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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov 2024-10-02 15:14:46 +03:00
parent 162a455402
commit ccc2547210
5 changed files with 588 additions and 133 deletions
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261
examples/talk-llama/llama-vocab.cpp
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@ -50,7 +50,7 @@ struct 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) {
std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) const {
if (len == 0 || offset == len) {
return std::make_pair(key, offset);
}
@ -79,6 +79,15 @@ struct naive_trie {
// impl
//
struct llm_tokenizer {
llm_tokenizer() {}
virtual ~llm_tokenizer() = default;
};
llama_vocab::~llama_vocab() {
delete tokenizer;
}
int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
GGML_ASSERT(token_left.find(' ') == std::string::npos);
GGML_ASSERT(token_left.find('\n') == std::string::npos);
@ -187,10 +196,15 @@ struct llm_bigram_spm {
size_t size;
};
struct llm_tokenizer_spm {
llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
struct llm_tokenizer_spm : llm_tokenizer {
llm_tokenizer_spm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
};
struct llm_tokenizer_spm_session {
llm_tokenizer_spm_session(const llama_vocab & vocab) : vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
// split string into utf8 chars
int index = 0;
size_t offs = 0;
@ -279,7 +293,6 @@ private:
if (left == -1 || right == -1) {
return;
}
const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
auto token = vocab.token_to_id.find(text);
@ -306,10 +319,11 @@ private:
}
const llama_vocab & vocab;
// currently unused
// const llm_tokenizer_spm * spm_tokenizer;
std::vector<llm_symbol> symbols;
llm_bigram_spm::queue work_queue;
std::map<std::string, std::pair<int, int>> rev_merge;
};
@ -352,8 +366,8 @@ struct llm_bigram_bpe {
size_t size;
};
struct llm_tokenizer_bpe {
llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {
struct llm_tokenizer_bpe : llm_tokenizer {
llm_tokenizer_bpe(const llama_vocab & vocab) : llm_tokenizer() {
GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
switch (vocab.type_pre) {
case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
@ -450,6 +464,20 @@ struct llm_tokenizer_bpe {
"[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_CHAMELEON:
// Note: in theory, the special token (sentinel and image token) regex_exprs below
// are unnecessary, as they are split in `tokenizer_st_partition` anyway.
// However, since the upstream pre-tokenizer uses them, they are also
// included here (see https://huggingface.co/facebook/chameleon-7b).
regex_exprs = {
"<sentinel:[0-9]+>", // Sentinel tokens
"(IMGIMG)((A|B|C|D|E|F|G|H|I){1,4})Z", // Image tokens
"([\\t\\n]| | )", // directly from tokenizer.json
"\\p{N}", // Individual digits
"[\\p{P}!-/:-@\\[-`{-~]", // Punctuation, Isolated
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
};
break;
default:
// default regex for BPE tokenization pre-processing
regex_exprs = {
@ -462,7 +490,14 @@ struct llm_tokenizer_bpe {
}
}
void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const {
std::vector<std::string> regex_exprs;
};
struct llm_tokenizer_bpe_session {
llm_tokenizer_bpe_session(const llama_vocab & vocab) : vocab(vocab),
bpe_tokenizer(static_cast<const llm_tokenizer_bpe *>(vocab.tokenizer)) {}
static void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) {
output.push_back(token_id);
}
@ -501,12 +536,11 @@ struct llm_tokenizer_bpe {
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
int final_prev_index = -1;
const auto word_collection = unicode_regex_split(text, regex_exprs);
const auto word_collection = unicode_regex_split(text, bpe_tokenizer->regex_exprs);
symbols_final.clear();
for (auto & word : word_collection) {
for (const auto & word : word_collection) {
work_queue = llm_bigram_bpe::queue();
symbols.clear();
@ -609,7 +643,6 @@ private:
if (left == -1 || right == -1) {
return;
}
std::string left_token = std::string(symbols[left].text, symbols[left].n);
std::string right_token = std::string(symbols[right].text, symbols[right].n);
@ -633,12 +666,10 @@ private:
}
const llama_vocab & vocab;
std::vector<std::string> regex_exprs;
const llm_tokenizer_bpe * bpe_tokenizer;
std::vector<llm_symbol> symbols;
std::vector<llm_symbol> symbols_final;
llm_bigram_bpe::queue work_queue;
};
@ -646,15 +677,17 @@ private:
// WPM tokenizer
//
struct llm_tokenizer_wpm {
llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
struct llm_tokenizer_wpm : llm_tokenizer {
llm_tokenizer_wpm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
};
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const {
struct llm_tokenizer_wpm_session {
llm_tokenizer_wpm_session(const llama_vocab & vocab) : vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
const auto & token_map = vocab.token_to_id;
// normalize and split by whitespace
std::vector<std::string> words = preprocess(text);
// bos token prepended already
// find the longest tokens that form the words
@ -699,7 +732,7 @@ struct llm_tokenizer_wpm {
}
// TODO: reduce string copies by using cpts_offs array
std::vector<std::string> preprocess(const std::string & text) const {
static std::vector<std::string> preprocess(const std::string & text) {
const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
std::vector<std::string> words(1, "");
@ -751,15 +784,18 @@ struct llm_tokenizer_wpm {
//(cpt >= 0xFF00 && cpt <= 0xFFEF);
}
private:
const llama_vocab & vocab;
// currently unused
// const llm_tokenizer_wpm * wpm_tokenizer;
};
//
// UGM tokenizer
//
struct llm_tokenizer_ugm {
llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) {
struct llm_tokenizer_ugm : llm_tokenizer {
llm_tokenizer_ugm(const llama_vocab & vocab) : llm_tokenizer() {
if (vocab.precompiled_charsmap.size() > 0) {
size_t charsmap_offset = 0;
@ -805,6 +841,30 @@ struct llm_tokenizer_ugm {
unknown_token_score = min_score - unknown_token_score_penalty;
}
// 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;
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;
};
struct llm_tokenizer_ugm_session {
llm_tokenizer_ugm_session(const llama_vocab & vocab) : vocab(vocab),
ugm_tokenizer(static_cast<const llm_tokenizer_ugm *>(vocab.tokenizer)) {}
/* 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,
@ -843,7 +903,7 @@ struct llm_tokenizer_ugm {
// 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];
const struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
const struct naive_trie * node = ugm_tokenizer->token_matcher.traverse(normalized[prefix_offset++]);
while (prefix_offset <= input_len && node != NULL) {
// check if we found valid token in prefix
@ -873,7 +933,7 @@ struct llm_tokenizer_ugm {
// 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;
const double challenger_score = current_best.score_sum + ugm_tokenizer->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) {
@ -905,7 +965,6 @@ struct llm_tokenizer_ugm {
}
private:
const llama_vocab & vocab;
// helper structure for returning normalization results
struct normalization_result {
@ -918,7 +977,7 @@ private:
normalized->clear();
normalized->reserve(input.size() * 3);
const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " ";
const std::string space = vocab.tokenizer_escape_whitespaces ? ugm_tokenizer->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;
@ -1000,13 +1059,21 @@ private:
size_t xcda_array_size;
};
// 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;
};
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);
auto user_defined_token_match =
ugm_tokenizer->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 };
}
@ -1014,8 +1081,8 @@ private:
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);
if (ugm_tokenizer->xcda_array_size > 0) {
struct xcda_array_view xcda_view(ugm_tokenizer->xcda_array, ugm_tokenizer->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
@ -1051,12 +1118,13 @@ private:
if (longest_prefix_length > 0) {
// we have a match, so return the replacement sequence
if (longest_prefix_offset >= prefix_replacements_size) {
if (longest_prefix_offset >= ugm_tokenizer->prefix_replacements_size) {
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
}
const char * prefix_replacement = &prefix_replacements[longest_prefix_offset];
const char * prefix_replacement = &(ugm_tokenizer->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
@ -1068,33 +1136,9 @@ private:
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;
const llama_vocab & vocab;
const llm_tokenizer_ugm * ugm_tokenizer;
};
//
@ -1155,8 +1199,8 @@ static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escape
return output;
}
struct llm_tokenizer_rwkv {
llm_tokenizer_rwkv(const llama_vocab & vocab): vocab(vocab) {
struct llm_tokenizer_rwkv : llm_tokenizer {
llm_tokenizer_rwkv(const llama_vocab & vocab) : llm_tokenizer() {
// RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens.
// For now, we decode the vocab here into the lookup we'll use for tokenization.
@ -1168,11 +1212,17 @@ struct llm_tokenizer_rwkv {
}
}
struct naive_trie token_matcher;
};
struct llm_tokenizer_rwkv_session {
llm_tokenizer_rwkv_session(const llama_vocab & vocab) : vocab(vocab),
rwkv_tokenizer(static_cast<const llm_tokenizer_rwkv &>(*vocab.tokenizer)) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
uint32_t position = 0;
while (position < text.size()) {
const struct naive_trie * node = token_matcher.traverse(text[position]);
const struct naive_trie * node = rwkv_tokenizer.token_matcher.traverse(text[position]);
if (node == NULL) {
// no matching token found, add unknown token
output.push_back(vocab.special_unk_id);
@ -1197,11 +1247,33 @@ struct llm_tokenizer_rwkv {
}
}
private:
const llama_vocab & vocab;
struct naive_trie token_matcher;
const llm_tokenizer_rwkv & rwkv_tokenizer;
};
void llama_vocab::init_tokenizer() {
switch (type) {
case LLAMA_VOCAB_TYPE_SPM:
tokenizer = new llm_tokenizer_spm(*this);
break;
case LLAMA_VOCAB_TYPE_BPE:
tokenizer = new llm_tokenizer_bpe(*this);
break;
case LLAMA_VOCAB_TYPE_WPM:
tokenizer = new llm_tokenizer_wpm(*this);
break;
case LLAMA_VOCAB_TYPE_UGM:
tokenizer = new llm_tokenizer_ugm(*this);
break;
case LLAMA_VOCAB_TYPE_RWKV:
tokenizer = new llm_tokenizer_rwkv(*this);
break;
default:
GGML_ABORT("unsupported vocab type");
}
}
//
// (de-) tokenize
//
@ -1263,7 +1335,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
// if a fragment is text ( not yet processed )
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto & raw_text = fragment.raw_text;
const auto & raw_text = fragment.raw_text;
auto raw_text_base_offset = fragment.offset;
auto raw_text_base_length = fragment.length;
@ -1362,7 +1434,13 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
}
}
std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
std::vector<llama_vocab::id> llama_tokenize_internal(
const llama_vocab & vocab,
std::string raw_text,
bool add_special,
bool parse_special) {
GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
std::vector<llama_vocab::id> output;
std::forward_list<fragment_buffer_variant> fragment_buffer;
@ -1399,9 +1477,9 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_spm tokenizer(vocab);
llama_escape_whitespace(raw_text);
tokenizer.tokenize(raw_text, output);
llm_tokenizer_spm_session session(vocab);
session.tokenize(raw_text, output);
is_prev_special = false;
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
@ -1423,10 +1501,11 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
} break;
case LLAMA_VOCAB_TYPE_BPE:
{
llm_tokenizer_bpe tokenizer(vocab);
llm_tokenizer_bpe_session session(vocab);
// it calls some other methods that are not exist in llm_tokenizer,
// here just cast it to bpe tokenizer object
if (add_special) {
tokenizer.append_bos(output);
session.append_bos(output);
}
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@ -1435,15 +1514,15 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#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);
session.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
tokenizer.append(fragment.token, output);
session.append(fragment.token, output);
}
}
if (add_special) {
tokenizer.append_eos(output);
tokenizer.check_double_bos_eos(output);
session.append_eos(output);
session.check_double_bos_eos(output);
}
} break;
case LLAMA_VOCAB_TYPE_WPM:
@ -1453,7 +1532,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
output.push_back(vocab.special_cls_id);
}
llm_tokenizer_wpm tokenizer(vocab);
llm_tokenizer_wpm_session session(vocab);
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@ -1462,7 +1541,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#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);
session.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
@ -1475,12 +1554,11 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
} break;
case LLAMA_VOCAB_TYPE_UGM:
{
llm_tokenizer_ugm tokenizer(vocab);
if (add_special && vocab.tokenizer_add_bos != 0) {
if (add_special && vocab.tokenizer_add_bos) {
GGML_ASSERT(vocab.special_bos_id != -1);
output.push_back(vocab.special_bos_id);
