ikawrakow/ik_llama.cpp
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Server: refactor and rename functions (#1151)

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* Server: rename functions and refactor code

rename functions

refactor update slots

rename params_base

rename timings

* change

* Revert kv cache name changes

* Revert 2

* fix test build error

---------

Co-authored-by: firecoperana <firecoperana>
This commit is contained in:
firecoperana
2026-01-18 00:16:57 -06:00
committed by GitHub
parent 7024fdbc72
commit d71a3ec315
38 changed files with 532 additions and 528 deletions
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20
examples/parallel/parallel.cpp
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@@ -51,7 +51,7 @@ static std::vector<std::string> k_prompts = {
struct client {
~client() {
if (ctx_sampling) {
llama_sampling_free(ctx_sampling);
common_sampler_free(ctx_sampling);
}
}
@@ -161,7 +161,7 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < clients.size(); ++i) {
auto & client = clients[i];
client.id = i;
client.ctx_sampling = llama_sampling_init(llama_get_model_vocab(model), params.sparams);
client.ctx_sampling = common_sampler_init(llama_get_model_vocab(model), params.sparams);
}
std::vector<llama_token> tokens_system;
@@ -190,7 +190,7 @@ int main(int argc, char ** argv) {
LOG_TEE("%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) {
@@ -214,7 +214,7 @@ int main(int argc, char ** argv) {
llama_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) {
@@ -224,7 +224,7 @@ 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;
}
@@ -253,14 +253,14 @@ int main(int argc, char ** argv) {
client.prompt = client.input + "\nAssistant:";
client.response = "";
llama_sampling_reset(llama_get_model_vocab(model), client.ctx_sampling);
common_sampler_reset(llama_get_model_vocab(model), client.ctx_sampling);
// do not prepend BOS because we have a system prompt!
std::vector<llama_token> tokens_prompt;
tokens_prompt = ::llama_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
@@ -341,9 +341,9 @@ 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 = llama_sampling_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i);
const llama_token id = common_sampler_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i);
llama_sampling_accept(client.ctx_sampling, ctx, id, true);
common_sampler_accept(client.ctx_sampling, ctx, id, true);
if (client.n_decoded == 1) {
// start measuring generation time after the first token to make sure all concurrent clients
@@ -351,7 +351,7 @@ 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;