Merge branch 'master' of github.com:tjohnman/llama.cpp into eternal-interactive-mode

This commit is contained in:
Johnman 2023-03-19 18:06:04 +01:00
commit 5ef2da2bf4
3 changed files with 55 additions and 37 deletions

View file

@ -805,7 +805,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.prompt.empty()) {
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
@ -850,7 +850,11 @@ int main(int argc, char ** argv) {
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);
std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
for (auto antiprompt : params.antiprompt) {
antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
}
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
@ -872,13 +876,16 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: interactive mode on.\n", __func__);
if(antiprompt_inp.size()) {
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
if(antipromptv_inp.size()) {
for (size_t apindex = 0; apindex < antipromptv_inp.size(); ++apindex) {
auto antiprompt_inp = antipromptv_inp.at(apindex);
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.at(apindex).c_str());
fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
}
fprintf(stderr, "\n");
}
fprintf(stderr, "\n");
}
}
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);
@ -935,35 +942,37 @@ int main(int argc, char ** argv) {
embd.clear();
if (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;
if (!is_interacting) {
// 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;
const int n_vocab = model.hparams.n_vocab;
const int n_vocab = model.hparams.n_vocab;
gpt_vocab::id id = 0;
gpt_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
{
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);
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);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
t_sample_us += ggml_time_us() - t_start_sample_us;
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;
}
// 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 (embd_inp.size() > input_consumed) {
@ -994,9 +1003,12 @@ int main(int argc, char ** argv) {
// 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;
for (auto antiprompt_inp : antipromptv_inp) {
if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
// reverse prompt found
is_interacting = true;
break;
}
}
if (is_interacting) {
// currently being interactive

View file

@ -71,10 +71,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
} else if (arg == "--color") {
params.use_color = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
params.antiprompt = argv[++i];
params.antiprompt.push_back(argv[++i]);
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, params);
exit(0);
} else if (arg == "--random-prompt") {
params.random_prompt = true;
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
gpt_print_usage(argc, argv, params);
@ -93,12 +95,14 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
fprintf(stderr, " -i, --interactive run in interactive mode\n");
fprintf(stderr, " --interactive-start run in interactive mode and poll user input at startup\n");
fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT\n");
fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT (can be\n");
fprintf(stderr, " specified more than once for multiple prompts).\n");
fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
fprintf(stderr, " prompt to start generation with (default: random)\n");
fprintf(stderr, " prompt to start generation with (default: empty)\n");
fprintf(stderr, " --random-prompt start with a randomized prompt.\n");
fprintf(stderr, " -f FNAME, --file FNAME\n");
fprintf(stderr, " prompt file to start generation.\n");
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);

View file

@ -30,11 +30,13 @@ struct gpt_params {
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt;
bool random_prompt = false;
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool interactive_start = false; // reverse prompt immediately
std::string antiprompt = ""; // string upon seeing which more user input is prompted
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
};
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);