From 1c4d573c5fdf42d7c849cd037ad92efd2ed32525 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 9 Oct 2024 14:16:01 +0200 Subject: [PATCH] examples : do not use common library in simple example --- examples/simple/CMakeLists.txt | 2 +- examples/simple/simple.cpp | 120 +++++++++++++++++---------------- 2 files changed, 62 insertions(+), 60 deletions(-) diff --git a/examples/simple/CMakeLists.txt b/examples/simple/CMakeLists.txt index 070cfbe7a..b63afbb8b 100644 --- a/examples/simple/CMakeLists.txt +++ b/examples/simple/CMakeLists.txt @@ -1,5 +1,5 @@ set(TARGET llama-simple) add_executable(${TARGET} simple.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index c2b7267c8..b35b2b930 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -1,41 +1,38 @@ -#include "arg.h" -#include "common.h" -#include "log.h" #include "llama.h" - +#include +#include #include static void print_usage(int, char ** argv) { - LOG("\nexample usage:\n"); - LOG("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]); - LOG("\n"); + printf("\nexample usage:\n"); + printf("\n %s [prompt]\n", argv[0]); + printf("\n"); } int main(int argc, char ** argv) { - gpt_params params; + std::string model_path; + std::string prompt = "Hello my name is"; + int n_predict = 32; - params.prompt = "Hello my name is"; - params.n_predict = 32; - - if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) { + if (argc < 2) { + print_usage(argc, argv); return 1; } + model_path = argv[1]; - gpt_init(); - - // total length of the sequence including the prompt - const int n_predict = params.n_predict; - - // init LLM - - llama_backend_init(); - llama_numa_init(params.numa); + if (argc > 2) { + prompt = argv[2]; + for (int i = 3; i < argc; i++) { + prompt += " "; + prompt += argv[i]; + } + } // initialize the model - llama_model_params model_params = llama_model_params_from_gpt_params(params); - - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model_params model_params = llama_model_default_params(); + model_params.n_gpu_layers = 99; // offload all layers to GPU + llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params); if (model == NULL) { fprintf(stderr , "%s: error: unable to load model\n" , __func__); @@ -44,8 +41,9 @@ int main(int argc, char ** argv) { // initialize the context - llama_context_params ctx_params = llama_context_params_from_gpt_params(params); - + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = 512; // maximum context size + ctx_params.no_perf = false; llama_context * ctx = llama_new_context_with_model(model, ctx_params); if (ctx == NULL) { @@ -54,9 +52,7 @@ int main(int argc, char ** argv) { } auto sparams = llama_sampler_chain_default_params(); - sparams.no_perf = false; - llama_sampler * smpl = llama_sampler_chain_init(sparams); llama_sampler_chain_add(smpl, llama_sampler_init_greedy()); @@ -64,44 +60,50 @@ int main(int argc, char ** argv) { // tokenize the prompt std::vector tokens_list; - tokens_list = ::llama_tokenize(ctx, params.prompt, true); + int n_tokens = llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true); + tokens_list.resize(-n_tokens); + if (llama_tokenize(model, prompt.c_str(), prompt.size(), tokens_list.data(), tokens_list.size(), true, true) < 0) { + fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__); + return 1; + } const int n_ctx = llama_n_ctx(ctx); const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size()); - LOG("\n"); - LOG_INF("%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req); + fprintf(stderr, "%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req); + // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { - LOG_ERR("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__); - LOG_ERR("%s: either reduce n_predict or increase n_ctx\n", __func__); + fprintf(stderr, "%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__); + fprintf(stderr, "%s: either reduce n_predict or increase n_ctx\n", __func__); return 1; } // print the prompt token-by-token - LOG("\n"); + fprintf(stderr, "\n"); for (auto id : tokens_list) { - LOG("%s", llama_token_to_piece(ctx, id).c_str()); + char buf[128]; + int n = llama_token_to_piece(model, id, buf, sizeof(buf), 0, true); + if (n < 0) { + fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__); + return 1; + } + std::string s(buf, n); + printf("%s", s.c_str()); } // create a llama_batch with size 512 // we use this object to submit token data for decoding - llama_batch batch = llama_batch_init(512, 0, 1); + llama_batch batch = llama_batch_get_one(tokens_list.data(), tokens_list.size(), 0, 0); // evaluate the initial prompt - for (size_t i = 0; i < tokens_list.size(); i++) { - llama_batch_add(batch, tokens_list[i], i, { 0 }, false); - } - - // llama_decode will output logits only for the last token of the prompt - batch.logits[batch.n_tokens - 1] = true; if (llama_decode(ctx, batch) != 0) { - LOG("%s: llama_decode() failed\n", __func__); + fprintf(stderr, "%s: llama_decode() failed\n", __func__); return 1; } @@ -114,24 +116,28 @@ int main(int argc, char ** argv) { while (n_cur <= n_predict) { // sample the next token + llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1); { - const llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1); // is it an end of generation? if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) { - LOG("\n"); + fprintf(stderr, "\n"); break; } - LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str()); + char buf[128]; + int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true); + if (n < 0) { + fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__); + return 1; + } + std::string s(buf, n); + printf("%s", s.c_str()); fflush(stdout); // prepare the next batch - llama_batch_clear(batch); - - // push this new token for next evaluation - llama_batch_add(batch, new_token_id, n_cur, { 0 }, true); + batch = llama_batch_get_one(&new_token_id, 1, n_cur, 0); n_decode += 1; } @@ -140,30 +146,26 @@ int main(int argc, char ** argv) { // evaluate the current batch with the transformer model if (llama_decode(ctx, batch)) { - LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1); + fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return 1; } } - LOG("\n"); + fprintf(stderr, "\n"); const auto t_main_end = ggml_time_us(); - LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", + fprintf(stderr, "%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); - LOG("\n"); + fprintf(stderr, "\n"); llama_perf_sampler_print(smpl); llama_perf_context_print(ctx); + fprintf(stderr, "\n"); - LOG("\n"); - - llama_batch_free(batch); llama_sampler_free(smpl); llama_free(ctx); llama_free_model(model); - llama_backend_free(); - return 0; }