mirror of
https://github.com/PABannier/bark.cpp
synced 2026-03-11 17:41:45 +01:00
ENH Add progress callback (#35)
This commit is contained in:
PAB
GitHub
parent
a18319369e
commit
0a030189d1
93
bark.cpp
93
bark.cpp
@ -1276,6 +1276,15 @@ bark_sequence bark_forward_text_encoder(
|
||||
const float min_eos_p) {
|
||||
|
||||
bark_sequence out;
|
||||
|
||||
bark_progress progress;
|
||||
progress.func = __func__;
|
||||
|
||||
int64_t t_sample_us = 0;
|
||||
int64_t t_predict_us = 0;
|
||||
|
||||
const int64_t t_main_start_us = ggml_time_us();
|
||||
|
||||
int n_past = 0;
|
||||
float eos_p = 0;
|
||||
|
||||
@ -1290,7 +1299,10 @@ bark_sequence bark_forward_text_encoder(
|
||||
|
||||
for (int i = 0; i < 768; i++) {
|
||||
merge_ctx = i == 0;
|
||||
|
||||
int64_t t_predict_start_us = ggml_time_us();
|
||||
gpt_eval(model, n_threads, n_past, merge_ctx, input, logits, mem_per_token);
|
||||
t_predict_us += (ggml_time_us() - t_predict_start_us);
|
||||
|
||||
float logits_pad_token = logits[SEMANTIC_PAD_TOKEN];
|
||||
logits.resize(SEMANTIC_VOCAB_SIZE);
|
||||
@ -1304,7 +1316,9 @@ bark_sequence bark_forward_text_encoder(
|
||||
|
||||
input.clear();
|
||||
|
||||
int64_t t_sample_start_us = ggml_time_us();
|
||||
bark_vocab::id next = gpt_sample(logits, rng, temp, &eos_p);
|
||||
t_sample_us += (ggml_time_us() - t_sample_start_us);
|
||||
|
||||
if (early_stop && ((next == SEMANTIC_VOCAB_SIZE) || (eos_p > min_eos_p)))
|
||||
break;
|
||||
@ -1312,14 +1326,22 @@ bark_sequence bark_forward_text_encoder(
|
||||
input.push_back(next);
|
||||
out.push_back(next);
|
||||
|
||||
float sum_logits = std::accumulate(logits.begin(), logits.end(), 0.0f);
|
||||
printf("%d :: %.6f :: %.3f (n=%zu)\n", next, eos_p, sum_logits, logits.size());
|
||||
progress.callback((float) i/768);
|
||||
|
||||
// float sum_logits = std::accumulate(logits.begin(), logits.end(), 0.0f);
|
||||
// printf("%d :: %.6f :: %.3f (n=%zu)\n", next, eos_p, sum_logits, logits.size());
|
||||
|
||||
// printf("%d ", next);
|
||||
// fflush(stdout);
|
||||
}
|
||||
|
||||
printf("\n\nsemantic sequence length: %zu\n\n", out.size());
|
||||
const int64_t t_main_end_us = ggml_time_us();
|
||||
|
||||
printf("\n\n");
|
||||
printf("%s: mem per token = %8.2f MB\n", __func__, mem_per_token/1000.0f/1000.0f);
|
||||
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
|
||||
printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
|
||||
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
|
||||
|
||||
return out;
|
||||
}
|
||||
@ -1335,6 +1357,14 @@ bark_codes bark_forward_coarse_encoder(
|
||||
bark_codes out_coarse(N_COARSE_CODEBOOKS);
|
||||
bark_sequence out;
|
||||
|
||||
bark_progress progress;
|
||||
progress.func = __func__;
|
||||
|
||||
int64_t t_sample_us = 0;
|
||||
int64_t t_predict_us = 0;
|
||||
|
||||
const int64_t t_main_start_us = ggml_time_us();
|
||||
|
||||
float semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS;
|
||||
int max_semantic_history = floorf(max_coarse_history / semantic_to_coarse_ratio);
|
||||
|
||||
@ -1383,7 +1413,9 @@ bark_codes bark_forward_coarse_encoder(
|
||||
