Merge branch 'master' into master

common/common.cpp

@@ -2804,125 +2804,87 @@ float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n)
 //
 
 static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
-    int32_t n_tensors;
-
-    size_t n_bytes = 0;
-
-    uint32_t max_direction_layer = 0;
-
     llama_control_vector_data result = { -1, {} };
 
-    // calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer
+    ggml_context * ctx = nullptr;
-    {
+    struct gguf_init_params meta_gguf_params = {
-        struct ggml_init_params meta_params = {
+        /* .no_alloc = */ false,
-            /* .mem_size   = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(),
+        /* .ctx      = */ &ctx,
-            /* .mem_buffer = */ nullptr,
+    };
-            /* .no_alloc   = */ true,
+    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
-        };
+    if (!ctx_gguf) {
-        ggml_context * meta_ctx = ggml_init(meta_params);
+        fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
-        struct gguf_init_params meta_gguf_params = {
+        return result;
-            /* .no_alloc = */ true,
-            /* .ctx      = */ &meta_ctx,
-        };
-        struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
-        if (!meta_ctx_gguf) {
-            fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
-            ggml_free(meta_ctx);
-            return result;
-        }
-
-        n_tensors = gguf_get_n_tensors(meta_ctx_gguf);
-        for (int i = 0; i < n_tensors; i++) {
-            std::string name = gguf_get_tensor_name(meta_ctx_gguf, i);
-
-            // split on '.'
-            size_t dotpos = name.find('.');
-            if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
-                try {
-                    uint32_t layer = std::stoi(name.substr(dotpos + 1));
-                    if (layer == 0) {
-                        fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
-                        ggml_free(meta_ctx);
-                        gguf_free(meta_ctx_gguf);
-                        return result;
-                    }
-                    if (layer > max_direction_layer) {
-                        max_direction_layer = layer;
-                    }
-                } catch (...) {
-                    fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
-                    ggml_free(meta_ctx);
-                    gguf_free(meta_ctx_gguf);
-                    return result;
-                }
-            }
-
-            struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str());
-            if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) {
-                fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
-                ggml_free(meta_ctx);
-                gguf_free(meta_ctx_gguf);
-                return result;
-            }
-            if (result.n_embd == -1) {
-                result.n_embd = ggml_nelements(tensor_meta);
-            } else if (ggml_nelements(tensor_meta) != result.n_embd) {
-                fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str());
-                ggml_free(meta_ctx);
-                gguf_free(meta_ctx_gguf);
-                return result;
-            }
-            n_bytes += ggml_nbytes(tensor_meta);
-        }
-        ggml_free(meta_ctx);
-        gguf_free(meta_ctx_gguf);
     }
 
+    int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
     if (n_tensors == 0) {
         fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
-        return result;
     }
 
-    // load and scale tensors into final control vector context
+    for (int i = 0; i < n_tensors; i++) {
-    struct ggml_init_params ggml_params = {
+        std::string name = gguf_get_tensor_name(ctx_gguf, i);
-        /* .mem_size   = */ ggml_tensor_overhead() * n_tensors + n_bytes,
-        /* .mem_buffer = */ nullptr,
-        /* .no_alloc   = */ false,
-    };
-    struct ggml_context * ctx = ggml_init(ggml_params);
 
-    struct gguf_init_params params = {
+        int layer_idx = -1;
-        /*.no_alloc = */ false,
-        /*.ctx      = */ &ctx,
-    };
-    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params);
-    if (!ctx_gguf) {
-        fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
-        ggml_free(ctx);
-        return result;
-    }
 
-    // do not store data for layer 0 (it's not used)
+        // split on '.'
-    result.data.resize(result.n_embd * max_direction_layer);
+        size_t dotpos = name.find('.');
-
+        if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
-    for (uint32_t il = 1; il <= max_direction_layer; il++) {
+            try {
-        const std::string name = "direction." + std::to_string(il);
+                layer_idx = std::stoi(name.substr(dotpos + 1));
-        const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
+            } catch (...) {
-
+                layer_idx = -1;
-        float * dst = result.data.data() + result.n_embd * (il - 1);
-
-        if (tensor) {
-            const float * src = (const float *) tensor->data;
-            for (int j = 0; j < result.n_embd; j++) {
-                dst[j] = src[j] * load_info.strength;
-            }
-        } else {
-            for (int j = 0; j < result.n_embd; j++) {
-                dst[j] = 0.0f;
             }
         }
+        if (layer_idx < 0) {
+            fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        } else if (layer_idx == 0) {
+            fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+
+        struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
+        if (tensor->type != GGML_TYPE_F32) {
+            fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+        if (ggml_n_dims(tensor) != 1) {
+            fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+
+        if (result.n_embd == -1) {
+            result.n_embd = ggml_nelements(tensor);
+        } else if (ggml_nelements(tensor) != result.n_embd) {
+            fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+
+        // extend if necessary - do not store data for layer 0 (it's not used)
+        result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
+
+        const float * src = (const float *) tensor->data;
+        float * dst = result.data.data() + result.n_embd * (layer_idx - 1);  // layer 1 at [0]
+        for (int j = 0; j < result.n_embd; j++) {
+            dst[j] += src[j] * load_info.strength;  // allows multiple directions for same layer in same file
+        }
+
     }
 
