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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20
examples/batched-bench/batched-bench.cpp
View file

@ -15,13 +15,13 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
return 1;
}
gpt_init();
common_init();
int is_pp_shared = params.is_pp_shared;
@ -36,7 +36,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = common_model_params_to_llama(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;
}
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = common_context_params_to_llama(params);
// ensure enough sequences are available
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
{
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)) {
@ -122,11 +122,11 @@ int main(int argc, char ** argv) {
continue;
}
llama_batch_clear(batch);
common_batch_clear(batch);
for (int i = 0; i < pp; ++i) {
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;
@ -151,10 +151,10 @@ int main(int argc, char ** argv) {
const auto t_tg_start = ggml_time_us();
for (int i = 0; i < tg; ++i) {
llama_batch_clear(batch);
common_batch_clear(batch);
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)) {

28
examples/batched/batched.cpp
View file

@ -15,16 +15,16 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.prompt = "Hello my name is";
params.n_predict = 32;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
return 1;
}
gpt_init();
common_init();
// number of parallel batches
int n_parallel = params.n_parallel;
@ -39,7 +39,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = common_model_params_to_llama(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
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;
// initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = common_context_params_to_llama(params);
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_predict, n_parallel);
@ -94,7 +94,7 @@ int main(int argc, char ** argv) {
LOG("\n");
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
@ -108,7 +108,7 @@ int main(int argc, char ** argv) {
// evaluate the initial prompt
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());
@ -123,8 +123,8 @@ int main(int argc, char ** argv) {
decoder_start_token_id = llama_token_bos(model);
}
llama_batch_clear(batch);
llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
common_batch_clear(batch);
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
@ -161,7 +161,7 @@ int main(int argc, char ** argv) {
while (n_cur <= n_predict) {
// prepare the next batch
llama_batch_clear(batch);
common_batch_clear(batch);
// sample the next token for each parallel sequence / stream
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 (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;
// 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;
}

2
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -872,7 +872,7 @@ static std::string basename(const std::string &path) {
}
int main(int argc, char ** argv) {
gpt_init();
common_init();
struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) {

16
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) {
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;

24
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) {
size_t n_tokens = tokens.size();
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);
}
}
@ -74,18 +74,18 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
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);
}
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
return 1;
}
gpt_init();
common_init();
params.embedding = true;
// For non-causal models, batch size must be equal to ubatch size
@ -95,7 +95,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model
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;
@ -122,7 +122,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
// split the prompt into lines
@ -135,7 +135,7 @@ int main(int argc, char ** argv) {
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
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) {
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);
@ -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: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
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");
}
@ -199,7 +199,7 @@ int main(int argc, char ** argv) {
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
s = 0;
llama_batch_clear(batch);
common_batch_clear(batch);
}
// add to batch
@ -263,7 +263,7 @@ int main(int argc, char ** argv) {
LOG("\n");
for (int i = 0; i < n_prompts; i++) {
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("%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)
LOG(" [");
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);
j++;
if (j < n_embd_count) LOG(", "); else break;

14
examples/eval-callback/eval-callback.cpp
View file

@ -126,10 +126,10 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
return true;
}
static bool run(llama_context * ctx, const gpt_params & params) {
static bool run(llama_context * ctx, const common_params & params) {
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))) {
LOG_ERR("%s : failed to eval\n", __func__);
@ -142,13 +142,13 @@ static bool run(llama_context * ctx, const gpt_params & params) {
int main(int argc, char ** argv) {
callback_data cb_data;
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
gpt_init();
common_init();
llama_backend_init();
llama_numa_init(params.numa);
@ -160,7 +160,7 @@ int main(int argc, char ** argv) {
params.warmup = false;
// init
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;
@ -172,7 +172,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
}

6
examples/export-lora/export-lora.cpp
View file

@ -128,7 +128,7 @@ struct lora_merge_ctx {
lora_merge_ctx(
std::string & base_fname,
std::vector<llama_lora_adapter_info> & lora_files,
std::vector<common_lora_adapter_info> & lora_files,
std::string & outfile,
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
@ -400,9 +400,9 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
return 1;
}

14
examples/gen-docs/gen-docs.cpp
View file

@ -11,7 +11,7 @@ static void write_table_header(std::ofstream & file) {
file << "| -------- | ----------- |\n";
}
static void write_table_entry(std::ofstream & file, const llama_arg & opt) {
static void write_table_entry(std::ofstream & file, const common_arg & opt) {
file << "| `";
// args
for (const auto & arg : opt.args) {
@ -40,7 +40,7 @@ static void write_table_entry(std::ofstream & file, const llama_arg & opt) {
file << "` | " << md_help << " |\n";
}
static void write_table(std::ofstream & file, std::vector<llama_arg *> & opts) {
static void write_table(std::ofstream & file, std::vector<common_arg *> & opts) {
write_table_header(file);
for (const auto & opt : opts) {
write_table_entry(file, *opt);
@ -50,12 +50,12 @@ static void write_table(std::ofstream & file, std::vector<llama_arg *> & opts) {
static void export_md(std::string fname, llama_example ex) {
std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
gpt_params params;
auto ctx_arg = gpt_params_parser_init(params, ex);
common_params params;
auto ctx_arg = common_params_parser_init(params, ex);
std::vector<llama_arg *> common_options;
std::vector<llama_arg *> sparam_options;
std::vector<llama_arg *> specific_options;
std::vector<common_arg *> common_options;
std::vector<common_arg *> sparam_options;
std::vector<common_arg *> specific_options;
for (auto & opt : ctx_arg.options) {
// in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
if (opt.is_sparam) {

36
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);
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];
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();
@ -28,7 +28,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
// inputs.push_back(llama_token_eos(model));
// 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
// 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)
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)
@ -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());
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);
#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);
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;
while (true) {
llama_batch_clear(bat);
common_batch_clear(bat);
{
const int32_t n_inputs = inputs.size();
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();
@ -127,7 +127,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
break;
}
std::string piece = llama_token_to_piece(ctx, token);
std::string piece = common_token_to_piece(ctx, token);
if (stream) {
std::printf("%s", piece.c_str());
std::fflush(stdout);
@ -152,16 +152,16 @@ static std::string gritlm_instruction(const std::string & instruction) {
}
int main(int argc, char * argv[]) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
gpt_init();
common_init();
llama_model_params mparams = llama_model_params_from_gpt_params(params);
llama_context_params cparams = llama_context_params_from_gpt_params(params);
llama_model_params mparams = common_model_params_to_llama(params);
llama_context_params cparams = common_context_params_to_llama(params);
llama_backend_init();
@ -199,10 +199,10 @@ int main(int argc, char * argv[]) {
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_d1 = llama_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_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].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 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].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 = 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[1].c_str(), cosine_sim_q0_d1);

18
examples/imatrix/imatrix.cpp
View file

@ -37,13 +37,13 @@ struct Stats {
class IMatrixCollector {
public:
IMatrixCollector() = default;
void set_params(gpt_params params) { m_params = std::move(params); }
void set_params(common_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix(int ncall = -1) const;
bool load_imatrix(const char * file_name);
private:
std::unordered_map<std::string, Stats> m_stats;
gpt_params m_params;
common_params m_params;
std::mutex m_mutex;
int m_last_call = 0;
std::vector<float> m_src1_data;
@ -428,7 +428,7 @@ static void process_logits(
}
}
static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
static bool compute_imatrix(llama_context * ctx, const common_params & params) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
const int n_ctx = llama_n_ctx(ctx);
@ -436,7 +436,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
auto tim1 = std::chrono::high_resolution_clock::now();
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();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -568,17 +568,17 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_ctx = 512;
params.logits_all = true;
params.escape = false;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
return 1;
}
gpt_init();
common_init();
params.n_batch = std::min(params.n_batch, params.n_ctx);
@ -607,7 +607,7 @@ int main(int argc, char ** argv) {
params.warmup = false;
// init
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;
@ -625,7 +625,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
if (!compute_imatrix(ctx, params)) {

66
examples/infill/infill.cpp
View file

@ -35,8 +35,8 @@
static llama_context ** g_ctx;
static llama_model ** g_model;
static gpt_sampler ** g_smpl;
static gpt_params * g_params;
static common_sampler ** g_smpl;
static common_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
@ -44,7 +44,7 @@ static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false;
static void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const llama_context * ctx, const common_params & params, const llama_model * model,
const std::vector<llama_token> & input_tokens, const std::string & output,
const std::vector<llama_token> & output_tokens
) {
@ -95,12 +95,12 @@ static void sigint_handler(int signo) {
} else {
console::cleanup();
LOG("\n");
gpt_perf_print(*g_ctx, *g_smpl);
common_perf_print(*g_ctx, *g_smpl);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
// make sure all logs are flushed
LOG("Interrupted by user\n");
gpt_log_pause(gpt_log_main());
common_log_pause(common_log_main());
_exit(130);
}
@ -109,14 +109,14 @@ static void sigint_handler(int signo) {
#endif
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
g_params = &params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
return 1;
}
gpt_init();
common_init();
auto & sparams = params.sparams;
@ -166,7 +166,7 @@ int main(int argc, char ** argv) {
llama_model * model = nullptr;
llama_context * ctx = nullptr;
gpt_sampler * smpl = nullptr;
common_sampler * smpl = nullptr;
g_model = &model;
g_ctx = &ctx;
@ -174,7 +174,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
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 = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
@ -195,15 +195,15 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
const bool add_bos = llama_add_bos_token(model);
GGML_ASSERT(!llama_add_eos_token(model));
std::vector<llama_token> embd_inp;
std::vector<llama_token> embd_end;
std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, 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_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: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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) {
LOG_INF("%s: static prompt based on n_keep: '", __func__);
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");
}
@ -298,11 +298,11 @@ int main(int argc, char ** argv) {
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
}
}
smpl = gpt_sampler_init(model, sparams);
smpl = common_sampler_init(model, sparams);
LOG_INF("sampler seed: %u\n", gpt_sampler_get_seed(smpl));
LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl));
LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
LOG_INF("sampler chain: %s\n", gpt_sampler_print(smpl).c_str());
LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str());
LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
@ -411,9 +411,9 @@ int main(int argc, char ** argv) {
embd.clear();
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
const llama_token id = gpt_sampler_sample(smpl, ctx, -1);
const llama_token id = common_sampler_sample(smpl, ctx, -1);
gpt_sampler_accept(smpl, id, true);
common_sampler_accept(smpl, id, true);
// LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
@ -434,7 +434,7 @@ int main(int argc, char ** argv) {
// push the prompt in the sampling context in order to apply repetition penalties later
// for the prompt, we don't apply grammar rules
gpt_sampler_accept(smpl, embd_inp[n_consumed], false);
common_sampler_accept(smpl, embd_inp[n_consumed], false);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
@ -446,7 +446,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo) {
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());
if (embd.size() > 1) {
@ -465,10 +465,10 @@ int main(int argc, char ** argv) {
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// deal with eot token in infill mode
if ((gpt_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
if ((common_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
if (is_interacting && !params.interactive_first) {
// 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");
console::set_display(console::user_input);
@ -505,8 +505,8 @@ int main(int argc, char ** argv) {
}
// tokenize new prefix and suffix
std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, 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_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
@ -529,7 +529,7 @@ int main(int argc, char ** argv) {
is_interacting = false;
}
// deal with end of generation tokens in interactive mode
else if (llama_token_is_eog(model, gpt_sampler_last(smpl))) {
else if (llama_token_is_eog(model, common_sampler_last(smpl))) {
LOG_DBG("found EOS token\n");
if (params.interactive) {
@ -579,7 +579,7 @@ int main(int argc, char ** argv) {
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());
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) {
const llama_token token = embd_inp[i];
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();
@ -601,7 +601,7 @@ int main(int argc, char ** argv) {
if (n_past > 0) {
if (is_interacting) {
gpt_sampler_reset(smpl);
common_sampler_reset(smpl);
}
is_interacting = false;
}
@ -620,17 +620,17 @@ int main(int argc, char ** argv) {
}
}
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");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
llama_free(ctx);
llama_free_model(model);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_backend_free();
return 0;

22
examples/llama.android/llama/src/main/cpp/llama-android.cpp
View file

@ -186,11 +186,11 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
for (nri = 0; nri < nr; nri++) {
LOGi("Benchmark prompt processing (pp)");
llama_batch_clear(*batch);
common_batch_clear(*batch);
const int n_tokens = pp;
for (i = 0; i < n_tokens; i++) {
llama_batch_add(*batch, 0, i, { 0 }, false);
common_batch_add(*batch, 0, i, { 0 }, false);
}
batch->logits[batch->n_tokens - 1] = true;
@ -210,9 +210,9 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
const auto t_tg_start = ggml_time_us();
for (i = 0; i < tg; i++) {
llama_batch_clear(*batch);
common_batch_clear(*batch);
for (j = 0; j < pl; j++) {
llama_batch_add(*batch, 0, i, { j }, true);
common_batch_add(*batch, 0, i, { j }, true);
}
LOGi("llama_decode() text generation: %d", i);
@ -357,7 +357,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
const auto tokens_list = llama_tokenize(context, text, 1);
const auto tokens_list = common_tokenize(context, text, 1);
auto n_ctx = llama_n_ctx(context);
auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
@ -369,14 +369,14 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
}
for (auto id : tokens_list) {
LOGi("%s", llama_token_to_piece(context, id).c_str());
LOGi("%s", common_token_to_piece(context, id).c_str());
}
llama_batch_clear(*batch);
common_batch_clear(*batch);
// evaluate the initial prompt
for (auto 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
@ -419,7 +419,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
return nullptr;
}
auto new_token_chars = llama_token_to_piece(context, new_token_id);
auto new_token_chars = common_token_to_piece(context, new_token_id);
cached_token_chars += new_token_chars;
jstring new_token = nullptr;
@ -431,8 +431,8 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
new_token = env->NewStringUTF("");
}
llama_batch_clear(*batch);
llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
common_batch_clear(*batch);
common_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
env->CallVoidMethod(intvar_ncur, la_int_var_inc);

44
examples/llava/llava-cli.cpp
View file

@ -37,21 +37,21 @@ 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){
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);
return true;
}
static const char * sample(struct gpt_sampler * smpl,
static const char * sample(struct common_sampler * smpl,
struct llama_context * ctx_llama,
int * n_past) {
const llama_token id = gpt_sampler_sample(smpl, ctx_llama, -1);
gpt_sampler_accept(smpl, id, true);
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>";
} else {
ret = llama_token_to_piece(ctx_llama, id);
ret = common_token_to_piece(ctx_llama, id);
}
eval_id(ctx_llama, id, n_past);
return ret.c_str();
@ -120,7 +120,7 @@ static void print_usage(int, char ** argv) {
LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
// load and preprocess the image
llava_image_embed * embed = NULL;
@ -146,7 +146,7 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
return embed;
}
static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) {
static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
int n_past = 0;
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
@ -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());
LOG_INF("system_prompt: %s\n", system_prompt.c_str());
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++) {
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());
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++) {
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 {
@ -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:";
user_prompt = prompt + "\nASSISTANT:";
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++) {
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());
}
}
}
@ -191,7 +191,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
LOG("\n");
struct gpt_sampler * smpl = gpt_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
exit(1);
@ -211,15 +211,15 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
fflush(stdout);
}
gpt_sampler_free(smpl);
common_sampler_free(smpl);
LOG("\n");
}
static struct llama_model * llava_init(gpt_params * params) {
static struct llama_model * llava_init(common_params * params) {
llama_backend_init();
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_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
if (model == NULL) {
@ -229,7 +229,7 @@ static struct llama_model * llava_init(gpt_params * params) {
return model;
}
static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
const char * clip_path = params->mmproj.c_str();
auto prompt = params->prompt;
@ -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);
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
llama_context_params ctx_params = common_context_params_to_llama(*params);
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);
@ -272,13 +272,13 @@ static void llava_free(struct llava_context * ctx_llava) {
int main(int argc, char ** argv) {
ggml_time_init();
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
return 1;
}
gpt_init();
common_init();
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
print_usage(argc, argv);

40
examples/llava/minicpmv-cli.cpp
View file

@ -25,11 +25,11 @@ static void show_additional_info(int /*argc*/, char ** argv) {
LOG("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llama_model * llava_init(gpt_params * params) {
static struct llama_model * llava_init(common_params * params) {
llama_backend_init();
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_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
if (model == NULL) {
@ -39,13 +39,13 @@ static struct llama_model * llava_init(gpt_params * params) {
return model;
}
static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
auto prompt = params->prompt;
if (prompt.empty()) {
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_to_llama(*params);
if (params->n_ctx < 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__);
@ -79,7 +79,7 @@ static void llava_free(struct llava_context * ctx_llava) {
llama_backend_free();
}
static struct clip_ctx * clip_init_context(gpt_params * params) {
static struct clip_ctx * clip_init_context(common_params * params) {
const char * clip_path = params->mmproj.c_str();
auto prompt = params->prompt;
@ -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){
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);
}
@ -129,7 +129,7 @@ static void process_eval_image_embed(struct llava_context * ctx_llava, const str
llava_image_embed_free(slice_embed);
}
static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, gpt_params * params, int &n_past) {
static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, common_params * params, int &n_past) {
std::string system_prompt;
int idx = 0;
int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip);
@ -162,22 +162,22 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e
LOG_INF("%s: image token past: %d\n", __func__, n_past);
}
static const char * sample(struct gpt_sampler * smpl,
static const char * sample(struct common_sampler * smpl,
struct llama_context * ctx_llama,
int * n_past) {
const llama_token id = gpt_sampler_sample(smpl, ctx_llama, -1);
gpt_sampler_accept(smpl, id, true);
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>";
} else {
ret = llama_token_to_piece(ctx_llama, id);
ret = common_token_to_piece(ctx_llama, id);
}
eval_id(ctx_llama, id, n_past);
return ret.c_str();
}
static struct llava_context * minicpmv_init(gpt_params * params, const std::string & fname, int &n_past){
static struct llava_context * minicpmv_init(common_params * params, const std::string & fname, int &n_past){
auto * ctx_clip = clip_init_context(params);
auto * embeds = llava_image_embed_make_with_filename(ctx_clip, params->cpuparams.n_threads, fname.c_str());
if (!embeds) {
@ -213,7 +213,7 @@ static struct llava_context * minicpmv_init(gpt_params * params, const std::stri
return ctx_llava;
}
static struct gpt_sampler * llama_init(struct llava_context * ctx_llava, gpt_params * params, const std::string & prompt, int & n_past, bool is_first = false){
static struct common_sampler * llama_init(struct llava_context * ctx_llava, common_params * params, const std::string & prompt, int & n_past, bool is_first = false){
std::string user_prompt = prompt;
int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip);
if (!is_first) {
@ -237,11 +237,11 @@ static struct gpt_sampler * llama_init(struct llava_context * ctx_llava, gpt_par
LOG_INF("\n");
struct gpt_sampler * smpl = gpt_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
return smpl;
}
static const char * llama_loop(struct llava_context * ctx_llava,struct gpt_sampler * smpl, int &n_past){
static const char * llama_loop(struct llava_context * ctx_llava,struct common_sampler * smpl, int &n_past){
const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past);
return tmp;
@ -250,13 +250,13 @@ static const char * llama_loop(struct llava_context * ctx_llava,struct gpt_sampl
int main(int argc, char ** argv) {
ggml_time_init();
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
return 1;
}
gpt_init();
common_init();
if (params.mmproj.empty() || (params.image.empty())) {
show_additional_info(argc, argv);
@ -290,7 +290,7 @@ int main(int argc, char ** argv) {
fflush(stdout);
}
gpt_sampler_free(smpl);
common_sampler_free(smpl);
}else {
while (true) {
LOG("<user>");
@ -309,7 +309,7 @@ int main(int argc, char ** argv) {
if (strstr(response.c_str(), "<user>")) break; // minicpm-v
fflush(stdout);
}
gpt_sampler_free(smpl);
common_sampler_free(smpl);
}
}
printf("\n");

48
examples/lookahead/lookahead.cpp
View file

@ -37,13 +37,13 @@ struct ngram_container {
};
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
gpt_init();
common_init();
const int W = 15; // lookahead window
const int N = 5; // n-gram size
@ -56,7 +56,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the target model
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;
@ -65,7 +65,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp;
std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx, params.prompt, true, true);
all = inp;
const int max_context_size = llama_n_ctx(ctx);
@ -79,7 +79,7 @@ int main(int argc, char ** argv) {
LOG("\n\n");
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);
@ -115,7 +115,7 @@ int main(int argc, char ** argv) {
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct gpt_sampler * smpl = gpt_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);
@ -156,12 +156,12 @@ int main(int argc, char ** argv) {
// sample first token
{
id = gpt_sampler_sample(smpl, ctx, 0);
id = common_sampler_sample(smpl, ctx, 0);
gpt_sampler_accept(smpl, id, true);
common_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());
fflush(stdout);
@ -172,7 +172,7 @@ int main(int argc, char ** argv) {
// debug
if (dump_kv_cache) {
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/
@ -201,10 +201,10 @@ int main(int argc, char ** argv) {
// V V V V V V
// id
{
llama_batch_clear(batch);
common_batch_clear(batch);
// 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
{
@ -229,7 +229,7 @@ int main(int argc, char ** argv) {
ngrams_cur[g].tokens [j + 1] = t;
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;
}
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
for (int j = 1; j < N - 1; j++) {
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);
}
}
}
@ -281,13 +281,13 @@ int main(int argc, char ** argv) {
}
// sample the next token
id = gpt_sampler_sample(smpl, ctx, i_batch);
id = common_sampler_sample(smpl, ctx, i_batch);
gpt_sampler_accept(smpl, id, true);
common_sampler_accept(smpl, id, true);
// 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) {
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)
if (0 && v == 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++) {
@ -336,7 +336,7 @@ int main(int argc, char ** argv) {
const int idx = id*(N - 1)*G + i*(N - 1);
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());
}
@ -358,7 +358,7 @@ int main(int argc, char ** argv) {
if (v == 0) {
// sample from the last level
for (int i = 0; i < W; i++) {
tokens_j[N - 2][i] = gpt_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
tokens_j[N - 2][i] = common_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
}
} else {
for (int i = 0; i < W; i++) {
@ -466,9 +466,9 @@ int main(int argc, char ** argv) {
LOG_INF("n_accept = %d\n", n_accept);
LOG_INF("\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_kv_cache_view_free(&kvc_view);

14
examples/lookup/lookup-create.cpp
View file

@ -12,9 +12,9 @@
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
@ -23,7 +23,7 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa);
// load the model
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;
@ -31,15 +31,15 @@ int main(int argc, char ** argv){
// tokenize the prompt
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__);
llama_ngram_cache ngram_cache;
llama_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
common_ngram_cache ngram_cache;
common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
common_ngram_cache_save(ngram_cache, params.lookup_cache_static);
return 0;
}

8
examples/lookup/lookup-merge.cpp
View file

@ -33,15 +33,15 @@ int main(int argc, char ** argv){
}
fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str());
llama_ngram_cache ngram_cache_merged = llama_ngram_cache_load(args[0]);
common_ngram_cache ngram_cache_merged = common_ngram_cache_load(args[0]);
for (size_t i = 1; i < args.size()-1; ++i) {
fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str());
llama_ngram_cache ngram_cache = llama_ngram_cache_load(args[i]);
common_ngram_cache ngram_cache = common_ngram_cache_load(args[i]);
llama_ngram_cache_merge(ngram_cache_merged, ngram_cache);
common_ngram_cache_merge(ngram_cache_merged, ngram_cache);
}
fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str());
llama_ngram_cache_save(ngram_cache_merged, args.back());
common_ngram_cache_save(ngram_cache_merged, args.back());
}

28
examples/lookup/lookup-stats.cpp
View file

@ -13,13 +13,13 @@
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
gpt_init();
common_init();
const int n_draft = params.n_draft;
@ -28,18 +28,18 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa);
// load the model
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;
// tokenize the prompt
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_dynamic;
llama_ngram_cache ngram_cache_static;
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
@ -48,7 +48,7 @@ int main(int argc, char ** argv){
if (!params.lookup_cache_static.empty()) {
try {
ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static);
} catch (std::ifstream::failure const &) {
LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
exit(1);
@ -57,7 +57,7 @@ int main(int argc, char ** argv){
if (!params.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
@ -86,7 +86,7 @@ int main(int argc, char ** argv){
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
common_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
@ -105,7 +105,7 @@ int main(int argc, char ** argv){
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
}
@ -115,7 +115,7 @@ int main(int argc, char ** argv){
pseudo_output.push_back(inp_slice[pseudo_output.size()]);
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
}
@ -133,7 +133,7 @@ int main(int argc, char ** argv){
}
// After each chunk, update the dynamic ngram cache with the context ngram cache:
llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
ngram_cache_context.clear();
}

54
examples/lookup/lookup.cpp
View file

@ -13,13 +13,13 @@
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
gpt_init();
common_init();
// max. number of additional tokens to draft if match is found
const int n_draft = params.n_draft;
@ -31,29 +31,29 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa);
// load the model
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;
// tokenize the prompt
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_dynamic;
llama_ngram_cache ngram_cache_static;
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
{
// Fill up context ngram cache with tokens from user input:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
if (!params.lookup_cache_static.empty()) {
try {
ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static);
} catch (std::ifstream::failure const &) {
LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
exit(1);
@ -62,7 +62,7 @@ int main(int argc, char ** argv){
if (!params.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
@ -80,7 +80,7 @@ int main(int argc, char ** argv){
LOG("\n\n");
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);
@ -102,7 +102,7 @@ int main(int argc, char ** argv){
bool has_eos = false;
struct gpt_sampler * smpl = gpt_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
std::vector<llama_token> draft;
@ -117,7 +117,7 @@ int main(int argc, char ** argv){
// debug
if (dump_kv_cache) {
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
@ -126,11 +126,11 @@ int main(int argc, char ** argv){
int i_dft = 0;
while (true) {
// sample from the target model
llama_token id = gpt_sampler_sample(smpl, ctx, i_dft);
llama_token id = common_sampler_sample(smpl, ctx, i_dft);
gpt_sampler_accept(smpl, id, true);
common_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) {
LOG("%s", token_str.c_str());
@ -152,7 +152,7 @@ int main(int argc, char ** argv){
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
@ -178,7 +178,7 @@ int main(int argc, char ** argv){
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
break;
@ -192,18 +192,18 @@ int main(int argc, char ** argv){
// clean the cache of draft tokens that weren't accepted
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
llama_batch_clear(batch_tgt);
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
common_batch_clear(batch_tgt);
common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// Draft already contains a single token sampled from the model:
GGML_ASSERT(draft.size() == 1);
GGML_ASSERT(draft[0] == inp.back());
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
common_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) {
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;
@ -218,8 +218,8 @@ int main(int argc, char ** argv){
auto t_dec_end = ggml_time_us();
// Update dynamic ngram cache with context ngram cache and save it to disk:
llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
common_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
LOG("\n\n");
@ -237,9 +237,9 @@ int main(int argc, char ** argv){
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_INF("\ntarget:\n\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_batch_free(batch_tgt);

92
examples/main/main.cpp
View file

@ -33,8 +33,8 @@
static llama_context ** g_ctx;
static llama_model ** g_model;
static gpt_sampler ** g_smpl;
static gpt_params * g_params;
static common_sampler ** g_smpl;
static common_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
@ -63,7 +63,7 @@ static bool file_is_empty(const std::string & path) {
}
static void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const llama_context * ctx, const common_params & params, const llama_model * model,
const std::vector<llama_token> & input_tokens, const std::string & output,
const std::vector<llama_token> & output_tokens
) {
@ -114,12 +114,12 @@ static void sigint_handler(int signo) {
} else {
console::cleanup();
LOG("\n");
gpt_perf_print(*g_ctx, *g_smpl);
common_perf_print(*g_ctx, *g_smpl);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
// make sure all logs are flushed
LOG("Interrupted by user\n");
gpt_log_pause(gpt_log_main());
common_log_pause(common_log_main());
_exit(130);
}
@ -127,22 +127,22 @@ static void sigint_handler(int signo) {
}
#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) {
llama_chat_msg new_msg{role, content};
auto formatted = llama_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
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) {
common_chat_msg new_msg{role, content};
auto formatted = common_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
chat_msgs.push_back({role, content});
LOG_DBG("formatted: '%s'\n", formatted.c_str());
return formatted;
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
g_params = &params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
return 1;
}
gpt_init();
common_init();
auto & sparams = params.sparams;
@ -187,9 +187,9 @@ int main(int argc, char ** argv) {
llama_model * model = nullptr;
llama_context * ctx = nullptr;
gpt_sampler * smpl = nullptr;
common_sampler * smpl = nullptr;
std::vector<llama_chat_msg> chat_msgs;
std::vector<common_chat_msg> chat_msgs;
g_model = &model;
g_ctx = &ctx;
@ -197,7 +197,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
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 = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
@ -246,7 +246,7 @@ int main(int argc, char ** argv) {
// print chat template example in conversation mode
if (params.conversation) {
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 {
LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
}
@ -255,7 +255,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
}
@ -296,7 +296,7 @@ int main(int argc, char ** argv) {
: params.prompt;
if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
LOG_DBG("tokenize the prompt\n");
embd_inp = ::llama_tokenize(ctx, prompt, true, true);
embd_inp = common_tokenize(ctx, prompt, true, true);
} else {
LOG_DBG("use session tokens\n");
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: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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) {
LOG_INF("%s: static prompt based on n_keep: '", __func__);
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");
}
@ -415,9 +415,9 @@ int main(int argc, char ** argv) {
for (const auto & antiprompt : params.antiprompt) {
LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str());
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++) {
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()) {
LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
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++) {
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());
}
}
}
@ -440,23 +440,23 @@ int main(int argc, char ** argv) {
if (!params.input_suffix.empty()) {
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
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++) {
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());
}
}
}
}
smpl = gpt_sampler_init(model, sparams);
smpl = common_sampler_init(model, sparams);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
return 1;
}
LOG_INF("sampler seed: %u\n", gpt_sampler_get_seed(smpl));
LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl));
LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
LOG_INF("sampler chain: %s\n", gpt_sampler_print(smpl).c_str());
LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str());
LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
@ -521,7 +521,7 @@ int main(int argc, char ** argv) {
antiprompt_ids.reserve(params.antiprompt.size());
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)) {
@ -679,9 +679,9 @@ int main(int argc, char ** argv) {
LOG_DBG("saved session to %s\n", path_session.c_str());
}
const llama_token id = gpt_sampler_sample(smpl, ctx, -1);
const llama_token id = common_sampler_sample(smpl, ctx, -1);
gpt_sampler_accept(smpl, id, /* accept_grammar= */ true);
common_sampler_accept(smpl, id, /* accept_grammar= */ true);
// LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
@ -702,7 +702,7 @@ int main(int argc, char ** argv) {
// push the prompt in the sampling context in order to apply repetition penalties later
// for the prompt, we don't apply grammar rules
gpt_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
common_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
@ -714,7 +714,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo && display) {
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
LOG("%s", token_str.c_str());
@ -743,7 +743,7 @@ int main(int argc, char ** argv) {
// check for reverse prompt in the last n_prev tokens
if (!params.antiprompt.empty()) {
const int n_prev = 32;
const std::string last_output = gpt_sampler_prev_str(smpl, ctx, n_prev);
const std::string last_output = common_sampler_prev_str(smpl, ctx, n_prev);
is_antiprompt = false;
// Check if each of the reverse prompts appears at the end of the output.
@ -765,7 +765,7 @@ int main(int argc, char ** argv) {
}
// check for reverse prompt using special tokens
llama_token last_token = gpt_sampler_last(smpl);
llama_token last_token = common_sampler_last(smpl);
for (std::vector<llama_token> ids : antiprompt_ids) {
if (ids.size() == 1 && last_token == ids[0]) {
if (params.interactive) {
@ -782,13 +782,13 @@ int main(int argc, char ** argv) {
}
// deal with end of generation tokens in interactive mode
if (llama_token_is_eog(model, gpt_sampler_last(smpl))) {
if (llama_token_is_eog(model, common_sampler_last(smpl))) {
LOG_DBG("found an EOG token\n");
if (params.interactive) {
if (!params.antiprompt.empty()) {
// 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());
is_antiprompt = true;
}
@ -803,8 +803,8 @@ int main(int argc, char ** argv) {
// if current token is not EOG, we add it to current assistant message
if (params.conversation) {
const auto id = gpt_sampler_last(smpl);
assistant_ss << llama_token_to_piece(ctx, id, false);
const auto id = common_sampler_last(smpl);
assistant_ss << common_token_to_piece(ctx, id, false);
}
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))
: 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)
const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = ::llama_tokenize(ctx, user_inp, false, format_chat);
const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = common_tokenize(ctx, user_inp, false, format_chat);
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());
@ -882,7 +882,7 @@ int main(int argc, char ** argv) {
for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i];
output_tokens.push_back(token);
output_ss << llama_token_to_piece(ctx, token);
output_ss << common_token_to_piece(ctx, token);
}
// reset assistant message
@ -899,7 +899,7 @@ int main(int argc, char ** argv) {
if (n_past > 0) {
if (is_interacting) {
gpt_sampler_reset(smpl);
common_sampler_reset(smpl);
}
is_interacting = false;
}
@ -925,10 +925,10 @@ int main(int argc, char ** argv) {
}
LOG("\n\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_free(ctx);
llama_free_model(model);

36
examples/parallel/parallel.cpp
View file

@ -54,7 +54,7 @@ static std::vector<std::string> k_prompts = {
struct client {
~client() {
if (smpl) {
gpt_sampler_free(smpl);
common_sampler_free(smpl);
}
}
@ -75,7 +75,7 @@ struct client {
std::string prompt;
std::string response;
struct gpt_sampler * smpl = nullptr;
struct common_sampler * smpl = nullptr;
};
static void print_date_time() {
@ -103,13 +103,13 @@ static std::vector<std::string> split_string(const std::string& input, char deli
int main(int argc, char ** argv) {
srand(1234);
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
return 1;
}
gpt_init();
common_init();
// number of simultaneous "clients" to simulate
const int32_t n_clients = params.n_parallel;
@ -130,7 +130,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the target model
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;
@ -160,11 +160,11 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < clients.size(); ++i) {
auto & client = clients[i];
client.id = i;
client.smpl = gpt_sampler_init(model, params.sparams);
client.smpl = common_sampler_init(model, params.sparams);
}
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();
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__);
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) {
@ -210,10 +210,10 @@ int main(int argc, char ** argv) {
while (true) {
if (dump_kv_cache) {
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
for (auto & client : clients) {
@ -223,7 +223,7 @@ int main(int argc, char ** argv) {
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;
}
@ -252,14 +252,14 @@ int main(int argc, char ** argv) {
client.prompt = client.input + "\nAssistant:";
client.response = "";
gpt_sampler_reset(client.smpl);
common_sampler_reset(client.smpl);
// do not prepend BOS because we have a system 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) {
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
@ -340,9 +340,9 @@ int main(int argc, char ** argv) {
//printf("client %d, seq %d, token %d, pos %d, batch %d\n",
// client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
const llama_token id = gpt_sampler_sample(client.smpl, ctx, client.i_batch - i);
const llama_token id = common_sampler_sample(client.smpl, ctx, client.i_batch - i);
gpt_sampler_accept(client.smpl, id, true);
common_sampler_accept(client.smpl, id, true);
if (client.n_decoded == 1) {
// start measuring generation time after the first token to make sure all concurrent clients
@ -350,7 +350,7 @@ int main(int argc, char ** argv) {
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.sampled = id;

28
examples/passkey/passkey.cpp
View file

@ -15,17 +15,17 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_junk = 250;
params.n_keep = 32;
params.i_pos = -1;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
return 1;
}
gpt_init();
common_init();
int n_junk = params.n_junk;
int n_keep = params.n_keep;
@ -61,7 +61,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = common_model_params_to_llama(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
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = common_context_params_to_llama(params);
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
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
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();
@ -137,10 +137,10 @@ int main(int argc, char ** argv) {
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++) {
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) {
@ -171,10 +171,10 @@ int main(int argc, char ** argv) {
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++) {
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) {
@ -229,15 +229,15 @@ int main(int argc, char ** argv) {
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;
// prepare the next batch
llama_batch_clear(batch);
common_batch_clear(batch);
// 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;

58
examples/perplexity/perplexity.cpp
View file

@ -35,7 +35,7 @@ struct results_log_softmax {
};
static void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const llama_context * ctx, const common_params & params, const llama_model * model,
const struct results_perplexity & results
) {
if (params.logdir.empty()) {
@ -339,7 +339,7 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
}
}
static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
static results_perplexity perplexity_v2(llama_context * ctx, const common_params & params) {
// Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
@ -350,7 +350,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
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);
@ -474,7 +474,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
return {tokens, std::exp(nll / count), logit_history, prob_history};
}
static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
static results_perplexity perplexity(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
if (params.ppl_stride > 0) {
return perplexity_v2(ctx, params);
}
@ -502,7 +502,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
auto tim1 = std::chrono::high_resolution_clock::now();
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();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -772,7 +772,7 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
}
}
static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
static void hellaswag_score(llama_context * ctx, const common_params & params) {
// Calculates hellaswag score (acc_norm) from prompt
//
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
@ -853,7 +853,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j = 0; j < 4; 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
@ -910,7 +910,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0;
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
// each task has 4 unique sequence ids - one for each ending
@ -926,7 +926,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}
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
n_logits += 1;
@ -936,7 +936,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// TODO: don't evaluate the last token of each sequence
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
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;
}
}
@ -1112,7 +1112,7 @@ static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string
* 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2
*
*/
static void winogrande_score(llama_context * ctx, const gpt_params & params) {
static void winogrande_score(llama_context * ctx, const common_params & params) {
constexpr int k_min_trailing_ctx = 3;
@ -1146,8 +1146,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
LOG_INF("%s : tokenizing selected tasks\n", __func__);
for (auto & task : data) {
task.seq_tokens[0] = ::llama_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[0] = common_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = common_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.common_prefix = 0;
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
@ -1162,8 +1162,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.seq_tokens[0].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_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size();
task.n_base1 = common_tokenize(ctx, task.first + task.choices[0], 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__);
@ -1195,7 +1195,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0;
size_t i_logits = 0;
llama_batch_clear(batch);
common_batch_clear(batch);
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
int n_logits = 0;
@ -1205,7 +1205,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
}
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;
n_logits += 1;
@ -1213,7 +1213,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
for (int s = 0; s < 2; ++s) {
// 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) {
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;
}
}
@ -1370,7 +1370,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
}
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();
for (auto& seq : task.seq_tokens) {
@ -1414,7 +1414,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
// git@hf.co:datasets/Stevross/mmlu
// https://huggingface.co/datasets/truthful_qa
//
static void multiple_choice_score(llama_context * ctx, const gpt_params & params) {
static void multiple_choice_score(llama_context * ctx, const common_params & params) {
std::istringstream strstream(params.prompt);
uint32_t n_task;
@ -1548,7 +1548,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
size_t i1 = i0;
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
// each task has 4 unique sequence ids - one for each ending
@ -1571,7 +1571,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
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, 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
n_logits += 1;
@ -1581,7 +1581,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
// TODO: don't evaluate the last token of each sequence
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
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;
}
}
@ -1695,7 +1695,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
LOG_INF("\n");
}
static void kl_divergence(llama_context * ctx, const gpt_params & params) {
static void kl_divergence(llama_context * ctx, const common_params & params) {
if (params.logits_file.empty()) {
LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
return;
@ -1968,17 +1968,17 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_ctx = 512;
params.logits_all = true;
params.escape = false;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
return 1;
}
gpt_init();
common_init();
const int32_t n_ctx = params.n_ctx;
@ -2017,7 +2017,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// 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 = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
@ -2036,7 +2036,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
struct results_perplexity results;

26
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) {
size_t n_tokens = tokens.size();
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);
}
}
@ -107,18 +107,18 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
float * out = output + batch.seq_id[i][0] * n_embd;
llama_embd_normalize(embd, out, n_embd);
common_embd_normalize(embd, out, n_embd);
}
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
return 1;
}
gpt_init();
common_init();
// For BERT models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
@ -149,7 +149,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model
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;
@ -176,7 +176,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
// max batch size
@ -185,7 +185,7 @@ int main(int argc, char ** argv) {
// tokenize the prompts and trim
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) {
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);
@ -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: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
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");
}
@ -232,7 +232,7 @@ int main(int argc, char ** argv) {
if (batch.n_tokens + n_toks > n_batch) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
llama_batch_clear(batch);
common_batch_clear(batch);
p += s;
s = 0;
}
@ -260,20 +260,20 @@ int main(int argc, char ** argv) {
while (true) {
LOG("Enter 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);
std::vector<float> query_emb(n_embd, 0);
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
llama_batch_clear(query_batch);
common_batch_clear(query_batch);
// compute cosine similarities
{
std::vector<std::pair<int, float>> similarities;
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));
}

18
examples/save-load-state/save-load-state.cpp
View file

@ -6,12 +6,12 @@
#include <cstdio>
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.prompt = "The quick brown fox";
params.sparams.seed = 1234;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
@ -28,7 +28,7 @@ int main(int argc, char ** argv) {
std::string result2;
// init
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;
@ -46,7 +46,7 @@ int main(int argc, char ** argv) {
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
// tokenize prompt
auto tokens = llama_tokenize(ctx, params.prompt, true);
auto tokens = common_tokenize(ctx, params.prompt, true);
// evaluate prompt
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++) {
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());
result0 += next_token_str;
@ -92,7 +92,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
// 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_to_llama(params));
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
@ -128,7 +128,7 @@ int main(int argc, char ** argv) {
// second run
for (auto i = 0; i < params.n_predict; i++) {
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());
result1 += next_token_str;
@ -152,7 +152,7 @@ int main(int argc, char ** argv) {
}
// 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_to_llama(params));
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
for (auto i = 0; i < params.n_predict; i++) {
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());
result2 += next_token_str;

80
examples/server/server.cpp
View file

@ -188,8 +188,8 @@ struct server_slot {
// sampling
json json_schema;
struct gpt_sampler_params sparams;
struct gpt_sampler * smpl = nullptr;
struct common_sampler_params sparams;
struct common_sampler * smpl = nullptr;
llama_token sampled;
@ -231,7 +231,7 @@ struct server_slot {
generated_token_probs.clear();
}
bool has_budget(gpt_params &global_params) {
bool has_budget(common_params &global_params) {
if (params.n_predict == -1 && global_params.n_predict == -1) {
return true; // limitless
}
@ -611,9 +611,9 @@ struct server_response {
struct server_context {
llama_model * model = nullptr;
llama_context * ctx = nullptr;
std::vector<llama_lora_adapter_container> loras;
std::vector<common_lora_adapter_container> loras;
gpt_params params;
common_params params;
llama_batch batch = {};
@ -655,20 +655,20 @@ struct server_context {
// Clear any sampling context
for (server_slot & slot : slots) {
if (slot.smpl != nullptr) {
gpt_sampler_free(slot.smpl);
common_sampler_free(slot.smpl);
}
}
llama_batch_free(batch);
}
bool load_model(const gpt_params & params_) {
bool load_model(const common_params & params_) {
params = params_;
// dedicate one sequence to the system prompt
params.n_parallel += 1;
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
@ -771,10 +771,10 @@ struct server_context {
std::vector<llama_token> p;
if (first) {
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
p = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
first = false;
} 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());
@ -788,7 +788,7 @@ struct server_context {
}
} else {
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;
@ -999,7 +999,7 @@ struct server_context {
slot.sparams.logit_bias.push_back({tok, bias});
}
} 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) {
slot.sparams.logit_bias.push_back({tok, bias});
}
@ -1031,7 +1031,7 @@ struct server_context {
sampler_names.emplace_back(name);
}
}
slot.sparams.samplers = gpt_sampler_types_from_names(sampler_names, false);
slot.sparams.samplers = common_sampler_types_from_names(sampler_names, false);
} else {
slot.sparams.samplers = default_sparams.samplers;
}
@ -1039,10 +1039,10 @@ struct server_context {
{
if (slot.smpl != nullptr) {
gpt_sampler_free(slot.smpl);
common_sampler_free(slot.smpl);
}
slot.smpl = gpt_sampler_init(model, slot.sparams);
slot.smpl = common_sampler_init(model, slot.sparams);
if (slot.smpl == nullptr) {
// for now, the only error that may happen here is invalid grammar
send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
@ -1073,7 +1073,7 @@ struct server_context {
system_tokens.clear();
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_tokens_prompt = system_tokens.size();
@ -1081,10 +1081,10 @@ struct server_context {
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);
llama_batch_clear(batch);
common_batch_clear(batch);
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) {
@ -1113,7 +1113,7 @@ struct server_context {
bool process_token(completion_token_output & result, server_slot & slot) {
// 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;
// search stop word and delete it
@ -1224,7 +1224,7 @@ struct server_context {
std::vector<std::string> samplers;
samplers.reserve(slot.sparams.samplers.size());
for (const auto & sampler : slot.sparams.samplers) {
samplers.emplace_back(gpt_sampler_type_to_str(sampler));
samplers.emplace_back(common_sampler_type_to_str(sampler));
}
return json {
@ -1232,7 +1232,7 @@ struct server_context {
{"n_predict", slot.n_predict}, // Server configured n_predict
{"model", params.model_alias},
{"seed", slot.sparams.seed},
{"seed_cur", slot.smpl ? gpt_sampler_get_seed(slot.smpl) : 0},
{"seed_cur", slot.smpl ? common_sampler_get_seed(slot.smpl) : 0},
{"temperature", slot.sparams.temp},
{"dynatemp_range", slot.sparams.dynatemp_range},
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
@ -1297,7 +1297,7 @@ struct server_context {
};
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_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) {
std::vector<completion_token_output> probs;
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());
probs = std::vector<completion_token_output>(
@ -1401,7 +1401,7 @@ struct server_context {
continue;
}
llama_embd_normalize(embd, embd_res.data(), n_embd);
common_embd_normalize(embd, embd_res.data(), n_embd);
res.data = json {
{"embedding", embd_res},
@ -1835,7 +1835,7 @@ struct server_context {
} break;
case SERVER_TASK_TYPE_SET_LORA:
{
llama_lora_adapters_apply(ctx, loras);
common_lora_adapters_apply(ctx, loras);
server_task_result result;
result.id = task.id;
result.stop = true;
@ -1921,7 +1921,7 @@ struct server_context {
}
// start populating the batch for this iteration
llama_batch_clear(batch);
common_batch_clear(batch);
// frist, add sampled tokens from any ongoing sequences
for (auto & slot : slots) {
@ -1935,7 +1935,7 @@ struct server_context {
// TODO: we always have to take into account the "system_tokens"
// 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;
@ -2092,7 +2092,7 @@ struct server_context {
GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
}
gpt_sampler_reset(slot.smpl);
common_sampler_reset(slot.smpl);
if (!slot.params.cache_prompt) {
slot.n_past_se = 0;
@ -2105,7 +2105,7 @@ struct server_context {
// push the prompt into the sampling context (do not apply grammar)
for (int i = 0; i < slot.n_past; ++i) {
gpt_sampler_accept(slot.smpl, slot.cache_tokens[i], false);
common_sampler_accept(slot.smpl, slot.cache_tokens[i], false);
}
}
}
@ -2159,7 +2159,7 @@ struct server_context {
slot.n_past_se = 0;
slot.ga_i = 0;
// TODO: is the system prompt ever in the sampling context?
gpt_sampler_reset(slot.smpl);
common_sampler_reset(slot.smpl);
}
// remove the non-common part from the cache
@ -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) {
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
@ -2322,9 +2322,9 @@ struct server_context {
}
completion_token_output result;
const llama_token id = gpt_sampler_sample(slot.smpl, ctx, slot.i_batch - i);
const llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i);
gpt_sampler_accept(slot.smpl, id, true);
common_sampler_accept(slot.smpl, id, true);
slot.n_decoded += 1;
if (slot.n_decoded == 1) {
@ -2335,7 +2335,7 @@ struct server_context {
result.tok = id;
const auto * cur_p = gpt_sampler_get_candidates(slot.smpl);
const auto * cur_p = common_sampler_get_candidates(slot.smpl);
for (size_t i = 0; i < (size_t) slot.sparams.n_probs; ++i) {
result.probs.push_back({
@ -2399,13 +2399,13 @@ inline void signal_handler(int signal) {
int main(int argc, char ** argv) {
// own arguments required by this example
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
return 1;
}
gpt_init();
common_init();
// enabling this will output extra debug information in the HTTP responses from the server
// see format_final_response_oaicompat()
@ -2427,7 +2427,7 @@ int main(int argc, char ** argv) {
LOG_INF("system info: n_threads = %d, n_threads_batch = %d, total_threads = %d\n", params.cpuparams.n_threads, params.cpuparams_batch.n_threads, std::thread::hardware_concurrency());
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
std::unique_ptr<httplib::Server> svr;
@ -3014,7 +3014,7 @@ int main(int argc, char ** argv) {
if (with_pieces) {
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;
// 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
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(
&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
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) {
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});
}
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());
return formatted_chat;
@ -246,7 +246,7 @@ 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;
@ -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
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
// (size > 1 meaning it's already a known token)

80
examples/speculative/speculative.cpp
View file

@ -26,20 +26,20 @@ struct seq_draft {
std::vector<llama_token> tokens;
std::vector<std::vector<llama_token_data>> dists;
struct gpt_sampler * smpl = nullptr;
struct common_sampler * smpl = nullptr;
};
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
// needed to get candidate probs even for temp <= 0.0
params.sparams.n_probs = 128;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
return 1;
}
gpt_init();
common_init();
if (params.model_draft.empty()) {
LOG_ERR("%s: --model-draft is required\n", __func__);
@ -66,7 +66,7 @@ int main(int argc, char ** argv) {
llama_context * ctx_dft = NULL;
// load the target model
llama_init_result llama_init_tgt = llama_init_from_gpt_params(params);
common_init_result llama_init_tgt = common_init_from_params(params);
model_tgt = llama_init_tgt.model;
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;
llama_init_result llama_init_dft = llama_init_from_gpt_params(params);
common_init_result llama_init_dft = common_init_from_params(params);
model_dft = llama_init_dft.model;
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) {
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,
llama_token_to_piece(ctx_tgt, i).c_str(),
llama_token_to_piece(ctx_dft, i).c_str());
common_token_to_piece(ctx_tgt, i).c_str(),
common_token_to_piece(ctx_dft, i).c_str());
return 1;
}
}
@ -134,7 +134,7 @@ int main(int argc, char ** argv) {
// Tokenize the prompt
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_tokens_list_size = max_context_size - 4;
@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
LOG("\n\n");
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();
@ -178,7 +178,7 @@ int main(int argc, char ** argv) {
bool has_eos = false;
// target model sampling context (reuse the llama_context's sampling instance)
struct gpt_sampler * smpl = gpt_sampler_init(model_tgt, params.sparams);
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
struct llama_sampler * softmax = llama_sampler_init_softmax();
@ -186,8 +186,8 @@ int main(int argc, char ** argv) {
std::vector<seq_draft> drafts(n_seq_dft);
for (int s = 0; s < n_seq_dft; ++s) {
// allocate gpt_sampler for each draft sequence
drafts[s].smpl = gpt_sampler_init(model_dft, params.sparams);
// allocate llama_sampler for each draft sequence
drafts[s].smpl = common_sampler_init(model_dft, params.sparams);
}
llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1);
@ -229,9 +229,9 @@ int main(int argc, char ** argv) {
bool accept = false;
if (params.sparams.temp > 0) {
// stochastic verification
gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
auto & dist_tgt = *gpt_sampler_get_candidates(smpl);
auto & dist_tgt = *common_sampler_get_candidates(smpl);
float p_tgt = 0.0f;
float p_dft = 0.0f;
@ -277,13 +277,13 @@ int main(int argc, char ** argv) {
s_keep = s;
accept = true;
token_id = drafts[s].tokens[i_dft];
token_str = llama_token_to_piece(ctx_tgt, token_id);
gpt_sampler_accept(smpl, token_id, true);
token_str = common_token_to_piece(ctx_tgt, token_id);
common_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());
break;
} 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;
// calculate residual probability
@ -349,19 +349,19 @@ int main(int argc, char ** argv) {
const int idx = dist(rng);
token_id = dist_tgt.data[idx].id;
gpt_sampler_accept(smpl, token_id, true);
token_str = llama_token_to_piece(ctx_tgt, token_id);
common_sampler_accept(smpl, token_id, true);
token_str = common_token_to_piece(ctx_tgt, token_id);
}
} else {
// greedy verification
// sample from the target model
LOG_DBG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
token_id = gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
token_id = common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
gpt_sampler_accept(smpl, token_id, true);
common_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) {
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].i_batch_tgt.push_back(0);
llama_batch_clear(batch_dft);
llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
common_batch_clear(batch_dft);
common_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
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());
@ -446,9 +446,9 @@ int main(int argc, char ** argv) {
}
if (drafts[0].smpl) {
gpt_sampler_free(drafts[0].smpl);
common_sampler_free(drafts[0].smpl);
}
drafts[0].smpl = gpt_sampler_clone(smpl);
drafts[0].smpl = common_sampler_clone(smpl);
int n_seq_cur = 1;
int n_past_cur = n_past_dft;
@ -461,8 +461,8 @@ int main(int argc, char ** argv) {
drafts[0].drafting = true;
drafts[0].i_batch_dft = 0;
llama_batch_clear(batch_tgt);
llama_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
common_batch_clear(batch_tgt);
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
for (int i = 0; i < n_draft; ++i) {
@ -477,13 +477,13 @@ int main(int argc, char ** argv) {
continue;
}
gpt_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
const auto * cur_p = gpt_sampler_get_candidates(drafts[s].smpl);
const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl);
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",
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);
@ -518,9 +518,9 @@ int main(int argc, char ** argv) {
drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
if (drafts[n_seq_cur].smpl) {
gpt_sampler_free(drafts[n_seq_cur].smpl);
common_sampler_free(drafts[n_seq_cur].smpl);
}
drafts[n_seq_cur].smpl = gpt_sampler_clone(drafts[s].smpl);
drafts[n_seq_cur].smpl = common_sampler_clone(drafts[s].smpl);
sa.push_back(n_seq_cur);
@ -536,7 +536,7 @@ int main(int argc, char ** argv) {
const int s = sa[is];
gpt_sampler_accept(drafts[s].smpl, id, true);
common_sampler_accept(drafts[s].smpl, id, true);
drafts[s].tokens.push_back(id);
// save cur_p.data into drafts[s].dists
@ -545,12 +545,12 @@ int main(int argc, char ** argv) {
// add unique drafted tokens to the target batch
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
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) {
drafts[s].drafting = false;
@ -617,11 +617,11 @@ int main(int argc, char ** argv) {
LOG_INF("\n");
LOG_INF("target:\n\n");
gpt_perf_print(ctx_tgt, smpl);
common_perf_print(ctx_tgt, smpl);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
for (int s = 0; s < n_seq_dft; ++s) {
gpt_sampler_free(drafts[s].smpl);
common_sampler_free(drafts[s].smpl);
}
llama_sampler_free(softmax);

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;
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) {
printf("[");
@ -380,7 +380,7 @@ int main(int raw_argc, char ** raw_argv) {
} else {
bool invalid_utf8 = false;
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) {
printf("' (utf-8 decode failure)\n");
} else {