mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
1552 lines
50 KiB
C++
1552 lines
50 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 "ggml.h"
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#include "gguf.h"
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#include "common.h"
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#include "log.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 <climits>
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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 <filesystem>
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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 <thread>
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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(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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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_LOW: p = BELOW_NORMAL_PRIORITY_CLASS; break;
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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_LOW: p = 5; break;
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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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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 common_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 <= common_log_verbosity_thold) {
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common_log_add(common_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 common_params_get_system_info(const common_params & params) {
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std::ostringstream os;
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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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}
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#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
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// 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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}
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//
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// String utils
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//
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std::string string_format(const char * fmt, ...) {
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va_list ap;
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va_list ap2;
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va_start(ap, fmt);
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va_copy(ap2, ap);
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int size = vsnprintf(NULL, 0, fmt, ap);
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GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
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std::vector<char> buf(size + 1);
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int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
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GGML_ASSERT(size2 == size);
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va_end(ap2);
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va_end(ap);
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return std::string(buf.data(), size);
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}
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std::string string_strip(const std::string & str) {
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size_t start = 0;
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size_t end = str.size();
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while (start < end && std::isspace(str[start])) {
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start++;
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}
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while (end > start && std::isspace(str[end - 1])) {
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end--;
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}
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return str.substr(start, end - start);
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}
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std::string string_get_sortable_timestamp() {
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using clock = std::chrono::system_clock;
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const clock::time_point current_time = clock::now();
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const time_t as_time_t = clock::to_time_t(current_time);
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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>(
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current_time.time_since_epoch() % 1000000000).count();
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char timestamp_ns[11];
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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) {
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if (search.empty()) {
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return;
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}
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std::string builder;
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builder.reserve(s.length());
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size_t pos = 0;
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size_t last_pos = 0;
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while ((pos = s.find(search, last_pos)) != std::string::npos) {
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builder.append(s, last_pos, pos - last_pos);
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builder.append(replace);
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last_pos = pos + search.length();
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}
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builder.append(s, last_pos, std::string::npos);
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s = std::move(builder);
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}
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bool string_ends_with(const std::string_view & str, const std::string_view & suffix) {
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return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
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}
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size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop) {
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if (!str.empty() && !stop.empty()) {
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const char text_last_char = str.back();
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for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
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if (stop[char_index] == text_last_char) {
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const auto current_partial = stop.substr(0, char_index + 1);
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if (string_ends_with(str, current_partial)) {
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return str.size() - char_index - 1;
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}
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}
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}
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}
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return std::string::npos;
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}
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std::string regex_escape(const std::string & s) {
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static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
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return std::regex_replace(s, special_chars, "\\$&");
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}
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std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
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std::ostringstream result;
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for (size_t i = 0; i < values.size(); ++i) {
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if (i > 0) {
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result << separator;
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}
|
|
result << values[i];
|
|
}
|
|
return result.str();
|
|
}
|
|
|
|
std::vector<std::string> string_split(const std::string & str, const std::string & delimiter) {
|
|
std::vector<std::string> parts;
|
|
size_t start = 0;
|
|
size_t end = str.find(delimiter);
|
|
|
|
while (end != std::string::npos) {
|
|
parts.push_back(str.substr(start, end - start));
|
|
start = end + delimiter.length();
|
|
end = str.find(delimiter, start);
|
|
}
|
|
|
|
parts.push_back(str.substr(start));
|
|
|
|
return parts;
|
|
}
|
|
|
|
std::string string_repeat(const std::string & str, size_t n) {
|
|
if (n == 0) {
|
|
return "";
|
|
}
|
|
|
|
std::string result;
|
|
result.reserve(str.length() * n);
|
|
|
|
for (size_t i = 0; i < n; ++i) {
|
|
result += str;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
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 = common_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 = common_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 {
|
|
#if defined(__clang__)
|
|
// disable C++17 deprecation warning for std::codecvt_utf8
|
|
# pragma clang diagnostic push
|
|
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
|
|
#elif defined(__GNUC__)
|
|
# pragma GCC diagnostic push
|
|
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
|
|
#endif
|
|
|
|
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
|
|
|
|
#if defined(__clang__)
|
|
# pragma clang diagnostic pop
|
|
#elif defined(__GNUC__)
|
|
# pragma GCC diagnostic pop
|
|
#endif
|
|
|
|
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;
|
|
}
|
|
|
|
#include <iostream>
|
|
|
|
|
|
// 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);
|
|
|
|
pos_slash += 1;
|
|
|
|
// skip the drive letter, in some systems it can return an access denied error
|
|
if (subpath.length() == 2 && subpath[1] == ':') {
|
|
continue;
|
|
}
|
|
|
|
const bool success = CreateDirectoryW(subpath.c_str(), 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;
|
|
}
|
|
}
|
|
}
|
|
|
|
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 {
|
|
#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
|
|
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");
|
|
#else
|
|
# error Unknown architecture
|
|
#endif
|
|
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 common_init_result common_init_from_params(common_params & params) {
|
|
common_init_result iparams;
|
|
auto mparams = common_model_params_to_llama(params);
|
|
|
|
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
|
|
if (model == NULL) {
|
|
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
|
|
return iparams;
|
|
}
|
|
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
auto cparams = common_context_params_to_llama(params);
|
|
|
|
llama_context * lctx = llama_init_from_model(model, cparams);
|
|
if (lctx == NULL) {
|
|
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
|
|
llama_model_free(model);
|
|
return iparams;
|
|
}
|
|
|
|
if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
|
|
LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
|
|
params.ctx_shift = false;
|
|
}
|
|
|
|
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_model_n_layer(model);
|
|
|
|
const auto cvec = common_control_vector_load(params.control_vectors);
|
|
if (cvec.n_embd == -1) {
|
|
llama_free(lctx);
|
|
llama_model_free(model);
|
|
|
|
return iparams;
|
|
}
|
|
|
|
int err = llama_apply_adapter_cvec(
|
|
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_model_free(model);
|
|
|
|
return iparams;
|
|
}
|
|
}
|
|
|
|
if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) {
|
|
bool ok = true;
|
|
|
|
if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
|
|
LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__);
|
|
ok = false;
|
|
}
|
|
|
|
bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
|
|
bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
|
|
|
|
if (!has_eos && !has_sep) {
|
|
LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
|
|
ok = false;
|
|
} else if (!has_eos) {
|
|
LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
|
|
} else if (!has_sep) {
|
|
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
|
|
ok = false;
|
|
}
|
|
|
|
if (!ok) {
|
|
llama_free(lctx);
|
|
llama_model_free(model);
|
|
|
|
return iparams;
|
|
}
|
|
}
|
|
|
|
// load and optionally apply lora adapters
|
|
for (auto & la : params.lora_adapters) {
|
|
llama_adapter_lora_ptr lora;
|
|
lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
|
|
if (lora == nullptr) {
|
|
LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
|
|
llama_free(lctx);
|
|
llama_model_free(model);
|
|
return iparams;
|
|
}
|
|
|
|
la.ptr = lora.get();
|
|
iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
|
|
}
|
|
|
|
if (!params.lora_init_without_apply) {
|
|
common_set_adapter_lora(lctx, params.lora_adapters);
|
|
}
|
|
|
|
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
|
|
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
|
|
params.sampling.ignore_eos = false;
|
|
}
|
|
|
|
if (params.sampling.ignore_eos) {
|
|
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
|
|
if (llama_vocab_is_eog(vocab, i)) {
|
|
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
|
|
params.sampling.logit_bias.push_back({i, -INFINITY});
|
|
}
|
|
}
|
|
}
|
|
|
|
if (params.sampling.penalty_last_n == -1) {
|
|
LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
|
|
params.sampling.penalty_last_n = llama_n_ctx(lctx);
|
|
}
|
|
|
|
if (params.sampling.dry_penalty_last_n == -1) {
|
|
LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
|
|
params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
|
|
}
|
|
|
|
if (params.warmup) {
|
|
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
|
|
|
|
llama_set_warmup(lctx, true);
|
|
|
|
std::vector<llama_token> tmp;
|
|
llama_token bos = llama_vocab_bos(vocab);
|
|
llama_token eos = llama_vocab_eos(vocab);
|
|
|
|
// 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()));
|
|
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
|
|
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
|
|
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)));
|
|
}
|
|
llama_memory_clear(llama_get_memory(lctx), true);
|
|
llama_synchronize(lctx);
|
|
llama_perf_context_reset(lctx);
|
|
llama_set_warmup(lctx, false);
|
|
}
|
|
|
|
iparams.model.reset(model);
|
|
iparams.context.reset(lctx);
|
|
|
|
return iparams;
|
|
}
|
|
|
|
std::string get_model_endpoint() {
|
|
const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
|
|
// We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
|
|
const char * hf_endpoint_env = getenv("HF_ENDPOINT");
|
|
const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env;
|
|
std::string model_endpoint = "https://huggingface.co/";
|
|
if (endpoint_env) {
|
|
model_endpoint = endpoint_env;
|
|
if (model_endpoint.back() != '/') model_endpoint += '/';
|
|
}
|
|
return model_endpoint;
|
|
}
|
|
|
|
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
|
|
llama_clear_adapter_lora(ctx);
|
|
for (auto & la : lora) {
|
|
if (la.scale != 0.0f) {
|
|
llama_set_adapter_lora(ctx, la.ptr, la.scale);
|
|
}
|
|
}
|
|
}
|
|
|
|
struct llama_model_params common_model_params_to_llama(common_params & params) {
|
|
auto mparams = llama_model_default_params();
|
|
|
|
if (!params.devices.empty()) {
|
|
mparams.devices = params.devices.data();
|
|
}
|
|
|
|
if (params.n_gpu_layers != -1) {
|
|
mparams.n_gpu_layers = params.n_gpu_layers;
|
|
}
|
|
|
|
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();
|
|
}
|
|
|
|
if (params.tensor_buft_overrides.empty()) {
|
|
mparams.tensor_buft_overrides = NULL;
|
|
} else {
|
|
GGML_ASSERT(params.tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern");
|
|
mparams.tensor_buft_overrides = params.tensor_buft_overrides.data();
|
|
}
|
|
|
|
mparams.progress_callback = params.load_progress_callback;
|
|
mparams.progress_callback_user_data = params.load_progress_callback_user_data;
|
|
|
|
return mparams;
|
|
}
|
|
|
|
struct llama_context_params common_context_params_to_llama(const common_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.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.op_offload = !params.no_op_offload;
|
|
cparams.swa_full = params.swa_full;
|
|
cparams.graph_reuse = params.graph_reuse;
|
|
|
|
cparams.type_k = params.cache_type_k;
|
|
cparams.type_v = 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;
|
|
}
|
|
|
|
//
|
|
// Batch utils
|
|
//
|
|
|
|
void common_batch_clear(struct llama_batch & batch) {
|
|
batch.n_tokens = 0;
|
|
}
|
|
|
|
void common_batch_add(
|
|
struct llama_batch & batch,
|
|
llama_token id,
|
|
llama_pos pos,
|
|
const std::vector<llama_seq_id> & seq_ids,
|
|
bool logits) {
|
|
GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
|
|
|
|
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++;
|
|
}
|
|
|
|
//
|
|
// Token utils
|
|
//
|
|
|
|
size_t common_lcp(const llama_tokens & a, const llama_tokens & b) {
|
|
size_t i;
|
|
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
|
|
|
|
return i;
|
|
}
|
|
|
|
size_t common_lcs(const llama_tokens & a, const llama_tokens & b) {
|
|
// check for empty sequences
|
|
if (a.empty() || b.empty()) {
|
|
return 0;
|
|
}
|
|
|
|
// get the lengths of the input sequences
|
|
size_t a_len = a.size();
|
|
size_t b_len = b.size();
|
|
|
|
// initialize the maximum length of the longest common subsequence (LCS)
|
|
size_t max_length = 0;
|
|
|
|
// use two rows instead of a 2D matrix to optimize space
|
|
std::vector<size_t> prev_row(b_len + 1, 0);
|
|
std::vector<size_t> curr_row(b_len + 1, 0);
|
|
|
|
// iterate through the elements of a
|
|
for (size_t i = 1; i <= a_len; i++) {
|
|
// iterate through the elements of b
|
|
for (size_t j = 1; j <= b_len; j++) {
|
|
// if elements at the current positions match
|
|
if (a[i - 1] == b[j - 1]) {
|
|
// if it's the first element of either sequences, set LCS length to 1
|
|
if (i == 1 || j == 1) {
|
|
curr_row[j] = 1;
|
|
} else {
|
|
// increment LCS length by 1 compared to the previous element
|
|
curr_row[j] = prev_row[j - 1] + 1;
|
|
}
|
|
|
|
// update max_length if necessary
|
|
if (curr_row[j] > max_length) {
|
|
max_length = curr_row[j];
|
|
}
|
|
} else {
|
|
// reset LCS length if elements don't match
|
|
curr_row[j] = 0;
|
|
}
|
|
}
|
|
|
|
// update the previous row for the next iteration
|
|
prev_row = curr_row;
|
|
}
|
|
|
|
// return the maximum length of the LCS
|
|
return max_length;
|
|
}
|
|
|
|
//
|
|
// Vocab utils
|
|
//
|
|
|
|
std::vector<llama_token> common_tokenize(
|
|
const struct llama_context * ctx,
|
|
const std::string & text,
|
|
bool add_special,
|
|
bool parse_special) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
return common_tokenize(vocab, text, add_special, parse_special);
|
|
}
|
|
|
|
std::vector<llama_token> common_tokenize(
|
|
const struct llama_vocab * vocab,
|
|
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(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
|
|
if (n_tokens == std::numeric_limits<int32_t>::min()) {
|
|
throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit");
|
|
}
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_tokenize(vocab, 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 common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
return common_token_to_piece(vocab, token, special);
|
|
}
|
|
|
|
std::string common_token_to_piece(const struct llama_vocab * vocab, 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(vocab, token, &piece[0], piece.size(), 0, special);
|
|
if (n_chars < 0) {
|
|
piece.resize(-n_chars);
|
|
int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
|
|
GGML_ASSERT(check == -n_chars);
|
|
}
|
|
else {
|
|
piece.resize(n_chars);
|
|
}
|
|
|
|
return piece;
|
|
}
|
|
|
|
std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
return common_detokenize(vocab, tokens, special);
|
|
}
|
|
|
|
std::string common_detokenize(const struct llama_vocab * vocab, 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(vocab, 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(vocab, 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;
|
|
}
|
|
|
|
//
|
|
// Embedding utils
|
|
//
|
|
|
|
void common_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 common_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 common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) {
|
|
common_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;
|
|
}
|
|
|
|
common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos) {
|
|
common_control_vector_data result = { -1, {} };
|
|
|
|
for (const auto & info : load_infos) {
|
|
auto cur = common_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;
|
|
}
|
|
|
|
ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
|
|
const int64_t ne_datapoint = llama_n_ctx(ctx);
|
|
const int64_t ndata = (tokens.size() - ne_datapoint - 1) / stride;
|
|
ggml_opt_dataset_t result = ggml_opt_dataset_init(
|
|
GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);
|
|
|
|
llama_token * data = (llama_token *) ggml_opt_dataset_data(result)->data;
|
|
llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;
|
|
|
|
for (int64_t idata = 0; idata < ndata; ++idata) {
|
|
memcpy(data + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
|
|
memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
|
|
}
|
|
|
|
return result;
|
|
}
|