vbatts/llama.cpp
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common : use common_ prefix for common library functions (#9805)

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* common : use common_ prefix for common library functions

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Diego Devesa 2024-10-10 22:57:42 +02:00 • committed by GitHub
parent 0e9f760eb1
commit 7eee341bee
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GPG key ID: B5690EEEBB952194
45 changed files with 1284 additions and 1284 deletions
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16
examples/cvector-generator/cvector-generator.cpp
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@ -31,7 +31,7 @@ template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin);
ret += common_token_to_piece(ctx, *begin);
}
return ret;
@ -272,8 +272,8 @@ struct tokenized_prompt {
tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true);
tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true);
tokens_pos = common_tokenize(ctx, pos, add_bos, true);
tokens_neg = common_tokenize(ctx, neg, add_bos, true);
max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
padding_seq(ctx, tokens_pos, 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) {
// 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();
while (tokens.size() < len) {
tokens.push_back(pad_tok);
@ -370,7 +370,7 @@ static void export_gguf(const std::vector<struct ggml_tensor *> & v_ctrl, const
* Load prompt files and completion file.
* Then format each pair of prompt + completion to make an entry.
*/
static int prepare_entries(gpt_params & params, train_context & ctx_train) {
static int prepare_entries(common_params & params, train_context & ctx_train) {
// load prompts
std::vector<std::string> positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true);
std::vector<std::string> negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true);
@ -388,9 +388,9 @@ static int prepare_entries(gpt_params & params, train_context & ctx_train) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
return 1;
}
@ -413,7 +413,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model to get hparams
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;