+    if (result.n_embd == -1) {
+        fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
+        result.data.clear();
+    }
+
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
+
     return result;
 }
 
@@ -2933,16 +2895,19 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
         auto cur = llama_control_vector_load_one(info);
 
         if (cur.n_embd == -1) {
-            return result;
+            result.n_embd = -1;
+            break;
         }
-        if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) {
+        if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
-            fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str());
+            fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
-            return result;
+            result.n_embd = -1;
+            break;
         }
 
         if (result.n_embd == -1) {
             result = std::move(cur);
         } else {
+            result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f);  // extend if necessary
             for (size_t i = 0; i < cur.data.size(); i++) {
                 result.data[i] += cur.data[i];
             }
@@ -2950,7 +2915,8 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
     }
 
     if (result.n_embd == -1) {
-        fprintf(stderr, "%s: no vectors passed\n", __func__);
+        fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
+        result.data.clear();
     }
 
     return result;

convert-hf-to-gguf-update.py

@@ -85,6 +85,7 @@ models = [
     {"name": "smaug-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
     {"name": "poro-chat",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
     {"name": "jina-v2-code",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
+    {"name": "viking",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
 ]
 
 

convert-hf-to-gguf.py

@@ -487,6 +487,9 @@ class Model:
         if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
             # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
             res = "jina-v2-code"
+        if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
+            # ref: https://huggingface.co/LumiOpen/Viking-7B
+            res = "viking"
 
         if res is None:
             logger.warning("\n")

examples/llama.android/llama/CMakeLists.txt

@@ -1,55 +0,0 @@
-
-# For more information about using CMake with Android Studio, read the
-# documentation: https://d.android.com/studio/projects/add-native-code.html.
-# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
-
-# Sets the minimum CMake version required for this project.
-cmake_minimum_required(VERSION 3.22.1)
-
-# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
-# Since this is the top level CMakeLists.txt, the project name is also accessible
-# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
-# build script scope).
-project("llama-android")
-
-## Fetch latest llama.cpp from GitHub
-#include(FetchContent)
-#FetchContent_Declare(
-#        llama
-#        GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
-#        GIT_TAG        master
-#)
-#
-## Also provides "common"
-#FetchContent_MakeAvailable(llama)
-
-# llama.cpp CI uses the code from the current branch
-# ref: https://github.com/ggerganov/llama.cpp/pull/7341#issuecomment-2117617700
-add_subdirectory(../../../../../../ build-llama)
-
-# Creates and names a library, sets it as either STATIC
-# or SHARED, and provides the relative paths to its source code.
-# You can define multiple libraries, and CMake builds them for you.
-# Gradle automatically packages shared libraries with your APK.
-#
-# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
-# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
-# is preferred for the same purpose.
-#
-# In order to load a library into your app from Java/Kotlin, you must call
-# System.loadLibrary() and pass the name of the library defined here;
-# for GameActivity/NativeActivity derived applications, the same library name must be
-# used in the AndroidManifest.xml file.
-add_library(${CMAKE_PROJECT_NAME} SHARED
-    # List C/C++ source files with relative paths to this CMakeLists.txt.
-        llama-android.cpp)
-
-# Specifies libraries CMake should link to your target library. You
-# can link libraries from various origins, such as libraries defined in this
-# build script, prebuilt third-party libraries, or Android system libraries.
-target_link_libraries(${CMAKE_PROJECT_NAME}
-    # List libraries link to the target library
-    llama
-    common
-    android
-    log)

examples/llama.android/llama/src/main/cpp/CMakeLists.txt

@@ -11,15 +11,15 @@ cmake_minimum_required(VERSION 3.22.1)
 # build script scope).
 project("llama-android")
 
