Add a simpler main example
This commit is contained in:
KerfuffleV2
parent
0a5eebb45d
commit
62c5c6f5c3
2 changed files with 419 additions and 1 deletions
5
Makefile
5
Makefile
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@ -1,5 +1,5 @@
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# Define the default target now so that it is always the first target
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# Define the default target now so that it is always the first target
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BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative tests/test-c.o
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BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple simple-inference save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative tests/test-c.o
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# Binaries only useful for tests
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# Binaries only useful for tests
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TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1
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TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1
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@ -549,6 +549,9 @@ beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o co
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speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
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speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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simple-inference: examples/simple-inference/simple-inference.cpp build-info.h ggml.o llama.o common.o console.o grammar-parser.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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ifdef LLAMA_METAL
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ifdef LLAMA_METAL
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metal: examples/metal/metal.cpp ggml.o $(OBJS)
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metal: examples/metal/metal.cpp ggml.o $(OBJS)
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$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
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$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
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415
examples/simple-inference/simple-inference.cpp
Normal file
415
examples/simple-inference/simple-inference.cpp
Normal file
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@ -0,0 +1,415 @@
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// Defines sigaction on msys:
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#ifndef _GNU_SOURCE
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#define _GNU_SOURCE
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#endif
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#include "common.h"
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#include "console.h"
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#include "llama.h"
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#include "build-info.h"
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#include "grammar-parser.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iostream>
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#include <sstream>
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#include <string>
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#include <vector>
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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#include <signal.h>
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#include <unistd.h>
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#elif defined (_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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#include <signal.h>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static llama_context ** g_ctx;
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static llama_model ** g_model;
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static gpt_params * g_params;
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static std::vector<llama_token> * g_input_tokens;
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static std::ostringstream * g_output_ss;
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static std::vector<llama_token> * g_output_tokens;
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void write_logfile(
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const llama_context * ctx, const gpt_params & params, const llama_model * model,
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const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
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if (params.logdir.empty()) {
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return;
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}
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const std::string timestamp = get_sortable_timestamp();
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const bool success = create_directory_with_parents(params.logdir);
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if (!success) {
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fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
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__func__, params.logdir.c_str());
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return;
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}
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const std::string logfile_path = params.logdir + timestamp + ".yml";
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FILE * logfile = fopen(logfile_path.c_str(), "w");
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if (logfile == NULL) {
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fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
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return;
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}
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fprintf(logfile, "binary: simple-inference\n");
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char model_desc[128];
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llama_model_desc(model, model_desc, sizeof(model_desc));
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dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
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fprintf(logfile, "\n");
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fprintf(logfile, "######################\n");
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fprintf(logfile, "# Generation Results #\n");
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fprintf(logfile, "######################\n");
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fprintf(logfile, "\n");
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dump_string_yaml_multiline(logfile, "output", output.c_str());
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dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
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llama_dump_timing_info_yaml(logfile, ctx);
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fclose(logfile);
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}
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
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void sigint_handler(int signo) {
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if (signo == SIGINT) {
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console::cleanup();
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printf("\n");
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llama_print_timings(*g_ctx);
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write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
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_exit(130);
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}
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}
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#endif
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bool check_unsupported(const gpt_params * params) {
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std::string nope;
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if (params->perplexity)
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nope = "perplexity";
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else if (params->embedding)
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nope = "embedding";
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else if (params->cfg_scale != 1.0f)
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nope = "cfg_scale";
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else if (!params->cfg_negative_prompt.empty())
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nope = "cfg_negative_prompt";
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else if (params->mem_test)
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nope = "mem test";
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else if (params->export_cgraph)
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nope = "export cgraph";
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else if (!params->path_prompt_cache.empty())
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nope = "prompt cache";
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else if (params->escape)
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nope = "prompt escaping";
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else if (params->interactive || params->interactive_first || params->instruct)
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nope = "interactive mode";
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else if (!params->input_prefix.empty() || !params->input_suffix.empty() || params->input_prefix_bos)
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nope = "input prefix or suffix";
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else if (params->hellaswag)
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nope = "hellaswag";
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else if (params->n_keep != 0)
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nope = "keep";
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else if (!params->antiprompt.empty())
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nope = "reverse prompt";
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if (!nope.empty()) {
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LOG_TEE("%s: error: We don't support %s here.\n", __func__, nope.c_str());
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return false;
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}
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return true;
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}
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bool initialize(llama_context **ctx_p, llama_model **model_p, gpt_params & params, std::vector<llama_token> & embd_inp, llama_grammar ** grammar_p) {
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// save choice to use color for later
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// (note for later: this is a slightly awkward choice)
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console::init(params.simple_io, params.use_color);
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atexit([]() { console::cleanup(); });
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if (params.rope_freq_base != 10000.0) {
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LOG_TEE("%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
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}
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if (params.rope_freq_scale != 1.0) {
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LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
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}
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if (params.n_ctx < 8) {
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LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
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params.n_ctx = 8;
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}
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LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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if (params.seed == LLAMA_DEFAULT_SEED) {
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params.seed = time(NULL);
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}
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LOG_TEE("%s: seed = %u\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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LOG("%s: llama backend init\n", __func__);
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llama_backend_init(params.numa);
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g_model = model_p;
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g_ctx = ctx_p;
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// load the model and apply lora adapter, if any
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LOG("%s: load the model and apply lora adapter, if any\n", __func__);
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std::tie(*model_p, *ctx_p) = llama_init_from_gpt_params(params);
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llama_model * model = *model_p;
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llama_context * ctx = *ctx_p;
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if (model == NULL) {
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LOG_TEE("%s: error: unable to load model\n", __func__);
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return false;
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}
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// print system information
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{
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LOG_TEE("\n");
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LOG_TEE("system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
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LOG("add_bos: %d\n", add_bos);
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if (!params.prompt.empty()) {
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LOG("tokenize the prompt\n");
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embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
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}
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LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
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LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
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// Should not run without any tokens
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if (embd_inp.empty()) {
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embd_inp.push_back(llama_token_bos(ctx));
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LOG("input was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
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}
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const int n_ctx = llama_n_ctx(ctx);
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LOG("n_ctx: %d\n", n_ctx);
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if ((int) embd_inp.size() > n_ctx - 4) {
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LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
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return 1;
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}
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if (params.verbose_prompt) {
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LOG_TEE("\n");
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LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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LOG_TEE("%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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LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
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}
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LOG_TEE("\n");
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}
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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struct sigaction sigint_action;
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sigint_action.sa_handler = sigint_handler;
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sigemptyset (&sigint_action.sa_mask);
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sigint_action.sa_flags = 0;
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sigaction(SIGINT, &sigint_action, NULL);
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#elif defined (_WIN32)
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auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
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return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
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};
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SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
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#endif
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LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
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params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
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LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
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LOG_TEE("\n\n");
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grammar_parser::parse_state parsed_grammar;
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if (!params.grammar.empty()) {
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parsed_grammar = grammar_parser::parse(params.grammar.c_str());
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// will be empty (default) if there are parse errors
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if (parsed_grammar.rules.empty()) {
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return false;
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}
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LOG_TEE("%s: grammar:\n", __func__);
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grammar_parser::print_grammar(stderr, parsed_grammar);
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LOG_TEE("\n");
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{
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auto it = params.logit_bias.find(llama_token_eos(ctx));
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if (it != params.logit_bias.end() && it->second == -INFINITY) {
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LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
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}
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}
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std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
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*grammar_p = llama_grammar_init(
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grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
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}
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return true;
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}
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bool feed_prompt(llama_context *ctx, const gpt_params * params, llama_token * tokens, int tokens_len, int n_past) {
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console::set_display(console::prompt);
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while (tokens_len > 0) {
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const int this_chunk_size = std::min(tokens_len, params->n_batch);
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if (llama_eval(ctx, tokens, this_chunk_size, n_past, params->n_threads)) {
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console::set_display(console::reset);
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LOG_TEE("%s : failed to eval\n", __func__);
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return false;
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}
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// display text
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for (int i = 0; i < this_chunk_size; i++) {
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const std::string token_str = llama_token_to_piece(ctx, tokens[i]);
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fputs(token_str.c_str(), stdout);
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}
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fflush(stdout);
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tokens += this_chunk_size;
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tokens_len -= this_chunk_size;
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n_past += this_chunk_size;
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}
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console::set_display(console::reset);
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return true;
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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g_params = ¶ms;
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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||||||
|
|
||||||
|
if (!check_unsupported(¶ms)) {
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
#ifndef LOG_DISABLE_LOGS
|
||||||
|
log_set_target(log_filename_generator("simple-inference", "log"));
|
||||||
|
LOG_TEE("Log start\n");
|
||||||
|
log_dump_cmdline(argc,argv);
|
||||||
|
#endif // LOG_DISABLE_LOGS
|
||||||
|
|
||||||
|
llama_context * ctx = NULL;
|
||||||
|
llama_model * model = NULL;
|
||||||
|
llama_grammar * grammar = NULL;
|
||||||
|
std::vector<llama_token> prompt_tokens;
|
||||||
|
|
||||||
|
if (!initialize(&ctx, &model, params, prompt_tokens, &grammar)) {
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
const int n_ctx = llama_n_ctx(ctx);
|
||||||
|
int n_remain = params.n_predict;
|
||||||
|
|
||||||
|
std::vector<int> input_tokens; g_input_tokens = &input_tokens;
|
||||||
|
std::vector<int> output_tokens; g_output_tokens = &output_tokens;
|
||||||
|
std::ostringstream output_ss; g_output_ss = &output_ss;
|
||||||
|
|
||||||
|
{
|
||||||
|
LOG("warming up the model with an empty run\n");
|
||||||
|
|
||||||
|
const std::vector<llama_token> tmp = { llama_token_bos(ctx), };
|
||||||
|
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
|
||||||
|
llama_reset_timings(ctx);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!feed_prompt(ctx, ¶ms, prompt_tokens.data(), prompt_tokens.size(), 0)) {
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (n_remain < 0 || n_remain + int(prompt_tokens.size()) > n_ctx) {
|
||||||
|
n_remain = n_ctx - prompt_tokens.size();
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<llama_token> last_tokens = prompt_tokens;
|
||||||
|
last_tokens.reserve(params.n_ctx);
|
||||||
|
|
||||||
|
std::vector<llama_token_data> candidates;
|
||||||
|
candidates.reserve(llama_n_vocab(ctx));
|
||||||
|
|
||||||
|
// Required to match output from main example with a specific seed - but why?
|
||||||
|
if (false) {
|
||||||
|
llama_token id = llama_sample_token(ctx, NULL, grammar, params, last_tokens, candidates);
|
||||||
|
if (llama_eval(ctx, &id, 1, last_tokens.size(), params.n_threads)) {
|
||||||
|
LOG_TEE("%s : failed to eval\n", __func__);
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
const std::string token_str = llama_token_to_piece(ctx, id);
|
||||||
|
fputs(token_str.c_str(), stdout);
|
||||||
|
fflush(stdout);
|
||||||
|
}
|
||||||
|
|
||||||
|
while (n_remain > 0) {
|
||||||
|
const llama_token id = llama_sample_token(ctx, NULL, grammar, params, last_tokens, candidates);
|
||||||
|
|
||||||
|
last_tokens.push_back(id);
|
||||||
|
output_tokens.push_back(id);
|
||||||
|
--n_remain;
|
||||||
|
|
||||||
|
LOG("n_remain: %d\n", n_remain);
|
||||||
|
|
||||||
|
// end of text token
|
||||||
|
if (id == llama_token_eos(ctx)) {
|
||||||
|
LOG_TEE(" [end of text]\n");
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
|
||||||
|
const std::string token_str = llama_token_to_piece(ctx, id);
|
||||||
|
|
||||||
|
output_ss << token_str;
|
||||||
|
fputs(token_str.c_str(), stdout);
|
||||||
|
fflush(stdout);
|
||||||
|
|
||||||
|
// predict
|
||||||
|
if (n_remain > 0 && llama_eval(ctx, &id, 1, last_tokens.size(), params.n_threads)) {
|
||||||
|
LOG_TEE("%s : failed to eval\n", __func__);
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
llama_print_timings(ctx);
|
||||||
|
write_logfile(ctx, params, model, prompt_tokens, output_ss.str(), output_tokens);
|
||||||
|
|
||||||
|
llama_free(ctx);
|
||||||
|
llama_free_model(model);
|
||||||
|
|
||||||
|
if (grammar != NULL) {
|
||||||
|
llama_grammar_free(grammar);
|
||||||
|
}
|
||||||
|
llama_backend_free();
|
||||||
|
|
||||||
|
#ifndef LOG_DISABLE_LOGS
|
||||||
|
LOG_TEE("Log end\n")
|
||||||
|
#endif // LOG_DISABLE_LOGS
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
Loading…
Add table
Add a link
Reference in a new issue