summaryrefslogtreecommitdiff
path: root/common/common.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'common/common.cpp')
-rw-r--r--common/common.cpp767
1 files changed, 767 insertions, 0 deletions
diff --git a/common/common.cpp b/common/common.cpp
new file mode 100644
index 00000000..d7e1a572
--- /dev/null
+++ b/common/common.cpp
@@ -0,0 +1,767 @@
+#include "common.h"
+
+#include <cassert>
+#include <iostream>
+#include <cstring>
+#include <fstream>
+#include <string>
+#include <iterator>
+#include <algorithm>
+#include <sstream>
+#include <unordered_set>
+#include <regex>
+
+#if defined(__APPLE__) && defined(__MACH__)
+#include <sys/types.h>
+#include <sys/sysctl.h>
+#endif
+
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#define NOMINMAX
+#include <windows.h>
+#include <fcntl.h>
+#include <io.h>
+#else
+#include <sys/ioctl.h>
+#include <unistd.h>
+#endif
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+int32_t get_num_physical_cores() {
+#ifdef __linux__
+ // enumerate the set of thread siblings, num entries is num cores
+ std::unordered_set<std::string> siblings;
+ for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
+ std::ifstream thread_siblings("/sys/devices/system/cpu"
+ + std::to_string(cpu) + "/topology/thread_siblings");
+ if (!thread_siblings.is_open()) {
+ break; // no more cpus
+ }
+ std::string line;
+ if (std::getline(thread_siblings, line)) {
+ siblings.insert(line);
+ }
+ }
+ if (siblings.size() > 0) {
+ return static_cast<int32_t>(siblings.size());
+ }
+#elif defined(__APPLE__) && defined(__MACH__)
+ int32_t num_physical_cores;
+ size_t len = sizeof(num_physical_cores);
+ int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
+ if (result == 0) {
+ return num_physical_cores;
+ }
+ result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
+ if (result == 0) {
+ return num_physical_cores;
+ }
+#elif defined(_WIN32)
+ //TODO: Implement
+#endif
+ unsigned int n_threads = std::thread::hardware_concurrency();
+ return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
+}
+
+void process_escapes(std::string& input) {
+ std::size_t input_len = input.length();
+ std::size_t output_idx = 0;
+
+ for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
+ if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
+ switch (input[++input_idx]) {
+ case 'n': input[output_idx++] = '\n'; break;
+ case 'r': input[output_idx++] = '\r'; break;
+ case 't': input[output_idx++] = '\t'; break;
+ case '\'': input[output_idx++] = '\''; break;
+ case '\"': input[output_idx++] = '\"'; break;
+ case '\\': input[output_idx++] = '\\'; break;
+ default: input[output_idx++] = '\\';
+ input[output_idx++] = input[input_idx]; break;
+ }
+ } else {
+ input[output_idx++] = input[input_idx];
+ }
+ }
+
+ input.resize(output_idx);
+}
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
+ bool invalid_param = false;
+ bool escape_prompt = false;
+ std::string arg;
+ gpt_params default_params;
+ const std::string arg_prefix = "--";
+
+ for (int i = 1; i < argc; i++) {
+ arg = argv[i];
+ if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+ std::replace(arg.begin(), arg.end(), '_', '-');
+ }
+
+ if (arg == "-s" || arg == "--seed") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.seed = std::stoul(argv[i]);
+ } else if (arg == "-t" || arg == "--threads") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_threads = std::stoi(argv[i]);
+ if (params.n_threads <= 0) {
+ params.n_threads = std::thread::hardware_concurrency();
+ }
+ } else if (arg == "-p" || arg == "--prompt") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.prompt = argv[i];
+ } else if (arg == "-e") {
+ escape_prompt = true;
+ } else if (arg == "--prompt-cache") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.path_prompt_cache = argv[i];
+ } else if (arg == "--prompt-cache-all") {
+ params.prompt_cache_all = true;
+ } else if (arg == "--prompt-cache-ro") {
+ params.prompt_cache_ro = true;
+ } else if (arg == "-f" || arg == "--file") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ std::ifstream file(argv[i]);
+ if (!file) {
+ fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+ invalid_param = true;
+ break;
+ }
+ 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 == "--rope-freq-base") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.rope_freq_base = std::stof(argv[i]);
+ } else if (arg == "--rope-freq-scale") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.rope_freq_scale = std::stof(argv[i]);
+ } else if (arg == "--rope-scale") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.rope_freq_scale = 1.0f/std::stof(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 == "--tfs") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.tfs_z = std::stof(argv[i]);
+ } else if (arg == "--typical") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.typical_p = 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 == "--frequency-penalty") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.frequency_penalty = std::stof(argv[i]);
+ } else if (arg == "--presence-penalty") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.presence_penalty = std::stof(argv[i]);
+ } else if (arg == "--mirostat") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.mirostat = std::stoi(argv[i]);
+ } else if (arg == "--mirostat-lr") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.mirostat_eta = std::stof(argv[i]);
+ } else if (arg == "--mirostat-ent") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.mirostat_tau = std::stof(argv[i]);
+ } else if (arg == "--cfg-negative-prompt") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.cfg_negative_prompt = argv[i];
+ } else if (arg == "--cfg-negative-prompt-file") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ std::ifstream file(argv[i]);
+ if (!file) {
+ fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+ invalid_param = true;
+ break;
+ }
+ std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.cfg_negative_prompt));
+ if (params.cfg_negative_prompt.back() == '\n') {
+ params.cfg_negative_prompt.pop_back();
+ }
+ } else if (arg == "--cfg-scale") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.cfg_scale = 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 == "--keep") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_keep = std::stoi(argv[i]);
+ } else if (arg == "--chunks") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_chunks = std::stoi(argv[i]);
+ } else if (arg == "-m" || arg == "--model") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.model = argv[i];
+ } else if (arg == "-a" || arg == "--alias") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.model_alias = argv[i];
+ } else if (arg == "--lora") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.lora_adapter = argv[i];
+ params.use_mmap = false;
+ } else if (arg == "--lora-base") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.lora_base = argv[i];
+ } else if (arg == "-i" || arg == "--interactive") {
+ params.interactive = true;
+ } else if (arg == "--embedding") {
+ params.embedding = true;
+ } else if (arg == "--interactive-first") {
+ params.interactive_first = true;
+ } else if (arg == "-ins" || arg == "--instruct") {
+ params.instruct = true;
+ } else if (arg == "--multiline-input") {
+ params.multiline_input = true;
+ } else if (arg == "--simple-io") {
+ params.simple_io = true;
+ } else if (arg == "--color") {
+ params.use_color = true;
+ } else if (arg == "--mlock") {
+ params.use_mlock = true;
+ } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
+ params.n_gpu_layers = std::stoi(argv[i]);
+#else
+ fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
+ fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+#endif
+ } else if (arg == "--main-gpu" || arg == "-mg") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+#ifdef GGML_USE_CUBLAS
+ params.main_gpu = std::stoi(argv[i]);
+#else
+ fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
+#endif
+ } else if (arg == "--tensor-split" || arg == "-ts") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+#ifdef GGML_USE_CUBLAS
+ std::string arg_next = argv[i];
+
+ // split string by , and /
+ const std::regex regex{R"([,/]+)"};
+ std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
+ std::vector<std::string> split_arg{it, {}};
+ GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
+
+ for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
+ if (i < split_arg.size()) {
+ params.tensor_split[i] = std::stof(split_arg[i]);
+ } else {
+ params.tensor_split[i] = 0.0f;
+ }
+ }
+#else
+ fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
+#endif // GGML_USE_CUBLAS
+ } else if (arg == "--mul-mat-q" || arg == "-mmq") {
+#ifdef GGML_USE_CUBLAS
+ params.mul_mat_q = true;
+#else
+ fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
+#endif // GGML_USE_CUBLAS
+ } else if (arg == "--low-vram" || arg == "-lv") {
+#ifdef GGML_USE_CUBLAS
+ params.low_vram = true;
+#else
+ fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
+#endif // GGML_USE_CUBLAS
+ } else if (arg == "--no-mmap") {
+ params.use_mmap = false;
+ } else if (arg == "--mtest") {
+ params.mem_test = true;
+ } else if (arg == "--numa") {
+ params.numa = true;
+ } else if (arg == "--export") {
+ params.export_cgraph = true;
+ } else if (arg == "--verbose-prompt") {
+ params.verbose_prompt = 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 == "--hellaswag") {
+ params.hellaswag = true;
+ } else if (arg == "--hellaswag-tasks") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.hellaswag_tasks = std::stoi(argv[i]);
+ } else if (arg == "--ignore-eos") {
+ params.ignore_eos = true;
+ } else if (arg == "--no-penalize-nl") {
+ params.penalize_nl = false;
+ } else if (arg == "-l" || arg == "--logit-bias") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ std::stringstream ss(argv[i]);
+ llama_token key;
+ char sign;
+ std::string value_str;
+ try {
+ if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
+ params.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
+ } else {
+ throw std::exception();
+ }
+ } catch (const std::exception&) {
+ invalid_param = true;
+ break;
+ }
+ } else if (arg == "-h" || arg == "--help") {
+ gpt_print_usage(argc, argv, default_params);
+ exit(0);
+ } else if (arg == "--random-prompt") {
+ params.random_prompt = true;
+ } else if (arg == "--in-prefix-bos") {
+ params.input_prefix_bos = true;
+ } else if (arg == "--in-prefix") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.input_prefix = argv[i];
+ } else if (arg == "--in-suffix") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.input_suffix = argv[i];
+ } else if (arg == "--grammar") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.grammar = argv[i];
+ } else if (arg == "--grammar-file") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ std::ifstream file(argv[i]);
+ if (!file) {
+ fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+ invalid_param = true;
+ break;
+ }
+ std::copy(
+ std::istreambuf_iterator<char>(file),
+ std::istreambuf_iterator<char>(),
+ std::back_inserter(params.grammar)
+ );
+ } else {
+ fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+ gpt_print_usage(argc, argv, default_params);
+ exit(1);
+ }
+ }
+ if (invalid_param) {
+ fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
+ gpt_print_usage(argc, argv, default_params);
+ exit(1);
+ }
+ if (params.prompt_cache_all &&
+ (params.interactive || params.interactive_first ||
+ params.instruct)) {
+ fprintf(stderr, "error: --prompt-cache-all not supported in interactive mode yet\n");
+ gpt_print_usage(argc, argv, default_params);
+ exit(1);
+ }
+
+ if (escape_prompt) {
+ process_escapes(params.prompt);
+ process_escapes(params.input_prefix);
+ process_escapes(params.input_suffix);
+ }
+
+ return true;
+}
+
+void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
+ fprintf(stdout, "usage: %s [options]\n", argv[0]);
+ fprintf(stdout, "\n");
+ fprintf(stdout, "options:\n");
+ fprintf(stdout, " -h, --help show this help message and exit\n");
+ fprintf(stdout, " -i, --interactive run in interactive mode\n");
+ fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n");
+ fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
+ fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
+ fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n");
+ fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n");
+ fprintf(stdout, " (can be specified more than once for multiple prompts).\n");
+ fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n");
+ fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
+ fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
+ fprintf(stdout, " -p PROMPT, --prompt PROMPT\n");
+ fprintf(stdout, " prompt to start generation with (default: empty)\n");
+ fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
+ fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
+ fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
+ fprintf(stdout, " not supported with --interactive or other interactive options\n");
+ fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
+ fprintf(stdout, " --random-prompt start with a randomized prompt.\n");
+ fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n");
+ fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n");
+ fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
+ fprintf(stdout, " -f FNAME, --file FNAME\n");
+ fprintf(stdout, " prompt file to start generation.\n");
+ fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
+ fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
+ fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
+ fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
+ fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
+ fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
+ fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
+ fprintf(stdout, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
+ fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
+ fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
+ fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
+ fprintf(stdout, " --mirostat N use Mirostat sampling.\n");
+ fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
+ fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
+ fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
+ fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
+ fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
+ fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n");
+ fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
+ fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
+ fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
+ fprintf(stdout, " --grammar-file FNAME file to read grammar from\n");
+ fprintf(stdout, " --cfg-negative-prompt PROMPT\n");
+ fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n");
+ fprintf(stdout, " --cfg-negative-prompt-file FNAME\n");
+ fprintf(stdout, " negative prompt file to use for guidance. (default: empty)\n");
+ fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
+ fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
+ fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
+ fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
+ fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
+ fprintf(stdout, " --no-penalize-nl do not penalize newline token\n");
+ fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
+ fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
+ fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp);
+ fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n");
+ fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
+ fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
+ fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
+ fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
+ if (llama_mlock_supported()) {
+ fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
+ }
+ if (llama_mmap_supported()) {
+ fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
+ }
+ fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n");
+ fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n");
+ fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n");
+#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
+ fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
+ fprintf(stdout, " number of layers to store in VRAM\n");
+ fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
+ fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
+ fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
+ fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
+ fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
+ fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
+ fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
+#endif
+ fprintf(stdout, " --mtest compute maximum memory usage\n");
+ fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
+ fprintf(stdout, " --verbose-prompt print prompt before generation\n");
+ fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
+ fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
+ fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
+ fprintf(stdout, " -m FNAME, --model FNAME\n");
+ fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
+ fprintf(stdout, "\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";
+}
+
+//
+// Model utils
+//
+
+struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
+ auto lparams = llama_context_default_params();
+
+ lparams.n_ctx = params.n_ctx;
+ lparams.n_batch = params.n_batch;
+ lparams.n_gpu_layers = params.n_gpu_layers;
+ lparams.main_gpu = params.main_gpu;
+ lparams.tensor_split = params.tensor_split;
+ lparams.low_vram = params.low_vram;
+ lparams.mul_mat_q = params.mul_mat_q;
+ lparams.seed = params.seed;
+ lparams.f16_kv = params.memory_f16;
+ lparams.use_mmap = params.use_mmap;
+ lparams.use_mlock = params.use_mlock;
+ lparams.logits_all = params.perplexity;
+ lparams.embedding = params.embedding;
+ lparams.rope_freq_base = params.rope_freq_base;
+ lparams.rope_freq_scale = params.rope_freq_scale;
+
+ return lparams;
+}
+
+std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
+ auto lparams = llama_context_params_from_gpt_params(params);
+
+ llama_model * model = llama_load_model_from_file(params.model.c_str(), lparams);
+ if (model == NULL) {
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+ return std::make_tuple(nullptr, nullptr);
+ }
+
+ llama_context * lctx = llama_new_context_with_model(model, lparams);
+ if (lctx == NULL) {
+ fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
+ llama_free_model(model);
+ return std::make_tuple(nullptr, nullptr);
+ }
+
+ if (!params.lora_adapter.empty()) {
+ int err = llama_model_apply_lora_from_file(model,
+ params.lora_adapter.c_str(),
+ params.lora_base.empty() ? NULL : params.lora_base.c_str(),
+ params.n_threads);
+ if (err != 0) {
+ fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
+ llama_free(lctx);
+ llama_free_model(model);
+ return std::make_tuple(nullptr, nullptr);
+ }
+ }
+
+ if (params.ignore_eos) {
+ params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
+ }
+
+ return std::make_tuple(model, lctx);
+}
+
+//
+// Vocab utils
+//
+
+std::vector<llama_token> llama_tokenize(
+ struct llama_context * ctx,
+ const std::string & text,
+ bool add_bos) {
+ // upper limit for the number of tokens
+ int n_tokens = text.length() + add_bos;
+ std::vector<llama_token> result(n_tokens);
+ n_tokens = llama_tokenize(ctx, text.c_str(), result.data(), result.size(), add_bos);
+ if (n_tokens < 0) {
+ result.resize(-n_tokens);
+ int check = llama_tokenize(ctx, text.c_str(), result.data(), result.size(), add_bos);
+ GGML_ASSERT(check == -n_tokens);
+ } else {
+ result.resize(n_tokens);
+ }
+ return result;
+}
+
+std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+ std::vector<char> result(8, 0);
+ const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
+ if (n_tokens < 0) {
+ result.resize(-n_tokens);
+ int check = llama_token_to_str(ctx, token, result.data(), result.size());
+ GGML_ASSERT(check == -n_tokens);
+ } else {
+ result.resize(n_tokens);
+ }
+
+ return std::string(result.data(), result.size());
+}
+
+std::vector<llama_token> llama_tokenize_bpe(
+ struct llama_context * ctx,
+ const std::string & text,
+ bool add_bos) {
+ int n_tokens = text.length() + add_bos;
+ std::vector<llama_token> result(n_tokens);
+ n_tokens = llama_tokenize_bpe(ctx, text.c_str(), result.data(), result.size(), add_bos);
+ if (n_tokens < 0) {
+ result.resize(-n_tokens);
+ int check = llama_tokenize_bpe(ctx, text.c_str(), result.data(), result.size(), add_bos);
+ GGML_ASSERT(check == -n_tokens);
+ } else {
+ result.resize(n_tokens);
+ }
+ return result;
+}
+
+std::string llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token) {
+ std::vector<char> result(8, 0);
+ const int n_tokens = llama_token_to_str_bpe(ctx, token, result.data(), result.size());
+ if (n_tokens < 0) {
+ result.resize(-n_tokens);
+ const int check = llama_token_to_str_bpe(ctx, token, result.data(), result.size());
+ GGML_ASSERT(check == -n_tokens);
+ } else {
+ result.resize(n_tokens);
+ }
+
+ return std::string(result.data(), result.size());
+}
+
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-17 01:56:54 +0000