}
llm_tokenizer_ugm_session session(vocab);
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@ -1488,26 +1566,27 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#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);
session.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) {
if (add_special && vocab.tokenizer_add_bos && 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) {
if (add_special && vocab.tokenizer_add_eos) {
GGML_ASSERT(vocab.special_eos_id != -1);
output.push_back(vocab.special_eos_id);
}
} break;
case LLAMA_VOCAB_TYPE_RWKV:
{
llm_tokenizer_rwkv_session session(vocab);
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);
@ -1516,8 +1595,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_rwkv tokenizer(vocab);
tokenizer.tokenize(raw_text, output);
session.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
@ -1713,11 +1791,13 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
// suppressing them like CONTROL tokens.
if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
return _try_copy(token_text.data(), token_text.size());
} else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
}
if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
std::string result = token_text;
llama_unescape_whitespace(result);
return _try_copy(result.data(), result.size());
} else if (attr & LLAMA_TOKEN_ATTR_BYTE) {
}
if (attr & LLAMA_TOKEN_ATTR_BYTE) {
char byte = (char) llama_token_to_byte(vocab, token);
return _try_copy((char*) &byte, 1);
}
@ -1728,7 +1808,8 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
// suppressing them like CONTROL tokens.
if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
return _try_copy(token_text.data(), token_text.size());
} else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
}
if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
std::string result = llama_decode_text(token_text);
return _try_copy(result.data(), result.size());
}
@ -1761,6 +1842,8 @@ int32_t llama_detokenize_impl(
int32_t text_len_max,
bool remove_special,
bool unparse_special) {
GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
int32_t avail = text_len_max;
int32_t total = 0;

9
examples/talk-llama/llama-vocab.h
View File

@ -8,6 +8,8 @@
#include <map>
#include <set>
struct llm_tokenizer;
struct llama_vocab {
using id = llama_token;
using token = std::string;
@ -65,7 +67,14 @@ struct llama_vocab {
std::vector<char> precompiled_charsmap;
llm_tokenizer * tokenizer = nullptr;
llama_vocab() = default;
~llama_vocab();
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
void init_tokenizer();
};
//

395
examples/talk-llama/llama.cpp
View File

@ -215,6 +215,8 @@ enum llm_arch {
LLM_ARCH_EXAONE,
LLM_ARCH_RWKV6,
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
LLM_ARCH_UNKNOWN,
};
@ -266,6 +268,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_EXAONE, "exaone" },
{ LLM_ARCH_RWKV6, "rwkv6" },
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -302,6 +306,7 @@ enum llm_kv {
LLM_KV_DECODER_START_TOKEN_ID,
LLM_KV_ATTN_LOGIT_SOFTCAPPING,
LLM_KV_FINAL_LOGIT_SOFTCAPPING,
LLM_KV_SWIN_NORM,
LLM_KV_RESCALE_EVERY_N_LAYERS,
LLM_KV_TIME_MIX_EXTRA_DIM,
LLM_KV_TIME_DECAY_EXTRA_DIM,
@ -409,6 +414,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
{ LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" },
{ LLM_KV_FINAL_LOGIT_SOFTCAPPING, "%s.final_logit_softcapping" },
{ LLM_KV_SWIN_NORM, "%s.swin_norm" },
{ LLM_KV_RESCALE_EVERY_N_LAYERS, "%s.rescale_every_n_layers" },
{ LLM_KV_TIME_MIX_EXTRA_DIM, "%s.time_mix_extra_dim" },
{ LLM_KV_TIME_DECAY_EXTRA_DIM, "%s.time_decay_extra_dim" },
@ -600,6 +606,8 @@ enum llm_tensor {
LLM_TENSOR_ENC_FFN_DOWN,
LLM_TENSOR_ENC_FFN_UP,
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@ -787,6 +795,8 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_CLS, "cls" },
{ LLM_TENSOR_CLS_OUT, "cls.output" },
},
},
{
@ -822,6 +832,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_CLS, "cls" },
},
},
{
@ -1467,6 +1478,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
