mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 06:39:25 +01:00
ecb217db4f
* mtl : export the LLaMA computation graph
* ci : disable temporary
* mtl : adapt the MNIST example as starter
* mtl : no need for mtl-export tool, add cli arg for main instead
* mtl : export just a small part of the graph for now to make it easier
* mtl : move MSL code into separate file for easy editing
* mtl : initial get_rows_q4_0 kernel
* mtl : confirmed get_rows_q4_0 is working correctly
* mtl : add rms_norm kernel + confirm working
* mtl : add mul kernel + confirm working
* mtl : initial mul_mat Q4 kernel (wrong results)
* mtl : mul_mat fixes (still wrong)
* mtl : another mul_mat Q4 (still does not work)
* mtl : working mul_mat q4
* ggml : fix handling of "view" ops in ggml_graph_import()
* mtl : add rope kernel
* mtl : add reshape and transpose handling
* ggml : store offset as opt arg for ggml_view_xd() operators
* mtl : add cpy kernel + handle view ops
* mtl : confirm f16 x f32 attention mul mat
* mtl : add scale kernel
* mtl : add diag_mask_inf kernel
* mtl : fix soft_max kernel
* ggml : update ggml_nbytes() to handle non-contiguous tensors
* mtl : verify V tensor contents
* mtl : add f32 -> f32 cpy kernel
* mtl : add silu kernel
* mtl : add non-broadcast mul kernel
* mtl : full GPU inference of the computation graph
* mtl : optimize rms_norm and soft_max kernels
* mtl : add f16 mat x f32 vec multiplication kernel
* mtl : fix bug in f16 x f32 mul mat + speed-up computation
* mtl : faster mul_mat_q4_0_f32 kernel
* mtl : fix kernel signature + roll inner loop
* mtl : more threads for rms_norm + better timing
* mtl : remove printfs from inner loop
* mtl : simplify implementation
* mtl : add save/load vocab to ggml file
* mtl : plug Metal inference into llama.cpp (very quick-n-dirty)
* mtl : make it work with main example
Lots of hacks but at least now it generates text
* mtl : preparing for merge
* mtl : clean-up ggml mtl interface + suport scratch / inplace
* mtl : remove temp / debug code
* metal : final refactoring and simplification
* Revert "ci : disable temporary"
This reverts commit 98c267fc77.
* metal : add comments
* metal : clean-up stuff, fix typos
* readme : add Metal instructions
* readme : add example for main
643 lines
25 KiB
C++
643 lines
25 KiB
C++
// 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 "llama.h"
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#include "build-info.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 <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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#define NOMINMAX
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#include <windows.h>
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#include <signal.h>
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#endif
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static console_state con_st;
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static llama_context ** g_ctx;
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static bool is_interacting = false;
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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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if (!is_interacting) {
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is_interacting=true;
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} else {
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console_cleanup(con_st);
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printf("\n");
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llama_print_timings(*g_ctx);
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_exit(130);
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}
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}
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}
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#endif
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int main(int argc, char ** argv) {
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gpt_params params;
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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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// save choice to use color for later
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// (note for later: this is a slightly awkward choice)
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con_st.use_color = params.use_color;
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con_st.multiline_input = params.multiline_input;
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console_init(con_st);
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atexit([]() { console_cleanup(con_st); });
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if (params.perplexity) {
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printf("\n************\n");
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printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (params.embedding) {
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printf("\n************\n");
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printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (params.n_ctx > 2048) {
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fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
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"expect poor results\n", __func__, params.n_ctx);
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}
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fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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if (params.seed < 0) {
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params.seed = time(NULL);
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}
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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.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_init_backend();
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llama_context * ctx;
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g_ctx = &ctx;
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// load the model and apply lora adapter, if any
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ctx = llama_init_from_gpt_params(params);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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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, "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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// determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters
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// uncomment the "used_mem" line in llama.cpp to see the results
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if (params.mem_test) {
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{
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const std::vector<llama_token> tmp(params.n_batch, llama_token_bos());
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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}
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{
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const std::vector<llama_token> tmp = { 0, };
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llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads);
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}
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llama_print_timings(ctx);
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llama_free(ctx);
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return 0;
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}
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// export the cgraph and exit
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if (params.export_cgraph) {
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llama_eval_export(ctx, "llama.ggml");
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llama_free(ctx);
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return 0;
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}
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std::string path_session = params.path_prompt_cache;
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std::vector<llama_token> session_tokens;
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if (!path_session.empty()) {
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fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
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// fopen to check for existing session
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FILE * fp = std::fopen(path_session.c_str(), "rb");
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if (fp != NULL) {
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std::fclose(fp);
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session_tokens.resize(params.n_ctx);
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size_t n_token_count_out = 0;
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if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
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fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
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return 1;
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}
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session_tokens.resize(n_token_count_out);
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llama_set_rng_seed(ctx, params.seed);
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fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
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} else {
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fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
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}
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}
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// tokenize the prompt
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std::vector<llama_token> embd_inp;
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if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
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// Add a space in front of the first character to match OG llama tokenizer behavior
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params.prompt.insert(0, 1, ' ');
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embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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} else {
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embd_inp = session_tokens;
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}
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const int n_ctx = llama_n_ctx(ctx);
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if ((int) embd_inp.size() > n_ctx - 4) {
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fprintf(stderr, "%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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// debug message about similarity of saved session, if applicable
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size_t n_matching_session_tokens = 0;
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if (session_tokens.size()) {
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for (llama_token id : session_tokens) {
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if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
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break;
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}
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n_matching_session_tokens++;
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}
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if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
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fprintf(stderr, "%s: using full prompt from session file\n", __func__);
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} else if (n_matching_session_tokens >= embd_inp.size()) {
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fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
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} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
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fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
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__func__, n_matching_session_tokens, embd_inp.size());
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} else {
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fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
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__func__, n_matching_session_tokens, embd_inp.size());
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}
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}
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// if we will use the cache for the full prompt without reaching the end of the cache, force
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// reevaluation of the last token token to recalculate the cached logits
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if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
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session_tokens.size() > embd_inp.size()) {
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session_tokens.resize(embd_inp.size() - 1);
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}
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// number of tokens to keep when resetting context
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if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) {
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params.n_keep = (int)embd_inp.size();
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}
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// prefix & suffix for instruct mode
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const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
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const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
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// in instruct mode, we inject a prefix and a suffix to each input by the user
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if (params.instruct) {
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params.interactive_first = true;
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params.antiprompt.push_back("### Instruction:\n\n");
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}
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// enable interactive mode if interactive start is specified
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if (params.interactive_first) {
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params.interactive = true;
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}
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// determine newline token
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auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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if (params.verbose_prompt) {
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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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fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
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}
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if (params.n_keep > 0) {
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fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
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for (int i = 0; i < params.n_keep; i++) {
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fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]));
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}
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fprintf(stderr, "'\n");
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}
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fprintf(stderr, "\n");
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}
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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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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(static_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
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#endif
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fprintf(stderr, "%s: interactive mode on.\n", __func__);
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if (params.antiprompt.size()) {
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for (auto antiprompt : params.antiprompt) {
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fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
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}
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}
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if (!params.input_prefix.empty()) {
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fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
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}
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if (!params.input_suffix.empty()) {
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fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str());
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}
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}
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fprintf(stderr, "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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fprintf(stderr, "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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fprintf(stderr, "\n\n");
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// TODO: replace with ring-buffer
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std::vector<llama_token> last_n_tokens(n_ctx);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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if (params.interactive) {
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const char *control_message;
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if (con_st.multiline_input) {
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control_message = " - To return control to LLaMa, end your input with '\\'.\n"
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" - To return control without starting a new line, end your input with '/'.\n";
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} else {
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control_message = " - Press Return to return control to LLaMa.\n"
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" - To return control without starting a new line, end your input with '/'.\n"
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" - If you want to submit another line, end your input with '\\'.\n";
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}
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fprintf(stderr, "== Running in interactive mode. ==\n"
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
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" - Press Ctrl+C to interject at any time.\n"
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#endif
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"%s\n", control_message);
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is_interacting = params.interactive_first;
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}
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bool is_antiprompt = false;
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bool input_echo = true;
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bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
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int n_past = 0;
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int n_remain = params.n_predict;
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int n_consumed = 0;
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int n_session_consumed = 0;
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// the first thing we will do is to output the prompt, so set color accordingly
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console_set_color(con_st, CONSOLE_COLOR_PROMPT);
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std::vector<llama_token> embd;
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while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
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// predict
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if (embd.size() > 0) {
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// infinite text generation via context swapping
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// if we run out of context:
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// - take the n_keep first tokens from the original prompt (via n_past)
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// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
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if (n_past + (int) embd.size() > n_ctx) {
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const int n_left = n_past - params.n_keep;
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// always keep the first token - BOS
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n_past = std::max(1, params.n_keep);
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// insert n_left/2 tokens at the start of embd from last_n_tokens
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embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
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// stop saving session if we run out of context
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path_session.clear();
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//printf("\n---\n");
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//printf("resetting: '");
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//for (int i = 0; i < (int) embd.size(); i++) {
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// printf("%s", llama_token_to_str(ctx, embd[i]));
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//}
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//printf("'\n");
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//printf("\n---\n");
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}
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// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
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if (n_session_consumed < (int) session_tokens.size()) {
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size_t i = 0;
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for ( ; i < embd.size(); i++) {
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if (embd[i] != session_tokens[n_session_consumed]) {
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session_tokens.resize(n_session_consumed);
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break;
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}
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n_past++;
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n_session_consumed++;
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if (n_session_consumed >= (int) session_tokens.size()) {
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++i;
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break;
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}
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}
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if (i > 0) {
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embd.erase(embd.begin(), embd.begin() + i);
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}
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}
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// evaluate tokens in batches
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// embd is typically prepared beforehand to fit within a batch, but not always
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for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
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int n_eval = (int) embd.size() - i;
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if (n_eval > params.n_batch) {
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n_eval = params.n_batch;
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}
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if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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n_past += n_eval;
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}
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if (embd.size() > 0 && !path_session.empty()) {
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session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
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n_session_consumed = session_tokens.size();
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}
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}
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embd.clear();
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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// out of user input, sample next token
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const float temp = params.temp;
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const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
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const float top_p = params.top_p;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
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const float repeat_penalty = params.repeat_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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|
|
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// optionally save the session on first sample (for faster prompt loading next time)
|
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if (!path_session.empty() && need_to_save_session) {
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need_to_save_session = false;
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llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
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}
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|
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llama_token id = 0;
|
|
|
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{
|
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auto logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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|
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// Apply params.logit_bias map
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for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
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logits[it->first] += it->second;
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}
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|
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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|
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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|
|
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// Apply penalties
|
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float nl_logit = logits[llama_token_nl()];
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auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
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llama_sample_repetition_penalty(ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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|
last_n_repeat, repeat_penalty);
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|
llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
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|
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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|
last_n_repeat, alpha_frequency, alpha_presence);
|
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if (!penalize_nl) {
|
|
logits[llama_token_nl()] = nl_logit;
|
|
}
|
|
|
|
if (temp <= 0) {
|
|
// Greedy sampling
|
|
id = llama_sample_token_greedy(ctx, &candidates_p);
|
|
} else {
|
|
if (mirostat == 1) {
|
|
static float mirostat_mu = 2.0f * mirostat_tau;
|
|
const int mirostat_m = 100;
|
|
llama_sample_temperature(ctx, &candidates_p, temp);
|
|
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
|
|
} else if (mirostat == 2) {
|
|
static float mirostat_mu = 2.0f * mirostat_tau;
|
|
llama_sample_temperature(ctx, &candidates_p, temp);
|
|
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
|
|
} else {
|
|
// Temperature sampling
|
|
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
|
|
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
|
|
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
|
|
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
|
|
llama_sample_temperature(ctx, &candidates_p, temp);
|
|
id = llama_sample_token(ctx, &candidates_p);
|
|
}
|
|
}
|
|
// printf("`%d`", candidates_p.size);
|
|
|
|
last_n_tokens.erase(last_n_tokens.begin());
|
|
last_n_tokens.push_back(id);
|
|
}
|
|
|
|
// replace end of text token with newline token when in interactive mode
|
|
if (id == llama_token_eos() && params.interactive && !params.instruct) {
|
|
id = llama_token_newline.front();
|
|
if (params.antiprompt.size() != 0) {
|
|
// tokenize and inject first reverse prompt
|
|
const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false);
|
|
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
|
|
}
|
|
}
|
|
|
|
// add it to the context
|
|
embd.push_back(id);
|
|
|
|
// echo this to console
|
|
input_echo = true;
|
|
|
|
// decrement remaining sampling budget
|
|
--n_remain;
|
|
} else {
|
|
// some user input remains from prompt or interaction, forward it to processing
|
|
while ((int) embd_inp.size() > n_consumed) {
|
|
embd.push_back(embd_inp[n_consumed]);
|
|
last_n_tokens.erase(last_n_tokens.begin());
|
|
last_n_tokens.push_back(embd_inp[n_consumed]);
|
|
++n_consumed;
|
|
if ((int) embd.size() >= params.n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// display text
|
|
if (input_echo) {
|
|
for (auto id : embd) {
|
|
printf("%s", llama_token_to_str(ctx, id));
|
|
}
|
|
fflush(stdout);
|
|
}
|
|
// reset color to default if we there is no pending user input
|
|
if (input_echo && (int)embd_inp.size() == n_consumed) {
|
|
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
|
|
}
|
|
|
|
// if not currently processing queued inputs;
|
|
if ((int) embd_inp.size() <= n_consumed) {
|
|
|
|
// check for reverse prompt
|
|
if (params.antiprompt.size()) {
|
|
std::string last_output;
|
|
for (auto id : last_n_tokens) {
|
|
last_output += llama_token_to_str(ctx, id);
|
|
}
|
|
|
|
is_antiprompt = false;
|
|
// Check if each of the reverse prompts appears at the end of the output.
|
|
// If we're not running interactively, the reverse prompt might be tokenized with some following characters
|
|
// so we'll compensate for that by widening the search window a bit.
|
|
for (std::string & antiprompt : params.antiprompt) {
|
|
size_t extra_padding = params.interactive ? 0 : 2;
|
|
size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
|
|
? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
|
|
: 0;
|
|
|
|
if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) {
|
|
if (params.interactive) {
|
|
is_interacting = true;
|
|
console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
|
|
}
|
|
is_antiprompt = true;
|
|
fflush(stdout);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (n_past > 0 && is_interacting) {
|
|
if (params.instruct) {
|
|
printf("\n> ");
|
|
}
|
|
|
|
std::string buffer;
|
|
if (!params.input_prefix.empty()) {
|
|
buffer += params.input_prefix;
|
|
printf("%s", buffer.c_str());
|
|
}
|
|
|
|
std::string line;
|
|
bool another_line = true;
|
|
do {
|
|
another_line = console_readline(con_st, line);
|
|
buffer += line;
|
|
} while (another_line);
|
|
|
|
// done taking input, reset color
|
|
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
|
|
|
|
// Add tokens to embd only if the input buffer is non-empty
|
|
// Entering a empty line lets the user pass control back
|
|
if (buffer.length() > 1) {
|
|
// append input suffix if any
|
|
if (!params.input_suffix.empty()) {
|
|
buffer += params.input_suffix;
|
|
printf("%s", params.input_suffix.c_str());
|
|
}
|
|
|
|
// instruct mode: insert instruction prefix
|
|
if (params.instruct && !is_antiprompt) {
|
|
n_consumed = embd_inp.size();
|
|
embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
|
|
}
|
|
|
|
auto line_inp = ::llama_tokenize(ctx, buffer, false);
|
|
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
|
|
|
|
// instruct mode: insert response suffix
|
|
if (params.instruct) {
|
|
embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
|
|
}
|
|
|
|
n_remain -= line_inp.size();
|
|
}
|
|
|
|
input_echo = false; // do not echo this again
|
|
}
|
|
|
|
if (n_past > 0) {
|
|
is_interacting = false;
|
|
}
|
|
}
|
|
|
|
// end of text token
|
|
if (!embd.empty() && embd.back() == llama_token_eos()) {
|
|
if (params.instruct) {
|
|
is_interacting = true;
|
|
} else {
|
|
fprintf(stderr, " [end of text]\n");
|
|
break;
|
|
}
|
|
}
|
|
|
|
// In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
|
|
if (params.interactive && n_remain <= 0 && params.n_predict != -1) {
|
|
n_remain = params.n_predict;
|
|
is_interacting = true;
|
|
}
|
|
}
|
|
|
|
if (!path_session.empty() && params.prompt_cache_all) {
|
|
fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
|
|
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
|
|
}
|
|
|
|
llama_print_timings(ctx);
|
|
llama_free(ctx);
|
|
|
|
return 0;
|
|
}
|