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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48
examples/lookahead/lookahead.cpp
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@ -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);