@ -1478,6 +1490,43 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_GRANITE_MOE,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_CHAMELEON,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -2341,6 +2390,7 @@ struct llama_hparams {
bool vocab_only;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
uint32_t n_vocab;
uint32_t n_ctx_train; // context size the model was trained on
@ -2396,7 +2446,7 @@ struct llama_hparams {
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;
// Additional scale factors (Granite)
// Additional scale factors (Granite/Granite MoE)
float f_residual_scale = 0.0f;
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
@ -2849,6 +2899,7 @@ struct llama_model {
llama_hparams hparams = {};
llama_vocab vocab;
// TODO: should init all tensors to nullptr
struct ggml_tensor * tok_embd;
struct ggml_tensor * type_embd;
struct ggml_tensor * pos_embd;
@ -2861,6 +2912,12 @@ struct llama_model {
struct ggml_tensor * output_b;
struct ggml_tensor * output_norm_enc;
// classifier
struct ggml_tensor * cls;
struct ggml_tensor * cls_b;
struct ggml_tensor * cls_out = nullptr;
struct ggml_tensor * cls_out_b = nullptr;
std::vector<llama_layer> layers;
llama_split_mode split_mode;
@ -5445,8 +5502,10 @@ static void llm_load_hparams(
}
} else {
switch (hparams.n_layer) {
case 16: model.type = e_model::MODEL_1B; break; // Llama 3.2 1B
case 22: model.type = e_model::MODEL_1B; break;
case 26: model.type = e_model::MODEL_3B; break;
case 28: model.type = e_model::MODEL_3B; break; // Llama 3.2 3B
// granite uses a vocab with len 49152
case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
case 36: model.type = e_model::MODEL_8B; break; // granite
@ -5559,7 +5618,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
hparams.f_max_alibi_bias = 8.0f;
switch (hparams.n_layer) {
@ -6048,6 +6107,7 @@ static void llm_load_hparams(
}
} break;
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
@ -6056,11 +6116,24 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_3B; break;
case 40: model.type = e_model::MODEL_3B; break;
// Add additional layer/vocab/etc checks here for other model sizes
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_CHAMELEON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
hparams.f_norm_eps = 1e-5; // eps for qk-norm, torch default
ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 48: model.type = e_model::MODEL_34B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
@ -6254,6 +6327,7 @@ static void llm_load_vocab(
tokenizer_pre == "phi-2" ||
tokenizer_pre == "jina-es" ||
tokenizer_pre == "jina-de" ||
tokenizer_pre == "jina-v1-en" ||
tokenizer_pre == "jina-v2-es" ||
tokenizer_pre == "jina-v2-de" ||
tokenizer_pre == "jina-v2-code") {
@ -6318,6 +6392,11 @@ static void llm_load_vocab(
} else if (
tokenizer_pre == "exaone") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
} else if (
tokenizer_pre == "chameleon") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
vocab.tokenizer_add_bos = true;
vocab.tokenizer_clean_spaces = false;
} else {
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
}
@ -6375,7 +6454,12 @@ static void llm_load_vocab(
for (uint32_t i = 0; i < n_vocab; i++) {
std::string word = gguf_get_arr_str(ctx, token_idx, i);
GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
//GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
if (word.empty()) {
LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
word = "[EMPTY_" + std::to_string(i) + "]";
}
vocab.token_to_id[word] = i;
vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
@ -6400,6 +6484,8 @@ static void llm_load_vocab(
}
GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
vocab.init_tokenizer();
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
// For Fill-In-the-Middle (FIM)/infill models which where converted
@ -6454,9 +6540,15 @@ static void llm_load_vocab(
vocab.linefeed_id = ids[0];
} else {
const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
//GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
if (ids.empty()) {
LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
vocab.linefeed_id = vocab.special_pad_id;
} else {
vocab.linefeed_id = ids[0];
}
}
// special tokens
{
@ -6810,7 +6902,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
