Merge branch 'master' into server-probs

.gitignore

@@ -48,6 +48,7 @@ models-mnt
 /Pipfile
 /embd-input-test
 /libllama.so
+/llama-bench
 build-info.h
 arm_neon.h
 compile_commands.json

Makefile

@@ -1,5 +1,5 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test llama-bench
 
 # Binaries only useful for tests
 TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
@@ -345,7 +345,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test build-info.h $(TEST_TARGETS)
+	rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test llama-bench build-info.h $(TEST_TARGETS)
 
 #
 # Examples
@@ -391,6 +391,9 @@ train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratc
 convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp    build-info.h ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
+llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
 build-info.h: $(wildcard .git/index) scripts/build-info.sh
 	@sh scripts/build-info.sh > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \

README.md

@@ -9,13 +9,13 @@
 
 Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
-**Hot topics:**
+### 🚧 Incoming breaking change + refactoring:
 
-- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998
+See PR https://github.com/ggerganov/llama.cpp/pull/2398 for more info.
-- k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001
+
-- New roadmap: https://github.com/users/ggerganov/projects/7
+To devs: avoid making big changes to `llama.h` / `llama.cpp` until merged
-- Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985
+
-- p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1
+----
 
 <details>
   <summary>Table of Contents</summary>
@@ -96,8 +96,10 @@ as the main playground for developing new features for the [ggml](https://github
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
 - 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)
 - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
+- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
 
 **UI:**
 

examples/CMakeLists.txt

@@ -45,6 +45,7 @@ else()
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(simple)
     add_subdirectory(embd-input)
+    add_subdirectory(llama-bench)
     if (LLAMA_METAL)
         add_subdirectory(metal)
     endif()

examples/llama-bench/CMakeLists.txt

@@ -0,0 +1,8 @@
+set(TARGET llama-bench)
+add_executable(${TARGET} llama-bench.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+  add_dependencies(${TARGET} BUILD_INFO)
+endif()

