Add common/common.h

common/common.h

@@ -0,0 +1,999 @@
+// Various helper functions and utilities
+
+#pragma once
+
+#include "ggml-opt.h"
+#include "ggml.h"
+#include "llama-cpp.h"
+
+#include <set>
+#include <sstream>
+#include <string>
+#include <string_view>
+#include <variant>
+#include <vector>
+#include <map>
+
+#if defined(_WIN32) && !defined(_WIN32_WINNT)
+#define _WIN32_WINNT 0x0A00
+#endif
+
+#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)
+
+struct common_time_meas {
+    common_time_meas(int64_t & t_acc, bool disable = false);
+    ~common_time_meas();
+
+    const int64_t t_start_us;
+
+    int64_t & t_acc;
+};
+
+struct common_adapter_lora_info {
+    std::string path;
+    float scale;
+
+    std::string task_name;
+    std::string prompt_prefix;
+
+    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;
+
+const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT);
+
+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_BATCHED,
+    LLAMA_EXAMPLE_DEBUG,
+    LLAMA_EXAMPLE_COMMON,
+    LLAMA_EXAMPLE_SPECULATIVE,
+    LLAMA_EXAMPLE_COMPLETION,
+    LLAMA_EXAMPLE_CLI,
+    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_FINETUNE,
+    LLAMA_EXAMPLE_FIT_PARAMS,
+    LLAMA_EXAMPLE_RESULTS,
+    LLAMA_EXAMPLE_EXPORT_GRAPH_OPS,
+
+    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,
+    COMMON_SAMPLER_TYPE_ADAPTIVE_P  = 12,
+};
+
+// 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;
+};
+
+enum common_params_sampling_config : uint64_t {
+    COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS        = 1 << 0,
+    COMMON_PARAMS_SAMPLING_CONFIG_TOP_K           = 1 << 1,
+    COMMON_PARAMS_SAMPLING_CONFIG_TOP_P           = 1 << 2,
+    COMMON_PARAMS_SAMPLING_CONFIG_MIN_P           = 1 << 3,
+    COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY = 1 << 4,
+    COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD   = 1 << 5,
+    COMMON_PARAMS_SAMPLING_CONFIG_TEMP            = 1 << 6,
+    COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N  = 1 << 7,
+    COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT  = 1 << 8,
+    COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT        = 1 << 9,
+    COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU    = 1 << 10,
+    COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA    = 1 << 11,
+};
+
+enum common_speculative_type {
+    COMMON_SPECULATIVE_TYPE_NONE,          // no speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT,         // draft model
+    COMMON_SPECULATIVE_TYPE_EAGLE3,        // eagle draft model
+    COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding
+    COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,   // self-speculative decoding with n-gram keys only
+    COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
+    COMMON_SPECULATIVE_TYPE_NGRAM_MOD,
+    COMMON_SPECULATIVE_TYPE_NGRAM_CACHE,   // self-speculative decoding with 3-level n-gram cache
+    COMMON_SPECULATIVE_TYPE_COUNT          // number of types, unknown type
+};
+
+// Grammar type enumeration
+enum common_grammar_type {
+    COMMON_GRAMMAR_TYPE_NONE,           // no grammar set
+    COMMON_GRAMMAR_TYPE_USER,           // user-provided GBNF (--grammar / "grammar" API field)
+    COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT,  // auto-generated from JSON schema (--json-schema / "json_schema" API field)
+    COMMON_GRAMMAR_TYPE_TOOL_CALLS,     // auto-generated by chat template parser for function calling
+};
+
+// Grammar variant struct with type and grammar string
+struct common_grammar {
+    common_grammar_type type = COMMON_GRAMMAR_TYPE_NONE;
+    std::string grammar;
+
+    // Default constructor - no grammar
+    common_grammar() = default;
+
+    // Constructor with type and grammar string
+    common_grammar(common_grammar_type t, std::string g) : type(t), grammar(std::move(g)) {
+        GGML_ASSERT(type != COMMON_GRAMMAR_TYPE_NONE || !grammar.empty());
+    }
+
+    // Check if a grammar is set
+    bool empty() const { return type == COMMON_GRAMMAR_TYPE_NONE || grammar.empty(); }
+};
+
+// Returns the raw grammar string, or empty string if no grammar is set.
+inline const std::string & common_grammar_value(const common_grammar & g) {
+    return g.grammar;
+}
+
+// Returns true when the generation_prompt should be prefilled into the grammar sampler.
+// Only output-format and tool-call grammars need prefill; user-supplied grammars must not be prefilled.
+inline bool common_grammar_needs_prefill(const common_grammar & g) {
+    return g.type == COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT
+        || g.type == COMMON_GRAMMAR_TYPE_TOOL_CALLS;
+}
+
+// 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)
+    float   adaptive_target    = -1.0f;  // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    float   adaptive_decay     = 0.90f;  // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
+    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;
+
+    uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers
+
+    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,
+    };
+
+    common_grammar              grammar;      // optional grammar constraint (user / output-format / tool-calls)
+    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
+
+    // The assistant generation prompt already prefilled into the prompt.
+    // Fed to the grammar sampler (to advance past pre-existing tokens) and used
+    // to determine the reasoning budget sampler's initial state.
+    // Only applied when the grammar is of output-format or tool-calls type.
+    std::string generation_prompt;
+
+    // reasoning budget sampler parameters
+    // these are populated by the server/CLI based on chat template params
+    int32_t                  reasoning_budget_tokens   = -1;   // -1 = disabled, >= 0 = token budget
+    std::vector<llama_token> reasoning_budget_start;           // start tag token sequence
+    std::vector<llama_token> reasoning_budget_end;             // end tag token sequence
+    std::vector<llama_token> reasoning_budget_forced;          // forced sequence (message + end tag)
+
+    bool backend_sampling = false;
+
+    bool has_logit_bias() const {
+        return !logit_bias.empty();
+    }
+
+    // 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
+    std::string docker_repo = ""; // Docker repo                                            // NOLINT
+    std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
+};
+
+struct common_ngram_mod;
+
+struct common_params_speculative {
+    common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding
+
+    // general-purpose speculative decoding parameters
+
+    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
+    float   p_split = 0.1f; // speculative decoding split probability
+    float   p_min   = 0.75f; // minimum speculative decoding probability (greedy)
+
+    // ngram-based speculative decoding
+
+    uint16_t ngram_size_n     = 12; // ngram size for lookup
+    uint16_t ngram_size_m     = 48; // mgram size for speculative tokens
+    uint16_t ngram_min_hits   =  1; // minimum hits at ngram/mgram lookup for mgram to be proposed
+
+    std::shared_ptr<common_ngram_mod> ngram_mod;
+
+    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
+
+    // draft-model speculative decoding
+
+    struct common_params_model mparams_dft;
+
+    llama_model * model_dft = nullptr; // a llama_model that can be shared by multiple speculative contexts
+
+    llama_context_params cparams_dft; // these are the parameters for the draft llama_context
+
+    int32_t n_ctx        = 0;  // draft context size
+    int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
+
+    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;
+
+    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
+    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
+    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+
+    bool has_dft() const {
+        return !mparams_dft.path.empty() || !mparams_dft.hf_repo.empty();
+    }
+};
+
+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
+};
+
+// reasoning API response format (not to be confused as chat template's reasoning format)
+// only used by server
+enum common_reasoning_format {
+    COMMON_REASONING_FORMAT_NONE,
+    COMMON_REASONING_FORMAT_AUTO,            // Same as deepseek, using `message.reasoning_content`
+    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.
+    // do not extend this enum unless you absolutely have to
+    // in most cases, use COMMON_REASONING_FORMAT_AUTO
+    // see: https://github.com/ggml-org/llama.cpp/pull/15408
+};
+
+
+struct lr_opt {
+    float    lr0          = 1e-5; // learning rate at first epoch
+    float    lr_min       = -1;
+    float    decay_epochs = -1;   // if >0, the learning rate starts at lr0 and decays to lr_min after this many epochs
+    float    scale_epoch  = 0;
+    float    wd           = 0;
+    unsigned epochs       = 2;
+
+    unsigned epoch; // set by optimizer outer (epochs) loop
+    // learning rate decay - constant LR per epoch only for now
+    float get_lr(float e) const;
+    float get_lr() const { return get_lr(epoch); }
+    // must call after arg parse, before get_lr
+    void init();
+};
+
+struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata);
+
+struct common_params {
+    int32_t n_predict             =    -1; // max. number of new tokens to predict, -1 == no limit
+    int32_t n_ctx                 =     0; // context size, 0 == context the model was trained with
+    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        = -1.0f; // YaRN low correction dim
+    float   yarn_beta_slow        = -1.0f; // YaRN high correction dim
+    int32_t yarn_orig_ctx         =     0; // YaRN original context length
+
+    // 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 is auto, <= -2 is all
+    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
+    bool    fit_params         = true; // whether to fit unset model/context parameters to free device memory
+    int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
+
+    // margin per device in bytes for fitting parameters to free memory:
+    std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
+
+    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
+    enum llama_flash_attn_type   flash_attn_type   = LLAMA_FLASH_ATTN_TYPE_AUTO; // whether to use Flash Attention
+
+    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::set<std::string> model_alias;     // model aliases                                                 // NOLINT
+    std::set<std::string> model_tags;      // model tags (informational, not used for routing)              // 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 logits_file          = ""; // file for saving *all* logits                                  // NOLINT
+
+    // llama-debug specific options
+    std::string logits_output_dir = "data"; // directory for saving logits output files                     // NOLINT
+    bool        save_logits       = false;  // whether to save logits to files                              // NOLINT
+    std::vector<std::string> tensor_filter; // filter tensor names for debug output (regex)                 // 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                  = 3;  // LOG_LEVEL_INFO
+    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 check             = false; // check rather than generate results for llama-results
+
+    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 no_perf           = false; // disable performance metrics
+    bool show_timings      = true;  // show timing information on CLI
+    bool ctx_shift         = false; // context shift on infinite 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;  // enable mmap to use filesystem cache
+    bool use_direct_io     = false; // read from disk without buffering
+    size_t expert_cache_size = 0;   // expert LRU cache size in bytes for MoE models (0 = disabled)
+    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 no_host           = false; // bypass host buffer allowing extra buffers to be used
+
+    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)
+    int image_min_tokens = -1;
+    int image_max_tokens = -1;
+
+    // finetune
+    struct lr_opt lr;
+    enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+    float val_split = 0.05f; // fraction of the data used for the validation set
+
+    // 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
+    bool    reuse_port          = false;         // allow multiple sockets to bind to the same 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
+    bool    cache_prompt        = true;  // whether to enable prompt caching
+    int32_t n_ctx_checkpoints   = 32;     // max number of context checkpoints per slot
+    int32_t checkpoint_every_nt = 8192;   // make a checkpoint every n tokens during prefill
+    int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
+
+    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 = true;                                                                                  // NOLINT
+    bool enable_chat_template = true;
+    bool force_pure_content_parser = false;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    int enable_reasoning = -1; // -1 = auto, 0 = disable, 1 = enable
+    int reasoning_budget = -1;
+    std::string reasoning_budget_message; // message injected before end tag when budget exhausted
+    bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
+    int sleep_idle_seconds = -1;   // if >0, server will sleep after this many seconds of idle time
+
+    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;
+
+    // webui configs
+    bool webui = true;
+    bool webui_mcp_proxy = false;
+    std::string webui_config_json;
+
+    // "advanced" endpoints are disabled by default for better security
+    bool endpoint_slots   = true;
+    bool endpoint_props   = false; // only control POST requests, not GET
+    bool endpoint_metrics = false;
+
+    // enable built-in tools
+    std::vector<std::string> server_tools;
+
+    // router server configs
+    std::string models_dir    = ""; // directory containing models for the router server
+    std::string models_preset = ""; // directory containing model presets for the router server
+    int models_max = 4;             // maximum number of models to load simultaneously
+    bool models_autoload = true;    // automatically load models when requested via the router server
+
+    bool log_json = false;
+
+    std::string slot_save_path;
+    std::string media_path; // path to directory for loading media files
+
+    float slot_prompt_similarity = 0.1f;
+
+    // batched-bench params
+    bool is_pp_shared   = false;
+    bool is_tg_separate = 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<>
+inline std::vector<std::string> string_split<std::string>(const std::string & str, char delim)
+{
+    std::vector<std::string> parts;
+    size_t begin_pos = 0;
+    size_t delim_pos = str.find(delim);
+    while (delim_pos != std::string::npos) {
+        std::string part = str.substr(begin_pos, delim_pos - begin_pos);
+        parts.emplace_back(part);
+        begin_pos = delim_pos + 1;
+        delim_pos = str.find(delim, begin_pos);
+    }
+    parts.emplace_back(str.substr(begin_pos));
+    return parts;
+}
+
+// remove when moving to c++20
+inline bool string_starts_with(std::string_view str, std::string_view prefix) {
+    return str.size() >= prefix.size() &&
+           str.compare(0, prefix.size(), prefix) == 0;
+}
+
+// remove when moving to c++20
+inline bool string_ends_with(std::string_view str, std::string_view suffix) {
+    return str.size() >= suffix.size() &&
+           str.compare(str.size() - suffix.size(), suffix.size(), suffix) == 0;
+}
+
+inline bool string_remove_suffix(std::string & str, std::string_view suffix) {
+    if (string_ends_with(str, suffix)) {
+        str.resize(str.size() - suffix.size());
+        return true;
+    }
+    return false;
+}
+
+inline size_t string_find_partial_stop(std::string_view str, std::string_view stop) {
+    if (!str.empty() && !stop.empty()) {
+        const size_t max_len = std::min(str.size(), stop.size());
+        const char last_char = str.back();
+        for (size_t len = max_len; len > 0; --len) {
+            if (stop[len - 1] == last_char) {
+                if (string_ends_with(str, stop.substr(0, len))) {
+                    return str.size() - len;
+                }
+            }
+        }
+    }
+    return std::string::npos;
+}
+
+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);
+
+bool glob_match(const std::string & pattern, const std::string & str);
+
+//
+// Filesystem utils
+//
+
+bool fs_validate_filename(const std::string & filename, bool allow_subdirs = false);
+bool fs_create_directory_with_parents(const std::string & path);
+bool fs_is_directory(const std::string & path);
+
+std::string fs_get_cache_directory();
+std::string fs_get_cache_file(const std::string & filename);
+
+struct common_file_info {
+    std::string path;
+    std::string name;
+    size_t      size = 0; // in bytes
+    bool        is_dir = false;
+};
+std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
+
+//
+// TTY utils
+//
+
+// Auto-detect if colors can be enabled based on terminal and environment
+bool tty_can_use_colors();
+
+//
+// Model utils
+//
+
+struct common_sampler;
+
+// note: defines the model, context, samplers, ets. lifetimes
+struct common_init_result {
+    common_init_result(common_params & params);
+    ~common_init_result();
+
+    llama_model * model();
+    llama_context * context();
+
+    common_sampler * sampler(llama_seq_id seq_id);
+    void reset_samplers();
+
+    std::vector<llama_adapter_lora_ptr> & lora();
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
+};
+
+using common_init_result_ptr = std::unique_ptr<common_init_result>;
+
+common_init_result_ptr 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);
+
+// decodes a single batch of tokens for a prompt and manages session tokens
+//
+// Note: We save state before the last token so that we can replay it to ensure
+// compatibility with all memory types. Recurrent/hybrid models cannot remove
+// tokens from memory, so this approach works across all model architectures.
+bool common_prompt_batch_decode(
+              struct llama_context * ctx,
+    const std::vector<llama_token> & embd,
+                               int & n_past,
+                               int   n_batch,
+                  std::string_view   state_path,
+                              bool   save_state);
+
+// replays the last token after loading state to regenerate logits
+// used after loading session state to ensure the sampling context has valid logits
+bool common_replay_last_token(struct llama_context * ctx, llama_token last_token, int32_t pos);
+
+//
+// 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: replace 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";
+
+}
+
+//
+// MoE utils
+//
+
+const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps";
+
+inline std::string llm_ffn_exps_block_regex(int idx) {
+    return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);
+}
+
+inline llama_model_tensor_buft_override llm_ffn_exps_cpu_override() {
+    return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() };
+}
+
+//
+// training utils
+//
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);
+
+// "adamw" or "sgd" (case insensitive)
+enum ggml_opt_optimizer_type common_opt_get_optimizer(const char *);