}
if (model.arch == LLM_ARCH_GRANITE) {
if (model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@ -6984,6 +7076,7 @@ static bool llm_load_tensors(
case LLM_ARCH_REFACT:
case LLM_ARCH_MINICPM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@ -7327,6 +7420,12 @@ static bool llm_load_tensors(
if (model.arch == LLM_ARCH_BERT) {
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_out = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
@ -7379,6 +7478,8 @@ static bool llm_load_tensors(
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias
model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
@ -8704,6 +8805,45 @@ static bool llm_load_tensors(
}
} break;
case LLM_ARCH_CHAMELEON:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head});
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv});
layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -10173,6 +10313,10 @@ struct llm_build_context {
struct ggml_tensor * cur;
switch (pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
cur = inp;
} break;
case LLAMA_POOLING_TYPE_MEAN:
{
struct ggml_tensor * inp_mean = build_inp_mean();
@ -10184,9 +10328,26 @@ struct llm_build_context {
struct ggml_tensor * inp_cls = build_inp_cls();
cur = ggml_get_rows(ctx0, inp, inp_cls);
} break;
case LLAMA_POOLING_TYPE_NONE:
case LLAMA_POOLING_TYPE_RANK:
{
cur = inp;
struct ggml_tensor * inp_cls = build_inp_cls();
inp = ggml_get_rows(ctx0, inp, inp_cls);
// classification head
// https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
GGML_ASSERT(model.cls != nullptr);
GGML_ASSERT(model.cls_b != nullptr);
cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls, inp), model.cls_b);
cur = ggml_tanh(ctx0, cur);
// some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
// https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
if (model.cls_out) {
GGML_ASSERT(model.cls_out_b != nullptr);
cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls_out, cur), model.cls_out_b);
}
} break;
default:
{
@ -11415,8 +11576,8 @@ struct llm_build_context {
inpL = cur;
}
// final output
cur = inpL;
cb(cur, "result_embd", -1);
ggml_build_forward_expand(gf, cur);
@ -15848,6 +16009,184 @@ struct llm_build_context {
return gf;
}
// ref: https://github.com/facebookresearch/chameleon
// based on the original build_llama() function, changes:
// * qk-norm
// * swin-norm
// * removed bias
// * removed MoE
struct ggml_cgraph * build_chameleon() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
// mutable variable, needed during the last layer of the computation to skip unused tokens
int32_t n_tokens = this->n_tokens;
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
if (hparams.swin_norm) {
cur = inpL;
} else {
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
}
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].attn_q_norm) {
Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur) * n_embd_head,
ggml_element_size(Qcur) * n_embd_head * n_head,
0);
cb(Qcur, "Qcur", il);
Qcur = llm_build_norm(ctx0, Qcur, hparams,
model.layers[il].attn_q_norm,
model.layers[il].attn_q_norm_b,
LLM_NORM, cb, il);
cb(Qcur, "Qcur", il);
}
if (model.layers[il].attn_k_norm) {
Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
ggml_element_size(Kcur) * n_embd_head,
ggml_element_size(Kcur) * n_embd_head * n_head_kv,
0);
cb(Kcur, "Kcur", il);
Kcur = llm_build_norm(ctx0, Kcur, hparams,
model.layers[il].attn_k_norm,
model.layers[il].attn_k_norm_b,
LLM_NORM, cb, il);
cb(Kcur, "Kcur", il);
}
Qcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, nullptr,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
if (hparams.swin_norm) {
cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
}
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
n_tokens = n_outputs;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
if (!hparams.swin_norm) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
}
cur = llm_build_ffn(ctx0, lctx, cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
if (hparams.swin_norm) {
cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output_with_img_logits", -1);
// TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.
