 44412cb94..b79de7a7d 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -392,15 +392,21 @@ struct test_case {
         struct callback_userdata {
             bool   ok;
             double max_err;
+            ggml_backend_t backend1;
+            ggml_backend_t backend2;
         };
 
         callback_userdata ud {
             true,
             max_nmse_err(),
+            backend1,
+            backend2
         };
 
         auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool {
             callback_userdata * ud = (callback_userdata *) user_data;
+            const char * bn1 = ggml_backend_name(ud->backend1);
+            const char * bn2 = ggml_backend_name(ud->backend2);
 
             if (t1->op == GGML_OP_NONE) {
                 // sentinels must be unchanged
@@ -422,7 +428,7 @@ struct test_case {
             for (size_t i = 0; i < f1.size(); i++) {
                 // check for nans
                 if (std::isnan(f1[i]) || std::isnan(f2[i])) {
-                    printf("[%s] NaN at index %zu (%f %f) ", ggml_op_desc(t1), i, f1[i], f2[i]);
+                    printf("[%s] NaN at index %zu (%s=%f %s=%f) ", ggml_op_desc(t1), i, bn1, f1[i], bn2, f2[i]);
                     ud->ok = false;
                     return true;
                 }
@@ -430,12 +436,12 @@ struct test_case {
                 if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
                     if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
                         if (std::signbit(f1[i]) != std::signbit(f2[i])) {
-                            printf("[%s] inf sign mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
+                            printf("[%s] inf sign mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
                             ud->ok = false;
                             return true;
                         }
                     } else {
-                        printf("[%s] inf mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
+                        printf("[%s] inf mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
                         ud->ok = false;
                         return true;
                     }

From 012cf349aec8ffb47c9def5dc018240fa3721e8b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 4 Jan 2024 19:56:33 +0200
Subject: [PATCH 38/60] server : send token probs for "stream == false" (#4714)

---
 examples/server/server.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index e45ea809a..d1469fb08 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1265,7 +1265,7 @@ struct llama_server_context
         {
             std::vector<completion_token_output> probs_output = {};
             const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
-            size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
+            size_t probs_pos      = std::min(slot.sent_token_probs_index,                       slot.generated_token_probs.size());
             size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
             if (probs_pos < probs_stop_pos)
             {
@@ -1325,7 +1325,7 @@ struct llama_server_context
             {
                 probs = std::vector<completion_token_output>(
                                     slot.generated_token_probs.begin(),
-                                    slot.generated_token_probs.begin() + slot.sent_token_probs_index);
+                                    slot.generated_token_probs.end());
             }
             res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
         }

From b3a7c20b5c035250257d2b62851c379b159c899a Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 4 Jan 2024 20:45:37 +0100
Subject: [PATCH 39/60] finetune : remove unused includes (#4756)

This commit removes unused includes from finetune.cpp.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---
 examples/finetune/finetune.cpp | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index e0520f64c..eaca42fc1 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -3,15 +3,9 @@
 #include "llama.h"
 #include "common.h"
 #include "train.h"
-#include <unordered_map>
 #include <vector>
-#include <cassert>
-#include <climits>
 #include <cstring>
-#include <cstdarg>
 #include <ctime>
-#include <random>
-#include <stdexcept>
 #include <algorithm>
 #include <string>
 

From 3681f22443d917e7328456b69c276d6927dafeec Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 5 Jan 2024 15:11:10 +0200
Subject: [PATCH 40/60] examples : add few-shot translation example (#4783)

---
 examples/base-translate.sh | 56 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100755 examples/base-translate.sh

diff --git a/examples/base-translate.sh b/examples/base-translate.sh
new file mode 100755
index 000000000..50fba025c
--- /dev/null
+++ b/examples/base-translate.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+#
+# Few-shot translation example.
+# Requires a base model (i.e. no fine-tuned or instruct models).
+#
+# Usage:
+#
+#   cd llama.cpp
+#   make -j
+#
+#   ./examples/base-translate.sh <model-base> "<text>"
+#
+
+if [ $# -ne 2 ]; then
+  echo "Usage: ./base-translate.sh <model-base> \"<text>\""
+  exit 1
+fi
+
+ftmp="__llama.cpp_example_tmp__.txt"
+trap "rm -f $ftmp" EXIT
+
+echo "Translate from English to French:
+
+===
+
+sea otter, peppermint, plush girafe:
+
+sea otter => loutre de mer
+peppermint => menthe poivrée
+plush girafe => girafe peluche
+
+===
+
+violin
+
+violin => violon
+
+===
+
+phone, computer, mouse, keyboard:
+
+phone => téléphone
+computer => ordinateur
+mouse => souris
+keyboard => clavier
+
+===
+" > $ftmp
+
+echo "$2
+" >> $ftmp
+
+model=$1
+
+# generate the most likely continuation, run on the CPU until the string "===" is found
+./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -ngl 0 -r "==="

From c1d7cb28d3fed97fbc95fc3c43f0c5e2113e546c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 5 Jan 2024 15:18:21 +0200
Subject: [PATCH 41/60] ggml : do not sched_yield when calling BLAS (#4761)

* ggml : do not sched_yield when calling BLAS

ggml-ci

* ggml : fix do_yield logic

ggml-ci

* ggml : simplify do_yield logic

ggml-ci
---
 ggml.c | 41 ++++++++++++++---------------------------
 1 file changed, 14 insertions(+), 27 deletions(-)

diff --git a/ggml.c b/ggml.c
index b124f14cc..62f0f18ef 100644
--- a/ggml.c
+++ b/ggml.c
@@ -9704,10 +9704,10 @@ static void ggml_compute_forward_group_norm(
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
 // helper function to determine if it is better to use BLAS or not
 // for large matrices, BLAS is faster
-static bool ggml_compute_forward_mul_mat_use_blas(
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
+static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     //const int64_t ne00 = src0->ne[0];
     //const int64_t ne01 = src0->ne[1];
 
@@ -9787,7 +9787,7 @@ static void ggml_compute_forward_mul_mat(
 #endif
 
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
+    if (ggml_compute_forward_mul_mat_use_blas(dst)) {
         if (params->ith != 0) {
             return;
         }
@@ -16301,24 +16301,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
 
                 //n_tasks = MIN(n_threads, MAX(1, nr0/128));
                 //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks);
-
-#if defined(GGML_USE_CUBLAS)
-                if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) {
-                    n_tasks = 1; // TODO: this actually is doing nothing
-                                 //       the threads are still spinning
-                }
-#elif defined(GGML_USE_CLBLAST)
-                if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
-                    n_tasks = 1; // TODO: this actually is doing nothing
-                                 //       the threads are still spinning
-                }
-#endif
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
-                    n_tasks = 1; // TODO: this actually is doing nothing
-                                 //       the threads are still spinning
-                }
-#endif
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
@@ -16491,6 +16473,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             state->shared->node_n += 1;
             return (thread_ret_t) GGML_EXIT_ABORTED;
         }
+
         if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
             // all other threads are finished and spinning
             // do finalize and init here so we don't have synchronize again
@@ -16556,14 +16539,18 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         } else {
             // wait for other threads to finish
             const int last = node_n;
+
+            const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
+
             while (true) {
                 // TODO: this sched_yield can have significant impact on the performance - either positive or negative
                 //       depending on the workload and the operating system.
                 //       since it is not clear what is the best approach, it should potentially become user-configurable
                 //       ref: https://github.com/ggerganov/ggml/issues/291
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                sched_yield();
-#endif
+                // UPD:  adding the do_yield flag seems to resolve the issue universally
+                if (do_yield) {
+                    sched_yield();
+                }
 
                 node_n = atomic_load(&state->shared->node_n);
                 if (node_n != last) break;
@@ -16642,7 +16629,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     } else
 #endif
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                    if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
+                    if (ggml_compute_forward_mul_mat_use_blas(node)) {
                         if (node->src[0]->type != GGML_TYPE_F32) {
                             // here we need memory just for single 2D matrix from src0
                             cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);

From 1bf681f90ef4cf37b36e6d604d3e30fc57eda650 Mon Sep 17 00:00:00 2001
From: Finn Voorhees <finnvoorhees@gmail.com>
Date: Wed, 3 Jan 2024 08:39:43 -0500
Subject: [PATCH 42/60] ggml : add error handling to graph_compute
 (whisper/1714)

---
 ggml-backend-impl.h |  2 +-
 ggml-backend.c      | 10 +++++++---
 ggml-backend.h      |  2 +-
 ggml-cuda.cu        |  4 +++-
 ggml-metal.h        |  2 +-
 ggml-metal.m        |  9 +++++----
 6 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h
index 05859935a..ca21b4743 100644
--- a/ggml-backend-impl.h
+++ b/ggml-backend-impl.h
@@ -90,7 +90,7 @@ extern "C" {
         void                      (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
         // compute graph without a plan
-        void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
         // check if the backend supports an operation
         bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
diff --git a/ggml-backend.c b/ggml-backend.c
index 2c3752067..53e741cb8 100644
--- a/ggml-backend.c
+++ b/ggml-backend.c
@@ -195,11 +195,14 @@ void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_
     ggml_backend_synchronize(backend);
 }
 
-void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    backend->iface.graph_compute(backend, cgraph);
+bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    if (!backend->iface.graph_compute(backend, cgraph)) {
+        return false;
+    }
 
     // TODO: optional sync
     ggml_backend_synchronize(backend);
+    return true;
 }
 
 bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@@ -597,7 +600,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac
     GGML_UNUSED(backend);
 }
 
-static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
@@ -611,6 +614,7 @@ static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
     cplan.work_data = cpu_ctx->work_data;
 
     ggml_graph_compute(cgraph, &cplan);
+    return true;
 }
 
 static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
diff --git a/ggml-backend.h b/ggml-backend.h
index a9d2fddd7..85ff67b0e 100644
--- a/ggml-backend.h
+++ b/ggml-backend.h
@@ -58,7 +58,7 @@ extern "C" {
 
     GGML_API void ggml_backend_graph_plan_free   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
     GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API void ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API bool ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
     GGML_API bool ggml_backend_supports_op       (ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 52d3cc6a6..10c21615e 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -9910,7 +9910,7 @@ static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_ba
     UNUSED(plan);
 }
 
-static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
     ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
 
     ggml_cuda_set_main_device(cuda_ctx->device);
@@ -9967,6 +9967,8 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
     }
 
     UNUSED(backend);
+
+    return true;
 }
 
 static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
diff --git a/ggml-metal.h b/ggml-metal.h
index b5e02b668..c4b7325da 100644
--- a/ggml-metal.h
+++ b/ggml-metal.h
@@ -87,7 +87,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
 
 // same as ggml_graph_compute but uses Metal
 // creates gf->n_threads command buffers in parallel
-void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
+bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
 
 //
 // backend API
diff --git a/ggml-metal.m b/ggml-metal.m
index 7aa92c14c..55cc1a872 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -977,7 +977,7 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
             return false;
     }
 }
-void ggml_metal_graph_compute(
+bool ggml_metal_graph_compute(
         struct ggml_metal_context * ctx,
                struct ggml_cgraph * gf) {
     @autoreleasepool {
@@ -2405,10 +2405,11 @@ void ggml_metal_graph_compute(
         MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
         if (status != MTLCommandBufferStatusCompleted) {
             GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
-            GGML_ASSERT(false);
+            return false;
         }
     }
 
+    return true;
     }
 }
 
@@ -2688,10 +2689,10 @@ static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggm
     UNUSED(backend);
 }
 
-static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
 
-    ggml_metal_graph_compute(metal_ctx, cgraph);
+    return ggml_metal_graph_compute(metal_ctx, cgraph);
 }
 
 static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {

From d061bf9405cc5fd50792fb2dbdff9c9ea53d6bf9 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 5 Jan 2024 15:36:04 +0200
Subject: [PATCH 43/60] ggml : fix q2_k bpw in comments (ggml/680)

---
 ggml-quants.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml-quants.h b/ggml-quants.h
index 70c12c274..62c1df6cb 100644
--- a/ggml-quants.h
+++ b/ggml-quants.h
@@ -70,7 +70,7 @@ static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block s
 // 2-bit quantization
 // weight is represented as x = a * q + b
 // 16 blocks of 16 elements each
-// Effectively 2.5625 bits per weight
+// Effectively 2.625 bits per weight
 typedef struct {
     uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
     uint8_t qs[QK_K/4];      // quants

From 91d38876dfa10332359ac671b62353aeceb448d3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 5 Jan 2024 16:30:52 +0200
Subject: [PATCH 44/60] metal : switch back to default.metallib (ggml/681)

ggml-ci
---
 CMakeLists.txt                                | 10 ++++++----
 .../llama.swiftui.xcodeproj/project.pbxproj   | 19 +------------------
 ggml-metal.m                                  |  6 +++---
 scripts/sync-ggml.last                        |  2 +-
 4 files changed, 11 insertions(+), 26 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 57ae4c2df..ce237cf45 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -177,27 +177,29 @@ if (LLAMA_METAL)
     if (LLAMA_METAL_SHADER_DEBUG)
         # custom command to do the following:
         #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o ggml.metallib
+        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
         #
         # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
         #       disabling fast math is needed in order to pass tests/test-backend-ops
         # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+        # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
+        #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
         set(XC_FLAGS -fno-fast-math -fno-inline -g)
         if (LLAMA_QKK_64)
             set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
         endif()
 
         add_custom_command(
-            OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
+            OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
             COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
-            COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
+            COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
             DEPENDS ggml-metal.metal
             COMMENT "Compiling Metal kernels"
         )
 
         add_custom_target(
             ggml-metal ALL
-            DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
+            DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
         )
     endif()
 
diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
index a70750a22..14b93f26c 100644
--- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
+++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
@@ -23,26 +23,10 @@
 		8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
 		8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
 		8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
-		F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
 		F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
+		F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
 /* End PBXBuildFile section */
 
-/* Begin PBXBuildRule section */
-		F1FE20DB2B465C2100B45541 /* PBXBuildRule */ = {
-			isa = PBXBuildRule;
-			compilerSpec = com.apple.compilers.proxy.script;
-			fileType = sourcecode.metal;
-			inputFiles = (
-			);
-			isEditable = 1;
-			outputFiles = (
-				"${DERIVED_FILES_DIR}/ggml-metal.air",
-				"${DERIVED_FILES_DIR}/ggml.metallib",
-			);
-			script = "# metal\nxcrun metal    -c \"${INPUT_FILE_PATH}\" -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\nxcrun metallib -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE%-metal}.metallib\" \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\n";
-		};
-/* End PBXBuildRule section */
-
 /* Begin PBXFileReference section */
 		542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
 		542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
@@ -209,7 +193,6 @@
 				8A1C83712AC328BD0096AF73 /* Resources */,
 			);
 			buildRules = (
-				F1FE20DB2B465C2100B45541 /* PBXBuildRule */,
 			);
 			dependencies = (
 			);
diff --git a/ggml-metal.m b/ggml-metal.m
index 55cc1a872..fbbdcd8c4 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -258,14 +258,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
         NSError * error = nil;
-        NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"];
+        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
         if (libPath != nil) {
             // pre-compiled library found
             NSURL * libURL = [NSURL fileURLWithPath:libPath];
             GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
             ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
         } else {
-            GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__);
+            GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
 