examples/llama-bench/llama-bench.cpp

@@ -0,0 +1,967 @@
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <chrono>
+#include <cinttypes>
+#include <cstring>
+#include <ctime>
+#include <iterator>
+#include <map>
+#include <numeric>
+#include <regex>
+#include <sstream>
+#include <stdio.h>
+#include <string>
+#include <vector>
+
+#include "ggml.h"
+#include "llama.h"
+#include "common.h"
+#include "build-info.h"
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+#endif
+
+// utils
+static uint64_t get_time_ns() {
+    using clock = std::chrono::high_resolution_clock;
+    return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();
+}
+
+template<class T>
+static std::string join(const std::vector<T> & values, const std::string & delim) {
+    std::ostringstream str;
+    for (size_t i = 0; i < values.size(); i++) {
+        str << values[i];
+        if (i < values.size() - 1) {
+            str << delim;
+        }
+    }
+    return str.str();
+}
+
+template<class T>
+static std::vector<T> split(const std::string & str, char delim) {
+    std::vector<T> values;
+    std::istringstream str_stream(str);
+    std::string token;
+    while (std::getline(str_stream, token, delim)) {
+        T value;
+        std::istringstream token_stream(token);
+        token_stream >> value;
+        values.push_back(value);
+    }
+    return values;
+}
+
+template<typename T>
+static T avg(const std::vector<T> & v) {
+    if (v.empty()) {
+        return 0;
+    }
+    T sum = std::accumulate(v.begin(), v.end(), T(0));
+    return sum / (T)v.size();
+}
+
+template<typename T>
+static T stdev(const std::vector<T> & v) {
+    if (v.size() <= 1) {
+        return 0;
+    }
+    T mean = avg(v);
+    T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0));
+    T stdev = std::sqrt(sq_sum / (T)(v.size() - 1) - mean * mean * (T)v.size() / (T)(v.size() - 1));
+    return stdev;
+}
+
+static bool ggml_cpu_has_metal() {
+#if defined(GGML_USE_METAL)
+    return true;
+#else
+    return false;
+#endif
+}
+
+static std::string get_cpu_info() {
+    std::string id;
+#ifdef __linux__
+    FILE * f = fopen("/proc/cpuinfo", "r");
+    if (f) {
+        char buf[1024];
+        while (fgets(buf, sizeof(buf), f)) {
+            if (strncmp(buf, "model name", 10) == 0) {
+                char * p = strchr(buf, ':');
+                if (p) {
+                    p++;
+                    while (std::isspace(*p)) {
+                        p++;
+                    }
+                    while (std::isspace(p[strlen(p) - 1])) {
+                        p[strlen(p) - 1] = '\0';
+                    }
+                    id = p;
+                    break;
+                }
+            }
+        }
+    }
+#endif
+    // TODO: other platforms
+    return id;
+}
+
+static std::string get_gpu_info() {
+    std::string id;
+#ifdef GGML_USE_CUBLAS
+    int count = ggml_cuda_get_device_count();
+    for (int i = 0; i < count; i++) {
+        char buf[128];
+        ggml_cuda_get_device_description(i, buf, sizeof(buf));
+        id += buf;
+        if (i < count - 1) {
+            id += "/";
+        }
+    }
+#endif
+    // TODO: other backends
+    return id;
+}
+
+// command line params
+enum output_formats {CSV, JSON, MARKDOWN, SQL};
+
+struct cmd_params {
+    std::vector<std::string> model;
+    std::vector<int> n_prompt;
+    std::vector<int> n_gen;
+    std::vector<int> n_batch;
+    std::vector<bool> f32_kv;
+    std::vector<int> n_threads;
+    std::vector<int> n_gpu_layers;
+    std::vector<int> main_gpu;
+    std::vector<bool> mul_mat_q;
+    std::vector<bool> low_vram;
+    std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
+    int reps;
+    bool verbose;
+    output_formats output_format;
+};
+
+static const cmd_params cmd_params_defaults = {
+    /* model         */ {"models/7B/ggml-model-q4_0.bin"},
+    /* n_prompt      */ {512},
+    /* n_gen         */ {128},
+    /* n_batch       */ {512},
+    /* f32_kv        */ {false},
+    /* n_threads     */ {get_num_physical_cores()},
+    /* n_gpu_layers  */ {99},
+    /* main_gpu      */ {0},
+    /* mul_mat_q     */ {true},
+    /* low_vram      */ {false},
+    /* tensor_split  */ {{}},
+    /* reps          */ 5,
+    /* verbose       */ false,
+    /* output_format */ MARKDOWN
+};
+
+static void print_usage(int /* argc */, char ** argv) {
+    fprintf(stdout, "usage: %s [options]\n", argv[0]);
+    fprintf(stdout, "\n");
+    fprintf(stdout, "options:\n");
+    fprintf(stdout, "  -h, --help\n");
+    fprintf(stdout, "  -m, --model <filename>            (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
+    fprintf(stdout, "  -p, --n-prompt <n>                (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
+    fprintf(stdout, "  -n, --n-gen <n>                   (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
+    fprintf(stdout, "  -b, --batch-size <n>              (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
+    fprintf(stdout, "  --memory-f32 <0|1>                (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
+    fprintf(stdout, "  -t, --threads <n>                 (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
+    fprintf(stdout, "  -ngl N, --n-gpu-layers <n>        (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
+    fprintf(stdout, "  -mg i, --main-gpu <n>             (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
+    fprintf(stdout, "  -lv, --low-vram <0|1>             (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
+    fprintf(stdout, "  -mmq, --mul-mat-q <0|1>           (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
+    fprintf(stdout, "  -ts, --tensor_split <ts>                       \n");
+    fprintf(stdout, "  -r, --repetitions <n>             (default: %d)\n", cmd_params_defaults.reps);
+    fprintf(stdout, "  -o, --output <csv|json|md|sql>    (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : "md");
+    fprintf(stdout, "  -v, --verbose                     (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
+    fprintf(stdout, "\n");
+    fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by repeating the parameter.\n");
+
+}
+
+static cmd_params parse_cmd_params(int argc, char ** argv) {
+    cmd_params params;
+    std::string arg;
+    bool invalid_param = false;
+    const std::string arg_prefix = "--";
+    const char split_delim = ',';
+
+    params.verbose = cmd_params_defaults.verbose;
+    params.output_format = cmd_params_defaults.output_format;
+    params.reps = cmd_params_defaults.reps;
+
+    for (int i = 1; i < argc; i++) {
+        arg = argv[i];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+
+        if (arg == "-h" || arg == "--help") {
+            print_usage(argc, argv);
+            exit(0);
+        } else if (arg == "-m" || arg == "--model") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<std::string>(argv[i], split_delim);
+            params.model.insert(params.model.end(), p.begin(), p.end());
+        } else if (arg == "-p" || arg == "--n-prompt") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());
+        } else if (arg == "-n" || arg == "--n-gen") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
+        } else if (arg == "-b" || arg == "--batch-size") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
+        } else if (arg == "--memory-f32") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.f32_kv.insert(params.f32_kv.end(), p.begin(), p.end());
+        } else if (arg == "-t" || arg == "--threads") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
+        } else if (arg == "-ngl" || arg == "--n-gpu-layers") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
+        } else if (arg == "-mg" || arg == "--main-gpu") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.main_gpu = split<int>(argv[i], split_delim);
+        } else if (arg == "-lv" || arg == "--low-vram") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.low_vram.insert(params.low_vram.end(), p.begin(), p.end());
+        } else if (arg == "-mmq" || arg == "--mul-mat-q") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
+        } else if (arg == "-ts" || arg == "--tensor-split") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            for (auto ts : split<std::string>(argv[i], split_delim)) {
+                // split string by ; and /
+                const std::regex regex{R"([;/]+)"};
+                std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
+                std::vector<std::string> split_arg{it, {}};
+                GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
+
+                std::array<float, LLAMA_MAX_DEVICES> tensor_split;
+                for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
+                    if (i < split_arg.size()) {
+                        tensor_split[i] = std::stof(split_arg[i]);
+                    } else {
+                        tensor_split[i] = 0.0f;
+                    }
+                }
+                params.tensor_split.push_back(tensor_split);
+            }
+        } else if (arg == "-r" || arg == "--repetitions") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.reps = std::stoi(argv[i]);
+        } else if (arg == "-o" || arg == "--output") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            if (argv[i] == std::string("csv")) {
+                params.output_format = CSV;
+            } else if (argv[i] == std::string("json")) {
+                params.output_format = JSON;
+            } else if (argv[i] == std::string("md")) {
+                params.output_format = MARKDOWN;
+            } else if (argv[i] == std::string("sql")) {
+                params.output_format = SQL;
+            } else {
+                invalid_param = true;
+                break;
+            }
+        } else if (arg == "-v" || arg == "--verbose") {
+            params.verbose = true;
+        } else {
+            invalid_param = true;
+            break;
+        }
+    }
+    if (invalid_param) {
+        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
+        print_usage(argc, argv);
+        exit(1);
+    }
+
+    // set defaults
+    if (params.model.empty())        { params.model = cmd_params_defaults.model; }
+    if (params.n_prompt.empty())     { params.n_prompt = cmd_params_defaults.n_prompt; }
+    if (params.n_gen.empty())        { params.n_gen = cmd_params_defaults.n_gen; }
+    if (params.n_batch.empty())      { params.n_batch = cmd_params_defaults.n_batch; }
+    if (params.f32_kv.empty())       { params.f32_kv = cmd_params_defaults.f32_kv; }
+    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.mul_mat_q.empty())    { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
+    if (params.low_vram.empty())     { params.low_vram = cmd_params_defaults.low_vram; }
+    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; }
+
+    return params;
+}
+
+struct cmd_params_instance {
+    std::string model;
+    int n_prompt;
+    int n_gen;
+    int n_batch;
+    bool f32_kv;
+    int n_threads;
+    int n_gpu_layers;
+    int main_gpu;
+    bool mul_mat_q;
+    bool low_vram;
+    std::array<float, LLAMA_MAX_DEVICES> tensor_split;
+
+    llama_context_params to_llama_params() const {
+        llama_context_params lparams = llama_context_default_params();
+        lparams.n_ctx = n_prompt + n_gen;
+        lparams.n_batch = n_batch;
+        lparams.f16_kv = !f32_kv;
+        lparams.n_gpu_layers = n_gpu_layers;
+        lparams.main_gpu = main_gpu;
+        lparams.mul_mat_q = mul_mat_q;
+        lparams.low_vram = low_vram;
+        lparams.tensor_split = tensor_split.data();
+
+        return lparams;
+    }
+};
+
+static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) {
+    std::vector<cmd_params_instance> instances;
+
+    for (const auto & m : params.model)
+    for (const auto & nb : params.n_batch)
+    for (const auto & fk : params.f32_kv)
+    for (const auto & nl : params.n_gpu_layers)
+    for (const auto & mg : params.main_gpu)
+    for (const auto & mmq : params.mul_mat_q)
+    for (const auto & lv : params.low_vram)
+    for (const auto & ts : params.tensor_split)
+    for (const auto & nt : params.n_threads) {
+        cmd_params_instance instance = {
+            /* .model        = */ m,
+            /* .n_prompt     = */ n_prompt,
+            /* .n_gen        = */ n_gen,
+            /* .n_batch      = */ nb,
+            /* .f32_kv       = */ fk,
+            /* .n_threads    = */ nt,
+            /* .n_gpu_layers = */ nl,
+            /* .main_gpu     = */ mg,
+            /* .mul_mat_q    = */ mmq,
+            /* .low_vram     = */ lv,
+            /* .tensor_split = */ ts,
+        };
+        instances.push_back(instance);
+    }
+    return instances;
+}
+
+static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_params & params) {
+    std::vector<cmd_params_instance> instances;
+
+    for (const auto & n_prompt : params.n_prompt) {
+        if (n_prompt == 0) {
+            continue;
+        }
+        auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt);
+        instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end());
+    }
+
+    for (const auto & n_gen : params.n_gen) {
+        if (n_gen == 0) {
+            continue;
+        }
+        auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0);
+        instances.insert(instances.end(), instances_gen.begin(), instances_gen.end());
+    }
+
+    return instances;
+}
+
+struct test {
+    static const std::string build_commit;
+    static const int build_number;
+    static const bool cuda;
+    static const bool opencl;
+    static const bool metal;
+    static const bool gpu_blas;
+    static const bool blas;
+    static const std::string cpu_info;
+    static const std::string gpu_info;
+    std::string model_filename;
+    std::string model_type;
+    int n_batch;
+    int n_threads;
+    bool f32_kv;
+    int n_gpu_layers;
+    int main_gpu;
+    bool mul_mat_q;
+    bool low_vram;
+    std::array<float, LLAMA_MAX_DEVICES> tensor_split;
+    int n_prompt;
+    int n_gen;
+    std::string test_time;
+    std::vector<uint64_t> samples_ns;
+
+    test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) {
+        model_filename = inst.model;
+        char buf[128];
+        llama_model_type(lmodel, buf, sizeof(buf));
+        model_type = buf;
+        n_batch = inst.n_batch;
+        n_threads = inst.n_threads;
+        f32_kv = inst.f32_kv;
+        n_gpu_layers = inst.n_gpu_layers;
+        main_gpu = inst.main_gpu;
+        mul_mat_q = inst.mul_mat_q;
+        low_vram = inst.low_vram;
+        tensor_split = inst.tensor_split;
+        n_prompt = inst.n_prompt;
+        n_gen = inst.n_gen;
+        // RFC 3339 date-time format
+        time_t t = time(NULL);
+        std::strftime(buf, sizeof(buf), "%FT%TZ", gmtime(&t));
+        test_time = buf;
+
+        (void) ctx;
+    }
+
+    uint64_t avg_ns() const {
+        return ::avg(samples_ns);
+    }
+
+    uint64_t stdev_ns() const {
+        return ::stdev(samples_ns);
+    }
+
+    std::vector<double> get_ts() const {
+        int n_tokens = n_prompt + n_gen;
+        std::vector<double> ts;
+        std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; });
+        return ts;
+    }
+
+    double avg_ts() const {
+        return ::avg(get_ts());
+    }
+
+    double stdev_ts() const {
