mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-02 09:40:42 +00:00
9c6c09cbd2
Major change to the backend that allows for pluggable versions of llama.cpp/ggml. This was squashed merged from dlopen_backend_5 where the history is preserved.
91 lines
2.4 KiB
C++
91 lines
2.4 KiB
C++
// Various helper functions and utilities
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#pragma once
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#include <string>
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#include <map>
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#include <vector>
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#include <random>
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#include <thread>
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//
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// CLI argument parsing
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//
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_predict = 200; // new tokens to predict
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// sampling parameters
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int32_t top_k = 40;
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float top_p = 0.9f;
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float temp = 0.9f;
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int32_t n_batch = 8; // batch size for prompt processing
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std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path
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std::string prompt;
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
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std::string gpt_random_prompt(std::mt19937 & rng);
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//
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// Vocab utils
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//
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struct gpt_vocab {
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using id = int32_t;
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using token = std::string;
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std::map<token, id> token_to_id;
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std::map<id, token> id_to_token;
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std::vector<std::string> special_tokens;
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void add_special_token(const std::string &token) {
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special_tokens.push_back(token);
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}
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};
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void replace(std::string & str, const std::string & needle, const std::string & replacement);
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// poor-man's JSON parsing
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std::map<std::string, int32_t> json_parse(const std::string & fname);
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// split text into tokens
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//
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// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
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//
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// Regex (Python):
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// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
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//
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// Regex (C++):
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// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
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//
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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
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// load the tokens from encoder.json
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bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
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// sample next token given probabilities for each embedding
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//
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// - consider only the top K tokens
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// - from them, consider only the top tokens with cumulative probability > P
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//
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// TODO: not sure if this implementation is correct
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//
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gpt_vocab::id gpt_sample_top_k_top_p(
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const size_t actualVocabSize,
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const int32_t * last_n_tokens_data,
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int last_n_tokens_size,
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const std::vector<float> logits,
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int top_k,
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double top_p,
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double temp,
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float repeat_penalty,
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std::mt19937 & rng);
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