             NSString * sourcePath;
             NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
@@ -295,7 +295,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #endif
             // try to disable fast-math
             // NOTE: this seems to have no effect whatsoever
-            //       instead, in order to disable fast-math, we have to build ggml.metallib from the command line
+            //       instead, in order to disable fast-math, we have to build default.metallib from the command line
             //       using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
             //       and go through the "pre-compiled library found" path above
             //[options setFastMathEnabled:false];
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 354246a26..fe7f3202f 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-3fd01e00e40583ccd4b393a7c6502d6a4455a1d5
+f96711108d55bdbbd277e6be07204dce6a94fb93

From be36bb946a6336238e92706464de6a30495fe825 Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Sat, 6 Jan 2024 01:02:44 +0900
Subject: [PATCH 45/60] flake.nix : fix typo (#4700)

betwen -> between
---
 .devops/nix/package.nix | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index 5f2a7c9f4..43bdbd755 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -9,7 +9,7 @@
   git,
   python3,
   mpi,
-  openblas, # TODO: Use the generic `blas` so users could switch betwen alternative implementations
+  openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations
   cudaPackages,
   darwin,
   rocmPackages,

From eec22a1c6378d9a013943cbddb4330c0da621442 Mon Sep 17 00:00:00 2001
From: a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com>
Date: Fri, 5 Jan 2024 08:04:40 -0800
Subject: [PATCH 46/60] cmake : check for openblas64 (#4134)

openblas v0.3.22 64-bit pkg-config file is named openblas64.pc
https://github.com/OpenMathLib/OpenBLAS/issues/3790
---
 CMakeLists.txt | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ce237cf45..668669c6d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -230,7 +230,11 @@ if (LLAMA_BLAS)
             if (${LLAMA_BLAS_VENDOR} MATCHES "Generic")
                 pkg_check_modules(DepBLAS REQUIRED blas)
             elseif (${LLAMA_BLAS_VENDOR} MATCHES "OpenBLAS")
-                pkg_check_modules(DepBLAS REQUIRED openblas)
+                # As of openblas v0.3.22, the 64-bit is named openblas64.pc
+                pkg_check_modules(DepBLAS openblas64)
+                if (NOT DepBLAS_FOUND)
+                    pkg_check_modules(DepBLAS REQUIRED openblas)
+                endif()
             elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME")
                 pkg_check_modules(DepBLAS REQUIRED blis)
             elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS")

From 96e80dabc6e73ff68b09b68947b1fc25883c5094 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 6 Jan 2024 11:40:24 +0200
Subject: [PATCH 47/60] examples : improve base-translate.sh script (#4783)

---
 examples/base-translate.sh | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/examples/base-translate.sh b/examples/base-translate.sh
index 50fba025c..00dedd0df 100755
--- a/examples/base-translate.sh
+++ b/examples/base-translate.sh
@@ -8,14 +8,19 @@
 #   cd llama.cpp
 #   make -j
 #
-#   ./examples/base-translate.sh <model-base> "<text>"
+#   ./examples/base-translate.sh <model-base> "<text>" [extra-main-args]
 #
 
-if [ $# -ne 2 ]; then
-  echo "Usage: ./base-translate.sh <model-base> \"<text>\""
+if [ $# -lt 2 ]; then
+  echo "Usage: ./base-translate.sh <model-base> \"<text>\" [extra-main-args]"
   exit 1
 fi
 
+eargs=""
+if [ $# -gt 2 ]; then
+  eargs="${@:3}"
+fi
+
 ftmp="__llama.cpp_example_tmp__.txt"
 trap "rm -f $ftmp" EXIT
 
@@ -52,5 +57,5 @@ echo "$2
 
 model=$1
 
-# generate the most likely continuation, run on the CPU until the string "===" is found
-./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -ngl 0 -r "==="
+# generate the most likely continuation until the string "===" is found
+./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs

From c75ca5d96f902564cbbbdd7f5cade80d53c288bb Mon Sep 17 00:00:00 2001
From: Daniel Illescas Romero <illescas.daniel@protonmail.com>
Date: Sat, 6 Jan 2024 16:12:59 +0100
Subject: [PATCH 48/60] llama.swiftui : use correct pointer for llama_token_eos
 (#4797)

---
 examples/llama.swiftui/llama.cpp.swift/LibLlama.swift | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 66244382f..8696b493c 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -161,7 +161,7 @@ actor LlamaContext {
             new_token_id = llama_sample_token_greedy(context, &candidates_p)
         }
 
-        if new_token_id == llama_token_eos(context) || n_cur == n_len {
+        if new_token_id == llama_token_eos(model) || n_cur == n_len {
             print("\n")
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
             temporary_invalid_cchars.removeAll()

From 67984921a70a7e680a24494aeb7575a66e90685d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 7 Jan 2024 08:45:26 +0200
Subject: [PATCH 49/60] server : fix n_predict check (#4798)

---
 examples/server/server.cpp | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index d1469fb08..6c7fcd176 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -447,8 +447,14 @@ struct llama_client_slot
     }
 
     bool has_budget(gpt_params &global_params) {
+        if (params.n_predict == -1 && global_params.n_predict == -1)
+        {
+            return true; // limitless
+        }
+
         n_remaining = -1;
-        if(params.n_predict != -1)
+
+        if (params.n_predict != -1)
         {
             n_remaining = params.n_predict - n_decoded;
         }
@@ -456,7 +462,8 @@ struct llama_client_slot
         {
             n_remaining = global_params.n_predict - n_decoded;
         }
-        return n_remaining > 0 || n_remaining == -1; // no budget || limitless
+
+        return n_remaining > 0; // no budget
     }
 
     bool available() const {
@@ -1102,7 +1109,7 @@ struct llama_server_context
         }
 
         // check the limits
-        if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
+        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
         {
             slot.stopped_limit = true;
             slot.has_next_token = false;
@@ -1703,7 +1710,6 @@ struct llama_server_context
 
             llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
 
-            slot.n_decoded += 1;
             slot.n_past += 1;
         }
 
@@ -1921,6 +1927,7 @@ struct llama_server_context
 
                 llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
 
+                slot.n_decoded += 1;
                 if (slot.n_decoded == 1)
                 {
                     slot.t_start_genereration = ggml_time_us();

From 63ee677efd92060b14894b984597c62e3742b8da Mon Sep 17 00:00:00 2001
From: Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
Date: Sun, 7 Jan 2024 01:52:42 -0500
Subject: [PATCH 50/60] ggml : use __builtin_amdgcn_sudot4 in __dp4a for gfx11
 (#4787)

---
 ggml-cuda.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 10c21615e..54b266be4 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -183,7 +183,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
 static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
 #if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
     c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(__gfx1100__)
+#elif defined(RDNA3)
     c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
 #elif defined(__gfx1010__) || defined(__gfx900__)
     int tmp1;

From 3418c03ecc149fd657527ebb06776239b60a3f3b Mon Sep 17 00:00:00 2001
From: Alex Azarov <alexander.azarov@mapbox.com>
Date: Sun, 7 Jan 2024 08:46:55 +0100
Subject: [PATCH 51/60] llama.swiftui : add visionOS target (#4805)

---
 .../llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
index 14b93f26c..9b1a9787b 100644
--- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
+++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
@@ -420,11 +420,13 @@
 				MARKETING_VERSION = 1.0;
 				PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
 				PRODUCT_NAME = "$(TARGET_NAME)";
+				SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
+				SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
 				SWIFT_EMIT_LOC_STRINGS = YES;
 				SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
 				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
 				SWIFT_VERSION = 5.0;
-				TARGETED_DEVICE_FAMILY = "1,2";
+				TARGETED_DEVICE_FAMILY = "1,2,7";
 			};
 			name = Debug;
 		};
@@ -453,10 +455,12 @@
 				MARKETING_VERSION = 1.0;
 				PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
 				PRODUCT_NAME = "$(TARGET_NAME)";
+				SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
+				SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
 				SWIFT_EMIT_LOC_STRINGS = YES;
 				SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
 				SWIFT_VERSION = 5.0;
-				TARGETED_DEVICE_FAMILY = "1,2";
+				TARGETED_DEVICE_FAMILY = "1,2,7";
 			};
 			name = Release;
 		};

From d117d4dc5dadb46831036bfa4d6e5e8c86babaf1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 7 Jan 2024 09:50:31 +0200
Subject: [PATCH 52/60] llama : print tensor meta for debugging

---
 llama.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/llama.cpp b/llama.cpp
index 3bb056dba..06db40303 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2180,7 +2180,11 @@ struct llama_model_loader {
                     type_max   = type;
                 }
 
-                // LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
+                // TODO: make runtime configurable
+#if 0
+                struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
+                LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
+#endif
             }
 
             switch (type_max) {

From 72d8407b3696dd1293bd233b6db392be108bc377 Mon Sep 17 00:00:00 2001
From: Alex Azarov <alexander.azarov@mapbox.com>
Date: Sun, 7 Jan 2024 09:20:50 +0100
Subject: [PATCH 53/60] llama.swiftui : use llama.cpp as SPM package (#4804)

---
 .../llama.cpp.swift/LibLlama.swift            |  5 +-
 .../llama.cpp.swift/bridging-header.h         |  5 --
 .../llama.swiftui.xcodeproj/project.pbxproj   | 80 +++----------------
 .../AccentColor.colorset/Contents.json        | 11 ---
 .../Preview Assets.xcassets/Contents.json     |  6 --
 5 files changed, 13 insertions(+), 94 deletions(-)
 delete mode 100644 examples/llama.swiftui/llama.cpp.swift/bridging-header.h
 delete mode 100644 examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json
 delete mode 100644 examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json

diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 8696b493c..fc79fd346 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -1,8 +1,5 @@
 import Foundation
-
-// To use this in your own project, add llama.cpp as a swift package dependency
-// and uncomment this import line.
-// import llama
+import llama
 
 enum LlamaError: Error {
     case couldNotInitializeContext
diff --git a/examples/llama.swiftui/llama.cpp.swift/bridging-header.h b/examples/llama.swiftui/llama.cpp.swift/bridging-header.h
deleted file mode 100644
index 6cd72c979..000000000
--- a/examples/llama.swiftui/llama.cpp.swift/bridging-header.h
+++ /dev/null
@@ -1,5 +0,0 @@
-//
-//  Use this file to import your target's public headers that you would like to expose to Swift.
-//
-
-#import "llama.h"
diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
index 9b1a9787b..a8848a49f 100644
--- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
+++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
@@ -7,52 +7,31 @@
 	objects = {
 
 /* Begin PBXBuildFile section */
-		542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
-		5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
-		542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
-		542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
-		542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
 		549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
-		549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */ = {isa = PBXBuildFile; fileRef = 549479C52AC9E0F200E0F78B /* ggml-metal.m */; settings = {COMPILER_FLAGS = "-fno-objc-arc -DGGML_SWIFT -DGGML_USE_METAL -O3"; }; };
 		7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
 		8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; };
 		8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; };
 		8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837A2AC328BE0096AF73 /* Assets.xcassets */; };
-		8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */; };
 		8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 8A39BE092AC7601000BFEB40 /* Accelerate.framework */; };
 		8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
 		8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
 		8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
-		F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
+		DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; };
 		F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
 /* End PBXBuildFile section */
 
 /* Begin PBXFileReference section */
-		542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
-		542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
-		542376092B0D9C40008E6A1C /* ggml-backend.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = "ggml-backend.h"; path = "../../ggml-backend.h"; sourceTree = "<group>"; };
-		5423760A2B0D9C4B008E6A1C /* ggml-backend.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.c"; path = "../../ggml-backend.c"; sourceTree = "<group>"; };
-		542EA09B2AC8723900A8AEE9 /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = ../../ggml.c; sourceTree = "<group>"; };
-		542EA09C2AC8723900A8AEE9 /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = ggml.h; path = ../../ggml.h; sourceTree = "<group>"; };
-		542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-alloc.h"; path = "../../ggml-alloc.h"; sourceTree = "<group>"; };
-		542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-alloc.c"; path = "../../ggml-alloc.c"; sourceTree = "<group>"; };
-		542EA0A12AC8729100A8AEE9 /* llama.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = llama.cpp; path = ../../llama.cpp; sourceTree = "<group>"; };
-		542EA0A22AC8729100A8AEE9 /* llama.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = llama.h; path = ../../llama.h; sourceTree = "<group>"; };
-		549479C52AC9E0F200E0F78B /* ggml-metal.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; name = "ggml-metal.m"; path = "../../ggml-metal.m"; sourceTree = "<group>"; };
-		549479C62AC9E0F200E0F78B /* ggml-metal.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-metal.h"; path = "../../ggml-metal.h"; sourceTree = "<group>"; };
-		549479C82AC9E10B00E0F78B /* ggml-metal.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; name = "ggml-metal.metal"; path = "../../ggml-metal.metal"; sourceTree = "<group>"; };
 		549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
 		7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = "<group>"; };
-		8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = "bridging-header.h"; sourceTree = "<group>"; };
 		8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; };
 		8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = "<group>"; };
 		8A1C83782AC328BD0096AF73 /* ContentView.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ContentView.swift; sourceTree = "<group>"; };
 		8A1C837A2AC328BE0096AF73 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
-		8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = "Preview Assets.xcassets"; sourceTree = "<group>"; };
 		8A39BE092AC7601000BFEB40 /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
 		8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = "<group>"; };
 		8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = "<group>"; };
 		8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = "<group>"; };
+		DF2D2FE72B4A59BE00FCB72D /* llama.cpp */ = {isa = PBXFileReference; lastKnownFileType = wrapper; name = llama.cpp; path = ../..; sourceTree = "<group>"; };
 		F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = "<group>"; };
 /* End PBXFileReference section */
 
@@ -61,6 +40,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				DF810E132B4A5BA200301144 /* llama in Frameworks */,
 				549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
 				8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
 			);
@@ -69,30 +49,10 @@
 /* End PBXFrameworksBuildPhase section */
 
 /* Begin PBXGroup section */
-		8A08D1F62AC7383900FE6CD4 /* llama.cpp */ = {
-			isa = PBXGroup;
-			children = (
-				5423760A2B0D9C4B008E6A1C /* ggml-backend.c */,
-				542376092B0D9C40008E6A1C /* ggml-backend.h */,
-				542376062B0D9BEA008E6A1C /* ggml-quants.h */,
-				542376072B0D9BFB008E6A1C /* ggml-quants.c */,
-				549479C82AC9E10B00E0F78B /* ggml-metal.metal */,
-				549479C62AC9E0F200E0F78B /* ggml-metal.h */,
-				549479C52AC9E0F200E0F78B /* ggml-metal.m */,
-				542EA09B2AC8723900A8AEE9 /* ggml.c */,
-				542EA09C2AC8723900A8AEE9 /* ggml.h */,
-				542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */,
-				542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */,
-				542EA0A12AC8729100A8AEE9 /* llama.cpp */,
-				542EA0A22AC8729100A8AEE9 /* llama.h */,
-			);
-			name = llama.cpp;
-			sourceTree = "<group>";
-		};
 		8A1C836A2AC328BD0096AF73 = {
 			isa = PBXGroup;
 			children = (
-				8A08D1F62AC7383900FE6CD4 /* llama.cpp */,
+				DF2D2FE72B4A59BE00FCB72D /* llama.cpp */,
 				8A907F312AC7134E006146EA /* llama.cpp.swift */,
 				8A3F84232AC4C891005E2EE8 /* models */,
 				8A1C83752AC328BD0096AF73 /* llama.swiftui */,
@@ -117,19 +77,10 @@
 				8A9F7C4A2AC332BF008AE1EA /* UI */,
 				8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */,
 				8A1C837A2AC328BE0096AF73 /* Assets.xcassets */,
-				8A1C837C2AC328BE0096AF73 /* Preview Content */,
 			);
 			path = llama.swiftui;
 			sourceTree = "<group>";
 		};
-		8A1C837C2AC328BE0096AF73 /* Preview Content */ = {
-			isa = PBXGroup;
-			children = (
-				8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */,
-			);
-			path = "Preview Content";
-			sourceTree = "<group>";
-		};
 		8A39BE082AC7601000BFEB40 /* Frameworks */ = {
 			isa = PBXGroup;
 			children = (
@@ -157,7 +108,6 @@
 		8A907F312AC7134E006146EA /* llama.cpp.swift */ = {
 			isa = PBXGroup;
 			children = (
-				8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */,
 				8A907F322AC7134E006146EA /* LibLlama.swift */,
 			);
 			path = llama.cpp.swift;
@@ -198,6 +148,7 @@
 			);
 			name = llama.swiftui;
 			packageProductDependencies = (
+				DF810E122B4A5BA200301144 /* llama */,
 			);
 			productName = llama.swiftui;
 			productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
@@ -244,9 +195,7 @@
 			isa = PBXResourcesBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
-				F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */,
 				8A3F84242AC4C891005E2EE8 /* models in Resources */,
-				8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
 				8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
@@ -258,18 +207,12 @@
 			isa = PBXSourcesBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
-				542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */,
-				549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */,
 				F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */,
-				542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */,
 				8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
-				542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
 				8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */,
 				8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
 				8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
 				7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */,
-				542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */,
-				5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
@@ -399,11 +342,9 @@
 			isa = XCBuildConfiguration;
 			buildSettings = {
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
 				DEVELOPMENT_TEAM = STLSG3FG8Q;
 				ENABLE_PREVIEWS = YES;
 				GENERATE_INFOPLIST_FILE = YES;
@@ -423,7 +364,6 @@
 				SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
 				SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
 				SWIFT_EMIT_LOC_STRINGS = YES;
-				SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
 				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
 				SWIFT_VERSION = 5.0;
 				TARGETED_DEVICE_FAMILY = "1,2,7";
@@ -434,11 +374,9 @@
 			isa = XCBuildConfiguration;
 			buildSettings = {
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
 				DEVELOPMENT_TEAM = STLSG3FG8Q;
 				ENABLE_PREVIEWS = YES;
 				GENERATE_INFOPLIST_FILE = YES;
@@ -458,7 +396,6 @@
 				SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
 				SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
 				SWIFT_EMIT_LOC_STRINGS = YES;
-				SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
 				SWIFT_VERSION = 5.0;
 				TARGETED_DEVICE_FAMILY = "1,2,7";
 			};
@@ -486,6 +423,13 @@
 			defaultConfigurationName = Release;
 		};
 /* End XCConfigurationList section */
+
+/* Begin XCSwiftPackageProductDependency section */
+		DF810E122B4A5BA200301144 /* llama */ = {
+			isa = XCSwiftPackageProductDependency;
+			productName = llama;
+		};
+/* End XCSwiftPackageProductDependency section */
 	};
 	rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
 }
diff --git a/examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json b/examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json
deleted file mode 100644
index eb8789700..000000000
--- a/examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json
+++ /dev/null
@@ -1,11 +0,0 @@
-{
-  "colors" : [
-    {
-      "idiom" : "universal"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}
diff --git a/examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json b/examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json
deleted file mode 100644
index 73c00596a..000000000
--- a/examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

From 3c36213df850a2353e95572b3636797c79b7c815 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 7 Jan 2024 11:21:53 +0200
Subject: [PATCH 54/60] llama : remove redundant GQA check (#4796)

---
 llama.cpp | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 06db40303..021e79a8f 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4776,7 +4776,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -4900,7 +4899,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * pos;
@@ -5001,7 +4999,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         const int64_t n_rot = n_embd_head_k / 2;
 
@@ -5215,7 +5212,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -5308,7 +5304,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -5404,7 +5399,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -5731,7 +5725,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * attn_norm_output;
@@ -5955,7 +5948,6 @@ struct llm_build_context {
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_gqa  == n_embd);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * pos;

From 9dede37d812604897496dd9d276ae9fbe13d1042 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 7 Jan 2024 14:29:36 +0200
Subject: [PATCH 55/60] llama : remove unused vars (#4796)

---
 llama.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 021e79a8f..91aa3f8e7 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4997,7 +4997,6 @@ struct llm_build_context {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         const int64_t n_rot = n_embd_head_k / 2;
@@ -5210,7 +5209,6 @@ struct llm_build_context {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;

From d5a410e8556191672465f7ff58682ea2474038b0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sun, 7 Jan 2024 17:24:08 +0100
Subject: [PATCH 56/60] CUDA: fixed redundant value dequantization (#4809)

---
 ggml-cuda.cu | 35 +++++++++++++++++++++++------------
 1 file changed, 23 insertions(+), 12 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 54b266be4..2df64b111 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -1872,14 +1872,6 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
     v.y = x[ib + iqs + 1];
 }
 
-static __device__ void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const float * x = (const float *) vx;
-
-    // automatic half -> float type cast if dfloat == float
-    v.x = x[ib + iqs + 0];
-    v.y = x[ib + iqs + 1];
-}
-
 static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
     const int ix = blockDim.x*blockIdx.x + threadIdx.x;
 
@@ -1983,7 +1975,7 @@ static __global__ void k_get_rows_float(
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
-    const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
+    const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
 
     if (i >= k) {
         return;
@@ -2002,6 +1994,19 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
     y[iybs + iqs + y_offset] = v.y;
 }
 
+template <typename src_t, typename dst_t>
+static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    const src_t * x = (src_t *) vx;
+
+    y[i] = x[i];
+}
+
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
 
@@ -5609,7 +5614,7 @@ static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, con
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
     dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
 }
 
@@ -5659,6 +5664,12 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu
 #endif
 }
 
+template <typename src_t, typename dst_t>
+static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
 static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_Q4_0:
@@ -5682,7 +5693,7 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
         case GGML_TYPE_Q6_K:
             return dequantize_row_q6_K_cuda;
         case GGML_TYPE_F32:
-            return dequantize_block_cuda<1, 1, convert_f32>;
+            return convert_unary_cuda<float>;
         default:
             return nullptr;
     }
@@ -5711,7 +5722,7 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
         case GGML_TYPE_Q6_K:
             return dequantize_row_q6_K_cuda;
         case GGML_TYPE_F16:
-            return dequantize_block_cuda<1, 1, convert_f16>;
+            return convert_unary_cuda<half>;
         default:
             return nullptr;
     }

From 226460cc0d5b185bc6685fb76f418fd9418d7add Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Sun, 7 Jan 2024 17:59:01 +0100
Subject: [PATCH 57/60] llama-bench : add no-kv-offload parameter (#4812)

---
 examples/llama-bench/llama-bench.cpp | 34 +++++++++++++++++++++++++---
 1 file changed, 31 insertions(+), 3 deletions(-)

diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 6617c050d..7f7186cde 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -138,6 +138,7 @@ struct cmd_params {
     std::vector<int> n_threads;
     std::vector<int> n_gpu_layers;
     std::vector<int> main_gpu;
+    std::vector<bool> no_kv_offload;
     std::vector<bool> mul_mat_q;
     std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
     int reps;
@@ -155,6 +156,7 @@ static const cmd_params cmd_params_defaults = {
     /* n_threads     */ {get_num_physical_cores()},
     /* n_gpu_layers  */ {99},
     /* main_gpu      */ {0},
+    /* no_kv_offload */ {false},
     /* mul_mat_q     */ {true},
     /* tensor_split  */ {{}},
     /* reps          */ 5,
@@ -176,6 +178,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -t, --threads <n>                 (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
     printf("  -ngl, --n-gpu-layers <n>          (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
     printf("  -mg, --main-gpu <i>               (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
+    printf("  -nkvo, --no-kv-offload <0|1>      (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
     printf("  -mmq, --mul-mat-q <0|1>           (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
     printf("  -ts, --tensor_split <ts0/ts1/..>               \n");
     printf("  -r, --repetitions <n>             (default: %d)\n", cmd_params_defaults.reps);
@@ -309,6 +312,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 break;
             }
             params.main_gpu = split<int>(argv[i], split_delim);
+        } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
         } else if (arg == "-mmq" || arg == "--mul-mat-q") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -383,6 +393,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.type_v.empty())       { params.type_v = cmd_params_defaults.type_v; }
     if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
     if (params.main_gpu.empty())     { params.main_gpu = cmd_params_defaults.main_gpu; }
+    if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
     if (params.mul_mat_q.empty())    { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
     if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
     if (params.n_threads.empty())    { params.n_threads = cmd_params_defaults.n_threads; }
@@ -400,6 +411,7 @@ struct cmd_params_instance {
     int n_threads;
     int n_gpu_layers;
     int main_gpu;
+    bool no_kv_offload;
     bool mul_mat_q;
     std::array<float, LLAMA_MAX_DEVICES> tensor_split;
 
@@ -428,6 +440,7 @@ struct cmd_params_instance {
         cparams.type_k = type_k;
         cparams.type_v = type_v;
         cparams.mul_mat_q = mul_mat_q;
+        cparams.offload_kqv = !no_kv_offload;
 
         return cparams;
     }
@@ -444,6 +457,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
     for (const auto & mmq : params.mul_mat_q)
+    for (const auto & nkvo : params.no_kv_offload)
     for (const auto & nt : params.n_threads) {
         cmd_params_instance instance = {
             /* .model        = */ m,
@@ -455,6 +469,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
             /* .n_threads    = */ nt,
             /* .n_gpu_layers = */ nl,
             /* .main_gpu     = */ mg,
+            /* .no_kv_offload= */ nkvo,
             /* .mul_mat_q    = */ mmq,
             /* .tensor_split = */ ts,
         };
@@ -476,6 +491,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
     for (const auto & mmq : params.mul_mat_q)
+    for (const auto & nkvo : params.no_kv_offload)
     for (const auto & nt : params.n_threads) {
         for (const auto & n_prompt : params.n_prompt) {
             if (n_prompt == 0) {
@@ -491,6 +507,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
                 /* .main_gpu     = */ mg,
+                /* .no_kv_offload= */ nkvo,
                 /* .mul_mat_q    = */ mmq,
                 /* .tensor_split = */ ts,
             };
@@ -511,6 +528,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
                 /* .main_gpu     = */ mg,
+                /* .no_kv_offload= */ nkvo,
                 /* .mul_mat_q    = */ mmq,
                 /* .tensor_split = */ ts,
             };
@@ -559,6 +577,7 @@ struct test {
     ggml_type type_v;
     int n_gpu_layers;
     int main_gpu;
+    bool no_kv_offload;
     bool mul_mat_q;
     std::array<float, LLAMA_MAX_DEVICES> tensor_split;
     int n_prompt;
@@ -579,6 +598,7 @@ struct test {
         type_v = inst.type_v;
         n_gpu_layers = inst.n_gpu_layers;
         main_gpu = inst.main_gpu;
+        no_kv_offload = inst.no_kv_offload;
         mul_mat_q = inst.mul_mat_q;
         tensor_split = inst.tensor_split;
         n_prompt = inst.n_prompt;
@@ -640,7 +660,8 @@ struct test {
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
             "n_batch", "n_threads", "type_k", "type_v",
-            "n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
+            "n_gpu_layers", "main_gpu", "no_kv_offload",
+            "mul_mat_q", "tensor_split",
             "n_prompt", "n_gen", "test_time",
             "avg_ns", "stddev_ns",
             "avg_ts", "stddev_ts"
@@ -659,7 +680,7 @@ struct test {
             return INT;
         }
         if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
-            field == "f16_kv" || field == "mul_mat_q") {
+            field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") {
             return BOOL;
         }
         if (field == "avg_ts" || field == "stddev_ts") {
@@ -690,7 +711,8 @@ struct test {
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
             std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
-            std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
+            std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload),
+            std::to_string(mul_mat_q), tensor_split_str,
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
             std::to_string(avg_ns()), std::to_string(stdev_ns()),
             std::to_string(avg_ts()), std::to_string(stdev_ts())
@@ -851,6 +873,9 @@ struct markdown_printer : public printer {
         if (field == "mul_mat_q") {
             return "mmq";
         }
+        if (field == "no_kv_offload") {
+            return "nkvo";
+        }
         if (field == "tensor_split") {
             return "ts";
         }
@@ -885,6 +910,9 @@ struct markdown_printer : public printer {
         if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
             fields.push_back("mul_mat_q");
         }
+        if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
+            fields.push_back("no_kv_offload");
+        }
         if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
             fields.push_back("tensor_split");
         }

From b7e7982953f80a656e03feb5cfb17a17a173eb26 Mon Sep 17 00:00:00 2001
From: Lars Grammel <lars.grammel@gmail.com>
Date: Sun, 7 Jan 2024 21:24:11 +0100
Subject: [PATCH 58/60] readme : add lgrammel/modelfusion JS/TS client for
 llama.cpp (#4814)

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index ca6d14e17..2f6e6ffee 100644
--- a/README.md
+++ b/README.md
@@ -118,6 +118,7 @@ as the main playground for developing new features for the [ggml](https://github
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
+- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
 - Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)

From b0034d93ce2949ce7d9c098ca02e56f66cd484e2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 8 Jan 2024 11:14:04 +0200
Subject: [PATCH 59/60] examples : add passkey test (#3856)

* examples : add passkey test

* passkey : better prints

* passkey : select pass key pos from CLI

* passkey : simplify n_past logic

* make : add passkey target

* passkey : add "self-extend"-like context extension (#4810)

* llama : "self-extend"-like context extension

* passkey : add comment

* passkey : add readme
---
 .gitignore                      |   1 +
 Makefile                        |   5 +-
 examples/CMakeLists.txt         |   1 +
 examples/batched/batched.cpp    |   1 +
 examples/passkey/CMakeLists.txt |   5 +
 examples/passkey/README.md      |  12 ++
 examples/passkey/passkey.cpp    | 296 ++++++++++++++++++++++++++++++++
 llama.cpp                       |  34 ++++
 llama.h                         |   7 +
 9 files changed, 361 insertions(+), 1 deletion(-)
 create mode 100644 examples/passkey/CMakeLists.txt
 create mode 100644 examples/passkey/README.md
 create mode 100644 examples/passkey/passkey.cpp

diff --git a/.gitignore b/.gitignore
index def74a1e9..cf1b692e9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -51,6 +51,7 @@ models-mnt
 /lookup
 /main
 /metal
+/passkey
 /perplexity
 /q8dot
 /quantize
diff --git a/Makefile b/Makefile
index 28c6d79bc..4c7e175bf 100644
--- a/Makefile
+++ b/Makefile
@@ -2,7 +2,7 @@
 BUILD_TARGETS = \
 	main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
-	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o
+	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = \
@@ -665,6 +665,9 @@ lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS
 lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
+passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
 ifdef LLAMA_METAL
 metal: examples/metal/metal.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 4cc13d6e9..0c71cbdf7 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -31,6 +31,7 @@ else()
     add_subdirectory(quantize-stats)
     add_subdirectory(save-load-state)
     add_subdirectory(simple)
+    add_subdirectory(passkey)
     add_subdirectory(speculative)
     add_subdirectory(lookahead)
     add_subdirectory(lookup)
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 22a4265df..b1775e0b0 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -69,6 +69,7 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> tokens_list;
     tokens_list = ::llama_tokenize(model, params.prompt, true);
+
     const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
 
     // initialize the context
diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt
new file mode 100644
index 000000000..3161bf3ef
--- /dev/null
+++ b/examples/passkey/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET passkey)
+add_executable(${TARGET} passkey.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/passkey/README.md b/examples/passkey/README.md
new file mode 100644
index 000000000..4a22bb559
--- /dev/null
+++ b/examples/passkey/README.md
@@ -0,0 +1,12 @@
+# llama.cpp/example/passkey
+
+See the following PRs for more info:
+
+- https://github.com/ggerganov/llama.cpp/pull/3856
+- https://github.com/ggerganov/llama.cpp/pull/4810
+
+### Usage
+
+```bash
+make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250
+```
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
new file mode 100644
index 000000000..5c0022832
--- /dev/null
+++ b/examples/passkey/passkey.cpp
@@ -0,0 +1,296 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+
+    if (argc == 1 || argv[1][0] == '-') {
+        printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]);
+        return 1 ;
+    }
+
+    int seed = -1;
+
+    int n_junk = 250; // number of times to repeat the junk text
+    int n_keep = 32;  // number of tokens in the prompt prefix
+    int n_grp  = 1;   // if more than 1 - perform LongLM SelfExtend
+    int i_pos  = -1;  // position of the passkey in the junk text
+
+    if (argc >= 2) {
+        params.model = argv[1];
+    }
+
+    if (argc >= 3) {
+        n_junk = std::stoi(argv[2]);
+    }
+
+    if (argc >= 4) {
+        n_grp = std::stoi(argv[3]);
+    }
+
+    if (argc >= 5) {
+        i_pos = std::stoi(argv[4]);
+    }
+
+    if (argc >= 6) {
+        seed = std::stoi(argv[5]);
+    }
+
+    if (seed == -1) {
+        seed = time(NULL);
+    }
+
+    srand(seed);
+
+    if (i_pos == -1) {
+        i_pos = rand() % n_junk;
+    }
+
+    const std::string prompt_prefix = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.";
+    const std::string prompt_suffix = " What is the pass key? The pass key is";
+
+    // generate junk text
+    params.prompt = prompt_prefix;
+
+    const int passkey = rand() % 50000 + 1;
+
+    for (int i = 0; i < n_junk; i++) {
+        if (i % n_junk == i_pos) {
+            params.prompt += " The pass key is " + std::to_string(passkey) + ". Remember it. " + std::to_string(passkey) + " is the pass key.";
+        }
+
+        params.prompt += " The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.";
+    }
+
+    params.prompt += prompt_suffix;
+
+    // init LLM
+
+    llama_backend_init(params.numa);
+
+    // initialize the model
+
+    llama_model_params model_params = llama_model_default_params();
+
+    model_params.n_gpu_layers = 99; // offload all layers to the GPU
+
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    // initialize the context
+
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.seed    = seed;
+    ctx_params.n_ctx   = llama_n_ctx_train(model)*n_grp + n_keep;
+    ctx_params.n_batch = 512;
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+
+    GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
+
+    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    // tokenize the prompt
+    std::vector<llama_token> tokens_list;
+    tokens_list = ::llama_tokenize(ctx, params.prompt, true);
+
+    // tokenize the prefix and use it as a sink
+    const int n_tokens_prefix = ::llama_tokenize(ctx, prompt_prefix, true).size();
+
+    const int n_tokens_all = tokens_list.size();
+
+    // we leave a margin of 16 tokens for the generated text - it should contain just the passkey
+    const int n_predict = 16;
+
+    // total length of the sequences including the prompt
+    const int n_len = n_tokens_all + n_predict;
+
+    const int n_ctx       = llama_n_ctx(ctx) - n_keep;
+    const int n_kv_req    = llama_n_ctx(ctx);
+    const int n_batch     = ctx_params.n_batch;
+    const int n_batch_grp = ctx_params.n_batch/n_grp;
+
+    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch);
+
+    // print the prompt token-by-token
+
+    LOG_TEE("\n");
+    LOG_TEE("prefix tokens: %d\n", n_tokens_prefix);
+    LOG_TEE("prompt tokens: %d\n", n_tokens_all);
+    //LOG_TEE("prompt: %s\n", params.prompt.c_str());
+
+    llama_batch batch = llama_batch_init(512, 0, 1);
+
+    int n_past = 0;
+
+    // fill the KV cache
+    for (int i = 0; i < n_ctx; i += n_batch) {
+        if (i > 0 && n_grp > 1) {
+            // if SelfExtend is enabled, we compress the position from the last batch by a factor of n_grp
+            const int ib = i/n_batch - 1;
+            const int bd = n_batch_grp*(n_grp - 1);
+
+            llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch,         n_past,         ib*bd);
+            llama_kv_cache_seq_div  (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+
+            n_past -= bd;
+        }
+
+        llama_batch_clear(batch);
+
+        for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
+            llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
+        }
+
+        if (i + n_batch >= n_tokens_all) {
+            batch.logits[batch.n_tokens - 1] = true;
+        }
+
+        if (llama_decode(ctx, batch) != 0) {
+            LOG_TEE("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+
+        LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
+
+        if (i + n_batch >= n_tokens_all) {
+            break;
+        }
+    }
+
+    for (int i = n_ctx; i < n_tokens_all; i += n_batch) {
+        const int n_discard = n_batch;
+
+        LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard);
+
+        llama_kv_cache_seq_rm   (ctx, 0, n_keep            , n_keep + n_discard);
+        llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+
+        n_past -= n_discard;
+
+        llama_batch_clear(batch);
+
+        for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
+            llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
+        }
+
+        if (i + n_batch >= n_tokens_all) {
+            batch.logits[batch.n_tokens - 1] = true;
+        }
+
+        if (llama_decode(ctx, batch) != 0) {
+            LOG_TEE("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+
+        LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
+    }
+
+    {
+        const int n_discard = n_past - n_ctx + n_predict;
+
+        if (n_discard > 0) {
+            LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
+
+            llama_kv_cache_seq_rm   (ctx, 0, n_keep            , n_keep + n_discard);
+            llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+
+            n_past -= n_discard;
+        }
+    }
+
+    LOG_TEE("\n");
+    LOG_TEE("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk);
+    LOG_TEE("\n");
+
+    // main loop
+
+    int n_cur    = n_tokens_all;
+    int n_decode = 0;
+
+    LOG_TEE("%s", prompt_suffix.c_str());
+    fflush(stdout);
+
+    const auto t_main_start = ggml_time_us();
+
+    while (n_cur <= n_len) {
+        // sample the next token
+        {
+            auto   n_vocab = llama_n_vocab(model);
+            auto * logits  = llama_get_logits_ith(ctx, batch.n_tokens - 1);
+
+            std::vector<llama_token_data> candidates;
+            candidates.reserve(n_vocab);
+
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+            }
+
+            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+            // sample the most likely token
+            const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
+
+            // is it an end of stream?
+            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+                LOG_TEE("\n");
+
+                break;
+            }
+
+            LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
+            fflush(stdout);
+
+            n_decode += 1;
+
+            // prepare the next batch
+            llama_batch_clear(batch);
+
+            // push this new token for next evaluation
+            llama_batch_add(batch, new_token_id, n_past++, { 0 }, true);
+        }
+
+        n_cur += 1;
+
+        // evaluate the current batch with the transformer model
+        if (llama_decode(ctx, batch)) {
+            fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
+            return 1;
+        }
+    }
+
+    LOG_TEE("\n");
+
+    const auto t_main_end = ggml_time_us();
+
+    LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
+            __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
+
+    llama_print_timings(ctx);
+
+    fprintf(stderr, "\n");
+
+    llama_batch_free(batch);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/llama.cpp b/llama.cpp
index 91aa3f8e7..63853d1c3 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1903,6 +1903,28 @@ static void llama_kv_cache_seq_shift(
     cache.head = new_head != cache.size ? new_head : 0;
 }
 
+static void llama_kv_cache_seq_div(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1,
+                          int   d) {
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.has_shift = true;
+
+            {
+                llama_pos p_old = cache.cells[i].pos;
+                cache.cells[i].pos   /= d;
+                cache.cells[i].delta += cache.cells[i].pos - p_old;
+            }
+        }
+    }
+}
+
 //
 // model loading and saving
 //
@@ -10140,9 +10162,21 @@ void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
 }
 
 void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+    if (delta == 0) {
+        return;
+    }
+
     llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
 }
 
+void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+    if (d == 1) {
+        return;
+    }
+
+    llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
+}
+
 // Returns the *maximum* size of the state
 size_t llama_get_state_size(const struct llama_context * ctx) {
     // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
diff --git a/llama.h b/llama.h
index 461d4604a..5305de90b 100644
--- a/llama.h
+++ b/llama.h
@@ -484,6 +484,13 @@ extern "C" {
                        llama_pos   p1,
                        llama_pos   delta);
 
+    LLAMA_API void llama_kv_cache_seq_div(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+                       llama_pos   p0,
+                       llama_pos   p1,
+                             int   d);
+
     //
     // State / sessions
     //

From 52531fdff88764282c1b233174721aab8347252d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 8 Jan 2024 11:18:32 +0200
Subject: [PATCH 60/60] main : add self-extend support (#4815)

* examples : add passkey test

* passkey : better prints

* passkey : select pass key pos from CLI

* passkey : simplify n_past logic

* llama : "self-extend"-like context extension

* passkey : add comment

* main : add Self-Extend support

* llama : add comment about llama_kv_cache_seq_div
---
 common/common.cpp      | 18 +++++++++
 common/common.h        |  2 +
 examples/main/main.cpp | 87 ++++++++++++++++++++++++++++++------------
 llama.h                |  4 ++
 4 files changed, 87 insertions(+), 24 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index eacaee18e..6b4913a65 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -220,6 +220,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
+        } else if (arg == "--grp-attn-n" || arg == "-gan") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+
+            params.grp_attn_n = std::stoi(argv[i]);
+        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+
+            params.grp_attn_w = std::stoi(argv[i]);
         } else if (arg == "--rope-freq-base") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -904,6 +918,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("                        Not recommended since this is both slower and uses more VRAM.\n");
 #endif // GGML_USE_CUBLAS
 #endif
+    printf("  -gan N, --grp-attn-n N\n");
+    printf("                        group-attention factor (default: %d)\n", params.grp_attn_n);
+    printf("  -gat N, --grp-attn-w N\n");
+    printf("                        group-attention width (default: %.1f)\n", (double)params.grp_attn_w);
     printf("  --verbose-prompt      print prompt before generation\n");
     printf("  -dkvc, --dump-kv-cache\n");
     printf("                        verbose print of the KV cache\n");
diff --git a/common/common.h b/common/common.h
index 9659aa045..e2bbfc258 100644
--- a/common/common.h
+++ b/common/common.h
@@ -62,6 +62,8 @@ struct gpt_params {
     int32_t main_gpu                        = 0;     // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0};   // how split tensors should be distributed across GPUs
     int32_t n_beams                         = 0;     // if non-zero then use beam search of given width.
+    int32_t grp_attn_n                      = 1;     // group-attention factor
+    int32_t grp_attn_w                      = 512;   // group-attention width
     float   rope_freq_base                  = 0.0f;  // RoPE base frequency
     float   rope_freq_scale                 = 0.0f;  // RoPE frequency scaling factor
     float   yarn_ext_factor                 = -1.0f; // YaRN extrapolation mix factor
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index c096f110b..5ea67051f 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -439,6 +439,21 @@ int main(int argc, char ** argv) {
     LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
     LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
     LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+
+    // group-attention state
+    // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
+    int ga_i = 0;
+
+    const int ga_n = params.grp_attn_n;
+    const int ga_w = params.grp_attn_w;
+
+    if (ga_n != 1) {
+        GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
+        GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
+      //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
+      //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
+        LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
+    }
     LOG_TEE("\n\n");
 
     if (params.interactive) {
@@ -500,37 +515,61 @@ int main(int argc, char ** argv) {
                 fflush(stdout);
             }
 
-            // infinite text generation via context swapping
-            // if we run out of context:
-            // - take the n_keep first tokens from the original prompt (via n_past)
-            // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
-            if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
-                if (params.n_predict == -2) {
-                    LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
-                    break;
+            if (ga_n == 1) {
+                // infinite text generation via context shifting
+                // if we run out of context:
+                // - take the n_keep first tokens from the original prompt (via n_past)
+                // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
+                if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
+                    if (params.n_predict == -2) {
+                        LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
+                        break;
+                    }
+
+                    const int n_left    = n_past - params.n_keep - 1;
+                    const int n_discard = n_left/2;
+
+                    LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
+                            n_past, n_left, n_ctx, params.n_keep, n_discard);
+
+                    llama_kv_cache_seq_rm   (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
+                    llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+
+                    n_past -= n_discard;
+
+                    if (ctx_guidance) {
+                        n_past_guidance -= n_discard;
+                    }
+
+                    LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
+
+                    LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
+
+                    LOG("clear session path\n");
+                    path_session.clear();
                 }
+            } else {
+                // context extension via Self-Extend
+                while (n_past >= ga_i + ga_w) {
+                    const int ib = (ga_n*ga_i)/ga_w;
+                    const int bd = (ga_w/ga_n)*(ga_n - 1);
+                    const int dd = (ga_w/ga_n) - ib*bd - ga_w;
 
-                const int n_left    = n_past - params.n_keep - 1;
-                const int n_discard = n_left/2;
+                    LOG("\n");
+                    LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd);
+                    LOG("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
+                    LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
 
-                LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
-                    n_past, n_left, n_ctx, params.n_keep, n_discard);
+                    llama_kv_cache_seq_shift(ctx, 0, ga_i,                n_past,              ib*bd);
+                    llama_kv_cache_seq_div  (ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
+                    llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
 
-                llama_kv_cache_seq_rm   (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+                    n_past -= bd;
 
-                n_past -= n_discard;
+                    ga_i += ga_w/ga_n;
 
-                if (ctx_guidance) {
-                    n_past_guidance -= n_discard;
+                    LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
                 }
-
-                LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
-
-                LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
-
-                LOG("clear session path\n");
-                path_session.clear();
             }
 
             // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
diff --git a/llama.h b/llama.h
index 5305de90b..869ff0acf 100644
--- a/llama.h
+++ b/llama.h
@@ -484,6 +484,10 @@ extern "C" {
                        llama_pos   p1,
                        llama_pos   delta);
 
+    // Integer division of the positions by factor of `d > 1`
+    // If the KV cache is RoPEd, the KV data is updated accordingly
+    // p0 < 0 : [0,  p1]
+    // p1 < 0 : [p0, inf)
     LLAMA_API void llama_kv_cache_seq_div(
             struct llama_context * ctx,
                     llama_seq_id   seq_id,