mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 11:24:35 +00:00
3a0dcb3920
This new mode works by first loading the model then listening for TCP connections on a port. When a connection is received, arguments will be parsed using a simple protocol: - First the number of arguments will be read followed by a newline character. - Then each argument will be read, separated by the 0 byte. - With this we build an argument vector, similar to what is passed to the program entry point. We pass this to gpt_params_parse. Finally `run` will be executed with the input/output streams connected to the socket. Signed-off-by: Thiago Padilha <thiago@padilha.cc>
168 lines
7.8 KiB
C++
168 lines
7.8 KiB
C++
#include "utils.h"
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#include <cassert>
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#include <cstring>
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#include <fstream>
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#include <string>
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#include <iterator>
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#include <algorithm>
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#if defined(_MSC_VER) || defined(__MINGW32__)
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#include <malloc.h> // using malloc.h with MSC/MINGW
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#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
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#include <alloca.h>
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#endif
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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// determine sensible default number of threads.
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// std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0.
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#ifdef __linux__
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std::ifstream cpuinfo("/proc/cpuinfo");
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params.n_threads = std::count(std::istream_iterator<std::string>(cpuinfo),
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std::istream_iterator<std::string>(),
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std::string("processor"));
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#endif
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if (params.n_threads == 0) {
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params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());
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}
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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 == "-f" || arg == "--file") {
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std::ifstream file(argv[++i]);
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
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if (params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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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 == "-c" || arg == "--ctx_size") {
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params.n_ctx = std::stoi(argv[++i]);
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} else if (arg == "--memory_f16") {
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params.memory_f16 = true;
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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 == "--repeat_last_n") {
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params.repeat_last_n = std::stoi(argv[++i]);
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} else if (arg == "--repeat_penalty") {
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params.repeat_penalty = 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 == "-i" || arg == "--interactive") {
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params.interactive = true;
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} else if (arg == "--interactive-first") {
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params.interactive_start = true;
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} else if (arg == "-ins" || arg == "--instruct") {
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params.instruct = true;
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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.push_back(argv[++i]);
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} else if (arg == "--perplexity") {
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params.perplexity = true;
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} else if (arg == "--ignore-eos") {
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params.ignore_eos = true;
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} else if (arg == "--n_parts") {
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params.n_parts = std::stoi(argv[++i]);
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#ifndef _WIN32
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} else if (arg == "-l" || arg == "--listen") {
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params.listen_port = argv[++i];
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#endif
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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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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 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, " -i, --interactive run in interactive mode\n");
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fprintf(stderr, " --interactive-first run in interactive mode and wait for input right away\n");
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fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
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fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
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fprintf(stderr, " run in interactive mode and 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, use random seed for <= 0)\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: 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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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, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
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fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
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fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
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fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
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fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n");
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
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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, " --perplexity compute perplexity over the prompt\n");
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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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#ifndef _WIN32
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fprintf(stderr, " -l PORT, --listen PORT\n");
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fprintf(stderr, " Run in TCP mode, listening on PORT\n");
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#endif
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fprintf(stderr, "\n");
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}
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std::string 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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// TODO: not great allocating this every time
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std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
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// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
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std::vector<llama_token> res(text.size() + (int)add_bos);
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int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
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assert(n >= 0);
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res.resize(n);
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return res;
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}
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