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llama : cache llama_token_to_piece (#7587)

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* llama : cache llama_token_to_piece

ggml-ci

* llama : use vectors and avoid has_cache

ggml-ci

* llama : throw on unknown tokenizer types

ggml-ci

* llama : print a log of the total cache size
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Georgi Gerganov 2024-05-30 19:01:41 +03:00 committed by GitHub
parent 5dcdf94676
commit 5921b8f089
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2 changed files with 119 additions and 84 deletions
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llama.cpp
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@ -1702,12 +1702,13 @@ struct llama_mlock {
};
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
// NOTE: avoid ever using this except for building the token_to_piece caches
static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
int check = llama_token_to_piece(model, token, result.data(), result.size(), special);
GGML_ASSERT(check == -n_tokens);
}
else {
@ -2162,7 +2163,9 @@ struct llama_vocab {
std::unordered_map<token, id> token_to_id;
std::vector<token_data> id_to_token;
std::vector<id> special_tokens_cache;
std::vector<id> cache_special_tokens;
std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = false);
std::vector<token> cache_token_to_piece_special; // llama_token_to_piece(special = true);
std::map<std::pair<std::string, std::string>, int> bpe_ranks;
@ -4592,20 +4595,14 @@ static void llm_load_vocab(
vocab.special_cls_id = 101;
vocab.special_mask_id = 103;
vocab.add_space_prefix = false;
} else {
if (tokenizer_model == "gpt2") {
} else if (tokenizer_model == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
}
} else {
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
vocab.type = LLAMA_VOCAB_TYPE_SPM;
return;
}
// read bpe merges and populate bpe ranks
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
if (merges_keyidx == -1) {
@ -4639,6 +4636,8 @@ static void llm_load_vocab(
vocab.special_pad_id = -1;
vocab.special_cls_id = -1;
vocab.special_mask_id = -1;
} else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
}
// for now, only BPE models have pre-tokenizers
@ -4833,17 +4832,38 @@ static void llm_load_vocab(
{
for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
vocab.special_tokens_cache.push_back(id);
vocab.cache_special_tokens.push_back(id);
}
}
std::sort( vocab.special_tokens_cache.begin(), vocab.special_tokens_cache.end(),
std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
[&] (const llama_vocab::id a, const llama_vocab::id b) {
return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
}
);
LLAMA_LOG_INFO("%s: special tokens cache size = %u.\n", __func__, (uint32_t)vocab.special_tokens_cache.size());
LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
}
// build token to piece caches
{
size_t size_cache = 0;
std::vector<llama_vocab::token> cache_token_to_piece (n_vocab);
std::vector<llama_vocab::token> cache_token_to_piece_special(n_vocab);
for (uint32_t id = 0; id < n_vocab; ++id) {
cache_token_to_piece[id] = llama_token_to_piece(&model, id, false);
cache_token_to_piece_special[id] = llama_token_to_piece(&model, id, true);
size_cache += cache_token_to_piece[id].size();
size_cache += cache_token_to_piece_special[id].size();
}
std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
std::swap(vocab.cache_token_to_piece_special, cache_token_to_piece_special);
LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
}
}
@ -13233,7 +13253,7 @@ struct fragment_buffer_variant {
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
// for each special token
for (const llama_vocab::id special_id : vocab.special_tokens_cache) {
for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
const auto & special_token = vocab.id_to_token[special_id].text;
// for each text fragment
@ -14392,7 +14412,7 @@ void llama_sample_repetition_penalties(
void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
GGML_ASSERT(ctx);
const int64_t t_start_sample_us = ggml_time_us();
int64_t t_start_sample_us = ggml_time_us();
bool allow_eog = false;
for (const auto & stack : grammar->stacks) {
@ -14404,12 +14424,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
candidates_decoded.reserve(candidates->size);
std::vector<llama_grammar_candidate> candidates_grammar;
candidates_grammar.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_piece(ctx, id, false);
const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id);
if (llama_token_is_eog(&ctx->model, id)) {
if (!allow_eog) {
@ -14609,7 +14630,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
GGML_ASSERT(false);
}
const std::string piece = llama_token_to_piece(ctx, token, false);
const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token);
// Note terminating 0 in decoded string
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
@ -18292,6 +18313,20 @@ static std::string llama_decode_text(const std::string & text) {
// does not write null-terminator to buf
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
// if we have a cache - use it
{
const auto & cache = special ? model->vocab.cache_token_to_piece_special : model->vocab.cache_token_to_piece;
if (!cache.empty()) {
const auto & res = cache.at(token);
if (length < (int) res.size()) {
return -(int) res.size();
}
memcpy(buf, res.c_str(), res.size());
return res.size();
}
}
if (0 <= token && token < llama_n_vocab(model)) {
switch (llama_vocab_get_type(model->vocab)) {
case LLAMA_VOCAB_TYPE_WPM: