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@sealad886
sealad886 authored May 30, 2024
2 parents 2c619b0 + 5921b8f commit 0449909
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12 changes: 11 additions & 1 deletion README.md
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Expand Up @@ -2,7 +2,9 @@

![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)

[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)

[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)

Expand Down Expand Up @@ -386,6 +388,14 @@ In order to build llama.cpp you have four different options.
CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
the instructions for use and activate this options in this document below.
### Homebrew
On Mac and Linux, the homebrew package manager can be used via
```
brew install llama.cpp
```
The formula is automatically updated with new `llama.cpp` releases.
### Metal Build
On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
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199 changes: 117 additions & 82 deletions llama.cpp
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Expand Up @@ -1702,12 +1702,13 @@ struct llama_mlock {
};
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;

static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
// NOTE: avoid ever using this except for building the token_to_piece caches
static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
int check = llama_token_to_piece(model, token, result.data(), result.size(), special);
GGML_ASSERT(check == -n_tokens);
}
else {
Expand Down Expand Up @@ -2162,7 +2163,9 @@ struct llama_vocab {
std::unordered_map<token, id> token_to_id;
std::vector<token_data> id_to_token;

std::vector<id> special_tokens_cache;
std::vector<id> cache_special_tokens;
std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = false);
std::vector<token> cache_token_to_piece_special; // llama_token_to_piece(special = true);

std::map<std::pair<std::string, std::string>, int> bpe_ranks;

Expand Down Expand Up @@ -4592,20 +4595,14 @@ static void llm_load_vocab(
vocab.special_cls_id = 101;
vocab.special_mask_id = 103;
vocab.add_space_prefix = false;
} else {
if (tokenizer_model == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;
} else if (tokenizer_model == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;

const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
}
} else {
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
vocab.type = LLAMA_VOCAB_TYPE_SPM;
return;
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
}

// read bpe merges and populate bpe ranks
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
if (merges_keyidx == -1) {
Expand Down Expand Up @@ -4639,6 +4636,8 @@ static void llm_load_vocab(
vocab.special_pad_id = -1;
vocab.special_cls_id = -1;
vocab.special_mask_id = -1;
} else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
}

// for now, only BPE models have pre-tokenizers
Expand Down Expand Up @@ -4833,17 +4832,38 @@ static void llm_load_vocab(
{
for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
vocab.special_tokens_cache.push_back(id);
vocab.cache_special_tokens.push_back(id);
}
}

std::sort( vocab.special_tokens_cache.begin(), vocab.special_tokens_cache.end(),
std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
[&] (const llama_vocab::id a, const llama_vocab::id b) {
return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
}
);

LLAMA_LOG_INFO("%s: special tokens cache size = %u.\n", __func__, (uint32_t)vocab.special_tokens_cache.size());
LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
}

// build token to piece caches
{
size_t size_cache = 0;

std::vector<llama_vocab::token> cache_token_to_piece (n_vocab);
std::vector<llama_vocab::token> cache_token_to_piece_special(n_vocab);

for (uint32_t id = 0; id < n_vocab; ++id) {
cache_token_to_piece[id] = llama_token_to_piece(&model, id, false);
cache_token_to_piece_special[id] = llama_token_to_piece(&model, id, true);

size_cache += cache_token_to_piece[id].size();
size_cache += cache_token_to_piece_special[id].size();
}

std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
std::swap(vocab.cache_token_to_piece_special, cache_token_to_piece_special);

LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
}
}

Expand Down Expand Up @@ -13233,7 +13253,7 @@ struct fragment_buffer_variant {

static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
// for each special token
for (const llama_vocab::id special_id : vocab.special_tokens_cache) {
for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
const auto & special_token = vocab.id_to_token[special_id].text;

// for each text fragment
Expand Down Expand Up @@ -14392,7 +14412,7 @@ void llama_sample_repetition_penalties(

void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
GGML_ASSERT(ctx);
const int64_t t_start_sample_us = ggml_time_us();
int64_t t_start_sample_us = ggml_time_us();

bool allow_eog = false;
for (const auto & stack : grammar->stacks) {
Expand All @@ -14404,12 +14424,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c

std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
candidates_decoded.reserve(candidates->size);
std::vector<llama_grammar_candidate> candidates_grammar;

std::vector<llama_grammar_candidate> candidates_grammar;
candidates_grammar.reserve(candidates->size);

for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_piece(ctx, id, false);
const llama_token id = candidates->data[i].id;
const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id);

if (llama_token_is_eog(&ctx->model, id)) {
if (!allow_eog) {
Expand Down Expand Up @@ -14609,7 +14630,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
GGML_ASSERT(false);
}

const std::string piece = llama_token_to_piece(ctx, token, false);
const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token);

// Note terminating 0 in decoded string
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
Expand Down Expand Up @@ -18292,69 +18313,83 @@ static std::string llama_decode_text(const std::string & text) {

// does not write null-terminator to buf
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
// if we have a cache - use it
{
const auto & cache = special ? model->vocab.cache_token_to_piece_special : model->vocab.cache_token_to_piece;

if (!cache.empty()) {
const auto & res = cache.at(token);
if (length < (int) res.size()) {
return -(int) res.size();
}
memcpy(buf, res.c_str(), res.size());
return res.size();
}
}

if (0 <= token && token < llama_n_vocab(model)) {
switch (llama_vocab_get_type(model->vocab)) {
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_SPM: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
llama_unescape_whitespace(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
if (length < 3) {
return -3;
}
memcpy(buf, "\xe2\x96\x85", 3);
return 3;
} else if (llama_is_byte_token(model->vocab, token)) {
if (length < 1) {
return -1;
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_SPM: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
llama_unescape_whitespace(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
if (length < 3) {
return -3;
}
memcpy(buf, "\xe2\x96\x85", 3);
return 3;
} else if (llama_is_byte_token(model->vocab, token)) {
if (length < 1) {
return -1;
}
buf[0] = llama_token_to_byte(model->vocab, token);
return 1;
}
buf[0] = llama_token_to_byte(model->vocab, token);
return 1;
break;
}
break;
}
case LLAMA_VOCAB_TYPE_BPE: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
result = llama_decode_text(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
case LLAMA_VOCAB_TYPE_BPE: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
result = llama_decode_text(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
break;
}
break;
}
default:
GGML_ASSERT(false);
default:
GGML_ASSERT(false);
}
}
return 0;
Expand Down
4 changes: 2 additions & 2 deletions llama.h
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Original file line number Diff line number Diff line change
Expand Up @@ -424,8 +424,8 @@ extern "C" {

LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);

LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);

LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
Expand Down

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