llama.cpp/common/common.cpp

#include "ggml.h"
#include "gguf.h"

#include "common.h"
#include "log.h"
#include "llama.h"
#include "sampling.h"
#include "unicode.h"

#include <algorithm>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <chrono>
#include <cstdarg>
#include <cstring>
#include <ctime>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <iterator>
#include <regex>
#include <sstream>
#include <string>
#include <thread>
#include <unordered_set>
#include <vector>

#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
#include <sys/sysctl.h>
#endif

#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#   define NOMINMAX
#endif
#include <locale>
#include <windows.h>
#include <string.h>
#include <fcntl.h>
#include <io.h>
#else
#include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h>
#endif

#if defined(__linux__)
#include <sys/types.h>
#include <pwd.h>
#endif

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

common_time_meas::common_time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}

common_time_meas::~common_time_meas() {
    if (t_start_us >= 0) {
        t_acc += ggml_time_us() - t_start_us;
    }
}

//
// CPU utils
//

int32_t cpu_get_num_physical_cores() {
#ifdef __linux__
    // enumerate the set of thread siblings, num entries is num cores
    std::unordered_set<std::string> siblings;
    for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
        std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
            + std::to_string(cpu) + "/topology/thread_siblings");
        if (!thread_siblings.is_open()) {
            break; // no more cpus
        }
        std::string line;
        if (std::getline(thread_siblings, line)) {
            siblings.insert(line);
        }
    }
    if (!siblings.empty()) {
        return static_cast<int32_t>(siblings.size());
    }
#elif defined(__APPLE__) && defined(__MACH__)
    int32_t num_physical_cores;
    size_t len = sizeof(num_physical_cores);
    int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
    if (result == 0) {
        return num_physical_cores;
    }
    result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
    if (result == 0) {
        return num_physical_cores;
    }
#elif defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
    // TODO: windows + arm64 + mingw64
    unsigned int n_threads_win = std::thread::hardware_concurrency();
    unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4;

    DWORD buffer_size = 0;
    if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) {
        if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) {
            return default_threads;
        }
    }

    std::vector<char> buffer(buffer_size);
    if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) {
        return default_threads;
    }

    int32_t num_physical_cores = 0;
    PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data());
    while (buffer_size > 0) {
        if (info->Relationship == RelationProcessorCore) {
            num_physical_cores += info->Processor.GroupCount;
        }
        buffer_size -= info->Size;
        info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size);
    }

    return num_physical_cores > 0 ? num_physical_cores : default_threads;
#endif
    unsigned int n_threads = std::thread::hardware_concurrency();
    return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}

#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
#include <pthread.h>

static void cpuid(unsigned leaf, unsigned subleaf,
                  unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
    __asm__("movq\t%%rbx,%%rsi\n\t"
            "cpuid\n\t"
            "xchgq\t%%rbx,%%rsi"
            : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
            : "0"(leaf), "2"(subleaf));
}

static int pin_cpu(int cpu) {
    cpu_set_t mask;
    CPU_ZERO(&mask);
    CPU_SET(cpu, &mask);
    return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
}

static bool is_hybrid_cpu(void) {
    unsigned eax, ebx, ecx, edx;
    cpuid(7, 0, &eax, &ebx, &ecx, &edx);
    return !!(edx & (1u << 15));
}

static bool is_running_on_efficiency_core(void) {
    unsigned eax, ebx, ecx, edx;
    cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
    int intel_atom = 0x20;
    int core_type = (eax & 0xff000000u) >> 24;
    return core_type == intel_atom;
}

static int cpu_count_math_cpus(int n_cpu) {
    int result = 0;
    for (int cpu = 0; cpu < n_cpu; ++cpu) {
        if (pin_cpu(cpu)) {
            return -1;
        }
        if (is_running_on_efficiency_core()) {
            continue; // efficiency cores harm lockstep threading
        }
        ++cpu; // hyperthreading isn't useful for linear algebra
        ++result;
    }
    return result;
}

#endif // __x86_64__ && __linux__

/**
 * Returns number of CPUs on system that are useful for math.
 */
int32_t cpu_get_num_math() {
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
    int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
    if (n_cpu < 1) {
        return cpu_get_num_physical_cores();
    }
    if (is_hybrid_cpu()) {
        cpu_set_t affinity;
        if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
            int result = cpu_count_math_cpus(n_cpu);
            pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
            if (result > 0) {
                return result;
            }
        }
    }
#endif
    return cpu_get_num_physical_cores();
}

// Helper for setting process priority

#if defined(_WIN32)

bool set_process_priority(enum ggml_sched_priority prio) {
    if (prio == GGML_SCHED_PRIO_NORMAL) {
        return true;
    }

    DWORD p = NORMAL_PRIORITY_CLASS;
    switch (prio) {
        case GGML_SCHED_PRIO_LOW:      p = BELOW_NORMAL_PRIORITY_CLASS; break;
        case GGML_SCHED_PRIO_NORMAL:   p = NORMAL_PRIORITY_CLASS;       break;
        case GGML_SCHED_PRIO_MEDIUM:   p = ABOVE_NORMAL_PRIORITY_CLASS; break;
        case GGML_SCHED_PRIO_HIGH:     p = HIGH_PRIORITY_CLASS;         break;
        case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS;     break;
    }

    if (!SetPriorityClass(GetCurrentProcess(), p)) {
        LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
        return false;
    }

    return true;
}

#else // MacOS and POSIX
#include <sys/types.h>
#include <sys/resource.h>

bool set_process_priority(enum ggml_sched_priority prio) {
    if (prio == GGML_SCHED_PRIO_NORMAL) {
        return true;
    }

    int p = 0;
    switch (prio) {
        case GGML_SCHED_PRIO_LOW:      p =  5;  break;
        case GGML_SCHED_PRIO_NORMAL:   p =  0;  break;
        case GGML_SCHED_PRIO_MEDIUM:   p = -5;  break;
        case GGML_SCHED_PRIO_HIGH:     p = -10; break;
        case GGML_SCHED_PRIO_REALTIME: p = -20; break;
    }

    if (setpriority(PRIO_PROCESS, 0, p) != 0) {
        LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
        return false;
    }
    return true;
}

