CONSOLE_STATE_DEFAULT=0,

CONSOLE_STATE_PROMPT,

CONSOLE_STATE_USER_INPUT

{

if (!con_use_color) return;

// only emit color code if state changed

if (new_st != con_st) {

con_st = new_st;

switch(con_st) {

case CONSOLE_STATE_DEFAULT:

printf(ANSI_COLOR_RESET);

return;

case CONSOLE_STATE_PROMPT:

printf(ANSI_COLOR_YELLOW);

return;

case CONSOLE_STATE_USER_INPUT:

printf(ANSI_BOLD ANSI_COLOR_GREEN);

return;

}

}

std::vector<double> probs(logits.size());

float max_logit = logits[0];

for (float v : logits) max_logit = std::max(max_logit, v);

double sum_exp = 0.0;

for (size_t i = 0; i < logits.size(); i++) {

// Subtract the maximum logit value from the current logit value for numerical stability

float logit = logits[i] - max_logit;

double exp_logit = std::exp(logit);

sum_exp += exp_logit;

probs[i] = exp_logit;

}

for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;

return probs;

// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research

// Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`

// Output: `perplexity: 13.5106 [114/114]`

auto tokens = ::llama_tokenize(ctx, params.prompt, true);

int count = 0;

double nll = 0.0;

int seq_count = tokens.size() / params.n_ctx;

fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);

for (int i = 0; i < seq_count; ++i) {

int start = i * params.n_ctx;

int end = start + params.n_ctx - 1;

std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);

auto start_t = std::chrono::high_resolution_clock::now();

if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {

fprintf(stderr, "%s : failed to eval\n", __func__);

return;

}

auto end_t = std::chrono::high_resolution_clock::now();

if (i == 0) {

double seconds = std::chrono::duration<double>(end_t - start_t).count();

printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));

}

// We get the logits for all the tokens in the context window (params.n_ctx)

// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,

// calculate the perplexity over the last half the window (so the model always has

// some context to predict the token).

//

// We rely on the fact that attention in the forward pass only looks at previous

// tokens here, so the logits returned for each token are an accurate representation

// of what the model would have predicted at that point.

//

// Example, we have a context window of 512, we will compute perplexity for each of the

// last 256 tokens. Then, we split the input up into context window size chunks to

// process the entire prompt.

auto logits = llama_get_logits(ctx);

for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {

// Calculate probability of next token, given the previous ones.

int n_vocab = llama_n_vocab(ctx);

std::vector<float> tok_logits(

logits + j * n_vocab,

logits + (j + 1) * n_vocab);

double prob = softmax(tok_logits)[tokens[start + j + 1]];

nll += -std::log(prob);

++count;

}

// perplexity is e^(average negative log-likelihood)

printf("[%d]%.4lf,", i + 1, std::exp(nll / count));

fflush(stdout);

}

printf("\n");

set_console_state(CONSOLE_STATE_DEFAULT);

printf("\n"); // this also force flush stdout.

if (signo == SIGINT) {

if (!is_interacting) {

is_interacting=true;

} else {

_exit(130);

}

}

// has to be called once at the start of the program to init ggml stuff

ggml_time_init();

gpt_params params;

params.model = "models/llama-7B/ggml-model.bin";

if (gpt_params_parse(argc, argv, params) == false) {

return 1;

}

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);

}

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);

}

// save choice to use color for later

// (note for later: this is a slightly awkward choice)

con_use_color = params.use_color;

llama_context * ctx;

// load the model

{

auto lparams = llama_context_default_params();

lparams.n_ctx = params.n_ctx;

lparams.n_parts = params.n_parts;

lparams.seed = params.seed;

lparams.f16_kv = params.memory_f16;

lparams.logits_all = params.perplexity;

ctx = llama_init_from_file(params.model.c_str(), lparams);

if (ctx == NULL) {

fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());

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 required inference memory per token:

// TODO: better way to do that

{

const std::vector<llama_token> tmp = { 0, 1, 2, 3 };

llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);

}

if (params.perplexity) {

perplexity(ctx, params);

exit(0);

}

int n_past = 0;

// Add a space in front of the first character to match OG llama tokenizer behavior

params.prompt.insert(0, 1, ' ');

// tokenize the prompt

auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);

const int n_ctx = llama_n_ctx(ctx);

params.n_predict = std::min(params.n_predict, n_ctx - (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 = true;

params.antiprompt.push_back("### Instruction:\n\n");

}

// enable interactive mode if reverse prompt is specified

if (params.antiprompt.size() != 0) {

params.interactive = true;

}

if (params.interactive_start) {

params.interactive = true;

}

// determine newline token

auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);

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]));

}

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)

signal(SIGINT, sigint_handler);

#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());

}

}

}

fprintf(stderr, "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);

fprintf(stderr, "\n\n");

std::vector<llama_token> embd;

int last_n_size = params.repeat_last_n;

std::vector<llama_token> last_n_tokens(last_n_size);

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_start || params.instruct;

}

int input_consumed = 0;

bool input_noecho = false;

int remaining_tokens = params.n_predict;

#if defined (_WIN32)

if (params.use_color) {

// Enable ANSI colors on Windows 10+

unsigned long dwMode = 0;

void* hConOut = GetStdHandle((unsigned long)-11); // STD_OUTPUT_HANDLE (-11)

if (hConOut && hConOut != (void*)-1 && GetConsoleMode(hConOut, &dwMode) && !(dwMode & 0x4)) {

SetConsoleMode(hConOut, dwMode | 0x4); // ENABLE_VIRTUAL_TERMINAL_PROCESSING (0x4)

}

}

#endif

// the first thing we will do is to output the prompt, so set color accordingly

set_console_state(CONSOLE_STATE_PROMPT);

while (remaining_tokens > 0 || params.interactive) {

// predict

if (embd.size() > 0) {

if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {

fprintf(stderr, "%s : failed to eval\n", __func__);

return 1;

}

}

n_past += embd.size();

embd.clear();

if ((int) embd_inp.size() <= input_consumed) {

// out of user input, sample next token

const float top_k = params.top_k;

const float top_p = params.top_p;

const float temp = params.temp;

const float repeat_penalty = params.repeat_penalty;

llama_token id = 0;

{

auto logits = llama_get_logits(ctx);

if (params.ignore_eos) {

// set the logit of the eos token to zero to avoid sampling it

//logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;

// TODO: this does not work of params.logits_all == true

assert(params.perplexity == false);

logits[llama_token_eos()] = 0;

}

id = llama_sample_top_p_top_k(ctx, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);

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) {

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_noecho = false;

// decrement remaining sampling budget

--remaining_tokens;

} else {

// some user input remains from prompt or interaction, forward it to processing

while ((int) 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;

if ((int) embd.size() >= params.n_batch) {

break;

}

}

}

// display text

if (!input_noecho) {

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_noecho && (int)embd_inp.size() == input_consumed) {

set_console_state(CONSOLE_STATE_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() <= input_consumed) {

// check for reverse prompt

std::string last_output;

for (auto id : last_n_tokens) {

last_output += llama_token_to_str(ctx, id);

}

// 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;

break;

}

}

if (is_interacting) {

// potentially set color to indicate we are taking user input

set_console_state(CONSOLE_STATE_USER_INPUT);

if (params.instruct) {

input_consumed = embd_inp.size();

embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());

printf("\n> ");

}

std::string buffer;

std::string line;

bool another_line = true;

do {

std::getline(std::cin, line);

if (line.empty() || line.back() != '\\') {

another_line = false;

} else {

line.pop_back(); // Remove the continue character

}

buffer += line + '\n'; // Append the line to the result

} while (another_line);

// done taking input, reset color

set_console_state(CONSOLE_STATE_DEFAULT);

auto line_inp = ::llama_tokenize(ctx, buffer, false);

embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());

if (params.instruct) {

embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());

}

remaining_tokens -= line_inp.size();

input_noecho = true; // do not echo this again

}

is_interacting = false;

}

// end of text token

if (embd.back() == llama_token_eos()) {

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 && remaining_tokens <= 0) {

remaining_tokens = params.n_predict;

is_interacting = true;

}

}

#if defined (_WIN32)

signal(SIGINT, SIG_DFL);

#endif

llama_print_timings(ctx);

llama_free(ctx);

set_console_state(CONSOLE_STATE_DEFAULT);

return 0;