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llama : implement Unigram tokenizer needed by T5 and FLAN-T5 model families (#5763)

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* llama : add T5 model architecture, tensors and model header parameters

* llama : add implementation of Unigram tokenizer with SentencePiece-like text normalization using precompiled charsmap

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
This commit is contained in:
fairydreaming 2024-06-25 21:14:35 +02:00 committed by GitHub
parent e6bf007744
commit 6fcbf68235
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GPG Key ID: B5690EEEBB952194
4 changed files with 586 additions and 38 deletions
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@ -226,6 +226,7 @@ enum llm_arch {
LLM_ARCH_ARCTIC,
LLM_ARCH_DEEPSEEK2,
LLM_ARCH_BITNET,
LLM_ARCH_T5,
LLM_ARCH_UNKNOWN,
};
@ -265,6 +266,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_ARCTIC, "arctic" },
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
{ LLM_ARCH_BITNET, "bitnet" },
{ LLM_ARCH_T5, "t5" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -297,6 +299,7 @@ enum llm_kv {
LLM_KV_EXPERT_WEIGHTS_SCALE,
LLM_KV_POOLING_TYPE,
LLM_KV_LOGIT_SCALE,
LLM_KV_DECODER_START_TOKEN_ID,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@ -309,6 +312,7 @@ enum llm_kv {
LLM_KV_ATTENTION_CAUSAL,
LLM_KV_ATTENTION_Q_LORA_RANK,
LLM_KV_ATTENTION_KV_LORA_RANK,
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@ -346,6 +350,8 @@ enum llm_kv {
LLM_KV_TOKENIZER_ADD_BOS,
LLM_KV_TOKENIZER_ADD_EOS,
LLM_KV_TOKENIZER_ADD_PREFIX,
LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
LLM_KV_TOKENIZER_PREFIX_ID,
@ -383,18 +389,20 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
{ LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
{ LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
{ LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
{ LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
{ LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
{ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
{ LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
{ LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
{ LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
{ LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
{ LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
{ LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
{ LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
{ LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
{ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
{ LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
{ LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
{ LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@ -415,29 +423,31 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
{ LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
{ LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
{ LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
{ LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
{ LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
{ LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
{ LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
{ LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
{ LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
{ LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
{ LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
{ LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
{ LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" },
{ LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" },
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
{ LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
};
struct LLM_KV {
@ -504,6 +514,34 @@ enum llm_tensor {
LLM_TENSOR_ATTN_KV_A_NORM,
LLM_TENSOR_ATTN_SUB_NORM,
LLM_TENSOR_FFN_SUB_NORM,
LLM_TENSOR_DEC_ATTN_NORM,
LLM_TENSOR_DEC_ATTN_Q,
LLM_TENSOR_DEC_ATTN_K,
LLM_TENSOR_DEC_ATTN_V,
LLM_TENSOR_DEC_ATTN_OUT,
LLM_TENSOR_DEC_ATTN_REL_B,
LLM_TENSOR_DEC_CROSS_ATTN_NORM,
LLM_TENSOR_DEC_CROSS_ATTN_Q,
LLM_TENSOR_DEC_CROSS_ATTN_K,
LLM_TENSOR_DEC_CROSS_ATTN_V,
LLM_TENSOR_DEC_CROSS_ATTN_OUT,
LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
LLM_TENSOR_DEC_FFN_NORM,
LLM_TENSOR_DEC_FFN_GATE,
LLM_TENSOR_DEC_FFN_DOWN,
LLM_TENSOR_DEC_FFN_UP,
LLM_TENSOR_DEC_OUTPUT_NORM,
LLM_TENSOR_ENC_ATTN_NORM,
LLM_TENSOR_ENC_ATTN_Q,
LLM_TENSOR_ENC_ATTN_K,
LLM_TENSOR_ENC_ATTN_V,
LLM_TENSOR_ENC_ATTN_OUT,
LLM_TENSOR_ENC_ATTN_REL_B,
LLM_TENSOR_ENC_FFN_NORM,
LLM_TENSOR_ENC_FFN_GATE,
LLM_TENSOR_ENC_FFN_DOWN,
LLM_TENSOR_ENC_FFN_UP,
LLM_TENSOR_ENC_OUTPUT_NORM,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@ -1135,6 +1173,41 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_SUB_NORM, "blk.%d.ffn_sub_norm" },
},
},
{
LLM_ARCH_T5,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_DEC_OUTPUT_NORM, "dec.output_norm" },
{ LLM_TENSOR_DEC_ATTN_NORM, "dec.blk.%d.attn_norm" },
{ LLM_TENSOR_DEC_ATTN_Q, "dec.blk.%d.attn_q" },
{ LLM_TENSOR_DEC_ATTN_K, "dec.blk.%d.attn_k" },
{ LLM_TENSOR_DEC_ATTN_V, "dec.blk.%d.attn_v" },
{ LLM_TENSOR_DEC_ATTN_OUT, "dec.blk.%d.attn_o" },
{ LLM_TENSOR_DEC_ATTN_REL_B, "dec.blk.%d.attn_rel_b" },
{ LLM_TENSOR_DEC_CROSS_ATTN_NORM, "dec.blk.%d.cross_attn_norm" },
{ LLM_TENSOR_DEC_CROSS_ATTN_Q, "dec.blk.%d.cross_attn_q" },
{ LLM_TENSOR_DEC_CROSS_ATTN_K, "dec.blk.%d.cross_attn_k" },
{ LLM_TENSOR_DEC_CROSS_ATTN_V, "dec.blk.%d.cross_attn_v" },
{ LLM_TENSOR_DEC_CROSS_ATTN_OUT, "dec.blk.%d.cross_attn_o" },
{ LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "dec.blk.%d.cross_attn_rel_b" },
{ LLM_TENSOR_DEC_FFN_NORM, "dec.blk.%d.ffn_norm" },
{ LLM_TENSOR_DEC_FFN_GATE, "dec.blk.%d.ffn_gate" },
{ LLM_TENSOR_DEC_FFN_DOWN, "dec.blk.%d.ffn_down" },
{ LLM_TENSOR_DEC_FFN_UP, "dec.blk.%d.ffn_up" },
{ LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
{ LLM_TENSOR_ENC_ATTN_NORM, "enc.blk.%d.attn_norm" },
{ LLM_TENSOR_ENC_ATTN_Q, "enc.blk.%d.attn_q" },
{ LLM_TENSOR_ENC_ATTN_K, "enc.blk.%d.attn_k" },
{ LLM_TENSOR_ENC_ATTN_V, "enc.blk.%d.attn_v" },
{ LLM_TENSOR_ENC_ATTN_OUT, "enc.blk.%d.attn_o" },
{ LLM_TENSOR_ENC_ATTN_REL_B, "enc.blk.%d.attn_rel_b" },
{ LLM_TENSOR_ENC_FFN_NORM, "enc.blk.%d.ffn_norm" },
{ LLM_TENSOR_ENC_FFN_GATE, "enc.blk.%d.ffn_gate" },
{ LLM_TENSOR_ENC_FFN_DOWN, "enc.blk.%d.ffn_down" },
{ LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -2356,6 +2429,11 @@ struct llama_vocab {
bool tokenizer_add_bos = false;
bool tokenizer_add_eos = false;
bool tokenizer_ignore_merges = false;
bool tokenizer_remove_extra_whitespaces = false;
bool tokenizer_escape_whitespaces = true;
bool tokenizer_treat_whitespace_as_suffix = false;
std::vector<char> precompiled_charsmap;
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
GGML_ASSERT(token_left.find(' ') == std::string::npos);
@ -4191,6 +4269,7 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
case LLAMA_VOCAB_TYPE_UGM: return "UGM";
default: return "unknown";
}
}
@ -4870,6 +4949,45 @@ static void llm_load_vocab(
vocab.special_pad_id = -1;
vocab.special_cls_id = -1;
vocab.special_mask_id = -1;
} else if (tokenizer_model == "t5") {
vocab.type = LLAMA_VOCAB_TYPE_UGM;
// default special tokens
vocab.special_bos_id = -1;
vocab.special_eos_id = 1;
vocab.special_unk_id = 2;
vocab.special_sep_id = -1;
vocab.special_pad_id = 0;
vocab.special_cls_id = -1;
vocab.special_mask_id = -1;
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.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
} // The default value of add_space_prefix is true.
const int remove_extra_whitespaces_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS).c_str());
if (remove_extra_whitespaces_keyidx != -1) {
vocab.tokenizer_remove_extra_whitespaces = gguf_get_val_bool(ctx, remove_extra_whitespaces_keyidx);
} // The default value of remove_extra_whitespaces is false.
const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
if (precompiled_charsmap_keyidx != -1) {
size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
const char * precompiled_charsmap = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
vocab.precompiled_charsmap.assign(precompiled_charsmap, precompiled_charsmap + n_precompiled_charsmap);
#ifdef IS_BIG_ENDIAN
// correct endiannes of data in precompiled_charsmap binary blob
uint32_t * xcda_blob_size = (uint32_t *) &vocab.precompiled_charsmap[0];
*xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
uint32_t * xcda_array = (uint32_t *) &vocab.precompiled_charsmap[sizeof(uint32_t)];
for (size_t i = 0; i < xcda_array_size; ++i) {
xcda_array[i] = __builtin_bswap32(xcda_array[i]);
}
#endif
}
} else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
}
@ -4952,6 +5070,10 @@ static void llm_load_vocab(
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
vocab.tokenizer_add_bos = true;
vocab.tokenizer_add_eos = false;
} else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
vocab.tokenizer_add_bos = false;
vocab.tokenizer_add_eos = true;
} else {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
}
@ -13213,12 +13335,18 @@ static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id
return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
}
static bool llama_is_unused_token(const llama_vocab& vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
}
static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
GGML_ASSERT(llama_is_byte_token(vocab, id));
const auto & token_data = vocab.id_to_token.at(id);
switch (llama_vocab_get_type(vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
case LLAMA_VOCAB_TYPE_SPM:
case LLAMA_VOCAB_TYPE_UGM: {
auto buf = token_data.text.substr(3, 2);
return strtol(buf.c_str(), NULL, 16);
}
@ -13238,7 +13366,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
static const char * hex = "0123456789ABCDEF";
switch (llama_vocab_get_type(vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
case LLAMA_VOCAB_TYPE_SPM:
case LLAMA_VOCAB_TYPE_UGM: {
const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
auto token = vocab.token_to_id.find(buf);
if (token != vocab.token_to_id.end()) {
@ -13826,6 +13955,383 @@ struct llm_tokenizer_wpm {
const llama_vocab & vocab;
};
struct naive_trie {
naive_trie() : has_value(false), value(0) {
}
void insert(const char * key, size_t len, int32_t value = 0) {
if (len == 0) {
this->has_value = true;
this->value = value;
return;
}
char c = key[0];
auto res = children.find(c);
if (res != children.end()) {
res->second.insert(key + 1, len - 1, value);
} else {
auto res = children.insert(std::make_pair(c, naive_trie()));
res.first->second.insert(key + 1, len - 1, value);
}
}
std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) {
if (len == 0 || offset == len) {
return std::make_pair(key, offset);
}
char c = key[offset];
auto res = children.find(c);
if (res != children.end()) {
return res->second.get_longest_prefix(key, len, offset + 1);
} else {
return std::make_pair(key, offset);
}
}
struct naive_trie * traverse(const char c) {
auto res = children.find(c);
if (res != children.end()) {
return &res->second;
} else {
return NULL;
}
}
std::map<char, struct naive_trie> children;
bool has_value;
llama_token value;
};
struct llm_tokenizer_ugm {
llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) {
if (vocab.precompiled_charsmap.size() > 0) {
size_t charsmap_offset = 0;
// First four bytes of precompiled_charsmap contains length of binary
// blob containing XOR-compressed compact double array (XCDA) entries
uint32_t xcda_blob_size = *(const uint32_t *) &vocab.precompiled_charsmap[0];
charsmap_offset += sizeof(xcda_blob_size);
if (xcda_blob_size + charsmap_offset >= vocab.precompiled_charsmap.size()) {
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
}
// Next xcda_blob_size bytes contain entries of XOR-compressed compact
// double array (XCDA). Each entry is bit-packed into a 32-bit integer.
