mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 06:49:54 +00:00
42c76d1358
* Introduce ggml_compute_threadpool - OpenMP functional: check - Vanilla ggml functional: Check - ggml w/threadpool functional: Check - OpenMP no regression: No glaring problems - Vanilla ggml no regression: No glaring problems - ggml w/threadpool no regression: No glaring problems * Minor fixes * fixed use after release bug * fixed a harmless race condition * Fix Android bulid issue * fix more race conditions * fix deadlock for cases where cgraph.n_nodes == 1 and fix --poll case * threadpool: use cpu_get_num_math to set the default number of threadpool threads This way we avoid using E-Cores and Hyperthreaded siblings. * bench: create fresh threadpool for each test For benchmarking it's better to start a fresh pool for each test with the exact number of threads needed for that test. Having larger pools is suboptimal (causes more load, etc). * atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior. * threadpool: make polling the default to match openmp behavior All command line args now allow for setting poll to 0 (false). * threadpool: do not wakeup threads in already paused threadpool * fix potential race condition in check_for_work * threadpool: do not create two threadpools if their params are identical * threadpool: reduce pause/resume/wakeup overhead in common cases We now start threadpool in paused state only if we have two. The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead. * threadpool: add support for hybrid polling poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var. poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ... The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms. We can tune this further as things evolve. * threadpool: reduce the number of barrier required New work is now indicated with an atomic counter that is incremented for each new graph that needs to be computed. This removes the need for extra barrier for clearing the "new_work" and removes the special case for trivial graphs. * threadpool: remove special-casing for disposable threadpools With the efficient hybrid polling there is no need to make disposable pools any different. This simplifies the overall logic and reduces branching. Include n_threads in debug print for disposable threadpool. Declare pause and stop flags as atomic_bool This doesn't actually generate any memory barriers and simply informs the thread sanitizer that these flags can be written & read by different threads without locking. * threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs) This fixes the race condition with very small graphs where the main thread happens to start a new graph while the workers are just about to exit from barriers. * threadpool: use relaxed order for chunk sync Full memory barrier is an overkill for this since each thread works on different chunk * threadpool: remove abort_callback from threadpool state * threadpool: better naming for thread/cpumask releated functions * threadpool: consistent use of int type for n_threads params * threadpool: add support for ggml_threadpool_params_default/init Also removes the need for explicit mask_specified param. all-zero cpumask means use default (usually inherited) cpu affinity mask. * threadpool: move typedef into ggml.h * threadpool: fix apply_priority() function name * threadpool: fix swift wrapper errors due to n_threads int type cleanup * threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled * threadpool: replace checks for compute_thread ret code with proper status check * threadpool: simplify threadpool init logic and fix main thread affinity application Most of the init code is now exactly the same between threadpool and openmp. * threadpool: update threadpool resume/pause function names * threadpool: enable openmp by default for now * threadpool: don't forget to free workers state when omp is enabled * threadpool: avoid updating process priority on the platforms that do not require it On Windows we need to change overall process priority class in order to set thread priorities, but on Linux, Mac, etc we do not need to touch the overall process settings. * threadpool: update calling thread prio and affinity only at start/resume This avoids extra syscalls for each graph_compute() * llama-bench: turn threadpool params into vectors, add output headers, etc * llama-bench: add support for cool off between tests --delay This helps for long running tests on platforms that are thermally limited (phones, laptops, etc). --delay (disabled by default) introduces the sleep for N seconds before starting each test. * threadpool: move process priority setting into the apps (bench and cli) This avoids changing the overall process priority on Windows for the apps that use ggml/llama.cpp directy. * threadpool: move all pause/resume logic into ggml * threadpool: futher api cleanup and prep for future refactoring All threadpool related functions and structs use ggml_threadpool prefix. * threadpool: minor indent fixes * threadpool: improve setprioty error message * Update examples/llama-bench/llama-bench.cpp Co-authored-by: slaren <slarengh@gmail.com> * threadpool: fix indent in set_threadpool call * use int32_t for n_thread type in public llama.cpp API * threadpool: use _new and _free instead of _create and _release * fix two more public APIs to use int32_t for n_threads * build: set _GNU_SOURCE for Adroid --------- Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com> Co-authored-by: fmz <quic_fzaghlou@quic.com> Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
3663 lines
151 KiB
C++
3663 lines
151 KiB
C++
#if defined(_MSC_VER)
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#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
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#endif
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#include "common.h"
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// Change JSON_ASSERT from assert() to GGML_ASSERT:
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#define JSON_ASSERT GGML_ASSERT
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#include "json.hpp"
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#include "json-schema-to-grammar.h"
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#include "llama.h"
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#include <algorithm>
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#include <cinttypes>
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#include <cmath>
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#include <codecvt>
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#include <cstdarg>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iostream>
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#include <iterator>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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#if defined(__APPLE__) && defined(__MACH__)
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#include <sys/types.h>
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#include <sys/sysctl.h>
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#endif
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <locale>
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#include <windows.h>
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#include <fcntl.h>
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#include <io.h>
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#else
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#include <sys/ioctl.h>
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#include <sys/stat.h>
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#include <unistd.h>
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#endif
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#if defined(LLAMA_USE_CURL)
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#include <curl/curl.h>
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#include <curl/easy.h>
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#include <thread>
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#include <future>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
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#define GGML_USE_CUDA_SYCL
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#endif
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#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
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#define GGML_USE_CUDA_SYCL_VULKAN
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#endif
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#if defined(LLAMA_USE_CURL)
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#ifdef __linux__
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#include <linux/limits.h>
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#elif defined(_WIN32)
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#define PATH_MAX MAX_PATH
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#else
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#include <sys/syslimits.h>
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#endif
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#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
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#endif // LLAMA_USE_CURL
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using json = nlohmann::ordered_json;
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//
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// Environment variable utils
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//
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template<typename T>
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static typename std::enable_if<std::is_same<T, std::string>::value, void>::type
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get_env(std::string name, T & target) {
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char * value = std::getenv(name.c_str());
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target = value ? std::string(value) : target;
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}
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template<typename T>
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static typename std::enable_if<!std::is_same<T, bool>::value && std::is_integral<T>::value, void>::type
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get_env(std::string name, T & target) {
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char * value = std::getenv(name.c_str());
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target = value ? std::stoi(value) : target;
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}
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template<typename T>
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static typename std::enable_if<std::is_floating_point<T>::value, void>::type
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get_env(std::string name, T & target) {
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char * value = std::getenv(name.c_str());
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target = value ? std::stof(value) : target;
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}
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template<typename T>
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static typename std::enable_if<std::is_same<T, bool>::value, void>::type
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get_env(std::string name, T & target) {
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char * value = std::getenv(name.c_str());
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if (value) {
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std::string val(value);
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target = val == "1" || val == "true";
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}
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}
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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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fprintf(stderr, "warn: 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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fprintf(stderr, "warn: 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 gpt_params_handle_model_default(gpt_params & params) {
|
|
if (!params.hf_repo.empty()) {
|
|
// short-hand to avoid specifying --hf-file -> default it to --model
|
|
if (params.hf_file.empty()) {
|
|
if (params.model.empty()) {
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throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
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}
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params.hf_file = params.model;
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} else if (params.model.empty()) {
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params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
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}
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} else if (!params.model_url.empty()) {
|
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if (params.model.empty()) {
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auto f = string_split(params.model_url, '#').front();
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f = string_split(f, '?').front();
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params.model = fs_get_cache_file(string_split(f, '/').back());
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}
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} else if (params.model.empty()) {
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params.model = DEFAULT_MODEL_PATH;
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}
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}
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|
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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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|
