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https://github.com/ggerganov/llama.cpp/pull/9418
2060 lines
74 KiB
C++
2060 lines
74 KiB
C++
#if defined(_MSC_VER)
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#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
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#endif
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#include "common.h"
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#include "log.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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#include <thread>
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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 <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(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) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
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// TODO: windows + arm64 + mingw64
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unsigned int n_threads_win = std::thread::hardware_concurrency();
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unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4;
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DWORD buffer_size = 0;
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if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) {
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if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) {
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return default_threads;
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}
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}
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std::vector<char> buffer(buffer_size);
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if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) {
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return default_threads;
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}
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int32_t num_physical_cores = 0;
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PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data());
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while (buffer_size > 0) {
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if (info->Relationship == RelationProcessorCore) {
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num_physical_cores += info->Processor.GroupCount;
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}
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buffer_size -= info->Size;
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info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size);
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}
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return num_physical_cores > 0 ? num_physical_cores : default_threads;
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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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// Helper for setting process priority
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#if defined(_WIN32)
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bool set_process_priority(enum ggml_sched_priority prio) {
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if (prio == GGML_SCHED_PRIO_NORMAL) {
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return true;
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}
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DWORD p = NORMAL_PRIORITY_CLASS;
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switch (prio) {
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case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break;
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case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break;
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case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break;
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case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break;
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}
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if (!SetPriorityClass(GetCurrentProcess(), p)) {
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LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
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return false;
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}
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return true;
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}
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#else // MacOS and POSIX
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#include <sys/types.h>
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#include <sys/resource.h>
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bool set_process_priority(enum ggml_sched_priority prio) {
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if (prio == GGML_SCHED_PRIO_NORMAL) {
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return true;
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}
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int p = 0;
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switch (prio) {
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case GGML_SCHED_PRIO_NORMAL: p = 0; break;
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case GGML_SCHED_PRIO_MEDIUM: p = -5; break;
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case GGML_SCHED_PRIO_HIGH: p = -10; break;
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case GGML_SCHED_PRIO_REALTIME: p = -20; break;
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}
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if (!setpriority(PRIO_PROCESS, 0, p)) {
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LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
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return false;
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}
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return true;
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}
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#endif
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//
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// CLI argument parsing
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//
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void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
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int32_t n_set = 0;
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if (cpuparams.n_threads < 0) {
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// Assuming everything about cpuparams is invalid
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if (role_model != nullptr) {
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cpuparams = *role_model;
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} else {
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cpuparams.n_threads = cpu_get_num_math();
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}
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}
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for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
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if (cpuparams.cpumask[i]) {
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n_set++;
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}
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}
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if (n_set && n_set < cpuparams.n_threads) {
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// Not enough set bits, may experience performance issues.
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LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
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}
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}
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bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
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size_t dash_loc = range.find('-');
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if (dash_loc == std::string::npos) {
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LOG_ERR("Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
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return false;
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}
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size_t start_i;
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size_t end_i;
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if (dash_loc == 0) {
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start_i = 0;
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} else {
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start_i = std::stoull(range.substr(0, dash_loc));
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if (start_i >= GGML_MAX_N_THREADS) {
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LOG_ERR("Start index out of bounds!\n");
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return false;
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}
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}
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|
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if (dash_loc == range.length() - 1) {
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end_i = GGML_MAX_N_THREADS - 1;
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} else {
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end_i = std::stoull(range.substr(dash_loc + 1));
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if (end_i >= GGML_MAX_N_THREADS) {
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LOG_ERR("End index out of bounds!\n");
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return false;
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}
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}
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for (size_t i = start_i; i <= end_i; i++) {
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boolmask[i] = true;
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}
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return true;
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}
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bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
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// Discard potential 0x prefix
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size_t start_i = 0;
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if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
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start_i = 2;
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}
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size_t num_digits = mask.length() - start_i;
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if (num_digits > 128) num_digits = 128;
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size_t end_i = num_digits + start_i;
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for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
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char c = mask.at(i);
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int8_t id = c;
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if ((c >= '0' && c <= '9')) {
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id -= '0';
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} else if (c >= 'a' && c <= 'f') {
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id -= 'a' - 10;
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} else if (c >= 'A' && c <= 'F') {
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id -= 'A' - 10;
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} else {
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LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
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return false;
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}
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boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0);
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boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
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boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
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boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
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}
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return true;
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}
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void gpt_init() {
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llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
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if (LOG_DEFAULT_LLAMA <= gpt_log_verbosity_thold) {
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gpt_log_add(gpt_log_main(), level, "%s", text);
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}
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}, NULL);
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|
|
#ifdef NDEBUG
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const char * build_type = "";
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#else
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const char * build_type = " (debug)";
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#endif
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LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
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}
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std::string gpt_params_get_system_info(const gpt_params & params) {
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std::ostringstream os;
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|
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os << "system_info: n_threads = " << params.cpuparams.n_threads;
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if (params.cpuparams_batch.n_threads != -1) {
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os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
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}
|
|
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
|
|
// TODO: windows + arm64 + mingw64
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DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS);
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os << " / " << logicalProcessorCount << " | " << llama_print_system_info();
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#else
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os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
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#endif
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|
|
return os.str();
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|
}
|
|
|
|
//
|
|
// 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) {
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|
std::string part = input.substr(0, separator_pos);
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|
parts.emplace_back(part);
|
|
input = input.substr(separator_pos + 1);
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|
separator_pos = input.find(separator);
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|
}
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|
parts.emplace_back(input);
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|
return parts;
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|
}
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|
|
|
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--;
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|
}
|
|
return str.substr(start, end - start);
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|
}
|
|
|
|
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];
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|
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
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|
|
|
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);
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|
|
|
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
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|
}
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|
|
|
void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
|
|
if (search.empty()) {
|
|
return;
|
|
}
|
|
std::string builder;
|
|
builder.reserve(s.length());
|
|
size_t pos = 0;
|
|
size_t last_pos = 0;
|
|
while ((pos = s.find(search, last_pos)) != std::string::npos) {
|
|
builder.append(s, last_pos, pos - last_pos);
|
|
builder.append(replace);
|
|
last_pos = pos + search.length();
|
|
}
|
|
builder.append(s, last_pos, std::string::npos);
|
|
s = std::move(builder);
|
|
}
|
|
|
|
std::string string_from(bool value) {
|
|
return value ? "true" : "false";
|
|
}
|
|
|
|
std::string string_from(const std::vector<int> & values) {
|
|
std::stringstream buf;
|
|
|
|
buf << "[ ";
|
|
bool first = true;
|
|
for (auto e : values) {
|
|
if (first) {
|
|
first = false;
|
|
} else {
|
|
buf << ", ";
|
|
}
|
|
buf << std::to_string(e);
|
|
}
|
|
buf << " ]";
|
|
|
|
return buf.str();
|
|
}
|
|
|
|
std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
std::stringstream buf;
|
|
|
|
buf << "[ ";
|
|
|
|
bool first = true;
|
|
for (const auto & token : tokens) {
|
|
if (!first) {
|
|
buf << ", ";
|
|
} else {
|
|
first = false;
|
|
}
|
|
|
|
auto detokenized = llama_token_to_piece(ctx, token);
|
|
|
|
detokenized.erase(
|
|
std::remove_if(
|
|
detokenized.begin(),
|
|
detokenized.end(),
|
|
[](const unsigned char c) { return !std::isprint(c); }),
|
|
detokenized.end());
|
|
|
|
buf << "'" << detokenized << "'"
|
|
<< ":" << std::to_string(token);
|
|
}
|
|
|
|
buf << " ]";
|
|
|
|
return buf.str();
|
|
}
|
|
|
|
std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch) {
|
|
std::stringstream buf;
|
|
|
|
buf << "[ ";
|
|
|
|
bool first = true;
|
|
for (int i = 0; i < batch.n_tokens; ++i) {
|
|
if (!first) {
|
|
buf << ", ";
|
|
} else {
|
|
first = false;
|
|
}
|
|
|
|
auto detokenized = llama_token_to_piece(ctx, batch.token[i]);
|
|
|
|
detokenized.erase(
|
|
std::remove_if(
|
|
detokenized.begin(),
|
|
detokenized.end(),
|
|
[](const unsigned char c) { return !std::isprint(c); }),
|
|
detokenized.end());
|
|
|
|
buf << "\n" << std::to_string(i)
|
|
<< ":token '" << detokenized << "'"
|
|
<< ":pos " << std::to_string(batch.pos[i])
|
|
<< ":n_seq_id " << std::to_string(batch.n_seq_id[i])
|
|
<< ":seq_id " << std::to_string(batch.seq_id[i][0])
|
|
<< ":logits " << std::to_string(batch.logits[i]);
|
|
}
|
|
|
|
buf << " ]";
|
|
|
|
return buf.str();
|
|
}
|
|
|
|
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) {
|
|
LOG_ERR("%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 {
|
|
LOG_ERR("%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) {
|
|
LOG_ERR("%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 {
|
|
LOG_ERR("%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
|
|
//
|
|
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
|
|
llama_init_result iparams;
|
|
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(), params.hf_token.c_str(), mparams);
|
|
} else if (!params.model_url.empty()) {
|
|
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
|
|
} else {
|
|
model = llama_load_model_from_file(params.model.c_str(), mparams);
|
|
}
|
|
|
|
if (model == NULL) {
|
|
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.c_str());
|
|
return iparams;
|
|
}
|
|
|
|
auto cparams = llama_context_params_from_gpt_params(params);
|
|
|
|
llama_context * lctx = llama_new_context_with_model(model, cparams);
|
|
if (lctx == NULL) {
|
|
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
|
|
llama_free_model(model);
|
|
return iparams;
|
|
}
|
|
|
|
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 iparams;
|
|
}
|
|
|
|
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 iparams;
|
|
}
|
|
}
|
|
|
|
// load and optionally apply lora adapters
|
|
for (auto & la : params.lora_adapters) {
|
|
llama_lora_adapter_container loaded_la;
|
|
loaded_la.path = la.path;
|
|
loaded_la.scale = la.scale;
|
|
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
|
|
if (loaded_la.adapter == nullptr) {
|
|
LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return iparams;
|
|
}
|
|
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
|
|
}
|
|
if (!params.lora_init_without_apply) {
|
|
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
|
|
}
|
|
|
|
if (params.sparams.ignore_eos && llama_token_eos(model) == -1) {
|
|
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
|
|
params.sparams.ignore_eos = false;
|
|
}
|
|
|
|
if (params.warmup) {
|
|
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
|
|
|
|
std::vector<llama_token> tmp;
|
|
llama_token bos = llama_token_bos(model);
|
|
llama_token eos = llama_token_eos(model);
|
|
// some models (e.g. T5) don't have a BOS token
|
|
if (bos != LLAMA_TOKEN_NULL) {
|
|
tmp.push_back(bos);
|
|
}
|
|
if (eos != LLAMA_TOKEN_NULL) {
|
|
tmp.push_back(eos);
|
|
}
|
|
if (tmp.empty()) {
|
|
tmp.push_back(0);
|
|
}
|
|
|
|
if (llama_model_has_encoder(model)) {
|
|
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
|
|
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
|
|
if (decoder_start_token_id == -1) {
|
|
decoder_start_token_id = bos;
|
|
}
|
|
tmp.clear();
|
|
tmp.push_back(decoder_start_token_id);
|
|
}
|
|
if (llama_model_has_decoder(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_perf_context_reset(lctx);
|
|
}
|
|
|
|
iparams.model = model;
|
|
iparams.context = lctx;
|
|
return iparams;
|
|
}
|
|
|
|
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
|
|
llama_lora_adapter_clear(ctx);
|
|
for (auto & la : lora_adapters) {
|
|
if (la.scale != 0.0f) {
|
|
llama_lora_adapter_set(ctx, la.adapter, la.scale);
|
|
}
|
|
}
|
|
}
|
|
|
|
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.cpuparams.n_threads;
|
|
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
|
|
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
|
|
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.attention_type = params.attention_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.no_perf = params.no_perf;
|
|
|
|
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;
|
|
}
|
|
|
|
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
|
|
struct ggml_threadpool_params tpp;
|
|
|
|
ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
|
|
|
|
if (params.mask_valid) {
|
|
std::memcpy(&tpp.cpumask, ¶ms.cpumask, GGML_MAX_N_THREADS);
|
|
}
|
|
|
|
tpp.prio = params.priority;
|
|
tpp.poll = params.poll;
|
|
tpp.strict_cpu = params.strict_cpu;
|
|
|
|
return tpp;
|
|
}
|
|
|
|
#ifdef LLAMA_USE_CURL
|
|
|
|
#define CURL_MAX_RETRY 3
|
|
#define CURL_RETRY_DELAY_SECONDS 2
|
|
|
|
|
|
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 curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
|
|
int remaining_attempts = max_attempts;
|
|
|
|
while (remaining_attempts > 0) {
|
|
LOG_INF("%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
|
|
|
|
CURLcode res = curl_easy_perform(curl);
|
|
if (res == CURLE_OK) {
|
|
return true;
|
|
}
|
|
|
|
int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
|
|
LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
|
|
|
|
remaining_attempts--;
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
|
|
}
|
|
|
|
LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
|
|
|
|
return false;
|
|
}
|
|
|
|
static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
|
|
|
|
// Initialize libcurl
|
|
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
|
|
if (!curl) {
|
|
LOG_ERR("%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);
|
|
|
|
// Check if hf-token or bearer-token was specified
|
|
if (!hf_token.empty()) {
|
|
std::string auth_header = "Authorization: Bearer ";
|
|
auth_header += hf_token.c_str();
|
|
struct curl_slist *http_headers = NULL;
|
|
http_headers = curl_slist_append(http_headers, auth_header.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
|
|
}
|
|
|
|
#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;
|
|
LOG_INF("%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) {
|
|
LOG_ERR("%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) {
|
|
LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
|
|
return false;
|
|
}
|
|
}
|
|
} else {
|
|
LOG_INF("%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);
|
|
|
|
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
|
|
if (!was_perform_successful) {
|
|
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;
|
|
LOG_ERR("%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) {
|
|
LOG_WRN("%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) {
|
|
LOG_WRN("%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) {
|
|
LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
|
|
if (remove(path.c_str()) != 0) {
|
|
LOG_ERR("%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) {
|
|
LOG_ERR("%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
|
|
LOG_INF("%s: trying to download model 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());
|
|
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
|
|
if (!was_perform_successful) {
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code < 200 || http_code >= 400) {
|
|
LOG_ERR("%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);
|
|
LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
|
|
|
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
|
|
LOG_ERR("%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 char * hf_token,
|
|
const struct llama_model_params & params) {
|
|
// Basic validation of the model_url
|
|
if (!model_url || strlen(model_url) == 0) {
|
|
LOG_ERR("%s: invalid model_url\n", __func__);
|
|
return NULL;
|
|
}
|
|
|
|
if (!llama_download_file(model_url, path_model, hf_token)) {
|
|
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) {
|
|
LOG_ERR("\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)) {
|
|
LOG_ERR("\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)) {
|
|
LOG_ERR("\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, hf_token](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, hf_token);
|
|
}, 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 char * hf_token,
|
|
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, hf_token, params);
|
|
}
|
|
|
|
#else
|
|
|
|
struct llama_model * llama_load_model_from_url(
|
|
const char * /*model_url*/,
|
|
const char * /*path_model*/,
|
|
const char * /*hf_token*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
LOG_WRN("%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 char * /*hf_token*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
LOG_WRN("%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::string piece;
|
|
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
|
|
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
|
|
if (n_chars < 0) {
|
|
piece.resize(-n_chars);
|
|
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
|
|
GGML_ASSERT(check == -n_chars);
|
|
}
|
|
else {
|
|
piece.resize(n_chars);
|
|
}
|
|
|
|
return piece;
|
|
}
|
|
|
|
std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
|
|
std::string text;
|
|
text.resize(std::max(text.capacity(), tokens.size()));
|
|
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
|
if (n_chars < 0) {
|
|
text.resize(-n_chars);
|
|
n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
|
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
|
|
}
|
|
|
|
text.resize(n_chars);
|
|
|
|
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
|
|
return text;
|
|
}
|
|
|
|
//
|
|
// Chat template utils
|
|
//
|
|
|
|
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;
|
|
}
|
|
|
|
std::string llama_chat_apply_template(const struct llama_model * model,
|
|
const std::string & tmpl,
|
|
const std::vector<llama_chat_msg> & msgs,
|
|
bool add_ass) {
|
|
int alloc_size = 0;
|
|
bool fallback = false; // indicate if we must fallback to default chatml
|
|
std::vector<llama_chat_message> chat;
|
|
for (auto & msg : msgs) {
|
|
chat.push_back({msg.role.c_str(), msg.content.c_str()});
|
|
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
|
|
}
|
|
|
|
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
|
|
std::vector<char> buf(alloc_size);
|
|
|
|
// run the first time to get the total output length
|
|
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
|
|
|
|
// error: chat template is not supported
|
|
if (res < 0) {
|
|
if (ptr_tmpl != nullptr) {
|
|
// if the custom "tmpl" is not supported, we throw an error
|
|
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
|
|
throw std::runtime_error("this custom template is not supported");
|
|
} else {
|
|
// If the built-in template is not supported, we default to chatml
|
|
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
|
|
fallback = true;
|
|
}
|
|
}
|
|
|
|
// if it turns out that our buffer is too small, we resize it
|
|
if ((size_t) res > buf.size()) {
|
|
buf.resize(res);
|
|
res = llama_chat_apply_template(
|
|
fallback ? nullptr : model,
|
|
fallback ? "chatml" : ptr_tmpl,
|
|
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
|
|
}
|
|
|
|
std::string formatted_chat(buf.data(), res);
|
|
return formatted_chat;
|
|
}
|
|
|
|
std::string llama_chat_format_single(const struct llama_model * model,
|
|
const std::string & tmpl,
|
|
const std::vector<llama_chat_msg> & past_msg,
|
|
const llama_chat_msg & new_msg,
|
|
bool add_ass) {
|
|
std::ostringstream ss;
|
|
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
|
|
std::vector<llama_chat_msg> chat_new(past_msg);
|
|
// if the past_msg ends with a newline, we must preserve it in the formatted version
|
|
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
|
|
ss << "\n";
|
|
};
|
|
// format chat with new_msg
|
|
chat_new.push_back(new_msg);
|
|
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
|
|
// get the diff part
|
|
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
|
|
return ss.str();
|
|
}
|
|
|
|
std::string llama_chat_format_example(const struct llama_model * model,
|
|
const std::string & tmpl) {
|
|
std::vector<llama_chat_msg> msgs = {
|
|
{"system", "You are a helpful assistant"},
|
|
{"user", "Hello"},
|
|
{"assistant", "Hi there"},
|
|
{"user", "How are you?"},
|
|
};
|
|
return llama_chat_apply_template(model, tmpl, msgs, true);
|
|
}
|
|
|
|
//
|
|
// 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) {
|
|
llama_control_vector_data result = { -1, {} };
|
|
|
|
ggml_context * ctx = nullptr;
|
|
struct gguf_init_params meta_gguf_params = {
|
|
/* .no_alloc = */ false,
|
|
/* .ctx = */ &ctx,
|
|
};
|
|
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
|
|
if (!ctx_gguf) {
|
|
LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
|
|
return result;
|
|
}
|
|
|
|
int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
|
|
if (n_tensors == 0) {
|
|
LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
|
|
}
|
|
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
std::string name = gguf_get_tensor_name(ctx_gguf, i);
|
|
|
|
int layer_idx = -1;
|
|
|
|
// split on '.'
|
|
size_t dotpos = name.find('.');
|
|
if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
|
|
try {
|
|
layer_idx = std::stoi(name.substr(dotpos + 1));
|
|
} catch (...) {
|
|
layer_idx = -1;
|
|
}
|
|
}
|
|
if (layer_idx < 0) {
|
|
LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
|
|
result.n_embd = -1;
|
|
break;
|
|
} else if (layer_idx == 0) {
|
|
LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
|
|
struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
|
|
if (tensor->type != GGML_TYPE_F32) {
|
|
LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
if (ggml_n_dims(tensor) != 1) {
|
|
LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
|
|
if (result.n_embd == -1) {
|
|
result.n_embd = ggml_nelements(tensor);
|
|
} else if (ggml_nelements(tensor) != result.n_embd) {
|
|
LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
|
|
// extend if necessary - do not store data for layer 0 (it's not used)
|
|
result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
|
|
|
|
const float * src = (const float *) tensor->data;
|
|
float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0]
|
|
for (int j = 0; j < result.n_embd; j++) {
|
|
dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file
|
|
}
|
|
|
|
}
|
|
|
|
if (result.n_embd == -1) {
|
|
LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
|
|
result.data.clear();
|
|
}
|
|
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
|
|
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) {
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
|
|
LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
|
|
if (result.n_embd == -1) {
|
|
result = std::move(cur);
|
|
} else {
|
|
result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary
|
|
for (size_t i = 0; i < cur.data.size(); i++) {
|
|
result.data[i] += cur.data[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
if (result.n_embd == -1) {
|
|
LOG_ERR("%s: no valid control vector files passed\n", __func__);
|
|
result.data.clear();
|
|
}
|
|
|
|
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 auto & 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_riscv_v: %s\n", ggml_cpu_has_riscv_v() ? "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);
|
|
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);
|
|
fprintf(stream, "ignore_eos: %s # default: false\n", sparams.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 (const auto & logit_bias : sparams.logit_bias) {
|
|
fprintf(stream, " %d: %f", logit_bias.token, logit_bias.bias);
|
|
}
|
|
|
|
fprintf(stream, "lora:\n");
|
|
for (auto & la : params.lora_adapters) {
|
|
if (la.scale == 1.0f) {
|
|
fprintf(stream, " - %s\n", la.path.c_str());
|
|
}
|
|
}
|
|
fprintf(stream, "lora_scaled:\n");
|
|
for (auto & la : params.lora_adapters) {
|
|
if (la.scale != 1.0f) {
|
|
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
|
|
}
|
|
}
|
|
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
|
|
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, "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.cpuparams.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, "typ_p: %f # default: 1.0\n", sparams.typ_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");
|
|
}
|