+        return ::stdev(get_ts());
+    }
+
+    static std::string get_backend() {
+        if (cuda) {
+            return "CUDA";
+        }
+        if (opencl) {
+            return "OpenCL";
+        }
+        if (metal) {
+            return "Metal";
+        }
+        if (gpu_blas) {
+            return "GPU BLAS";
+        }
+        if (blas) {
+            return "BLAS";
+        }
+        return "CPU";
+    }
+
+    static const std::vector<std::string> & get_fields() {
+        static const std::vector<std::string> fields = {
+            "build_commit", "build_number",
+            "cuda", "opencl", "metal", "gpu_blas", "blas",
+            "cpu_info", "gpu_info",
+            "model_filename", "model_type",
+            "n_batch", "n_threads", "f16_kv",
+            "n_gpu_layers", "main_gpu", "mul_mat_q", "low_vram", "tensor_split",
+            "n_prompt", "n_gen", "test_time",
+            "avg_ns", "stddev_ns",
+            "avg_ts", "stddev_ts"
+        };
+        return fields;
+    }
+
+    enum field_type {STRING, BOOL, INT, FLOAT};
+
+    static field_type get_field_type(const std::string & field) {
+        if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
+            field == "n_gpu_layers" || field == "main_gpu" ||
+            field == "n_prompt" || field == "n_gen" ||
+            field == "avg_ns" || field == "stddev_ns") {
+            return INT;
+        }
+        if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
+            field == "f16_kv" || field == "mul_mat_q" || field == "low_vram") {
+            return BOOL;
+        }
+        if (field == "avg_ts" || field == "stddev_ts") {
+            return FLOAT;
+        }
+        return STRING;
+    }
+
+    std::vector<std::string> get_values() const {
+        std::string tensor_split_str;
+        int max_nonzero = 0;
+        for (int i = 0; i < LLAMA_MAX_DEVICES; i++) {
+            if (tensor_split[i] > 0) {
+                max_nonzero = i;
+            }
+        }
+        for (int i = 0; i <= max_nonzero; i++) {
+            char buf[32];
+            snprintf(buf, sizeof(buf), "%.2f", tensor_split[i]);
+            tensor_split_str += buf;
+            if (i < max_nonzero) {
+                tensor_split_str += "/";
+            }
+        }
+        std::vector<std::string> values = {
+            build_commit, std::to_string(build_number),
+            std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
+            cpu_info, gpu_info,
+            model_filename, model_type,
+            std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
+            std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), std::to_string(low_vram), 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())
+        };
+        return values;
+    }
+
+    std::map<std::string, std::string> get_map() const {
+        std::map<std::string, std::string> map;
+        auto fields = get_fields();
+        auto values = get_values();
+        std::transform(fields.begin(), fields.end(), values.begin(),
+                std::inserter(map, map.end()), std::make_pair<const std::string &, const std::string &>);
+        return map;
+    }
+};
+
+const std::string test::build_commit = BUILD_COMMIT;
+const int         test::build_number = BUILD_NUMBER;
+const bool        test::cuda         = !!ggml_cpu_has_cublas();
+const bool        test::opencl       = !!ggml_cpu_has_clblast();
+const bool        test::metal        = !!ggml_cpu_has_metal();
+const bool        test::gpu_blas     = !!ggml_cpu_has_gpublas();
+const bool        test::blas         = !!ggml_cpu_has_blas();
+const std::string test::cpu_info     = get_cpu_info();
+const std::string test::gpu_info     = get_gpu_info();
+
+struct printer {
+    FILE * fout;
+    virtual void print_header(const cmd_params & params) { (void) params; };
+    virtual void print_test(const test & t) = 0;
+    virtual void print_footer() { };
+};
+
+struct csv_printer : public printer {
+    static std::string escape_csv(const std::string & field) {
+        std::string escaped = "\"";
+        for (auto c : field) {
+            if (c == '"') {
+                escaped += "\"";
+            }
+            escaped += c;
+        }
+        escaped += "\"";
+        return escaped;
+    }
+
+    void print_header(const cmd_params & params) override  {
+        std::vector<std::string> fields = test::get_fields();
+        fprintf(fout, "%s\n", join(fields, ",").c_str());
+        (void) params;
+    }
+
+    void print_test(const test & t) override {
+        std::vector<std::string> values = t.get_values();
+        std::transform(values.begin(), values.end(), values.begin(), escape_csv);
+        fprintf(fout, "%s\n", join(values, ",").c_str());
+    }
+};
+
+struct json_printer : public printer {
+    bool first = true;
+
+    static std::string escape_json(const std::string & value) {
+        std::string escaped;
+        for (auto c : value) {
+            if (c == '"') {
+                escaped += "\\\"";
+            } else if (c == '\\') {
+                escaped += "\\\\";
+            } else  if (c <= 0x1f) {
+                char buf[8];
+                snprintf(buf, sizeof(buf), "\\u%04x", c);
+                escaped += buf;
+            } else {
+                escaped += c;
+            }
+        }
+        return escaped;
+    }
+
+    static std::string format_value(const std::string & field, const std::string & value) {
+        switch (test::get_field_type(field)) {
+            case test::STRING:
+                return "\"" + escape_json(value) + "\"";
+            case test::BOOL:
+                return value == "0" ? "false" : "true";
+            default:
+                return value;
+        }
+    }
+
+    void print_header(const cmd_params & params) override {
+        fprintf(fout, "[\n");
+        (void) params;
+    }
+
+    void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
+        assert(fields.size() == values.size());
+        for (size_t i = 0; i < fields.size(); i++) {
+            fprintf(fout, "    \"%s\": %s,\n", fields.at(i).c_str(), format_value(fields.at(i), values.at(i)).c_str());
+        }
+    }
+
+    void print_test(const test & t) override {
+        if (first) {
+            first = false;
+        } else {
+            fprintf(fout, ",\n");
+        }
+        fprintf(fout, "  {\n");
+        print_fields(test::get_fields(), t.get_values());
+        fprintf(fout, "    \"samples_ns\": [ %s ],\n", join(t.samples_ns, ", ").c_str());
+        fprintf(fout, "    \"samples_ts\": [ %s ]\n", join(t.get_ts(), ", ").c_str());
+        fprintf(fout, "  }");
+        fflush(fout);
+    }
+
+    void print_footer() override {
+        fprintf(fout, "\n]\n");
+    }
+};
+
+struct markdown_printer : public printer {
+    std::vector<std::string> fields;
+
+    static int get_field_width(const std::string & field) {
+        if (field == "model") {
+            return -30;
+        }
+        if (field == "t/s") {
+            return 15;
+        }
+        int width = std::max((int)field.length(), 10);
+
+        if (test::get_field_type(field) == test::STRING) {
+            return -width;
+        }
+        return width;
+    }
+
+    void print_header(const cmd_params & params) override {
+        // select fields to print
+        fields = { "model", "backend" };
+        bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
+        if (!is_cpu_backend) {
+            fields.push_back("n_gpu_layers");
+        }
+        if (params.n_batch.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
+            fields.push_back("n_threads");
+        }
+        if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
+            fields.push_back("n_batch");
+        }
+        if (params.f32_kv.size() > 1 || params.f32_kv != cmd_params_defaults.f32_kv) {
+            fields.push_back("f16_kv");
+        }
+        if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
+            fields.push_back("main_gpu");
+        }
+        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.low_vram.size() > 1 || params.low_vram != cmd_params_defaults.low_vram) {
+            fields.push_back("low_vram");
+        }
+        if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
+            fields.push_back("tensor_split");
+        }
+        fields.push_back("test");
+        fields.push_back("t/s");
+
+        fprintf(fout, "|");
+        for (const auto & field : fields) {
+            fprintf(fout, " %*s |", get_field_width(field), field.c_str());
+        }
+        fprintf(fout, "\n");
+        fprintf(fout, "|");
+        for (const auto & field : fields) {
+            int width = get_field_width(field);
+            fprintf(fout, " %s%s |", std::string(std::abs(width) - 1, '-').c_str(), width > 0 ? ":" : "-");
+        }
+        fprintf(fout, "\n");
+    }
+
+    void print_test(const test & t) override {
+        std::map<std::string, std::string> vmap = t.get_map();
+
+        fprintf(fout, "|");
+        for (const auto & field : fields) {
+            std::string value;
+            if (field == "model") {
+                value = t.model_type;
+            } else if (field == "backend") {
+                value = test::get_backend();
+            } else if (field == "test") {
+                char buf[128];
+                if (t.n_prompt > 0 && t.n_gen == 0) {
+                    snprintf(buf, sizeof(buf), "pp %d", t.n_prompt);
+                } else if (t.n_gen > 0 && t.n_prompt == 0) {
+                    snprintf(buf, sizeof(buf), "tg %d", t.n_gen);
+                } else {
+                    assert(false);
+                    exit(1);
+                }
+                value = buf;
+            } else if (field == "t/s") {
+                char buf[128];
+                snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts());
+                value = buf;
+            } else if (vmap.find(field) != vmap.end()) {
+                value = vmap.at(field);
+            } else {
+                assert(false);
+                exit(1);
+            }
+
+            int width = get_field_width(field);
+            if (field == "t/s") {
+                // HACK: the utf-8 character is 2 bytes
+                width += 1;
+            }
+            fprintf(fout, " %*s |", width, value.c_str());
+        }
+        fprintf(fout, "\n");
+    }
+
+    void print_footer() override {
+        fprintf(fout, "\nbuild: %s (%d)\n", test::build_commit.c_str(), test::build_number);
+    }
+};
+
+struct sql_printer : public printer {
+    static std::string get_sql_field_type(const std::string & field) {
+        switch (test::get_field_type(field)) {
+            case test::STRING:
+                return "TEXT";
+            case test::BOOL:
+            case test::INT:
+                return "INTEGER";
+            case test::FLOAT:
+                return "REAL";
+            default:
+                assert(false);
+                exit(1);
+        }
+    }
+
+    void print_header(const cmd_params & params) override {
+        std::vector<std::string> fields = test::get_fields();
+        fprintf(fout, "CREATE TABLE IF NOT EXISTS test (\n");
+        for (size_t i = 0; i < fields.size(); i++) {
+            fprintf(fout, "  %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(),  i < fields.size() - 1 ? "," : "");
+        }
+        fprintf(fout, ");\n");
+        fprintf(fout, "\n");
+        (void) params;
+    }
+
+    void print_test(const test & t) override {
+        fprintf(fout, "INSERT INTO test (%s) ", join(test::get_fields(), ", ").c_str());
+        fprintf(fout, "VALUES (");
+        std::vector<std::string> values = t.get_values();
+        for (size_t i = 0; i < values.size(); i++) {
+            fprintf(fout, "'%s'%s", values.at(i).c_str(), i < values.size() - 1 ? ", " : "");
+        }
+        fprintf(fout, ");\n");
+    }
+};
+
+static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
+    std::vector<llama_token> tokens(n_batch, llama_token_bos());
+    int n_processed = 0;
+    while (n_processed < n_prompt) {
+        int n_tokens = std::min(n_prompt - n_processed, n_batch);
+        llama_eval(ctx, tokens.data(), n_tokens, n_past + n_processed, n_threads);
+        n_processed += n_tokens;
+    }
+}
+
+static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
+    llama_token token = llama_token_bos();
+    for (int i = 0; i < n_gen; i++) {
+        llama_eval(ctx, &token, 1, n_past + i, n_threads);
+    }
+}
+
+static void llama_null_log_callback(enum llama_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) text;
+    (void) user_data;
+}
+
+int main(int argc, char ** argv) {
+#if !defined(NDEBUG)
+    fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
+#endif
+
+#if (defined(_MSC_VER) && defined(_DEBUG)) || (!defined(_MSC_VER) && !defined(__OPTIMIZE__))
+    fprintf(stderr, "warning: debug build, performance may be affected\n");
+#endif
+
+#if defined(__SANITIZE_ADDRESS__) || defined(__SANITIZE_THREAD__)
+    fprintf(stderr, "warning: sanitizer enabled, performance may be affected\n");
+#endif
+
+    cmd_params params = parse_cmd_params(argc, argv);
+
+    // initialize llama.cpp
+    if (!params.verbose) {
+        llama_log_set(llama_null_log_callback, NULL);
+    }
+    bool numa = false;
+    llama_backend_init(numa);
+
+    // initialize printer
+    std::unique_ptr<printer> p;
+    switch (params.output_format) {
+        case CSV:
+            p.reset(new csv_printer());
+            break;
+        case JSON:
+            p.reset(new json_printer());
+            break;
+        case MARKDOWN:
+            p.reset(new markdown_printer());
+            break;
+        case SQL:
+            p.reset(new sql_printer());
+            break;
+        default:
+            assert(false);
+            exit(1);
+    }
+    p->fout = stdout;
+    p->print_header(params);
+
+    std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
+
+    for (const auto & inst : params_instances) {
+        // TODO: keep the model between tests when possible
+        llama_context_params lparams = inst.to_llama_params();
+
+        llama_model * lmodel  = llama_load_model_from_file(inst.model.c_str(), lparams);
+        if (lmodel == NULL) {
+            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
+            return 1;
+        }
+
+        llama_context * ctx = llama_new_context_with_model(lmodel, lparams);
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
+            llama_free_model(lmodel);
+            return 1;
+        }
+
+        test t(inst, lmodel, ctx);
+
+        // warmup run
+        test_gen(ctx, 1, 0, t.n_threads);
+
+        for (int i = 0; i < params.reps; i++) {
+            uint64_t t_start = get_time_ns();
+            if (t.n_prompt > 0) {
+                test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
+            }
+            if (t.n_gen > 0) {
+                test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
+            }
+            uint64_t t_ns = get_time_ns() - t_start;
+            t.samples_ns.push_back(t_ns);
+        }
+
+        p->print_test(t);
+
+        llama_print_timings(ctx);
+
+        llama_free(ctx);
+        llama_free_model(lmodel);
+    }
+
+    p->print_footer();
+
+    llama_backend_free();
+
+    return 0;
+}

examples/perplexity/perplexity.cpp

@@ -5,6 +5,7 @@
 #include <cmath>
 #include <ctime>
 #include <sstream>
+#include <cstring>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -88,7 +89,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
                 fprintf(stderr, "%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
             }
-            fprintf(stderr, "%d minutes\n", total_seconds / 60);
+            fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
         // We get the logits for all the tokens in the context window (params.n_ctx)
@@ -209,17 +210,19 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     double acc = 0.0f;
     const int n_vocab = llama_n_vocab(ctx);
 
+    std::vector<float> tok_logits(n_vocab);
+
     for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
 
         // Tokenize the context to count tokens
         std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
         size_t context_size = context_embd.size();
 
-        for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
+        // Do the 1st ending
-
+        // In this case we include the context when evaluating
-            // Tokenize the query
+        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], prepend_bos);
-            std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
+        auto query_size = query_embd.size();
-            size_t query_size = query_embd.size();
+        //printf("First query: %d\n",(int)query_size);
 
         // Stop if query wont fit the ctx window
         if (query_size > (size_t)params.n_ctx) {
@@ -238,19 +241,64 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             return;
         }
 
-            const auto query_logits = llama_get_logits(ctx);
+        auto query_logits = llama_get_logits(ctx);
-            std::vector<float> logits;
-            logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
 
-            hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
+        std::memcpy(tok_logits.data(), query_logits + (context_size-1)*n_vocab, n_vocab*sizeof(float));
-            hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
+        const auto first_probs = softmax(tok_logits);
+
+        hs_data[task_idx].ending_logprob_count[0] = 1;
+        hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);
 
