utils.h

// Various helper functions and utilities

#pragma once

#include <string>
#include <unordered_map>
#include <vector>
#include <random>
#include <thread>

//
// CLI argument parsing
//

struct gpt_params {
    int32_t seed          = -1;  // RNG seed
    int32_t n_threads     = std::min(4, (int32_t) std::thread::hardware_concurrency());
    int32_t n_predict     = 128; // new tokens to predict
    int32_t repeat_last_n = 64;  // last n tokens to penalize
    int32_t n_parts       = -1;  // amount of model parts (-1 = determine from model dimensions)
    int32_t n_ctx         = 512; //context size

    // sampling parameters
    int32_t top_k = 40;
    float   top_p = 0.95f;
    float   temp  = 0.80f;
    float   repeat_penalty  = 1.10f;

    int32_t n_batch = 8; // batch size for prompt processing

    std::string model  = "models/lamma-7B/ggml-model.bin"; // model path
    std::string prompt = "";

    std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted

    bool memory_f16        = false; // use f16 instead of f32 for memory kv
    bool random_prompt     = false; // do not randomize prompt if none provided
    bool use_color         = false; // use color to distinguish generations and inputs
    bool interactive       = false; // interactive mode
    bool interactive_start = false; // reverse prompt immediately
    bool instruct          = false; // instruction mode (used for Alpaca models)
    bool ignore_eos        = false; // do not stop generating after eos
    bool perplexity        = false; // compute perplexity over the prompt
};

bool gpt_params_parse(int argc, char ** argv, gpt_params & params);

void gpt_print_usage(int argc, char ** argv, const gpt_params & params);

std::string gpt_random_prompt(std::mt19937 & rng);

//
// Model file parsing
//

#define FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
#define FILE_MAGIC 0x67676d66 // 'ggmf' in hex
#define FILE_VERSION 1

//
// Vocab utils
//

struct llama_vocab {
    using id    = int32_t;
    using token = std::string;

    struct token_score {
        token tok;
        float score;
    };

    std::unordered_map<token, id> token_to_id;
    std::vector<token_score> id_to_token;
};

void replace(std::string & str, const std::string & needle, const std::string & replacement);

// poor-man's JSON parsing
std::unordered_map<std::string, int32_t> json_parse(const std::string & fname);

// TODO: temporary until #77 is merged, need this now for some tokenizer tests
bool llama_vocab_load(const std::string & fname, llama_vocab & vocab);

// TODO: this is probably wrong, but I cannot figure out how this tokenizer works ..
// ref: https://github.com/google/sentencepiece
std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos);

// sample next token given probabilities for each embedding
//
//   - consider only the top K tokens
//   - from them, consider only the top tokens with cumulative probability > P
//
llama_vocab::id llama_sample_top_p_top_k(
        const llama_vocab & vocab,
        const float * logits,
        std::vector<llama_vocab::id> & last_n_tokens,
        double repeat_penalty,
        int top_k,
        double top_p,
        double temp,
        std::mt19937 & rng);

// filer to top K tokens from list of logits
void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k);

//
// Quantization
//

size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist);
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist);