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llama.cpp/main.cpp

311 lines
10 KiB

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#include "ggml.h"
#include "llama.h"
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#include "utils.h"
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#endif
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
// determine number of model parts based on the dimension
static const std::map<int, int> LLAMA_N_PARTS = {
{ 4096, 1 },
{ 5120, 2 },
{ 6656, 4 },
{ 8192, 8 },
};
static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
void sigint_handler(int signo) {
if (signo == SIGINT) {
if (!is_interacting) {
is_interacting=true;
} else {
_exit(130);
}
}
}
#endif
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int main(int argc, char ** argv) {
ggml_time_init();
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const int64_t t_main_start_us = ggml_time_us();
gpt_params params;
params.model = "models/7B/ggml-model-q4_0.bin";
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if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
if (params.seed < 0) {
params.seed = time(NULL);
}
printf("%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.prompt.empty()) {
params.prompt = gpt_random_prompt(rng);
}
// params.prompt = R"(// this function checks if the number n is prime
//bool is_prime(int n) {)";
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int64_t t_load_us = 0;
gpt_vocab vocab;
llama_model model;
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!llama_model_load(params.model, model, vocab, 512)) { // TODO: set context from user input ??
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return 1;
}
t_load_us = ggml_time_us() - t_start_us;
}
int n_past = 0;
int64_t t_sample_us = 0;
int64_t t_predict_us = 0;
std::vector<float> logits;
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
// tokenize the reverse prompt
std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
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printf("\n");
printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
}
printf("\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);
#endif
printf("%s: interactive mode on.\n", __func__);
if(antiprompt_inp.size()) {
printf("%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
printf("%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
printf("%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
}
printf("\n");
}
}
printf("sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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printf("\n\n");
std::vector<gpt_vocab::id> embd;
// determine the required inference memory per token:
size_t mem_per_token = 0;
llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
int last_n_size = params.repeat_last_n;
std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
if (params.interactive) {
printf("== Running in interactive mode. ==\n"
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
" - 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");
}
int remaining_tokens = params.n_predict;
int input_consumed = 0;
bool input_noecho = false;
// prompt user immediately after the starting prompt has been loaded
if (params.interactive_start) {
is_interacting = true;
}
// set the color for the prompt which will be output initially
if (params.use_color) {
printf(ANSI_COLOR_YELLOW);
}
while (remaining_tokens > 0) {
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// predict
if (embd.size() > 0) {
const int64_t t_start_us = ggml_time_us();
if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
printf("Failed to predict\n");
return 1;
}
t_predict_us += ggml_time_us() - t_start_us;
}
n_past += embd.size();
embd.clear();
if (embd_inp.size() <= input_consumed) {
// out of user input, sample next token
const float top_k = params.top_k;
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const float top_p = params.top_p;
const float temp = params.temp;
const float repeat_penalty = params.repeat_penalty;
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const int n_vocab = model.hparams.n_vocab;
gpt_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
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t_sample_us += ggml_time_us() - t_start_sample_us;
}
// add it to the context
embd.push_back(id);
// echo this to console
input_noecho = false;
// decrement remaining sampling budget
--remaining_tokens;
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} else {
// some user input remains from prompt or interaction, forward it to processing
while (embd_inp.size() > input_consumed) {
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[input_consumed]);
++input_consumed;
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if (embd.size() > params.n_batch) {
break;
}
}
}
// display text
if (!input_noecho) {
for (auto id : embd) {
printf("%s", vocab.id_to_token[id].c_str());
}
// reset color to default if we there is no pending user input
if (params.use_color && embd_inp.size() <= input_consumed) {
printf(ANSI_COLOR_RESET);
}
fflush(stdout);
}
// in interactive mode, and not currently processing queued inputs;
// check if we should prompt the user for more
if (params.interactive && embd_inp.size() <= input_consumed) {
// check for reverse prompt
if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
// reverse prompt found
is_interacting = true;
}
if (is_interacting) {
// currently being interactive
bool another_line=true;
while (another_line) {
fflush(stdout);
char buf[256] = {0};
int n_read;
if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) {
// presumable empty line, consume the newline
scanf("%*c");
n_read=0;
}
if(params.use_color) printf(ANSI_COLOR_RESET);
if (n_read > 0 && buf[n_read-1]=='\\') {
another_line = true;
buf[n_read-1] = '\n';
buf[n_read] = 0;
} else {
another_line = false;
buf[n_read] = '\n';
buf[n_read+1] = 0;
}
std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buf, false);
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
remaining_tokens -= line_inp.size();
input_noecho = true; // do not echo this again
}
is_interacting = false;
}
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}
// end of text token
if (embd.back() == 2) {
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printf(" [end of text]\n");
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break;
}
}
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// report timing
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n\n");
printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
}
ggml_free(model.ctx);
return 0;
}