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https://github.com/LostRuins/koboldcpp.git
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fixed other arch
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Concedo
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abb9ad789c
9 changed files with 61 additions and 456 deletions
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@ -3,78 +3,7 @@
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#include <fstream>
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#include <regex>
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bool utils_gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-s" || arg == "--seed") {
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params.seed = std::stoi(argv[++i]);
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(argv[++i]);
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = argv[++i];
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} else if (arg == "-n" || arg == "--n_predict") {
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params.n_predict = std::stoi(argv[++i]);
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} else if (arg == "--top_k") {
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params.top_k = std::stoi(argv[++i]);
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} else if (arg == "--top_p") {
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params.top_p = std::stof(argv[++i]);
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} else if (arg == "--temp") {
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params.temp = std::stof(argv[++i]);
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} else if (arg == "-b" || arg == "--batch_size") {
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params.n_batch = std::stoi(argv[++i]);
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} else if (arg == "-m" || arg == "--model") {
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params.model = argv[++i];
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} else if (arg == "-h" || arg == "--help") {
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utils_gpt_print_usage(argc, argv, params);
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exit(0);
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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utils_gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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return true;
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}
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void utils_gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\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, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stderr, " -m FNAME, --model FNAME\n");
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fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
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fprintf(stderr, "\n");
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}
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std::string utils_gpt_random_prompt(std::mt19937 & rng) {
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const int r = rng() % 10;
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switch (r) {
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case 0: return "So";
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case 1: return "Once upon a time";
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case 2: return "When";
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case 3: return "The";
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case 4: return "After";
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case 5: return "If";
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case 6: return "import";
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case 7: return "He";
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case 8: return "She";
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case 9: return "They";
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default: return "To";
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}
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return "The";
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}
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void utreplace(std::string & str, const std::string & needle, const std::string & replacement) {
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size_t pos = 0;
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@ -175,6 +104,31 @@ std::map<std::string, int32_t> json_parse(const std::string & fname) {
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return result;
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}
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void gpt_vocab::add_special_token(const std::string & token) {
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special_tokens.push_back(token);
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}
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static void append_utf8(char32_t ch, std::string & out) {
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if (ch <= 0x7F) {
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out.push_back(static_cast<unsigned char>(ch));
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} else if (ch <= 0x7FF) {
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out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
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out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
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} else if (ch <= 0xFFFF) {
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out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
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out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
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out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
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} else if (ch <= 0x10FFFF) {
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out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
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out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
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out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
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out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
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} else {
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printf("Invalid Unicode code point\n");
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}
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}
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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
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std::vector<std::string> words;
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@ -208,7 +162,8 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
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if (it != vocab.token_to_id.end()) {
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tokens.push_back(it->second);
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i = j;
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break;
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j = n;
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continue;
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}
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--j;
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}
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@ -230,202 +185,6 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
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return tokens;
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}
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bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
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printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
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vocab.token_to_id = ::json_parse(fname);
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for (const auto & kv : vocab.token_to_id) {
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vocab.id_to_token[kv.second] = kv.first;
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}
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printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
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// print the vocabulary
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//for (auto kv : vocab.token_to_id) {
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// printf("'%s' -> %d\n", kv.first.data(), kv.second);
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//}
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return true;
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}
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void gptj_sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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}
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gpt_vocab::id gptj_sample_top_p_top_k(
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const gpt_vocab & vocab,
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const float * logits,
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std::vector<gpt_vocab::id> & last_n_tokens,
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double repeat_penalty,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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for (int i = 0; i < n_logits; ++i) {
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// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
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// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
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if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
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// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
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if (logits[i] < 0.0) {
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logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
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}
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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}
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}
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gptj_sample_top_k(logits_id, top_k > 0 ? std::min(top_k, n_logits) : n_logits);
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < (int) probs.size(); i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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probs.resize(i + 1);
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logits_id.resize(i + 1);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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//printf("\n");
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//for (int i = 0; i < (int) 10; i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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//}
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//printf("\n\n");
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//exit(0);
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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gpt_vocab::id gpt_sample_top_k_top_p(
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const gpt_vocab & vocab,
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const float * logits,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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for (int i = 0; i < n_logits; ++i) {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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}
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < top_k; i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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top_k = i + 1;
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probs.resize(top_k);
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logits_id.resize(top_k);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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//printf("\n");
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//for (int i = 0; i < (int) probs.size(); i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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//}
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//exit(0);
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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bool should_transpose_layer(std::string name)
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{
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