Merge branch 'ggerganov:master' into master

.devops/llama-server-cuda.Dockerfile

@@ -30,8 +30,10 @@ RUN make -j$(nproc) llama-server
 FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
 
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
 
 COPY --from=build /app/llama-server /llama-server
 
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
 ENTRYPOINT [ "/llama-server" ]

.devops/llama-server-intel.Dockerfile

@@ -20,10 +20,12 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
+    apt-get install -y libcurl4-openssl-dev curl
 
 COPY --from=build /app/build/bin/llama-server /llama-server
 
 ENV LC_ALL=C.utf8
 
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
 ENTRYPOINT [ "/llama-server" ]

.devops/llama-server-rocm.Dockerfile

@@ -43,8 +43,10 @@ ENV CXX=/opt/rocm/llvm/bin/clang++
 # Enable cURL
 ENV LLAMA_CURL=1
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
+    apt-get install -y libcurl4-openssl-dev curl
 
 RUN make -j$(nproc) llama-server
 
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
 ENTRYPOINT [ "/app/llama-server" ]

.devops/llama-server-vulkan.Dockerfile

@@ -5,15 +5,11 @@ FROM ubuntu:$UBUNTU_VERSION as build
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget
 
-# Install Vulkan SDK
+# Install Vulkan SDK and cURL
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
     wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
     apt update -y && \
-    apt-get install -y vulkan-sdk
-
-# Install cURL
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
+    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
 
 # Build it
 WORKDIR /app
@@ -28,4 +24,6 @@ RUN cp /app/build/bin/llama-server /llama-server && \
 
 ENV LC_ALL=C.utf8
 
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
 ENTRYPOINT [ "/llama-server" ]

.devops/llama-server.Dockerfile

@@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION as build
 
 RUN apt-get update && \
-    apt-get install -y build-essential git libcurl4-openssl-dev
+    apt-get install -y build-essential git libcurl4-openssl-dev curl
 
 WORKDIR /app
 
@@ -22,4 +22,6 @@ COPY --from=build /app/llama-server /llama-server
 
 ENV LC_ALL=C.utf8
 
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
 ENTRYPOINT [ "/llama-server" ]

.github/workflows/docker.yml

@@ -10,7 +10,7 @@
 name: Publish Docker image
 
 on:
-  pull_request:
+  #pull_request:
   push:
     branches:
       - master
@@ -22,7 +22,7 @@ concurrency:
 jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
-    if: github.event.pull_request.draft == false
+    #if: github.event.pull_request.draft == false
 
     runs-on: ubuntu-latest
     env:

common/common.cpp

@@ -1263,11 +1263,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     // cvector params
-    if (arg == "--completions-file") {
-        CHECK_ARG
-        params.cvector_completions_file = argv[i];
-        return true;
-    }
     if (arg == "--positive-file") {
         CHECK_ARG
         params.cvector_positive_file = argv[i];
@@ -1278,11 +1273,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.cvector_negative_file = argv[i];
         return true;
     }
-    if (arg == "--completions") {
-        CHECK_ARG
-        params.n_completions = std::stoi(argv[i]);
-        return true;
-    }
     if (arg == "--pca-batch") {
         CHECK_ARG
         params.n_pca_batch = std::stoi(argv[i]);
@@ -1293,6 +1283,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.n_pca_iterations = std::stoi(argv[i]);
         return true;
     }
+    if (arg == "--method") {
+        CHECK_ARG
+        std::string value(argv[i]);
+        /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
+        else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
+        else { invalid_param = true; }
+        return true;
+    }
 #ifndef LOG_DISABLE_LOGS
     // Parse args for logging parameters
     if (log_param_single_parse(argv[i])) {
@@ -1444,7 +1442,10 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "main",        "       --cfg-negative-prompt-file FNAME",
                                                                         "negative prompt file to use for guidance" });
     options.push_back({ "main",        "       --cfg-scale N",          "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
-
+    options.push_back({ "main",        "       --chat-template JINJA_TEMPLATE",
+                                                                        "set custom jinja chat template (default: template taken from model's metadata)\n"
+                                                                        "only commonly used templates are accepted:\n"
+                                                                        "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
     options.push_back({ "grammar" });
     options.push_back({ "*",           "       --grammar GRAMMAR",      "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
     options.push_back({ "*",           "       --grammar-file FNAME",   "file to read grammar from" });
@@ -1538,9 +1539,11 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "*",           "       --lora FNAME",           "apply LoRA adapter (implies --no-mmap)" });
     options.push_back({ "*",           "       --lora-scaled FNAME S",  "apply LoRA adapter with user defined scaling S (implies --no-mmap)" });
     options.push_back({ "*",           "       --lora-base FNAME",      "optional model to use as a base for the layers modified by the LoRA adapter" });
-    options.push_back({ "*",           "       --control-vector FNAME", "add a control vector" });
+    options.push_back({ "*",           "       --control-vector FNAME", "add a control vector\n"
+                                                                        "note: this argument can be repeated to add multiple control vectors" });
     options.push_back({ "*",           "       --control-vector-scaled FNAME SCALE",
-                                                                        "add a control vector with user defined scaling SCALE" });
+                                                                        "add a control vector with user defined scaling SCALE\n"
+                                                                        "note: this argument can be repeated to add multiple scaled control vectors" });
     options.push_back({ "*",           "       --control-vector-layer-range START END",
                                                                         "layer range to apply the control vector(s) to, start and end inclusive" });
     options.push_back({ "*",           "-m,    --model FNAME",          "model path (default: models/$filename with filename from --hf-file\n"
@@ -1621,11 +1624,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "cvector",     "-o,    --output FNAME",         "output file (default: '%s')", params.cvector_outfile.c_str() });
     options.push_back({ "cvector",     "       --positive-file FNAME",  "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
     options.push_back({ "cvector",     "       --negative-file FNAME",  "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
-    options.push_back({ "cvector",     "       --completions-file FNAME",
-                                                                        "completions file (default: '%s')", params.cvector_completions_file.c_str() });
-    options.push_back({ "cvector",     "       --completions N",        "number of lines of completions file to use (default: %d)", params.n_completions });
     options.push_back({ "cvector",     "       --pca-batch N",          "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
     options.push_back({ "cvector",     "       --pca-iter N",           "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
+    options.push_back({ "cvector",     "       --method {pca,mean}",    "dimensionality reduction method to be used (default: pca)" });
 
     printf("usage: %s [options]\n", argv[0]);
 
@@ -2602,12 +2603,67 @@ bool llama_should_add_bos_token(const llama_model * model) {
     return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
 }
 
+//
+// Chat template utils
+//
+
 bool llama_chat_verify_template(const std::string & tmpl) {
     llama_chat_message chat[] = {{"user", "test"}};
     int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
     return res >= 0;
 }
 
+std::string llama_chat_apply_template(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & msgs,
+        bool add_ass) {
+    int alloc_size = 0;
+    std::vector<llama_chat_message> chat;
+    for (auto & msg : msgs) {
+        chat.push_back({msg.role.c_str(), msg.content.c_str()});
+        alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
+    }
+
+    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
+    std::vector<char> buf(alloc_size);
+
+    // run the first time to get the total output length
+    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+
+    // if it turns out that our buffer is too small, we resize it
+    if ((size_t) res > buf.size()) {
+        buf.resize(res);
+        res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+    }
+
+    std::string formatted_chat(buf.data(), res);
+    return formatted_chat;
+}
+
+std::string llama_chat_format_single(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & past_msg,
+        const llama_chat_msg & new_msg,
+        bool add_ass) {
+    auto fmt_past_msg = llama_chat_apply_template(model, tmpl, past_msg, false);
+    std::vector<llama_chat_msg> chat_new(past_msg);
+    chat_new.push_back(new_msg);
+    auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
+    auto formatted = fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
+    return formatted;
+}
+
+std::string llama_chat_format_example(const struct llama_model * model,
+        const std::string & tmpl) {
+    std::vector<llama_chat_msg> msgs = {
+        {"system",    "You are a helpful assistant"},
+        {"user",      "Hello"},
+        {"assistant", "Hi there"},
+        {"user",      "How are you?"},
+    };
+    return llama_chat_apply_template(model, tmpl, msgs, true);
+}
+
 //
 // KV cache utils
 //

common/common.h

@@ -52,6 +52,12 @@ int32_t cpu_get_num_math();
 // CLI argument parsing
 //
 
+// dimensionality reduction methods, used by cvector-generator
+enum dimre_method {
+    DIMRE_METHOD_PCA,
+    DIMRE_METHOD_MEAN,
+};
+
 struct gpt_params {
     uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
 
@@ -238,13 +244,12 @@ struct gpt_params {
     bool compute_ppl    = true;  // whether to compute perplexity
 
     // cvector-generator params
-    int n_completions = 64;
-    int n_pca_batch = 20;
+    int n_pca_batch = 100;
     int n_pca_iterations = 1000;
-    std::string cvector_outfile          = "control_vector.gguf";
-    std::string cvector_completions_file = "examples/cvector-generator/completions.txt";
-    std::string cvector_positive_file    = "examples/cvector-generator/positive.txt";
-    std::string cvector_negative_file    = "examples/cvector-generator/negative.txt";
+    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
+    std::string cvector_outfile       = "control_vector.gguf";
+    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
+    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
 };
 
 void gpt_params_handle_model_default(gpt_params & params);
@@ -365,9 +370,32 @@ bool llama_should_add_bos_token(const llama_model * model);
 // Chat template utils
 //
 
+// same with llama_chat_message, but uses std::string
+struct llama_chat_msg {
+    std::string role;
+    std::string content;
+};
+
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
 bool llama_chat_verify_template(const std::string & tmpl);
 
+// CPP wrapper for llama_chat_apply_template
+std::string llama_chat_apply_template(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & chat,
+        bool add_ass);
+
+// Format single message, while taking into account the position of that message in chat history
+std::string llama_chat_format_single(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & past_msg,
+        const llama_chat_msg & new_msg,
+        bool add_ass);
+
+// Returns an example of formatted chat
+std::string llama_chat_format_example(const struct llama_model * model,
+        const std::string & tmpl);
+
 //
 // KV cache utils
 //