mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
646ef4a9cf
* add parameters for embeddings --embd-normalize --embd-output-format --embd-separator description in the README.md * Update README.md fix tipo * Trailing whitespace * fix json generation, use " not ' * fix merge master * fix code formating group of parameters // embedding print usage for embedding parameters --------- Co-authored-by: Brian <mofosyne@gmail.com>
3121 lines
128 KiB
C++
3121 lines
128 KiB
C++
#include "common.h"
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// Change JSON_ASSERT from assert() to GGML_ASSERT:
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#define JSON_ASSERT GGML_ASSERT
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#include "json.hpp"
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#include "json-schema-to-grammar.h"
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#include "llama.h"
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#include <algorithm>
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#include <cinttypes>
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#include <cmath>
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#include <codecvt>
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#include <cstdarg>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iostream>
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#include <iterator>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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#if defined(__APPLE__) && defined(__MACH__)
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#include <sys/types.h>
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#include <sys/sysctl.h>
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#endif
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <locale>
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#include <windows.h>
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#include <fcntl.h>
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#include <io.h>
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#else
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#include <sys/ioctl.h>
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#include <sys/stat.h>
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#include <unistd.h>
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#endif
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#if defined(LLAMA_USE_CURL)
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#include <curl/curl.h>
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#include <curl/easy.h>
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#include <thread>
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#include <future>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
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#define GGML_USE_CUDA_SYCL
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#endif
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#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
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#define GGML_USE_CUDA_SYCL_VULKAN
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#endif
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#if defined(LLAMA_USE_CURL)
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#ifdef __linux__
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#include <linux/limits.h>
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#elif defined(_WIN32)
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#define PATH_MAX MAX_PATH
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#else
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#include <sys/syslimits.h>
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#endif
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#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
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#endif // LLAMA_USE_CURL
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using json = nlohmann::ordered_json;
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//
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// CPU utils
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//
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int32_t cpu_get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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std::unordered_set<std::string> siblings;
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for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
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std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
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+ std::to_string(cpu) + "/topology/thread_siblings");
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if (!thread_siblings.is_open()) {
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break; // no more cpus
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}
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std::string line;
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if (std::getline(thread_siblings, line)) {
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siblings.insert(line);
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}
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}
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if (!siblings.empty()) {
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return static_cast<int32_t>(siblings.size());
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}
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#elif defined(__APPLE__) && defined(__MACH__)
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int32_t num_physical_cores;
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size_t len = sizeof(num_physical_cores);
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int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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#elif defined(_WIN32)
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//TODO: Implement
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#endif
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unsigned int n_threads = std::thread::hardware_concurrency();
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return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
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}
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#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
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#include <pthread.h>
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static void cpuid(unsigned leaf, unsigned subleaf,
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unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
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__asm__("movq\t%%rbx,%%rsi\n\t"
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"cpuid\n\t"
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"xchgq\t%%rbx,%%rsi"
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: "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
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: "0"(leaf), "2"(subleaf));
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}
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static int pin_cpu(int cpu) {
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cpu_set_t mask;
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CPU_ZERO(&mask);
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CPU_SET(cpu, &mask);
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return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
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}
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static bool is_hybrid_cpu(void) {
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unsigned eax, ebx, ecx, edx;
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cpuid(7, 0, &eax, &ebx, &ecx, &edx);
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return !!(edx & (1u << 15));
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}
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static bool is_running_on_efficiency_core(void) {
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unsigned eax, ebx, ecx, edx;
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cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
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int intel_atom = 0x20;
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int core_type = (eax & 0xff000000u) >> 24;
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return core_type == intel_atom;
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}
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static int cpu_count_math_cpus(int n_cpu) {
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int result = 0;
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for (int cpu = 0; cpu < n_cpu; ++cpu) {
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if (pin_cpu(cpu)) {
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return -1;
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}
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if (is_running_on_efficiency_core()) {
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continue; // efficiency cores harm lockstep threading
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}
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++cpu; // hyperthreading isn't useful for linear algebra
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++result;
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}
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return result;
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}
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#endif // __x86_64__ && __linux__
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/**
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* Returns number of CPUs on system that are useful for math.
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*/
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int32_t cpu_get_num_math() {
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#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
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int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
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if (n_cpu < 1) {
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return cpu_get_num_physical_cores();
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}
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if (is_hybrid_cpu()) {
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cpu_set_t affinity;
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if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
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int result = cpu_count_math_cpus(n_cpu);
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pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
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if (result > 0) {
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return result;
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}
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}
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}
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#endif
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return cpu_get_num_physical_cores();
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}
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//
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// CLI argument parsing
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//
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void gpt_params_handle_model_default(gpt_params & params) {
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if (!params.hf_repo.empty()) {
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// short-hand to avoid specifying --hf-file -> default it to --model
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if (params.hf_file.empty()) {
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if (params.model.empty()) {
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throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
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}
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params.hf_file = params.model;
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} else if (params.model.empty()) {
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params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
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}
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} else if (!params.model_url.empty()) {
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if (params.model.empty()) {
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auto f = string_split(params.model_url, '#').front();
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f = string_split(f, '?').front();
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params.model = fs_get_cache_file(string_split(f, '/').back());
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}
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} else if (params.model.empty()) {
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params.model = DEFAULT_MODEL_PATH;
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}
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}
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bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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bool invalid_param = false;
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std::string arg;
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const std::string arg_prefix = "--";
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llama_sampling_params & sparams = params.sparams;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
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std::replace(arg.begin(), arg.end(), '_', '-');
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}
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if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
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throw std::invalid_argument("error: unknown argument: " + arg);
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}
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if (invalid_param) {
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throw std::invalid_argument("error: invalid parameter for argument: " + arg);
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}
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}
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if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
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throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
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}
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gpt_params_handle_model_default(params);
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if (params.escape) {
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string_process_escapes(params.prompt);
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string_process_escapes(params.input_prefix);
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string_process_escapes(params.input_suffix);
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string_process_escapes(sparams.cfg_negative_prompt);
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for (auto & antiprompt : params.antiprompt) {
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string_process_escapes(antiprompt);
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}
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}
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if (!params.kv_overrides.empty()) {
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params.kv_overrides.emplace_back();
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params.kv_overrides.back().key[0] = 0;
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}
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return true;
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}
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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const auto params_org = params; // the example can modify the default params
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try {
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if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
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params = params_org;
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params.usage = true;
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return false;
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}
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} catch (const std::invalid_argument & ex) {
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fprintf(stderr, "%s\n", ex.what());
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params = params_org;
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return false;
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}
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return true;
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}
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#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
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bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
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const char split_delim = ',';
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llama_sampling_params & sparams = params.sparams;
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if (arg == "-s" || arg == "--seed") {
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CHECK_ARG
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// TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
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params.seed = std::stoul(argv[i]);
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sparams.seed = std::stoul(argv[i]);
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return true;
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}
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if (arg == "-t" || arg == "--threads") {
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CHECK_ARG
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params.n_threads = std::stoi(argv[i]);
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if (params.n_threads <= 0) {
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params.n_threads = std::thread::hardware_concurrency();
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}
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return true;
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}
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if (arg == "-tb" || arg == "--threads-batch") {
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CHECK_ARG
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params.n_threads_batch = std::stoi(argv[i]);
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if (params.n_threads_batch <= 0) {
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params.n_threads_batch = std::thread::hardware_concurrency();
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}
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return true;
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}
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if (arg == "-td" || arg == "--threads-draft") {
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CHECK_ARG
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params.n_threads_draft = std::stoi(argv[i]);
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if (params.n_threads_draft <= 0) {
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params.n_threads_draft = std::thread::hardware_concurrency();
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}
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return true;
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}
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if (arg == "-tbd" || arg == "--threads-batch-draft") {
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CHECK_ARG
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params.n_threads_batch_draft = std::stoi(argv[i]);
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if (params.n_threads_batch_draft <= 0) {
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params.n_threads_batch_draft = std::thread::hardware_concurrency();
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}
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return true;
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}
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if (arg == "-p" || arg == "--prompt") {
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CHECK_ARG
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params.prompt = argv[i];
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return true;
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}
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if (arg == "-e" || arg == "--escape") {
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params.escape = true;
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return true;
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}
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if (arg == "--no-escape") {
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params.escape = false;
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return true;
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}
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if (arg == "--prompt-cache") {
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CHECK_ARG
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params.path_prompt_cache = argv[i];
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return true;
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}
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if (arg == "--prompt-cache-all") {
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params.prompt_cache_all = true;
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return true;
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}
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if (arg == "--prompt-cache-ro") {
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params.prompt_cache_ro = true;
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return true;
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}
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if (arg == "-bf" || arg == "--binary-file") {
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CHECK_ARG
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std::ifstream file(argv[i], std::ios::binary);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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return true;
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}
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// store the external file name in params
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params.prompt_file = argv[i];
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std::ostringstream ss;
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ss << file.rdbuf();
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params.prompt = ss.str();
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fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
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return true;
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}
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if (arg == "-f" || arg == "--file") {
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CHECK_ARG
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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return true;
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}
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// store the external file name in params
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params.prompt_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.empty() && params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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return true;
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}
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if (arg == "--in-file") {
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CHECK_ARG
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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return true;
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}
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params.in_files.push_back(argv[i]);
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return true;
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}
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if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
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CHECK_ARG
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params.n_predict = std::stoi(argv[i]);
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return true;
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}
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if (arg == "--top-k") {
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CHECK_ARG
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sparams.top_k = std::stoi(argv[i]);
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return true;
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}
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if (arg == "-c" || arg == "--ctx-size") {
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CHECK_ARG
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params.n_ctx = std::stoi(argv[i]);
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return true;
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}
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if (arg == "--grp-attn-n" || arg == "-gan") {
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CHECK_ARG
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params.grp_attn_n = std::stoi(argv[i]);
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return true;
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}
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if (arg == "--grp-attn-w" || arg == "-gaw") {
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CHECK_ARG
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params.grp_attn_w = std::stoi(argv[i]);
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return true;
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}
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if (arg == "--rope-freq-base") {
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CHECK_ARG
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params.rope_freq_base = std::stof(argv[i]);
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return true;
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}
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if (arg == "--rope-freq-scale") {
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CHECK_ARG
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params.rope_freq_scale = std::stof(argv[i]);
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return true;
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}
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if (arg == "--rope-scaling") {
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CHECK_ARG
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std::string value(argv[i]);
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/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
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else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
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else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
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else { invalid_param = true; }
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return true;
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}
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if (arg == "--rope-scale") {
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CHECK_ARG
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params.rope_freq_scale = 1.0f / std::stof(argv[i]);
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return true;
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}
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if (arg == "--yarn-orig-ctx") {
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CHECK_ARG
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params.yarn_orig_ctx = std::stoi(argv[i]);
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return true;
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}
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if (arg == "--yarn-ext-factor") {
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CHECK_ARG
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params.yarn_ext_factor = std::stof(argv[i]);
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return true;
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}
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if (arg == "--yarn-attn-factor") {
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CHECK_ARG
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params.yarn_attn_factor = std::stof(argv[i]);
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return true;
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}
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if (arg == "--yarn-beta-fast") {
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CHECK_ARG
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params.yarn_beta_fast = std::stof(argv[i]);
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return true;
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}
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if (arg == "--yarn-beta-slow") {
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CHECK_ARG
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params.yarn_beta_slow = std::stof(argv[i]);
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return true;
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}
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if (arg == "--pooling") {
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CHECK_ARG
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std::string value(argv[i]);
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/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
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else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
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else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
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else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
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else { invalid_param = true; }
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return true;
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}
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if (arg == "--defrag-thold" || arg == "-dt") {
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CHECK_ARG
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params.defrag_thold = std::stof(argv[i]);
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return true;
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}
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if (arg == "--samplers") {
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CHECK_ARG
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const auto sampler_names = string_split(argv[i], ';');
|
|
sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
|
|
return true;
|
|
}
|
|
if (arg == "--sampling-seq") {
|
|
CHECK_ARG
|
|
sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--top-p") {
|
|
CHECK_ARG
|
|
sparams.top_p = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--min-p") {
|
|
CHECK_ARG
|
|
sparams.min_p = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--temp") {
|
|
CHECK_ARG
|
|
sparams.temp = std::stof(argv[i]);
|
|
sparams.temp = std::max(sparams.temp, 0.0f);
|
|
return true;
|
|
}
|
|
if (arg == "--tfs") {
|
|
CHECK_ARG
|
|
sparams.tfs_z = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--typical") {
|
|
CHECK_ARG
|
|
sparams.typical_p = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--repeat-last-n") {
|
|
CHECK_ARG
|
|
sparams.penalty_last_n = std::stoi(argv[i]);
|
|
sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
|
|
return true;
|
|
}
|
|
if (arg == "--repeat-penalty") {
|
|
CHECK_ARG
|
|
sparams.penalty_repeat = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--frequency-penalty") {
|
|
CHECK_ARG
|
|
sparams.penalty_freq = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--presence-penalty") {
|
|
CHECK_ARG
|
|
sparams.penalty_present = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--dynatemp-range") {
|
|
CHECK_ARG
|
|
sparams.dynatemp_range = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--dynatemp-exp") {
|
|
CHECK_ARG
|
|
sparams.dynatemp_exponent = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mirostat") {
|
|
CHECK_ARG
|
|
sparams.mirostat = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mirostat-lr") {
|
|
CHECK_ARG
|
|
sparams.mirostat_eta = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mirostat-ent") {
|
|
CHECK_ARG
|
|
sparams.mirostat_tau = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--cfg-negative-prompt") {
|
|
CHECK_ARG
|
|
sparams.cfg_negative_prompt = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--cfg-negative-prompt-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
|
|
if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
|
|
sparams.cfg_negative_prompt.pop_back();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--cfg-scale") {
|
|
CHECK_ARG
|
|
sparams.cfg_scale = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-b" || arg == "--batch-size") {
|
|
CHECK_ARG
|
|
params.n_batch = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ub" || arg == "--ubatch-size") {
|
|
CHECK_ARG
|
|
params.n_ubatch = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--keep") {
|
|
CHECK_ARG
|
|
params.n_keep = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--draft") {
|
|
CHECK_ARG
|
|
params.n_draft = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--chunks") {
|
|
CHECK_ARG
|
|
params.n_chunks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-np" || arg == "--parallel") {
|
|
CHECK_ARG
|
|
params.n_parallel = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ns" || arg == "--sequences") {
|
|
CHECK_ARG
|
|
params.n_sequences = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--p-split" || arg == "-ps") {
|
|
CHECK_ARG
|
|
params.p_split = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-m" || arg == "--model") {
|
|
CHECK_ARG
|
|
params.model = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-md" || arg == "--model-draft") {
|
|
CHECK_ARG
|
|
params.model_draft = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-a" || arg == "--alias") {
|
|
CHECK_ARG
|
|
params.model_alias = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-mu" || arg == "--model-url") {
|
|
CHECK_ARG
|
|
params.model_url = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-hfr" || arg == "--hf-repo") {
|
|
CHECK_ARG
|
|
params.hf_repo = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-hff" || arg == "--hf-file") {
|
|
CHECK_ARG
|
|
params.hf_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--lora") {
|
|
CHECK_ARG
|
|
params.lora_adapter.emplace_back(argv[i], 1.0f);
|
|
params.use_mmap = false;
|
|
return true;
|
|
}
|
|
if (arg == "--lora-scaled") {
|
|
CHECK_ARG
|
|
const char* lora_adapter = argv[i];
|
|
CHECK_ARG
|
|
params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
|
|
params.use_mmap = false;
|
|
return true;
|
|
}
|
|
if (arg == "--lora-base") {
|
|
CHECK_ARG
|
|
params.lora_base = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--control-vector") {
|
|
CHECK_ARG
|
|
params.control_vectors.push_back({ 1.0f, argv[i], });
|
|
return true;
|
|
}
|
|
if (arg == "--control-vector-scaled") {
|
|
CHECK_ARG
|
|
const char* fname = argv[i];
|
|
CHECK_ARG
|
|
params.control_vectors.push_back({ std::stof(argv[i]), fname, });
|
|
return true;
|
|
}
|
|
if (arg == "--control-vector-layer-range") {
|
|
CHECK_ARG
|
|
params.control_vector_layer_start = std::stoi(argv[i]);
|
|
CHECK_ARG
|
|
params.control_vector_layer_end = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mmproj") {
|
|
CHECK_ARG
|
|
params.mmproj = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--image") {
|
|
CHECK_ARG
|
|
params.image.emplace_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-i" || arg == "--interactive") {
|
|
params.interactive = true;
|
|
return true;
|
|
}
|
|
if (arg == "-sp" || arg == "--special") {
|
|
params.special = true;
|
|
return true;
|
|
}
|
|
if (arg == "--embedding" || arg == "--embeddings") {
|
|
params.embedding = true;
|
|
return true;
|
|
}
|
|
if (arg == "--embd-normalize") {
|
|
CHECK_ARG
|
|
params.embd_normalize = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--embd-output-format") {
|
|
CHECK_ARG
|
|
params.embd_out = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--embd-separator") {
|
|
CHECK_ARG
|
|
params.embd_sep = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-if" || arg == "--interactive-first") {
|
|
params.interactive_first = true;
|
|
return true;
|
|
}
|
|
if (arg == "-cnv" || arg == "--conversation") {
|
|
params.conversation = true;
|
|
return true;
|
|
}
|
|
if (arg == "--infill") {
|
|
params.infill = true;
|
|
return true;
|
|
}
|
|
if (arg == "-dkvc" || arg == "--dump-kv-cache") {
|
|
params.dump_kv_cache = true;
|
|
return true;
|
|
}
|
|
if (arg == "-nkvo" || arg == "--no-kv-offload") {
|
|
params.no_kv_offload = true;
|
|
return true;
|
|
}
|
|
if (arg == "-ctk" || arg == "--cache-type-k") {
|
|
params.cache_type_k = argv[++i];
|
|
return true;
|
|
}
|
|
if (arg == "-ctv" || arg == "--cache-type-v") {
|
|
params.cache_type_v = argv[++i];
|
|
return true;
|
|
}
|
|
if (arg == "--multiline-input") {
|
|
params.multiline_input = true;
|
|
return true;
|
|
}
|
|
if (arg == "--simple-io") {
|
|
params.simple_io = true;
|
|
return true;
|
|
}
|
|
if (arg == "-cb" || arg == "--cont-batching") {
|
|
params.cont_batching = true;
|
|
return true;
|
|
}
|
|
if (arg == "-fa" || arg == "--flash-attn") {
|
|
params.flash_attn = true;
|
|
return true;
|
|
}
|
|
if (arg == "-co" || arg == "--color") {
|
|
params.use_color = true;
|
|
return true;
|
|
}
|
|
if (arg == "--mlock") {
|
|
params.use_mlock = true;
|
|
return true;
|
|
}
|
|
if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
|
|
CHECK_ARG
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
if (!llama_supports_gpu_offload()) {
|
|
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
|
|
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") {
|
|
CHECK_ARG
|
|
params.n_gpu_layers_draft = std::stoi(argv[i]);
|
|
if (!llama_supports_gpu_offload()) {
|
|
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
|
|
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--main-gpu" || arg == "-mg") {
|
|
CHECK_ARG
|
|
params.main_gpu = std::stoi(argv[i]);
|
|
#ifndef GGML_USE_CUDA_SYCL_VULKAN
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
|
|
#endif // GGML_USE_CUDA_SYCL_VULKAN
|
|
return true;
|
|
}
|
|
if (arg == "--split-mode" || arg == "-sm") {
|
|
CHECK_ARG
|
|
std::string arg_next = argv[i];
|
|
if (arg_next == "none") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_NONE;
|
|
}
|
|
else if (arg_next == "layer") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
|
|
}
|
|
else if (arg_next == "row") {
|
|
#ifdef GGML_USE_SYCL
|
|
fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
|
|
exit(1);
|
|
#endif // GGML_USE_SYCL
|
|
params.split_mode = LLAMA_SPLIT_MODE_ROW;
|
|
}
|
|
else {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
#ifndef GGML_USE_CUDA_SYCL_VULKAN
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
|
|
#endif // GGML_USE_CUDA_SYCL_VULKAN
|
|
return true;
|
|
}
|
|
if (arg == "--tensor-split" || arg == "-ts") {
|
|
CHECK_ARG
|
|
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, {} };
|
|
if (split_arg.size() >= llama_max_devices()) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
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;
|
|
}
|
|
}
|
|
#ifndef GGML_USE_CUDA_SYCL_VULKAN
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
|
|
#endif // GGML_USE_CUDA_SYCL_VULKAN
|
|
return true;
|
|
}
|
|
if (arg == "--rpc") {
|
|
CHECK_ARG
|
|
params.rpc_servers = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--no-mmap") {
|
|
params.use_mmap = false;
|
|
return true;
|
|
}
|
|
if (arg == "--numa") {
|
|
CHECK_ARG
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
|
|
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
|
|
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
|
|
else { invalid_param = true; }
|
|
return true;
|
|
}
|
|
if (arg == "-v" || arg == "--verbose") {
|
|
params.verbosity = 1;
|
|
return true;
|
|
}
|
|
if (arg == "--verbosity") {
|
|
CHECK_ARG
|
|
params.verbosity = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--verbose-prompt") {
|
|
params.verbose_prompt = true;
|
|
return true;
|
|
}
|
|
if (arg == "--no-display-prompt") {
|
|
params.display_prompt = false;
|
|
return true;
|
|
}
|
|
if (arg == "-r" || arg == "--reverse-prompt") {
|
|
CHECK_ARG
|
|
params.antiprompt.emplace_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ld" || arg == "--logdir") {
|
|
CHECK_ARG
|
|
params.logdir = argv[i];
|
|
|
|
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
|
|
params.logdir += DIRECTORY_SEPARATOR;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-lcs" || arg == "--lookup-cache-static") {
|
|
CHECK_ARG
|
|
params.lookup_cache_static = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
|
|
CHECK_ARG
|
|
params.lookup_cache_dynamic = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
|
|
CHECK_ARG
|
|
params.logits_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--perplexity" || arg == "--all-logits") {
|
|
params.logits_all = true;
|
|
return true;
|
|
}
|
|
if (arg == "--ppl-stride") {
|
|
CHECK_ARG
|
|
params.ppl_stride = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--ppl-output-type") {
|
|
CHECK_ARG
|
|
params.ppl_output_type = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ptc" || arg == "--print-token-count") {
|
|
CHECK_ARG
|
|
params.n_print = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--check-tensors") {
|
|
params.check_tensors = true;
|
|
return true;
|
|
}
|
|
if (arg == "--hellaswag") {
|
|
params.hellaswag = true;
|
|
return true;
|
|
}
|
|
if (arg == "--hellaswag-tasks") {
|
|
CHECK_ARG
|
|
params.hellaswag_tasks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--winogrande") {
|
|
params.winogrande = true;
|
|
return true;
|
|
}
|
|
if (arg == "--winogrande-tasks") {
|
|
CHECK_ARG
|
|
params.winogrande_tasks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--multiple-choice") {
|
|
params.multiple_choice = true;
|
|
return true;
|
|
}
|
|
if (arg == "--multiple-choice-tasks") {
|
|
CHECK_ARG
|
|
params.multiple_choice_tasks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--kl-divergence") {
|
|
params.kl_divergence = true;
|
|
return true;
|
|
}
|
|
if (arg == "--ignore-eos") {
|
|
params.ignore_eos = true;
|
|
return true;
|
|
}
|
|
if (arg == "--penalize-nl") {
|
|
sparams.penalize_nl = true;
|
|
return true;
|
|
}
|
|
if (arg == "-l" || arg == "--logit-bias") {
|
|
CHECK_ARG
|
|
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 == '-')) {
|
|
sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
|
|
}
|
|
else {
|
|
throw std::exception();
|
|
}
|
|
}
|
|
catch (const std::exception&) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-h" || arg == "--help" || arg == "--usage" ) {
|
|
params.usage = true;
|
|
return true;
|
|
}
|
|
if (arg == "--version") {
|
|
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
|
|
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
|
|
exit(0);
|
|
}
|
|
if (arg == "--in-prefix-bos") {
|
|
params.input_prefix_bos = true;
|
|
return true;
|
|
}
|
|
if (arg == "--in-prefix") {
|
|
CHECK_ARG
|
|
params.input_prefix = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--in-suffix") {
|
|
CHECK_ARG
|
|
params.input_suffix = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--grammar") {
|
|
CHECK_ARG
|
|
sparams.grammar = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--grammar-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(sparams.grammar)
|
|
);
|
|
return true;
|
|
}
|
|
if (arg == "-j" || arg == "--json-schema") {
|
|
CHECK_ARG
|
|
sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
|
|
return true;
|
|
}
|
|
if (arg == "--override-kv") {
|
|
CHECK_ARG
|
|
if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
|
|
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--host") {
|
|
CHECK_ARG
|
|
params.hostname = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--port") {
|
|
CHECK_ARG
|
|
params.port = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--path") {
|
|
CHECK_ARG
|
|
params.public_path = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--api-key") {
|
|
CHECK_ARG
|
|
params.api_keys.push_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--api-key-file") {
|
|
CHECK_ARG
|
|
std::ifstream key_file(argv[i]);
|
|
if (!key_file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::string key;
|
|
while (std::getline(key_file, key)) {
|
|
if (!key.empty()) {
|
|
params.api_keys.push_back(key);
|
|
}
|
|
}
|
|
key_file.close();
|
|
return true;
|
|
}
|
|
if (arg == "--ssl-key-file") {
|
|
CHECK_ARG
|
|
params.ssl_file_key = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--ssl-cert-file") {
|
|
CHECK_ARG
|
|
params.ssl_file_cert = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--timeout" || arg == "-to") {
|
|
CHECK_ARG
|
|
params.timeout_read = std::stoi(argv[i]);
|
|
params.timeout_write = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--threads-http") {
|
|
CHECK_ARG
|
|
params.n_threads_http = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-spf" || arg == "--system-prompt-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::string system_prompt;
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(system_prompt)
|
|
);
|
|
params.system_prompt = system_prompt;
|
|
return true;
|
|
}
|
|
if (arg == "--log-format") {
|
|
CHECK_ARG
|
|
if (std::strcmp(argv[i], "json") == 0) {
|
|
params.log_json = true;
|
|
} else if (std::strcmp(argv[i], "text") == 0) {
|
|
params.log_json = false;
|
|
} else {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--no-slots") {
|
|
params.endpoint_slots = false;
|
|
return true;
|
|
}
|
|
if (arg == "--metrics") {
|
|
params.endpoint_metrics = true;
|
|
return true;
|
|
}
|
|
if (arg == "--slot-save-path") {
|
|
CHECK_ARG
|
|
params.slot_save_path = argv[i];
|
|
// if doesn't end with DIRECTORY_SEPARATOR, add it
|
|
if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
|
|
params.slot_save_path += DIRECTORY_SEPARATOR;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--chat-template") {
|
|
CHECK_ARG
|
|
if (!llama_chat_verify_template(argv[i])) {
|
|
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
|
|
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
params.chat_template = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--slot-prompt-similarity" || arg == "-sps") {
|
|
CHECK_ARG
|
|
params.slot_prompt_similarity = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-pps") {
|
|
params.is_pp_shared = true;
|
|
return true;
|
|
}
|
|
if (arg == "-npp") {
|
|
CHECK_ARG
|
|
auto p = string_split<int>(argv[i], split_delim);
|
|
params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
|
|
return true;
|
|
}
|
|
if (arg == "-ntg") {
|
|
CHECK_ARG
|
|
auto p = string_split<int>(argv[i], split_delim);
|
|
params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
|
|
return true;
|
|
}
|
|
if (arg == "-npl") {
|
|
CHECK_ARG
|
|
auto p = string_split<int>(argv[i], split_delim);
|
|
params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
|
|
return true;
|
|
}
|
|
if (arg == "--context-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i], std::ios::binary);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
params.context_files.push_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--chunk-size") {
|
|
CHECK_ARG
|
|
params.chunk_size = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--chunk-separator") {
|
|
CHECK_ARG
|
|
params.chunk_separator = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--junk") {
|
|
CHECK_ARG
|
|
params.n_junk = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--pos") {
|
|
CHECK_ARG
|
|
params.i_pos = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-o" || arg == "--output" || arg == "--output-file") {
|
|
CHECK_ARG
|
|
params.out_file = argv[i];
|
|
params.cvector_outfile = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-ofreq" || arg == "--output-frequency") {
|
|
CHECK_ARG
|
|
params.n_out_freq = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--save-frequency") {
|
|
CHECK_ARG
|
|
params.n_save_freq = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--process-output") {
|
|
params.process_output = true;
|
|
return true;
|
|
}
|
|
if (arg == "--no-ppl") {
|
|
params.compute_ppl = false;
|
|
return true;
|
|
}
|
|
if (arg == "--chunk" || arg == "--from-chunk") {
|
|
CHECK_ARG
|
|
params.i_chunk = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
// cvector params
|
|
if (arg == "--completions-file") {
|
|
CHECK_ARG
|
|
params.cvector_completions_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--positive-file") {
|
|
CHECK_ARG
|
|
params.cvector_positive_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--negative-file") {
|
|
CHECK_ARG
|
|
params.cvector_negative_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--completions") {
|
|
CHECK_ARG
|
|
params.n_completions = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--pca-batch") {
|
|
CHECK_ARG
|
|
params.n_pca_batch = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--pca-iter") {
|
|
CHECK_ARG
|
|
params.n_pca_iterations = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
#ifndef LOG_DISABLE_LOGS
|
|
// Parse args for logging parameters
|
|
if (log_param_single_parse(argv[i])) {
|
|
// Do nothing, log_param_single_parse automatically does it's thing
|
|
// and returns if a match was found and parsed.
|
|
return true;
|
|
}
|
|
if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
|
|
// We have a matching known parameter requiring an argument,
|
|
// now we need to check if there is anything after this argv
|
|
// and flag invalid_param or parse it.
|
|
CHECK_ARG
|
|
if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
// End of Parse args for logging parameters
|
|
#endif // LOG_DISABLE_LOGS
|
|
|
|
return false;
|
|
}
|
|
|
|
#ifdef __GNUC__
|
|
#ifdef __MINGW32__
|
|
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
|
|
#else
|
|
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
|
|
#endif
|
|
#else
|
|
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
|
|
#endif
|
|
|
|
void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|
const llama_sampling_params & sparams = params.sparams;
|
|
|
|
std::string sampler_type_chars;
|
|
std::string sampler_type_names;
|
|
for (const auto sampler_type : sparams.samplers_sequence) {
|
|
sampler_type_chars += static_cast<char>(sampler_type);
|
|
sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
|
|
}
|
|
sampler_type_names.pop_back();
|
|
|
|
struct option_info {
|
|
LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
|
|
option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
|
|
va_list args_list;
|
|
va_start(args_list, desc);
|
|
char buffer[1024];
|
|
vsnprintf(buffer, sizeof(buffer), desc, args_list);
|
|
va_end(args_list);
|
|
this->desc = buffer;
|
|
}
|
|
|
|
option_info(const std::string & grp) : grp(grp) {}
|
|
|
|
std::string tags;
|
|
std::string args;
|
|
std::string desc;
|
|
std::string grp;
|
|
};
|
|
|
|
std::vector<option_info> options;
|
|
|
|
// TODO: filter by tags
|
|
|
|
options.push_back({ "general" });
|
|
options.push_back({ "*", "-h, --help, --usage", "print usage and exit" });
|
|
options.push_back({ "*", " --version", "show version and build info" });
|
|
options.push_back({ "*", "-v, --verbose", "print verbose information" });
|
|
options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity });
|
|
options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
|
|
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
|
|
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
|
|
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
|
|
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads });
|
|
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
|
|
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
|
|
options.push_back({ "speculative", "-tbd, --threads-batch-draft N",
|
|
"number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
|
|
options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
|
|
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
|
|
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
|
|
"path to static lookup cache to use for lookup decoding (not updated by generation)" });
|
|
options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME",
|
|
"path to dynamic lookup cache to use for lookup decoding (updated by generation)" });
|
|
|
|
options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
|
|
options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
|
|
options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch });
|
|
options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch });
|
|
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
|
|
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
|
|
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
|
|
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: '%s')", params.prompt.c_str() });
|
|
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
|
|
options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" });
|
|
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
|
|
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
|
|
options.push_back({ "*", " --no-escape", "do not process escape sequences" });
|
|
options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print });
|
|
options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" });
|
|
options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n"
|
|
"not supported with --interactive or other interactive options" });
|
|
options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" });
|
|
options.push_back({ "main", "-r, --reverse-prompt PROMPT",
|
|
"halt generation at PROMPT, return control in interactive mode\n"
|
|
"can be specified more than once for multiple prompts" });
|
|
options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
|
|
options.push_back({ "main", "-cnv, --conversation", "run in conversation mode (does not print special tokens and suffix/prefix) (default: %s)", params.conversation ? "true" : "false" });
|
|
options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
|
|
options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
|
|
options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" });
|
|
options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" });
|
|
options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" });
|
|
options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" });
|
|
|
|
options.push_back({ "sampling" });
|
|
options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n"
|
|
"(default: %s)", sampler_type_names.c_str() });
|
|
options.push_back({ "*", " --sampling-seq SEQUENCE",
|
|
"simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
|
|
options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
|
|
options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
|
|
options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp });
|
|
options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
|
|
options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
|
|
options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
|
|
options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
|
|
options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
|
|
options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
|
|
options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
|
|
options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
|
|
options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
|
|
options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
|
|
options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
|
|
options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n"
|
|
"Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
|
|
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
|
|
options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
|
|
options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
|
|
options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n"
|
|
"i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
|
|
"or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
|
|
options.push_back({ "main", " --cfg-negative-prompt PROMPT",
|
|
"negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
|
|
options.push_back({ "main", " --cfg-negative-prompt-file FNAME",
|
|
"negative prompt file to use for guidance" });
|
|
options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
|
|
|
|
options.push_back({ "grammar" });
|
|
options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
|
|
options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" });
|
|
options.push_back({ "*", "-j, --json-schema SCHEMA",
|
|
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
|
|
"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
|
|
|
|
options.push_back({ "embedding" });
|
|
options.push_back({ "embedding", " --pooling {none,mean,cls}",
|
|
"pooling type for embeddings, use model default if unspecified" });
|
|
|
|
options.push_back({ "context hacking" });
|
|
options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
|
|
"RoPE frequency scaling method, defaults to linear unless specified by the model" });
|
|
options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" });
|
|
options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
|
|
options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" });
|
|
options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
|
|
options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
|
|
options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
|
|
options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
|
|
options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
|
|
options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n });
|
|
options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w });
|
|
options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" });
|
|
options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" });
|
|
options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
|
|
options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
|
|
|
|
options.push_back({ "perplexity" });
|
|
options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
|
|
options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" });
|
|
options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
|
|
options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" });
|
|
options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
|
|
options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" });
|
|
options.push_back({ "perplexity", " --multiple-choice-tasks N",
|
|
"number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
|
|
options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" });
|
|
options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride });
|
|
options.push_back({ "perplexity", " --ppl-output-type {0,1}",
|
|
"output type for perplexity calculation (default: %d)", params.ppl_output_type });
|
|
|
|
options.push_back({ "parallel" });
|
|
options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
|
|
options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel });
|
|
options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences });
|
|
options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
|
|
|
|
options.push_back({ "multi-modality" });
|
|
options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
|
|
options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" });
|
|
|
|
options.push_back({ "backend" });
|
|
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });
|
|
|
|
if (llama_supports_mlock()) {
|
|
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
|
|
}
|
|
if (llama_supports_mmap()) {
|
|
options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
|
|
}
|
|
options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n"
|
|
" - distribute: spread execution evenly over all nodes\n"
|
|
" - isolate: only spawn threads on CPUs on the node that execution started on\n"
|
|
" - numactl: use the CPU map provided by numactl\n"
|
|
"if run without this previously, it is recommended to drop the system page cache before using this\n"
|
|
"see https://github.com/ggerganov/llama.cpp/issues/1437" });
|
|
|
|
if (llama_supports_gpu_offload()) {
|
|
options.push_back({ "*", "-ngl, --gpu-layers N",
|
|
"number of layers to store in VRAM" });
|
|
options.push_back({ "*", "-ngld, --gpu-layers-draft N",
|
|
"number of layers to store in VRAM for the draft model" });
|
|
options.push_back({ "*", "-sm, --split-mode SPLIT_MODE",
|
|
"how to split the model across multiple GPUs, one of:\n"
|
|
" - none: use one GPU only\n"
|
|
" - layer (default): split layers and KV across GPUs\n"
|
|
" - row: split rows across GPUs" });
|
|
options.push_back({ "*", "-ts, --tensor-split SPLIT",
|
|
"fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
|
|
options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n"
|
|
"or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
|
|
}
|
|
|
|
options.push_back({ "model" });
|
|
options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
|
|
options.push_back({ "*", " --override-kv KEY=TYPE:VALUE",
|
|
"advanced option to override model metadata by key. may be specified multiple times.\n"
|
|
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
|
|
options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (implies --no-mmap)" });
|
|
options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (implies --no-mmap)" });
|
|
options.push_back({ "*", " --lora-base FNAME", "optional model to use as a base for the layers modified by the LoRA adapter" });
|
|
options.push_back({ "*", " --control-vector FNAME", "add a control vector" });
|
|
options.push_back({ "*", " --control-vector-scaled FNAME SCALE",
|
|
"add a control vector with user defined scaling SCALE" });
|
|
options.push_back({ "*", " --control-vector-layer-range START END",
|
|
"layer range to apply the control vector(s) to, start and end inclusive" });
|
|
options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n"
|
|
"or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
|
|
options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" });
|
|
options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" });
|
|
options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" });
|
|
options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" });
|
|
|
|
options.push_back({ "retrieval" });
|
|
options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" });
|
|
options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size });
|
|
options.push_back({ "retrieval", " --chunk-separator STRING",
|
|
"separator between chunks (default: '%s')", params.chunk_separator.c_str() });
|
|
|
|
options.push_back({ "passkey" });
|
|
options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk });
|
|
options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos });
|
|
|
|
options.push_back({ "imatrix" });
|
|
options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() });
|
|
options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq });
|
|
options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
|
|
options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
|
|
options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
|
|
options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk });
|
|
|
|
options.push_back({ "bench" });
|
|
options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
|
|
options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" });
|
|
options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" });
|
|
options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" });
|
|
|
|
options.push_back({ "embedding" });
|
|
options.push_back({ "embedding", " --embd-normalize", "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
|
|
options.push_back({ "embedding", " --embd-output-format", "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
|
|
options.push_back({ "embedding", " --embd-separator", "separator of embendings (default \\n) for example \"<#sep#>\"" });
|
|
|
|
options.push_back({ "server" });
|
|
options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() });
|
|
options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port });
|
|
options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() });
|
|
options.push_back({ "server", " --embedding(s)", "enable embedding endpoint (default: %s)", params.embedding ? "enabled" : "disabled" });
|
|
options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" });
|
|
options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" });
|
|
options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" });
|
|
options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" });
|
|
options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read });
|
|
options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
|
|
options.push_back({ "server", " --system-prompt-file FNAME",
|
|
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
|
|
options.push_back({ "server", " --log-format {text,json}",
|
|
"log output format: json or text (default: json)" });
|
|
options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
|
|
options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
|
|
options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" });
|
|
options.push_back({ "server", " --chat-template JINJA_TEMPLATE",
|
|
"set custom jinja chat template (default: template taken from model's metadata)\n"
|
|
"only commonly used templates are accepted:\n"
|
|
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
|
|
options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY",
|
|
"how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
|
|
|
|
#ifndef LOG_DISABLE_LOGS
|
|
options.push_back({ "logging" });
|
|
options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" });
|
|
options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" });
|
|
options.push_back({ "logging", " --log-test", "Run simple logging test" });
|
|
options.push_back({ "logging", " --log-disable", "Disable trace logs" });
|
|
options.push_back({ "logging", " --log-enable", "Enable trace logs" });
|
|
options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" });
|
|
options.push_back({ "logging", " --log-new", "Create a separate new log file on start. "
|
|
"Each log file will have unique name: \"<name>.<ID>.log\"" });
|
|
options.push_back({ "logging", " --log-append", "Don't truncate the old log file." });
|
|
#endif // LOG_DISABLE_LOGS
|
|
|
|
options.push_back({ "cvector" });
|
|
options.push_back({ "cvector", "-o, --output FNAME", "output file (default: '%s')", params.cvector_outfile.c_str() });
|
|
options.push_back({ "cvector", " --positive-file FNAME", "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
|
|
options.push_back({ "cvector", " --negative-file FNAME", "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
|
|
options.push_back({ "cvector", " --completions-file FNAME",
|
|
"completions file (default: '%s')", params.cvector_completions_file.c_str() });
|
|
options.push_back({ "cvector", " --completions N", "number of lines of completions file to use (default: %d)", params.n_completions });
|
|
options.push_back({ "cvector", " --pca-batch N", "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
|
|
options.push_back({ "cvector", " --pca-iter N", "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
|
|
|
|
printf("usage: %s [options]\n", argv[0]);
|
|
|
|
for (const auto & o : options) {
|
|
if (!o.grp.empty()) {
|
|
printf("\n%s:\n\n", o.grp.c_str());
|
|
continue;
|
|
}
|
|
printf(" %-32s", o.args.c_str());
|
|
if (o.args.length() > 30) {
|
|
printf("\n%34s", "");
|
|
}
|
|
|
|
const auto desc = o.desc;
|
|
size_t start = 0;
|
|
size_t end = desc.find('\n');
|
|
while (end != std::string::npos) {
|
|
printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
|
|
start = end + 1;
|
|
end = desc.find('\n', start);
|
|
}
|
|
|
|
printf("%s\n", desc.substr(start).c_str());
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
std::string gpt_params_get_system_info(const gpt_params & params) {
|
|
std::ostringstream os;
|
|
|
|
os << "system_info: n_threads = " << params.n_threads;
|
|
if (params.n_threads_batch != -1) {
|
|
os << " (n_threads_batch = " << params.n_threads_batch << ")";
|
|
}
|
|
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
|
|
|
|
return os.str();
|
|
}
|
|
|
|
//
|
|
// String utils
|
|
//
|
|
|
|
std::vector<std::string> string_split(std::string input, char separator) {
|
|
std::vector<std::string> parts;
|
|
size_t separator_pos = input.find(separator);
|
|
while (separator_pos != std::string::npos) {
|
|
std::string part = input.substr(0, separator_pos);
|
|
parts.emplace_back(part);
|
|
input = input.substr(separator_pos + 1);
|
|
separator_pos = input.find(separator);
|
|
}
|
|
parts.emplace_back(input);
|
|
return parts;
|
|
}
|
|
|
|
std::string string_strip(const std::string & str) {
|
|
size_t start = 0;
|
|
size_t end = str.size();
|
|
while (start < end && std::isspace(str[start])) {
|
|
start++;
|
|
}
|
|
while (end > start && std::isspace(str[end - 1])) {
|
|
end--;
|
|
}
|
|
return str.substr(start, end - start);
|
|
}
|
|
|
|
std::string string_get_sortable_timestamp() {
|
|
using clock = std::chrono::system_clock;
|
|
|
|
const clock::time_point current_time = clock::now();
|
|
const time_t as_time_t = clock::to_time_t(current_time);
|
|
char timestamp_no_ns[100];
|
|
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
|
|
|
|
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
|
|
current_time.time_since_epoch() % 1000000000).count();
|
|
char timestamp_ns[11];
|
|
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
|
|
|
|
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
|
|
}
|
|
|
|
void string_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;
|
|
case 'x':
|
|
// Handle \x12, etc
|
|
if (input_idx + 2 < input_len) {
|
|
const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
|
|
char *err_p = nullptr;
|
|
const long val = std::strtol(x, &err_p, 16);
|
|
if (err_p == x + 2) {
|
|
input_idx += 2;
|
|
input[output_idx++] = char(val);
|
|
break;
|
|
}
|
|
}
|
|
// fall through
|
|
default: input[output_idx++] = '\\';
|
|
input[output_idx++] = input[input_idx]; break;
|
|
}
|
|
} else {
|
|
input[output_idx++] = input[input_idx];
|
|
}
|
|
}
|
|
|
|
input.resize(output_idx);
|
|
}
|
|
|
|
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
|
|
const char * sep = strchr(data, '=');
|
|
if (sep == nullptr || sep - data >= 128) {
|
|
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
|
|
return false;
|
|
}
|
|
llama_model_kv_override kvo;
|
|
std::strncpy(kvo.key, data, sep - data);
|
|
kvo.key[sep - data] = 0;
|
|
sep++;
|
|
if (strncmp(sep, "int:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
|
|
kvo.val_i64 = std::atol(sep);
|
|
} else if (strncmp(sep, "float:", 6) == 0) {
|
|
sep += 6;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
|
|
kvo.val_f64 = std::atof(sep);
|
|
} else if (strncmp(sep, "bool:", 5) == 0) {
|
|
sep += 5;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
|
|
if (std::strcmp(sep, "true") == 0) {
|
|
kvo.val_bool = true;
|
|
} else if (std::strcmp(sep, "false") == 0) {
|
|
kvo.val_bool = false;
|
|
} else {
|
|
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
|
|
return false;
|
|
}
|
|
} else if (strncmp(sep, "str:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
|
|
if (strlen(sep) > 127) {
|
|
fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
|
|
return false;
|
|
}
|
|
strncpy(kvo.val_str, sep, 127);
|
|
kvo.val_str[127] = '\0';
|
|
} else {
|
|
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
|
|
return false;
|
|
}
|
|
overrides.emplace_back(std::move(kvo));
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// Filesystem utils
|
|
//
|
|
|
|
// Validate if a filename is safe to use
|
|
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
|
|
bool fs_validate_filename(const std::string & filename) {
|
|
if (!filename.length()) {
|
|
// Empty filename invalid
|
|
return false;
|
|
}
|
|
if (filename.length() > 255) {
|
|
// Limit at common largest possible filename on Linux filesystems
|
|
// to avoid unnecessary further validation
|
|
// (On systems with smaller limits it will be caught by the OS)
|
|
return false;
|
|
}
|
|
|
|
std::u32string filename_utf32;
|
|
try {
|
|
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
|
|
filename_utf32 = converter.from_bytes(filename);
|
|
|
|
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
|
|
// or invalid encodings were encountered. Reject such attempts
|
|
std::string filename_reencoded = converter.to_bytes(filename_utf32);
|
|
if (filename_reencoded != filename) {
|
|
return false;
|
|
}
|
|
} catch (const std::exception &) {
|
|
return false;
|
|
}
|
|
|
|
// Check for forbidden codepoints:
|
|
// - Control characters
|
|
// - Unicode equivalents of illegal characters
|
|
// - UTF-16 surrogate pairs
|
|
// - UTF-8 replacement character
|
|
// - Byte order mark (BOM)
|
|
// - Illegal characters: / \ : * ? " < > |
|
|
for (char32_t c : filename_utf32) {
|
|
if (c <= 0x1F // Control characters (C0)
|
|
|| c == 0x7F // Control characters (DEL)
|
|
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|
|
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|
|
|| c == 0x2215 // Division Slash (forward slash equivalent)
|
|
|| c == 0x2216 // Set Minus (backslash equivalent)
|
|
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|
|
|| c == 0xFFFD // Replacement Character (UTF-8)
|
|
|| c == 0xFEFF // Byte Order Mark (BOM)
|
|
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|
|
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
|
|
// Unicode and other whitespace is not affected, only 0x20 space
|
|
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
|
|
return false;
|
|
}
|
|
|
|
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
|
|
if (filename.find("..") != std::string::npos) {
|
|
return false;
|
|
}
|
|
|
|
// Reject "."
|
|
if (filename == ".") {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// returns true if successful, false otherwise
|
|
bool fs_create_directory_with_parents(const std::string & path) {
|
|
#ifdef _WIN32
|
|
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
|
|
std::wstring wpath = converter.from_bytes(path);
|
|
|
|
// if the path already exists, check whether it's a directory
|
|
const DWORD attributes = GetFileAttributesW(wpath.c_str());
|
|
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
|
|
return true;
|
|
}
|
|
|
|
size_t pos_slash = 0;
|
|
|
|
// process path from front to back, procedurally creating directories
|
|
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
|
|
const std::wstring subpath = wpath.substr(0, pos_slash);
|
|
const wchar_t * test = subpath.c_str();
|
|
|
|
const bool success = CreateDirectoryW(test, NULL);
|
|
if (!success) {
|
|
const DWORD error = GetLastError();
|
|
|
|
// if the path already exists, ensure that it's a directory
|
|
if (error == ERROR_ALREADY_EXISTS) {
|
|
const DWORD attributes = GetFileAttributesW(subpath.c_str());
|
|
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
pos_slash += 1;
|
|
}
|
|
|
|
return true;
|
|
#else
|
|
// if the path already exists, check whether it's a directory
|
|
struct stat info;
|
|
if (stat(path.c_str(), &info) == 0) {
|
|
return S_ISDIR(info.st_mode);
|
|
}
|
|
|
|
size_t pos_slash = 1; // skip leading slashes for directory creation
|
|
|
|
// process path from front to back, procedurally creating directories
|
|
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
|
|
const std::string subpath = path.substr(0, pos_slash);
|
|
struct stat info;
|
|
|
|
// if the path already exists, ensure that it's a directory
|
|
if (stat(subpath.c_str(), &info) == 0) {
|
|
if (!S_ISDIR(info.st_mode)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
// create parent directories
|
|
const int ret = mkdir(subpath.c_str(), 0755);
|
|
if (ret != 0) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
pos_slash += 1;
|
|
}
|
|
|
|
return true;
|
|
#endif // _WIN32
|
|
}
|
|
|
|
std::string fs_get_cache_directory() {
|
|
std::string cache_directory = "";
|
|
auto ensure_trailing_slash = [](std::string p) {
|
|
// Make sure to add trailing slash
|
|
if (p.back() != DIRECTORY_SEPARATOR) {
|
|
p += DIRECTORY_SEPARATOR;
|
|
}
|
|
return p;
|
|
};
|
|
if (getenv("LLAMA_CACHE")) {
|
|
cache_directory = std::getenv("LLAMA_CACHE");
|
|
} else {
|
|
#ifdef __linux__
|
|
if (std::getenv("XDG_CACHE_HOME")) {
|
|
cache_directory = std::getenv("XDG_CACHE_HOME");
|
|
} else {
|
|
cache_directory = std::getenv("HOME") + std::string("/.cache/");
|
|
}
|
|
#elif defined(__APPLE__)
|
|
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
|
|
#elif defined(_WIN32)
|
|
cache_directory = std::getenv("LOCALAPPDATA");
|
|
#endif // __linux__
|
|
cache_directory = ensure_trailing_slash(cache_directory);
|
|
cache_directory += "llama.cpp";
|
|
}
|
|
return ensure_trailing_slash(cache_directory);
|
|
}
|
|
|
|
std::string fs_get_cache_file(const std::string & filename) {
|
|
GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
|
|
std::string cache_directory = fs_get_cache_directory();
|
|
const bool success = fs_create_directory_with_parents(cache_directory);
|
|
if (!success) {
|
|
throw std::runtime_error("failed to create cache directory: " + cache_directory);
|
|
}
|
|
return cache_directory + filename;
|
|
}
|
|
|
|
|
|
//
|
|
// Model utils
|
|
//
|
|
|
|
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
|
|
auto mparams = llama_model_params_from_gpt_params(params);
|
|
|
|
llama_model * model = nullptr;
|
|
|
|
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
|
|
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
|
|
} else if (!params.model_url.empty()) {
|
|
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
|
|
} else {
|
|
model = llama_load_model_from_file(params.model.c_str(), mparams);
|
|
}
|
|
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
|
|
return std::make_tuple(nullptr, nullptr);
|
|
}
|
|
|
|
auto cparams = llama_context_params_from_gpt_params(params);
|
|
|
|
llama_context * lctx = llama_new_context_with_model(model, cparams);
|
|
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.control_vectors.empty()) {
|
|
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
|
|
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
|
|
|
|
const auto cvec = llama_control_vector_load(params.control_vectors);
|
|
if (cvec.n_embd == -1) {
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return std::make_tuple(nullptr, nullptr);
|
|
}
|
|
|
|
int err = llama_control_vector_apply(lctx,
|
|
cvec.data.data(),
|
|
cvec.data.size(),
|
|
cvec.n_embd,
|
|
params.control_vector_layer_start,
|
|
params.control_vector_layer_end);
|
|
if (err) {
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return std::make_tuple(nullptr, nullptr);
|
|
}
|
|
}
|
|
|
|
for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
|
|
const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
|
|
float lora_scale = std::get<1>(params.lora_adapter[i]);
|
|
int err = llama_model_apply_lora_from_file(model,
|
|
lora_adapter.c_str(),
|
|
lora_scale,
|
|
((i > 0) || 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.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
if (params.warmup) {
|
|
LOG("warming up the model with an empty run\n");
|
|
|
|
std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
|
|
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
|
|
llama_kv_cache_clear(lctx);
|
|
llama_synchronize(lctx);
|
|
llama_reset_timings(lctx);
|
|
}
|
|
|
|
return std::make_tuple(model, lctx);
|
|
}
|
|
|
|
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
|
|
auto mparams = llama_model_default_params();
|
|
|
|
if (params.n_gpu_layers != -1) {
|
|
mparams.n_gpu_layers = params.n_gpu_layers;
|
|
}
|
|
mparams.rpc_servers = params.rpc_servers.c_str();
|
|
mparams.main_gpu = params.main_gpu;
|
|
mparams.split_mode = params.split_mode;
|
|
mparams.tensor_split = params.tensor_split;
|
|
mparams.use_mmap = params.use_mmap;
|
|
mparams.use_mlock = params.use_mlock;
|
|
mparams.check_tensors = params.check_tensors;
|
|
if (params.kv_overrides.empty()) {
|
|
mparams.kv_overrides = NULL;
|
|
} else {
|
|
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
|
|
mparams.kv_overrides = params.kv_overrides.data();
|
|
}
|
|
|
|
return mparams;
|
|
}
|
|
|
|
static ggml_type kv_cache_type_from_str(const std::string & s) {
|
|
if (s == "f32") {
|
|
return GGML_TYPE_F32;
|
|
}
|
|
if (s == "f16") {
|
|
return GGML_TYPE_F16;
|
|
}
|
|
if (s == "q8_0") {
|
|
return GGML_TYPE_Q8_0;
|
|
}
|
|
if (s == "q4_0") {
|
|
return GGML_TYPE_Q4_0;
|
|
}
|
|
if (s == "q4_1") {
|
|
return GGML_TYPE_Q4_1;
|
|
}
|
|
if (s == "iq4_nl") {
|
|
return GGML_TYPE_IQ4_NL;
|
|
}
|
|
if (s == "q5_0") {
|
|
return GGML_TYPE_Q5_0;
|
|
}
|
|
if (s == "q5_1") {
|
|
return GGML_TYPE_Q5_1;
|
|
}
|
|
|
|
throw std::runtime_error("Invalid cache type: " + s);
|
|
}
|
|
|
|
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
|
|
auto cparams = llama_context_default_params();
|
|
|
|
cparams.n_ctx = params.n_ctx;
|
|
cparams.n_seq_max = params.n_parallel;
|
|
cparams.n_batch = params.n_batch;
|
|
cparams.n_ubatch = params.n_ubatch;
|
|
cparams.n_threads = params.n_threads;
|
|
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
|
|
cparams.seed = params.seed;
|
|
cparams.logits_all = params.logits_all;
|
|
cparams.embeddings = params.embedding;
|
|
cparams.rope_scaling_type = params.rope_scaling_type;
|
|
cparams.rope_freq_base = params.rope_freq_base;
|
|
cparams.rope_freq_scale = params.rope_freq_scale;
|
|
cparams.yarn_ext_factor = params.yarn_ext_factor;
|
|
cparams.yarn_attn_factor = params.yarn_attn_factor;
|
|
cparams.yarn_beta_fast = params.yarn_beta_fast;
|
|
cparams.yarn_beta_slow = params.yarn_beta_slow;
|
|
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
|
|
cparams.pooling_type = params.pooling_type;
|
|
cparams.defrag_thold = params.defrag_thold;
|
|
cparams.cb_eval = params.cb_eval;
|
|
cparams.cb_eval_user_data = params.cb_eval_user_data;
|
|
cparams.offload_kqv = !params.no_kv_offload;
|
|
cparams.flash_attn = params.flash_attn;
|
|
|
|
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
|
|
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
|
|
|
|
return cparams;
|
|
}
|
|
|
|
#ifdef LLAMA_USE_CURL
|
|
|
|
static bool starts_with(const std::string & str, const std::string & prefix) {
|
|
// While we wait for C++20's std::string::starts_with...
|
|
return str.rfind(prefix, 0) == 0;
|
|
}
|
|
|
|
static bool llama_download_file(const std::string & url, const std::string & path) {
|
|
|
|
// Initialize libcurl
|
|
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
|
|
if (!curl) {
|
|
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
bool force_download = false;
|
|
|
|
// Set the URL, allow to follow http redirection
|
|
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
|
|
|
|
#if defined(_WIN32)
|
|
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
|
|
// operating system. Currently implemented under MS-Windows.
|
|
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
|
|
#endif
|
|
|
|
// Check if the file already exists locally
|
|
struct stat model_file_info;
|
|
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
|
|
|
|
// If the file exists, check its JSON metadata companion file.
|
|
std::string metadata_path = path + ".json";
|
|
nlohmann::json metadata;
|
|
std::string etag;
|
|
std::string last_modified;
|
|
|
|
if (file_exists) {
|
|
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
|
|
std::ifstream metadata_in(metadata_path);
|
|
if (metadata_in.good()) {
|
|
try {
|
|
metadata_in >> metadata;
|
|
fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
|
|
if (metadata.contains("url") && metadata.at("url").is_string()) {
|
|
auto previous_url = metadata.at("url").get<std::string>();
|
|
if (previous_url != url) {
|
|
fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
|
|
etag = metadata.at("etag");
|
|
}
|
|
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
|
|
last_modified = metadata.at("lastModified");
|
|
}
|
|
} catch (const nlohmann::json::exception & e) {
|
|
fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
|
|
return false;
|
|
}
|
|
}
|
|
} else {
|
|
fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
|
|
}
|
|
|
|
// Send a HEAD request to retrieve the etag and last-modified headers
|
|
struct llama_load_model_from_url_headers {
|
|
std::string etag;
|
|
std::string last_modified;
|
|
};
|
|
llama_load_model_from_url_headers headers;
|
|
{
|
|
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
|
|
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
|
|
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
|
|
|
|
static std::regex header_regex("([^:]+): (.*)\r\n");
|
|
static std::regex etag_regex("ETag", std::regex_constants::icase);
|
|
static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
|
|
|
|
std::string header(buffer, n_items);
|
|
std::smatch match;
|
|
if (std::regex_match(header, match, header_regex)) {
|
|
const std::string & key = match[1];
|
|
const std::string & value = match[2];
|
|
if (std::regex_match(key, match, etag_regex)) {
|
|
headers->etag = value;
|
|
} else if (std::regex_match(key, match, last_modified_regex)) {
|
|
headers->last_modified = value;
|
|
}
|
|
}
|
|
return n_items;
|
|
};
|
|
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
|
|
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
|
|
|
|
CURLcode res = curl_easy_perform(curl.get());
|
|
if (res != CURLE_OK) {
|
|
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code != 200) {
|
|
// HEAD not supported, we don't know if the file has changed
|
|
// force trigger downloading
|
|
force_download = true;
|
|
fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
|
|
}
|
|
}
|
|
|
|
bool should_download = !file_exists || force_download;
|
|
if (!should_download) {
|
|
if (!etag.empty() && etag != headers.etag) {
|
|
fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
|
|
should_download = true;
|
|
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
|
|
fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
|
|
should_download = true;
|
|
}
|
|
}
|
|
if (should_download) {
|
|
std::string path_temporary = path + ".downloadInProgress";
|
|
if (file_exists) {
|
|
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
|
|
if (remove(path.c_str()) != 0) {
|
|
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Set the output file
|
|
|
|
struct FILE_deleter {
|
|
void operator()(FILE * f) const {
|
|
fclose(f);
|
|
}
|
|
};
|
|
|
|
std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
|
|
if (!outfile) {
|
|
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
|
|
return false;
|
|
}
|
|
|
|
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
|
|
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
|
|
return fwrite(data, size, nmemb, (FILE *)fd);
|
|
};
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
|
|
|
|
// display download progress
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
|
|
|
|
// helper function to hide password in URL
|
|
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
|
|
std::size_t protocol_pos = url.find("://");
|
|
if (protocol_pos == std::string::npos) {
|
|
return url; // Malformed URL
|
|
}
|
|
|
|
std::size_t at_pos = url.find('@', protocol_pos + 3);
|
|
if (at_pos == std::string::npos) {
|
|
return url; // No password in URL
|
|
}
|
|
|
|
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
|
|
};
|
|
|
|
// start the download
|
|
fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
|
|
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
|
|
auto res = curl_easy_perform(curl.get());
|
|
if (res != CURLE_OK) {
|
|
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code < 200 || http_code >= 400) {
|
|
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
|
|
return false;
|
|
}
|
|
|
|
// Causes file to be closed explicitly here before we rename it.
|
|
outfile.reset();
|
|
|
|
// Write the updated JSON metadata file.
|
|
metadata.update({
|
|
{"url", url},
|
|
{"etag", headers.etag},
|
|
{"lastModified", headers.last_modified}
|
|
});
|
|
std::ofstream(metadata_path) << metadata.dump(4);
|
|
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
|
|
|
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
|
|
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_url(
|
|
const char * model_url,
|
|
const char * path_model,
|
|
const struct llama_model_params & params) {
|
|
// Basic validation of the model_url
|
|
if (!model_url || strlen(model_url) == 0) {
|
|
fprintf(stderr, "%s: invalid model_url\n", __func__);
|
|
return NULL;
|
|
}
|
|
|
|
if (!llama_download_file(model_url, path_model)) {
|
|
return NULL;
|
|
}
|
|
|
|
// check for additional GGUFs split to download
|
|
int n_split = 0;
|
|
{
|
|
struct gguf_init_params gguf_params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ NULL,
|
|
};
|
|
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
|
|
if (!ctx_gguf) {
|
|
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
|
|
return NULL;
|
|
}
|
|
|
|
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
|
|
if (key_n_split >= 0) {
|
|
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
|
|
}
|
|
|
|
gguf_free(ctx_gguf);
|
|
}
|
|
|
|
if (n_split > 1) {
|
|
char split_prefix[PATH_MAX] = {0};
|
|
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
|
|
// Verify the first split file format
|
|
// and extract split URL and PATH prefixes
|
|
{
|
|
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
|
|
fprintf(stderr, "\n%s: unexpected model file name: %s"
|
|
" n_split=%d\n", __func__, path_model, n_split);
|
|
return NULL;
|
|
}
|
|
|
|
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
|
|
fprintf(stderr, "\n%s: unexpected model url: %s"
|
|
" n_split=%d\n", __func__, model_url, n_split);
|
|
return NULL;
|
|
}
|
|
}
|
|
|
|
// Prepare download in parallel
|
|
std::vector<std::future<bool>> futures_download;
|
|
for (int idx = 1; idx < n_split; idx++) {
|
|
futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split](int download_idx) -> bool {
|
|
char split_path[PATH_MAX] = {0};
|
|
llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
|
|
|
|
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
|
|
|
|
return llama_download_file(split_url, split_path);
|
|
}, idx));
|
|
}
|
|
|
|
// Wait for all downloads to complete
|
|
for (auto & f : futures_download) {
|
|
if (!f.get()) {
|
|
return NULL;
|
|
}
|
|
}
|
|
}
|
|
|
|
return llama_load_model_from_file(path_model, params);
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_hf(
|
|
const char * repo,
|
|
const char * model,
|
|
const char * path_model,
|
|
const struct llama_model_params & params) {
|
|
// construct hugging face model url:
|
|
//
|
|
// --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
|
|
// https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
|
|
//
|
|
// --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
|
|
// https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
|
|
//
|
|
|
|
std::string model_url = "https://huggingface.co/";
|
|
model_url += repo;
|
|
model_url += "/resolve/main/";
|
|
model_url += model;
|
|
|
|
return llama_load_model_from_url(model_url.c_str(), path_model, params);
|
|
}
|
|
|
|
#else
|
|
|
|
struct llama_model * llama_load_model_from_url(
|
|
const char * /*model_url*/,
|
|
const char * /*path_model*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_hf(
|
|
const char * /*repo*/,
|
|
const char * /*model*/,
|
|
const char * /*path_model*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
#endif // LLAMA_USE_CURL
|
|
|
|
//
|
|
// Batch utils
|
|
//
|
|
|
|
void llama_batch_clear(struct llama_batch & batch) {
|
|
batch.n_tokens = 0;
|
|
}
|
|
|
|
void llama_batch_add(
|
|
struct llama_batch & batch,
|
|
llama_token id,
|
|
llama_pos pos,
|
|
const std::vector<llama_seq_id> & seq_ids,
|
|
bool logits) {
|
|
batch.token [batch.n_tokens] = id;
|
|
batch.pos [batch.n_tokens] = pos;
|
|
batch.n_seq_id[batch.n_tokens] = seq_ids.size();
|
|
for (size_t i = 0; i < seq_ids.size(); ++i) {
|
|
batch.seq_id[batch.n_tokens][i] = seq_ids[i];
|
|
}
|
|
batch.logits [batch.n_tokens] = logits;
|
|
|
|
batch.n_tokens++;
|
|
}
|
|
|
|
//
|
|
// Vocab utils
|
|
//
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_context * ctx,
|
|
const std::string & text,
|
|
bool add_special,
|
|
bool parse_special) {
|
|
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
|
|
}
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_model * model,
|
|
const std::string & text,
|
|
bool add_special,
|
|
bool parse_special) {
|
|
// upper limit for the number of tokens
|
|
int n_tokens = text.length() + 2 * add_special;
|
|
std::vector<llama_token> result(n_tokens);
|
|
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
|
|
GGML_ASSERT(check == -n_tokens);
|
|
} else {
|
|
result.resize(n_tokens);
|
|
}
|
|
return result;
|
|
}
|
|
|
|
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
|
|
std::vector<char> result(8, 0);
|
|
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
|
GGML_ASSERT(check == -n_tokens);
|
|
} else {
|
|
result.resize(n_tokens);
|
|
}
|
|
|
|
return std::string(result.data(), result.size());
|
|
}
|
|
|
|
std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
const llama_token bos_id = llama_token_bos(llama_get_model(ctx));
|
|
|
|
std::string piece;
|
|
std::string result;
|
|
|
|
for (size_t i = 0; i < tokens.size(); ++i) {
|
|
piece = llama_token_to_piece(ctx, tokens[i]);
|
|
|
|
// remove the leading space of the first non-BOS token
|
|
if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
|
|
piece = piece.substr(1);
|
|
}
|
|
|
|
result += piece;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
std::string piece;
|
|
std::string result;
|
|
|
|
for (size_t i = 0; i < tokens.size(); ++i) {
|
|
piece = llama_token_to_piece(ctx, tokens[i]);
|
|
|
|
result += piece;
|
|
}
|
|
|
|
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
|
|
return result;
|
|
}
|
|
|
|
bool llama_should_add_bos_token(const llama_model * model) {
|
|
const int add_bos = llama_add_bos_token(model);
|
|
|
|
return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
|
|
}
|
|
|
|
bool llama_chat_verify_template(const std::string & tmpl) {
|
|
llama_chat_message chat[] = {{"user", "test"}};
|
|
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
|
|
return res >= 0;
|
|
}
|
|
|
|
//
|
|
// KV cache utils
|
|
//
|
|
|
|
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
|
|
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
|
|
|
|
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
|
|
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
|
|
|
|
llama_kv_cache_view_cell * c_curr = view.cells;
|
|
llama_seq_id * cs_curr = view.cells_sequences;
|
|
|
|
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
|
|
if (i % row_size == 0) {
|
|
printf("\n%5d: ", i);
|
|
}
|
|
int seq_count = 0;
|
|
for (int j = 0; j < view.n_seq_max; j++) {
|
|
if (cs_curr[j] >= 0) { seq_count++; }
|
|
}
|
|
putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
|
|
}
|
|
|
|
printf("\n=== Done dumping\n");
|
|
}
|
|
|
|
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
|
|
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
|
|
|
|
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
|
|
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
|
|
|
|
std::unordered_map<llama_seq_id, size_t> seqs;
|
|
llama_kv_cache_view_cell * c_curr = view.cells;
|
|
llama_seq_id * cs_curr = view.cells_sequences;
|
|
|
|
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
|
|
for (int j = 0; j < view.n_seq_max; j++) {
|
|
if (cs_curr[j] < 0) { continue; }
|
|
if (seqs.find(cs_curr[j]) == seqs.end()) {
|
|
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
|
|
const size_t sz = seqs.size();
|
|
seqs[cs_curr[j]] = sz;
|
|
}
|
|
}
|
|
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
|
|
}
|
|
|
|
printf("=== Sequence legend: ");
|
|
for (const auto & it : seqs) {
|
|
printf("%zu=%d, ", it.second, it.first);
|
|
}
|
|
printf("'+'=other sequence ids");
|
|
|
|
c_curr = view.cells;
|
|
cs_curr = view.cells_sequences;
|
|
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
|
|
if (i % row_size == 0) {
|
|
printf("\n%5d: ", i);
|
|
}
|
|
for (int j = 0; j < view.n_seq_max; j++) {
|
|
if (cs_curr[j] >= 0) {
|
|
const auto & it = seqs.find(cs_curr[j]);
|
|
putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
|
|
} else {
|
|
putchar('.');
|
|
}
|
|
}
|
|
putchar(' ');
|
|
}
|
|
|
|
printf("\n=== Done dumping\n");
|
|
}
|
|
|
|
//
|
|
// Embedding utils
|
|
//
|
|
|
|
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
|
|
double sum = 0.0;
|
|
|
|
switch (embd_norm) {
|
|
case -1: // no normalisation
|
|
sum = 1.0;
|
|
break;
|
|
case 0: // max absolute
|
|
for (int i = 0; i < n; i++) {
|
|
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
|
|
}
|
|
sum /= 32760.0; // make an int16 range
|
|
break;
|
|
case 2: // euclidean
|
|
for (int i = 0; i < n; i++) {
|
|
sum += inp[i] * inp[i];
|
|
}
|
|
sum = std::sqrt(sum);
|
|
break;
|
|
default: // p-norm (euclidean is p-norm p=2)
|
|
for (int i = 0; i < n; i++) {
|
|
sum += std::pow(std::abs(inp[i]), embd_norm);
|
|
}
|
|
sum = std::pow(sum, 1.0 / embd_norm);
|
|
break;
|
|
}
|
|
|
|
const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
out[i] = inp[i] * norm;
|
|
}
|
|
}
|
|
|
|
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
|
|
double sum = 0.0;
|
|
double sum1 = 0.0;
|
|
double sum2 = 0.0;
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
sum += embd1[i] * embd2[i];
|
|
sum1 += embd1[i] * embd1[i];
|
|
sum2 += embd2[i] * embd2[i];
|
|
}
|
|
|
|
// Handle the case where one or both vectors are zero vectors
|
|
if (sum1 == 0.0 || sum2 == 0.0) {
|
|
if (sum1 == 0.0 && sum2 == 0.0) {
|
|
return 1.0f; // two zero vectors are similar
|
|
}
|
|
return 0.0f;
|
|
}
|
|
|
|
return sum / (sqrt(sum1) * sqrt(sum2));
|
|
}
|
|
|
|
//
|
|
// Control vector utils
|
|
//
|
|
|
|
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
|
|
int32_t n_tensors;
|
|
|
|
size_t n_bytes = 0;
|
|
|
|
uint32_t max_direction_layer = 0;
|
|
|
|
llama_control_vector_data result = { -1, {} };
|
|
|
|
// calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer
|
|
{
|
|
struct ggml_init_params meta_params = {
|
|
/* .mem_size = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(),
|
|
/* .mem_buffer = */ nullptr,
|
|
/* .no_alloc = */ true,
|
|
};
|
|
ggml_context * meta_ctx = ggml_init(meta_params);
|
|
struct gguf_init_params meta_gguf_params = {
|
|
/* .no_alloc = */ true,
|
|
/* .ctx = */ &meta_ctx,
|
|
};
|
|
struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
|
|
if (!meta_ctx_gguf) {
|
|
fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
|
|
ggml_free(meta_ctx);
|
|
return result;
|
|
}
|
|
|
|
n_tensors = gguf_get_n_tensors(meta_ctx_gguf);
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
std::string name = gguf_get_tensor_name(meta_ctx_gguf, i);
|
|
|
|
// split on '.'
|
|
size_t dotpos = name.find('.');
|
|
if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
|
|
try {
|
|
uint32_t layer = std::stoi(name.substr(dotpos + 1));
|
|
if (layer == 0) {
|
|
fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
|
|
ggml_free(meta_ctx);
|
|
gguf_free(meta_ctx_gguf);
|
|
return result;
|
|
}
|
|
if (layer > max_direction_layer) {
|
|
max_direction_layer = layer;
|
|
}
|
|
} catch (...) {
|
|
fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
|
|
ggml_free(meta_ctx);
|
|
gguf_free(meta_ctx_gguf);
|
|
return result;
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str());
|
|
if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) {
|
|
fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
|
|
ggml_free(meta_ctx);
|
|
gguf_free(meta_ctx_gguf);
|
|
return result;
|
|
}
|
|
if (result.n_embd == -1) {
|
|
result.n_embd = ggml_nelements(tensor_meta);
|
|
} else if (ggml_nelements(tensor_meta) != result.n_embd) {
|
|
fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str());
|
|
ggml_free(meta_ctx);
|
|
gguf_free(meta_ctx_gguf);
|
|
return result;
|
|
}
|
|
n_bytes += ggml_nbytes(tensor_meta);
|
|
}
|
|
ggml_free(meta_ctx);
|
|
gguf_free(meta_ctx_gguf);
|
|
}
|
|
|
|
if (n_tensors == 0) {
|
|
fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
|
|
return result;
|
|
}
|
|
|
|
// load and scale tensors into final control vector context
|
|
struct ggml_init_params ggml_params = {
|
|
/* .mem_size = */ ggml_tensor_overhead() * n_tensors + n_bytes,
|
|
/* .mem_buffer = */ nullptr,
|
|
/* .no_alloc = */ false,
|
|
};
|
|
struct ggml_context * ctx = ggml_init(ggml_params);
|
|
|
|
struct gguf_init_params params = {
|
|
/*.no_alloc = */ false,
|
|
/*.ctx = */ &ctx,
|
|
};
|
|
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params);
|
|
if (!ctx_gguf) {
|
|
fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
|
|
ggml_free(ctx);
|
|
return result;
|
|
}
|
|
|
|
// do not store data for layer 0 (it's not used)
|
|
result.data.resize(result.n_embd * max_direction_layer);
|
|
|
|
for (uint32_t il = 1; il <= max_direction_layer; il++) {
|
|
const std::string name = "direction." + std::to_string(il);
|
|
const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
|
|
|
|
float * dst = result.data.data() + result.n_embd * (il - 1);
|
|
|
|
if (tensor) {
|
|
const float * src = (const float *) tensor->data;
|
|
for (int j = 0; j < result.n_embd; j++) {
|
|
dst[j] = src[j] * load_info.strength;
|
|
}
|
|
} else {
|
|
for (int j = 0; j < result.n_embd; j++) {
|
|
dst[j] = 0.0f;
|
|
}
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
|
|
llama_control_vector_data result = { -1, {} };
|
|
|
|
for (const auto & info : load_infos) {
|
|
auto cur = llama_control_vector_load_one(info);
|
|
|
|
if (cur.n_embd == -1) {
|
|
return result;
|
|
}
|
|
if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) {
|
|
fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str());
|
|
return result;
|
|
}
|
|
|
|
if (result.n_embd == -1) {
|
|
result = std::move(cur);
|
|
} else {
|
|
for (size_t i = 0; i < cur.data.size(); i++) {
|
|
result.data[i] += cur.data[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
if (result.n_embd == -1) {
|
|
fprintf(stderr, "%s: no vectors passed\n", __func__);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
//
|
|
// YAML utils
|
|
//
|
|
|
|
void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
|
|
if (data.empty()) {
|
|
fprintf(stream, "%s:\n", prop_name);
|
|
return;
|
|
}
|
|
|
|
fprintf(stream, "%s: [", prop_name);
|
|
for (size_t i = 0; i < data.size() - 1; ++i) {
|
|
fprintf(stream, "%e, ", data[i]);
|
|
}
|
|
fprintf(stream, "%e]\n", data.back());
|
|
}
|
|
|
|
void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
|
|
if (data.empty()) {
|
|
fprintf(stream, "%s:\n", prop_name);
|
|
return;
|
|
}
|
|
|
|
fprintf(stream, "%s: [", prop_name);
|
|
for (size_t i = 0; i < data.size() - 1; ++i) {
|
|
fprintf(stream, "%d, ", data[i]);
|
|
}
|
|
fprintf(stream, "%d]\n", data.back());
|
|
}
|
|
|
|
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
|
|
std::string data_str(data == NULL ? "" : data);
|
|
|
|
if (data_str.empty()) {
|
|
fprintf(stream, "%s:\n", prop_name);
|
|
return;
|
|
}
|
|
|
|
size_t pos_start = 0;
|
|
size_t pos_found = 0;
|
|
|
|
if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
|
|
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
|
|
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
|
|
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
|
|
data_str = "\"" + data_str + "\"";
|
|
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
|
|
return;
|
|
}
|
|
|
|
if (data_str.find('\n') == std::string::npos) {
|
|
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
|
|
return;
|
|
}
|
|
|
|
fprintf(stream, "%s: |\n", prop_name);
|
|
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
|
|
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
|
|
pos_start = pos_found + 1;
|
|
}
|
|
}
|
|
|
|
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
|
|
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
|
|
const llama_sampling_params & sparams = params.sparams;
|
|
|
|
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
|
|
fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
|
|
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
|
|
|
|
#ifdef NDEBUG
|
|
fprintf(stream, "debug: false\n");
|
|
#else
|
|
fprintf(stream, "debug: true\n");
|
|
#endif // NDEBUG
|
|
|
|
fprintf(stream, "model_desc: %s\n", model_desc);
|
|
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
|
|
|
|
#ifdef __OPTIMIZE__
|
|
fprintf(stream, "optimize: true\n");
|
|
#else
|
|
fprintf(stream, "optimize: false\n");
|
|
#endif // __OPTIMIZE__
|
|
|
|
fprintf(stream, "time: %s\n", timestamp.c_str());
|
|
|
|
fprintf(stream, "\n");
|
|
fprintf(stream, "###############\n");
|
|
fprintf(stream, "# User Inputs #\n");
|
|
fprintf(stream, "###############\n");
|
|
fprintf(stream, "\n");
|
|
|
|
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
|
|
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
|
|
yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
|
|
fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
|
|
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
|
|
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
|
|
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
|
|
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
|
|
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
|
|
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
|
|
yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
|
|
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
|
|
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
|
|
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
|
|
|
|
const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
|
|
const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
|
|
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
|
|
|
|
yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
|
|
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
|
|
yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
|
|
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
|
|
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
|
|
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
|
|
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
|
|
|
|
fprintf(stream, "logit_bias:\n");
|
|
for (std::pair<llama_token, float> lb : sparams.logit_bias) {
|
|
if (ignore_eos && lb.first == logit_bias_eos->first) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " %d: %f", lb.first, lb.second);
|
|
}
|
|
|
|
fprintf(stream, "lora:\n");
|
|
for (std::tuple<std::string, float> la : params.lora_adapter) {
|
|
if (std::get<1>(la) != 1.0f) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " - %s\n", std::get<0>(la).c_str());
|
|
}
|
|
fprintf(stream, "lora_scaled:\n");
|
|
for (std::tuple<std::string, float> la : params.lora_adapter) {
|
|
if (std::get<1>(la) == 1.0f) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la));
|
|
}
|
|
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
|
|
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
|
|
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
|
|
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
|
|
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
|
|
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
|
|
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
|
|
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
|
|
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
|
|
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
|
|
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
|
|
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
|
|
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
|
|
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
|
|
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
|
|
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
|
|
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
|
|
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
|
|
yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
|
|
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
|
|
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
|
|
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
|
|
yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
|
|
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
|
|
|
|
fprintf(stream, "reverse_prompt:\n");
|
|
for (std::string ap : params.antiprompt) {
|
|
size_t pos = 0;
|
|
while ((pos = ap.find('\n', pos)) != std::string::npos) {
|
|
ap.replace(pos, 1, "\\n");
|
|
pos += 1;
|
|
}
|
|
|
|
fprintf(stream, " - %s\n", ap.c_str());
|
|
}
|
|
|
|
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
|
|
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
|
|
fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
|
|
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
|
|
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
|
|
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
|
|
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
|
|
|
|
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
|
|
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
|
|
|
|
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
|
|
fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
|
|
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
|
|
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
|
|
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
|
|
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
|
|
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
|
|
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
|
|
}
|