mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 23:29:44 +00:00
Use fprintf for diagnostic output (#48)
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
GitHub
parent
84d9015c4a
commit
671d5cac15
1 changed files with 46 additions and 46 deletions
92
main.cpp
92
main.cpp
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@ -85,7 +85,7 @@ struct llama_model {
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// load the model's weights from a file
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bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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std::vector<char> f_buf(1024*1024);
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@ -127,16 +127,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
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n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_mult = %d\n", __func__, hparams.n_mult);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
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printf("%s: f16 = %d\n", __func__, hparams.f16);
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printf("%s: n_ff = %d\n", __func__, n_ff);
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printf("%s: n_parts = %d\n", __func__, n_parts);
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fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd);
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fprintf(stderr, "%s: n_mult = %d\n", __func__, hparams.n_mult);
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fprintf(stderr, "%s: n_head = %d\n", __func__, hparams.n_head);
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fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
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fprintf(stderr, "%s: n_rot = %d\n", __func__, hparams.n_rot);
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fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
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fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff);
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fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
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}
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// load vocab
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@ -161,7 +161,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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vocab.id_to_token[i] = word;
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//if (i < 30000) {
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// printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
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// fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
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//}
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}
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}
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@ -220,7 +220,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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ctx_size += (5 + 10*n_layer)*256; // object overhead
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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// create the ggml context
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@ -307,7 +307,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
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printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
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fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
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}
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const size_t file_offset = fin.tellg();
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@ -325,7 +325,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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fname_part += "." + std::to_string(i);
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}
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printf("%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
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fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
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fin = std::ifstream(fname_part, std::ios::binary);
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fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
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@ -336,7 +336,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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int n_tensors = 0;
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size_t total_size = 0;
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printf("%s: ", __func__);
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fprintf(stderr, "%s: ", __func__);
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while (true) {
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int32_t n_dims;
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@ -436,7 +436,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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if (0) {
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static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
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printf("%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
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fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
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}
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size_t bpe = 0;
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@ -499,16 +499,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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total_size += ggml_nbytes(tensor)/n_parts;
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}
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//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);
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//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);
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if (++n_tensors % 8 == 0) {
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printf(".");
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fflush(stdout);
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fprintf(stderr, ".");
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fflush(stderr);
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}
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}
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printf(" done\n");
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fprintf(stderr, " done\n");
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printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
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fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
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}
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fin.close();
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@ -552,7 +552,7 @@ bool llama_eval(
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if (mem_per_token > 0 && mem_per_token*N > buf_size) {
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const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
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//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
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//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
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// reallocate
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buf_size = buf_size_new;
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@ -744,7 +744,7 @@ bool llama_eval(
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if (mem_per_token == 0) {
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mem_per_token = ggml_used_mem(ctx0)/N;
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}
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//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
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//fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
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ggml_free(ctx0);
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@ -780,7 +780,7 @@ int main(int argc, char ** argv) {
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params.seed = time(NULL);
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}
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printf("%s: seed = %d\n", __func__, params.seed);
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fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.prompt.empty()) {
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@ -822,13 +822,13 @@ int main(int argc, char ** argv) {
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// tokenize the reverse prompt
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std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
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printf("\n");
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printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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for (int i = 0; i < (int) embd_inp.size(); i++) {
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printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
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fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
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}
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printf("\n");
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fprintf(stderr, "\n");
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if (params.interactive) {
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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struct sigaction sigint_action;
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@ -838,19 +838,19 @@ int main(int argc, char ** argv) {
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sigaction(SIGINT, &sigint_action, NULL);
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#endif
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printf("%s: interactive mode on.\n", __func__);
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fprintf(stderr, "%s: interactive mode on.\n", __func__);
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if(antiprompt_inp.size()) {
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printf("%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
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printf("%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
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fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
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printf("%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
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fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
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}
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printf("\n");
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fprintf(stderr, "\n");
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}
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}
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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);
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printf("\n\n");
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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);
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fprintf(stderr, "\n\n");
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std::vector<gpt_vocab::id> embd;
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@ -864,7 +864,7 @@ int main(int argc, char ** argv) {
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if (params.interactive) {
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printf("== Running in interactive mode. ==\n"
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fprintf(stderr, "== Running in interactive mode. ==\n"
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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" - Press Ctrl+C to interject at any time.\n"
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#endif
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@ -892,7 +892,7 @@ int main(int argc, char ** argv) {
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const int64_t t_start_us = ggml_time_us();
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if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
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printf("Failed to predict\n");
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fprintf(stderr, "Failed to predict\n");
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return 1;
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}
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@ -1005,7 +1005,7 @@ int main(int argc, char ** argv) {
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// end of text token
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if (embd.back() == 2) {
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printf(" [end of text]\n");
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fprintf(stderr, " [end of text]\n");
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break;
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}
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}
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@ -1015,12 +1015,12 @@ int main(int argc, char ** argv) {
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{
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const int64_t t_main_end_us = ggml_time_us();
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printf("\n\n");
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printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
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printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
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printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
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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);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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fprintf(stderr, "\n\n");
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fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
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fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
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fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
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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);
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fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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}
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ggml_free(model.ctx);
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