mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 23:29:44 +00:00
7a9b6c3a8b
* Reduce memory usage and allocate enough memory for large contexts * Simpler scratch buffer usage * Reenable BLAS for quantized mul_mat * Fix number of layers in 30B and 65B * Fix KV cache size for F32
241 lines
9.9 KiB
C++
241 lines
9.9 KiB
C++
#include "ggml.h"
|
|
|
|
#include "utils.h"
|
|
|
|
#include <cassert>
|
|
#include <cstring>
|
|
#include <fstream>
|
|
#include <string>
|
|
#include <iterator>
|
|
#include <algorithm>
|
|
|
|
#if defined(_MSC_VER) || defined(__MINGW32__)
|
|
#include <malloc.h> // using malloc.h with MSC/MINGW
|
|
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
|
|
#include <alloca.h>
|
|
#endif
|
|
|
|
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|
// determine sensible default number of threads.
|
|
// std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0.
|
|
#ifdef __linux__
|
|
std::ifstream cpuinfo("/proc/cpuinfo");
|
|
params.n_threads = std::count(std::istream_iterator<std::string>(cpuinfo),
|
|
std::istream_iterator<std::string>(),
|
|
std::string("processor"));
|
|
#endif
|
|
if (params.n_threads == 0) {
|
|
params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());
|
|
}
|
|
|
|
bool invalid_param = false;
|
|
std::string arg;
|
|
for (int i = 1; i < argc; i++) {
|
|
arg = argv[i];
|
|
|
|
if (arg == "-s" || arg == "--seed") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.seed = std::stoi(argv[i]);
|
|
} else if (arg == "-t" || arg == "--threads") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads = std::stoi(argv[i]);
|
|
} else if (arg == "-p" || arg == "--prompt") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.prompt = argv[i];
|
|
} else if (arg == "-f" || arg == "--file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream file(argv[i]);
|
|
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
|
|
if (params.prompt.back() == '\n') {
|
|
params.prompt.pop_back();
|
|
}
|
|
} else if (arg == "-n" || arg == "--n_predict") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_predict = std::stoi(argv[i]);
|
|
} else if (arg == "--top_k") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.top_k = std::stoi(argv[i]);
|
|
} else if (arg == "-c" || arg == "--ctx_size") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_ctx = std::stoi(argv[i]);
|
|
} else if (arg == "--memory_f32") {
|
|
params.memory_f16 = false;
|
|
} else if (arg == "--top_p") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.top_p = std::stof(argv[i]);
|
|
} else if (arg == "--temp") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.temp = std::stof(argv[i]);
|
|
} else if (arg == "--repeat_last_n") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.repeat_last_n = std::stoi(argv[i]);
|
|
} else if (arg == "--repeat_penalty") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.repeat_penalty = std::stof(argv[i]);
|
|
} else if (arg == "-b" || arg == "--batch_size") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_batch = std::stoi(argv[i]);
|
|
params.n_batch = std::min(512, params.n_batch);
|
|
} else if (arg == "-m" || arg == "--model") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model = argv[i];
|
|
} else if (arg == "-i" || arg == "--interactive") {
|
|
params.interactive = true;
|
|
} else if (arg == "--embedding") {
|
|
params.embedding = true;
|
|
} else if (arg == "--interactive-start") {
|
|
params.interactive = true;
|
|
} else if (arg == "--interactive-first") {
|
|
params.interactive_start = true;
|
|
} else if (arg == "-ins" || arg == "--instruct") {
|
|
params.instruct = true;
|
|
} else if (arg == "--color") {
|
|
params.use_color = true;
|
|
} else if (arg == "--mlock") {
|
|
params.use_mlock = true;
|
|
} else if (arg == "--mtest") {
|
|
params.mem_test = true;
|
|
} else if (arg == "-r" || arg == "--reverse-prompt") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.antiprompt.push_back(argv[i]);
|
|
} else if (arg == "--perplexity") {
|
|
params.perplexity = true;
|
|
} else if (arg == "--ignore-eos") {
|
|
params.ignore_eos = true;
|
|
} else if (arg == "--n_parts") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_parts = std::stoi(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);
|
|
exit(1);
|
|
}
|
|
}
|
|
if (invalid_param) {
|
|
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
|
|
gpt_print_usage(argc, argv, params);
|
|
exit(1);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|
fprintf(stderr, "usage: %s [options]\n", argv[0]);
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "options:\n");
|
|
fprintf(stderr, " -h, --help show this help message and exit\n");
|
|
fprintf(stderr, " -i, --interactive run in interactive mode\n");
|
|
fprintf(stderr, " --interactive-first run in interactive mode and wait for input right away\n");
|
|
fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
|
|
fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
|
|
fprintf(stderr, " run in interactive mode and 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, use random seed for <= 0)\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: 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);
|
|
fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
|
|
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
|
|
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
|
|
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
|
|
fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
|
|
fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
|
|
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
|
|
fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
|
|
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
|
|
if (ggml_mlock_supported()) {
|
|
fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
fprintf(stderr, " --mtest compute maximum memory usage\n");
|
|
fprintf(stderr, " -m FNAME, --model FNAME\n");
|
|
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
|
|
fprintf(stderr, "\n");
|
|
}
|
|
|
|
std::string gpt_random_prompt(std::mt19937 & rng) {
|
|
const int r = rng() % 10;
|
|
switch (r) {
|
|
case 0: return "So";
|
|
case 1: return "Once upon a time";
|
|
case 2: return "When";
|
|
case 3: return "The";
|
|
case 4: return "After";
|
|
case 5: return "If";
|
|
case 6: return "import";
|
|
case 7: return "He";
|
|
case 8: return "She";
|
|
case 9: return "They";
|
|
default: return "To";
|
|
}
|
|
|
|
return "The";
|
|
}
|
|
|
|
// TODO: not great allocating this every time
|
|
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
|
|
// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
|
|
std::vector<llama_token> res(text.size() + (int)add_bos);
|
|
int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
|
|
assert(n >= 0);
|
|
res.resize(n);
|
|
|
|
return res;
|
|
}
|