// Various helper functions and utilities #pragma once #include #include #include #include #include // // 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 // sampling parameters int32_t top_k = 40; float top_p = 0.9f; float temp = 1.0f; int32_t n_batch = 8; // batch size for prompt processing std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path std::string 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); // // Vocab utils // struct gpt_vocab { using id = int32_t; using token = std::string; std::map token_to_id; std::map id_to_token; }; void replace(std::string & str, const std::string & needle, const std::string & replacement); // poor-man's JSON parsing std::map json_parse(const std::string & fname); // 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_tokenize(const gpt_vocab & vocab, const std::string & text); // 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);