         // Calculate the logprobs over the ending
-            for (size_t j = context_size-1; j < query_size - 1; j++) {
+        for (size_t j = context_size; j < query_size - 1; j++) {
-                // Calculate probability of next token, given the previous ones.
+
-                const std::vector<float> tok_logits(
+            std::memcpy(tok_logits.data(), query_logits + j*n_vocab, n_vocab*sizeof(float));
-                    logits.begin() + (j + 0) * n_vocab,
+
-                    logits.begin() + (j + 1) * n_vocab);
+            const float prob = softmax(tok_logits)[query_embd[j + 1]];
+
+            hs_data[task_idx].ending_logprob[0] += std::log(prob);
+            hs_data[task_idx].ending_logprob_count[0]++;
+        }
+
+        // Calculate the mean token logprob for acc_norm
+        hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];
+
+        // Do the remaining endings
+        // For these, we use the bare ending with n_past = context_size
+        //
+        for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
+
+            // Tokenize the query
+            query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
+            query_size = query_embd.size();
+            //printf("Second query: %d\n",(int)query_size);
+
+            // Stop if query wont fit the ctx window
+            if (context_size + query_size > (size_t)params.n_ctx) {
+                fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
+                return;
+            }
+
+            // Speedup small evaluations by evaluating atleast 32 tokens
+            // No, resizing to 32 is actually slightly slower (at least on CUDA)
+            //if (query_size < 32) {
+            //    query_embd.resize(32);
+            //}
+
+            // Evaluate the query
+            if (llama_eval(ctx, query_embd.data(), query_embd.size(), context_size, params.n_threads)) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return;
+            }
+
+            query_logits = llama_get_logits(ctx);
+
+            hs_data[task_idx].ending_logprob_count[ending_idx] = 1;
+            hs_data[task_idx].ending_logprob[ending_idx] = std::log(first_probs[query_embd[0]]);
+
+            // Calculate the logprobs over the ending
+            for (size_t j = 0; j < query_size - 1; j++) {
+                std::memcpy(tok_logits.data(), query_logits + j*n_vocab, n_vocab*sizeof(float));
 
                 const float prob = softmax(tok_logits)[query_embd[j + 1]];
 
@@ -267,9 +315,9 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         }
 
         // Find the ending with maximum logprob
-        size_t ending_logprob_max_idx = -1;
+        size_t ending_logprob_max_idx = 0;
-        double ending_logprob_max_val = -INFINITY;
+        double ending_logprob_max_val = hs_data[task_idx].ending_logprob[0];
-        for (size_t j=0; j < 4; j++) {
+        for (size_t j = 1; j < 4; j++) {
             if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
                 ending_logprob_max_idx = j;
                 ending_logprob_max_val =  hs_data[task_idx].ending_logprob[j];

examples/server/deps.sh

@@ -11,8 +11,10 @@ echo >> $PUBLIC/index.js # add newline
 
 FILES=$(ls $PUBLIC)
 
+cd $PUBLIC
 for FILE in $FILES; do
-  func=$(echo $FILE | tr '.' '_')
+  echo "generate $FILE.hpp"
-  echo "generate $FILE.hpp ($func)"
+
-  xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
+  # use simple flag for old version of xxd
+  xxd -i $FILE > $DIR/$FILE.hpp
 done

examples/server/public/index.html

@@ -161,12 +161,12 @@
     import { SchemaConverter } from '/json-schema-to-grammar.mjs';
 
     const session = signal({
-      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
+      prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
       template: "{{prompt}}\n\n{{history}}\n{{char}}:",
       historyTemplate: "{{name}}: {{message}}",
       transcript: [],
       type: "chat",
-      char: "llama",
+      char: "Llama",
       user: "User",
     })
 

ggml-cuda.cu

@@ -6469,3 +6469,15 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
     func(tensor->src[0], tensor->src[1], tensor);
     return true;
 }
+
+int ggml_cuda_get_device_count() {
+    int device_count;
+    CUDA_CHECK(cudaGetDeviceCount(&device_count));
+    return device_count;
+}
+
+void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
+    cudaDeviceProp prop;
+    CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
+    snprintf(description, description_size, "%s", prop.name);
+}

ggml-cuda.h

@@ -8,29 +8,25 @@ extern "C" {
 
 #define GGML_CUDA_MAX_DEVICES       16
 
-void   ggml_init_cublas(void);
+GGML_API void   ggml_init_cublas(void);
-void   ggml_cuda_set_tensor_split(const float * tensor_split);
+GGML_API void * ggml_cuda_host_malloc(size_t size);
+GGML_API void   ggml_cuda_host_free(void * ptr);
 
-void   ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cuda_set_tensor_split(const float * tensor_split);
-size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
-void   ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
+GGML_API void   ggml_cuda_free_data(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_set_main_device(int main_device);
+GGML_API void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
+GGML_API void   ggml_cuda_set_scratch_size(size_t scratch_size);
+GGML_API void   ggml_cuda_free_scratch(void);
+GGML_API bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
 
-// TODO: export these with GGML_API
+GGML_API int    ggml_cuda_get_device_count(void);
-void * ggml_cuda_host_malloc(size_t size);
+GGML_API void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
-void   ggml_cuda_host_free(void * ptr);
-
-void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
-
-void   ggml_cuda_free_data(struct ggml_tensor * tensor);
-void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
-void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
-void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
-void   ggml_cuda_set_main_device(int main_device);
-void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
-void   ggml_cuda_set_scratch_size(size_t scratch_size);
-void   ggml_cuda_free_scratch(void);
-bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
 
 #ifdef  __cplusplus
 }

ggml.c

@@ -1643,11 +1643,37 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
 static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 
 static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+    [GGML_TYPE_I8] = {
+        .type_name                = "i8",
+        .blck_size                = 1,
+        .type_size                = sizeof(int8_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I16] = {
+        .type_name                = "i16",
+        .blck_size                = 1,
+        .type_size                = sizeof(int16_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I32] = {
+        .type_name                = "i32",
+        .blck_size                = 1,
+        .type_size                = sizeof(int32_t),
+        .is_quantized             = false,
+    },
     [GGML_TYPE_F32] = {
+        .type_name                = "f32",
+        .blck_size                = 1,
+        .type_size                = sizeof(float),
+        .is_quantized             = false,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
         .vec_dot_type             = GGML_TYPE_F32,
     },
     [GGML_TYPE_F16] = {
+        .type_name                = "f16",
+        .blck_size                = 1,
+        .type_size                = sizeof(ggml_fp16_t),
+        .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
         .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
@@ -1655,6 +1681,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_F16,
     },
     [GGML_TYPE_Q4_0] = {
+        .type_name                = "q4_0",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
         .from_float               = quantize_row_q4_0,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
@@ -1662,6 +1692,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q4_1] = {
+        .type_name                = "q4_1",
+        .blck_size                = QK4_1,
+        .type_size                = sizeof(block_q4_1),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
         .from_float               = quantize_row_q4_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
@@ -1669,6 +1703,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q5_0] = {
+        .type_name                = "q5_0",
+        .blck_size                = QK5_0,
+        .type_size                = sizeof(block_q5_0),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
         .from_float               = quantize_row_q5_0,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
@@ -1676,6 +1714,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q5_1] = {
+        .type_name                = "q5_1",
+        .blck_size                = QK5_1,
+        .type_size                = sizeof(block_q5_1),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
         .from_float               = quantize_row_q5_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
@@ -1683,6 +1725,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q8_0] = {
+        .type_name                = "q8_0",
+        .blck_size                = QK8_0,
+        .type_size                = sizeof(block_q8_0),
+        .is_quantized             = true,
         .to_float                 = dequantize_row_q8_0,
         .from_float               = quantize_row_q8_0,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
@@ -1690,12 +1736,20 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q8_1] = {
+        .type_name                = "q8_1",
+        .blck_size                = QK8_1,
+        .type_size                = sizeof(block_q8_1),
+        .is_quantized             = true,
         .from_float               = quantize_row_q8_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
 #ifdef GGML_USE_K_QUANTS
     [GGML_TYPE_Q2_K] = {
+        .type_name                = "q2_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q2_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
         .from_float               = quantize_row_q2_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
@@ -1703,6 +1757,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q3_K] = {
+        .type_name                = "q3_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q3_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
         .from_float               = quantize_row_q3_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
@@ -1710,6 +1768,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q4_K] = {
+        .type_name                = "q4_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q4_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
         .from_float               = quantize_row_q4_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
@@ -1717,6 +1779,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q5_K] = {
+        .type_name                = "q5_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q5_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
         .from_float               = quantize_row_q5_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
@@ -1724,6 +1790,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q6_K] = {
+        .type_name                = "q6_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q6_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
         .from_float               = quantize_row_q6_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
@@ -1731,15 +1801,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q8_K] = {
+        .type_name                = "q8_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q8_K),
+        .is_quantized             = true,
         .from_float               = quantize_row_q8_K,
     }
 #endif
 };
 
 // For internal test use
-ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) {
+ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
-    GGML_ASSERT(i < GGML_TYPE_COUNT);
+    GGML_ASSERT(type < GGML_TYPE_COUNT);
-    return type_traits[i];
+    return type_traits[type];
 }
 
 
@@ -3648,99 +3722,6 @@ inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) {
     *s = idx;
 }
 
-//
-// data types
-//
-
-static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = 1,
-    [GGML_TYPE_F16]  = 1,
-    [GGML_TYPE_Q4_0] = QK4_0,
-    [GGML_TYPE_Q4_1] = QK4_1,
-    [GGML_TYPE_Q5_0] = QK5_0,
-    [GGML_TYPE_Q5_1] = QK5_1,
-    [GGML_TYPE_Q8_0] = QK8_0,
-    [GGML_TYPE_Q8_1] = QK8_1,
-#ifdef GGML_USE_K_QUANTS
-    [GGML_TYPE_Q2_K] = QK_K,
-    [GGML_TYPE_Q3_K] = QK_K,
-    [GGML_TYPE_Q4_K] = QK_K,
-    [GGML_TYPE_Q5_K] = QK_K,
-    [GGML_TYPE_Q6_K] = QK_K,
-    [GGML_TYPE_Q8_K] = QK_K,
-#endif
-    [GGML_TYPE_I8]   = 1,
-    [GGML_TYPE_I16]  = 1,
-    [GGML_TYPE_I32]  = 1,
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_BLCK_SIZE is outdated");
-
-static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = sizeof(float),
-    [GGML_TYPE_F16]  = sizeof(ggml_fp16_t),
-    [GGML_TYPE_Q4_0] = sizeof(block_q4_0),
-    [GGML_TYPE_Q4_1] = sizeof(block_q4_1),
-    [GGML_TYPE_Q5_0] = sizeof(block_q5_0),
-    [GGML_TYPE_Q5_1] = sizeof(block_q5_1),
-    [GGML_TYPE_Q8_0] = sizeof(block_q8_0),
-    [GGML_TYPE_Q8_1] = sizeof(block_q8_1),
-#ifdef GGML_USE_K_QUANTS
-    [GGML_TYPE_Q2_K] = sizeof(block_q2_K),
-    [GGML_TYPE_Q3_K] = sizeof(block_q3_K),
-    [GGML_TYPE_Q4_K] = sizeof(block_q4_K),
-    [GGML_TYPE_Q5_K] = sizeof(block_q5_K),
-    [GGML_TYPE_Q6_K] = sizeof(block_q6_K),
-    [GGML_TYPE_Q8_K] = sizeof(block_q8_K),
-#endif
-    [GGML_TYPE_I8]   = sizeof(int8_t),
-    [GGML_TYPE_I16]  = sizeof(int16_t),
-    [GGML_TYPE_I32]  = sizeof(int32_t),
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_SIZE is outdated");
-
-
-static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = "f32",
-    [GGML_TYPE_F16]  = "f16",
-    [GGML_TYPE_Q4_0] = "q4_0",
-    [GGML_TYPE_Q4_1] = "q4_1",
-    [GGML_TYPE_Q5_0] = "q5_0",
-    [GGML_TYPE_Q5_1] = "q5_1",
-    [GGML_TYPE_Q8_0] = "q8_0",
-    [GGML_TYPE_Q8_1] = "q8_1",
-    [GGML_TYPE_Q2_K] = "q2_K",
-    [GGML_TYPE_Q3_K] = "q3_K",
-    [GGML_TYPE_Q4_K] = "q4_K",
-    [GGML_TYPE_Q5_K] = "q5_K",
-    [GGML_TYPE_Q6_K] = "q6_K",
-    [GGML_TYPE_Q8_K] = "q8_K",
-    [GGML_TYPE_I8]   = "i8",
-    [GGML_TYPE_I16]  = "i16",
-    [GGML_TYPE_I32]  = "i32",
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_NAME is outdated");
-
-static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = false,
-    [GGML_TYPE_F16]  = false,
-    [GGML_TYPE_Q4_0] = true,
-    [GGML_TYPE_Q4_1] = true,
-    [GGML_TYPE_Q5_0] = true,
-    [GGML_TYPE_Q5_1] = true,
-    [GGML_TYPE_Q8_0] = true,
-    [GGML_TYPE_Q8_1] = true,
-    [GGML_TYPE_Q2_K] = true,
-    [GGML_TYPE_Q3_K] = true,
-    [GGML_TYPE_Q4_K] = true,
-    [GGML_TYPE_Q5_K] = true,
-    [GGML_TYPE_Q6_K] = true,
-    [GGML_TYPE_Q8_K] = true,
-    [GGML_TYPE_I8]   = false,
-    [GGML_TYPE_I16]  = false,
-    [GGML_TYPE_I32]  = false,
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_IS_QUANTIZED is outdated");
-
 static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "NONE",
 
@@ -4110,29 +4091,33 @@ size_t ggml_nbytes(const struct ggml_tensor * tensor) {
     //
     // is enough, but just in case, adding the second part
 
-    return GGML_PAD(MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]), GGML_MEM_ALIGN);
+    return GGML_PAD(MAX(tensor->ne[3]*tensor->nb[3], ggml_nelements(tensor)*ggml_type_size(tensor->type))/ggml_blck_size(tensor->type), GGML_MEM_ALIGN);
 }
 
 size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
-    return (nrows_split*tensor->ne[0]*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
+    return (nrows_split*tensor->ne[0]*ggml_type_size(tensor->type))/ggml_blck_size(tensor->type);
 }
 
 int ggml_blck_size(enum ggml_type type) {
-    return GGML_BLCK_SIZE[type];
+    return type_traits[type].blck_size;
 }
 
 size_t ggml_type_size(enum ggml_type type) {
-    return GGML_TYPE_SIZE[type];
+    return type_traits[type].type_size;
 }
 
 float ggml_type_sizef(enum ggml_type type) {
-    return ((float)(GGML_TYPE_SIZE[type]))/GGML_BLCK_SIZE[type];
+    return ((float)(type_traits[type].type_size))/type_traits[type].blck_size;
 }
 
 const char * ggml_type_name(enum ggml_type type) {
-    return GGML_TYPE_NAME[type];
+    return type_traits[type].type_name;
+}
+
+bool ggml_is_quantized(enum ggml_type type) {
+    return type_traits[type].is_quantized;
 }
 
 const char * ggml_op_name(enum ggml_op op) {
@@ -4144,7 +4129,7 @@ const char * ggml_op_symbol(enum ggml_op op) {
 }
 
 size_t ggml_element_size(const struct ggml_tensor * tensor) {
-    return GGML_TYPE_SIZE[tensor->type];
+    return ggml_type_size(tensor->type);
 }
 
 static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) {
@@ -4182,10 +4167,6 @@ static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct
         (t0->ne[3] == t1->ne[3]);
 }
 
-bool ggml_is_quantized(enum ggml_type type) {
-    return GGML_IS_QUANTIZED[type];
-}
-
 enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
     enum ggml_type wtype = GGML_TYPE_COUNT;
 
