vbatts/llama.cpp
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common : use common_ prefix for common library functions

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slaren 2024-10-09 12:26:27 +02:00
parent 3dc48fe75a
commit 4f7e4b5e19
33 changed files with 326 additions and 326 deletions
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82
common/common.cpp
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@ -493,7 +493,7 @@ std::string string_from(const struct llama_context * ctx, const std::vector<llam
first = false; first = false;
} }
auto detokenized = llama_token_to_piece(ctx, token); auto detokenized = common_token_to_piece(ctx, token);
detokenized.erase( detokenized.erase(
std::remove_if( std::remove_if(
@ -524,7 +524,7 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
first = false; first = false;
} }
auto detokenized = llama_token_to_piece(ctx, batch.token[i]); auto detokenized = common_token_to_piece(ctx, batch.token[i]);
detokenized.erase( detokenized.erase(
std::remove_if( std::remove_if(
@ -819,16 +819,16 @@ std::string fs_get_cache_file(const std::string & filename) {
// //
// Model utils // Model utils
// //
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) { struct common_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams; common_init_result iparams;
auto mparams = llama_model_params_from_gpt_params(params); auto mparams = common_model_params_from_gpt_params(params);
llama_model * model = nullptr; llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) { if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); model = common_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else if (!params.model_url.empty()) { } else if (!params.model_url.empty()) {
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else { } else {
model = llama_load_model_from_file(params.model.c_str(), mparams); model = llama_load_model_from_file(params.model.c_str(), mparams);
} }
@ -863,7 +863,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
} }
} }
auto cparams = llama_context_params_from_gpt_params(params); auto cparams = common_context_params_from_gpt_params(params);
llama_context * lctx = llama_new_context_with_model(model, cparams); llama_context * lctx = llama_new_context_with_model(model, cparams);
if (lctx == NULL) { if (lctx == NULL) {
@ -900,7 +900,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
// load and optionally apply lora adapters // load and optionally apply lora adapters
for (auto & la : params.lora_adapters) { for (auto & la : params.lora_adapters) {
llama_lora_adapter_container loaded_la; common_lora_adapter_container loaded_la;
loaded_la.path = la.path; loaded_la.path = la.path;
loaded_la.scale = la.scale; loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str()); loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
@ -913,7 +913,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
} }
if (!params.lora_init_without_apply) { if (!params.lora_init_without_apply) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters); common_lora_adapters_apply(lctx, iparams.lora_adapters);
} }
if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) { if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
@ -961,7 +961,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
return iparams; return iparams;
} }
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) { void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx); llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) { for (auto & la : lora_adapters) {
if (la.scale != 0.0f) { if (la.scale != 0.0f) {
@ -970,7 +970,7 @@ void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lor
} }
} }
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) { struct llama_model_params common_model_params_from_gpt_params(const gpt_params & params) {
auto mparams = llama_model_default_params(); auto mparams = llama_model_default_params();
if (params.n_gpu_layers != -1) { if (params.n_gpu_layers != -1) {
@ -1022,7 +1022,7 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
throw std::runtime_error("Invalid cache type: " + s); throw std::runtime_error("Invalid cache type: " + s);
} }
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { struct llama_context_params common_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params(); auto cparams = llama_context_default_params();
cparams.n_ctx = params.n_ctx; cparams.n_ctx = params.n_ctx;
@ -1430,7 +1430,7 @@ struct llama_model * llama_load_model_from_hf(
#else #else
struct llama_model * llama_load_model_from_url( struct llama_model * common_load_model_from_url(
const char * /*model_url*/, const char * /*model_url*/,
const char * /*path_model*/, const char * /*path_model*/,
const char * /*hf_token*/, const char * /*hf_token*/,
@ -1439,7 +1439,7 @@ struct llama_model * llama_load_model_from_url(
return nullptr; return nullptr;
} }
struct llama_model * llama_load_model_from_hf( struct llama_model * common_load_model_from_hf(
const char * /*repo*/, const char * /*repo*/,
const char * /*model*/, const char * /*model*/,
const char * /*path_model*/, const char * /*path_model*/,
@ -1455,11 +1455,11 @@ struct llama_model * llama_load_model_from_hf(
// Batch utils // Batch utils
// //
void llama_batch_clear(struct llama_batch & batch) { void common_batch_clear(struct llama_batch & batch) {
batch.n_tokens = 0; batch.n_tokens = 0;
} }
void llama_batch_add( void common_batch_add(
struct llama_batch & batch, struct llama_batch & batch,
llama_token id, llama_token id,
llama_pos pos, llama_pos pos,
@ -1482,15 +1482,15 @@ void llama_batch_add(
// Vocab utils // Vocab utils
// //
std::vector<llama_token> llama_tokenize( std::vector<llama_token> common_tokenize(
const struct llama_context * ctx, const struct llama_context * ctx,
const std::string & text, const std::string & text,
bool add_special, bool add_special,
bool parse_special) { bool parse_special) {
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special); return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
} }
std::vector<llama_token> llama_tokenize( std::vector<llama_token> common_tokenize(
const struct llama_model * model, const struct llama_model * model,
const std::string & text, const std::string & text,
bool add_special, bool add_special,
@ -1509,7 +1509,7 @@ std::vector<llama_token> llama_tokenize(
return result; return result;
} }
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
std::string piece; std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n' 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(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
@ -1525,7 +1525,7 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t
return piece; return piece;
} }
std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) { std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string text; std::string text;
text.resize(std::max(text.capacity(), tokens.size())); 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(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
@ -1551,9 +1551,9 @@ bool llama_chat_verify_template(const std::string & tmpl) {
return res >= 0; return res >= 0;
} }
std::string llama_chat_apply_template(const struct llama_model * model, std::string common_chat_apply_template(const struct llama_model * model,
const std::string & tmpl, const std::string & tmpl,
const std::vector<llama_chat_msg> & msgs, const std::vector<common_chat_msg> & msgs,
bool add_ass) { bool add_ass) {
int alloc_size = 0; int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml bool fallback = false; // indicate if we must fallback to default chatml
@ -1595,42 +1595,42 @@ std::string llama_chat_apply_template(const struct llama_model * model,
return formatted_chat; return formatted_chat;
} }
std::string llama_chat_format_single(const struct llama_model * model, std::string common_chat_format_single(const struct llama_model * model,
const std::string & tmpl, const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg, const std::vector<common_chat_msg> & past_msg,
const llama_chat_msg & new_msg, const common_chat_msg & new_msg,
bool add_ass) { bool add_ass) {
std::ostringstream ss; std::ostringstream ss;
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false); auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false);
std::vector<llama_chat_msg> chat_new(past_msg); std::vector<common_chat_msg> chat_new(past_msg);
// if the past_msg ends with a newline, we must preserve it in the formatted version // if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
ss << "\n"; ss << "\n";
}; };
// format chat with new_msg // format chat with new_msg
chat_new.push_back(new_msg); chat_new.push_back(new_msg);
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass); auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass);
// get the diff part // get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str(); return ss.str();
} }
std::string llama_chat_format_example(const struct llama_model * model, std::string common_chat_format_example(const struct llama_model * model,
const std::string & tmpl) { const std::string & tmpl) {
std::vector<llama_chat_msg> msgs = { std::vector<common_chat_msg> msgs = {
{"system", "You are a helpful assistant"}, {"system", "You are a helpful assistant"},
{"user", "Hello"}, {"user", "Hello"},
{"assistant", "Hi there"}, {"assistant", "Hi there"},
{"user", "How are you?"}, {"user", "How are you?"},
}; };
return llama_chat_apply_template(model, tmpl, msgs, true); return common_chat_apply_template(model, tmpl, msgs, true);
} }
// //
// KV cache utils // KV cache utils
// //
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) { void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
@ -1653,7 +1653,7 @@ void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
printf("\n=== Done dumping\n"); printf("\n=== Done dumping\n");
} }
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) { void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
@ -1705,7 +1705,7 @@ void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_siz
// Embedding utils // Embedding utils
// //
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) { void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
double sum = 0.0; double sum = 0.0;
switch (embd_norm) { switch (embd_norm) {
@ -1739,7 +1739,7 @@ void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm)
} }
} }
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){ float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
double sum = 0.0; double sum = 0.0;
double sum1 = 0.0; double sum1 = 0.0;
double sum2 = 0.0; double sum2 = 0.0;
@ -1765,8 +1765,8 @@ float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n)
// Control vector utils // Control vector utils
// //
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) { static common_control_vector_data llama_control_vector_load_one(const common_control_vector_load_info & load_info) {
llama_control_vector_data result = { -1, {} }; common_control_vector_data result = { -1, {} };
ggml_context * ctx = nullptr; ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = { struct gguf_init_params meta_gguf_params = {
@ -1850,8 +1850,8 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
return result; return result;
} }
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) { common_control_vector_data llama_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos) {
llama_control_vector_data result = { -1, {} }; common_control_vector_data result = { -1, {} };
for (const auto & info : load_infos) { for (const auto & info : load_infos) {
auto cur = llama_control_vector_load_one(info); auto cur = llama_control_vector_load_one(info);

66
common/common.h
View file

@ -24,12 +24,12 @@
#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf" #define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
struct llama_lora_adapter_info { struct common_lora_adapter_info {
std::string path; std::string path;
float scale; float scale;
}; };
struct llama_lora_adapter_container : llama_lora_adapter_info { struct common_lora_adapter_container : common_lora_adapter_info {
struct llama_lora_adapter * adapter; struct llama_lora_adapter * adapter;
}; };
@ -39,7 +39,7 @@ extern char const * LLAMA_COMMIT;
extern char const * LLAMA_COMPILER; extern char const * LLAMA_COMPILER;
extern char const * LLAMA_BUILD_TARGET; extern char const * LLAMA_BUILD_TARGET;
struct llama_control_vector_load_info; struct common_control_vector_load_info;
// //
// CPU utils // CPU utils
@ -208,9 +208,9 @@ struct gpt_params {
std::vector<llama_model_kv_override> kv_overrides; 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) 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<llama_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale std::vector<common_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
int32_t verbosity = 0; int32_t verbosity = 0;
int32_t control_vector_layer_start = -1; // layer range for control vector int32_t control_vector_layer_start = -1; // layer range for control vector
@ -404,29 +404,29 @@ std::string fs_get_cache_file(const std::string & filename);
// Model utils // Model utils
// //
struct llama_init_result { struct common_init_result {
struct llama_model * model = nullptr; struct llama_model * model = nullptr;
struct llama_context * context = nullptr; struct llama_context * context = nullptr;
std::vector<llama_lora_adapter_container> lora_adapters; std::vector<common_lora_adapter_container> lora_adapters;
}; };
struct llama_init_result llama_init_from_gpt_params(gpt_params & params); struct common_init_result llama_init_from_gpt_params(gpt_params & params);
struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); struct llama_model_params common_model_params_from_gpt_params (const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params (const gpt_params & params); struct llama_context_params common_context_params_from_gpt_params (const gpt_params & params);
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params); struct llama_model * common_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params); struct llama_model * common_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
// clear LoRA adapters from context, then apply new list of adapters // clear LoRA adapters from context, then apply new list of adapters
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters); void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters);
// Batch utils // Batch utils
void llama_batch_clear(struct llama_batch & batch); void common_batch_clear(struct llama_batch & batch);
void llama_batch_add( void common_batch_add(
struct llama_batch & batch, struct llama_batch & batch,
llama_token id, llama_token id,
llama_pos pos, llama_pos pos,
@ -439,13 +439,13 @@ void llama_batch_add(
// tokenizes a string into a vector of tokens // tokenizes a string into a vector of tokens
// should work similar to Python's `tokenizer.encode` // should work similar to Python's `tokenizer.encode`
std::vector<llama_token> llama_tokenize( std::vector<llama_token> common_tokenize(
const struct llama_context * ctx, const struct llama_context * ctx,
const std::string & text, const std::string & text,
bool add_special, bool add_special,
bool parse_special = false); bool parse_special = false);
std::vector<llama_token> llama_tokenize( std::vector<llama_token> common_tokenize(
const struct llama_model * model, const struct llama_model * model,
const std::string & text, const std::string & text,
bool add_special, bool add_special,
@ -453,7 +453,7 @@ std::vector<llama_token> llama_tokenize(
// tokenizes a token into a piece, optionally renders special/control tokens // tokenizes a token into a piece, optionally renders special/control tokens
// should work similar to Python's `tokenizer.id_to_piece` // should work similar to Python's `tokenizer.id_to_piece`
std::string llama_token_to_piece( std::string common_token_to_piece(
const struct llama_context * ctx, const struct llama_context * ctx,
llama_token token, llama_token token,
bool special = true); bool special = true);
@ -461,7 +461,7 @@ std::string llama_token_to_piece(
// detokenizes a vector of tokens into a string // detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode` // should work similar to Python's `tokenizer.decode`
// optionally renders special/control tokens // optionally renders special/control tokens
std::string llama_detokenize( std::string common_detokenize(
llama_context * ctx, llama_context * ctx,
const std::vector<llama_token> & tokens, const std::vector<llama_token> & tokens,
bool special = true); bool special = true);
@ -471,7 +471,7 @@ std::string llama_detokenize(
// //
// same with llama_chat_message, but uses std::string // same with llama_chat_message, but uses std::string
struct llama_chat_msg { struct common_chat_msg {
std::string role; std::string role;
std::string content; std::string content;
}; };
@ -482,20 +482,20 @@ bool llama_chat_verify_template(const std::string & tmpl);
// CPP wrapper for llama_chat_apply_template // CPP wrapper for llama_chat_apply_template
// If the built-in template is not supported, we default to chatml // If the built-in template is not supported, we default to chatml
// If the custom "tmpl" is not supported, we throw an error // If the custom "tmpl" is not supported, we throw an error
std::string llama_chat_apply_template(const struct llama_model * model, std::string common_chat_apply_template(const struct llama_model * model,
const std::string & tmpl, const std::string & tmpl,
const std::vector<llama_chat_msg> & chat, const std::vector<common_chat_msg> & chat,
bool add_ass); bool add_ass);
// Format single message, while taking into account the position of that message in chat history // Format single message, while taking into account the position of that message in chat history
std::string llama_chat_format_single(const struct llama_model * model, std::string common_chat_format_single(const struct llama_model * model,
const std::string & tmpl, const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg, const std::vector<common_chat_msg> & past_msg,
const llama_chat_msg & new_msg, const common_chat_msg & new_msg,
bool add_ass); bool add_ass);
// Returns an example of formatted chat // Returns an example of formatted chat
std::string llama_chat_format_example(const struct llama_model * model, std::string common_chat_format_example(const struct llama_model * model,
const std::string & tmpl); const std::string & tmpl);
// //
@ -503,31 +503,31 @@ std::string llama_chat_format_example(const struct llama_model * model,
// //
// Dump the KV cache view with the number of sequences per cell. // Dump the KV cache view with the number of sequences per cell.
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80); void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
// Dump the KV cache view showing individual sequences in each cell (long output). // Dump the KV cache view showing individual sequences in each cell (long output).
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40); void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
// //
// Embedding utils // Embedding utils
// //
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2); void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n); float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);
// //
// Control vector utils // Control vector utils
// //
struct llama_control_vector_data { struct common_control_vector_data {
int n_embd; int n_embd;
// stores data for layers [1, n_layer] where n_layer = data.size() / n_embd // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
std::vector<float> data; std::vector<float> data;
}; };
struct llama_control_vector_load_info { struct common_control_vector_load_info {
float strength; float strength;
std::string fname; std::string fname;
@ -535,7 +535,7 @@ struct llama_control_vector_load_info {
// Load control vectors, scale each by strength, and add them together. // Load control vectors, scale each by strength, and add them together.
// On error, returns {-1, empty} // On error, returns {-1, empty}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos); common_control_vector_data llama_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos);
// //
// Split utils // Split utils

2
common/sampling.cpp
View file

@ -358,7 +358,7 @@ std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx_main,
GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen"); GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen");
result += llama_token_to_piece(ctx_main, id); result += common_token_to_piece(ctx_main, id);
} }
return result; return result;

14
examples/batched-bench/batched-bench.cpp
View file

@ -36,7 +36,7 @@ int main(int argc, char ** argv) {
// initialize the model // initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model_params model_params = common_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -45,7 +45,7 @@ int main(int argc, char ** argv) {
return 1; return 1;
} }
llama_context_params ctx_params = llama_context_params_from_gpt_params(params); llama_context_params ctx_params = common_context_params_from_gpt_params(params);
// ensure enough sequences are available // ensure enough sequences are available
ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end()); ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
@ -92,7 +92,7 @@ int main(int argc, char ** argv) {
// warm up // warm up
{ {
for (int i = 0; i < 16; ++i) { for (int i = 0; i < 16; ++i) {
llama_batch_add(batch, 0, i, { 0 }, false); common_batch_add(batch, 0, i, { 0 }, false);
} }
if (!decode_helper(ctx, batch, ctx_params.n_batch)) { if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
@ -122,11 +122,11 @@ int main(int argc, char ** argv) {
continue; continue;
} }
llama_batch_clear(batch); common_batch_clear(batch);
for (int i = 0; i < pp; ++i) { for (int i = 0; i < pp; ++i) {
for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) { for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
llama_batch_add(batch, 0, i, { j }, false); common_batch_add(batch, 0, i, { j }, false);
} }
} }
batch.logits[batch.n_tokens - 1] = true; batch.logits[batch.n_tokens - 1] = true;
@ -151,10 +151,10 @@ int main(int argc, char ** argv) {
const auto t_tg_start = ggml_time_us(); const auto t_tg_start = ggml_time_us();
for (int i = 0; i < tg; ++i) { for (int i = 0; i < tg; ++i) {
llama_batch_clear(batch); common_batch_clear(batch);
for (int j = 0; j < pl; ++j) { for (int j = 0; j < pl; ++j) {
llama_batch_add(batch, 0, pp + i, { j }, true); common_batch_add(batch, 0, pp + i, { j }, true);
} }
if (!decode_helper(ctx, batch, ctx_params.n_batch)) { if (!decode_helper(ctx, batch, ctx_params.n_batch)) {

22
examples/batched/batched.cpp
View file

@ -39,7 +39,7 @@ int main(int argc, char ** argv) {
// initialize the model // initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model_params model_params = common_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -51,13 +51,13 @@ int main(int argc, char ** argv) {
// tokenize the prompt // tokenize the prompt
std::vector<llama_token> tokens_list; std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(model, params.prompt, true); tokens_list = ::common_tokenize(model, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel; const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
// initialize the context // initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params); llama_context_params ctx_params = common_context_params_from_gpt_params(params);
ctx_params.n_ctx = n_kv_req; ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_predict, n_parallel); ctx_params.n_batch = std::max(n_predict, n_parallel);
@ -94,7 +94,7 @@ int main(int argc, char ** argv) {
LOG("\n"); LOG("\n");
for (auto id : tokens_list) { for (auto id : tokens_list) {
LOG("%s", llama_token_to_piece(ctx, id).c_str()); LOG("%s", common_token_to_piece(ctx, id).c_str());
} }
// create a llama_batch // create a llama_batch
@ -108,7 +108,7 @@ int main(int argc, char ** argv) {
// evaluate the initial prompt // evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); ++i) { for (size_t i = 0; i < tokens_list.size(); ++i) {
llama_batch_add(batch, tokens_list[i], i, seq_ids, false); common_batch_add(batch, tokens_list[i], i, seq_ids, false);
} }
GGML_ASSERT(batch.n_tokens == (int) tokens_list.size()); GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
@ -123,8 +123,8 @@ int main(int argc, char ** argv) {
decoder_start_token_id = llama_token_bos(model); decoder_start_token_id = llama_token_bos(model);
} }
llama_batch_clear(batch); common_batch_clear(batch);
llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false); common_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
} }
// llama_decode will output logits only for the last token of the prompt // llama_decode will output logits only for the last token of the prompt
@ -161,7 +161,7 @@ int main(int argc, char ** argv) {
while (n_cur <= n_predict) { while (n_cur <= n_predict) {
// prepare the next batch // prepare the next batch
llama_batch_clear(batch); common_batch_clear(batch);
// sample the next token for each parallel sequence / stream // sample the next token for each parallel sequence / stream
for (int32_t i = 0; i < n_parallel; ++i) { for (int32_t i = 0; i < n_parallel; ++i) {
@ -185,15 +185,15 @@ int main(int argc, char ** argv) {
// if there is only one stream, we print immediately to stdout // if there is only one stream, we print immediately to stdout
if (n_parallel == 1) { if (n_parallel == 1) {
LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str()); LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
} }
streams[i] += llama_token_to_piece(ctx, new_token_id); streams[i] += common_token_to_piece(ctx, new_token_id);
i_batch[i] = batch.n_tokens; i_batch[i] = batch.n_tokens;
// push this new token for next evaluation // push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_cur, { i }, true); common_batch_add(batch, new_token_id, n_cur, { i }, true);
n_decode += 1; n_decode += 1;
} }

10
examples/cvector-generator/cvector-generator.cpp
View file

@ -31,7 +31,7 @@ template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret; std::string ret;
for (; begin != end; ++begin) { for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin); ret += common_token_to_piece(ctx, *begin);
} }
return ret; return ret;
@ -272,8 +272,8 @@ struct tokenized_prompt {
tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) { tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true); tokens_pos = ::common_tokenize(ctx, pos, add_bos, true);
tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true); tokens_neg = ::common_tokenize(ctx, neg, add_bos, true);
max_seq_len = std::max(tokens_pos.size(), tokens_neg.size()); max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
padding_seq(ctx, tokens_pos, max_seq_len); padding_seq(ctx, tokens_pos, max_seq_len);
padding_seq(ctx, tokens_neg, max_seq_len); padding_seq(ctx, tokens_neg, max_seq_len);
@ -281,7 +281,7 @@ struct tokenized_prompt {
void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) { void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) {
// TODO: customize padding token // TODO: customize padding token
std::vector<llama_token> pad_tokens = ::llama_tokenize(ctx, " ", false); std::vector<llama_token> pad_tokens = ::common_tokenize(ctx, " ", false);
llama_token pad_tok = pad_tokens.back(); llama_token pad_tok = pad_tokens.back();
while (tokens.size() < len) { while (tokens.size() < len) {
tokens.push_back(pad_tok); tokens.push_back(pad_tok);
@ -413,7 +413,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model to get hparams // load the model to get hparams
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;

16
examples/embedding/embedding.cpp
View file

@ -28,7 +28,7 @@ static std::vector<std::string> split_lines(const std::string & s, const std::st
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) { static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size(); size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) { for (size_t i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, true); common_batch_add(batch, tokens[i], i, { seq_id }, true);
} }
} }
@ -74,7 +74,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
} }
float * out = output + embd_pos * n_embd; float * out = output + embd_pos * n_embd;
llama_embd_normalize(embd, out, n_embd, embd_norm); common_embd_normalize(embd, out, n_embd, embd_norm);
} }
} }
@ -95,7 +95,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model // load the model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
@ -135,7 +135,7 @@ int main(int argc, char ** argv) {
// tokenize the prompts and trim // tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs; std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) { for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true, true); auto inp = ::common_tokenize(ctx, prompt, true, true);
if (inp.size() > n_batch) { if (inp.size() > n_batch) {
LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n", LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch); __func__, (long long int) inp.size(), (long long int) n_batch);
@ -159,7 +159,7 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str()); LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size()); LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
for (int j = 0; j < (int) inputs[i].size(); j++) { for (int j = 0; j < (int) inputs[i].size(); j++) {
LOG("%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str()); LOG("%6d -> '%s'\n", inputs[i][j], common_token_to_piece(ctx, inputs[i][j]).c_str());
} }
LOG("\n\n"); LOG("\n\n");
} }
@ -199,7 +199,7 @@ int main(int argc, char ** argv) {
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s; e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
s = 0; s = 0;
llama_batch_clear(batch); common_batch_clear(batch);
} }
// add to batch // add to batch
@ -263,7 +263,7 @@ int main(int argc, char ** argv) {
LOG("\n"); LOG("\n");
for (int i = 0; i < n_prompts; i++) { for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) { for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
LOG("%6.2f ", sim); LOG("%6.2f ", sim);
} }
LOG("%1.10s", prompts[i].c_str()); LOG("%1.10s", prompts[i].c_str());
@ -296,7 +296,7 @@ int main(int argc, char ** argv) {
for (int i = 0;;) { // at least two iteration (n_embd_count > 1) for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
LOG(" ["); LOG(" [");
for (int j = 0;;) { // at least two iteration (n_embd_count > 1) for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
LOG("%6.2f", sim); LOG("%6.2f", sim);
j++; j++;
if (j < n_embd_count) LOG(", "); else break; if (j < n_embd_count) LOG(", "); else break;

4
examples/eval-callback/eval-callback.cpp
View file

@ -129,7 +129,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
static bool run(llama_context * ctx, const gpt_params & params) { static bool run(llama_context * ctx, const gpt_params & params) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos); std::vector<llama_token> tokens = ::common_tokenize(ctx, params.prompt, add_bos);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
LOG_ERR("%s : failed to eval\n", __func__); LOG_ERR("%s : failed to eval\n", __func__);
@ -160,7 +160,7 @@ int main(int argc, char ** argv) {
params.warmup = false; params.warmup = false;
// init // init
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;

2
examples/export-lora/export-lora.cpp
View file

@ -128,7 +128,7 @@ struct lora_merge_ctx {
lora_merge_ctx( lora_merge_ctx(
std::string & base_fname, std::string & base_fname,
std::vector<llama_lora_adapter_info> & lora_files, std::vector<common_lora_adapter_info> & lora_files,
std::string & outfile, std::string & outfile,
int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) { int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
fout.exceptions(std::ofstream::failbit); // fail fast on write errors fout.exceptions(std::ofstream::failbit); // fail fast on write errors

30
examples/gritlm/gritlm.cpp
View file

@ -15,11 +15,11 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1); llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
for (uint64_t i = 0; i < sentences.size(); i++) { for (uint64_t i = 0; i < sentences.size(); i++) {
llama_batch_clear(batch); common_batch_clear(batch);
const std::string input_string = instruction + sentences[i]; const std::string input_string = instruction + sentences[i];
std::vector<llama_token> inputs = llama_tokenize(model, input_string, true, false); std::vector<llama_token> inputs = common_tokenize(model, input_string, true, false);
const int32_t n_toks = inputs.size(); const int32_t n_toks = inputs.size();
@ -28,7 +28,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
// inputs.push_back(llama_token_eos(model)); // inputs.push_back(llama_token_eos(model));
// we want to ignore instruction tokens for mean pooling // we want to ignore instruction tokens for mean pooling
const int32_t n_inst = llama_tokenize(model, instruction, true, false).size(); const int32_t n_inst = common_tokenize(model, instruction, true, false).size();
#ifdef GRIT_DEBUG #ifdef GRIT_DEBUG
// debug tokens - should be matching as referenced in the GritLM sample // debug tokens - should be matching as referenced in the GritLM sample
@ -40,7 +40,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
// add input to batch (this increments n_tokens) // add input to batch (this increments n_tokens)
for (int32_t j = 0; j < n_toks; j++) { for (int32_t j = 0; j < n_toks; j++) {
llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst); common_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
} }
// clear previous kv_cache values (irrelevant for embeddings) // clear previous kv_cache values (irrelevant for embeddings)
@ -75,7 +75,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
} }
std::vector<float> emb_norm(emb_unorm.size()); std::vector<float> emb_norm(emb_unorm.size());
llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd); common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
result.push_back(emb_norm); result.push_back(emb_norm);
#ifdef GRIT_DEBUG #ifdef GRIT_DEBUG
@ -105,16 +105,16 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1); llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
std::vector<llama_token> inputs = llama_tokenize(model, prompt, false, true); std::vector<llama_token> inputs = common_tokenize(model, prompt, false, true);
int32_t i_current_token = 0; int32_t i_current_token = 0;
while (true) { while (true) {
llama_batch_clear(bat); common_batch_clear(bat);
{ {
const int32_t n_inputs = inputs.size(); const int32_t n_inputs = inputs.size();
for (int32_t i = 0; i < n_inputs; i++) { for (int32_t i = 0; i < n_inputs; i++) {
llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1); common_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
} }
} }
inputs.clear(); inputs.clear();
@ -127,7 +127,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
break; break;
} }
std::string piece = llama_token_to_piece(ctx, token); std::string piece = common_token_to_piece(ctx, token);
if (stream) { if (stream) {
std::printf("%s", piece.c_str()); std::printf("%s", piece.c_str());
std::fflush(stdout); std::fflush(stdout);
@ -160,8 +160,8 @@ int main(int argc, char * argv[]) {
gpt_init(); gpt_init();
llama_model_params mparams = llama_model_params_from_gpt_params(params); llama_model_params mparams = common_model_params_from_gpt_params(params);
llama_context_params cparams = llama_context_params_from_gpt_params(params); llama_context_params cparams = common_context_params_from_gpt_params(params);
llama_backend_init(); llama_backend_init();
@ -199,10 +199,10 @@ int main(int argc, char * argv[]) {
const int n_embd = llama_n_embd(model); const int n_embd = llama_n_embd(model);
const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd); const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd); const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd); const float cosine_sim_q1_d0 = common_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd); const float cosine_sim_q1_d1 = common_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);

4
examples/imatrix/imatrix.cpp
View file

@ -436,7 +436,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
auto tim1 = std::chrono::high_resolution_clock::now(); auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__); LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true); std::vector<llama_token> tokens = ::common_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now(); auto tim2 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count()); LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -607,7 +607,7 @@ int main(int argc, char ** argv) {
params.warmup = false; params.warmup = false;
// init // init
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;

24
examples/infill/infill.cpp
View file

@ -174,7 +174,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any // load the model and apply lora adapter, if any
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
model = llama_init.model; model = llama_init.model;
ctx = llama_init.context; ctx = llama_init.context;
@ -202,8 +202,8 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd_inp; std::vector<llama_token> embd_inp;
std::vector<llama_token> embd_end; std::vector<llama_token> embd_end;
std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false); std::vector<llama_token> inp_pfx = ::common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false); std::vector<llama_token> inp_sfx = ::common_tokenize(ctx, params.input_suffix, false);
GGML_ASSERT(llama_token_prefix(model) >= 0); GGML_ASSERT(llama_token_prefix(model) >= 0);
GGML_ASSERT(llama_token_suffix(model) >= 0); GGML_ASSERT(llama_token_suffix(model) >= 0);
@ -257,13 +257,13 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) { for (int i = 0; i < (int) embd_inp.size(); i++) {
LOG_INF("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str());
} }
if (params.n_keep > 0) { if (params.n_keep > 0) {
LOG_INF("%s: static prompt based on n_keep: '", __func__); LOG_INF("%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) { for (int i = 0; i < params.n_keep; i++) {
LOG_CNT("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str());
} }
LOG_CNT("'\n"); LOG_CNT("'\n");
} }
@ -446,7 +446,7 @@ int main(int argc, char ** argv) {
// display text // display text
if (input_echo) { if (input_echo) {
for (auto id : embd) { for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id); const std::string token_str = common_token_to_piece(ctx, id);
LOG("%s", token_str.c_str()); LOG("%s", token_str.c_str());
if (embd.size() > 1) { if (embd.size() > 1) {
@ -468,7 +468,7 @@ int main(int argc, char ** argv) {
if ((gpt_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){ if ((gpt_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
if (is_interacting && !params.interactive_first) { if (is_interacting && !params.interactive_first) {
// print an eot token // print an eot token
LOG("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
} }
LOG("\n"); LOG("\n");
console::set_display(console::user_input); console::set_display(console::user_input);
@ -505,8 +505,8 @@ int main(int argc, char ** argv) {
} }
// tokenize new prefix and suffix // tokenize new prefix and suffix
std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false); std::vector<llama_token> inp_pfx = ::common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false); std::vector<llama_token> inp_sfx = ::common_tokenize(ctx, params.input_suffix, false);
inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
@ -579,7 +579,7 @@ int main(int argc, char ** argv) {
const size_t original_size = embd_inp.size(); const size_t original_size = embd_inp.size();
const auto line_inp = ::llama_tokenize(ctx, buffer, false); const auto line_inp = ::common_tokenize(ctx, buffer, false);
LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str()); LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
@ -587,7 +587,7 @@ int main(int argc, char ** argv) {
for (size_t i = original_size; i < embd_inp.size(); ++i) { for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i]; const llama_token token = embd_inp[i];
output_tokens.push_back(token); output_tokens.push_back(token);
output_ss << llama_token_to_piece(ctx, token); output_ss << common_token_to_piece(ctx, token);
} }
n_remain -= line_inp.size(); n_remain -= line_inp.size();
@ -620,7 +620,7 @@ int main(int argc, char ** argv) {
} }
} }
if (!params.interactive && n_remain <= 0) { if (!params.interactive && n_remain <= 0) {
LOG("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
} }
LOG("\n"); LOG("\n");

20
examples/llava/llava-cli.cpp
View file

@ -37,7 +37,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str; std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true); std::vector<llama_token> embd_inp = ::common_tokenize(ctx_llama, str2, add_bos, true);
eval_tokens(ctx_llama, embd_inp, n_batch, n_past); eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
return true; return true;
} }
@ -51,7 +51,7 @@ static const char * sample(struct gpt_sampler * smpl,
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) { if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>"; ret = "</s>";
} else { } else {
ret = llama_token_to_piece(ctx_llama, id); ret = common_token_to_piece(ctx_llama, id);
} }
eval_id(ctx_llama, id, n_past); eval_id(ctx_llama, id, n_past);
return ret.c_str(); return ret.c_str();
@ -159,16 +159,16 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
user_prompt = prompt.substr(image_pos + std::string("<image>").length()); user_prompt = prompt.substr(image_pos + std::string("<image>").length());
LOG_INF("system_prompt: %s\n", system_prompt.c_str()); LOG_INF("system_prompt: %s\n", system_prompt.c_str());
if (params->verbose_prompt) { if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); auto tmp = ::common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) { for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
} }
} }
LOG_INF("user_prompt: %s\n", user_prompt.c_str()); LOG_INF("user_prompt: %s\n", user_prompt.c_str());
if (params->verbose_prompt) { if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); auto tmp = ::common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) { for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
} }
} }
} else { } else {
@ -176,9 +176,9 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:"; system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
user_prompt = prompt + "\nASSISTANT:"; user_prompt = prompt + "\nASSISTANT:";
if (params->verbose_prompt) { if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); auto tmp = ::common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) { for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
} }
} }
} }
@ -219,7 +219,7 @@ static struct llama_model * llava_init(gpt_params * params) {
llama_backend_init(); llama_backend_init();
llama_numa_init(params->numa); llama_numa_init(params->numa);
llama_model_params model_params = llama_model_params_from_gpt_params(*params); llama_model_params model_params = common_model_params_from_gpt_params(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
if (model == NULL) { if (model == NULL) {
@ -240,7 +240,7 @@ static struct llava_context * llava_init_context(gpt_params * params, llama_mode
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params); llama_context_params ctx_params = common_context_params_from_gpt_params(*params);
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);

8
examples/llava/minicpmv-cli.cpp
View file

@ -29,7 +29,7 @@ static struct llama_model * llava_init(gpt_params * params) {
llama_backend_init(); llama_backend_init();
llama_numa_init(params->numa); llama_numa_init(params->numa);
llama_model_params model_params = llama_model_params_from_gpt_params(*params); llama_model_params model_params = common_model_params_from_gpt_params(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
if (model == NULL) { if (model == NULL) {
@ -45,7 +45,7 @@ static struct llava_context * llava_init_context(gpt_params * params, llama_mode
prompt = "describe the image in detail."; prompt = "describe the image in detail.";
} }
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params); llama_context_params ctx_params = common_context_params_from_gpt_params(*params);
if (params->n_ctx < 2048) { if (params->n_ctx < 2048) {
// warn user here, "Image processing requires at least 2048 context, setting context to 2048" // warn user here, "Image processing requires at least 2048 context, setting context to 2048"
LOG_WRN("%s: Image processing requires at least 2048 context, setting context to 2048\n" , __func__); LOG_WRN("%s: Image processing requires at least 2048 context, setting context to 2048\n" , __func__);
@ -114,7 +114,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str; std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true); std::vector<llama_token> embd_inp = ::common_tokenize(ctx_llama, str2, add_bos, true);
return eval_tokens(ctx_llama, embd_inp, n_batch, n_past); return eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
} }
@ -171,7 +171,7 @@ static const char * sample(struct gpt_sampler * smpl,
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) { if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>"; ret = "</s>";
} else { } else {
ret = llama_token_to_piece(ctx_llama, id); ret = common_token_to_piece(ctx_llama, id);
} }
eval_id(ctx_llama, id, n_past); eval_id(ctx_llama, id, n_past);
return ret.c_str(); return ret.c_str();

26
examples/lookahead/lookahead.cpp
View file

@ -56,7 +56,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the target model // load the target model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
@ -65,7 +65,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp; std::vector<llama_token> inp;
std::vector<llama_token> all; std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, true, true); inp = ::common_tokenize(ctx, params.prompt, true, true);
all = inp; all = inp;
const int max_context_size = llama_n_ctx(ctx); const int max_context_size = llama_n_ctx(ctx);
@ -79,7 +79,7 @@ int main(int argc, char ** argv) {
LOG("\n\n"); LOG("\n\n");
for (auto id : inp) { for (auto id : inp) {
LOG("%s", llama_token_to_piece(ctx, id).c_str()); LOG("%s", common_token_to_piece(ctx, id).c_str());
} }
fflush(stderr); fflush(stderr);
@ -161,7 +161,7 @@ int main(int argc, char ** argv) {
gpt_sampler_accept(smpl, id, true); gpt_sampler_accept(smpl, id, true);
{ {
const std::string token_str = llama_token_to_piece(ctx, id); const std::string token_str = common_token_to_piece(ctx, id);
LOG("%s", token_str.c_str()); LOG("%s", token_str.c_str());
fflush(stdout); fflush(stdout);
@ -172,7 +172,7 @@ int main(int argc, char ** argv) {
// debug // debug
if (dump_kv_cache) { if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view); llama_kv_cache_view_update(ctx, &kvc_view);
llama_kv_cache_dump_view_seqs(kvc_view, 40); common_kv_cache_dump_view_seqs(kvc_view, 40);
} }
// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/ // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
@ -201,10 +201,10 @@ int main(int argc, char ** argv) {
// V V V V V V // V V V V V V
// id // id
{ {
llama_batch_clear(batch); common_batch_clear(batch);
// current token - first token of the first level // current token - first token of the first level
llama_batch_add(batch, id, n_past, seq_id_all, true); common_batch_add(batch, id, n_past, seq_id_all, true);
// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation // verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
{ {
@ -229,7 +229,7 @@ int main(int argc, char ** argv) {
ngrams_cur[g].tokens [j + 1] = t; ngrams_cur[g].tokens [j + 1] = t;
ngrams_cur[g].i_batch[j + 1] = batch.n_tokens; ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true); common_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
} }
} }
} }
@ -241,13 +241,13 @@ int main(int argc, char ** argv) {
seq_id_look[j] = i + j + 1; seq_id_look[j] = i + j + 1;
} }
llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false); common_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
} }
// fill the rest of the levels // fill the rest of the levels
for (int j = 1; j < N - 1; j++) { for (int j = 1; j < N - 1; j++) {
for (int i = 0; i < W; i++) { for (int i = 0; i < W; i++) {
llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2); common_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
} }
} }
} }
@ -287,7 +287,7 @@ int main(int argc, char ** argv) {
// print // print
{ {
const std::string token_str = llama_token_to_piece(ctx, id); const std::string token_str = common_token_to_piece(ctx, id);
if (v == 0) { if (v == 0) {
LOG("%s", token_str.c_str()); LOG("%s", token_str.c_str());
@ -327,7 +327,7 @@ int main(int argc, char ** argv) {
// print known n-grams starting with token id (debug) // print known n-grams starting with token id (debug)
if (0 && v == 0) { if (0 && v == 0) {
if (ngrams_observed.cnt[id] > 0) { if (ngrams_observed.cnt[id] > 0) {
LOG("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str()); LOG("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], common_token_to_piece(ctx, id).c_str());
} }
for (int i = 0; i < ngrams_observed.cnt[id]; i++) { for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
@ -336,7 +336,7 @@ int main(int argc, char ** argv) {
const int idx = id*(N - 1)*G + i*(N - 1); const int idx = id*(N - 1)*G + i*(N - 1);
for (int j = 0; j < N - 1; j++) { for (int j = 0; j < N - 1; j++) {
const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]); const std::string token_str = common_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
LOG("%s", token_str.c_str()); LOG("%s", token_str.c_str());
} }

4
examples/lookup/lookup-create.cpp
View file

@ -23,7 +23,7 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model // load the model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
@ -31,7 +31,7 @@ int main(int argc, char ** argv){
// tokenize the prompt // tokenize the prompt
std::vector<llama_token> inp; std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, true, true); inp = ::common_tokenize(ctx, params.prompt, true, true);
fprintf(stderr, "%s: tokenization done\n", __func__); fprintf(stderr, "%s: tokenization done\n", __func__);

4
examples/lookup/lookup-stats.cpp
View file

@ -28,14 +28,14 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model // load the model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
// tokenize the prompt // tokenize the prompt
std::vector<llama_token> inp; std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, true, true); inp = ::common_tokenize(ctx, params.prompt, true, true);
llama_ngram_cache ngram_cache_context; llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic; llama_ngram_cache ngram_cache_dynamic;

16
examples/lookup/lookup.cpp
View file

@ -31,14 +31,14 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model // load the model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
// tokenize the prompt // tokenize the prompt
std::vector<llama_token> inp; std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, true, true); inp = ::common_tokenize(ctx, params.prompt, true, true);
llama_ngram_cache ngram_cache_context; llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic; llama_ngram_cache ngram_cache_dynamic;
@ -80,7 +80,7 @@ int main(int argc, char ** argv){
LOG("\n\n"); LOG("\n\n");
for (auto id : inp) { for (auto id : inp) {
LOG("%s", llama_token_to_piece(ctx, id).c_str()); LOG("%s", common_token_to_piece(ctx, id).c_str());
} }
fflush(stderr); fflush(stderr);
@ -117,7 +117,7 @@ int main(int argc, char ** argv){
// debug // debug
if (dump_kv_cache) { if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view); llama_kv_cache_view_update(ctx, &kvc_view);
llama_kv_cache_dump_view_seqs(kvc_view, 40); common_kv_cache_dump_view_seqs(kvc_view, 40);
} }
// print current draft sequence // print current draft sequence
@ -130,7 +130,7 @@ int main(int argc, char ** argv){
gpt_sampler_accept(smpl, id, true); gpt_sampler_accept(smpl, id, true);
const std::string token_str = llama_token_to_piece(ctx, id); const std::string token_str = common_token_to_piece(ctx, id);
if (!params.use_color) { if (!params.use_color) {
LOG("%s", token_str.c_str()); LOG("%s", token_str.c_str());
@ -192,8 +192,8 @@ int main(int argc, char ** argv){
// clean the cache of draft tokens that weren't accepted // clean the cache of draft tokens that weren't accepted
llama_kv_cache_seq_rm(ctx, 0, n_past, -1); llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
llama_batch_clear(batch_tgt); common_batch_clear(batch_tgt);
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true); common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// Draft already contains a single token sampled from the model: // Draft already contains a single token sampled from the model:
GGML_ASSERT(draft.size() == 1); GGML_ASSERT(draft.size() == 1);
@ -203,7 +203,7 @@ int main(int argc, char ** argv){
llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static); llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
for (size_t i = 1; i < draft.size(); ++i) { for (size_t i = 1; i < draft.size(); ++i) {
llama_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true); common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
} }
t_draft_us += ggml_time_us() - t_start_draft_us; t_draft_us += ggml_time_us() - t_start_draft_us;

46
examples/main/main.cpp
View file

@ -127,9 +127,9 @@ static void sigint_handler(int signo) {
} }
#endif #endif
static std::string chat_add_and_format(struct llama_model * model, std::vector<llama_chat_msg> & chat_msgs, const std::string & role, const std::string & content) { static std::string chat_add_and_format(struct llama_model * model, std::vector<common_chat_msg> & chat_msgs, const std::string & role, const std::string & content) {
llama_chat_msg new_msg{role, content}; common_chat_msg new_msg{role, content};
auto formatted = llama_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user"); auto formatted = common_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
chat_msgs.push_back({role, content}); chat_msgs.push_back({role, content});
LOG_DBG("formatted: '%s'\n", formatted.c_str()); LOG_DBG("formatted: '%s'\n", formatted.c_str());
return formatted; return formatted;
@ -189,7 +189,7 @@ int main(int argc, char ** argv) {
llama_context * ctx = nullptr; llama_context * ctx = nullptr;
gpt_sampler * smpl = nullptr; gpt_sampler * smpl = nullptr;
std::vector<llama_chat_msg> chat_msgs; std::vector<common_chat_msg> chat_msgs;
g_model = &model; g_model = &model;
g_ctx = &ctx; g_ctx = &ctx;
@ -197,7 +197,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any // load the model and apply lora adapter, if any
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
model = llama_init.model; model = llama_init.model;
ctx = llama_init.context; ctx = llama_init.context;
@ -246,7 +246,7 @@ int main(int argc, char ** argv) {
// print chat template example in conversation mode // print chat template example in conversation mode
if (params.conversation) { if (params.conversation) {
if (params.enable_chat_template) { if (params.enable_chat_template) {
LOG_INF("%s: chat template example:\n%s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str()); LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(model, params.chat_template).c_str());
} else { } else {
LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__); LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
} }
@ -296,7 +296,7 @@ int main(int argc, char ** argv) {
: params.prompt; : params.prompt;
if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
LOG_DBG("tokenize the prompt\n"); LOG_DBG("tokenize the prompt\n");
embd_inp = ::llama_tokenize(ctx, prompt, true, true); embd_inp = ::common_tokenize(ctx, prompt, true, true);
} else { } else {
LOG_DBG("use session tokens\n"); LOG_DBG("use session tokens\n");
embd_inp = session_tokens; embd_inp = session_tokens;
@ -379,13 +379,13 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) { for (int i = 0; i < (int) embd_inp.size(); i++) {
LOG_INF("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str());
} }
if (params.n_keep > add_bos) { if (params.n_keep > add_bos) {
LOG_INF("%s: static prompt based on n_keep: '", __func__); LOG_INF("%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) { for (int i = 0; i < params.n_keep; i++) {
LOG_CNT("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str());
} }
LOG_CNT("'\n"); LOG_CNT("'\n");
} }
@ -415,9 +415,9 @@ int main(int argc, char ** argv) {
for (const auto & antiprompt : params.antiprompt) { for (const auto & antiprompt : params.antiprompt) {
LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str()); LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str());
if (params.verbose_prompt) { if (params.verbose_prompt) {
auto tmp = ::llama_tokenize(ctx, antiprompt, false, true); auto tmp = ::common_tokenize(ctx, antiprompt, false, true);
for (int i = 0; i < (int) tmp.size(); i++) { for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
} }
} }
} }
@ -430,9 +430,9 @@ int main(int argc, char ** argv) {
if (!params.input_prefix.empty()) { if (!params.input_prefix.empty()) {
LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str()); LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
if (params.verbose_prompt) { if (params.verbose_prompt) {
auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true); auto tmp = ::common_tokenize(ctx, params.input_prefix, true, true);
for (int i = 0; i < (int) tmp.size(); i++) { for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
} }
} }
} }
@ -440,9 +440,9 @@ int main(int argc, char ** argv) {
if (!params.input_suffix.empty()) { if (!params.input_suffix.empty()) {
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str()); LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
if (params.verbose_prompt) { if (params.verbose_prompt) {
auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true); auto tmp = ::common_tokenize(ctx, params.input_suffix, false, true);
for (int i = 0; i < (int) tmp.size(); i++) { for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
} }
} }
} }
@ -521,7 +521,7 @@ int main(int argc, char ** argv) {
antiprompt_ids.reserve(params.antiprompt.size()); antiprompt_ids.reserve(params.antiprompt.size());
for (const std::string & antiprompt : params.antiprompt) { for (const std::string & antiprompt : params.antiprompt) {
antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true)); antiprompt_ids.emplace_back(::common_tokenize(ctx, antiprompt, false, true));
} }
if (llama_model_has_encoder(model)) { if (llama_model_has_encoder(model)) {
@ -714,7 +714,7 @@ int main(int argc, char ** argv) {
// display text // display text
if (input_echo && display) { if (input_echo && display) {
for (auto id : embd) { for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id, params.special); const std::string token_str = common_token_to_piece(ctx, id, params.special);
// Console/Stream Output // Console/Stream Output
LOG("%s", token_str.c_str()); LOG("%s", token_str.c_str());
@ -788,7 +788,7 @@ int main(int argc, char ** argv) {
if (params.interactive) { if (params.interactive) {
if (!params.antiprompt.empty()) { if (!params.antiprompt.empty()) {
// tokenize and inject first reverse prompt // tokenize and inject first reverse prompt
const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true); const auto first_antiprompt = ::common_tokenize(ctx, params.antiprompt.front(), false, true);
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
is_antiprompt = true; is_antiprompt = true;
} }
@ -804,7 +804,7 @@ int main(int argc, char ** argv) {
// if current token is not EOG, we add it to current assistant message // if current token is not EOG, we add it to current assistant message
if (params.conversation) { if (params.conversation) {
const auto id = gpt_sampler_last(smpl); const auto id = gpt_sampler_last(smpl);
assistant_ss << llama_token_to_piece(ctx, id, false); assistant_ss << common_token_to_piece(ctx, id, false);
} }
if (n_past > 0 && is_interacting) { if (n_past > 0 && is_interacting) {
@ -862,9 +862,9 @@ int main(int argc, char ** argv) {
? chat_add_and_format(model, chat_msgs, "user", std::move(buffer)) ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
: std::move(buffer); : std::move(buffer);
// TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix) // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); const auto line_pfx = ::common_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = ::llama_tokenize(ctx, user_inp, false, format_chat); const auto line_inp = ::common_tokenize(ctx, user_inp, false, format_chat);
const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); const auto line_sfx = ::common_tokenize(ctx, params.input_suffix, false, true);
LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str()); LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
@ -882,7 +882,7 @@ int main(int argc, char ** argv) {
for (size_t i = original_size; i < embd_inp.size(); ++i) { for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i]; const llama_token token = embd_inp[i];
output_tokens.push_back(token); output_tokens.push_back(token);
output_ss << llama_token_to_piece(ctx, token); output_ss << common_token_to_piece(ctx, token);
} }
// reset assistant message // reset assistant message

18
examples/parallel/parallel.cpp
View file

@ -130,7 +130,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the target model // load the target model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
@ -164,7 +164,7 @@ int main(int argc, char ** argv) {
} }
std::vector<llama_token> tokens_system; std::vector<llama_token> tokens_system;
tokens_system = ::llama_tokenize(ctx, k_system, true); tokens_system = ::common_tokenize(ctx, k_system, true);
const int32_t n_tokens_system = tokens_system.size(); const int32_t n_tokens_system = tokens_system.size();
llama_seq_id g_seq_id = 0; llama_seq_id g_seq_id = 0;
@ -189,7 +189,7 @@ int main(int argc, char ** argv) {
LOG_INF("%s: Evaluating the system prompt ...\n", __func__); LOG_INF("%s: Evaluating the system prompt ...\n", __func__);
for (int32_t i = 0; i < n_tokens_system; ++i) { for (int32_t i = 0; i < n_tokens_system; ++i) {
llama_batch_add(batch, tokens_system[i], i, { 0 }, false); common_batch_add(batch, tokens_system[i], i, { 0 }, false);
} }
if (llama_decode(ctx, batch) != 0) { if (llama_decode(ctx, batch) != 0) {
@ -210,10 +210,10 @@ int main(int argc, char ** argv) {
while (true) { while (true) {
if (dump_kv_cache) { if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view); llama_kv_cache_view_update(ctx, &kvc_view);
llama_kv_cache_dump_view_seqs(kvc_view, 40); common_kv_cache_dump_view_seqs(kvc_view, 40);
} }
llama_batch_clear(batch); common_batch_clear(batch);
// decode any currently ongoing sequences // decode any currently ongoing sequences
for (auto & client : clients) { for (auto & client : clients) {
@ -223,7 +223,7 @@ int main(int argc, char ** argv) {
client.i_batch = batch.n_tokens; client.i_batch = batch.n_tokens;
llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true); common_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
client.n_decoded += 1; client.n_decoded += 1;
} }
@ -256,10 +256,10 @@ int main(int argc, char ** argv) {
// do not prepend BOS because we have a system prompt! // do not prepend BOS because we have a system prompt!
std::vector<llama_token> tokens_prompt; std::vector<llama_token> tokens_prompt;
tokens_prompt = ::llama_tokenize(ctx, client.prompt, false); tokens_prompt = ::common_tokenize(ctx, client.prompt, false);
for (size_t i = 0; i < tokens_prompt.size(); ++i) { for (size_t i = 0; i < tokens_prompt.size(); ++i) {
llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false); common_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
} }
// extract the logits only for the last token // extract the logits only for the last token
@ -350,7 +350,7 @@ int main(int argc, char ** argv) {
client.t_start_gen = ggml_time_us(); client.t_start_gen = ggml_time_us();
} }
const std::string token_str = llama_token_to_piece(ctx, id); const std::string token_str = common_token_to_piece(ctx, id);
client.response += token_str; client.response += token_str;
client.sampled = id; client.sampled = id;

22
examples/passkey/passkey.cpp
View file

@ -61,7 +61,7 @@ int main(int argc, char ** argv) {
// initialize the model // initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model_params model_params = common_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -72,7 +72,7 @@ int main(int argc, char ** argv) {
// initialize the context // initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params); llama_context_params ctx_params = common_context_params_from_gpt_params(params);
ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
@ -92,10 +92,10 @@ int main(int argc, char ** argv) {
// tokenize the prompt // tokenize the prompt
std::vector<llama_token> tokens_list; std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true); tokens_list = ::common_tokenize(ctx, params.prompt, true);
// tokenize the prefix and use it as a sink // tokenize the prefix and use it as a sink
const int n_tokens_prefix = ::llama_tokenize(ctx, prompt_prefix, true).size(); const int n_tokens_prefix = ::common_tokenize(ctx, prompt_prefix, true).size();
const int n_tokens_all = tokens_list.size(); const int n_tokens_all = tokens_list.size();
@ -137,10 +137,10 @@ int main(int argc, char ** argv) {
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
} }
llama_batch_clear(batch); common_batch_clear(batch);
for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) { for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
} }
if (i + n_batch >= n_tokens_all) { if (i + n_batch >= n_tokens_all) {
@ -171,10 +171,10 @@ int main(int argc, char ** argv) {
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
llama_batch_clear(batch); common_batch_clear(batch);
for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) { for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
} }
if (i + n_batch >= n_tokens_all) { if (i + n_batch >= n_tokens_all) {
@ -229,15 +229,15 @@ int main(int argc, char ** argv) {
break; break;
} }
LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str()); LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
n_decode += 1; n_decode += 1;
// prepare the next batch // prepare the next batch
llama_batch_clear(batch); common_batch_clear(batch);
// push this new token for next evaluation // push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_past++, { 0 }, true); common_batch_add(batch, new_token_id, n_past++, { 0 }, true);
} }
n_cur += 1; n_cur += 1;

36
examples/perplexity/perplexity.cpp
View file

@ -348,7 +348,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
LOG_INF("%s: tokenizing the input ..\n", __func__); LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true); std::vector<llama_token> tokens = ::common_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx); const int n_ctx = llama_n_ctx(ctx);
@ -500,7 +500,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
auto tim1 = std::chrono::high_resolution_clock::now(); auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__); LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true); std::vector<llama_token> tokens = ::common_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now(); auto tim2 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count()); LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -844,7 +844,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j = 0; j < 4; j++) { for (size_t j = 0; j < 4; j++) {
hs_cur.ending[j] = prompt_lines[idx*6+2+j]; hs_cur.ending[j] = prompt_lines[idx*6+2+j];
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true); hs_cur.seq_tokens[j] = ::common_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
} }
// determine the common prefix of the endings // determine the common prefix of the endings
@ -900,7 +900,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0; size_t i1 = i0;
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch); common_batch_clear(batch);
// batch as much tasks as possible into the available context // batch as much tasks as possible into the available context
// each task has 4 unique sequence ids - one for each ending // each task has 4 unique sequence ids - one for each ending
@ -916,7 +916,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
} }
for (size_t i = 0; i < hs_cur.common_prefix; ++i) { for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false); common_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
} }
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1; n_logits += 1;
@ -926,7 +926,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// TODO: don't evaluate the last token of each sequence // TODO: don't evaluate the last token of each sequence
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) { for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1; const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits); common_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits; n_logits += needs_logits;
} }
} }
@ -1136,8 +1136,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
LOG_INF("%s : tokenizing selected tasks\n", __func__); LOG_INF("%s : tokenizing selected tasks\n", __func__);
for (auto & task : data) { for (auto & task : data) {
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true); task.seq_tokens[0] = ::common_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true); task.seq_tokens[1] = ::common_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.common_prefix = 0; task.common_prefix = 0;
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) { for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
@ -1152,8 +1152,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.seq_tokens[0].size() - task.common_prefix + task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix; task.seq_tokens[1].size() - task.common_prefix;
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size(); task.n_base1 = ::common_tokenize(ctx, task.first + task.choices[0], true).size();
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size(); task.n_base2 = ::common_tokenize(ctx, task.first + task.choices[1], true).size();
} }
LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__); LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__);
@ -1184,7 +1184,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0; size_t i1 = i0;
size_t i_logits = 0; size_t i_logits = 0;
llama_batch_clear(batch); common_batch_clear(batch);
while (n_cur + (int) data[i1].required_tokens <= n_ctx) { while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
int n_logits = 0; int n_logits = 0;
@ -1194,7 +1194,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
} }
for (size_t i = 0; i < data[i1].common_prefix; ++i) { for (size_t i = 0; i < data[i1].common_prefix; ++i) {
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false); common_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
} }
batch.logits[batch.n_tokens - 1] = true; batch.logits[batch.n_tokens - 1] = true;
n_logits += 1; n_logits += 1;
@ -1202,7 +1202,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
for (int s = 0; s < 2; ++s) { for (int s = 0; s < 2; ++s) {
// TODO: end before the last token, no need to predict past the end of the sequences // TODO: end before the last token, no need to predict past the end of the sequences
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) { for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true); common_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
n_logits += 1; n_logits += 1;
} }
} }
@ -1359,7 +1359,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
} }
return false; return false;
} }
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true)); task.seq_tokens.emplace_back(::common_tokenize(ctx, task.question + " " + answer, true));
} }
auto min_len = task.seq_tokens.front().size(); auto min_len = task.seq_tokens.front().size();
for (auto& seq : task.seq_tokens) { for (auto& seq : task.seq_tokens) {
@ -1536,7 +1536,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
size_t i1 = i0; size_t i1 = i0;
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch); common_batch_clear(batch);
// batch as much tasks as possible into the available context // batch as much tasks as possible into the available context
// each task has 4 unique sequence ids - one for each ending // each task has 4 unique sequence ids - one for each ending
@ -1559,7 +1559,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
for (size_t i = 0; i < cur_task.common_prefix; ++i) { for (size_t i = 0; i < cur_task.common_prefix; ++i) {
//llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false); //llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false); common_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
} }
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1; n_logits += 1;
@ -1569,7 +1569,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
// TODO: don't evaluate the last token of each sequence // TODO: don't evaluate the last token of each sequence
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) { for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1; const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits); common_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits; n_logits += needs_logits;
} }
} }
@ -2004,7 +2004,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model and apply lora adapter, if any // load the model and apply lora adapter, if any
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;

18
examples/retrieval/retrieval.cpp
View file

@ -77,7 +77,7 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) { static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size(); size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) { for (size_t i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, true); common_batch_add(batch, tokens[i], i, { seq_id }, true);
} }
} }
@ -107,7 +107,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
} }
float * out = output + batch.seq_id[i][0] * n_embd; float * out = output + batch.seq_id[i][0] * n_embd;
llama_embd_normalize(embd, out, n_embd); common_embd_normalize(embd, out, n_embd);
} }
} }
@ -149,7 +149,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa); llama_numa_init(params.numa);
// load the model // load the model
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
@ -185,7 +185,7 @@ int main(int argc, char ** argv) {
// tokenize the prompts and trim // tokenize the prompts and trim
for (auto & chunk : chunks) { for (auto & chunk : chunks) {
auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false); auto inp = ::common_tokenize(ctx, chunk.textdata, true, false);
if (inp.size() > n_batch) { if (inp.size() > n_batch) {
LOG_ERR("%s: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n", LOG_ERR("%s: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch); __func__, (long long int) inp.size(), (long long int) n_batch);
@ -204,7 +204,7 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str()); LOG_INF("%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size()); LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
for (int j = 0; j < (int) chunks[i].tokens.size(); j++) { for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str()); LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], common_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
} }
LOG_INF("\n\n"); LOG_INF("\n\n");
} }
@ -232,7 +232,7 @@ int main(int argc, char ** argv) {
if (batch.n_tokens + n_toks > n_batch) { if (batch.n_tokens + n_toks > n_batch) {
float * out = emb + p * n_embd; float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd); batch_decode(ctx, batch, out, s, n_embd);
llama_batch_clear(batch); common_batch_clear(batch);
p += s; p += s;
s = 0; s = 0;
} }
@ -260,20 +260,20 @@ int main(int argc, char ** argv) {
while (true) { while (true) {
LOG("Enter query: "); LOG("Enter query: ");
std::getline(std::cin, query); std::getline(std::cin, query);
std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true); std::vector<int32_t> query_tokens = common_tokenize(ctx, query, true);
batch_add_seq(query_batch, query_tokens, 0); batch_add_seq(query_batch, query_tokens, 0);
std::vector<float> query_emb(n_embd, 0); std::vector<float> query_emb(n_embd, 0);
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd); batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
llama_batch_clear(query_batch); common_batch_clear(query_batch);
// compute cosine similarities // compute cosine similarities
{ {
std::vector<std::pair<int, float>> similarities; std::vector<std::pair<int, float>> similarities;
for (int i = 0; i < n_chunks; i++) { for (int i = 0; i < n_chunks; i++) {
float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd); float sim = common_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
similarities.push_back(std::make_pair(i, sim)); similarities.push_back(std::make_pair(i, sim));
} }

14
examples/save-load-state/save-load-state.cpp
View file

@ -28,7 +28,7 @@ int main(int argc, char ** argv) {
std::string result2; std::string result2;
// init // init
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model; llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context; llama_context * ctx = llama_init.context;
@ -46,7 +46,7 @@ int main(int argc, char ** argv) {
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed)); llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
// tokenize prompt // tokenize prompt
auto tokens = llama_tokenize(ctx, params.prompt, true); auto tokens = common_tokenize(ctx, params.prompt, true);
// evaluate prompt // evaluate prompt
llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0)); llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
@ -72,7 +72,7 @@ int main(int argc, char ** argv) {
for (auto i = 0; i < params.n_predict; i++) { for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl, ctx, -1); auto next_token = llama_sampler_sample(smpl, ctx, -1);
auto next_token_str = llama_token_to_piece(ctx, next_token); auto next_token_str = common_token_to_piece(ctx, next_token);
printf("%s", next_token_str.c_str()); printf("%s", next_token_str.c_str());
result0 += next_token_str; result0 += next_token_str;
@ -92,7 +92,7 @@ int main(int argc, char ** argv) {
llama_free(ctx); llama_free(ctx);
// make new context // make new context
auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params)); auto * ctx2 = llama_new_context_with_model(model, common_context_params_from_gpt_params(params));
llama_sampler * smpl2 = llama_sampler_chain_init(sparams); llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
@ -128,7 +128,7 @@ int main(int argc, char ** argv) {
// second run // second run
for (auto i = 0; i < params.n_predict; i++) { for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl2, ctx2, -1); auto next_token = llama_sampler_sample(smpl2, ctx2, -1);
auto next_token_str = llama_token_to_piece(ctx2, next_token); auto next_token_str = common_token_to_piece(ctx2, next_token);
printf("%s", next_token_str.c_str()); printf("%s", next_token_str.c_str());
result1 += next_token_str; result1 += next_token_str;
@ -152,7 +152,7 @@ int main(int argc, char ** argv) {
} }
// make new context // make new context
auto * ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params)); auto * ctx3 = llama_new_context_with_model(model, common_context_params_from_gpt_params(params));
llama_sampler * smpl3 = llama_sampler_chain_init(sparams); llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
@ -216,7 +216,7 @@ int main(int argc, char ** argv) {
// third run with seq 1 instead of 0 // third run with seq 1 instead of 0
for (auto i = 0; i < params.n_predict; i++) { for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl3, ctx3, -1); auto next_token = llama_sampler_sample(smpl3, ctx3, -1);
auto next_token_str = llama_token_to_piece(ctx3, next_token); auto next_token_str = common_token_to_piece(ctx3, next_token);
printf("%s", next_token_str.c_str()); printf("%s", next_token_str.c_str());
result2 += next_token_str; result2 += next_token_str;

38
examples/server/server.cpp
View file

@ -611,7 +611,7 @@ struct server_response {
struct server_context { struct server_context {
llama_model * model = nullptr; llama_model * model = nullptr;
llama_context * ctx = nullptr; llama_context * ctx = nullptr;
std::vector<llama_lora_adapter_container> loras; std::vector<common_lora_adapter_container> loras;
gpt_params params; gpt_params params;
@ -668,7 +668,7 @@ struct server_context {
// dedicate one sequence to the system prompt // dedicate one sequence to the system prompt
params.n_parallel += 1; params.n_parallel += 1;
llama_init_result llama_init = llama_init_from_gpt_params(params); common_init_result llama_init = llama_init_from_gpt_params(params);
model = llama_init.model; model = llama_init.model;
ctx = llama_init.context; ctx = llama_init.context;
@ -771,10 +771,10 @@ struct server_context {
std::vector<llama_token> p; std::vector<llama_token> p;
if (first) { if (first) {
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); p = ::common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
first = false; first = false;
} else { } else {
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); p = ::common_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
} }
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
@ -788,7 +788,7 @@ struct server_context {
} }
} else { } else {
auto s = json_prompt.template get<std::string>(); auto s = json_prompt.template get<std::string>();
prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); prompt_tokens = ::common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
} }
return prompt_tokens; return prompt_tokens;
@ -999,7 +999,7 @@ struct server_context {
slot.sparams.logit_bias.push_back({tok, bias}); slot.sparams.logit_bias.push_back({tok, bias});
} }
} else if (el[0].is_string()) { } else if (el[0].is_string()) {
auto toks = llama_tokenize(model, el[0].get<std::string>(), false); auto toks = common_tokenize(model, el[0].get<std::string>(), false);
for (auto tok : toks) { for (auto tok : toks) {
slot.sparams.logit_bias.push_back({tok, bias}); slot.sparams.logit_bias.push_back({tok, bias});
} }
@ -1073,7 +1073,7 @@ struct server_context {
system_tokens.clear(); system_tokens.clear();
if (!system_prompt.empty()) { if (!system_prompt.empty()) {
system_tokens = ::llama_tokenize(ctx, system_prompt, true); system_tokens = ::common_tokenize(ctx, system_prompt, true);
const int32_t n_batch = llama_n_batch(ctx); const int32_t n_batch = llama_n_batch(ctx);
const int32_t n_tokens_prompt = system_tokens.size(); const int32_t n_tokens_prompt = system_tokens.size();
@ -1081,10 +1081,10 @@ struct server_context {
for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) { for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i); const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
llama_batch_clear(batch); common_batch_clear(batch);
for (int32_t j = 0; j < n_tokens; ++j) { for (int32_t j = 0; j < n_tokens; ++j) {
llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false); common_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
} }
if (llama_decode(ctx, batch) != 0) { if (llama_decode(ctx, batch) != 0) {
@ -1113,7 +1113,7 @@ struct server_context {
bool process_token(completion_token_output & result, server_slot & slot) { bool process_token(completion_token_output & result, server_slot & slot) {
// remember which tokens were sampled - used for repetition penalties during sampling // remember which tokens were sampled - used for repetition penalties during sampling
const std::string token_str = llama_token_to_piece(ctx, result.tok, params.special); const std::string token_str = common_token_to_piece(ctx, result.tok, params.special);
slot.sampled = result.tok; slot.sampled = result.tok;
// search stop word and delete it // search stop word and delete it
@ -1297,7 +1297,7 @@ struct server_context {
}; };
if (slot.sparams.n_probs > 0) { if (slot.sparams.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); const std::vector<llama_token> to_send_toks = common_tokenize(ctx, tkn.text_to_send, false);
const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size());
const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
@ -1347,7 +1347,7 @@ struct server_context {
if (slot.sparams.n_probs > 0) { if (slot.sparams.n_probs > 0) {
std::vector<completion_token_output> probs; std::vector<completion_token_output> probs;
if (!slot.params.stream && slot.stopped_word) { if (!slot.params.stream && slot.stopped_word) {
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); const std::vector<llama_token> stop_word_toks = common_tokenize(ctx, slot.stopping_word, false);
size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
probs = std::vector<completion_token_output>( probs = std::vector<completion_token_output>(
@ -1401,7 +1401,7 @@ struct server_context {
continue; continue;
} }
llama_embd_normalize(embd, embd_res.data(), n_embd); common_embd_normalize(embd, embd_res.data(), n_embd);
res.data = json { res.data = json {
{"embedding", embd_res}, {"embedding", embd_res},
@ -1835,7 +1835,7 @@ struct server_context {
} break; } break;
case SERVER_TASK_TYPE_SET_LORA: case SERVER_TASK_TYPE_SET_LORA:
{ {
llama_lora_adapters_apply(ctx, loras); common_lora_adapters_apply(ctx, loras);
server_task_result result; server_task_result result;
result.id = task.id; result.id = task.id;
result.stop = true; result.stop = true;
@ -1921,7 +1921,7 @@ struct server_context {
} }
// start populating the batch for this iteration // start populating the batch for this iteration
llama_batch_clear(batch); common_batch_clear(batch);
// frist, add sampled tokens from any ongoing sequences // frist, add sampled tokens from any ongoing sequences
for (auto & slot : slots) { for (auto & slot : slots) {
@ -1935,7 +1935,7 @@ struct server_context {
// TODO: we always have to take into account the "system_tokens" // TODO: we always have to take into account the "system_tokens"
// this is not great and needs to be improved somehow // this is not great and needs to be improved somehow
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true); common_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
slot.n_past += 1; slot.n_past += 1;
@ -2184,7 +2184,7 @@ struct server_context {
} }
} }
llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false); common_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
if (slot.params.cache_prompt) { if (slot.params.cache_prompt) {
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
@ -3014,7 +3014,7 @@ int main(int argc, char ** argv) {
if (with_pieces) { if (with_pieces) {
for (const auto& token : tokens) { for (const auto& token : tokens) {
std::string piece = llama_token_to_piece(ctx_server.ctx, token); std::string piece = common_token_to_piece(ctx_server.ctx, token);
json piece_json; json piece_json;
// Check if the piece is valid UTF-8 // Check if the piece is valid UTF-8
@ -3357,7 +3357,7 @@ int main(int argc, char ** argv) {
} }
// print sample chat example to make it clear which template is used // print sample chat example to make it clear which template is used
LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), llama_chat_format_example(ctx_server.model, params.chat_template).c_str()); LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), common_chat_format_example(ctx_server.model, params.chat_template).c_str());
ctx_server.queue_tasks.on_new_task(std::bind( ctx_server.queue_tasks.on_new_task(std::bind(
&server_context::process_single_task, &ctx_server, std::placeholders::_1)); &server_context::process_single_task, &ctx_server, std::placeholders::_1));

8
examples/server/utils.hpp
View file

@ -57,7 +57,7 @@ static T json_value(const json & body, const std::string & key, const T & defaul
// Format given chat. If tmpl is empty, we take the template from model metadata // Format given chat. If tmpl is empty, we take the template from model metadata
inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) { inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
std::vector<llama_chat_msg> chat; std::vector<common_chat_msg> chat;
for (size_t i = 0; i < messages.size(); ++i) { for (size_t i = 0; i < messages.size(); ++i) {
const auto & curr_msg = messages[i]; const auto & curr_msg = messages[i];
@ -84,7 +84,7 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
chat.push_back({role, content}); chat.push_back({role, content});
} }
const auto formatted_chat = llama_chat_apply_template(model, tmpl, chat, true); const auto formatted_chat = common_chat_apply_template(model, tmpl, chat, true);
LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str()); LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str());
return formatted_chat; return formatted_chat;
@ -246,7 +246,7 @@ template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret; std::string ret;
for (; begin != end; ++begin) { for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin); ret += common_token_to_piece(ctx, *begin);
} }
return ret; return ret;
@ -254,7 +254,7 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
// format incomplete utf-8 multibyte character for output // format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); std::string out = token == -1 ? "" : common_token_to_piece(ctx, token);
// if the size is 1 and first bit is 1, meaning it's a partial character // if the size is 1 and first bit is 1, meaning it's a partial character
// (size > 1 meaning it's already a known token) // (size > 1 meaning it's already a known token)

16
examples/simple/simple.cpp
View file

@ -33,7 +33,7 @@ int main(int argc, char ** argv) {
// initialize the model // initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model_params model_params = common_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -44,7 +44,7 @@ int main(int argc, char ** argv) {
// initialize the context // initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params); llama_context_params ctx_params = common_context_params_from_gpt_params(params);
llama_context * ctx = llama_new_context_with_model(model, ctx_params); llama_context * ctx = llama_new_context_with_model(model, ctx_params);
@ -64,7 +64,7 @@ int main(int argc, char ** argv) {
// tokenize the prompt // tokenize the prompt
std::vector<llama_token> tokens_list; std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true); tokens_list = ::common_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx); const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size()); const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size());
@ -84,7 +84,7 @@ int main(int argc, char ** argv) {
LOG("\n"); LOG("\n");
for (auto id : tokens_list) { for (auto id : tokens_list) {
LOG("%s", llama_token_to_piece(ctx, id).c_str()); LOG("%s", common_token_to_piece(ctx, id).c_str());
} }
// create a llama_batch with size 512 // create a llama_batch with size 512
@ -94,7 +94,7 @@ int main(int argc, char ** argv) {
// evaluate the initial prompt // evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); i++) { for (size_t i = 0; i < tokens_list.size(); i++) {
llama_batch_add(batch, tokens_list[i], i, { 0 }, false); common_batch_add(batch, tokens_list[i], i, { 0 }, false);
} }
// llama_decode will output logits only for the last token of the prompt // llama_decode will output logits only for the last token of the prompt
@ -124,14 +124,14 @@ int main(int argc, char ** argv) {
break; break;
} }
LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str()); LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout); fflush(stdout);
// prepare the next batch // prepare the next batch
llama_batch_clear(batch); common_batch_clear(batch);
// push this new token for next evaluation // push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_cur, { 0 }, true); common_batch_add(batch, new_token_id, n_cur, { 0 }, true);
n_decode += 1; n_decode += 1;
} }

34
examples/speculative/speculative.cpp
View file

@ -66,7 +66,7 @@ int main(int argc, char ** argv) {
llama_context * ctx_dft = NULL; llama_context * ctx_dft = NULL;
// load the target model // load the target model
llama_init_result llama_init_tgt = llama_init_from_gpt_params(params); common_init_result llama_init_tgt = llama_init_from_gpt_params(params);
model_tgt = llama_init_tgt.model; model_tgt = llama_init_tgt.model;
ctx_tgt = llama_init_tgt.context; ctx_tgt = llama_init_tgt.context;
@ -78,7 +78,7 @@ int main(int argc, char ** argv) {
} }
params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads; params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
llama_init_result llama_init_dft = llama_init_from_gpt_params(params); common_init_result llama_init_dft = llama_init_from_gpt_params(params);
model_dft = llama_init_dft.model; model_dft = llama_init_dft.model;
ctx_dft = llama_init_dft.context; ctx_dft = llama_init_dft.context;
@ -124,8 +124,8 @@ int main(int argc, char ** argv) {
if (std::strcmp(token_text_tgt, token_text_dft) != 0) { if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__); LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i, LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
llama_token_to_piece(ctx_tgt, i).c_str(), common_token_to_piece(ctx_tgt, i).c_str(),
llama_token_to_piece(ctx_dft, i).c_str()); common_token_to_piece(ctx_dft, i).c_str());
return 1; return 1;
} }
} }
@ -134,7 +134,7 @@ int main(int argc, char ** argv) {
// Tokenize the prompt // Tokenize the prompt
std::vector<llama_token> inp; std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx_tgt, params.prompt, true, true); inp = ::common_tokenize(ctx_tgt, params.prompt, true, true);
const int max_context_size = llama_n_ctx(ctx_tgt); const int max_context_size = llama_n_ctx(ctx_tgt);
const int max_tokens_list_size = max_context_size - 4; const int max_tokens_list_size = max_context_size - 4;
@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
LOG("\n\n"); LOG("\n\n");
for (auto id : inp) { for (auto id : inp) {
LOG("%s", llama_token_to_piece(ctx_tgt, id).c_str()); LOG("%s", common_token_to_piece(ctx_tgt, id).c_str());
} }
const int n_input = inp.size(); const int n_input = inp.size();
@ -277,13 +277,13 @@ int main(int argc, char ** argv) {
s_keep = s; s_keep = s;
accept = true; accept = true;
token_id = drafts[s].tokens[i_dft]; token_id = drafts[s].tokens[i_dft];
token_str = llama_token_to_piece(ctx_tgt, token_id); token_str = common_token_to_piece(ctx_tgt, token_id);
gpt_sampler_accept(smpl, token_id, true); gpt_sampler_accept(smpl, token_id, true);
LOG_DBG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str()); LOG_DBG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
break; break;
} else { } else {
LOG_DBG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str()); LOG_DBG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], common_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
drafts[s].active = false; drafts[s].active = false;
// calculate residual probability // calculate residual probability
@ -350,7 +350,7 @@ int main(int argc, char ** argv) {
token_id = dist_tgt.data[idx].id; token_id = dist_tgt.data[idx].id;
gpt_sampler_accept(smpl, token_id, true); gpt_sampler_accept(smpl, token_id, true);
token_str = llama_token_to_piece(ctx_tgt, token_id); token_str = common_token_to_piece(ctx_tgt, token_id);
} }
} else { } else {
// greedy verification // greedy verification
@ -361,7 +361,7 @@ int main(int argc, char ** argv) {
gpt_sampler_accept(smpl, token_id, true); gpt_sampler_accept(smpl, token_id, true);
token_str = llama_token_to_piece(ctx_tgt, token_id); token_str = common_token_to_piece(ctx_tgt, token_id);
for (int s = 0; s < n_seq_dft; ++s) { for (int s = 0; s < n_seq_dft; ++s) {
if (!drafts[s].active) { if (!drafts[s].active) {
@ -431,8 +431,8 @@ int main(int argc, char ** argv) {
drafts[0].dists.push_back(std::vector<llama_token_data>()); drafts[0].dists.push_back(std::vector<llama_token_data>());
drafts[0].i_batch_tgt.push_back(0); drafts[0].i_batch_tgt.push_back(0);
llama_batch_clear(batch_dft); common_batch_clear(batch_dft);
llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true); common_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
// LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str()); // LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
@ -461,8 +461,8 @@ int main(int argc, char ** argv) {
drafts[0].drafting = true; drafts[0].drafting = true;
drafts[0].i_batch_dft = 0; drafts[0].i_batch_dft = 0;
llama_batch_clear(batch_tgt); common_batch_clear(batch_tgt);
llama_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true); common_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
// sample n_draft tokens from the draft model using tree-based sampling // sample n_draft tokens from the draft model using tree-based sampling
for (int i = 0; i < n_draft; ++i) { for (int i = 0; i < n_draft; ++i) {
@ -483,7 +483,7 @@ int main(int argc, char ** argv) {
for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) { for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) {
LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n", LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
k, s, i, cur_p->data[k].id, cur_p->data[k].p, llama_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); k, s, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
} }
std::vector<int> sa(1, s); std::vector<int> sa(1, s);
@ -545,12 +545,12 @@ int main(int argc, char ** argv) {
// add unique drafted tokens to the target batch // add unique drafted tokens to the target batch
drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens); drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true); common_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true);
// add the token to the batch for batched decoding with the draft model // add the token to the batch for batched decoding with the draft model
drafts[s].i_batch_dft = batch_dft.n_tokens; drafts[s].i_batch_dft = batch_dft.n_tokens;
llama_batch_add(batch_dft, id, n_past_cur, { s }, true); common_batch_add(batch_dft, id, n_past_cur, { s }, true);
if (batch_tgt.n_tokens > n_draft) { if (batch_tgt.n_tokens > n_draft) {
drafts[s].drafting = false; drafts[s].drafting = false;

4
examples/tokenize/tokenize.cpp
View file

@ -365,7 +365,7 @@ int main(int raw_argc, char ** raw_argv) {
const bool parse_special = !no_parse_special; const bool parse_special = !no_parse_special;
std::vector<llama_token> tokens; std::vector<llama_token> tokens;
tokens = ::llama_tokenize(model, prompt, add_bos, parse_special); tokens = ::common_tokenize(model, prompt, add_bos, parse_special);
if (printing_ids) { if (printing_ids) {
printf("["); printf("[");
@ -380,7 +380,7 @@ int main(int raw_argc, char ** raw_argv) {
} else { } else {
bool invalid_utf8 = false; bool invalid_utf8 = false;
printf("%6d -> '", tokens[i]); printf("%6d -> '", tokens[i]);
write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8); write_utf8_cstr_to_stdout(common_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8);
if (invalid_utf8) { if (invalid_utf8) {
printf("' (utf-8 decode failure)\n"); printf("' (utf-8 decode failure)\n");
} else { } else {

10
tests/test-chat-template.cpp
View file

@ -140,11 +140,11 @@ int main(void) {
// test llama_chat_format_single for system message // test llama_chat_format_single for system message
printf("\n\n=== llama_chat_format_single (system message) ===\n\n"); printf("\n\n=== llama_chat_format_single (system message) ===\n\n");
std::vector<llama_chat_msg> chat2; std::vector<common_chat_msg> chat2;
llama_chat_msg sys_msg{"system", "You are a helpful assistant"}; common_chat_msg sys_msg{"system", "You are a helpful assistant"};
auto fmt_sys = [&](std::string tmpl) { auto fmt_sys = [&](std::string tmpl) {
auto output = llama_chat_format_single(nullptr, tmpl, chat2, sys_msg, false); auto output = common_chat_format_single(nullptr, tmpl, chat2, sys_msg, false);
printf("fmt_sys(%s) : %s\n", tmpl.c_str(), output.c_str()); printf("fmt_sys(%s) : %s\n", tmpl.c_str(), output.c_str());
printf("-------------------------\n"); printf("-------------------------\n");
return output; return output;
@ -160,10 +160,10 @@ int main(void) {
chat2.push_back({"system", "You are a helpful assistant"}); chat2.push_back({"system", "You are a helpful assistant"});
chat2.push_back({"user", "Hello"}); chat2.push_back({"user", "Hello"});
chat2.push_back({"assistant", "I am assistant"}); chat2.push_back({"assistant", "I am assistant"});
llama_chat_msg new_msg{"user", "How are you"}; common_chat_msg new_msg{"user", "How are you"};
auto fmt_single = [&](std::string tmpl) { auto fmt_single = [&](std::string tmpl) {
auto output = llama_chat_format_single(nullptr, tmpl, chat2, new_msg, true); auto output = common_chat_format_single(nullptr, tmpl, chat2, new_msg, true);
printf("fmt_single(%s) : %s\n", tmpl.c_str(), output.c_str()); printf("fmt_single(%s) : %s\n", tmpl.c_str(), output.c_str());
printf("-------------------------\n"); printf("-------------------------\n");
return output; return output;

14
tests/test-tokenizer-0.cpp
View file

@ -202,7 +202,7 @@ int main(int argc, char **argv) {
for (int i = 0; i < nthread; i++) { for (int i = 0; i < nthread; i++) {
threads[i] = std::thread([&, i]() { threads[i] = std::thread([&, i]() {
for (const auto & test_kv : k_tests) { for (const auto & test_kv : k_tests) {
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false); const std::vector<llama_token> res = common_tokenize(ctx, test_kv.first, add_special, false);
// here only print the result of the first thread // here only print the result of the first thread
// because the other threads are running the same tests // because the other threads are running the same tests
@ -212,7 +212,7 @@ int main(int argc, char **argv) {
printf("\n"); printf("\n");
printf("src: '%s'\n", test_kv.first.c_str()); printf("src: '%s'\n", test_kv.first.c_str());
printf("res: '%s'\n", llama_detokenize(ctx, res).c_str()); printf("res: '%s'\n", common_detokenize(ctx, res).c_str());
printf("tok: "); printf("tok: ");
for (const auto & tok : res) { for (const auto & tok : res) {
printf("%d ", tok); printf("%d ", tok);
@ -229,16 +229,16 @@ int main(int argc, char **argv) {
if (!correct) { if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
llama_detokenize(ctx, res).c_str(), common_detokenize(ctx, res).c_str(),
llama_detokenize(ctx, test_kv.second).c_str()); common_detokenize(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__); fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) { for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str()); fprintf(stderr, "%6d '%s', ", t, common_token_to_piece(ctx, t).c_str());
} }
fprintf(stderr, "\n"); fprintf(stderr, "\n");
fprintf(stderr, "%s : got tokens: ", __func__); fprintf(stderr, "%s : got tokens: ", __func__);
for (const auto & t : res) { for (const auto & t : res) {
fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str()); fprintf(stderr, "%6d '%s', ", t, common_token_to_piece(ctx, t).c_str());
} }
fprintf(stderr, "\n"); fprintf(stderr, "\n");
@ -273,7 +273,7 @@ int main(int argc, char **argv) {
{ {
const auto t_start = ggml_time_us(); const auto t_start = ggml_time_us();
res = llama_tokenize(ctx, text, add_special, false); res = common_tokenize(ctx, text, add_special, false);
const auto t_end = ggml_time_us(); const auto t_end = ggml_time_us();

10
tests/test-tokenizer-1-bpe.cpp
View file

@ -78,10 +78,10 @@ int main(int argc, char **argv) {
const int n_vocab = llama_n_vocab(model); const int n_vocab = llama_n_vocab(model);
for (int i = 0; i < n_vocab; ++i) { for (int i = 0; i < n_vocab; ++i) {
std::string str = llama_detokenize(ctx, std::vector<int>(1, i)); std::string str = common_detokenize(ctx, std::vector<int>(1, i));
try { try {
auto cps = unicode_cpts_from_utf8(str); auto cps = unicode_cpts_from_utf8(str);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true); std::vector<llama_token> tokens = common_tokenize(ctx, str, false, true);
if (ignore_merges && tokens.size() > 1) { if (ignore_merges && tokens.size() > 1) {
fprintf(stderr, fprintf(stderr,
"%s : error: token %d detokenizes to '%s'(%zu) but " "%s : error: token %d detokenizes to '%s'(%zu) but "
@ -94,7 +94,7 @@ int main(int argc, char **argv) {
fprintf(stderr, "]\n"); fprintf(stderr, "]\n");
return 2; return 2;
} }
std::string check = llama_detokenize(ctx, tokens); std::string check = common_detokenize(ctx, tokens);
if (check != str) { if (check != str) {
fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n", fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n",
__func__, i, str.c_str(), str.length(), check.c_str(), check.length()); __func__, i, str.c_str(), str.length(), check.c_str(), check.length());
@ -123,8 +123,8 @@ int main(int argc, char **argv) {
} }
std::string str = unicode_cpt_to_utf8(cp); std::string str = unicode_cpt_to_utf8(cp);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false); std::vector<llama_token> tokens = common_tokenize(ctx, str, false);
std::string check = llama_detokenize(ctx, tokens); std::string check = common_detokenize(ctx, tokens);
if (cp != 9601 && str != check) { if (cp != 9601 && str != check) {
fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
cp, check.c_str(), check.length(), str.c_str(), str.length()); cp, check.c_str(), check.length(), str.c_str(), str.length());

10
tests/test-tokenizer-1-spm.cpp
View file

@ -66,9 +66,9 @@ int main(int argc, char ** argv) {
const int n_vocab = llama_n_vocab(model); const int n_vocab = llama_n_vocab(model);
for (int i = 0; i < n_vocab; ++i) { for (int i = 0; i < n_vocab; ++i) {
std::string str = llama_detokenize(ctx, std::vector<int>(1, i), true); std::string str = common_detokenize(ctx, std::vector<int>(1, i), true);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true); std::vector<llama_token> tokens = common_tokenize(ctx, str, false, true);
std::string check = llama_detokenize(ctx, tokens); std::string check = common_detokenize(ctx, tokens);
if (check != str) { if (check != str) {
fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n", fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n",
__func__, i, str.c_str(), str.length(), check.c_str(), check.length()); __func__, i, str.c_str(), str.length(), check.c_str(), check.length());
@ -93,8 +93,8 @@ int main(int argc, char ** argv) {
} }
std::string str = unicode_cpt_to_utf8(cp); std::string str = unicode_cpt_to_utf8(cp);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true); std::vector<llama_token> tokens = common_tokenize(ctx, str, false, true);
std::string check = llama_detokenize(ctx, tokens); std::string check = common_detokenize(ctx, tokens);
if (cp != 9601 && str != check) { if (cp != 9601 && str != check) {
fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
cp, check.c_str(), check.length(), str.c_str(), str.length()); cp, check.c_str(), check.length(), str.c_str(), str.length());