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#if defined(_MSC_VER) |
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#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING |
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#endif |
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|
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#include "common.h" |
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#include "log.h" |
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|
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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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|
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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 <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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|
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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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|
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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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|
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#if defined(_MSC_VER) |
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#pragma warning(disable: 4244 4267) |
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#endif |
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|
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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 |
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#endif |
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using json = nlohmann::ordered_json; |
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int32_t cpu_get_num_physical_cores() { |
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#ifdef __linux__ |
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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; |
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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__) |
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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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|
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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; |
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} |
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++cpu; |
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++result; |
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} |
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return result; |
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} |
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#endif |
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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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#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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|
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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 |
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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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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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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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 * ) { |
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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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|
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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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|
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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__) |
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|
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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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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); |
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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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|
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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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|
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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); |
|
} |
|
|
|
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; |
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|
|
buf << "[ "; |
|
bool first = true; |
|
for (auto e : values) { |
|
if (first) { |
|
first = false; |
|
} else { |
|
buf << ", "; |
|
} |
|
buf << std::to_string(e); |
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} |
|
buf << " ]"; |
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|
|
return buf.str(); |
|
} |
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|
|
std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens) { |
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std::stringstream buf; |
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|
|
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': |
|
|
|
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; |
|
} |
|
} |
|
|
|
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; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
bool fs_validate_filename(const std::string & filename) { |
|
if (!filename.length()) { |
|
|
|
return false; |
|
} |
|
if (filename.length() > 255) { |
|
|
|
|
|
|
|
return false; |
|
} |
|
|
|
std::u32string filename_utf32; |
|
try { |
|
#if defined(__clang__) |
|
|
|
# pragma clang diagnostic push |
|
# pragma clang diagnostic ignored "-Wdeprecated-declarations" |
|
#endif |
|
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter; |
|
|
|
#if defined(__clang__) |
|
# pragma clang diagnostic pop |
|
#endif |
|
|
|
filename_utf32 = converter.from_bytes(filename); |
|
|
|
|
|
|
|
std::string filename_reencoded = converter.to_bytes(filename_utf32); |
|
if (filename_reencoded != filename) { |
|
return false; |
|
} |
|
} catch (const std::exception &) { |
|
return false; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for (char32_t c : filename_utf32) { |
|
if (c <= 0x1F |
|
|| c == 0x7F |
|
|| (c >= 0x80 && c <= 0x9F) |
|
|| c == 0xFF0E |
|
|| c == 0x2215 |
|
|| c == 0x2216 |
|
|| (c >= 0xD800 && c <= 0xDFFF) |
|
|| c == 0xFFFD |
|
|| c == 0xFEFF |
|
|| c == '/' || c == '\\' || c == ':' || c == '*' |
|
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') { |
|
return false; |
|
} |
|
} |
|
|
|
|
|
|
|
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') { |
|
return false; |
|
} |
|
|
|
|
|
if (filename.find("..") != std::string::npos) { |
|
return false; |
|
} |
|
|
|
|
|
if (filename == ".") { |
|
return false; |
|
} |
|
|
|
return true; |
|
} |
|
|
|
|
|
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); |
|
|
|
|
|
const DWORD attributes = GetFileAttributesW(wpath.c_str()); |
|
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { |
|
return true; |
|
} |
|
|
|
size_t pos_slash = 0; |
|
|
|
|
|
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 (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 |
|
|
|
struct stat info; |
|
if (stat(path.c_str(), &info) == 0) { |
|
return S_ISDIR(info.st_mode); |
|
} |
|
|
|
size_t pos_slash = 1; |
|
|
|
|
|
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { |
|
const std::string subpath = path.substr(0, pos_slash); |
|
struct stat info; |
|
|
|
|
|
if (stat(subpath.c_str(), &info) == 0) { |
|
if (!S_ISDIR(info.st_mode)) { |
|
return false; |
|
} |
|
} else { |
|
|
|
const int ret = mkdir(subpath.c_str(), 0755); |
|
if (ret != 0) { |
|
return false; |
|
} |
|
} |
|
|
|
pos_slash += 1; |
|
} |
|
|
|
return true; |
|
#endif |
|
} |
|
|
|
std::string fs_get_cache_directory() { |
|
std::string cache_directory = ""; |
|
auto ensure_trailing_slash = [](std::string p) { |
|
|
|
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 |
|
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; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
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 = nullptr; |
|
|
|
if (!params.hf_repo.empty() && !params.hf_file.empty()) { |
|
model = common_load_model_from_hf(params.hf_repo, params.hf_file, params.model, params.hf_token, mparams); |
|
} else if (!params.model_url.empty()) { |
|
model = common_load_model_from_url(params.model_url, params.model, params.hf_token, 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; |
|
} |
|
|
|
if (params.reranking) { |
|
bool ok = true; |
|
|
|
if (llama_token_bos(model) == LLAMA_TOKEN_NULL) { |
|
LOG_WRN("%s: warning: model does not have a BOS token, reranking will not work\n", __func__); |
|
ok = false; |
|
} |
|
|
|
if (llama_token_eos(model) == LLAMA_TOKEN_NULL) { |
|
LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__); |
|
ok = false; |
|
} |
|
|
|
if (llama_token_sep(model) == LLAMA_TOKEN_NULL) { |
|
LOG_WRN("%s: warning: model does not have a SEP token, reranking will not work\n", __func__); |
|
ok = false; |
|
} |
|
|
|
if (!ok) { |
|
llama_free_model(model); |
|
|
|
return iparams; |
|
} |
|
} |
|
|
|
auto cparams = common_context_params_to_llama(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.ctx_shift && !llama_kv_cache_can_shift(lctx)) { |
|
LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__); |
|
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 = common_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; |
|
} |
|
} |
|
|
|
|
|
for (auto & la : params.lora_adapters) { |
|
common_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); |
|
} |
|
if (!params.lora_init_without_apply) { |
|
common_lora_adapters_apply(lctx, iparams.lora_adapters); |
|
} |
|
|
|
if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) { |
|
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__); |
|
params.sampling.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); |
|
|
|
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 == -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))); |
|
} |
|
llama_kv_cache_clear(lctx); |
|
llama_synchronize(lctx); |
|
llama_perf_context_reset(lctx); |
|
} |
|
|
|
iparams.model = model; |
|
iparams.context = lctx; |
|
|
|
return iparams; |
|
} |
|
|
|
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_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 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.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 == "bf16") { |
|
return GGML_TYPE_BF16; |
|
} |
|
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("Unsupported cache type: " + s); |
|
} |
|
|
|
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.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; |
|
|
|
if (params.reranking) { |
|
cparams.embeddings = true; |
|
cparams.pooling_type = LLAMA_POOLING_TYPE_RANK; |
|
} |
|
|
|
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); |
|
|
|
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) { |
|
|
|
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 common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) { |
|
|
|
|
|
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; |
|
|
|
|
|
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); |
|
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); |
|
|
|
|
|
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) |
|
|
|
|
|
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); |
|
#endif |
|
|
|
|
|
struct stat model_file_info; |
|
auto file_exists = (stat(path.c_str(), &model_file_info) == 0); |
|
|
|
|
|
std::string metadata_path = path + ".json"; |
|
nlohmann::json metadata; |
|
std::string etag; |
|
std::string last_modified; |
|
|
|
if (file_exists) { |
|
|
|
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()); |
|
} |
|
|
|
|
|
struct common_load_model_from_url_headers { |
|
std::string etag; |
|
std::string last_modified; |
|
}; |
|
common_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_t n_items, void * userdata) -> size_t { |
|
common_load_model_from_url_headers *headers = (common_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); |
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); |
|
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) { |
|
|
|
|
|
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; |
|
} |
|
} |
|
|
|
|
|
|
|
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()); |
|
|
|
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L); |
|
|
|
|
|
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; |
|
} |
|
|
|
std::size_t at_pos = url.find('@', protocol_pos + 3); |
|
if (at_pos == std::string::npos) { |
|
return url; |
|
} |
|
|
|
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos); |
|
}; |
|
|
|
|
|
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; |
|
} |
|
|
|
|
|
outfile.reset(); |
|
|
|
|
|
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 * common_load_model_from_url( |
|
const std::string & model_url, |
|
const std::string & local_path, |
|
const std::string & hf_token, |
|
const struct llama_model_params & params) { |
|
|
|
if (model_url.empty()) { |
|
LOG_ERR("%s: invalid model_url\n", __func__); |
|
return NULL; |
|
} |
|
|
|
if (!common_download_file(model_url, local_path, hf_token)) { |
|
return NULL; |
|
} |
|
|
|
|
|
int n_split = 0; |
|
{ |
|
struct gguf_init_params gguf_params = { |
|
true, |
|
NULL, |
|
}; |
|
auto * ctx_gguf = gguf_init_from_file(local_path.c_str(), gguf_params); |
|
if (!ctx_gguf) { |
|
LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, local_path.c_str()); |
|
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}; |
|
|
|
|
|
|
|
{ |
|
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), local_path.c_str(), 0, n_split)) { |
|
LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, local_path.c_str(), n_split); |
|
return NULL; |
|
} |
|
|
|
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url.c_str(), 0, n_split)) { |
|
LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url.c_str(), n_split); |
|
return NULL; |
|
} |
|
} |
|
|
|
|
|
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 common_download_file(split_url, split_path, hf_token); |
|
}, idx)); |
|
} |
|
|
|
|
|
for (auto & f : futures_download) { |
|
if (!f.get()) { |
|
return NULL; |
|
} |
|
} |
|
} |
|
|
|
return llama_load_model_from_file(local_path.c_str(), params); |
|
} |
|
|
|
struct llama_model * common_load_model_from_hf( |
|
const std::string & repo, |
|
const std::string & remote_path, |
|
const std::string & local_path, |
|
const std::string & hf_token, |
|
const struct llama_model_params & params) { |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
std::string model_url = "https://huggingface.co/"; |
|
model_url += repo; |
|
model_url += "/resolve/main/"; |
|
model_url += remote_path; |
|
|
|
return common_load_model_from_url(model_url, local_path, hf_token, params); |
|
} |
|
|
|
#else |
|
|
|
struct llama_model * common_load_model_from_url( |
|
const std::string & , |
|
const std::string & , |
|
const std::string & , |
|
const struct llama_model_params & ) { |
|
LOG_WRN("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); |
|
return nullptr; |
|
} |
|
|
|
struct llama_model * common_load_model_from_hf( |
|
const std::string & , |
|
const std::string & , |
|
const std::string & , |
|
const std::string & , |
|
const struct llama_model_params & ) { |
|
LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); |
|
return nullptr; |
|
} |
|
|
|
#endif |
|
|
|
|
|
|
|
|
|
|
|
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++; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
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) { |
|
|
|
if (a.empty() || b.empty()) { |
|
return 0; |
|
} |
|
|
|
|
|
size_t a_len = a.size(); |
|
size_t b_len = b.size(); |
|
|
|
|
|
size_t max_length = 0; |
|
|
|
|
|
std::vector<size_t> prev_row(b_len + 1, 0); |
|
std::vector<size_t> curr_row(b_len + 1, 0); |
|
|
|
|
|
for (size_t i = 1; i <= a_len; i++) { |
|
|
|
for (size_t j = 1; j <= b_len; j++) { |
|
|
|
if (a[i - 1] == b[j - 1]) { |
|
|
|
if (i == 1 || j == 1) { |
|
curr_row[j] = 1; |
|
} else { |
|
|
|
curr_row[j] = prev_row[j - 1] + 1; |
|
} |
|
|
|
|
|
if (curr_row[j] > max_length) { |
|
max_length = curr_row[j]; |
|
} |
|
} else { |
|
|
|
curr_row[j] = 0; |
|
} |
|
} |
|
|
|
|
|
prev_row = curr_row; |
|
} |
|
|
|
|
|
return max_length; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
std::vector<llama_token> common_tokenize( |
|
const struct llama_context * ctx, |
|
const std::string & text, |
|
bool add_special, |
|
bool parse_special) { |
|
return common_tokenize(llama_get_model(ctx), text, add_special, parse_special); |
|
} |
|
|
|
std::vector<llama_token> common_tokenize( |
|
const struct llama_model * model, |
|
const std::string & text, |
|
bool add_special, |
|
bool parse_special) { |
|
|
|
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 common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { |
|
std::string piece; |
|
piece.resize(piece.capacity()); |
|
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 common_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()); |
|
} |
|
|
|
text.resize(n_chars); |
|
|
|
|
|
return text; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
bool common_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 common_chat_apply_template(const struct llama_model * model, |
|
const std::string & tmpl, |
|
const std::vector<common_chat_msg> & msgs, |
|
bool add_ass) { |
|
int alloc_size = 0; |
|
bool fallback = false; |
|
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); |
|
|
|
|
|
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size()); |
|
|
|
|
|
if (res < 0) { |
|
if (ptr_tmpl != nullptr) { |
|
|
|
|
|
throw std::runtime_error("this custom template is not supported"); |
|
} else { |
|
|
|
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size()); |
|
fallback = true; |
|
} |
|
} |
|
|
|
|
|
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 common_chat_format_single(const struct llama_model * model, |
|
const std::string & tmpl, |
|
const std::vector<common_chat_msg> & past_msg, |
|
const common_chat_msg & new_msg, |
|
bool add_ass) { |
|
std::ostringstream ss; |
|
auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false); |
|
std::vector<common_chat_msg> chat_new(past_msg); |
|
|
|
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { |
|
ss << "\n"; |
|
}; |
|
|
|
chat_new.push_back(new_msg); |
|
auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass); |
|
|
|
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); |
|
return ss.str(); |
|
} |
|
|
|
std::string common_chat_format_example(const struct llama_model * model, |
|
const std::string & tmpl) { |
|
std::vector<common_chat_msg> msgs = { |
|
{"system", "You are a helpful assistant"}, |
|
{"user", "Hello"}, |
|
{"assistant", "Hi there"}, |
|
{"user", "How are you?"}, |
|
}; |
|
return common_chat_apply_template(model, tmpl, msgs, true); |
|
} |
|
|
|
|
|
|
|
|
|
|
|
void common_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 common_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"); |
|
} |
|
|
|
|
|
|
|
|
|
|
|
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) { |
|
double sum = 0.0; |
|
|
|
switch (embd_norm) { |
|
case -1: |
|
sum = 1.0; |
|
break; |
|
case 0: |
|
for (int i = 0; i < n; i++) { |
|
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]); |
|
} |
|
sum /= 32760.0; |
|
break; |
|
case 2: |
|
for (int i = 0; i < n; i++) { |
|
sum += inp[i] * inp[i]; |
|
} |
|
sum = std::sqrt(sum); |
|
break; |
|
default: |
|
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]; |
|
} |
|
|
|
|
|
if (sum1 == 0.0 || sum2 == 0.0) { |
|
if (sum1 == 0.0 && sum2 == 0.0) { |
|
return 1.0f; |
|
} |
|
return 0.0f; |
|
} |
|
|
|
return sum / (sqrt(sum1) * sqrt(sum2)); |
|
} |
|
|
|
|
|
|
|
|
|
|
|
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 = { |
|
false, |
|
&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; |
|
|
|
|
|
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; |
|
} |
|
|
|
|
|
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); |
|
for (int j = 0; j < result.n_embd; j++) { |
|
dst[j] += src[j] * load_info.strength; |
|
} |
|
|
|
} |
|
|
|
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); |
|
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; |
|
} |
|
|
|
|