mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 14:20:31 +01:00
629 lines
24 KiB
C++
629 lines
24 KiB
C++
// Defines sigaction on msys:
|
|
#ifndef _GNU_SOURCE
|
|
#define _GNU_SOURCE
|
|
#endif
|
|
|
|
#include "common.h"
|
|
#include "llama.h"
|
|
#include "build-info.h"
|
|
|
|
#include <cassert>
|
|
#include <cinttypes>
|
|
#include <cmath>
|
|
#include <cstdio>
|
|
#include <cstring>
|
|
#include <ctime>
|
|
#include <fstream>
|
|
#include <iostream>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
|
|
#include <signal.h>
|
|
#include <unistd.h>
|
|
#elif defined (_WIN32)
|
|
#define WIN32_LEAN_AND_MEAN
|
|
#define NOMINMAX
|
|
#include <windows.h>
|
|
#include <signal.h>
|
|
#endif
|
|
|
|
static console_state con_st;
|
|
static llama_context ** g_ctx;
|
|
|
|
static bool is_interacting = false;
|
|
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
|
|
void sigint_handler(int signo) {
|
|
set_console_color(con_st, CONSOLE_COLOR_DEFAULT);
|
|
printf("\n"); // this also force flush stdout.
|
|
if (signo == SIGINT) {
|
|
if (!is_interacting) {
|
|
is_interacting=true;
|
|
} else {
|
|
llama_print_timings(*g_ctx);
|
|
_exit(130);
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
int main(int argc, char ** argv) {
|
|
gpt_params params;
|
|
params.model = "models/llama-7B/ggml-model.bin";
|
|
|
|
if (gpt_params_parse(argc, argv, params) == false) {
|
|
return 1;
|
|
}
|
|
|
|
// save choice to use color for later
|
|
// (note for later: this is a slightly awkward choice)
|
|
con_st.use_color = params.use_color;
|
|
|
|
#if defined (_WIN32)
|
|
win32_console_init(params.use_color);
|
|
#endif
|
|
|
|
if (params.perplexity) {
|
|
printf("\n************\n");
|
|
printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
|
|
printf("************\n\n");
|
|
|
|
return 0;
|
|
}
|
|
|
|
if (params.embedding) {
|
|
printf("\n************\n");
|
|
printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
|
|
printf("************\n\n");
|
|
|
|
return 0;
|
|
}
|
|
|
|
if (params.n_ctx > 2048) {
|
|
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
|
|
"expect poor results\n", __func__, params.n_ctx);
|
|
}
|
|
|
|
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
|
|
|
|
if (params.seed < 0) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
|
|
|
|
std::mt19937 rng(params.seed);
|
|
if (params.random_prompt) {
|
|
params.prompt = gpt_random_prompt(rng);
|
|
}
|
|
|
|
// params.prompt = R"(// this function checks if the number n is prime
|
|
//bool is_prime(int n) {)";
|
|
|
|
llama_context * ctx;
|
|
g_ctx = &ctx;
|
|
|
|
// load the model and apply lora adapter, if any
|
|
ctx = llama_init_from_gpt_params(params);
|
|
if (ctx == NULL) {
|
|
fprintf(stderr, "%s: error: unable to load model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// print system information
|
|
{
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
|
|
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
|
|
}
|
|
|
|
// determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters
|
|
// uncomment the "used_mem" line in llama.cpp to see the results
|
|
if (params.mem_test) {
|
|
{
|
|
const std::vector<llama_token> tmp(params.n_batch, 0);
|
|
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
|
|
}
|
|
|
|
{
|
|
const std::vector<llama_token> tmp = { 0, };
|
|
llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads);
|
|
}
|
|
|
|
llama_print_timings(ctx);
|
|
llama_free(ctx);
|
|
|
|
return 0;
|
|
}
|
|
|
|
// Add a space in front of the first character to match OG llama tokenizer behavior
|
|
params.prompt.insert(0, 1, ' ');
|
|
|
|
std::string path_session = params.path_session;
|
|
std::vector<llama_token> session_tokens;
|
|
|
|
if (!path_session.empty()) {
|
|
fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
|
|
|
|
// fopen to check for existing session
|
|
FILE * fp = std::fopen(path_session.c_str(), "rb");
|
|
if (fp != NULL) {
|
|
std::fclose(fp);
|
|
|
|
session_tokens.resize(params.n_ctx);
|
|
size_t n_token_count_out = 0;
|
|
if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
|
|
fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
|
|
return 1;
|
|
}
|
|
session_tokens.resize(n_token_count_out);
|
|
|
|
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
|
|
} else {
|
|
fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
|
|
}
|
|
}
|
|
|
|
// tokenize the prompt
|
|
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
|
|
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
|
|
if ((int) embd_inp.size() > n_ctx - 4) {
|
|
fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
|
|
return 1;
|
|
}
|
|
|
|
// debug message about similarity of saved session, if applicable
|
|
size_t n_matching_session_tokens = 0;
|
|
if (session_tokens.size()) {
|
|
for (llama_token id : session_tokens) {
|
|
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
|
|
break;
|
|
}
|
|
n_matching_session_tokens++;
|
|
}
|
|
if (n_matching_session_tokens >= embd_inp.size()) {
|
|
fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
|
|
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
|
|
fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
|
|
__func__, n_matching_session_tokens, embd_inp.size());
|
|
} else {
|
|
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
|
|
__func__, n_matching_session_tokens, embd_inp.size());
|
|
}
|
|
}
|
|
|
|
// number of tokens to keep when resetting context
|
|
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) {
|
|
params.n_keep = (int)embd_inp.size();
|
|
}
|
|
|
|
// prefix & suffix for instruct mode
|
|
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
|
|
const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
|
|
|
|
// in instruct mode, we inject a prefix and a suffix to each input by the user
|
|
if (params.instruct) {
|
|
params.interactive_first = true;
|
|
params.antiprompt.push_back("### Instruction:\n\n");
|
|
}
|
|
|
|
// enable interactive mode if reverse prompt or interactive start is specified
|
|
if (params.antiprompt.size() != 0 || params.interactive_first) {
|
|
params.interactive = true;
|
|
}
|
|
|
|
// determine newline token
|
|
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
|
|
|
|
if (params.verbose_prompt) {
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
|
|
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
|
|
for (int i = 0; i < (int) embd_inp.size(); i++) {
|
|
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
|
|
}
|
|
if (params.n_keep > 0) {
|
|
fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
|
|
for (int i = 0; i < params.n_keep; i++) {
|
|
fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]));
|
|
}
|
|
fprintf(stderr, "'\n");
|
|
}
|
|
fprintf(stderr, "\n");
|
|
}
|
|
|
|
if (params.interactive) {
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
|
|
struct sigaction sigint_action;
|
|
sigint_action.sa_handler = sigint_handler;
|
|
sigemptyset (&sigint_action.sa_mask);
|
|
sigint_action.sa_flags = 0;
|
|
sigaction(SIGINT, &sigint_action, NULL);
|
|
#elif defined (_WIN32)
|
|
auto console_ctrl_handler = [](DWORD ctrl_type) -> BOOL {
|
|
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
|
|
};
|
|
SetConsoleCtrlHandler(static_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
|
|
#endif
|
|
|
|
fprintf(stderr, "%s: interactive mode on.\n", __func__);
|
|
|
|
if (params.antiprompt.size()) {
|
|
for (auto antiprompt : params.antiprompt) {
|
|
fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
|
|
}
|
|
}
|
|
|
|
if (!params.input_prefix.empty()) {
|
|
fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
|
|
}
|
|
|
|
if (!params.input_suffix.empty()) {
|
|
fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str());
|
|
}
|
|
}
|
|
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",
|
|
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);
|
|
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);
|
|
fprintf(stderr, "\n\n");
|
|
|
|
// TODO: replace with ring-buffer
|
|
std::vector<llama_token> last_n_tokens(n_ctx);
|
|
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
|
|
|
|
if (params.interactive) {
|
|
fprintf(stderr, "== Running in interactive mode. ==\n"
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
|
|
" - Press Ctrl+C to interject at any time.\n"
|
|
#endif
|
|
" - Press Return to return control to LLaMa.\n"
|
|
" - If you want to submit another line, end your input in '\\'.\n\n");
|
|
is_interacting = params.interactive_first;
|
|
}
|
|
|
|
bool is_antiprompt = false;
|
|
bool input_echo = true;
|
|
|
|
// HACK - because session saving incurs a non-negligible delay, for now skip re-saving session
|
|
// if we loaded a session with at least 75% similarity. It's currently just used to speed up the
|
|
// initial prompt so it doesn't need to be an exact match.
|
|
bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < (embd_inp.size() * 3 / 4);
|
|
|
|
|
|
int n_past = 0;
|
|
int n_remain = params.n_predict;
|
|
int n_consumed = 0;
|
|
int n_session_consumed = 0;
|
|
|
|
// the first thing we will do is to output the prompt, so set color accordingly
|
|
set_console_color(con_st, CONSOLE_COLOR_PROMPT);
|
|
|
|
std::vector<llama_token> embd;
|
|
|
|
while (n_remain != 0 || params.interactive) {
|
|
// predict
|
|
if (embd.size() > 0) {
|
|
// infinite text generation via context swapping
|
|
// if we run out of context:
|
|
// - take the n_keep first tokens from the original prompt (via n_past)
|
|
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
|
|
if (n_past + (int) embd.size() > n_ctx) {
|
|
const int n_left = n_past - params.n_keep;
|
|
|
|
n_past = params.n_keep;
|
|
|
|
// insert n_left/2 tokens at the start of embd from last_n_tokens
|
|
embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
|
|
|
|
// stop saving session if we run out of context
|
|
path_session = "";
|
|
|
|
//printf("\n---\n");
|
|
//printf("resetting: '");
|
|
//for (int i = 0; i < (int) embd.size(); i++) {
|
|
// printf("%s", llama_token_to_str(ctx, embd[i]));
|
|
//}
|
|
//printf("'\n");
|
|
//printf("\n---\n");
|
|
}
|
|
|
|
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
|
|
// REVIEW
|
|
if (n_session_consumed < (int) session_tokens.size()) {
|
|
size_t i = 0;
|
|
for ( ; i < embd.size(); i++) {
|
|
if (embd[i] != session_tokens[n_session_consumed]) {
|
|
session_tokens.resize(n_session_consumed);
|
|
break;
|
|
}
|
|
|
|
n_past++;
|
|
n_session_consumed++;
|
|
|
|
if (n_session_consumed >= (int) session_tokens.size()) {
|
|
++i;
|
|
break;
|
|
}
|
|
}
|
|
if (i > 0) {
|
|
embd.erase(embd.begin(), embd.begin() + i);
|
|
}
|
|
}
|
|
|
|
// evaluate tokens in batches
|
|
// embd is typically prepared beforehand to fit within a batch, but not always
|
|
for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
|
|
int n_eval = (int) embd.size() - i;
|
|
if (n_eval > params.n_batch) {
|
|
n_eval = params.n_batch;
|
|
}
|
|
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
|
|
fprintf(stderr, "%s : failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
n_past += n_eval;
|
|
}
|
|
|
|
if (embd.size() > 0 && !path_session.empty()) {
|
|
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
|
|
n_session_consumed = session_tokens.size();
|
|
}
|
|
}
|
|
|
|
embd.clear();
|
|
|
|
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
|
|
// out of user input, sample next token
|
|
const float temp = params.temp;
|
|
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
|
|
const float top_p = params.top_p;
|
|
const float tfs_z = params.tfs_z;
|
|
const float typical_p = params.typical_p;
|
|
const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
|
|
const float repeat_penalty = params.repeat_penalty;
|
|
const float alpha_presence = params.presence_penalty;
|
|
const float alpha_frequency = params.frequency_penalty;
|
|
const int mirostat = params.mirostat;
|
|
const float mirostat_tau = params.mirostat_tau;
|
|
const float mirostat_eta = params.mirostat_eta;
|
|
const bool penalize_nl = params.penalize_nl;
|
|
|
|
// optionally save the session on first sample (for faster prompt loading next time)
|
|
if (!path_session.empty() && need_to_save_session) {
|
|
need_to_save_session = false;
|
|
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
|
|
}
|
|
|
|
llama_token id = 0;
|
|
|
|
{
|
|
auto logits = llama_get_logits(ctx);
|
|
auto n_vocab = llama_n_vocab(ctx);
|
|
|
|
// Apply params.logit_bias map
|
|
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
|
|
logits[it->first] += it->second;
|
|
}
|
|
|
|
std::vector<llama_token_data> candidates;
|
|
candidates.reserve(n_vocab);
|
|
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
|
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
|
}
|
|
|
|
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
|
|
|
// Apply penalties
|
|
float nl_logit = logits[llama_token_nl()];
|
|
auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
|
|
llama_sample_repetition_penalty(ctx, &candidates_p,
|
|
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
|
|
last_n_repeat, repeat_penalty);
|
|
llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
|
|
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
|
|
last_n_repeat, alpha_frequency, alpha_presence);
|
|
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);
|
|
llama_sample_tail_free(ctx, &candidates_p, tfs_z);
|
|
llama_sample_typical(ctx, &candidates_p, typical_p);
|
|
llama_sample_top_p(ctx, &candidates_p, top_p);
|
|
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) {
|
|
set_console_color(con_st, CONSOLE_COLOR_DEFAULT);
|
|
}
|
|
|
|
// in interactive mode, and not currently processing queued inputs;
|
|
// check if we should prompt the user for more
|
|
if (params.interactive && (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.
|
|
for (std::string & antiprompt : params.antiprompt) {
|
|
if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
|
|
is_interacting = true;
|
|
is_antiprompt = true;
|
|
set_console_color(con_st, CONSOLE_COLOR_USER_INPUT);
|
|
fflush(stdout);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (n_past > 0 && is_interacting) {
|
|
// potentially set color to indicate we are taking user input
|
|
set_console_color(con_st, CONSOLE_COLOR_USER_INPUT);
|
|
|
|
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 {
|
|
#if defined(_WIN32)
|
|
std::wstring wline;
|
|
if (!std::getline(std::wcin, wline)) {
|
|
// input stream is bad or EOF received
|
|
return 0;
|
|
}
|
|
win32_utf8_encode(wline, line);
|
|
#else
|
|
if (!std::getline(std::cin, line)) {
|
|
// input stream is bad or EOF received
|
|
return 0;
|
|
}
|
|
#endif
|
|
if (!line.empty()) {
|
|
if (line.back() == '\\') {
|
|
line.pop_back(); // Remove the continue character
|
|
} else {
|
|
another_line = false;
|
|
}
|
|
buffer += line + '\n'; // Append the line to the result
|
|
}
|
|
} while (another_line);
|
|
|
|
// done taking input, reset color
|
|
set_console_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;
|
|
}
|
|
}
|
|
|
|
llama_print_timings(ctx);
|
|
llama_free(ctx);
|
|
|
|
set_console_color(con_st, CONSOLE_COLOR_DEFAULT);
|
|
|
|
return 0;
|
|
}
|