@@ -4223,8 +4204,8 @@ bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/GGML_BLCK_SIZE[tensor->type] &&
+        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
         tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
@@ -4233,7 +4214,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
         tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
@@ -4248,7 +4229,7 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
         tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
@@ -4567,7 +4548,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
     size_t data_size = 0;
 
     if (data == NULL && !ctx->no_alloc) {
-        data_size += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
+        data_size += ggml_type_size(type)*(ne[0]/ggml_blck_size(type));
         for (int i = 1; i < n_dims; i++) {
             data_size *= ne[i];
         }
@@ -4622,8 +4603,8 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         result->ne[i] = ne[i];
     }
 
-    result->nb[0] = GGML_TYPE_SIZE[type];
+    result->nb[0] = ggml_type_size(type);
-    result->nb[1] = result->nb[0]*(result->ne[0]/GGML_BLCK_SIZE[type]);
+    result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type));
     for (int i = 2; i < GGML_MAX_DIMS; i++) {
         result->nb[i] = result->nb[i - 1]*result->ne[i - 1];
     }
@@ -7745,7 +7726,7 @@ static void ggml_compute_forward_dup_same_cont(
         memcpy(
             ((char *)  dst->data + ie0*nb0),
             ((char *) src0->data + ie0*nb00),
-            (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]);
+            (ie1 - ie0) * ggml_type_size(src0->type));
     }
 
 }
@@ -7779,7 +7760,7 @@ static void ggml_compute_forward_dup_f16(
 
     if (src0->type == dst->type &&
         ne00 == ne0 &&
-        nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) {
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
         // copy by rows
         const size_t rs = ne00*nb00;
         for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -7837,7 +7818,7 @@ static void ggml_compute_forward_dup_f16(
                 float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
 
                 size_t id = 0;
-                size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
                 char * dst_ptr = (char *) dst->data;
 
                 for (int i03 = 0; i03 < ne03; i03++) {
@@ -8050,7 +8031,7 @@ static void ggml_compute_forward_dup_f32(
 
     if (src0->type == dst->type &&
         ne00 == ne0 &&
-        nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) {
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
         // copy by rows
         const size_t rs = ne00*nb00;
         for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -8089,7 +8070,7 @@ static void ggml_compute_forward_dup_f32(
                 ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
 
                 size_t id = 0;
-                size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
                 char * dst_ptr = (char *) dst->data;
 
                 for (int i03 = 0; i03 < ne03; i03++) {
@@ -8501,7 +8482,7 @@ static void ggml_compute_forward_add_q_f32(
     ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0 or src1
-    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == ggml_type_size(type));
     GGML_ASSERT(nb10 == sizeof(float));
 
     // dst cannot be transposed or permuted
@@ -8775,7 +8756,7 @@ static void ggml_compute_forward_add1_q_f32(
     ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0
-    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == ggml_type_size(type));
 
     // dst cannot be transposed or permuted
     GGML_ASSERT(nb0 <= nb1);
@@ -10629,7 +10610,7 @@ static void ggml_compute_forward_mul_mat(
     GGML_ASSERT(ne3 == ne13);
 
     // we don't support permuted src0 or src1
-    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == ggml_type_size(type));
     GGML_ASSERT(nb10 == sizeof(float));
 
     // dst cannot be transposed or permuted
@@ -10712,7 +10693,7 @@ static void ggml_compute_forward_mul_mat(
     if (params->type == GGML_TASK_INIT) {
         if (src1->type != vec_dot_type) {
             char * wdata = params->wdata;
-            const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+            const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
 
             for (int64_t i13 = 0; i13 < ne13; ++i13) {
                 for (int64_t i12 = 0; i12 < ne12; ++i12) {
@@ -10732,7 +10713,7 @@ static void ggml_compute_forward_mul_mat(
     }
 
     const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
-    const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+    const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
 
     const int64_t nr0 = ne01;           // src0 rows
     const int64_t nr1 = ne11*ne12*ne13; // src1 rows
@@ -11205,7 +11186,7 @@ static void ggml_compute_forward_get_rows_q(
 
     assert( dst->ne[0] == nc);
     assert( dst->ne[1] == nr);
-    assert(src0->nb[0] == GGML_TYPE_SIZE[type]);
+    assert(src0->nb[0] == ggml_type_size(type));
 
     for (int i = 0; i < nr; ++i) {
         const int r = ((int32_t *) src1->data)[i];
@@ -16382,7 +16363,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
 
                     size_t cur = 0;
                     if (ggml_is_quantized(node->type)) {
-                        cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->ne[0] * n_tasks;
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                     }
 
                     work_size = MAX(work_size, cur);
@@ -16395,7 +16376,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     size_t cur = 0;
 
                     if (ggml_is_quantized(node->src[0]->type)) {
-                        cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src[0]->ne[0] * n_tasks;
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
                     }
 
                     work_size = MAX(work_size, cur);
@@ -16407,7 +16388,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     size_t cur = 0;
 
                     if (ggml_is_quantized(node->src[0]->type)) {
-                        cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src[1]->ne[0] * n_tasks;
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
                     }
 
                     work_size = MAX(work_size, cur);
@@ -16490,12 +16471,12 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                                      //       the threads are still spinning
                         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]);
+                            cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
                         }
                     } else
 #endif
                     if (node->src[1]->type != vec_dot_type) {
-                        cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src[1])/GGML_BLCK_SIZE[vec_dot_type];
+                        cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type);
                     } else {
                         cur = 0;
                     }
@@ -18301,8 +18282,8 @@ enum ggml_opt_result ggml_opt_resume(
         struct ggml_tensor * f) {
 
     // build forward + backward compute graphs
-    struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0));
+    struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
-    struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0));
+    struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
 
     struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
     struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;

ggml.h

@@ -1740,6 +1740,10 @@ extern "C" {
     typedef void (*ggml_vec_dot_t)   (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
 
     typedef struct {
+        const char      * type_name;
+        int               blck_size;
+        size_t            type_size;
+        bool              is_quantized;
         ggml_to_float_t   to_float;
         ggml_from_float_t from_float;
         ggml_from_float_t from_float_reference;
@@ -1747,7 +1751,7 @@ extern "C" {
         enum ggml_type    vec_dot_type;
     } ggml_type_traits_t;
 
-    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
+    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
 
 #ifdef  __cplusplus
 }

llama.cpp

@@ -115,9 +115,9 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
 // memory sizes (calculated for n_batch == 512)
 //
 
-static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0(int n_ctx)
+static std::map<e_model, size_t> MEM_REQ_SCRATCH0(int n_ctx)
 {
-    static std::map<e_model, size_t> k_sizes = {
+    std::map<e_model, size_t> k_sizes = {
         { MODEL_3B,   ((size_t) n_ctx / 16ull +  92ull) * MB },
         { MODEL_7B,   ((size_t) n_ctx / 16ull + 100ull) * MB },
         { MODEL_13B,  ((size_t) n_ctx / 12ull + 120ull) * MB },
@@ -996,7 +996,7 @@ static const char *llama_file_version_name(llama_file_version version) {
     return "unknown";
 }
 
-static const char *llama_ftype_name(enum llama_ftype ftype) {
+const char * llama_ftype_name(enum llama_ftype ftype) {
     switch (ftype) {
         case LLAMA_FTYPE_ALL_F32:     return "all F32";
         case LLAMA_FTYPE_MOSTLY_F16:  return "mostly F16";
@@ -1799,6 +1799,13 @@ static bool llama_eval_internal(
 
     LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
 
+    LLAMA_ASSERT(n_tokens > 0);
+    LLAMA_ASSERT(n_past >= 0);
+    LLAMA_ASSERT(n_threads > 0);
+    // TODO: keep the values of n_batch and n_ctx
+    // LLAMA_ASSERT(n_tokens <= n_batch);
+    // LLAMA_ASSERT(n_past + n_tokens <= n_ctx);
+
     const int64_t t_start_us = ggml_time_us();
 
 #ifdef GGML_USE_MPI
@@ -2077,37 +2084,81 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
 // grammar - internal
 //
 