if (step_ix >= n_steps)
|
||||
continue;
|
||||
|
||||
int64_t t_predict_start_us = ggml_time_us();
|
||||
gpt_eval(model, n_threads, n_past, false, input_in, logits, mem_per_token);
|
||||
t_predict_us += (ggml_time_us() - t_predict_start_us);
|
||||
|
||||
n_past += input_in.size();
|
||||
input_in.clear();
|
||||
@ -1393,16 +1425,21 @@ bark_codes bark_forward_coarse_encoder(
|
||||
int end_ix = SEMANTIC_VOCAB_SIZE + (2 - is_major) * CODEBOOK_SIZE;
|
||||
std::vector<float> relevant_logits(logits.begin() + start_ix, logits.begin() + end_ix);
|
||||
|
||||
int64_t t_sample_start_us = ggml_time_us();
|
||||
bark_vocab::id next = gpt_sample(relevant_logits, rng, temp, NULL);
|
||||
t_sample_us += (ggml_time_us() - t_sample_start_us);
|
||||
|
||||
next += start_ix;
|
||||
|
||||
input_in.push_back(next);
|
||||
out.push_back(next);
|
||||
|
||||
printf("%d ", next);
|
||||
fflush(stdout);
|
||||
// printf("%d ", next);
|
||||
// fflush(stdout);
|
||||
|
||||
step_ix += 1;
|
||||
|
||||
progress.callback((float) (i*sliding_window_size+j)/n_steps);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1416,7 +1453,13 @@ bark_codes bark_forward_coarse_encoder(
|
||||
out_coarse[1].push_back(out[i] - SEMANTIC_VOCAB_SIZE - CODEBOOK_SIZE);
|
||||
}
|
||||
|
||||
printf("\n\ncoarse sequence length: %zu\n\n", out.size());
|
||||
const int64_t t_main_end_us = ggml_time_us();
|
||||
|
||||
printf("\n\n");
|
||||
printf("%s: mem per token = %8.2f MB\n", __func__, mem_per_token/1000.0f/1000.0f);
|
||||
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
|
||||
printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/step_ix);
|
||||
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
|
||||
|
||||
return out_coarse;
|
||||
}
|
||||
@ -1427,11 +1470,20 @@ bark_codes bark_forward_fine_encoder(
|
||||
std::mt19937 & rng,
|
||||
const int n_threads,
|
||||
const float temp) {
|
||||
|
||||
bark_codes input = tokens;
|
||||
std::vector<std::vector<float>> logits;
|
||||
|
||||
bark_progress progress;
|
||||
progress.func = __func__;
|
||||
|
||||
int64_t t_sample_us = 0;
|
||||
int64_t t_predict_us = 0;
|
||||
|
||||
size_t mem_per_token = 0;
|
||||
|
||||
const int64_t t_main_start_us = ggml_time_us();
|
||||
|
||||
int n_coarse = input.size();
|
||||
int original_seq_len = input[0].size();
|
||||
int n_remove_from_end = 0;
|
||||
@ -1471,15 +1523,23 @@ bark_codes bark_forward_fine_encoder(
|
||||
}
|
||||
|
||||
for (int nn = n_coarse; nn < N_FINE_CODEBOOKS; nn++) {
|
||||
int64_t t_predict_start_us = ggml_time_us();
|
||||
fine_gpt_eval(model, n_threads, nn, in_buffer, logits, mem_per_token);
|
||||
t_predict_us += (ggml_time_us() - t_predict_start_us);
|
||||
|
||||
bark_sequence predictions(CODEBOOK_SIZE - rel_start_fill_ix);
|
||||
|
||||
for (int i = 0; i < (int) logits.size(); i++) {
|
||||
logits[i].resize(CODEBOOK_SIZE);
|
||||
|
||||
int64_t t_sample_start_us = ggml_time_us();
|
||||
bark_vocab::id next = gpt_sample(logits[i], rng, temp, NULL);
|
||||
t_sample_us += (ggml_time_us() - t_sample_start_us);
|
||||
|
||||
in_buffer[nn][rel_start_fill_ix+i] = next;
|
||||
}
|
||||
|
||||