#endif

//
// CLI argument parsing
//


void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
    int32_t n_set = 0;

    if (cpuparams.n_threads < 0) {
        // Assuming everything about cpuparams is invalid
        if (role_model != nullptr) {
            cpuparams = *role_model;
        } else {
            cpuparams.n_threads = cpu_get_num_math();
        }
    }

    for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
        if (cpuparams.cpumask[i]) {
            n_set++;
        }
    }

    if (n_set && n_set < cpuparams.n_threads) {
        // Not enough set bits, may experience performance issues.
        LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
    }
}

bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
    size_t dash_loc = range.find('-');
    if (dash_loc == std::string::npos) {
        LOG_ERR("Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
        return false;
    }

    size_t start_i;
    size_t end_i;

    if (dash_loc == 0) {
        start_i = 0;
    } else {
        start_i = std::stoull(range.substr(0, dash_loc));
        if (start_i >= GGML_MAX_N_THREADS) {
            LOG_ERR("Start index out of bounds!\n");
            return false;
        }
    }

    if (dash_loc == range.length() - 1) {
        end_i = GGML_MAX_N_THREADS - 1;
    } else {
        end_i = std::stoull(range.substr(dash_loc + 1));
        if (end_i >= GGML_MAX_N_THREADS) {
            LOG_ERR("End index out of bounds!\n");
            return false;
        }
    }

    for (size_t i = start_i; i <= end_i; i++) {
        boolmask[i] = true;
    }

    return true;
}

bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
    // Discard potential 0x prefix
    size_t start_i = 0;
    if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
        start_i = 2;
    }

    size_t num_digits = mask.length() - start_i;
    if (num_digits > 128) num_digits = 128;

    size_t end_i = num_digits + start_i;

    for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
        char c = mask.at(i);
        int8_t id = c;

        if ((c >= '0' && c <= '9')) {
            id -= '0';
        } else if (c >= 'a' && c <= 'f') {
            id -= 'a' - 10;
        } else if (c >= 'A' && c <= 'F') {
            id -= 'A' - 10;
        } else {
            LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
            return false;
        }

        boolmask[  n  ] = boolmask[  n  ] || ((id & 8) != 0);
        boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
        boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
        boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
    }

    return true;
}

void common_init() {
    llama_log_set(common_log_default_callback, NULL);

#ifdef NDEBUG
    const char * build_type = "";
#else
    const char * build_type = " (debug)";
#endif

    LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
}

std::string common_params_get_system_info(const common_params & params) {
    std::ostringstream os;

    os << "system_info: n_threads = " << params.cpuparams.n_threads;
    if (params.cpuparams_batch.n_threads != -1) {
        os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
    }
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
    // TODO: windows + arm64 + mingw64
    DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS);
    os << " / " << logicalProcessorCount << " | " << llama_print_system_info();
#else
    os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
#endif

    return os.str();
}

//
// String utils
//

std::string string_format(const char * fmt, ...) {
    va_list ap;
    va_list ap2;
    va_start(ap, fmt);
    va_copy(ap2, ap);
    int size = vsnprintf(NULL, 0, fmt, ap);
    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
    std::vector<char> buf(size + 1);
    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
    GGML_ASSERT(size2 == size);
    va_end(ap2);
    va_end(ap);
    return std::string(buf.data(), size);
}

std::string string_strip(const std::string & str) {
    size_t start = 0;
    size_t end = str.size();
    while (start < end && std::isspace(str[start])) {
        start++;
    }
    while (end > start && std::isspace(str[end - 1])) {
        end--;
    }
    return str.substr(start, end - start);
}

std::string string_get_sortable_timestamp() {
    using clock = std::chrono::system_clock;

    const clock::time_point current_time = clock::now();
    const time_t as_time_t = clock::to_time_t(current_time);
    char timestamp_no_ns[100];
    std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));

    const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
        current_time.time_since_epoch() % 1000000000).count();
    char timestamp_ns[11];
    snprintf(timestamp_ns, 11, "%09" PRId64, ns);

    return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}

void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
    if (search.empty()) {
        return;
    }
    std::string builder;
    builder.reserve(s.length());
    size_t pos = 0;
    size_t last_pos = 0;
    while ((pos = s.find(search, last_pos)) != std::string::npos) {
        builder.append(s, last_pos, pos - last_pos);
        builder.append(replace);
        last_pos = pos + search.length();
    }
    builder.append(s, last_pos, std::string::npos);
    s = std::move(builder);
}

std::string regex_escape(const std::string & s) {
    static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
    return std::regex_replace(s, special_chars, "\\$&");
}

std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
    std::ostringstream result;
    for (size_t i = 0; i < values.size(); ++i) {
        if (i > 0) {
            result << separator;
        }
        result << values[i];
    }
    return result.str();
}

std::vector<std::string> string_split(const std::string & str, const std::string & delimiter) {
    std::vector<std::string> parts;
    size_t start = 0;
    size_t end = str.find(delimiter);

    while (end != std::string::npos) {
        parts.push_back(str.substr(start, end - start));
        start = end + delimiter.length();
        end = str.find(delimiter, start);
    }

    parts.push_back(str.substr(start));

    return parts;
}

std::string string_repeat(const std::string & str, size_t n) {
    if (n == 0) {
        return "";
    }

    std::string result;
    result.reserve(str.length() * n);

    for (size_t i = 0; i < n; ++i) {
        result += str;
    }

    return result;
}

std::string string_from(bool value) {
    return value ? "true" : "false";
}

std::string string_from(const std::vector<int> & values) {
    std::stringstream buf;

    buf << "[ ";
    bool first = true;
    for (auto e : values) {
        if (first) {
            first = false;
        } else {
            buf << ", ";
        }
        buf << std::to_string(e);
    }
    buf << " ]";

    return buf.str();
}

std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens) {
    std::stringstream buf;

    buf << "[ ";

    bool first = true;
    for (const auto & token : tokens) {
        if (!first) {
            buf << ", ";
        } else {
            first = false;
        }

        auto detokenized = common_token_to_piece(ctx, token);

        buf << "'" << detokenized << "'"
            << ":" << std::to_string(token);
    }

    buf << " ]";

    return buf.str();
}

std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch) {
    std::stringstream buf;

    buf << "[ ";

    bool first = true;
    for (int i = 0; i < batch.n_tokens; ++i) {
        if (!first) {
            buf << ", ";
        } else {
            first = false;
        }

        auto detokenized = common_token_to_piece(ctx, batch.token[i]);

        buf << "\n"          << std::to_string(i)
            << ", token '"   << detokenized << "'"
            << ", pos "      << std::to_string(batch.pos[i])
            << ", n_seq_id " << std::to_string(batch.n_seq_id[i])
            << ", seq_id "   << std::to_string(batch.seq_id[i][0])
            << ", logits "   << std::to_string(batch.logits[i]);
    }

    buf << " ]";

    return buf.str();
}

void string_process_escapes(std::string & input) {
    std::size_t input_len = input.length();
    std::size_t output_idx = 0;

    for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
        if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
            switch (input[++input_idx]) {
                case 'n':  input[output_idx++] = '\n'; break;
                case 'r':  input[output_idx++] = '\r'; break;
                case 't':  input[output_idx++] = '\t'; break;
                case '\'': input[output_idx++] = '\''; break;
                case '\"': input[output_idx++] = '\"'; break;
                case '\\': input[output_idx++] = '\\'; break;
                case 'x':
                    // Handle \x12, etc
                    if (input_idx + 2 < input_len) {
                        const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
                        char *err_p = nullptr;
                        const long val = std::strtol(x, &err_p, 16);
                        if (err_p == x + 2) {
                            input_idx += 2;
                            input[output_idx++] = char(val);
                            break;
                        }
                    }
                    // fall through
                default:   input[output_idx++] = '\\';
                           input[output_idx++] = input[input_idx]; break;
            }
        } else {
            input[output_idx++] = input[input_idx];
        }
    }

    input.resize(output_idx);
}

bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
    const char * sep = strchr(data, '=');
    if (sep == nullptr || sep - data >= 128) {
        LOG_ERR("%s: malformed KV override '%s'\n", __func__, data);
        return false;
    }
    llama_model_kv_override kvo;
    std::strncpy(kvo.key, data, sep - data);
    kvo.key[sep - data] = 0;
    sep++;
    if (strncmp(sep, "int:", 4) == 0) {
        sep += 4;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
        kvo.val_i64 = std::atol(sep);
    } else if (strncmp(sep, "float:", 6) == 0) {
        sep += 6;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
        kvo.val_f64 = std::atof(sep);
    } else if (strncmp(sep, "bool:", 5) == 0) {
        sep += 5;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
        if (std::strcmp(sep, "true") == 0) {
            kvo.val_bool = true;
        } else if (std::strcmp(sep, "false") == 0) {
            kvo.val_bool = false;
        } else {
            LOG_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data);
            return false;
        }
    } else if (strncmp(sep, "str:", 4) == 0) {
        sep += 4;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
        if (strlen(sep) > 127) {
            LOG_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
            return false;
        }
        strncpy(kvo.val_str, sep, 127);
        kvo.val_str[127] = '\0';
    } else {
        LOG_ERR("%s: invalid type for KV override '%s'\n", __func__, data);
        return false;
    }
    overrides.emplace_back(std::move(kvo));
    return true;
}

//
// Filesystem utils
//

// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
    if (!filename.length()) {
        // Empty filename invalid
        return false;
    }
    if (filename.length() > 255) {
        // Limit at common largest possible filename on Linux filesystems
        // to avoid unnecessary further validation
        // (On systems with smaller limits it will be caught by the OS)
        return false;
    }

    size_t offset = 0;
    while (offset < filename.size()) {
        utf8_parse_result result = parse_utf8_codepoint(filename, offset);

        if (result.status != utf8_parse_result::SUCCESS) {
            return false;
        }
        uint32_t c = result.codepoint;

        if ((result.bytes_consumed == 2 && c < 0x80) ||
            (result.bytes_consumed == 3 && c < 0x800) ||
            (result.bytes_consumed == 4 && c < 0x10000)) {
            return false;
        }

        // Check for forbidden codepoints:
        // - Control characters
        // - Unicode equivalents of illegal characters
        // - UTF-16 surrogate pairs
        // - UTF-8 replacement character
        // - Byte order mark (BOM)
        // - Illegal characters: / \ : * ? " < > |
        if (c <= 0x1F // Control characters (C0)
            || c == 0x7F // Control characters (DEL)
            || (c >= 0x80 && c <= 0x9F) // Control characters (C1)
            || c == 0xFF0E // Fullwidth Full Stop (period equivalent)
            || c == 0x2215 // Division Slash (forward slash equivalent)
            || c == 0x2216 // Set Minus (backslash equivalent)
            || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
            || c > 0x10FFFF // Max Unicode limit
            || c == 0xFFFD // Replacement Character (UTF-8)
            || c == 0xFEFF // Byte Order Mark (BOM)
            || c == ':' || c == '*' // Illegal characters
            || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
            return false;
        }
        if (!allow_subdirs && (c == '/' || c == '\\')) {
            // Subdirectories not allowed, reject path separators
            return false;
        }
        offset += result.bytes_consumed;
    }

    // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
    // Unicode and other whitespace is not affected, only 0x20 space
    if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
        return false;
    }

    // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
    if (filename.find("..") != std::string::npos) {
        return false;
    }

    // Reject "."
    if (filename == ".") {
        return false;
    }

    return true;
}

#include <iostream>


#ifdef _WIN32
static std::wstring utf8_to_wstring(const std::string & str) {
    if (str.empty()) {
        return std::wstring();
    }

    int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);

    if (size <= 0) {
        return std::wstring();
    }

    std::wstring wstr(size, 0);
    MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);

    return wstr;
}
#endif

// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
    std::wstring wpath = utf8_to_wstring(path);

    // if the path already exists, check whether it's a directory
    const DWORD attributes = GetFileAttributesW(wpath.c_str());
    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
        return true;
    }

    size_t pos_slash = 0;

    // process path from front to back, procedurally creating directories
    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
        const std::wstring subpath = wpath.substr(0, pos_slash);

        pos_slash += 1;

        // skip the drive letter, in some systems it can return an access denied error
        if (subpath.length() == 2 && subpath[1] == ':') {
            continue;
        }

        const bool success = CreateDirectoryW(subpath.c_str(), NULL);

        if (!success) {
            const DWORD error = GetLastError();

            // if the path already exists, ensure that it's a directory
            if (error == ERROR_ALREADY_EXISTS) {
                const DWORD attributes = GetFileAttributesW(subpath.c_str());
                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
                    return false;
                }
            } else {
                return false;
            }
        }
    }

    return true;
#else
    // if the path already exists, check whether it's a directory
    struct stat info;
    if (stat(path.c_str(), &info) == 0) {
        return S_ISDIR(info.st_mode);
    }

    size_t pos_slash = 1; // skip leading slashes for directory creation

    // process path from front to back, procedurally creating directories
    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
        const std::string subpath = path.substr(0, pos_slash);
        struct stat info;

        // if the path already exists, ensure that it's a directory
        if (stat(subpath.c_str(), &info) == 0) {
            if (!S_ISDIR(info.st_mode)) {
                return false;
            }
        } else {
            // create parent directories
            const int ret = mkdir(subpath.c_str(), 0755);
            if (ret != 0) {
                return false;
            }
        }

        pos_slash += 1;
    }

    return true;
#endif // _WIN32
}

bool fs_is_directory(const std::string & path) {
    std::filesystem::path dir(path);
    return std::filesystem::exists(dir) && std::filesystem::is_directory(dir);
}

std::string fs_get_cache_directory() {
    std::string cache_directory = "";
    auto ensure_trailing_slash = [](std::string p) {
        // Make sure to add trailing slash
        if (p.back() != DIRECTORY_SEPARATOR) {
            p += DIRECTORY_SEPARATOR;
        }
        return p;
    };
    if (getenv("LLAMA_CACHE")) {
        cache_directory = std::getenv("LLAMA_CACHE");
    } else {
#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || \
        defined(__OpenBSD__) || defined(__NetBSD__)
        if (std::getenv("XDG_CACHE_HOME")) {
            cache_directory = std::getenv("XDG_CACHE_HOME");
        } else if (std::getenv("HOME")) {
            cache_directory = std::getenv("HOME") + std::string("/.cache/");
        } else {
#if defined(__linux__)
            /* no $HOME is defined, fallback to getpwuid */
            struct passwd *pw = getpwuid(getuid());
            if ((!pw) || (!pw->pw_dir)) {
                throw std::runtime_error("Failed to find $HOME directory");
            }

            cache_directory = std::string(pw->pw_dir) + std::string("/.cache/");
#else /* defined(__linux__) */
            throw std::runtime_error("Failed to find $HOME directory");
#endif /* defined(__linux__) */
        }
#elif defined(__APPLE__)
        cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
        cache_directory = std::getenv("LOCALAPPDATA");
#elif defined(__EMSCRIPTEN__)
        GGML_ABORT("not implemented on this platform");
#else
#  error Unknown architecture
#endif
        cache_directory = ensure_trailing_slash(cache_directory);
        cache_directory += "llama.cpp";
    }
    return ensure_trailing_slash(cache_directory);
}

std::string fs_get_cache_file(const std::string & filename) {
    GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
    std::string cache_directory = fs_get_cache_directory();
    const bool success = fs_create_directory_with_parents(cache_directory);
    if (!success) {
        throw std::runtime_error("failed to create cache directory: " + cache_directory);
    }
    return cache_directory + filename;
}

std::vector<common_file_info> fs_list(const std::string & path, bool include_directories) {
    std::vector<common_file_info> files;
    if (path.empty()) return files;

    std::filesystem::path dir(path);
    if (!std::filesystem::exists(dir) || !std::filesystem::is_directory(dir)) {
        return files;
    }

    for (const auto & entry : std::filesystem::directory_iterator(dir)) {
        try {
            // Only include regular files (skip directories)
            const auto & p = entry.path();
            if (std::filesystem::is_regular_file(p)) {
                common_file_info info;
                info.path   = p.string();
                info.name   = p.filename().string();
                info.is_dir = false;
                try {
                    info.size = static_cast<size_t>(std::filesystem::file_size(p));
                } catch (const std::filesystem::filesystem_error &) {
                    info.size = 0;
                }
                files.push_back(std::move(info));
            } else if (include_directories && std::filesystem::is_directory(p)) {
                common_file_info info;
                info.path   = p.string();
                info.name   = p.filename().string();
                info.size   = 0; // Directories have no size
                info.is_dir = true;
                files.push_back(std::move(info));
            }
        } catch (const std::filesystem::filesystem_error &) {
            // skip entries we cannot inspect
            continue;
        }
    }

    return files;
}

//
// TTY utils
//

bool tty_can_use_colors() {
    // Check NO_COLOR environment variable (https://no-color.org/)
    if (const char * no_color = std::getenv("NO_COLOR")) {
        if (no_color[0] != '\0') {
            return false;
        }
    }

    // Check TERM environment variable
    if (const char * term = std::getenv("TERM")) {
        if (std::strcmp(term, "dumb") == 0) {
            return false;
        }
    }

    // Check if stdout and stderr are connected to a terminal
    // We check both because log messages can go to either
    bool stdout_is_tty = isatty(fileno(stdout));
    bool stderr_is_tty = isatty(fileno(stderr));

    return stdout_is_tty || stderr_is_tty;
}

//
// Model utils
//

// TODO: move to common/sampling
static void common_init_sampler_from_model(
    const llama_model * model,
    common_params_sampling & sparams) {

    const uint64_t config = sparams.user_sampling_config;

    auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
        if (config & user_config) {
            return;
        }

        char buf[64] = {0};
        if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
            char * end = nullptr;
            int32_t v = strtol(buf, &end, 10);
            if (end && end != buf) {
                dst = v;
            }
        }
    };

    auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
        if (config & user_config) {
            return;
        }

        char buf[128] = {0};
        if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
            char * end = nullptr;
            float v = strtof(buf, &end);
            if (end && end != buf) {
                dst = v;
            }
        }
    };

    // Sampling sequence
    if (!(config & common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS)) {
        char buf[512] = {0};
        if (llama_model_meta_val_str(model, llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE), buf, sizeof(buf)) > 0) {
            const std::vector<std::string> sampler_names = string_split<std::string>(std::string(buf), ';');
            if (!sampler_names.empty()) {
                sparams.samplers = common_sampler_types_from_names(sampler_names, true);
            }
        }
    }

    get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_K),           sparams.top_k,           common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_P),           sparams.top_p,           common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIN_P),           sparams.min_p,           common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY), sparams.xtc_probability, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD),   sparams.xtc_threshold,   common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TEMP),            sparams.temp,            common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP);
    get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N),  sparams.penalty_last_n,  common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT),  sparams.penalty_repeat,  common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT);
    get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT),        sparams.mirostat,        common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU),    sparams.mirostat_tau,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU);
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA),    sparams.mirostat_eta,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
}

struct common_init_result::impl {
    impl() = default;
    ~impl() = default;

    // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top

    llama_model_ptr   model;
    llama_context_ptr context;

    std::vector<llama_adapter_lora_ptr> lora;

    std::vector<common_sampler_ptr> samplers;
    std::vector<llama_sampler_seq_config> samplers_seq_config;
};

common_init_result::common_init_result(common_params & params) :
    pimpl(new impl{}) {
    auto mparams = common_model_params_to_llama(params);
    auto cparams = common_context_params_to_llama(params);

    if (params.fit_params) {
        LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
        llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
            params.tensor_split,
            params.tensor_buft_overrides.data(),
            params.fit_params_target.data(),
            params.fit_params_min_ctx,
            params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
    }

    llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
    if (model == NULL) {
        return;
    }

    pimpl->model.reset(model);

    const llama_vocab * vocab = llama_model_get_vocab(model);

    // load and optionally apply lora adapters (must be loaded before context creation)
    for (auto & la : params.lora_adapters) {
        llama_adapter_lora_ptr lora;
        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
        if (lora == nullptr) {
            LOG_ERR("%s: failed to load lora adapter '%s'\n", __func__, la.path.c_str());
            pimpl->model.reset(model);
            return;
        }

        char buf[1024];
        la.ptr = lora.get();
        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
        la.task_name = buf;
        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
        la.prompt_prefix = buf;
        pimpl->lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
    }

    // updates params.sampling
    // TODO: fix naming
    common_init_sampler_from_model(model, params.sampling);

    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
        params.sampling.ignore_eos = false;
    }

    // initialize once
    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
        if (llama_vocab_is_eog(vocab, i)) {
            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
        }
    }

    if (params.sampling.ignore_eos) {
        // add EOG biases to the active set of logit biases
        params.sampling.logit_bias.insert(
                params.sampling.logit_bias.end(),
                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
    }

    //if (params.sampling.penalty_last_n == -1) {
    //    LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
    //}

    //if (params.sampling.dry_penalty_last_n == -1) {
    //    LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
    //}

    // init the backend samplers as part of the context creation
    pimpl->samplers.resize(cparams.n_seq_max);
    pimpl->samplers_seq_config.resize(cparams.n_seq_max);

    for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
        pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) };
    }

    if (params.sampling.backend_sampling) {
        cparams.samplers   = pimpl->samplers_seq_config.data();
        cparams.n_samplers = pimpl->samplers_seq_config.size();
    }

    llama_context * lctx = llama_init_from_model(model, cparams);
    if (lctx == NULL) {
        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
        return;
    }

    pimpl->context.reset(lctx);
}

llama_model * common_init_result::model() {
    return pimpl->model.get();
}

llama_context * common_init_result::context() {
    return pimpl->context.get();
}

common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
    return pimpl->samplers[seq_id].get();
}

void common_init_result::reset_samplers() {
    for (int i = 0; i < (int) pimpl->samplers.size(); ++i) {
        llama_sampler_reset(common_sampler_get(pimpl->samplers[i].get()));
    }
}

std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
    return pimpl->lora;
}

common_init_result_ptr common_init_from_params(common_params & params) {
    common_init_result_ptr res(new common_init_result(params));

    llama_model * model = res->model();
    if (model == NULL) {
        LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
        return res;
    }

    llama_context * lctx = res->context();
    if (lctx == NULL) {
        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
        return res;
    }

