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15
llama.cpp
15
llama.cpp
@ -2293,6 +2293,8 @@ struct llama_vocab {
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enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
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enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
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int max_token_len = 0; // used for optimizing longest token search
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std::unordered_map<token, id> token_to_id;
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std::vector<token_data> id_to_token;
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@ -4939,6 +4941,7 @@ static void llm_load_vocab(
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GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
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vocab.token_to_id[word] = i;
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vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
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auto & token_data = vocab.id_to_token[i];
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token_data.text = std::move(word);
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@ -5249,6 +5252,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
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if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
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LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
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if (model.arch == LLM_ARCH_DEEPSEEK2) {
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LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
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LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
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@ -13488,7 +13493,7 @@ private:
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struct llm_tokenizer_wpm {
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llm_tokenizer_wpm(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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void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const {
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const auto & token_map = vocab.token_to_id;
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// normalize and split by whitespace
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@ -13514,7 +13519,7 @@ struct llm_tokenizer_wpm {
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for (int i = 0; i < n; ++i) {
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// loop through possible match length
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bool match = false;
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for (int j = n; j > i; j--) {
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for (int j = std::min(n, i + vocab.max_token_len + 1); j > i; j--) {
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auto it = token_map.find(word1.substr(i, j - i));
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if (it != token_map.end()) {
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output.push_back(it->second);
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@ -13537,7 +13542,8 @@ struct llm_tokenizer_wpm {
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}
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}
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std::vector<std::string> preprocess(const std::string & text) {
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// TODO: reduce string copies by using cpts_offs array
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std::vector<std::string> preprocess(const std::string & text) const {
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const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
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std::vector<std::string> words(1, "");
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@ -13832,6 +13838,8 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
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output.push_back(vocab.special_cls_id);
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}
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llm_tokenizer_wpm tokenizer(vocab);
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for (const auto & fragment : fragment_buffer) {
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if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
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auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
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@ -13839,7 +13847,6 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
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#ifdef PRETOKENIZERDEBUG
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LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
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#endif
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llm_tokenizer_wpm tokenizer(vocab);
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tokenizer.tokenize(raw_text, output);
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} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
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output.push_back(fragment.token);
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@ -596,6 +596,7 @@ std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & c
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std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
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std::vector<uint32_t> result;
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result.reserve(utf8.size());
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size_t offset = 0;
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while (offset < utf8.size()) {
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result.push_back(unicode_cpt_from_utf8(utf8, offset));
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