ollama/llama/llama.cpp/common/common.h

// Various helper functions and utilities

#pragma once

#include "llama-cpp.h"

#include <set>
#include <string>
#include <string_view>
#include <vector>
#include <map>
#include <sstream>

#ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\'
#else
#define DIRECTORY_SEPARATOR '/'
#endif // _WIN32

#define die(msg)          do { fputs("error: " msg "\n", stderr);                exit(1); } while (0)
#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)

#define print_build_info() do {                                                                     \
    fprintf(stderr, "%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);      \
    fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
} while(0)

#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"

struct common_adapter_lora_info {
    std::string path;
    float scale;

    struct llama_adapter_lora * ptr;
};

using llama_tokens = std::vector<llama_token>;

// build info
extern int LLAMA_BUILD_NUMBER;
extern const char * LLAMA_COMMIT;
extern const char * LLAMA_COMPILER;
extern const char * LLAMA_BUILD_TARGET;

struct common_control_vector_load_info;

//
// CPU utils
//

struct cpu_params {
    int      n_threads                   = -1;
    bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
    bool     mask_valid                  = false;   // Default: any CPU
    enum ggml_sched_priority  priority   = GGML_SCHED_PRIO_NORMAL;  // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
    bool     strict_cpu                  = false;   // Use strict CPU placement
    uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
};

int32_t cpu_get_num_physical_cores();
int32_t cpu_get_num_math();

//
// Common params
//

enum llama_example {
    LLAMA_EXAMPLE_COMMON,
    LLAMA_EXAMPLE_SPECULATIVE,
    LLAMA_EXAMPLE_MAIN,
    LLAMA_EXAMPLE_EMBEDDING,
    LLAMA_EXAMPLE_PERPLEXITY,
    LLAMA_EXAMPLE_RETRIEVAL,
    LLAMA_EXAMPLE_PASSKEY,
    LLAMA_EXAMPLE_IMATRIX,
    LLAMA_EXAMPLE_BENCH,
    LLAMA_EXAMPLE_SERVER,
    LLAMA_EXAMPLE_CVECTOR_GENERATOR,
    LLAMA_EXAMPLE_EXPORT_LORA,
    LLAMA_EXAMPLE_MTMD,
    LLAMA_EXAMPLE_LOOKUP,
    LLAMA_EXAMPLE_PARALLEL,
    LLAMA_EXAMPLE_TTS,
    LLAMA_EXAMPLE_DIFFUSION,

    LLAMA_EXAMPLE_COUNT,
};

enum common_sampler_type {
    COMMON_SAMPLER_TYPE_NONE        = 0,
    COMMON_SAMPLER_TYPE_DRY         = 1,
    COMMON_SAMPLER_TYPE_TOP_K       = 2,
    COMMON_SAMPLER_TYPE_TOP_P       = 3,
    COMMON_SAMPLER_TYPE_MIN_P       = 4,
  //COMMON_SAMPLER_TYPE_TFS_Z       = 5,
    COMMON_SAMPLER_TYPE_TYPICAL_P   = 6,
    COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
    COMMON_SAMPLER_TYPE_XTC         = 8,
    COMMON_SAMPLER_TYPE_INFILL      = 9,
    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
    COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
};

// dimensionality reduction methods, used by cvector-generator
enum dimre_method {
    DIMRE_METHOD_PCA,
    DIMRE_METHOD_MEAN,
};

enum common_conversation_mode {
    COMMON_CONVERSATION_MODE_DISABLED = 0,
    COMMON_CONVERSATION_MODE_ENABLED  = 1,
    COMMON_CONVERSATION_MODE_AUTO     = 2,
};

enum common_grammar_trigger_type {
    COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN,
    COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
};

struct common_grammar_trigger {
    common_grammar_trigger_type type;
    std::string value;
    llama_token token = LLAMA_TOKEN_NULL;
};

// sampling parameters
struct common_params_sampling {
    uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler

    int32_t n_prev             = 64;    // number of previous tokens to remember
    int32_t n_probs            = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
    int32_t min_keep           = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
    int32_t top_k              = 40;    // <= 0 to use vocab size
    float   top_p              = 0.95f; // 1.0 = disabled
    float   min_p              = 0.05f; // 0.0 = disabled
    float   xtc_probability    = 0.00f; // 0.0 = disabled
    float   xtc_threshold      = 0.10f; // > 0.5 disables XTC
    float   typ_p              = 1.00f; // typical_p, 1.0 = disabled
    float   temp               = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
    float   dynatemp_range     = 0.00f; // 0.0 = disabled
    float   dynatemp_exponent  = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
    int32_t penalty_last_n     = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
    float   penalty_repeat     = 1.00f; // 1.0 = disabled
    float   penalty_freq       = 0.00f; // 0.0 = disabled
    float   penalty_present    = 0.00f; // 0.0 = disabled
    float   dry_multiplier     = 0.0f;  // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
    float   dry_base           = 1.75f; // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
    int32_t dry_allowed_length = 2;     // tokens extending repetitions beyond this receive penalty
    int32_t dry_penalty_last_n = -1;    // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
    int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
    float   top_n_sigma        = -1.00f;// -1.0 = disabled
    float   mirostat_tau       = 5.00f; // target entropy
    float   mirostat_eta       = 0.10f; // learning rate
    bool    ignore_eos         = false;
    bool    no_perf            = false; // disable performance metrics
    bool    timing_per_token   = false;

    std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY


    std::vector<enum common_sampler_type> samplers = {
        COMMON_SAMPLER_TYPE_PENALTIES,
        COMMON_SAMPLER_TYPE_DRY,
        COMMON_SAMPLER_TYPE_TOP_N_SIGMA,
        COMMON_SAMPLER_TYPE_TOP_K,
        COMMON_SAMPLER_TYPE_TYPICAL_P,
        COMMON_SAMPLER_TYPE_TOP_P,
        COMMON_SAMPLER_TYPE_MIN_P,
        COMMON_SAMPLER_TYPE_XTC,
        COMMON_SAMPLER_TYPE_TEMPERATURE,
    };

    std::string                         grammar; // optional BNF-like grammar to constrain sampling
    bool                                grammar_lazy = false;
    std::vector<common_grammar_trigger> grammar_triggers; // optional triggers (for lazy grammars)
    std::set<llama_token>               preserved_tokens;

    std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
    std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens

    // print the parameters into a string
    std::string print() const;
};

struct common_params_model {
    std::string path    = ""; // model local path                                           // NOLINT
    std::string url     = ""; // model url to download                                      // NOLINT
    std::string hf_repo = ""; // HF repo                                                    // NOLINT
    std::string hf_file = ""; // HF file                                                    // NOLINT
};

struct common_params_speculative {
    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading

    int32_t n_ctx        =     0; // draft context size
    int32_t n_max        =    16; // maximum number of tokens to draft during speculative decoding
    int32_t n_min        =     0; // minimum number of draft tokens to use for speculative decoding
    int32_t n_gpu_layers =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
    float   p_split      =  0.1f; // speculative decoding split probability
    float   p_min        = 0.75f; // minimum speculative decoding probability (greedy)
    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements

    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V

    struct cpu_params cpuparams;
    struct cpu_params cpuparams_batch;

    struct common_params_model model;
};

struct common_params_vocoder {
    struct common_params_model model;

    std::string speaker_file = ""; // speaker file path                                      // NOLINT

    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy            // NOLINT
};

struct common_params_diffusion {
    int32_t steps         = 128;
    bool    visual_mode   = false;

    float   eps           = 0;        // epsilon for timesteps
    int32_t block_length  = 0;        // block length for generation

    int32_t algorithm     = 4;        // default algorithm: low-confidence
    float   alg_temp      = 0.0f;     // algorithm temperature

    float   cfg_scale     = 0;        // classifier-free guidance scale
    bool    add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0
};

enum common_reasoning_format {
    COMMON_REASONING_FORMAT_NONE,
    COMMON_REASONING_FORMAT_AUTO,
    COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
    COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
};

struct common_params {
    int32_t n_predict             =    -1; // new tokens to predict
    int32_t n_ctx                 =  4096; // context size
    int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
    int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
    int32_t n_keep                =     0; // number of tokens to keep from initial prompt
    int32_t n_chunks              =    -1; // max number of chunks to process (-1 = unlimited)
    int32_t n_parallel            =     1; // number of parallel sequences to decode
    int32_t n_sequences           =     1; // number of sequences to decode
    int32_t grp_attn_n            =     1; // group-attention factor
    int32_t grp_attn_w            =   512; // group-attention width
    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
    float   rope_freq_base        =  0.0f; // RoPE base frequency
    float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
    float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
    float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
    int32_t yarn_orig_ctx         =     0; // YaRN original context length
    float   defrag_thold          =  0.1f; // KV cache defragmentation threshold

    // offload params
    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading

    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs

    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs

    struct cpu_params cpuparams;
    struct cpu_params cpuparams_batch;

    ggml_backend_sched_eval_callback cb_eval = nullptr;
    void * cb_eval_user_data                 = nullptr;

    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;

    enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
    enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings

    struct common_params_sampling    sampling;
    struct common_params_speculative speculative;
    struct common_params_vocoder     vocoder;
    struct common_params_diffusion   diffusion;

    struct common_params_model model;

    std::string model_alias          = ""; // model alias                                                   // NOLINT
    std::string hf_token             = ""; // HF token                                                      // NOLINT
    std::string prompt               = "";                                                                  // NOLINT
    std::string system_prompt        = "";                                                                  // NOLINT
    std::string prompt_file          = ""; // store the external prompt file name                           // NOLINT
    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state             // NOLINT
    std::string input_prefix         = ""; // string to prefix user inputs with                             // NOLINT
    std::string input_suffix         = ""; // string to suffix user inputs with                             // NOLINT
    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
    std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT

    std::vector<std::string> in_files;   // all input files
    std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
    std::vector<llama_model_kv_override> kv_overrides;
    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;

    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
    std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale

    std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale

    int32_t verbosity                  = 0;
    int32_t control_vector_layer_start = -1; // layer range for control vector
    int32_t control_vector_layer_end   = -1; // layer range for control vector
    bool    offline                    = false;

    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
                                     //                                       (which is more convenient to use for plotting)
                                     //
    bool   hellaswag        = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
    size_t hellaswag_tasks  = 400;   // number of tasks to use when computing the HellaSwag score

    bool   winogrande       = false; // compute Winogrande score over random tasks from datafile supplied in prompt
    size_t winogrande_tasks = 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed

    bool   multiple_choice  = false;  // compute TruthfulQA score over random tasks from datafile supplied in prompt
    size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed

    bool   kl_divergence    = false; // compute KL divergence

    bool usage             = false; // print usage
    bool completion        = false; // print source-able completion script
    bool use_color         = false; // use color to distinguish generations and inputs
    bool special           = false; // enable special token output
    bool interactive       = false; // interactive mode
    bool interactive_first = false; // wait for user input immediately
    bool prompt_cache_all  = false; // save user input and generations to prompt cache
    bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it

    bool escape            = true;  // escape "\n", "\r", "\t", "\'", "\"", and "\\"
    bool multiline_input   = false; // reverse the usage of `\`
    bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
    bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
    bool flash_attn        = false; // flash attention
    bool no_perf           = false; // disable performance metrics
    bool ctx_shift         = true;  // context shift on inifinite text generation
    bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
    bool kv_unified        = false; // enable unified KV cache

    bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
    bool use_mmap          = true;  // use mmap for faster loads
    bool use_mlock         = false; // use mlock to keep model in memory
    bool verbose_prompt    = false; // print prompt tokens before generation
    bool display_prompt    = true;  // print prompt before generation
    bool no_kv_offload     = false; // disable KV offloading
    bool warmup            = true;  // warmup run
    bool check_tensors     = false; // validate tensor data
    bool no_op_offload     = false; // globally disable offload host tensor operations to device
    bool no_extra_bufts    = false; // disable extra buffer types (used for weight repacking)

    bool single_turn       = false; // single turn chat conversation

    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V

    common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;

    // multimodal models (see tools/mtmd)
    struct common_params_model mmproj;
    bool mmproj_use_gpu = true;     // use GPU for multimodal model
    bool no_mmproj = false;         // explicitly disable multimodal model
    std::vector<std::string> image; // path to image file(s)

    // embedding
    bool embedding         = false; // get only sentence embedding
    int32_t embd_normalize = 2;     // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
    std::string embd_out   = "";    // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
    std::string embd_sep   = "\n";  // separator of embeddings
    std::string cls_sep    = "\t";  // separator of classification sequences

    // server params
    int32_t port           = 8080;         // server listens on this network port
    int32_t timeout_read   = 600;          // http read timeout in seconds
    int32_t timeout_write  = timeout_read; // http write timeout in seconds
    int32_t n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
    int32_t n_cache_reuse  = 0;            // min chunk size to reuse from the cache via KV shifting

    std::string hostname      = "127.0.0.1";
    std::string public_path   = "";                                                                         // NOLINT
    std::string api_prefix    = "";                                                                         // NOLINT
    std::string chat_template = "";                                                                         // NOLINT
    bool use_jinja = false;                                                                                 // NOLINT
    bool enable_chat_template = true;
    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
    int reasoning_budget = -1;
    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response

    std::vector<std::string> api_keys;

    std::string ssl_file_key  = "";                                                                         // NOLINT
    std::string ssl_file_cert = "";                                                                         // NOLINT

    std::map<std::string, std::string> default_template_kwargs;

    // "advanced" endpoints are disabled by default for better security
    bool webui            = true;
    bool endpoint_slots   = false;
    bool endpoint_props   = false; // only control POST requests, not GET
    bool endpoint_metrics = false;

    bool log_json = false;

    std::string slot_save_path;

    float slot_prompt_similarity = 0.5f;

    // batched-bench params
    bool is_pp_shared = false;

    std::vector<int32_t> n_pp;
    std::vector<int32_t> n_tg;
    std::vector<int32_t> n_pl;

    // retrieval params
    std::vector<std::string> context_files; // context files to embed

    int32_t chunk_size = 64; // chunk size for context embedding

    std::string chunk_separator = "\n"; // chunk separator for context embedding

    // passkey params
    int32_t n_junk = 250; // number of times to repeat the junk text
    int32_t i_pos  = -1;  // position of the passkey in the junk text

    // imatrix params
    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
    int32_t i_chunk     =  0; // start processing from this chunk
    int8_t  imat_dat    =  0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat)

    bool process_output  = false; // collect data for the output tensor
    bool compute_ppl     = true;  // whether to compute perplexity
    bool show_statistics = false; // show imatrix statistics per tensor
    bool parse_special   = false; // whether to parse special tokens during imatrix tokenization

    // cvector-generator params
    int n_pca_batch = 100;
    int n_pca_iterations = 1000;
    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
    std::string cvector_positive_file = "tools/cvector-generator/positive.txt";
    std::string cvector_negative_file = "tools/cvector-generator/negative.txt";

    bool spm_infill = false; // suffix/prefix/middle pattern for infill

    // batched-bench params
    bool batched_bench_output_jsonl = false;

    // common params
    std::string out_file; // output filename for all example programs
    // optional callback for model loading progress and cancellation:
    // called with a progress value between 0.0 and 1.0.
    // return false from callback to abort model loading or true to continue
    llama_progress_callback load_progress_callback = NULL;
    void *                  load_progress_callback_user_data = NULL;
};

// call once at the start of a program if it uses libcommon
// initializes the logging system and prints info about the build
void common_init();

std::string common_params_get_system_info(const common_params & params);

bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
bool set_process_priority(enum ggml_sched_priority prio);

//
// String utils
//

#ifdef __GNUC__
#    if defined(__MINGW32__) && !defined(__clang__)
#        define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#    else
#        define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#    endif
#else
#    define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
#endif

LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
std::string string_format(const char * fmt, ...);

std::string string_strip(const std::string & str);
std::string string_get_sortable_timestamp();

std::string string_join(const std::vector<std::string> & values, const std::string & separator);
std::vector<std::string> string_split(const std::string & str, const std::string & delimiter);
std::string string_repeat(const std::string & str, size_t n);

void string_replace_all(std::string & s, const std::string & search, const std::string & replace);

std::string regex_escape(const std::string & s);

template<class T>
static std::vector<T> string_split(const std::string & str, char delim) {
    static_assert(!std::is_same<T, std::string>::value, "Please use the specialized version for std::string");
    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<>
std::vector<std::string> string_split<std::string>(const std::string & input, char separator)
{
    std::vector<std::string> parts;
    size_t begin_pos = 0;
    size_t separator_pos = input.find(separator);
    while (separator_pos != std::string::npos) {
        std::string part = input.substr(begin_pos, separator_pos - begin_pos);
        parts.emplace_back(part);
        begin_pos = separator_pos + 1;
        separator_pos = input.find(separator, begin_pos);
    }
    parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos));
    return parts;
}

static bool string_starts_with(const std::string & str,
                               const std::string & prefix) {  // While we wait for C++20's std::string::starts_with...
    return str.rfind(prefix, 0) == 0;
}

// While we wait for C++20's std::string::ends_with...
bool string_ends_with(const std::string_view & str, const std::string_view & suffix);
bool string_remove_suffix(std::string & str, const std::string_view & suffix);
size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop);

bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
void string_process_escapes(std::string & input);

std::string string_from(bool value);
std::string string_from(const std::vector<int> & values);
std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens);
std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch);

//
// Filesystem utils
//

bool fs_validate_filename(const std::string & filename);
bool fs_create_directory_with_parents(const std::string & path);

std::string fs_get_cache_directory();
std::string fs_get_cache_file(const std::string & filename);

//
// Model utils
//

// note: defines object's lifetime
struct common_init_result {
    llama_model_ptr   model;
    llama_context_ptr context;

    std::vector<llama_adapter_lora_ptr> lora;
};

struct common_init_result     common_init_from_params(common_params & params);

struct llama_model_params     common_model_params_to_llama  (      common_params & params);
struct llama_context_params   common_context_params_to_llama(const common_params & params);
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);

// clear LoRA adapters from context, then apply new list of adapters
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);

std::string                   get_model_endpoint();

//
// Batch utils
//

void common_batch_clear(struct llama_batch & batch);

void common_batch_add(
                 struct llama_batch & batch,
                        llama_token   id,
                          llama_pos   pos,
    const std::vector<llama_seq_id> & seq_ids,
                               bool   logits);

//
// Token utils
//

// longest common prefix
size_t common_lcp(const llama_tokens & a, const llama_tokens & b);

// longet common subsequence
size_t common_lcs(const llama_tokens & a, const llama_tokens & b);

//
// Vocab utils
//

// tokenizes a string into a vector of tokens
// should work similar to Python's `tokenizer.encode`
std::vector<llama_token> common_tokenize(
  const struct llama_context * ctx,
           const std::string & text,
                        bool   add_special,
                        bool   parse_special = false);

std::vector<llama_token> common_tokenize(
    const struct llama_vocab * vocab,
           const std::string & text,
                        bool   add_special,
                        bool   parse_special = false);

// tokenizes a token into a piece, optionally renders special/control tokens
// should work similar to Python's `tokenizer.id_to_piece`
std::string common_token_to_piece(
        const struct llama_context * ctx,
                       llama_token   token,
                       bool          special = true);

std::string common_token_to_piece(
          const struct llama_vocab * vocab,
                       llama_token   token,
                       bool          special = true);

// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// optionally renders special/control tokens
std::string common_detokenize(
            const struct llama_context * ctx,
        const std::vector<llama_token> & tokens,
                                  bool   special = true);

std::string common_detokenize(
              const struct llama_vocab * vocab,
        const std::vector<llama_token> & tokens,
                                  bool   special = true);

//
// Embedding utils
//

// TODO: repace embd_norm with an enum
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);

float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);

//
// Control vector utils
//

struct common_control_vector_data {
    int n_embd;

    // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
    std::vector<float> data;
};

struct common_control_vector_load_info {
    float strength;

    std::string fname;
};

// Load control vectors, scale each by strength, and add them together.
// On error, returns {-1, empty}
common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos);

//
// Split utils
//

namespace {

const char * const LLM_KV_SPLIT_NO            = "split.no";
const char * const LLM_KV_SPLIT_COUNT         = "split.count";
const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";

}

//
// training utils
//

ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);