struct llama_vocab {
using id = int32_t;
using token = std::string;
- using ttype = llama_token_type;
+ using tattr = llama_token_attr;
struct token_data {
token text;
float score;
- ttype type;
+ tattr attr;
};
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
auto & token_data = vocab.id_to_token[i];
token_data.text = std::move(word);
token_data.score = scores ? scores[i] : 0.0f;
- token_data.type = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
+ token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;
+
+ if (toktypes) { //TODO: remove, required until per token attributes are available from GGUF file
+ switch(toktypes[i]) {
+ case LLAMA_TOKEN_TYPE_UNKNOWN: token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN; break;
+ case LLAMA_TOKEN_TYPE_UNUSED: token_data.attr = LLAMA_TOKEN_ATTR_UNUSED; break;
+ case LLAMA_TOKEN_TYPE_NORMAL: token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; break;
+ case LLAMA_TOKEN_TYPE_CONTROL: token_data.attr = LLAMA_TOKEN_ATTR_CONTROL; break;
+ case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
+ case LLAMA_TOKEN_TYPE_BYTE: token_data.attr = LLAMA_TOKEN_ATTR_BYTE; break;
+ case LLAMA_TOKEN_TYPE_UNDEFINED: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break;
+ default: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break;
+ }
+ }
}
GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
// build special tokens cache
{
for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
- if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
+ if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
vocab.cache_special_tokens.push_back(id);
}
}
LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
}
+
+ // Handle per token attributes
+ //NOTE: Each model customizes per token attributes.
+ //NOTE: Per token attributes are missing from the GGUF file.
+ //TODO: Extract attributes from GGUF file.
+ {
+ auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
+ for (auto substr : substrs) {
+ if (str.find(substr) < std::string::npos) {
+ return true;
+ }
+ }
+ return false;
+ };
+
+ auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
+ uint32_t current = vocab.id_to_token.at(id).attr;
+ current = value ? (current | attr) : (current & ~attr);
+ vocab.id_to_token[id].attr = (llama_token_attr) current;
+ };
+
+ auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+ _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
+ };
+
+ std::string model_name;
+ std::string tokenizer_pre;
+
+ ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
+ ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
+
+ // model name to lowercase
+ std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+ [] (const std::string::value_type x) {
+ return std::tolower(x);
+ }
+ );
+
+ // set attributes by model/tokenizer name
+ if (_contains_any(tokenizer_pre, {"jina-v2-es", "jina-v2-de"})) {
+ _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
+ } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+ for (auto id : vocab.cache_special_tokens) {
+ _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+ }
+ for (auto token : {"</s>"}) {
+ _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+ }
+ for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
+ _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+ }
+ }
+ }
}
static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
- return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
+ return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
}
static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
- return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
+ return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
}
static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
- return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
+ return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
}
static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
- return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
+ return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
}
static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
- return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
+ return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
}
static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
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.cache_special_tokens) {
- const auto & special_token = vocab.id_to_token[special_id].text;
+ const auto & data = vocab.id_to_token[special_id];
+ const auto & special_token = data.text;
// for each text fragment
std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
if (match > raw_text_base_offset) {
// left
const int64_t left_reminder_offset = raw_text_base_offset + 0;
- const int64_t left_reminder_length = match - raw_text_base_offset;
- buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
+ int64_t left_reminder_length = match - raw_text_base_offset;
+
+ if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
+ while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
+ left_reminder_length--;
+ }
+ }
+
+ if (left_reminder_length > 0) {
+ buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
+ it++;
+ }
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
#endif
- it++;
}
// special token
// right
if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
- const int64_t right_reminder_offset = match + special_token.length();
- const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
- buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
+ int64_t right_reminder_offset = match + special_token.length();
+ int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+
+ if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
+ while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
+ right_reminder_offset++;
+ right_reminder_length--;
+ }
+ }
+
+ if (right_reminder_length > 0) {
+ buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
+ it++;
+ }
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
#endif
- it++;
-
if (source == 0) {
buffer.erase_after(buffer.before_begin());
} else {
// tokenizer.encode('', add_special_tokens=True) returns [1]
// tokenizer.encode('', add_special_tokens=False) returns []
- static const bool rtrim = true; //TODO: as param
bool is_prev_special = false;
- bool special_token_rtrim = false;
if (add_special && vocab.special_add_bos != 0) {
GGML_ASSERT(vocab.special_bos_id != -1);
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
- // without adding this leading whitespace, we do not get the same results as the original tokenizer
-
- // TODO: It's likely possible to get rid of this string copy entirely
- // by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
- // and passing 'add space prefix' as bool argument
- //
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
- if (special_token_rtrim) {
- size_t num_whitespaces = 0;
- while (isspace(raw_text[num_whitespaces])) {
- num_whitespaces++;
- }
- if (num_whitespaces == raw_text.size()) {
- continue; // skip if all whitespaces
- }
- raw_text = raw_text.substr(num_whitespaces);
- }
-
if (vocab.add_space_prefix) {
if (!output.size() || is_prev_special) { // prefix with space if first token
raw_text = " " + raw_text;
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
is_prev_special = true;
- // phi-3 special tokens without rtrim, works fine for llama-spm too
- special_token_rtrim = rtrim
- && fragment.token != vocab.special_bos_id
- && fragment.token != vocab.special_unk_id
- && fragment.token != vocab.special_eos_id;
}
}
return model->vocab.id_to_token[token].score;
}
-llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
+llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
- return model->vocab.id_to_token[token].type;
+ return model->vocab.id_to_token[token].attr;
}
bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
'<s>a', # Phi-3 fail
'<unk><|endoftext|><s>', # Phi-3 fail
'a\na', # TODO: Bert fail
+ 'a </s> b', # rstrip phi-3
+ 'a <mask> b', # lstrip jina-v2
]
-def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]:
- special_tokens = set(tokenizer.all_special_tokens)
- special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"])
- special_tokens = list(sorted(special_tokens))
+def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
+ """Brute force check all vocab words"""
+ yield from vocab
+
+
+def generator_added_lr_strip(tokenizer) -> Iterator[str]:
+ WHITESPACES = ["", " ", " ", " "]
+ special_tokens = list(tokenizer.all_special_tokens)
+ added_tokens = list(tokenizer.added_tokens_encoder)
+ all_tokens = list(sorted(set(special_tokens + added_tokens)))
+ for token in all_tokens:
+ for lstrip in WHITESPACES:
+ for rstrip in WHITESPACES:
+ yield lstrip + token + rstrip
+ yield "a" + lstrip + token + rstrip
+ yield lstrip + token + rstrip + "z"
+ yield "a" + lstrip + token + rstrip + "z"
+
+
+def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]:
+ special_tokens = list(tokenizer.all_special_tokens)
+ added_tokens = list(tokenizer.added_tokens_encoder)
+ separations = [" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]
+ all_tokens = list(sorted(set(special_tokens + added_tokens + separations)))
rand = random.Random()
for m in range(iterations):
rand.seed(m)
- words = rand.choices(special_tokens, k=500)
+ words = rand.choices(all_tokens, k=500)
if words[0] == tokenizer.bos_token: # skip spam warning of double BOS
while len(words) > 1 and words[1] == tokenizer.bos_token: # leave one starting BOS
words.pop(0)
yield "".join(words)
-def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
- """Brute force check all vocab words"""
- yield from vocab
-
-
def generator_random_chars(iterations=100) -> Iterator[str]:
"""Brute force random text with simple characters"""
ids2 = func_tokenize2(text)
if ids1 != ids2:
i = find_first_mismatch(ids1, ids2)
- ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
- ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
+ ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
+ ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
logger.info(" TokenIDs: " + str(ids1))
logger.info(" Expected: " + str(ids2))
raise Exception()
vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
- test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
+ test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer))
+ test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000))
if __name__ == "__main__":
# main()
- path_tokenizers = "./models/tokenizers/"
+ path_tokenizers = "./models/tokenizers/"
path_vocab_format = "./models/ggml-vocab-%s.gguf"
# import os
# tokenizers = os.listdir(path_tokenizers)
tokenizers = [
- # "llama-spm", # SPM
- # "phi-3", # SPM
+ "llama-spm", # SPM
+ "phi-3", # SPM
"jina-v2-en", # WPM
"bert-bge", # WPM
]
overview / pkg / ggml / sources / llama.cpp / commitdiff