common : use common_ prefix for common library functions (#9805)
* common : use common_ prefix for common library functions --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Diego Devesa
GitHub
parent
0e9f760eb1
commit
7eee341bee
45 changed files with 1284 additions and 1284 deletions
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@ -362,10 +362,10 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
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return true;
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}
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void gpt_init() {
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void common_init() {
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llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
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if (LOG_DEFAULT_LLAMA <= gpt_log_verbosity_thold) {
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gpt_log_add(gpt_log_main(), level, "%s", text);
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if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) {
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common_log_add(common_log_main(), level, "%s", text);
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}
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}, NULL);
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@ -378,7 +378,7 @@ void gpt_init() {
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LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
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}
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std::string gpt_params_get_system_info(const gpt_params & params) {
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std::string common_params_get_system_info(const common_params & params) {
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std::ostringstream os;
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os << "system_info: n_threads = " << params.cpuparams.n_threads;
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@ -493,7 +493,7 @@ std::string string_from(const struct llama_context * ctx, const std::vector<llam
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first = false;
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}
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auto detokenized = llama_token_to_piece(ctx, token);
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auto detokenized = common_token_to_piece(ctx, token);
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detokenized.erase(
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std::remove_if(
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@ -524,7 +524,7 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
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first = false;
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}
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auto detokenized = llama_token_to_piece(ctx, batch.token[i]);
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auto detokenized = common_token_to_piece(ctx, batch.token[i]);
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detokenized.erase(
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std::remove_if(
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@ -819,16 +819,16 @@ std::string fs_get_cache_file(const std::string & filename) {
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//
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// Model utils
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//
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struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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llama_init_result iparams;
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auto mparams = llama_model_params_from_gpt_params(params);
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struct common_init_result common_init_from_params(common_params & params) {
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common_init_result iparams;
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auto mparams = common_model_params_to_llama(params);
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llama_model * model = nullptr;
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if (!params.hf_repo.empty() && !params.hf_file.empty()) {
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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);
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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);
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} else if (!params.model_url.empty()) {
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model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
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model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
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} else {
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model = llama_load_model_from_file(params.model.c_str(), mparams);
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}
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@ -863,7 +863,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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}
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}
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auto cparams = llama_context_params_from_gpt_params(params);
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auto cparams = common_context_params_to_llama(params);
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llama_context * lctx = llama_new_context_with_model(model, cparams);
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if (lctx == NULL) {
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@ -876,7 +876,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
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if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
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const auto cvec = llama_control_vector_load(params.control_vectors);
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const auto cvec = common_control_vector_load(params.control_vectors);
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if (cvec.n_embd == -1) {
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llama_free(lctx);
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llama_free_model(model);
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@ -900,7 +900,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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// load and optionally apply lora adapters
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for (auto & la : params.lora_adapters) {
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llama_lora_adapter_container loaded_la;
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common_lora_adapter_container loaded_la;
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loaded_la.path = la.path;
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loaded_la.scale = la.scale;
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loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
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@ -913,7 +913,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
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}
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if (!params.lora_init_without_apply) {
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llama_lora_adapters_apply(lctx, iparams.lora_adapters);
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common_lora_adapters_apply(lctx, iparams.lora_adapters);
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}
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if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
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@ -961,7 +961,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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return iparams;
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}
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void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
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void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters) {
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llama_lora_adapter_clear(ctx);
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for (auto & la : lora_adapters) {
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if (la.scale != 0.0f) {
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@ -970,7 +970,7 @@ void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lor
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}
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}
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struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
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struct llama_model_params common_model_params_to_llama(const common_params & params) {
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auto mparams = llama_model_default_params();
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if (params.n_gpu_layers != -1) {
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@ -1022,7 +1022,7 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
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throw std::runtime_error("Invalid cache type: " + s);
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}
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struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
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struct llama_context_params common_context_params_to_llama(const common_params & params) {
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auto cparams = llama_context_default_params();
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cparams.n_ctx = params.n_ctx;
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@ -1112,7 +1112,7 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
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return false;
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}
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static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
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static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
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// Initialize libcurl
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std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
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@ -1182,15 +1182,15 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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}
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// Send a HEAD request to retrieve the etag and last-modified headers
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struct llama_load_model_from_url_headers {
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struct common_load_model_from_url_headers {
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std::string etag;
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std::string last_modified;
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};
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llama_load_model_from_url_headers headers;
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common_load_model_from_url_headers headers;
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{
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typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
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auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
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llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
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common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata;
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static std::regex header_regex("([^:]+): (.*)\r\n");
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static std::regex etag_regex("ETag", std::regex_constants::icase);
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@ -1326,7 +1326,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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return true;
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}
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struct llama_model * llama_load_model_from_url(
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struct llama_model * common_load_model_from_url(
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const char * model_url,
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const char * path_model,
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const char * hf_token,
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@ -1337,7 +1337,7 @@ struct llama_model * llama_load_model_from_url(
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return NULL;
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}
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if (!llama_download_file(model_url, path_model, hf_token)) {
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if (!common_download_file(model_url, path_model, hf_token)) {
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return NULL;
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}
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@ -1390,7 +1390,7 @@ struct llama_model * llama_load_model_from_url(
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char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
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llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
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return llama_download_file(split_url, split_path, hf_token);
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return common_download_file(split_url, split_path, hf_token);
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}, idx));
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}
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@ -1405,7 +1405,7 @@ struct llama_model * llama_load_model_from_url(
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return llama_load_model_from_file(path_model, params);
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}
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struct llama_model * llama_load_model_from_hf(
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struct llama_model * common_load_model_from_hf(
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const char * repo,
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const char * model,
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const char * path_model,
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@ -1425,12 +1425,12 @@ struct llama_model * llama_load_model_from_hf(
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model_url += "/resolve/main/";
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model_url += model;
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return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
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return common_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
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}
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#else
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struct llama_model * llama_load_model_from_url(
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struct llama_model * common_load_model_from_url(
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const char * /*model_url*/,
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const char * /*path_model*/,
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const char * /*hf_token*/,
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@ -1439,7 +1439,7 @@ struct llama_model * llama_load_model_from_url(
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return nullptr;
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}
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struct llama_model * llama_load_model_from_hf(
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struct llama_model * common_load_model_from_hf(
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const char * /*repo*/,
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const char * /*model*/,
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const char * /*path_model*/,
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@ -1455,11 +1455,11 @@ struct llama_model * llama_load_model_from_hf(
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// Batch utils
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//
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void llama_batch_clear(struct llama_batch & batch) {
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void common_batch_clear(struct llama_batch & batch) {
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batch.n_tokens = 0;
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}
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void llama_batch_add(
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void common_batch_add(
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struct llama_batch & batch,
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llama_token id,
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llama_pos pos,
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@ -1482,15 +1482,15 @@ void llama_batch_add(
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// Vocab utils
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//
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std::vector<llama_token> llama_tokenize(
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std::vector<llama_token> common_tokenize(
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const struct llama_context * ctx,
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const std::string & text,
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bool add_special,
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bool parse_special) {
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return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
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return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
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}
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std::vector<llama_token> llama_tokenize(
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std::vector<llama_token> common_tokenize(
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const struct llama_model * model,
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const std::string & text,
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bool add_special,
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@ -1509,7 +1509,7 @@ std::vector<llama_token> llama_tokenize(
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return result;
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}
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std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
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std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
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std::string piece;
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piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
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const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
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@ -1525,7 +1525,7 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t
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return piece;
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}
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std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
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std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
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std::string text;
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text.resize(std::max(text.capacity(), tokens.size()));
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int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
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@ -1545,15 +1545,15 @@ std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token>
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// Chat template utils
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//
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bool llama_chat_verify_template(const std::string & tmpl) {
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bool common_chat_verify_template(const std::string & tmpl) {
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llama_chat_message chat[] = {{"user", "test"}};
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int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
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return res >= 0;
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}
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std::string llama_chat_apply_template(const struct llama_model * model,
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std::string common_chat_apply_template(const struct llama_model * model,
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const std::string & tmpl,
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const std::vector<llama_chat_msg> & msgs,
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const std::vector<common_chat_msg> & msgs,
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bool add_ass) {
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int alloc_size = 0;
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bool fallback = false; // indicate if we must fallback to default chatml
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@ -1595,42 +1595,42 @@ std::string llama_chat_apply_template(const struct llama_model * model,
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return formatted_chat;
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}
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std::string llama_chat_format_single(const struct llama_model * model,
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std::string common_chat_format_single(const struct llama_model * model,
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const std::string & tmpl,
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const std::vector<llama_chat_msg> & past_msg,
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const llama_chat_msg & new_msg,
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const std::vector<common_chat_msg> & past_msg,
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const common_chat_msg & new_msg,
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bool add_ass) {
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std::ostringstream ss;
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auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
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std::vector<llama_chat_msg> chat_new(past_msg);
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auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false);
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std::vector<common_chat_msg> chat_new(past_msg);
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// if the past_msg ends with a newline, we must preserve it in the formatted version
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if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
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ss << "\n";
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};
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// format chat with new_msg
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chat_new.push_back(new_msg);
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auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
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auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass);
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// get the diff part
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ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
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return ss.str();
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}
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std::string llama_chat_format_example(const struct llama_model * model,
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std::string common_chat_format_example(const struct llama_model * model,
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const std::string & tmpl) {
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std::vector<llama_chat_msg> msgs = {
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std::vector<common_chat_msg> msgs = {
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{"system", "You are a helpful assistant"},
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{"user", "Hello"},
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{"assistant", "Hi there"},
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{"user", "How are you?"},
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};
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return llama_chat_apply_template(model, tmpl, msgs, true);
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return common_chat_apply_template(model, tmpl, msgs, true);
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}
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//
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// KV cache utils
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//
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void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
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void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
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static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
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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",
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@ -1653,7 +1653,7 @@ void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
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printf("\n=== Done dumping\n");
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}
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void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
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void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
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static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
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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",
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@ -1705,7 +1705,7 @@ void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_siz
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// Embedding utils
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//
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void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
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||||
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
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||||
double sum = 0.0;
|
||||
|
||||
switch (embd_norm) {
|
||||
|
|
@ -1739,7 +1739,7 @@ void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm)
|
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}
|
||||
}
|
||||
|
||||
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
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||||
float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
|
||||
double sum = 0.0;
|
||||
double sum1 = 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
|
||||
//
|
||||
|
||||
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
|
||||
llama_control_vector_data result = { -1, {} };
|
||||
static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) {
|
||||
common_control_vector_data result = { -1, {} };
|
||||
|
||||
ggml_context * ctx = nullptr;
|
||||
struct gguf_init_params meta_gguf_params = {
|
||||
|
|
@ -1850,11 +1850,11 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
|
|||
return result;
|
||||
}
|
||||
|
||||
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
|
||||
llama_control_vector_data result = { -1, {} };
|
||||
common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos) {
|
||||
common_control_vector_data result = { -1, {} };
|
||||
|
||||
for (const auto & info : load_infos) {
|
||||
auto cur = llama_control_vector_load_one(info);
|
||||
auto cur = common_control_vector_load_one(info);
|
||||
|
||||
if (cur.n_embd == -1) {
|
||||
result.n_embd = -1;
|
||||
|
|
@ -1946,7 +1946,7 @@ void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const cha
|
|||
}
|
||||
}
|
||||
|
||||
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
|
||||
void yaml_dump_non_result_info(FILE * stream, const common_params & params, const llama_context * lctx,
|
||||
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
|
||||
const auto & sparams = params.sparams;
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue