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common : reimplement logging (#9418)

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https://github.com/ggerganov/llama.cpp/pull/9418
This commit is contained in:
Georgi Gerganov 2024-09-15 20:46:12 +03:00 • committed by GitHub
parent e6deac31f7
commit 6262d13e0b
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54 changed files with 2092 additions and 2419 deletions
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107
examples/embedding/embedding.cpp
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@ -1,5 +1,6 @@
#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include <ctime>
@ -39,16 +40,16 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
llama_kv_cache_clear(ctx);
// run model
fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
// encoder-only model
if (llama_encode(ctx, batch) < 0) {
fprintf(stderr, "%s : failed to encode\n", __func__);
LOG_ERR("%s : failed to encode\n", __func__);
}
} else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
// decoder-only model
if (llama_decode(ctx, batch) < 0) {
fprintf(stderr, "%s : failed to decode\n", __func__);
LOG_ERR("%s : failed to decode\n", __func__);
}
}
@ -84,12 +85,12 @@ int main(int argc, char ** argv) {
return 1;
}
gpt_init();
params.embedding = true;
// For non-causal models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
print_build_info();
llama_backend_init();
llama_numa_init(params.numa);
@ -99,7 +100,7 @@ int main(int argc, char ** argv) {
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
@ -109,19 +110,19 @@ int main(int argc, char ** argv) {
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
if (llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
fprintf(stderr, "%s: error: computing embeddings in encoder-decoder models is not supported\n", __func__);
LOG_ERR("%s: computing embeddings in encoder-decoder models is not supported\n", __func__);
return 1;
}
if (n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
LOG_WRN("%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
}
// split the prompt into lines
@ -136,7 +137,7 @@ int main(int argc, char ** argv) {
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true, false);
if (inp.size() > n_batch) {
fprintf(stderr, "%s: error: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
return 1;
}
@ -147,20 +148,20 @@ int main(int argc, char ** argv) {
// it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_token_sep(model)) {
fprintf(stderr, "%s: warning: last token in the prompt is not SEP\n", __func__);
fprintf(stderr, "%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
LOG_WRN("%s: last token in the prompt is not SEP\n", __func__);
LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
}
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) inputs.size(); i++) {
fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
for (int j = 0; j < (int) inputs[i].size(); j++) {
fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str());
LOG("%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str());
}
fprintf(stderr, "\n\n");
LOG("\n\n");
}
}
@ -211,57 +212,57 @@ int main(int argc, char ** argv) {
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
if (params.embd_out.empty()) {
fprintf(stdout, "\n");
LOG("\n");
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
for (int j = 0; j < n_embd_count; j++) {
fprintf(stdout, "embedding %d: ", j);
LOG("embedding %d: ", j);
for (int i = 0; i < std::min(3, n_embd); i++) {
if (params.embd_normalize == 0) {
fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
LOG("%6.0f ", emb[j * n_embd + i]);
} else {
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
LOG("%9.6f ", emb[j * n_embd + i]);
}
}
fprintf(stdout, " ... ");
LOG(" ... ");
for (int i = n_embd - 3; i < n_embd; i++) {
if (params.embd_normalize == 0) {
fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
LOG("%6.0f ", emb[j * n_embd + i]);
} else {
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
LOG("%9.6f ", emb[j * n_embd + i]);
}
}
fprintf(stdout, "\n");
LOG("\n");
}
} else {
// print the first part of the embeddings or for a single prompt, the full embedding
for (int j = 0; j < n_prompts; j++) {
fprintf(stdout, "embedding %d: ", j);
LOG("embedding %d: ", j);
for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
if (params.embd_normalize == 0) {
fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
LOG("%6.0f ", emb[j * n_embd + i]);
} else {
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
LOG("%9.6f ", emb[j * n_embd + i]);
}
}
fprintf(stdout, "\n");
LOG("\n");
}
// print cosine similarity matrix
if (n_prompts > 1) {
fprintf(stdout, "\n");
printf("cosine similarity matrix:\n\n");
LOG("\n");
LOG("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
fprintf(stdout, "%6.6s ", prompts[i].c_str());
LOG("%6.6s ", prompts[i].c_str());
}
fprintf(stdout, "\n");
LOG("\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f ", sim);
LOG("%6.2f ", sim);
}
fprintf(stdout, "%1.10s", prompts[i].c_str());
fprintf(stdout, "\n");
LOG("%1.10s", prompts[i].c_str());
LOG("\n");
}
}
}
@ -270,42 +271,42 @@ int main(int argc, char ** argv) {
if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") {
const bool notArray = params.embd_out != "array";
fprintf(stdout, notArray ? "{\n \"object\": \"list\",\n \"data\": [\n" : "[");
LOG(notArray ? "{\n \"object\": \"list\",\n \"data\": [\n" : "[");
for (int j = 0;;) { // at least one iteration (one prompt)
if (notArray) fprintf(stdout, " {\n \"object\": \"embedding\",\n \"index\": %d,\n \"embedding\": ",j);
fprintf(stdout, "[");
if (notArray) LOG(" {\n \"object\": \"embedding\",\n \"index\": %d,\n \"embedding\": ",j);
LOG("[");
for (int i = 0;;) { // at least one iteration (n_embd > 0)
fprintf(stdout, params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]);
LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]);
i++;
if (i < n_embd) fprintf(stdout, ","); else break;
if (i < n_embd) LOG(","); else break;
}
fprintf(stdout, notArray ? "]\n }" : "]");
LOG(notArray ? "]\n }" : "]");
j++;
if (j < n_embd_count) fprintf(stdout, notArray ? ",\n" : ","); else break;
if (j < n_embd_count) LOG(notArray ? ",\n" : ","); else break;
}
fprintf(stdout, notArray ? "\n ]" : "]\n");
LOG(notArray ? "\n ]" : "]\n");
if (params.embd_out == "json+" && n_prompts > 1) {
fprintf(stdout, ",\n \"cosineSimilarity\": [\n");
LOG(",\n \"cosineSimilarity\": [\n");
for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
fprintf(stdout, " [");
LOG(" [");
for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f", sim);
LOG("%6.2f", sim);
j++;
if (j < n_embd_count) fprintf(stdout, ", "); else break;
if (j < n_embd_count) LOG(", "); else break;
}
fprintf(stdout, " ]");
LOG(" ]");
i++;
if (i < n_embd_count) fprintf(stdout, ",\n"); else break;
if (i < n_embd_count) LOG(",\n"); else break;
}
fprintf(stdout, "\n ]");
LOG("\n ]");
}
if (notArray) fprintf(stdout, "\n}\n");
if (notArray) LOG("\n}\n");
}
LOG_TEE("\n");
LOG("\n");
llama_perf_context_print(ctx);
// clean up