progress.callback((float) (n*(N_FINE_CODEBOOKS-n_coarse)+(nn-n_coarse))/(n_loops*(N_FINE_CODEBOOKS-n_coarse)));
|
||||
}
|
||||
|
||||
// transfer over info into model_in
|
||||
@ -1498,6 +1558,14 @@ bark_codes bark_forward_fine_encoder(
|
||||
|
||||
BARK_ASSERT(tokens[0].size() == in_arr[0].size());
|
||||
|
||||
const int64_t t_main_end_us = ggml_time_us();
|
||||
|
||||
printf("\n\n");
|
||||
printf("%s: mem per token = %8.2f MB\n", __func__, mem_per_token/1000.0f/1000.0f);
|
||||
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
|
||||
printf("%s: predict time = %8.2f ms\n", __func__, t_predict_us/1000.0f);
|
||||
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
|
||||
|
||||
return in_arr;
|
||||
}
|
||||
|
||||
@ -1533,19 +1601,22 @@ bool bark_generate_audio(
|
||||
printf("%d ", tokens[i]);
|
||||
}
|
||||
|
||||
printf("\n\n");
|
||||
printf("\n");
|
||||
|
||||
// encode text (text model)
|
||||
// semantic encoding
|
||||
bark_sequence out_semantic = bark_forward_text_encoder(
|
||||
tokens, model.text_model, rng, n_threads, temp, early_stop, min_eos_p);
|
||||
tokens, model.text_model, rng, n_threads, temp, early_stop, min_eos_p);
|
||||
printf("\n");
|
||||
|
||||
// coarse encoding (coarse model)
|
||||
bark_codes out_coarse = bark_forward_coarse_encoder(
|
||||
out_semantic, model.coarse_model, rng, n_threads, temp, max_coarse_history, sliding_window_size);
|
||||
out_semantic, model.coarse_model, rng, n_threads, temp, max_coarse_history, sliding_window_size);
|
||||
printf("\n");
|
||||
|
||||
// fine encoding (fine model)
|
||||
bark_codes out_fine = bark_forward_fine_encoder(
|
||||
out_coarse, model.fine_model, rng, n_threads, fine_temp);
|
||||
out_coarse, model.fine_model, rng, n_threads, fine_temp);
|
||||
printf("\n");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
24
bark.h
24
bark.h
@ -160,7 +160,7 @@ bark_sequence bark_forward_text_encoder(
|
||||
const float min_eos_p);
|
||||
|
||||
bark_codes bark_forward_coarse_encoder(
|
||||
const std::vector<bark_vocab::id> & tokens,
|
||||
const bark_sequence & tokens,
|
||||
const gpt_model model,
|
||||
std::mt19937 & rng,
|
||||
const int n_threads,
|
||||
@ -174,3 +174,25 @@ bark_codes bark_forward_fine_encoder(
|
||||
std::mt19937 & rng,
|
||||
const int n_threads,
|
||||
const float temp);
|
||||
|
||||
struct bark_progress {
|
||||
float current = 0.0f;
|
||||
const char * func = NULL;
|
||||
|
||||
bark_progress() {}
|
||||
|
||||
void callback(float progress) {
|
||||
float percentage = progress * 100;
|
||||
if (percentage == 0.0f && func != NULL) {
|
||||
fprintf(stderr, "%s: ", func);
|
||||
}
|
||||
while (percentage > current) {
|
||||
current = percentage;
|
||||
fprintf(stderr, ".");
|
||||
fflush(stderr);
|
||||
if (percentage >= 100) {
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
#ifndef BUILD_INFO_H
|
||||
#define BUILD_INFO_H
|
||||
|
||||
#define BUILD_NUMBER 22
|
||||
#define BUILD_COMMIT "c54e6be"
|
||||
#define BUILD_NUMBER 33
|
||||
#define BUILD_COMMIT "a183193"
|
||||
|
||||
#endif // BUILD_INFO_H
|
||||
|
||||
Loading…
Reference in New Issue
Block a user