    const llama_vocab * vocab = llama_model_get_vocab(model);

    if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
        LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
        params.ctx_shift = false;
    }

    if (!params.control_vectors.empty()) {
        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_model_n_layer(model);

        const auto cvec = common_control_vector_load(params.control_vectors);
        if (cvec.n_embd == -1) {
            return res;
        }

        int err = llama_set_adapter_cvec(
                lctx,
                cvec.data.data(),
                cvec.data.size(),
                cvec.n_embd,
                params.control_vector_layer_start,
                params.control_vector_layer_end);
        if (err) {
            return res;
        }
    }

    if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) {
        bool ok = true;

        if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
            LOG_WRN("%s: warning: vocab does not have a  BOS token, reranking will not work\n", __func__);
            ok = false;
        }

        bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
        bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
        bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL;

        if (!has_eos && !has_sep && !has_rerank_prompt) {
            LOG_WRN("%s: warning: vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n", __func__);
            ok = false;
        } else if (!has_eos) {
            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
        }

        if (!ok) {
            return res;
        }
    }

    if (!params.lora_init_without_apply) {
        common_set_adapter_lora(lctx, params.lora_adapters);
    }

    if (params.warmup) {
        LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);

        llama_set_warmup(lctx, true);

        std::vector<llama_token> tmp;
        llama_token bos = llama_vocab_bos(vocab);
        llama_token eos = llama_vocab_eos(vocab);

        // some models (e.g. T5) don't have a BOS token
        if (bos != LLAMA_TOKEN_NULL) {
            tmp.push_back(bos);
        }
        if (eos != LLAMA_TOKEN_NULL) {
            tmp.push_back(eos);
        }
        if (tmp.empty()) {
            tmp.push_back(0);
        }

        if (llama_model_has_encoder(model)) {
            llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
            llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
            if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
                decoder_start_token_id = bos;
            }
            tmp.clear();
            tmp.push_back(decoder_start_token_id);
        }
        if (llama_model_has_decoder(model)) {
            llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch)));
        }
        llama_memory_clear(llama_get_memory(lctx), true);
        llama_synchronize(lctx);
        llama_perf_context_reset(lctx);
        llama_set_warmup(lctx, false);

        // reset samplers to reset RNG state after warmup to the seeded state
        res->reset_samplers();
    }

    return res;
}

common_init_result::~common_init_result() = default;

std::string get_model_endpoint() {
    const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
    // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
    const char * hf_endpoint_env = getenv("HF_ENDPOINT");
    const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env;
    std::string model_endpoint = "https://huggingface.co/";
    if (endpoint_env) {
        model_endpoint = endpoint_env;
        if (model_endpoint.back() != '/') {
            model_endpoint += '/';
        }
    }
    return model_endpoint;
}

void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
    std::vector<llama_adapter_lora *> loras;
    std::vector<float> scales;

    for (auto & la: lora) {
        loras.push_back(la.ptr);
        scales.push_back(la.scale);
    }

    llama_set_adapters_lora(ctx, loras.data(), loras.size(), scales.data());
}

struct llama_model_params common_model_params_to_llama(common_params & params) {
    auto mparams = llama_model_default_params();

    if (!params.devices.empty()) {
        mparams.devices = params.devices.data();
    }

    mparams.n_gpu_layers    = params.n_gpu_layers;
    mparams.main_gpu        = params.main_gpu;
    mparams.split_mode      = params.split_mode;
    mparams.tensor_split    = params.tensor_split;
    mparams.use_mmap        = params.use_mmap;
    mparams.use_direct_io   = params.use_direct_io;
    mparams.use_mlock       = params.use_mlock;
    mparams.check_tensors   = params.check_tensors;
    mparams.use_extra_bufts = !params.no_extra_bufts;
    mparams.no_host         = params.no_host;

    if (params.kv_overrides.empty()) {
        mparams.kv_overrides = NULL;
    } else {
        GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
        mparams.kv_overrides = params.kv_overrides.data();
    }

    if (params.tensor_buft_overrides.empty()) {
        mparams.tensor_buft_overrides = NULL;
    } else {
        GGML_ASSERT(params.tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern");
        mparams.tensor_buft_overrides = params.tensor_buft_overrides.data();
    }

    mparams.progress_callback           = params.load_progress_callback;
    mparams.progress_callback_user_data = params.load_progress_callback_user_data;

    return mparams;
}

struct llama_context_params common_context_params_to_llama(const common_params & params) {
    auto cparams = llama_context_default_params();

    cparams.n_ctx             = params.n_ctx;
    cparams.n_seq_max         = params.n_parallel;
    cparams.n_batch           = params.n_batch;
    cparams.n_ubatch          = params.n_ubatch;
    cparams.n_threads         = params.cpuparams.n_threads;
    cparams.n_threads_batch   = params.cpuparams_batch.n_threads == -1 ?
                                params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
    cparams.embeddings        = params.embedding;
    cparams.rope_scaling_type = params.rope_scaling_type;
    cparams.rope_freq_base    = params.rope_freq_base;
    cparams.rope_freq_scale   = params.rope_freq_scale;
    cparams.yarn_ext_factor   = params.yarn_ext_factor;
    cparams.yarn_attn_factor  = params.yarn_attn_factor;
    cparams.yarn_beta_fast    = params.yarn_beta_fast;
    cparams.yarn_beta_slow    = params.yarn_beta_slow;
    cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
    cparams.pooling_type      = params.pooling_type;
    cparams.attention_type    = params.attention_type;
    cparams.flash_attn_type   = params.flash_attn_type;
    cparams.cb_eval           = params.cb_eval;
    cparams.cb_eval_user_data = params.cb_eval_user_data;
    cparams.offload_kqv       = !params.no_kv_offload;
    cparams.no_perf           = params.no_perf;
    cparams.op_offload        = !params.no_op_offload;
    cparams.swa_full          = params.swa_full;
    cparams.kv_unified        = params.kv_unified;

    cparams.type_k = params.cache_type_k;
    cparams.type_v = params.cache_type_v;

    return cparams;
}

struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
    struct ggml_threadpool_params tpp;

    ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults

    if (params.mask_valid) {
        std::memcpy(&tpp.cpumask, &params.cpumask, GGML_MAX_N_THREADS);
    }

    tpp.prio       = params.priority;
    tpp.poll       = params.poll;
    tpp.strict_cpu = params.strict_cpu;

    return tpp;
}

//
// Batch utils
//

void common_batch_clear(struct llama_batch & batch) {
    batch.n_tokens = 0;
}

void common_batch_add(
                 struct llama_batch & batch,
                        llama_token   id,
                          llama_pos   pos,
    const std::vector<llama_seq_id> & seq_ids,
                               bool   logits) {
    GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");

    batch.token   [batch.n_tokens] = id;
    batch.pos     [batch.n_tokens] = pos;
    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
    for (size_t i = 0; i < seq_ids.size(); ++i) {
        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
    }
    batch.logits  [batch.n_tokens] = logits;

    batch.n_tokens++;
}

//
// Vocab utils
//

std::vector<llama_token> common_tokenize(
  const struct llama_context * ctx,
           const std::string & text,
                        bool   add_special,
                        bool   parse_special) {
    const llama_model * model = llama_get_model(ctx);
    const llama_vocab * vocab = llama_model_get_vocab(model);
    return common_tokenize(vocab, text, add_special, parse_special);
}

std::vector<llama_token> common_tokenize(
    const struct llama_vocab * vocab,
           const std::string & text,
                        bool   add_special,
                        bool   parse_special) {
    // upper limit for the number of tokens
    int n_tokens = text.length() + 2 * add_special;
    std::vector<llama_token> result(n_tokens);
    n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
    if (n_tokens == std::numeric_limits<int32_t>::min()) {
        throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit");
    }
    if (n_tokens < 0) {
        result.resize(-n_tokens);
        int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
        GGML_ASSERT(check == -n_tokens);
    } else {
        result.resize(n_tokens);
    }
    return result;
}

std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
    const llama_model * model = llama_get_model(ctx);
    const llama_vocab * vocab = llama_model_get_vocab(model);
    return common_token_to_piece(vocab, token, special);
}

std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
    std::string piece;
    piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
    const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
    if (n_chars < 0) {
        piece.resize(-n_chars);
        int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
        GGML_ASSERT(check == -n_chars);
    }
    else {
        piece.resize(n_chars);
    }

    return piece;
}

std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
    const llama_model * model = llama_get_model(ctx);
    const llama_vocab * vocab = llama_model_get_vocab(model);
    return common_detokenize(vocab, tokens, special);
}

std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
    std::string text;
    text.resize(std::max(text.capacity(), tokens.size()));
    int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
    if (n_chars < 0) {
        text.resize(-n_chars);
        n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
        GGML_ASSERT(n_chars <= (int32_t)text.size());  // whitespace trimming is performed after per-token detokenization
    }

    text.resize(n_chars);

    // NOTE: the original tokenizer decodes bytes after collecting the pieces.
    return text;
}

//
// Embedding utils
//

void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
    double sum = 0.0;

    switch (embd_norm) {
        case -1: // no normalisation
            sum = 1.0;
            break;
        case 0: // max absolute
            for (int i = 0; i < n; i++) {
                if (sum < std::abs(inp[i])) {
                    sum = std::abs(inp[i]);
                }
            }
            sum /= 32760.0; // make an int16 range
            break;
        case 2: // euclidean
            for (int i = 0; i < n; i++) {
                sum += inp[i] * inp[i];
            }
            sum = std::sqrt(sum);
            break;
        default: // p-norm (euclidean is p-norm p=2)
            for (int i = 0; i < n; i++) {
                sum += std::pow(std::abs(inp[i]), embd_norm);
            }
            sum = std::pow(sum, 1.0 / embd_norm);
            break;
    }

    const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;

    for (int i = 0; i < n; i++) {
        out[i] = inp[i] * norm;
    }
}

float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
    double sum  = 0.0;
    double sum1 = 0.0;
    double sum2 = 0.0;

    for (int i = 0; i < n; i++) {
        sum  += embd1[i] * embd2[i];
        sum1 += embd1[i] * embd1[i];
        sum2 += embd2[i] * embd2[i];
    }

    // Handle the case where one or both vectors are zero vectors
    if (sum1 == 0.0 || sum2 == 0.0) {
        if (sum1 == 0.0 && sum2 == 0.0) {
            return 1.0f; // two zero vectors are similar
        }
        return 0.0f;
    }

    return sum / (sqrt(sum1) * sqrt(sum2));
}

//
// Control vector utils
//

static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) {
    common_control_vector_data result = { -1, {} };

    ggml_context * ctx = nullptr;
    struct gguf_init_params meta_gguf_params = {
        /* .no_alloc = */ false,
        /* .ctx      = */ &ctx,
    };
    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
    if (!ctx_gguf) {
        LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
        return result;
    }

    int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
    if (n_tensors == 0) {
        LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
    }

    for (int i = 0; i < n_tensors; i++) {
        std::string name = gguf_get_tensor_name(ctx_gguf, i);

        int layer_idx = -1;

        // split on '.'
        size_t dotpos = name.find('.');
        if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
            try {
                layer_idx = std::stoi(name.substr(dotpos + 1));
            } catch (...) {
                layer_idx = -1;
            }
        }
        if (layer_idx < 0) {
            LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        } else if (layer_idx == 0) {
            LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
        if (tensor->type != GGML_TYPE_F32) {
            LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }
        if (ggml_n_dims(tensor) != 1) {
            LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        if (result.n_embd == -1) {
            result.n_embd = ggml_nelements(tensor);
        } else if (ggml_nelements(tensor) != result.n_embd) {
            LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        // extend if necessary - do not store data for layer 0 (it's not used)
        result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);

        const float * src = (const float *) tensor->data;
        float * dst = result.data.data() + result.n_embd * (layer_idx - 1);  // layer 1 at [0]
        for (int j = 0; j < result.n_embd; j++) {
            dst[j] += src[j] * load_info.strength;  // allows multiple directions for same layer in same file
        }

    }

    if (result.n_embd == -1) {
        LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
        result.data.clear();
    }

    gguf_free(ctx_gguf);
    ggml_free(ctx);

    return result;
}

common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos) {
    common_control_vector_data result = { -1, {} };

    for (const auto & info : load_infos) {
        auto cur = common_control_vector_load_one(info);

        if (cur.n_embd == -1) {
            result.n_embd = -1;
            break;
        }
        if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
            LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        if (result.n_embd == -1) {
            result = std::move(cur);
        } else {
            result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f);  // extend if necessary
            for (size_t i = 0; i < cur.data.size(); i++) {
                result.data[i] += cur.data[i];
            }
        }
    }

    if (result.n_embd == -1) {
        LOG_ERR("%s: no valid control vector files passed\n", __func__);
        result.data.clear();
    }

    return result;
}

ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
    const int64_t ne_datapoint = llama_n_ctx(ctx);
    const int64_t ndata        = (tokens.size() - ne_datapoint - 1) / stride;
    ggml_opt_dataset_t result = ggml_opt_dataset_init(
        GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);

    llama_token * data   = (llama_token *) ggml_opt_dataset_data(result)->data;
    llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;

    for (int64_t idata = 0; idata < ndata; ++idata) {
        memcpy(data   + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
        memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
    }

    return result;
}

ggml_opt_optimizer_params common_opt_lr_pars(void * userdata) {
    ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(nullptr);
    const lr_opt &            d      = *(lr_opt *) userdata;
    result.adamw.alpha = result.sgd.alpha = d.get_lr(d.epoch);
    result.sgd.wd = result.adamw.wd = d.wd;
    return result;
}

// TODO make all command line args case-insensitive
static inline bool eq_case_insensitive(char const* a, char const* b) {
    return !
#if defined(_MSC_VER)
        _stricmp
#else
        strcasecmp
#endif // defined(_MSC_VER)
        (a, b);
}

enum ggml_opt_optimizer_type common_opt_get_optimizer(const char * n) {
    if (eq_case_insensitive("adamw", n)) {
        return GGML_OPT_OPTIMIZER_TYPE_ADAMW;
    }
    if (eq_case_insensitive("sgd", n)) {
        return GGML_OPT_OPTIMIZER_TYPE_SGD;
    }
    return GGML_OPT_OPTIMIZER_TYPE_COUNT;
}

// TODO simplify to use just log and exp
static float const k_log_2 = std::log(2.f);

void lr_opt::init() {
    if (lr_min > 0 && lr_min < lr0) {
        float nhalf = std::log(lr0 / lr_min) / k_log_2;
        float e     = epochs;
        if (decay_epochs > 0 && decay_epochs < e) {
            e = decay_epochs;
        } else {
            decay_epochs = e;
        }
        scale_epoch = nhalf / e;
    }
}

float lr_opt::get_lr(float epoch) const {
    float r = lr_min <= 0 ? lr0 :
        epoch >= decay_epochs ? lr_min :
        lr0 * std::pow(0.5f, epoch * scale_epoch);
    LOG_INF("epoch %.2g lr=%.2g\n", epoch, r);
    return r;
}

bool common_replay_last_token(struct llama_context * ctx, llama_token last_token, int32_t pos) {
    llama_batch batch = llama_batch_get_one(&last_token, 1);
    batch.pos = &pos;
    if (llama_decode(ctx, batch)) {
        LOG_ERR("%s: failed to replay last token\n", __func__);
        return false;
    }
    return true;
}

bool common_prompt_batch_decode(
              struct llama_context * ctx,
    const std::vector<llama_token> & tokens,
                               int & n_past,
                               int   n_batch,
                  std::string_view   state_path,
                              bool   save_state) {
    const int n_eval = tokens.size();
    if (n_eval == 0) {
        return true;
    }

    if (save_state && n_eval > 1) {
        const int n_tokens_before_last = n_eval - 1;

        GGML_ASSERT(n_eval <= n_batch);

        // Decode all but the last token so we can save the memory state before decoding the last token.
        // This is done so we can restore the session state later and replay the last token.
        // Memory implementations in recurrent/hybrid models don't support removing tokens from their
        // memory, so we can't just remove the last token from the memory and replay the last token which
        // is the reason for this logic.
        if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(tokens.data()), n_tokens_before_last))) {
            LOG_ERR("%s : failed to eval\n", __func__);
            return false;
        }
        n_past += n_tokens_before_last;

        llama_state_save_file(ctx, state_path.data(), tokens.data(), n_tokens_before_last);
        LOG_INF("saved session before last token to %s, n_tokens = %d\n", state_path.data(), n_tokens_before_last);

        llama_token last_token = tokens.back();
        llama_batch batch = llama_batch_get_one(&last_token, 1);
        int32_t pos = n_past;
        batch.pos = &pos;

        if (llama_decode(ctx, batch)) {
            LOG_ERR("%s : failed to eval last token\n", __func__);
            return false;
        }
        n_past++;
    } else {
        if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(tokens.data()), n_eval))) {
            LOG_ERR("%s : failed to eval\n", __func__);
            return false;
        }
        n_past += n_eval;
    }

    return true;
}