common : reimplement logging (#9418)
https://github.com/ggerganov/llama.cpp/pull/9418
This commit is contained in:
Georgi Gerganov
GitHub
parent
e6deac31f7
commit
6262d13e0b
54 changed files with 2092 additions and 2419 deletions
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@ -1,14 +1,16 @@
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#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama.h"
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#include <algorithm>
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#include <fstream>
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#include <iostream> // TODO: remove me
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static void print_usage(int, char ** argv) {
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LOG_TEE("\nexample usage:\n");
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LOG_TEE("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]);
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LOG_TEE("\n");
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LOG("\nexample usage:\n");
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LOG("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]);
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LOG("\n");
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}
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struct chunk {
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@ -17,7 +19,7 @@ struct chunk {
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// original file position
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size_t filepos;
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// original text data
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std::string textdata = "";
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std::string textdata;
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// tokenized text data
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std::vector<llama_token> tokens;
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// embedding
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@ -31,14 +33,14 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
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std::ifstream f(filename.c_str());
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if (!f.is_open()) {
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fprintf(stderr, "Error: could not open file %s\n", filename.c_str());
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LOG_ERR("could not open file %s\n", filename.c_str());
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return chunks;
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}
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chunk current_chunk;
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char buffer[1024];
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int64_t filepos = 0;
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std::string current = "";
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std::string current;
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while (f.read(buffer, 1024)) {
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current += std::string(buffer, f.gcount());
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size_t pos;
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@ -84,9 +86,9 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
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llama_kv_cache_clear(ctx);
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// run model
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fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
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LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
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if (llama_decode(ctx, batch) < 0) {
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fprintf(stderr, "%s : failed to decode\n", __func__);
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LOG_ERR("%s : failed to decode\n", __func__);
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}
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for (int i = 0; i < batch.n_tokens; i++) {
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@ -99,7 +101,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
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if (embd == NULL) {
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embd = llama_get_embeddings_ith(ctx, i);
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if (embd == NULL) {
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fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
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LOG_ERR("%s: failed to get embeddings for token %d\n", __func__, i);
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continue;
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}
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}
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@ -116,24 +118,24 @@ int main(int argc, char ** argv) {
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return 1;
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}
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gpt_init();
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// For BERT models, batch size must be equal to ubatch size
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params.n_ubatch = params.n_batch;
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params.embedding = true;
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if (params.chunk_size <= 0) {
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fprintf(stderr, "chunk_size must be positive\n");
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LOG_ERR("chunk_size must be positive\n");
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return 1;
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}
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if (params.context_files.empty()) {
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fprintf(stderr, "context_files must be specified\n");
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LOG_ERR("context_files must be specified\n");
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return 1;
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}
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print_build_info();
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printf("processing files:\n");
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LOG_INF("processing files:\n");
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for (auto & context_file : params.context_files) {
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printf("%s\n", context_file.c_str());
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LOG_INF("%s\n", context_file.c_str());
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}
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std::vector<chunk> chunks;
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@ -141,7 +143,7 @@ int main(int argc, char ** argv) {
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std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
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chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
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}
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printf("Number of chunks: %ld\n", chunks.size());
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LOG_INF("Number of chunks: %ld\n", chunks.size());
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llama_backend_init();
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llama_numa_init(params.numa);
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@ -153,7 +155,7 @@ int main(int argc, char ** argv) {
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llama_context * ctx = llama_init.context;
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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LOG_ERR("%s: unable to load model\n", __func__);
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return 1;
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}
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@ -162,19 +164,19 @@ int main(int argc, char ** argv) {
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const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
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if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
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fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
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LOG_ERR("%s: pooling type NONE not supported\n", __func__);
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return 1;
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}
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if (n_ctx > n_ctx_train) {
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fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
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LOG_WRN("%s: warning: model was trained on only %d context tokens (%d specified)\n",
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__func__, n_ctx_train, n_ctx);
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}
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
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LOG_INF("\n");
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LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
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}
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// max batch size
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@ -185,7 +187,7 @@ int main(int argc, char ** argv) {
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for (auto & chunk : chunks) {
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auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
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if (inp.size() > n_batch) {
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fprintf(stderr, "%s: error: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
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LOG_ERR("%s: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
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__func__, (long long int) inp.size(), (long long int) n_batch);
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return 1;
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}
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@ -199,12 +201,12 @@ int main(int argc, char ** argv) {
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// tokenization stats
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if (params.verbose_prompt) {
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for (int i = 0; i < (int) chunks.size(); i++) {
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fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
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LOG_INF("%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
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LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
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for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
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fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
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LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
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}
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fprintf(stderr, "\n\n");
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LOG_INF("\n\n");
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}
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}
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@ -256,7 +258,7 @@ int main(int argc, char ** argv) {
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// start loop, receive query and return top k similar chunks based on cosine similarity
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std::string query;
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while (true) {
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printf("Enter query: ");
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LOG("Enter query: ");
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std::getline(std::cin, query);
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std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
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@ -280,18 +282,18 @@ int main(int argc, char ** argv) {
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return a.second > b.second;
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});
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printf("Top %d similar chunks:\n", params.sparams.top_k);
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LOG("Top %d similar chunks:\n", params.sparams.top_k);
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for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
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printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
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printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
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printf("similarity: %f\n", similarities[i].second);
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printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
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printf("--------------------\n");
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LOG("filename: %s\n", chunks[similarities[i].first].filename.c_str());
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LOG("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
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LOG("similarity: %f\n", similarities[i].second);
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LOG("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
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LOG("--------------------\n");
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}
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}
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}
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LOG_TEE("\n");
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LOG("\n");
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llama_perf_context_print(ctx);
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// clean up
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