common-base.h

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// Various helper functions and utilities
 
#pragma once
 
#include "llama.h"
 
#include "sampling.h"
 
#include <cmath>
#include <string>
#include <vector>
#include <random>
#include <thread>
#include <unordered_map>
#include <tuple>
 
 
 
struct llama_control_vector_load_info;
 
//
// CPU utils
//
 
int32_t cpu_get_num_physical_cores();
int32_t cpu_get_num_math();
 
//
// CLI argument parsing
//
 
// dimensionality reduction methods, used by cvector-generator
enum dimre_method {
    DIMRE_METHOD_PCA,
    DIMRE_METHOD_MEAN,
};
 
struct gpt_params {
    uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
 
    int32_t n_threads = cpu_get_num_math();
    int32_t n_threads_draft = -1;
    int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
    int32_t n_threads_batch_draft = -1;
    int32_t n_predict = -1; // new tokens to predict
    int32_t n_ctx = 0; // 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_draft = 5; // number of tokens to draft during speculative decoding
    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
    float   p_split = 0.1f; // speculative decoding split probability
    int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
    int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-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
    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 = -1.0f; // KV cache defragmentation threshold
 
    ggml_backend_sched_eval_callback cb_eval = nullptr;
    void* cb_eval_user_data = nullptr;
 
    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
 
    enum llama_split_mode        split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
    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
 
    // // sampling parameters
    struct llama_sampling_params sparams;
 
    std::string model = ""; // model path
    std::string model_draft = ""; // draft model for speculative decoding
    std::string model_alias = "unknown"; // model alias
    std::string model_url = ""; // model url to download
    std::string hf_token = ""; // HF token
    std::string hf_repo = ""; // HF repo
    std::string hf_file = ""; // HF file
    std::string prompt = "";
    std::string prompt_file = ""; // store the external prompt file name
    std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
    std::string input_prefix = ""; // string to prefix user inputs with
    std::string input_suffix = ""; // string to suffix user inputs with
    std::string logdir = ""; // directory in which to save YAML log files
    std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding
    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
    std::string logits_file = ""; // file for saving *all* logits
    std::string rpc_servers = ""; // comma separated list of RPC servers
 
    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;
 
    // TODO: avoid tuple, use struct
    std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
 
    std::vector<llama_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
 
    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 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 conversation = false; // conversation mode (does not print special tokens and suffix/prefix)
    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 input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
    bool ignore_eos = false; // ignore generated EOS tokens
    bool logits_all = false; // return logits for all tokens in the batch
    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 infill = false; // use infill mode
    bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
    bool no_kv_offload = false; // disable KV offloading
    bool warmup = true;  // warmup run
    bool check_tensors = false; // validate tensor data
 
    std::string cache_type_k = "f16"; // KV cache data type for the K
    std::string cache_type_v = "f16"; // KV cache data type for the V
 
    // multimodal models (see examples/llava)
    std::string mmproj = "";        // path to multimodal projector
    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 embendings (-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 embendings
 
    // 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
 
    std::string hostname = "127.0.0.1";
    std::string public_path = "";
    std::string chat_template = "";
    std::string system_prompt = "";
    bool enable_chat_template = true;
 
    std::vector<std::string> api_keys;
 
    std::string ssl_file_key = "";
    std::string ssl_file_cert = "";
 
    bool endpoint_slots = true;
    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
    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
 
    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
 
    bool process_output = false; // collect data for the output tensor
    bool compute_ppl = true;  // whether to compute perplexity
 
    // cvector-generator params
    int n_pca_batch = 100;
    int n_pca_iterations = 1000;
    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
    std::string cvector_outfile = "control_vector.gguf";
    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
 
    bool spm_infill = false; // suffix/prefix/middle pattern for infill
 
    std::string lora_outfile = "ggml-lora-merged-f16.gguf";
};