+struct llama_partial_utf8 {
+    uint32_t value;    // bit value so far (unshifted)
+    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
+};
+
 struct llama_grammar {
     const std::vector<std::vector<llama_grammar_element>>   rules;
     std::vector<std::vector<const llama_grammar_element *>> stacks;
+
+    // buffer for partially generated UTF-8 sequence from accepted tokens
+    llama_partial_utf8                                      partial_utf8;
 };
 
 struct llama_grammar_candidate {
     size_t               index;
     const uint32_t     * code_points;
+    llama_partial_utf8   partial_utf8;
 };
 
-// NOTE: assumes valid utf8 (but checks for overrun)
+// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
-// adds a terminating 0 for use as pointer
+// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
-std::vector<uint32_t> decode_utf8(const char * src) {
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
-    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
+        const char         * src,
+        llama_partial_utf8   partial_start) {
+    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
     const char          * pos      = src;
     std::vector<uint32_t> code_points;
+    uint32_t              value    = partial_start.value;
+    int                   n_remain = partial_start.n_remain;
+
+    // continue previous decode, if applicable
+    while (*pos != 0 && n_remain > 0) {
+        uint8_t next_byte = static_cast<uint8_t>(*pos);
+        if ((next_byte >> 6) != 2) {
+            // invalid sequence, abort
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
+        }
+        value = (value << 6) + (next_byte & 0x3F);
+        ++pos;
+        --n_remain;
+    }
+
+    if (partial_start.n_remain > 0 && n_remain == 0) {
+        code_points.push_back(value);
+    }
+
+    // decode any subsequent utf-8 sequences, which may end in an incomplete one
     while (*pos != 0) {
         uint8_t  first_byte = static_cast<uint8_t>(*pos);
         uint8_t  highbits   = first_byte >> 4;
-        int      len        = lookup[highbits];
+                 n_remain   = lookup[highbits] - 1;
-        uint8_t  mask       = (1 << (8 - len)) - 1;
+
-        uint32_t value      = first_byte & mask;
+        if (n_remain < 0) {
-        const char * end    = pos + len; // may overrun!
+            // invalid sequence, abort
-        ++pos;
+            code_points.clear();
-        for ( ; pos < end && *pos != 0; ++pos) {
+            code_points.push_back(0);
-            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
         }
+
+        uint8_t  mask       = (1 << (7 - n_remain)) - 1;
+                 value      = first_byte & mask;
+        ++pos;
+        while (*pos != 0 && n_remain > 0) {
+            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
+            ++pos;
+            --n_remain;
+        }
+        if (n_remain == 0) {
             code_points.push_back(value);
         }
+    }
     code_points.push_back(0);
-    return code_points;
+
+    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
 }
 
 // returns true iff pos points to the end of one of the definitions of a rule
@@ -2144,6 +2195,56 @@ static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
     return std::make_pair(found == is_positive_char, pos);
 }
 
+// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
+// range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static bool llama_grammar_match_partial_char(
+        const llama_grammar_element * pos,
+        const llama_partial_utf8      partial_utf8) {
+
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    LLAMA_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
+
+    uint32_t partial_value = partial_utf8.value;
+    int      n_remain      = partial_utf8.n_remain;
+
+    // invalid sequence or 7-bit char split across 2 bytes (overlong)
+    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
+        return false;
+    }
+
+    // range of possible code points this partial UTF-8 sequence could complete to
+    uint32_t low  = partial_value << (n_remain * 6);
+    uint32_t high = low | ((1 << (n_remain * 6)) - 1);
+
+    if (low == 0) {
+        if (n_remain == 2) {
+            low = 1 << 11;
+        } else if (n_remain == 3) {
+            low = 1 << 16;
+        }
+    }
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            if (pos->value <= high && low <= pos[1].value) {
+                return is_positive_char;
+            }
+            pos += 2;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            if (low <= pos->value && pos->value <= high) {
+                return is_positive_char;
+            }
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return !is_positive_char;
+}
+
+
 // transforms a grammar pushdown stack into N possible stacks, all ending
 // at a character range (terminal element)
 static void llama_grammar_advance_stack(
@@ -2244,8 +2345,11 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
     std::vector<llama_grammar_candidate> rejects;
 
     if (stack.empty()) {
-        // accept nothing; EOS is handled elsewhere
+        for (auto tok : candidates) {
-        rejects.insert(rejects.end(), candidates.begin(), candidates.end());
+            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
+                rejects.push_back(tok);
+            }
+        }
         return rejects;
     }
 
@@ -2253,10 +2357,15 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
 
     std::vector<llama_grammar_candidate> next_candidates;
     for (auto tok : candidates) {
-        if (llama_grammar_match_char(stack_pos, tok.code_points[0]).first) {
+        if (*tok.code_points == 0) {
-            if (tok.code_points[1] != 0) {
+            // reached end of full codepoints in token, reject iff it ended in a partial sequence
-                next_candidates.push_back({ tok.index, tok.code_points + 1 });
+            // that cannot satisfy this position in grammar
+            if (tok.partial_utf8.n_remain != 0 &&
+                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
+                rejects.push_back(tok);
             }
+        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
+            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
         } else {
             rejects.push_back(tok);
         }
@@ -2274,7 +2383,7 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
 
     auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
     for (auto tok : next_rejects) {
-        rejects.push_back({ tok.index, tok.code_points - 1 });
+        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
     }
 
     return rejects;
@@ -2339,7 +2448,7 @@ struct llama_grammar * llama_grammar_init(
         }
     } while (true);
 
-    return new llama_grammar{ std::move(vec_rules), std::move(stacks) };
+    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
 }
 
 void llama_grammar_free(struct llama_grammar * grammar) {
@@ -2645,7 +2754,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
 
     const llama_token eos = llama_token_eos();
 
-    std::vector<std::vector<uint32_t>>   candidates_decoded;
+    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
     std::vector<llama_grammar_candidate>                              candidates_grammar;
 
     for (size_t i = 0; i < candidates->size; ++i) {
@@ -2658,8 +2767,10 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
         } else if (*str == 0) {
             candidates->data[i].logit = -INFINITY;
         } else {
-            candidates_decoded.push_back(decode_utf8(str));
+            candidates_decoded.push_back(decode_utf8(str, grammar->partial_utf8));
-            candidates_grammar.push_back({ i, candidates_decoded.back().data() });
+            candidates_grammar.push_back({
+                i, candidates_decoded.back().first.data(), candidates_decoded.back().second
+            });
         }
     }
 
@@ -2860,11 +2971,14 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
     }
 
     const char * str = llama_token_to_str(ctx, token);
+
     // Note terminating 0 in decoded string
-    auto code_points = decode_utf8(str);
+    const auto   decoded     = decode_utf8(str, grammar->partial_utf8);
+    const auto & code_points = decoded.first;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
         grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
     }
+    grammar->partial_utf8 = decoded.second;
     LLAMA_ASSERT(!grammar->stacks.empty());
 
     ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
@@ -4167,6 +4281,10 @@ int llama_n_embd(const struct llama_context * ctx) {
     return ctx->model.hparams.n_embd;
 }
 
+int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) {
+    return snprintf(buf, buf_size, "LLaMA %s %s", llama_model_type_name(model->type), llama_ftype_name(model->hparams.ftype));
+}
+
 int llama_get_vocab_from_model(
         const struct llama_model * model,
         const char * * strings,

llama.h

@@ -351,6 +351,8 @@ extern "C" {
     LLAMA_API int llama_n_ctx_from_model  (const struct llama_model * model);
     LLAMA_API int llama_n_embd_from_model (const struct llama_model * model);
 
+    LLAMA_API int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size);
+
     // Get the vocabulary as output parameters.
     // Returns number of results.
     LLAMA_API int llama_get_vocab(

tests/test-llama-grammar.cpp

@@ -199,7 +199,7 @@ int main()
         uint32_t *cp = new uint32_t[2]; // dynamically allocate memory for code_point
         cp[0] = 37 + i;
         cp[1] = 0;
-        next_candidates[i] = {i, cp};
+        next_candidates[i] = {i, cp, {}};
     }
 
     std::vector<std::vector<std::pair<uint32_t, uint16_t>>> expected_reject = {