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llama : add llama_vocab, functions -> methods, naming (ggml-org#11110)
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* llama : functions -> methods (ggml-org#11110)

* llama : add struct llama_vocab to the API (ggml-org#11156)

ggml-ci

* hparams : move vocab params to llama_vocab (ggml-org#11159)

ggml-ci

* vocab : more pimpl (ggml-org#11165)

ggml-ci

* vocab : minor tokenization optimizations (ggml-org#11160)

ggml-ci

Co-authored-by: Diego Devesa <[email protected]>

* lora : update API names (ggml-org#11167)

ggml-ci

* llama : update API names to use correct prefix (ggml-org#11174)

* llama : update API names to use correct prefix

ggml-ci

* cont

ggml-ci

* cont

ggml-ci

* minor [no ci]

* vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (ggml-org#11174)

ggml-ci

* vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (ggml-org#11174)

ggml-ci

---------

Co-authored-by: Diego Devesa <[email protected]>
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@ggerganov @slaren
ggerganov and slaren authored Jan 12, 2025
1 parent c05e8c9 commit afa8a9e
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Showing 68 changed files with 6,015 additions and 5,560 deletions.
105 changes: 61 additions & 44 deletions common/common.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -857,21 +857,23 @@ struct common_init_result common_init_from_params(common_params & params) {
return iparams;
}

const llama_vocab * vocab = llama_model_get_vocab(model);

if (params.reranking) {
bool ok = true;

if (llama_token_bos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have a BOS token, reranking will not work\n", __func__);
if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__);
ok = false;
}

if (llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__);
if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
ok = false;
}

if (llama_token_sep(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have a SEP token, reranking will not work\n", __func__);
if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
ok = false;
}

Expand All @@ -884,7 +886,7 @@ struct common_init_result common_init_from_params(common_params & params) {

auto cparams = common_context_params_to_llama(params);

llama_context * lctx = llama_new_context_with_model(model, cparams);
llama_context * lctx = llama_init_from_model(model, cparams);
if (lctx == NULL) {
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_model_free(model);
Expand All @@ -898,7 +900,7 @@ struct common_init_result common_init_from_params(common_params & params) {

if (!params.control_vectors.empty()) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_model_n_layer(model);

const auto cvec = common_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
Expand All @@ -908,12 +910,13 @@ struct common_init_result common_init_from_params(common_params & params) {
return iparams;
}

int err = llama_control_vector_apply(lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
int err = llama_apply_adapter_cvec(
lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_model_free(model);
Expand All @@ -924,8 +927,8 @@ struct common_init_result common_init_from_params(common_params & params) {

// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_lora_adapter_ptr lora;
lora.reset(llama_lora_adapter_init(model, la.path.c_str()));
llama_adapter_lora_ptr lora;
lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
if (lora == nullptr) {
LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
Expand All @@ -938,17 +941,17 @@ struct common_init_result common_init_from_params(common_params & params) {
}

if (!params.lora_init_without_apply) {
common_lora_adapters_apply(lctx, params.lora_adapters);
common_set_adapter_lora(lctx, params.lora_adapters);
}

if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sampling.ignore_eos = false;
}

if (params.sampling.ignore_eos) {
for (llama_token i = 0; i < llama_n_vocab(model); i++) {
if (llama_token_is_eog(model, i)) {
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
params.sampling.logit_bias.push_back({i, -INFINITY});
}
Expand All @@ -969,8 +972,9 @@ struct common_init_result common_init_from_params(common_params & params) {
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);

std::vector<llama_token> tmp;
llama_token bos = llama_token_bos(model);
llama_token eos = llama_token_eos(model);
llama_token bos = llama_vocab_bos(vocab);
llama_token eos = llama_vocab_eos(vocab);

// some models (e.g. T5) don't have a BOS token
if (bos != LLAMA_TOKEN_NULL) {
tmp.push_back(bos);
Expand Down Expand Up @@ -1005,11 +1009,11 @@ struct common_init_result common_init_from_params(common_params & params) {
return iparams;
}

void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_info> & lora) {
llama_lora_adapter_clear(ctx);
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
llama_clear_adapter_lora(ctx);
for (auto & la : lora) {
if (la.scale != 0.0f) {
llama_lora_adapter_set(ctx, la.ptr, la.scale);
llama_set_adapter_lora(ctx, la.ptr, la.scale);
}
}
}
Expand Down Expand Up @@ -1559,21 +1563,23 @@ std::vector<llama_token> common_tokenize(
const std::string & text,
bool add_special,
bool parse_special) {
return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_tokenize(vocab, text, add_special, parse_special);
}

std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const struct llama_vocab * vocab,
const std::string & text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
Expand All @@ -1582,12 +1588,18 @@ std::vector<llama_token> common_tokenize(
}

std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_token_to_piece(vocab, token, special);
}

std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
}
else {
Expand All @@ -1597,13 +1609,19 @@ std::string common_token_to_piece(const struct llama_context * ctx, llama_token
return piece;
}

std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_detokenize(vocab, tokens, special);
}

std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
if (n_chars < 0) {
text.resize(-n_chars);
n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
}

Expand Down Expand Up @@ -1631,7 +1649,7 @@ std::string common_get_builtin_chat_template(const struct llama_model * model) {

bool common_chat_verify_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}

Expand All @@ -1642,35 +1660,34 @@ std::string common_chat_apply_template(const struct llama_model * model,
int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml
std::vector<llama_chat_message> chat;
for (auto & msg : msgs) {
for (const auto & msg : msgs) {
chat.push_back({msg.role.c_str(), msg.content.c_str()});
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
}

const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
const char * ptr_tmpl = tmpl.empty() ? llama_model_chat_template(model) : tmpl.c_str();
std::vector<char> buf(alloc_size);

// run the first time to get the total output length
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
int32_t res = llama_chat_apply_template(ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());

// error: chat template is not supported
if (res < 0) {
if (ptr_tmpl != nullptr) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
} else {
// If the built-in template is not supported, we default to chatml
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
fallback = true;
}

// If the built-in template is not supported, we default to chatml
res = llama_chat_apply_template("chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
fallback = true;
}

// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(
fallback ? nullptr : model,
fallback ? "chatml" : ptr_tmpl,
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
}
Expand Down
26 changes: 18 additions & 8 deletions common/common.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -24,11 +24,11 @@

#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"

struct common_lora_adapter_info {
struct common_adapter_lora_info {
std::string path;
float scale;

struct llama_lora_adapter * ptr;
struct llama_adapter_lora * ptr;
};

using llama_tokens = std::vector<llama_token>;
Expand Down Expand Up @@ -246,8 +246,8 @@ struct common_params {
std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
std::vector<llama_model_kv_override> kv_overrides;

bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
std::vector<common_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale

std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale

Expand Down Expand Up @@ -481,7 +481,7 @@ struct common_init_result {
llama_model_ptr model;
llama_context_ptr context;

std::vector<llama_lora_adapter_ptr> lora;
std::vector<llama_adapter_lora_ptr> lora;
};

struct common_init_result common_init_from_params(common_params & params);
Expand All @@ -503,7 +503,7 @@ struct llama_model * common_load_model_from_hf(
const struct llama_model_params & params);

// clear LoRA adapters from context, then apply new list of adapters
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_info> & lora);
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);

//
// Batch utils
Expand Down Expand Up @@ -541,7 +541,7 @@ std::vector<llama_token> common_tokenize(
bool parse_special = false);

std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const struct llama_vocab * vocab,
const std::string & text,
bool add_special,
bool parse_special = false);
Expand All @@ -553,11 +553,21 @@ std::string common_token_to_piece(
llama_token token,
bool special = true);

std::string common_token_to_piece(
const struct llama_vocab * vocab,
llama_token token,
bool special = true);

// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// optionally renders special/control tokens
std::string common_detokenize(
llama_context * ctx,
const struct llama_context * ctx,
const std::vector<llama_token> & tokens,
bool special = true);

std::string common_detokenize(
const struct llama_vocab * vocab,
const std::vector<llama_token> & tokens,
bool special = true);

Expand Down
17 changes: 11 additions & 6 deletions common/sampling.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,10 @@ struct common_sampler {
void set_logits(struct llama_context * ctx, int idx) {
const auto * logits = llama_get_logits_ith(ctx, idx);

const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);

const int n_vocab = llama_vocab_n_tokens(vocab);

cur.resize(n_vocab);

Expand Down Expand Up @@ -142,13 +145,15 @@ std::string common_params_sampling::print() const {
}

struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
const llama_vocab * vocab = llama_model_get_vocab(model);

llama_sampler_chain_params lparams = llama_sampler_chain_default_params();

lparams.no_perf = params.no_perf;

auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
/* .grmr = */ llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"),
/* .chain = */ llama_sampler_chain_init(lparams),
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
/* .cur = */ {},
Expand All @@ -157,7 +162,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co

llama_sampler_chain_add(result->chain,
llama_sampler_init_logit_bias(
llama_n_vocab(model),
llama_vocab_n_tokens(vocab),
params.logit_bias.size(),
params.logit_bias.data()));

Expand All @@ -172,7 +177,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
c_breakers.push_back(str.c_str());
}

llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
case COMMON_SAMPLER_TYPE_TOP_K:
Expand All @@ -194,7 +199,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
case COMMON_SAMPLER_TYPE_INFILL:
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model));
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab));
break;
case COMMON_SAMPLER_TYPE_PENALTIES:
llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
Expand All @@ -206,7 +211,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
} else if (params.mirostat == 2) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
Expand Down
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