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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36
examples/parallel/parallel.cpp
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@ -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;