CS348Project/llama.cpp
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Use fprintf for diagnostic output (#48)

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keep printf only for printing model output

one can now use ./main ... 2>dev/null to suppress any diagnostic output
This commit is contained in:
Pavol Rusnak
2023-03-13 17:39:56 +01:00
committed by GitHub
parent 84d9015c4a
commit 671d5cac15
1 changed files with 46 additions and 46 deletions
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92
main.cpp
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@@ -85,7 +85,7 @@ struct llama_model {
// load the model's weights from a file
bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) {
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
std::vector<char> f_buf(1024*1024);
@@ -127,16 +127,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_mult = %d\n", __func__, hparams.n_mult);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
printf("%s: f16 = %d\n", __func__, hparams.f16);
printf("%s: n_ff = %d\n", __func__, n_ff);
printf("%s: n_parts = %d\n", __func__, n_parts);
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd);
fprintf(stderr, "%s: n_mult = %d\n", __func__, hparams.n_mult);
fprintf(stderr, "%s: n_head = %d\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
fprintf(stderr, "%s: n_rot = %d\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
}
// load vocab
@@ -161,7 +161,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
vocab.id_to_token[i] = word;
//if (i < 30000) {
// printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
// fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
//}
}
}
@@ -220,7 +220,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
ctx_size += (5 + 10*n_layer)*256; // object overhead
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
@@ -307,7 +307,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
const size_t file_offset = fin.tellg();
@@ -325,7 +325,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
fname_part += "." + std::to_string(i);
}
printf("%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
fin = std::ifstream(fname_part, std::ios::binary);
fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
@@ -336,7 +336,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
int n_tensors = 0;
size_t total_size = 0;
printf("%s: ", __func__);
fprintf(stderr, "%s: ", __func__);
while (true) {
int32_t n_dims;
@@ -436,7 +436,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
if (0) {
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
printf("%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
}
size_t bpe = 0;
@@ -499,16 +499,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
total_size += ggml_nbytes(tensor)/n_parts;
}
//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
//fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
if (++n_tensors % 8 == 0) {
printf(".");
fflush(stdout);
fprintf(stderr, ".");
fflush(stderr);
}
}
printf(" done\n");
fprintf(stderr, " done\n");
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
}
fin.close();
@@ -552,7 +552,7 @@ bool llama_eval(
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
// reallocate
buf_size = buf_size_new;
@@ -744,7 +744,7 @@ bool llama_eval(
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
//fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
ggml_free(ctx0);
@@ -780,7 +780,7 @@ int main(int argc, char ** argv) {
params.seed = time(NULL);
}
printf("%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.prompt.empty()) {
@@ -822,13 +822,13 @@ int main(int argc, char ** argv) {
// tokenize the reverse prompt
std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
printf("\n");
printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
}
printf("\n");
fprintf(stderr, "\n");
if (params.interactive) {
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
@@ -838,19 +838,19 @@ int main(int argc, char ** argv) {
sigaction(SIGINT, &sigint_action, NULL);
#endif
printf("%s: interactive mode on.\n", __func__);
fprintf(stderr, "%s: interactive mode on.\n", __func__);
if(antiprompt_inp.size()) {
printf("%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
printf("%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
printf("%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
}
printf("\n");
fprintf(stderr, "\n");
}
}
printf("sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
printf("\n\n");
fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
fprintf(stderr, "\n\n");
std::vector<gpt_vocab::id> embd;
@@ -864,7 +864,7 @@ int main(int argc, char ** argv) {
if (params.interactive) {
printf("== Running in interactive mode. ==\n"
fprintf(stderr, "== Running in interactive mode. ==\n"
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
" - Press Ctrl+C to interject at any time.\n"
#endif
@@ -892,7 +892,7 @@ int main(int argc, char ** argv) {
const int64_t t_start_us = ggml_time_us();
if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
printf("Failed to predict\n");
fprintf(stderr, "Failed to predict\n");
return 1;
}
@@ -1005,7 +1005,7 @@ int main(int argc, char ** argv) {
// end of text token
if (embd.back() == 2) {
printf(" [end of text]\n");
fprintf(stderr, " [end of text]\n");
break;
}
}
@@ -1015,12 +1015,12 @@ int main(int argc, char ** argv) {
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n\n");
printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
printf("%s: load time = %8.2f ms\n", __func__, t_load_us/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);
fprintf(stderr, "\n\n");
fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
fprintf(stderr, "%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
}
ggml_free(model.ctx);