Introduce C-style API (#370)
* Major refactoring - introduce C-style API * Clean up * Add <cassert> * Add <iterator> * Add <algorithm> .... * Fix timing reporting and accumulation * Measure eval time only for single-token calls * Change llama_tokenize return meaning
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14 changed files with 1954 additions and 1752 deletions
519
utils.cpp
519
utils.cpp
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@ -3,12 +3,9 @@
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#include <cassert>
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#include <cstring>
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#include <fstream>
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#include <regex>
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#include <iostream>
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#include <iterator>
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#include <queue>
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#include <string>
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#include <math.h>
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#include <iterator>
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#include <algorithm>
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#if defined(_MSC_VER) || defined(__MINGW32__)
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#include <malloc.h> // using malloc.h with MSC/MINGW
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@ -147,509 +144,11 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
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return "The";
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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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size_t pos = 0;
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while ((pos = str.find(needle, pos)) != std::string::npos) {
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str.replace(pos, needle.length(), replacement);
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pos += replacement.length();
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}
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}
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std::unordered_map<std::string, int32_t> json_parse(const std::string & fname) {
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std::unordered_map<std::string, int32_t> result;
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// read file into string
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std::string json;
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{
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std::ifstream ifs(fname);
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if (!ifs) {
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fprintf(stderr, "Failed to open %s\n", fname.c_str());
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exit(1);
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}
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json = std::string((std::istreambuf_iterator<char>(ifs)),
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(std::istreambuf_iterator<char>()));
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}
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if (json[0] != '{') {
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return result;
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}
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// parse json
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{
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bool has_key = false;
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bool in_token = false;
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std::string str_key = "";
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std::string str_val = "";
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int n = json.size();
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for (int i = 1; i < n; ++i) {
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if (!in_token) {
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if (json[i] == ' ') continue;
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if (json[i] == '"') {
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in_token = true;
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continue;
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}
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} else {
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if (json[i] == '\\' && i+1 < n) {
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if (has_key == false) {
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str_key += json[i];
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} else {
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str_val += json[i];
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}
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++i;
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} else if (json[i] == '"') {
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if (has_key == false) {
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has_key = true;
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++i;
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while (json[i] == ' ') ++i;
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++i; // :
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while (json[i] == ' ') ++i;
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if (json[i] != '\"') {
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while (json[i] != ',' && json[i] != '}') {
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str_val += json[i++];
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}
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has_key = false;
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} else {
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in_token = true;
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continue;
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}
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} else {
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has_key = false;
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}
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::replace(str_key, "\\u0120", " " ); // \u0120 -> space
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::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
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::replace(str_key, "\\\"", "\""); // \\\" -> "
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try {
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result[str_key] = std::stoi(str_val);
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} catch (...) {
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//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
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}
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str_key = "";
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str_val = "";
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in_token = false;
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continue;
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}
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if (has_key == false) {
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str_key += json[i];
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} else {
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str_val += json[i];
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}
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}
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}
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}
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return result;
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}
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static size_t utf8_len(char src) {
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const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
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uint8_t highbits = static_cast<uint8_t>(src) >> 4;
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return lookup[highbits];
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}
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struct llama_sp_symbol {
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using index = int;
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index prev;
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index next;
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const char * text;
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size_t n;
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};
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struct llama_sp_bigram {
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struct comparator {
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bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
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return (l.score < r.score) || (l.score == r.score && l.left > r.left);
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}
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};
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using queue_storage = std::vector<llama_sp_bigram>;
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using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
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llama_sp_symbol::index left;
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llama_sp_symbol::index right;
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float score;
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size_t size;
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};
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// original implementation:
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// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
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struct llama_tokenizer {
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llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
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void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
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// split string into utf8 chars
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int index = 0;
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size_t offs = 0;
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while (offs < text.size()) {
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llama_sp_symbol sym;
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size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
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sym.text = text.c_str() + offs;
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sym.n = char_len;
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offs += char_len;
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sym.prev = index - 1;
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sym.next = offs == text.size() ? -1 : index + 1;
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index++;
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symbols_.emplace_back(std::move(sym));
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}
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// seed the work queue with all possible 2-character tokens.
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for (size_t i = 1; i < symbols_.size(); ++i) {
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try_add_bigram(i - 1, i);
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}
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// keep substituting the highest frequency pairs for as long as we can.
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while (!work_queue_.empty()) {
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auto bigram = work_queue_.top();
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work_queue_.pop();
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auto & left_sym = symbols_[bigram.left];
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auto & right_sym = symbols_[bigram.right];
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// if one of the symbols already got merged, skip it.
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if (left_sym.n == 0 || right_sym.n == 0 ||
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left_sym.n + right_sym.n != bigram.size) {
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continue;
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}
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// merge the right sym into the left one
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left_sym.n += right_sym.n;
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right_sym.n = 0;
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//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
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// remove the right sym from the chain
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left_sym.next = right_sym.next;
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if (right_sym.next >= 0) {
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symbols_[right_sym.next].prev = bigram.left;
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}
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// find more substitutions
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try_add_bigram(left_sym.prev, bigram.left);
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try_add_bigram(bigram.left, left_sym.next);
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}
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for (int i = 0; i != -1; i = symbols_[i].next) {
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auto & symbol = symbols_[i];
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auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
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if (token == vocab_.token_to_id.end()) {
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// output any symbols that did not form tokens as bytes.
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for (int j = 0; j < (int) symbol.n; ++j) {
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llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
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output.push_back(token_id);
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}
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} else {
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output.push_back((*token).second);
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}
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}
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}
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private:
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void try_add_bigram(int left, int right) {
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if (left == -1 || right == -1) {
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return;
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}
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const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
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auto token = vocab_.token_to_id.find(text);
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if (token == vocab_.token_to_id.end()) {
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return;
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}
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if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
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return;
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}
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const auto &tok_score = vocab_.id_to_token[(*token).second];
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llama_sp_bigram bigram;
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bigram.left = left;
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bigram.right = right;
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bigram.score = tok_score.score;
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bigram.size = text.size();
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work_queue_.push(bigram);
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}
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const llama_vocab & vocab_;
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std::vector<llama_sp_symbol> symbols_;
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llama_sp_bigram::queue work_queue_;
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};
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// TODO: temporary code duplication with llama.cpp
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// will resolve after #77 is merged
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bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
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std::ifstream fin(fname, std::ios::binary);
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if (!fin.is_open()) {
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return false;
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}
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int n_vocab = 0;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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std::string word;
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std::vector<char> tmp(64);
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vocab.id_to_token.resize(n_vocab);
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for (int i = 0; i < n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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word.resize(len);
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if (len > 0) {
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tmp.resize(len);
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fin.read(tmp.data(), len);
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word.assign(tmp.data(), len);
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} else {
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word.clear();
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}
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float score;
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fin.read((char *) &score, sizeof(score));
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vocab.token_to_id[word] = i;
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auto &tok_score = vocab.id_to_token[i];
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tok_score.tok = word;
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tok_score.score = score;
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}
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return true;
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}
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std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
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llama_tokenizer tokenizer(vocab);
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std::vector<llama_vocab::id> output;
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if (text.size() == 0) {
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return output;
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}
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if (bos) {
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output.push_back(1);
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}
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tokenizer.tokenize(text, output);
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return output;
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}
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void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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}
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llama_vocab::id llama_sample_top_p_top_k(
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const llama_vocab & vocab,
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const float * logits,
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std::vector<llama_vocab::id> & last_n_tokens,
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double repeat_penalty,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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std::vector<std::pair<double, llama_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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for (int i = 0; i < n_logits; ++i) {
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// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
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// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
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if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
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// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
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if (logits[i] < 0.0) {
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logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
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}
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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}
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}
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sample_top_k(logits_id, top_k);
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < (int) probs.size(); i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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probs.resize(i + 1);
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logits_id.resize(i + 1);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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//printf("\n");
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//for (int i = 0; i < (int) 10; i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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//}
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//printf("\n\n");
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//exit(0);
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
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const int nb = k / qk;
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const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
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const size_t row_size = nb*bs;
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assert(k % qk == 0);
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const size_t pp_size = qk / 2;
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uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
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char * pdst = (char *) dst;
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for (int j = 0; j < n; j += k) {
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uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
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uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
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for (int i = 0; i < nb; i++) {
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float amax = 0.0f; // absolute max
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{
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for (int l = 0; l < qk; l++) {
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const float v = src[j + i*qk + l];
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amax = std::max(amax, fabsf(v));
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}
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const float d = amax / ((1 << 3) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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*(float *) pd = d;
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pd += bs;
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for (int l = 0; l < qk; l += 2) {
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const float v0 = (src[j + i*qk + l + 0])*id;
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const float v1 = (src[j + i*qk + l + 1])*id;
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const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
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const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
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assert(vi0 >= 0 && vi0 < 16);
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assert(vi1 >= 0 && vi1 < 16);
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hist[vi0]++;
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hist[vi1]++;
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pp[l/2] = vi0 | (vi1 << 4);
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}
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memcpy(pb, pp, pp_size);
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pb += bs;
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}
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}
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}
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return (n/k)*row_size;
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}
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size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
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const int nb = k / qk;
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const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
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const size_t row_size = nb*bs;
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assert(k % qk == 0);
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const size_t pp_size = qk / 2;
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uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
|
||||
|
||||
char * pdst = (char *) dst;
|
||||
|
||||
for (int j = 0; j < n; j += k) {
|
||||
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
|
||||
uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
|
||||
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
|
||||
|
||||
//printf("n = %d, k = %d, nb = %d, row_size = %d, j = %d, pm = %p, pd = %p, pb = %p\n", n, k, nb, row_size, j, pm, pd, pb);
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
float min = std::numeric_limits<float>::max();
|
||||
float max = std::numeric_limits<float>::min();
|
||||
|
||||
{
|
||||
for (int l = 0; l < qk; l++) {
|
||||
const float v = src[j + i*qk + l];
|
||||
if (v < min) min = v;
|
||||
if (v > max) max = v;
|
||||
}
|
||||
|
||||
const float d = (max - min) / ((1 << 4) - 1);
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
*(float *) pd = d;
|
||||
*(float *) pm = min;
|
||||
pd += bs;
|
||||
pm += bs;
|
||||
|
||||
for (int l = 0; l < qk; l += 2) {
|
||||
const float v0 = (src[j + i*qk + l + 0] - min)*id;
|
||||
const float v1 = (src[j + i*qk + l + 1] - min)*id;
|
||||
|
||||
const uint8_t vi0 = round(v0);
|
||||
const uint8_t vi1 = round(v1);
|
||||
|
||||
assert(vi0 >= 0 && vi0 < 16);
|
||||
assert(vi1 >= 0 && vi1 < 16);
|
||||
|
||||
hist[vi0]++;
|
||||
hist[vi1]++;
|
||||
|
||||
pp[l/2] = vi0 | (vi1 << 4);
|
||||
}
|
||||
|
||||
memcpy(pb, pp, pp_size);
|
||||
pb += bs;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return (n/k)*row_size;
|
||||
// TODO: not great allocating this every time
|
||||
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
|
||||
std::vector<llama_token> res(8096);
|
||||
int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
|
||||
res.resize(n);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue