mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
669912d9a5
* sampler: turn lazy grammar trigger words to regexes * add scripts/tool_bench.sh & .py * constrain llama json output regardless of function name if matches at beginning * update relaxed newline space rule in grammar tests * support add_generation_prompt query parameter (useful for /apply_template) * Update src/llama-grammar.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2055 lines
69 KiB
C++
2055 lines
69 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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// Change JSON_ASSERT from assert() to GGML_ASSERT:
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#define JSON_ASSERT GGML_ASSERT
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#include "json.hpp"
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#include "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(LLAMA_USE_CURL)
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#include <curl/curl.h>
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#include <curl/easy.h>
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#include <future>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#if defined(LLAMA_USE_CURL)
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#ifdef __linux__
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#include <linux/limits.h>
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#elif defined(_WIN32)
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# if !defined(PATH_MAX)
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# define PATH_MAX MAX_PATH
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# endif
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#else
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#include <sys/syslimits.h>
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#endif
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#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
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//
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// CURL utils
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//
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using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
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// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
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struct curl_slist_ptr {
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struct curl_slist * ptr = nullptr;
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~curl_slist_ptr() {
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if (ptr) {
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curl_slist_free_all(ptr);
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}
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}
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};
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#endif // LLAMA_USE_CURL
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using json = nlohmann::ordered_json;
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//
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// CPU utils
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//
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int32_t cpu_get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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std::unordered_set<std::string> siblings;
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for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
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std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
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+ std::to_string(cpu) + "/topology/thread_siblings");
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if (!thread_siblings.is_open()) {
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break; // no more cpus
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}
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std::string line;
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if (std::getline(thread_siblings, line)) {
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siblings.insert(line);
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}
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}
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if (!siblings.empty()) {
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return static_cast<int32_t>(siblings.size());
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}
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#elif defined(__APPLE__) && defined(__MACH__)
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int32_t num_physical_cores;
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size_t len = sizeof(num_physical_cores);
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int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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#elif defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
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// TODO: windows + arm64 + mingw64
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unsigned int n_threads_win = std::thread::hardware_concurrency();
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unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4;
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DWORD buffer_size = 0;
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if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) {
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if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) {
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return default_threads;
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}
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}
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std::vector<char> buffer(buffer_size);
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if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) {
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return default_threads;
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}
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int32_t num_physical_cores = 0;
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PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data());
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while (buffer_size > 0) {
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if (info->Relationship == RelationProcessorCore) {
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num_physical_cores += info->Processor.GroupCount;
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}
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buffer_size -= info->Size;
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info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size);
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}
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return num_physical_cores > 0 ? num_physical_cores : default_threads;
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#endif
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unsigned int n_threads = std::thread::hardware_concurrency();
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return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
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}
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#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
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#include <pthread.h>
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static void cpuid(unsigned leaf, unsigned subleaf,
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unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
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__asm__("movq\t%%rbx,%%rsi\n\t"
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"cpuid\n\t"
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"xchgq\t%%rbx,%%rsi"
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: "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
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: "0"(leaf), "2"(subleaf));
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}
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static int pin_cpu(int cpu) {
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cpu_set_t mask;
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CPU_ZERO(&mask);
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CPU_SET(cpu, &mask);
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return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
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}
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static bool is_hybrid_cpu(void) {
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unsigned eax, ebx, ecx, edx;
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cpuid(7, 0, &eax, &ebx, &ecx, &edx);
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return !!(edx & (1u << 15));
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}
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static bool is_running_on_efficiency_core(void) {
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unsigned eax, ebx, ecx, edx;
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cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
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int intel_atom = 0x20;
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int core_type = (eax & 0xff000000u) >> 24;
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return core_type == intel_atom;
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}
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static int cpu_count_math_cpus(int n_cpu) {
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int result = 0;
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for (int cpu = 0; cpu < n_cpu; ++cpu) {
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if (pin_cpu(cpu)) {
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return -1;
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}
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if (is_running_on_efficiency_core()) {
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continue; // efficiency cores harm lockstep threading
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}
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++cpu; // hyperthreading isn't useful for linear algebra
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++result;
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}
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return result;
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}
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#endif // __x86_64__ && __linux__
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/**
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* Returns number of CPUs on system that are useful for math.