xcda_array = (const uint32_t *) &vocab.precompiled_charsmap[charsmap_offset];
xcda_array_size = xcda_blob_size / sizeof(uint32_t);
charsmap_offset += xcda_blob_size;
// Remaining bytes of precompiled charsmap contain null-terminated
// replacement strings for prefixes matched by the XCDA.
prefix_replacements = &vocab.precompiled_charsmap[charsmap_offset];
prefix_replacements_size = vocab.precompiled_charsmap.size() - charsmap_offset;
}
for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
const auto &token_data = vocab.id_to_token[id];
if (llama_is_normal_token(vocab, id)) {
min_score = std::min<float>(min_score, token_data.score);
max_score = std::max<float>(max_score, token_data.score);
}
if (llama_is_normal_token(vocab, id) ||
llama_is_user_defined_token(vocab, id) ||
llama_is_unused_token(vocab, id)) {
token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
}
if (llama_is_user_defined_token(vocab, id)) {
user_defined_token_matcher.insert(token_data.text.data(), token_data.text.size());
}
}
unknown_token_score = min_score - unknown_token_score_penalty;
}
/* This implementation is based on SentencePiece optimized Viterbi algorithm for
* unigram language models. The general idea is to:
* - move along the input sequence in steps of one UTF code point,
* - at each step find all possible tokenizations of the prefix by
* traversing the tokens trie,
* - for each tokenization store the best one so far (by higher score)
* - use the position in sequence after given token as an index to store
* results
* - if there was no valid tokenization of the current UTF code point
* then use unknown token with additional score penalty
* After processing the whole sequence we backtrack from the end to get
* the best tokenization.
*/
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
// normalize the input first
std::string normalized;
normalize(text, &normalized);
size_t input_len = normalized.size();
// initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
std::vector<struct best_tokenization> tokenization_results(input_len + 1, {0, 0, -FLT_MAX});
// at the beginning tokenization score is zero
tokenization_results[0] = { 0, 0, 0 };
for (size_t input_offset = 0; input_offset < input_len;) {
size_t prefix_offset = input_offset;
// calculate how many code units are in the currently processed UTF code point
size_t n_utf8_code_units = std::min<size_t>(utf8_len(normalized[input_offset]), input_len - input_offset);
// traverse the token matcher trie to find a matching token
bool single_codepoint_token_found = false;
const struct best_tokenization & current_best = tokenization_results[input_offset];
struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
while (prefix_offset <= input_len && node != NULL) {
// check if we found valid token in prefix
if (node->has_value) {
// check if it corresponds to the whole UTF code point
if (prefix_offset - input_offset == n_utf8_code_units) {
single_codepoint_token_found = true;
}
llama_token token_id = node->value;
const auto &token_data = vocab.id_to_token[token_id];
// we set the user-defined token scores to 0 to make them more likely to be selected
// (normal token scores are log probabilities, so they are negative)
// score type is double here to make tokenization results exactly
// the same as in the HF tokenizer using SentencePiece
const double token_score = llama_is_user_defined_token(vocab, token_id) ? 0.0 : token_data.score;
const double challenger_score = current_best.score_sum + token_score;
struct best_tokenization & current_champ = tokenization_results[prefix_offset];
if (challenger_score > current_champ.score_sum) {
struct best_tokenization challenger = { token_id, input_offset, (float) challenger_score };
current_champ = challenger;
}
}
node = node->traverse(normalized[prefix_offset++]);
}
// if we didn't find a valid token corresponding to the whole UTF code point
// then use unknown token as the tokenization of this UTF code point
if (!single_codepoint_token_found) {
const double challenger_score = current_best.score_sum + unknown_token_score;
prefix_offset = input_offset + n_utf8_code_units;
struct best_tokenization & current_champ = tokenization_results[prefix_offset];
if (challenger_score > current_champ.score_sum) {
struct best_tokenization challenger = { vocab.special_unk_id, input_offset, (float) challenger_score };
current_champ = challenger;
}
}
// move to the next UTF code point
input_offset += n_utf8_code_units;
}
// now backtrack from the end to gather token ids of the best tokenization
// merge sequences of consecutive unknown tokens into single unknown tokens
bool is_prev_unknown = false;
for (struct best_tokenization & tokenization = tokenization_results[input_len]; ; tokenization = tokenization_results[tokenization.input_offset]) {
bool is_unknown = tokenization.token_id == vocab.special_unk_id;
if (!(is_prev_unknown && is_unknown)) {
output.push_back(tokenization.token_id);
}
if (tokenization.input_offset == 0) {
break;
}
is_prev_unknown = is_unknown;
}
// reverse the output since we added tokens starting from the end of the input
std::reverse(output.begin(), output.end());
}
private:
const llama_vocab & vocab;
// helper structure for returning normalization results
struct normalization_result {
const char * normalized;
size_t normalized_len;
size_t consumed_input;
};
void normalize(const std::string& input, std::string * normalized) {
normalized->clear();
normalized->reserve(input.size() * 3);
const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " ";
bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
bool shall_merge_spaces = vocab.tokenizer_remove_extra_whitespaces;
bool is_space_prepended = false;
bool processing_non_ws = false;
size_t input_len = input.size();
for (size_t input_offset = 0; input_offset < input_len; ) {
auto norm_res = normalize_prefix(input, input_offset);
for (size_t i = 0; i < norm_res.normalized_len; i++) {
char c = norm_res.normalized[i];
if (c != ' ') {
if (!processing_non_ws) {
processing_non_ws = true;
if ((shall_prepend_space && !is_space_prepended) || shall_merge_spaces) {
normalized->append(space);
is_space_prepended = true;
}
}
normalized->push_back(c);
} else {
if (processing_non_ws) {
processing_non_ws = false;
}
if (!shall_merge_spaces) {
normalized->append(space);
}
}
}
input_offset += norm_res.consumed_input;
}
if (shall_append_space) {
normalized->append(space);
}
}
/*
* This structure is a view wrapper for XOR-compressed double array (XCDA)
* See Shunsuke Kanda (2018). Space- and Time-Efficient String Dictionaries.
* Eeach bit-packed entry contains:
* - BASE array value in bits 10-30
* - LCHECK array value in bits 0-7
* - LEAF array value in bit 9
* Entries containing indexes of replacement sequences have set bit 31
*/
struct xcda_array_view {
public:
xcda_array_view(const uint32_t * xcda_array, size_t xcda_array_size) : xcda_array(xcda_array), xcda_array_size(xcda_array_size) {
}
uint32_t get_base(size_t index) {
uint32_t packed_node = get_node(index);
return (packed_node >> 10) << ((packed_node & (1U << 9)) >> 6);
}
uint32_t get_lcheck(size_t index) {
uint32_t packed_node = get_node(index);
return packed_node & ((1U << 31) | 0xff);
}
bool get_leaf(size_t index) {
uint32_t packed_node = get_node(index);
return (packed_node >> 8) & 1;
}
uint32_t get_value(size_t index) {
uint32_t packed_node = get_node(index);
return packed_node & ((1U << 31) - 1);
}
private:
uint32_t get_node(size_t index) {
if (index > xcda_array_size) {
throw std::runtime_error("Index out of array bounds in XCDA array!");
}
return xcda_array[index];
}
const uint32_t * xcda_array;
size_t xcda_array_size;
};
struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
if (input_offset == input.size()) {
return { &input[input_offset], 0, 0 };
}
// if input prefix matches some user-defined token return this token as normalization result
auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
if (user_defined_token_match.second > 0) {
return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
}
size_t longest_prefix_length = 0;
size_t longest_prefix_offset = 0;
if (xcda_array_size > 0) {
struct xcda_array_view xcda_view(xcda_array, xcda_array_size);
// Find the longest normalized sequence matching the input prefix by walking
// the XOR-compressed compact double array (XCDA) starting from the root node
// We find the index of the next node by calculating BASE[s] ^ c where s is
// the index of the previous node and c is a numerical character value
uint32_t node_index = 0;
// get BASE of the root node
node_index = xcda_view.get_base(node_index);
for (size_t prefix_offset = input_offset; prefix_offset < input.size(); prefix_offset++) {
unsigned char c = input[prefix_offset];
if (c == 0) {
break;
}
node_index ^= c;
// if value of LCHECK is not c it means that this is not a child of
// the previous node, so we stop matching
if (xcda_view.get_lcheck(node_index) != c) {
break;
}
bool is_leaf = xcda_view.get_leaf(node_index);
// get BASE of the current node
node_index ^= xcda_view.get_base(node_index);
// if LEAF of the current node is true, it means that its BASE points to the node
// containing index of replacement sequence for currently matched input prefix
if (is_leaf)
{
longest_prefix_length = prefix_offset - input_offset + 1;
// get index of replacement sequence for currently matched input prefix
longest_prefix_offset = xcda_view.get_value(node_index);
}
}
}
if (longest_prefix_length > 0) {
// we have a match, so return the replacement sequence
if (longest_prefix_offset >= prefix_replacements_size) {
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
}
const char * prefix_replacement = &prefix_replacements[longest_prefix_offset];
return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
} else {
// check if the input prefix contains a valid sequence of UTF-8 code units
try {
// if yes, return this sequence unmodified
size_t prefix_offset = input_offset;
unicode_cpt_from_utf8(input, prefix_offset);
return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
} catch(std::invalid_argument & ex) {
// if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
return { "\xEF\xBF\xBD", 3, 1 };
}
}
}
// escaped space symbol - U+2581 (Lower One Eighth Block)
const std::string escaped_space = "\xE2\x96\x81";
const char * prefix_replacements = NULL;
size_t prefix_replacements_size = 0;
const uint32_t * xcda_array = NULL;
size_t xcda_array_size = 0;
struct naive_trie user_defined_token_matcher;
// this structure stores the best tokenization so far at input_offset
struct best_tokenization {
llama_token token_id;
size_t input_offset;
float score_sum;
};
float min_score = FLT_MAX;
float max_score = -FLT_MAX;
float unknown_token_score_penalty = 10.0;
float unknown_token_score;
struct naive_trie token_matcher;
};
typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
@ -14086,6 +14592,39 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
output.push_back(vocab.special_sep_id);
}
} break;
case LLAMA_VOCAB_TYPE_UGM:
{
llm_tokenizer_ugm tokenizer(vocab);
if (add_special && vocab.tokenizer_add_bos != 0) {
GGML_ASSERT(vocab.special_bos_id != -1);
output.push_back(vocab.special_bos_id);
}
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
tokenizer.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
}
if (add_special && vocab.tokenizer_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
LLAMA_LOG_WARN(
"%s: Added a BOS token to the prompt as specified by the model but the prompt "
"also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
"Are you sure this is what you want?\n", __FUNCTION__);
}
if (add_special && vocab.tokenizer_add_eos == 1) {
GGML_ASSERT(vocab.special_eos_id != -1);
output.push_back(vocab.special_eos_id);
}
} break;
case LLAMA_VOCAB_TYPE_NONE:
GGML_ASSERT(false);
}
@ -16964,6 +17503,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_BLOOM:
case LLM_ARCH_MAMBA:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_T5:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
@ -18659,6 +19199,10 @@ llama_token llama_token_eot(const struct llama_model * model) {
return model->vocab.special_eot_id;
}
llama_token llama_token_pad(const struct llama_model * model) {
return model->vocab.special_pad_id;
}
int32_t llama_tokenize(
const struct llama_model * model,
const char * text,
@ -18725,7 +19269,8 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
if (0 <= token && token < llama_n_vocab(model)) {
switch (llama_vocab_get_type(model->vocab)) {
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_SPM: {
case LLAMA_VOCAB_TYPE_SPM:
case LLAMA_VOCAB_TYPE_UGM: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {

2
llama.h
View File

@ -67,6 +67,7 @@ extern "C" {
LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
};
// pre-tokenization types
@ -857,6 +858,7 @@ extern "C" {
LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding
// Returns -1 if unknown, 1 for true or 0 for false.
LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);

2
unicode.cpp
View File

@ -23,7 +23,7 @@ static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
return result;
}
static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
assert(offset < utf8.size());
if (!(utf8[offset + 0] & 0x80)) {
auto result = utf8[offset + 0];

1
unicode.h
View File

@ -48,6 +48,7 @@ struct codepoint_flags {
std::string unicode_cpt_to_utf8(uint32_t cp);
uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);