// Needs to be removed once image outputs are supported.
int img_token_end_idx = 8196;
int img_token_start_idx = 4;
int num_img_tokens = img_token_end_idx - img_token_start_idx;
// creates 1d tensor of size num_img_tokens and values -FLT_MAX,
// which ensures that text token values are always at least larger than image token values
struct ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens);
img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX);
cb(img_logits, "img_logits", -1);
cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@ -15930,6 +16269,7 @@ static struct ggml_cgraph * llama_build_graph(
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
result = llm.build_llama();
} break;
@ -16107,6 +16447,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_rwkv6();
} break;
case LLM_ARCH_CHAMELEON:
{
result = llm.build_chameleon();
} break;
default:
GGML_ABORT("fatal error");
}
@ -16393,7 +16737,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
@ -16408,7 +16754,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
const llama_seq_id seq_id = batch.seq_id[s][0];
// TODO: adapt limits to n_seqs when batch.equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
for (int i = 0; i < n_seq_tokens; ++i) {
const llama_pos pos = batch.pos[s*n_seq_tokens + i];
@ -16679,12 +17025,6 @@ static void llama_graph_compute(
ggml_cgraph * gf,
int n_threads,
ggml_threadpool * threadpool) {
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(lctx.backend_metal)) {
ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
}
#endif
if (lctx.backend_cpu != nullptr) {
ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
ggml_backend_cpu_set_threadpool(lctx.backend_cpu, threadpool);
@ -16948,6 +17288,20 @@ static int llama_decode_internal(
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_RANK:
{
// extract the rerank score - a single float per sequence
auto & embd_seq_out = lctx.embd_seq;
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(1);
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ABORT("unknown pooling type");
@ -17154,6 +17508,13 @@ static int llama_encode_internal(
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_RANK:
{
// TODO: this likely should be the same logic as in llama_decoder_internal, but better to
// wait for an encoder model that requires this pooling type in order to test it
// https://github.com/ggerganov/llama.cpp/pull/9510
GGML_ABORT("RANK pooling not implemented yet");
}
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ABORT("unknown pooling type");
@ -19231,6 +19592,8 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2

7
examples/talk-llama/llama.h
View File

@ -102,6 +102,7 @@ extern "C" {
LLAMA_VOCAB_PRE_TYPE_BLOOM = 23,
LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24,
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
};
enum llama_rope_type {
@ -192,6 +193,7 @@ extern "C" {
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
LLAMA_POOLING_TYPE_LAST = 3,
LLAMA_POOLING_TYPE_RANK = 4, // used by reranking models to attach the classification head to the graph
};
enum llama_attention_type {
@ -871,7 +873,8 @@ extern "C" {
// Get the embeddings for a sequence id
// Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
// shape: [n_embd] (1-dimensional)
// when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[1] with the rank of the sequence
// otherwise: float[n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
//
@ -910,6 +913,8 @@ extern "C" {
//
// Tokenization
//
// The API is thread-safe.
//
/// @details Convert the provided text into tokens.
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.

5
src/whisper.cpp
View File

@ -204,11 +204,6 @@ static bool ggml_graph_compute_helper(
if (ggml_backend_is_blas(backend)) {
ggml_backend_blas_set_n_threads(backend, n_threads);
}
#endif
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(backend)) {
ggml_backend_metal_set_n_cb(backend, n_threads);
}
#endif
}