vbatts/llama.cpp
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We could use std::unordered_map over std::map (#305)

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* Improve performance by changing std::map to std::unordered_map and std::map<id, token> id_to_token; to std::vector<token> id_to_token;

* fix last commit on gpt_vocab_init add vocab.id_to_token.resize(vocab.token_to_id.size());

* Removed include <map>

* Nest struct token score inside gpt_vocab

* renamed token to tok
This commit is contained in:
Fabio R. Sluzala 2023-03-21 14:21:50 -03:00 • committed by GitHub
parent 89d5d90f3b
commit 353ec251a4
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GPG key ID: 4AEE18F83AFDEB23
4 changed files with 36 additions and 24 deletions
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20
utils.cpp
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@ -155,8 +155,8 @@ void replace(std::string & str, const std::string & needle, const std::string &
}
}
std::map<std::string, int32_t> json_parse(const std::string & fname) {
std::map<std::string, int32_t> result;
std::unordered_map<std::string, int32_t> json_parse(const std::string & fname) {
std::unordered_map<std::string, int32_t> result;
// read file into string
std::string json;
@ -360,16 +360,16 @@ private:
return;
}
auto score = vocab_.score.find((*token).second);
if (score == vocab_.score.end()) {
if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
return;
}
const auto &tok_score = vocab_.id_to_token[(*token).second];
llama_sp_bigram bigram;
bigram.left = left;
bigram.right = right;
bigram.score = (*score).second;
bigram.score = tok_score.score;
bigram.size = text.size();
work_queue_.push(bigram);
}
@ -393,6 +393,8 @@ bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
std::string word;
std::vector<char> tmp(64);
vocab.id_to_token.resize(n_vocab);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
@ -410,8 +412,10 @@ bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
fin.read((char *) &score, sizeof(score));
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
vocab.score[i] = score;
auto &tok_score = vocab.id_to_token[i];
tok_score.tok = word;
tok_score.score = score;
}
return true;