-include(FetchContent)
+#include(FetchContent)
-FetchContent_Declare(
+#FetchContent_Declare(
-        llama
+#        llama
-        GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
+#        GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
-        GIT_TAG        master
+#        GIT_TAG        master
-)
+#)
 
 # Also provides "common"
-FetchContent_MakeAvailable(llama)
+#FetchContent_MakeAvailable(llama)
 
 # Creates and names a library, sets it as either STATIC
 # or SHARED, and provides the relative paths to its source code.
@@ -30,6 +30,10 @@ FetchContent_MakeAvailable(llama)
 # the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
 # is preferred for the same purpose.
 #
+
+#load local llama.cpp
+add_subdirectory(../../../../../../ build-llama)
+
 # In order to load a library into your app from Java/Kotlin, you must call
 # System.loadLibrary() and pass the name of the library defined here;
 # for GameActivity/NativeActivity derived applications, the same library name must be

flake.lock

@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1718318537,
+        "lastModified": 1718895438,
-        "narHash": "sha256-4Zu0RYRcAY/VWuu6awwq4opuiD//ahpc2aFHg2CWqFY=",
+        "narHash": "sha256-k3JqJrkdoYwE3fHE6xGDY676AYmyh4U2Zw+0Bwe5DLU=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "e9ee548d90ff586a6471b4ae80ae9cfcbceb3420",
+        "rev": "d603719ec6e294f034936c0d0dc06f689d91b6c3",
         "type": "github"
       },
       "original": {

ggml/src/ggml-cuda/mmq.cuh

@@ -2475,7 +2475,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
 
     const dim3 block_nums_mmq(nsm, 1, 1);
 
-    ggml_cuda_pool & pool = ctx.pool();
+    ggml_cuda_pool & pool = ctx.pool(id);
     ggml_cuda_pool_alloc<float> tmp_fixup(pool, block_nums_mmq.x * mmq_x*mmq_y);
 
     if (args.ne01 % mmq_y == 0) {

include/llama.h

@@ -88,6 +88,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_DBRX           = 13,
         LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
         LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
+        LLAMA_VOCAB_PRE_TYPE_VIKING         = 16,
     };
 
     // note: these values should be synchronized with ggml_rope

src/llama.cpp

@@ -2060,6 +2060,7 @@ enum e_model {
     MODEL_8x22B,
     MODEL_16x12B,
     MODEL_10B_128x3_66B,
+    MODEL_57B_A14B,
     MODEL_9B,
     MODEL_27B,
 };
@@ -4293,6 +4294,7 @@ static const char * llama_model_type_name(e_model type) {
         case MODEL_8x22B:         return "8x22B";
         case MODEL_16x12B:        return "16x12B";
         case MODEL_10B_128x3_66B: return "10B+128x3.66B";
+        case MODEL_57B_A14B:      return "57B.A14B";
         default:                  return "?B";
     }
 }
@@ -4614,6 +4616,7 @@ static void llm_load_hparams(
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_A2_7B; break;
+                    case 28: model.type = e_model::MODEL_57B_A14B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -5103,6 +5106,9 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "poro-chat") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
+            } else if (
+                tokenizer_pre == "viking") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -5188,10 +5194,10 @@ static void llm_load_vocab(
         if (gen_name.find("code") != std::string::npos) {
             if (model.arch == LLM_ARCH_LLAMA
               && 32010 < vocab.id_to_token.size()
-              && vocab.id_to_token[32007].text == "<PRE>"
+              && vocab.id_to_token[32007].text.find("<PRE>") != std::string::npos
-              && vocab.id_to_token[32008].text == "<SUF>"
+              && vocab.id_to_token[32008].text.find("<SUF>") != std::string::npos
-              && vocab.id_to_token[32009].text == "<MID>"
+              && vocab.id_to_token[32009].text.find("<MID>") != std::string::npos
-              && vocab.id_to_token[32010].text == "<EOT>") {
+              && vocab.id_to_token[32010].text.find("<EOT>") != std::string::npos) {
                 vocab.special_prefix_id = 32007;
                 vocab.special_suffix_id = 32008;
                 vocab.special_middle_id = 32009;
@@ -13896,6 +13902,12 @@ struct llm_tokenizer_bpe {
                     " ?[^(\\s|.,!?…。，、।۔،)]+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_VIKING:
+                regex_exprs = {
+                    "\\p{N}",
+                    " ?[^(\\s|.,!?…。，、।۔،)]+",
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {