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// Various helper functions and utilities
#pragma once
#include <string>
#include <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 = 200; // new tokens to predict
int32_t n_batch = 8; // batch size for prompt processing
// sampling parameters
int32_t top_k = 40;
float top_p = 0.9f;
float temp = 0.9f;
int32_t repeat_last_n = 64;
float repeat_penalty = 1.00f;
std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path
std::string prompt = "";
std::string token_test = "";
bool interactive = false;
int32_t interactive_port = -1;
int32_t n_gpu_layers = 0;
};
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);
//
// Vocab utils
//
std::string trim(const std::string & s);
std::string replace(
const std::string & s,
const std::string & from,
const std::string & to);
struct gpt_vocab {
using id = int32_t;
using token = std::string;
std::map<token, id> token_to_id;
std::map<id, token> id_to_token;
std::vector<std::string> special_tokens;
void add_special_token(const std::string & token);
};
// poor-man's JSON parsing
std::map<std::string, int32_t> json_parse(const std::string & fname);
std::string convert_to_utf8(const std::wstring & input);
std::wstring convert_to_wstring(const std::string & input);
void gpt_split_words(std::string str, std::vector<std::string>& words);
// split text into tokens
//
// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
//
// Regex (Python):
// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
//
// Regex (C++):
// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
//
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
// test outputs of gpt_tokenize
//
// - compare with tokens generated by the huggingface tokenizer
// - test cases are chosen based on the model's main language (under 'prompt' directory)
// - if all sentences are tokenized identically, print 'All tests passed.'
// - otherwise, print sentence, huggingface tokens, ggml tokens
//
void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test);
// load the tokens from encoder.json
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
// 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
//
// TODO: not sure if this implementation is correct
// TODO: temperature is not implemented
//
gpt_vocab::id gpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const float * logits,
int top_k,
double top_p,
double temp,
std::mt19937 & rng);
gpt_vocab::id gpt_sample_top_k_top_p_repeat(
const gpt_vocab & vocab,
const float * logits,
const int32_t * last_n_tokens_data,
size_t last_n_tokens_data_size,
int top_k,
double top_p,
double temp,
int repeat_last_n,
float repeat_penalty,
std::mt19937 & rng);