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*/
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int32_t cpu_get_num_math() {
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#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
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int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
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if (n_cpu < 1) {
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return cpu_get_num_physical_cores();
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}
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if (is_hybrid_cpu()) {
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cpu_set_t affinity;
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if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
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int result = cpu_count_math_cpus(n_cpu);
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pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
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if (result > 0) {
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return result;
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}
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}
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}
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#endif
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return cpu_get_num_physical_cores();
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}
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// Helper for setting process priority
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#if defined(_WIN32)
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bool set_process_priority(enum ggml_sched_priority prio) {
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if (prio == GGML_SCHED_PRIO_NORMAL) {
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return true;
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}
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DWORD p = NORMAL_PRIORITY_CLASS;
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switch (prio) {
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case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break;
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case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break;
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case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break;
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case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break;
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}
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if (!SetPriorityClass(GetCurrentProcess(), p)) {
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LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
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return false;
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}
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return true;
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}
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#else // MacOS and POSIX
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#include <sys/types.h>
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#include <sys/resource.h>
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bool set_process_priority(enum ggml_sched_priority prio) {
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if (prio == GGML_SCHED_PRIO_NORMAL) {
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return true;
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}
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int p = 0;
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switch (prio) {
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case GGML_SCHED_PRIO_NORMAL: p = 0; break;
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case GGML_SCHED_PRIO_MEDIUM: p = -5; break;
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case GGML_SCHED_PRIO_HIGH: p = -10; break;
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case GGML_SCHED_PRIO_REALTIME: p = -20; break;
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}
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if (!setpriority(PRIO_PROCESS, 0, p)) {
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LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
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return false;
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}
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return true;
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}
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#endif
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//
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// CLI argument parsing
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//
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void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
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int32_t n_set = 0;
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if (cpuparams.n_threads < 0) {
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// Assuming everything about cpuparams is invalid
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if (role_model != nullptr) {
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cpuparams = *role_model;
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} else {
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cpuparams.n_threads = cpu_get_num_math();
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}
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}
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for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
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if (cpuparams.cpumask[i]) {
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n_set++;
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}
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}
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if (n_set && n_set < cpuparams.n_threads) {
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// Not enough set bits, may experience performance issues.
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LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
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}
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}
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bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
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size_t dash_loc = range.find('-');
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if (dash_loc == std::string::npos) {
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LOG_ERR("Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
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return false;
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}
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size_t start_i;
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size_t end_i;
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if (dash_loc == 0) {
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start_i = 0;
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} else {
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start_i = std::stoull(range.substr(0, dash_loc));
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if (start_i >= GGML_MAX_N_THREADS) {
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LOG_ERR("Start index out of bounds!\n");
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return false;
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}
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}
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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();
|
|
}
|
|
builder.append(s, last_pos, std::string::npos);
|
|
s = std::move(builder);
|
|
}
|
|
|
|
std::string regex_escape(const std::string & s) {
|
|
static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
|
|
return std::regex_replace(s, special_chars, "\\$0");
|
|
}
|
|
|
|
std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
|
|
std::ostringstream result;
|
|
for (size_t i = 0; i < values.size(); ++i) {
|
|
if (i > 0) {
|
|
result << separator;
|
|
}
|
|
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"
|
|
#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);
|
|
|
|
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
|
|
// or invalid encodings were encountered. Reject such attempts
|
|
std::string filename_reencoded = converter.to_bytes(filename_utf32);
|
|
if (filename_reencoded != filename) {
|
|
return false;
|
|
}
|
|
} catch (const std::exception &) {
|
|
return false;
|
|
}
|
|
|
|
// Check for forbidden codepoints:
|
|
// - Control characters
|
|
// - Unicode equivalents of illegal characters
|
|
// - UTF-16 surrogate pairs
|
|
// - UTF-8 replacement character
|
|
// - Byte order mark (BOM)
|
|
// - Illegal characters: / \ : * ? " < > |
|
|
for (char32_t c : filename_utf32) {
|
|
if (c <= 0x1F // Control characters (C0)
|
|
|| c == 0x7F // Control characters (DEL)
|
|
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|
|
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|
|
|| c == 0x2215 // Division Slash (forward slash equivalent)
|
|
|| c == 0x2216 // Set Minus (backslash equivalent)
|
|
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|
|
|| c == 0xFFFD // Replacement Character (UTF-8)
|
|
|| c == 0xFEFF // Byte Order Mark (BOM)
|
|
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|
|
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
|
|
// Unicode and other whitespace is not affected, only 0x20 space
|
|
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
|
|
return false;
|
|
}
|
|
|
|
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
|
|
if (filename.find("..") != std::string::npos) {
|
|
return false;
|
|
}
|
|
|
|
// Reject "."
|
|
if (filename == ".") {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// returns true if successful, false otherwise
|
|
bool fs_create_directory_with_parents(const std::string & path) {
|
|
#ifdef _WIN32
|
|
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
|
|
std::wstring wpath = converter.from_bytes(path);
|
|
|
|
// if the path already exists, check whether it's a directory
|
|
const DWORD attributes = GetFileAttributesW(wpath.c_str());
|
|
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
|
|
return true;
|
|
}
|
|
|
|
size_t pos_slash = 0;
|
|
|
|
// process path from front to back, procedurally creating directories
|
|
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
|
|
const std::wstring subpath = wpath.substr(0, pos_slash);
|
|
const wchar_t * test = subpath.c_str();
|
|
|
|
const bool success = CreateDirectoryW(test, NULL);
|
|
if (!success) {
|
|
const DWORD error = GetLastError();
|
|
|
|
// if the path already exists, ensure that it's a directory
|
|
if (error == ERROR_ALREADY_EXISTS) {
|
|
const DWORD attributes = GetFileAttributesW(subpath.c_str());
|
|
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
pos_slash += 1;
|
|
}
|
|
|
|
return true;
|
|
#else
|
|
// if the path already exists, check whether it's a directory
|
|
struct stat info;
|
|
if (stat(path.c_str(), &info) == 0) {
|
|
return S_ISDIR(info.st_mode);
|
|
}
|
|
|
|
size_t pos_slash = 1; // skip leading slashes for directory creation
|
|
|
|
// process path from front to back, procedurally creating directories
|
|
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
|
|
const std::string subpath = path.substr(0, pos_slash);
|
|
struct stat info;
|
|
|
|
// if the path already exists, ensure that it's a directory
|
|
if (stat(subpath.c_str(), &info) == 0) {
|
|
if (!S_ISDIR(info.st_mode)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
// create parent directories
|
|
const int ret = mkdir(subpath.c_str(), 0755);
|
|
if (ret != 0) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
pos_slash += 1;
|
|
}
|
|
|
|
return true;
|
|
#endif // _WIN32
|
|
}
|
|
|
|
std::string fs_get_cache_directory() {
|
|
std::string cache_directory = "";
|
|
auto ensure_trailing_slash = [](std::string p) {
|
|
// Make sure to add trailing slash
|
|
if (p.back() != DIRECTORY_SEPARATOR) {
|
|
p += DIRECTORY_SEPARATOR;
|
|
}
|
|
return p;
|
|
};
|
|
if (getenv("LLAMA_CACHE")) {
|
|
cache_directory = std::getenv("LLAMA_CACHE");
|
|
} else {
|
|
#ifdef __linux__
|
|
if (std::getenv("XDG_CACHE_HOME")) {
|
|
cache_directory = std::getenv("XDG_CACHE_HOME");
|
|
} else {
|
|
cache_directory = std::getenv("HOME") + std::string("/.cache/");
|
|
}
|
|
#elif defined(__APPLE__)
|
|
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
|
|
#elif defined(_WIN32)
|
|
cache_directory = std::getenv("LOCALAPPDATA");
|
|
#endif // __linux__
|
|
cache_directory = ensure_trailing_slash(cache_directory);
|
|
cache_directory += "llama.cpp";
|
|
}
|
|
return ensure_trailing_slash(cache_directory);
|
|
}
|
|
|
|
std::string fs_get_cache_file(const std::string & filename) {
|
|
GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
|
|
std::string cache_directory = fs_get_cache_directory();
|
|
const bool success = fs_create_directory_with_parents(cache_directory);
|
|
if (!success) {
|
|
throw std::runtime_error("failed to create cache directory: " + cache_directory);
|
|
}
|
|
return cache_directory + filename;
|
|
}
|
|
|
|
|
|
//
|
|
// Model utils
|
|
//
|
|
struct 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_model_load_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;
|
|
}
|
|
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
if (params.reranking) {
|
|
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;
|
|
}
|
|
|
|
if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
|
|
LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
|
|
ok = false;
|
|
}
|
|
|
|
if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
|
|
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
|
|
ok = false;
|
|
}
|
|
|
|
if (!ok) {
|
|
llama_model_free(model);
|
|
|
|
return iparams;
|
|
}
|
|
}
|
|
|
|
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.c_str());
|
|
llama_model_free(model);
|
|
return iparams;
|
|
}
|
|
|
|
if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) {
|
|
LOG_WRN("%s: KV cache shifting is not supported for this model, 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;
|
|
}
|
|
}
|
|
|
|
// 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__);
|
|
|
|
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_kv_cache_clear(lctx);
|
|
llama_synchronize(lctx);
|
|
llama_perf_context_reset(lctx);
|
|
}
|
|
|
|
iparams.model.reset(model);
|
|
iparams.context.reset(lctx);
|
|
|
|
return iparams;
|
|
}
|
|
|
|
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();
|
|
}
|
|
|
|
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.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 = 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;
|
|
}
|
|
|
|
#ifdef LLAMA_USE_CURL
|
|
|
|
#define CURL_MAX_RETRY 3
|
|
#define CURL_RETRY_DELAY_SECONDS 2
|
|
|
|
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) {
|
|
// Initialize libcurl
|
|
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
|
|
curl_slist_ptr http_headers;
|
|
if (!curl) {
|
|
LOG_ERR("%s: error initializing libcurl\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
bool force_download = false;
|
|
|
|
// Set the URL, allow to follow http redirection
|
|
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
|
|
|
|
// Check if hf-token or bearer-token was specified
|
|
if (!hf_token.empty()) {
|
|
std::string auth_header = "Authorization: Bearer " + hf_token;
|
|
http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
|
|
// operating system. Currently implemented under MS-Windows.
|
|
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
|
|
#endif
|
|
|
|
// Check if the file already exists locally
|
|
auto file_exists = std::filesystem::exists(path);
|
|
|
|
// If the file exists, check its JSON metadata companion file.
|
|
std::string metadata_path = path + ".json";
|
|
nlohmann::json metadata;
|
|
std::string etag;
|
|
std::string last_modified;
|
|
|
|
if (file_exists) {
|
|
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
|
|
std::ifstream metadata_in(metadata_path);
|
|
if (metadata_in.good()) {
|
|
try {
|
|
metadata_in >> metadata;
|
|
LOG_INF("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
|
|
if (metadata.contains("url") && metadata.at("url").is_string()) {
|
|
auto previous_url = metadata.at("url").get<std::string>();
|
|
if (previous_url != url) {
|
|
LOG_ERR("%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
|
|
etag = metadata.at("etag");
|
|
}
|
|
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
|
|
last_modified = metadata.at("lastModified");
|
|
}
|
|
} catch (const nlohmann::json::exception & e) {
|
|
LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
|
|
return false;
|
|
}
|
|
}
|
|
} else {
|
|
LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
|
|
}
|
|
|
|
// Send a HEAD request to retrieve the etag and last-modified headers
|
|
struct 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*/, 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); // will trigger the HEAD verb
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
|
|
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
|
|
|
|
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
|
|
if (!was_perform_successful) {
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code != 200) {
|
|
// HEAD not supported, we don't know if the file has changed
|
|
// force trigger downloading
|
|
force_download = true;
|
|
LOG_ERR("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
|
|
}
|
|
}
|
|
|
|
bool should_download = !file_exists || force_download;
|
|
if (!should_download) {
|
|
if (!etag.empty() && etag != headers.etag) {
|
|
LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
|
|
should_download = true;
|
|
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
|
|
LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
|
|
should_download = true;
|
|
}
|
|
}
|
|
if (should_download) {
|
|
std::string path_temporary = path + ".downloadInProgress";
|
|
if (file_exists) {
|
|
LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
|
|
if (remove(path.c_str()) != 0) {
|
|
LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Set the output file
|
|
|
|
struct FILE_deleter {
|
|
void operator()(FILE * f) const {
|
|
fclose(f);
|
|
}
|
|
};
|
|
|
|
std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
|
|
if (!outfile) {
|
|
LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path.c_str());
|
|
return false;
|
|
}
|
|
|
|
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
|
|
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
|
|
return fwrite(data, size, nmemb, (FILE *)fd);
|
|
};
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
|
|
|
|
// display download progress
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
|
|
|
|
// helper function to hide password in URL
|
|
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
|
|
std::size_t protocol_pos = url.find("://");
|
|
if (protocol_pos == std::string::npos) {
|
|
return url; // Malformed URL
|
|
}
|
|
|
|
std::size_t at_pos = url.find('@', protocol_pos + 3);
|
|
if (at_pos == std::string::npos) {
|
|
return url; // No password in URL
|
|
}
|
|
|
|
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
|
|
};
|
|
|
|
// start the download
|
|
LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
|
|
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
|
|
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
|
|
if (!was_perform_successful) {
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code < 200 || http_code >= 400) {
|
|
LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
|
|
return false;
|
|
}
|
|
|
|
// Causes file to be closed explicitly here before we rename it.
|
|
outfile.reset();
|
|
|
|
// Write the updated JSON metadata file.
|
|
metadata.update({
|
|
{"url", url},
|
|
{"etag", headers.etag},
|
|
{"lastModified", headers.last_modified}
|
|
});
|
|
std::ofstream(metadata_path) << metadata.dump(4);
|
|
LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
|
|
|
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
|
|
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
struct llama_model * 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) {
|
|
// Basic validation of the model_url
|
|
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;
|
|
}
|
|
|
|
// check for additional GGUFs split to download
|
|
int n_split = 0;
|
|
{
|
|
struct gguf_init_params gguf_params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ NULL,
|
|
};
|
|
auto * ctx_gguf = gguf_init_from_file(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};
|
|
|
|
// Verify the first split file format
|
|
// and extract split URL and PATH prefixes
|
|
{
|
|
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;
|
|
}
|
|
}
|
|
|
|
// Prepare download in parallel
|
|
std::vector<std::future<bool>> futures_download;
|
|
for (int idx = 1; idx < n_split; idx++) {
|
|
futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
|
|
char split_path[PATH_MAX] = {0};
|
|
llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
|
|
|
|
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
|
|
|
|
return common_download_file(split_url, split_path, hf_token);
|
|
}, idx));
|
|
}
|
|
|
|
// Wait for all downloads to complete
|
|
for (auto & f : futures_download) {
|
|
if (!f.get()) {
|
|
return NULL;
|
|
}
|
|
}
|
|
}
|
|
|
|
return llama_model_load_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) {
|
|
// construct hugging face model url:
|
|
//
|
|
// --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
|
|
// https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
|
|
//
|
|
// --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
|
|
// https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
|
|
//
|
|
|
|
std::string model_url = "https://huggingface.co/";
|
|
model_url += repo;
|
|
model_url += "/resolve/main/";
|
|
model_url += remote_path;
|
|
|
|
return common_load_model_from_url(model_url, local_path, hf_token, params);
|
|
}
|
|
|
|
/**
|
|
* Allow getting the HF file from the HF repo with tag (like ollama), for example:
|
|
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
|
|
* - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
|
|
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
|
|
* Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
|
|
*
|
|
* Return pair of <repo, file> (with "repo" already having tag removed)
|
|
*
|
|
* Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
|
|
*/
|
|
std::pair<std::string, std::string> common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & hf_token) {
|
|
auto parts = string_split<std::string>(hf_repo_with_tag, ':');
|
|
std::string tag = parts.size() > 1 ? parts.back() : "latest";
|
|
std::string hf_repo = parts[0];
|
|
if (string_split<std::string>(hf_repo, '/').size() != 2) {
|
|
throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
|
|
}
|
|
|
|
// fetch model info from Hugging Face Hub API
|
|
json model_info;
|
|
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
|
|
curl_slist_ptr http_headers;
|
|
std::string res_str;
|
|
std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag;
|
|
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
|
|
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
|
|
auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
|
|
static_cast<std::string *>(data)->append((char * ) ptr, size * nmemb);
|
|
return size * nmemb;
|
|
};
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str);
|
|
#if defined(_WIN32)
|
|
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
|
|
#endif
|
|
if (!hf_token.empty()) {
|
|
std::string auth_header = "Authorization: Bearer " + hf_token;
|
|
http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
|
|
}
|
|
// Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
|
|
http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
|
|
http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json");
|
|
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
|
|
|
|
CURLcode res = curl_easy_perform(curl.get());
|
|
|
|
if (res != CURLE_OK) {
|
|
throw std::runtime_error("error: cannot make GET request to HF API");
|
|
}
|
|
|
|
long res_code;
|
|
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
|
|
if (res_code == 200) {
|
|
model_info = json::parse(res_str);
|
|
} else if (res_code == 401) {
|
|
throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
|
|
} else {
|
|
throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
|
|
}
|
|
|
|
// check response
|
|
if (!model_info.contains("ggufFile")) {
|
|
throw std::runtime_error("error: model does not have ggufFile");
|
|
}
|
|
json & gguf_file = model_info.at("ggufFile");
|
|
if (!gguf_file.contains("rfilename")) {
|
|
throw std::runtime_error("error: ggufFile does not have rfilename");
|
|
}
|
|
|
|
return std::make_pair(hf_repo, gguf_file.at("rfilename"));
|
|
}
|
|
|
|
#else
|
|
|
|
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*/) {
|
|
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 & /*repo*/,
|
|
const std::string & /*remote_path*/,
|
|
const std::string & /*local_path*/,
|
|
const std::string & /*hf_token*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
std::pair<std::string, std::string> common_get_hf_file(const std::string &, const std::string &) {
|
|
LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
|
|
return std::make_pair("", "");
|
|
}
|
|
|
|
#endif // LLAMA_USE_CURL
|
|
|
|
//
|
|
// 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 < 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;
|
|
}
|
|
|
|
//
|
|
// KV cache utils
|
|
//
|
|
|
|
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");
|
|
}
|
|
|
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//
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// Embedding utils
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//
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void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
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double sum = 0.0;
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switch (embd_norm) {
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case -1: // no normalisation
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sum = 1.0;
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break;
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case 0: // max absolute
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for (int i = 0; i < n; i++) {
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if (sum < std::abs(inp[i])) {
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sum = std::abs(inp[i]);
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}
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}
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sum /= 32760.0; // make an int16 range
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break;
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case 2: // euclidean
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for (int i = 0; i < n; i++) {
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sum += inp[i] * inp[i];
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}
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sum = std::sqrt(sum);
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break;
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default: // p-norm (euclidean is p-norm p=2)
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for (int i = 0; i < n; i++) {
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sum += std::pow(std::abs(inp[i]), embd_norm);
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}
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sum = std::pow(sum, 1.0 / embd_norm);
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break;
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}
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const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
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for (int i = 0; i < n; i++) {
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out[i] = inp[i] * norm;
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}
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}
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float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
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double sum = 0.0;
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double sum1 = 0.0;
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double sum2 = 0.0;
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|
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for (int i = 0; i < n; i++) {
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sum += embd1[i] * embd2[i];
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sum1 += embd1[i] * embd1[i];
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sum2 += embd2[i] * embd2[i];
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}
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|
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// Handle the case where one or both vectors are zero vectors
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if (sum1 == 0.0 || sum2 == 0.0) {
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if (sum1 == 0.0 && sum2 == 0.0) {
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return 1.0f; // two zero vectors are similar
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}
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return 0.0f;
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}
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|
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return sum / (sqrt(sum1) * sqrt(sum2));
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}
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|
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//
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// Control vector utils
|
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//
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|
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static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) {
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common_control_vector_data result = { -1, {} };
|
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|
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ggml_context * ctx = nullptr;
|
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struct gguf_init_params meta_gguf_params = {
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/* .no_alloc = */ false,
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/* .ctx = */ &ctx,
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};
|
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struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
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if (!ctx_gguf) {
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LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
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return result;
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}
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|
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int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
|
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if (n_tensors == 0) {
|
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LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
|
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}
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|
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for (int i = 0; i < n_tensors; i++) {
|
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std::string name = gguf_get_tensor_name(ctx_gguf, i);
|
|
|
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int layer_idx = -1;
|
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|
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// split on '.'
|
|
size_t dotpos = name.find('.');
|
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if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
|
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try {
|
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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;
|
|
}
|
|
|
|
template <>
|
|
json common_grammar_trigger::to_json() const {
|
|
json out {
|
|
{"type", (int) type},
|
|
{"value", value},
|
|
};
|
|
if (type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) {
|
|
out["token"] = (int) token;
|
|
}
|
|
return out;
|
|
}
|
|
|
|
template <>
|
|
common_grammar_trigger common_grammar_trigger::from_json(const json & in) {
|
|
common_grammar_trigger out;
|
|
out.type = (common_grammar_trigger_type) in.at("type").get<int>();
|
|
out.value = in.at("value").get<std::string>();
|
|
if (out.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) {
|
|
out.token = (llama_token) in.at("token").get<int>();
|
|
}
|
|
return out;
|
|
}
|