Merge branch 'master' of github.com:tjohnman/llama.cpp into eternal-interactive-mode
This commit is contained in:
commit
5ef2da2bf4
3 changed files with 55 additions and 37 deletions
78
main.cpp
78
main.cpp
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@ -805,7 +805,7 @@ int main(int argc, char ** argv) {
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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.prompt.empty()) {
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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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@ -850,7 +850,11 @@ int main(int argc, char ** argv) {
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params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
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// tokenize the reverse prompt
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std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
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std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
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for (auto antiprompt : params.antiprompt) {
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antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
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}
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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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@ -872,13 +876,16 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "%s: interactive mode on.\n", __func__);
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if(antiprompt_inp.size()) {
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fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
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fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
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fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
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if(antipromptv_inp.size()) {
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for (size_t apindex = 0; apindex < antipromptv_inp.size(); ++apindex) {
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auto antiprompt_inp = antipromptv_inp.at(apindex);
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fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.at(apindex).c_str());
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fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
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fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
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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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}
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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);
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@ -935,35 +942,37 @@ int main(int argc, char ** argv) {
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embd.clear();
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if (embd_inp.size() <= input_consumed) {
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// out of user input, sample next token
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const float top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const float repeat_penalty = params.repeat_penalty;
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if (!is_interacting) {
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// out of user input, sample next token
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const float top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const float repeat_penalty = params.repeat_penalty;
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const int n_vocab = model.hparams.n_vocab;
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const int n_vocab = model.hparams.n_vocab;
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gpt_vocab::id id = 0;
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gpt_vocab::id id = 0;
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{
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const int64_t t_start_sample_us = ggml_time_us();
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{
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const int64_t t_start_sample_us = ggml_time_us();
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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);
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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);
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(id);
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(id);
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t_sample_us += ggml_time_us() - t_start_sample_us;
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t_sample_us += ggml_time_us() - t_start_sample_us;
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}
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// add it to the context
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embd.push_back(id);
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// echo this to console
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input_noecho = false;
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// decrement remaining sampling budget
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--remaining_tokens;
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}
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// add it to the context
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embd.push_back(id);
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// echo this to console
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input_noecho = false;
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// decrement remaining sampling budget
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--remaining_tokens;
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} else {
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// some user input remains from prompt or interaction, forward it to processing
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while (embd_inp.size() > input_consumed) {
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@ -994,9 +1003,12 @@ int main(int argc, char ** argv) {
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// check if we should prompt the user for more
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if (params.interactive && embd_inp.size() <= input_consumed) {
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// check for reverse prompt
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if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
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// reverse prompt found
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is_interacting = true;
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for (auto antiprompt_inp : antipromptv_inp) {
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if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
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// reverse prompt found
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is_interacting = true;
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break;
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}
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}
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if (is_interacting) {
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// currently being interactive
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10
utils.cpp
10
utils.cpp
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@ -71,10 +71,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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} else if (arg == "--color") {
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params.use_color = true;
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} else if (arg == "-r" || arg == "--reverse-prompt") {
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params.antiprompt = argv[++i];
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params.antiprompt.push_back(argv[++i]);
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} else if (arg == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, params);
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exit(0);
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} else if (arg == "--random-prompt") {
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params.random_prompt = true;
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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gpt_print_usage(argc, argv, params);
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@ -93,12 +95,14 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
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fprintf(stderr, " -i, --interactive run in interactive mode\n");
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fprintf(stderr, " --interactive-start run in interactive mode and poll user input at startup\n");
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fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
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fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT\n");
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fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT (can be\n");
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fprintf(stderr, " specified more than once for multiple prompts).\n");
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fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
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fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stderr, " prompt to start generation with (default: random)\n");
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fprintf(stderr, " prompt to start generation with (default: empty)\n");
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fprintf(stderr, " --random-prompt start with a randomized prompt.\n");
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fprintf(stderr, " -f FNAME, --file FNAME\n");
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fprintf(stderr, " prompt file to start generation.\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
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4
utils.h
4
utils.h
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@ -30,11 +30,13 @@ struct gpt_params {
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt;
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bool random_prompt = false;
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bool use_color = false; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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bool interactive_start = false; // reverse prompt immediately
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std::string antiprompt = ""; // string upon seeing which more user input is prompted
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std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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