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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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|
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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) {
|
|
// Not enough set bits, may experience performance issues.
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fprintf(stderr, "warn: 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 gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
|
|
bool invalid_param = false;
|
|
std::string arg;
|
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const std::string arg_prefix = "--";
|
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llama_sampling_params & sparams = params.sparams;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
|
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if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
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std::replace(arg.begin(), arg.end(), '_', '-');
|
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}
|
|
if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
|
|
throw std::invalid_argument("error: unknown argument: " + arg);
|
|
}
|
|
if (invalid_param) {
|
|
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
|
|
}
|
|
}
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|
|
postprocess_cpu_params(params.cpuparams, nullptr);
|
|
postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams);
|
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postprocess_cpu_params(params.draft_cpuparams, ¶ms.cpuparams);
|
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postprocess_cpu_params(params.draft_cpuparams_batch, ¶ms.cpuparams_batch);
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|
|
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
|
|
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
|
|
}
|
|
|
|
gpt_params_handle_model_default(params);
|
|
|
|
if (params.hf_token.empty()) {
|
|
get_env("HF_TOKEN", params.hf_token);
|
|
}
|
|
|
|
if (params.escape) {
|
|
string_process_escapes(params.prompt);
|
|
string_process_escapes(params.input_prefix);
|
|
string_process_escapes(params.input_suffix);
|
|
string_process_escapes(sparams.cfg_negative_prompt);
|
|
for (auto & antiprompt : params.antiprompt) {
|
|
string_process_escapes(antiprompt);
|
|
}
|
|
}
|
|
|
|
if (!params.kv_overrides.empty()) {
|
|
params.kv_overrides.emplace_back();
|
|
params.kv_overrides.back().key[0] = 0;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void gpt_params_parse_from_env(gpt_params & params) {
|
|
// we only care about server-related params for now
|
|
get_env("LLAMA_ARG_MODEL", params.model);
|
|
get_env("LLAMA_ARG_MODEL_URL", params.model_url);
|
|
get_env("LLAMA_ARG_MODEL_ALIAS", params.model_alias);
|
|
get_env("LLAMA_ARG_HF_REPO", params.hf_repo);
|
|
get_env("LLAMA_ARG_HF_FILE", params.hf_file);
|
|
get_env("LLAMA_ARG_THREADS", params.cpuparams.n_threads);
|
|
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
|
|
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
|
|
get_env("LLAMA_ARG_BATCH", params.n_batch);
|
|
get_env("LLAMA_ARG_UBATCH", params.n_ubatch);
|
|
get_env("LLAMA_ARG_N_GPU_LAYERS", params.n_gpu_layers);
|
|
get_env("LLAMA_ARG_THREADS_HTTP", params.n_threads_http);
|
|
get_env("LLAMA_ARG_CHAT_TEMPLATE", params.chat_template);
|
|
get_env("LLAMA_ARG_N_PREDICT", params.n_predict);
|
|
get_env("LLAMA_ARG_ENDPOINT_METRICS", params.endpoint_metrics);
|
|
get_env("LLAMA_ARG_ENDPOINT_SLOTS", params.endpoint_slots);
|
|
get_env("LLAMA_ARG_EMBEDDINGS", params.embedding);
|
|
get_env("LLAMA_ARG_FLASH_ATTN", params.flash_attn);
|
|
get_env("LLAMA_ARG_DEFRAG_THOLD", params.defrag_thold);
|
|
get_env("LLAMA_ARG_CONT_BATCHING", params.cont_batching);
|
|
get_env("LLAMA_ARG_HOST", params.hostname);
|
|
get_env("LLAMA_ARG_PORT", params.port);
|
|
}
|
|
|
|
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|
const auto params_org = params; // the example can modify the default params
|
|
|
|
try {
|
|
if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
|
|
params = params_org;
|
|
params.usage = true;
|
|
return false;
|
|
}
|
|
} catch (const std::invalid_argument & ex) {
|
|
fprintf(stderr, "%s\n", ex.what());
|
|
params = params_org;
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
|
|
size_t dash_loc = range.find('-');
|
|
if (dash_loc == std::string::npos) {
|
|
fprintf(stderr, "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
|
|
return false;
|
|
}
|
|
|
|
size_t start_i;
|
|
size_t end_i;
|
|
|
|
if (dash_loc == 0) {
|
|
start_i = 0;
|
|
} else {
|
|
start_i = std::stoull(range.substr(0, dash_loc));
|
|
if (start_i >= GGML_MAX_N_THREADS) {
|
|
fprintf(stderr, "Start index out of bounds!\n");
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (dash_loc == range.length() - 1) {
|
|
end_i = GGML_MAX_N_THREADS - 1;
|
|
} else {
|
|
end_i = std::stoull(range.substr(dash_loc + 1));
|
|
if (end_i >= GGML_MAX_N_THREADS) {
|
|
fprintf(stderr, "End index out of bounds!\n");
|
|
return false;
|
|
}
|
|
}
|
|
|
|
for (size_t i = start_i; i <= end_i; i++) {
|
|
boolmask[i] = true;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
|
|
// Discard potential 0x prefix
|
|
size_t start_i = 0;
|
|
if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
|
|
start_i = 2;
|
|
}
|
|
|
|
size_t num_digits = mask.length() - start_i;
|
|
if (num_digits > 128) num_digits = 128;
|
|
|
|
size_t end_i = num_digits + start_i;
|
|
|
|
for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
|
|
char c = mask.at(i);
|
|
int8_t id = c;
|
|
|
|
if ((c >= '0' && c <= '9')) {
|
|
id -= '0';
|
|
} else if (c >= 'a' && c <= 'f') {
|
|
id -= 'a' - 10;
|
|
} else if (c >= 'A' && c <= 'F') {
|
|
id -= 'A' - 10;
|
|
} else {
|
|
fprintf(stderr, "Invalid hex character '%c' at position %d\n", c, int32_t(i));
|
|
return false;
|
|
}
|
|
|
|
boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0);
|
|
boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
|
|
boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
|
|
boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
|
|
|
|
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
|
|
const char split_delim = ',';
|
|
|
|
llama_sampling_params & sparams = params.sparams;
|
|
|
|
if (arg == "-s" || arg == "--seed") {
|
|
CHECK_ARG
|
|
// TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
|
|
params.seed = std::stoul(argv[i]);
|
|
sparams.seed = std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-t" || arg == "--threads") {
|
|
CHECK_ARG
|
|
params.cpuparams.n_threads = std::stoi(argv[i]);
|
|
if (params.cpuparams.n_threads <= 0) {
|
|
params.cpuparams.n_threads = std::thread::hardware_concurrency();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-C" || arg == "--cpu-mask") {
|
|
CHECK_ARG
|
|
std::string mask = argv[i];
|
|
params.cpuparams.mask_valid = true;
|
|
invalid_param = !parse_cpu_mask(mask, params.cpuparams.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "-Cr" || arg == "--cpu-range") {
|
|
CHECK_ARG
|
|
std::string range = argv[i];
|
|
params.cpuparams.mask_valid = true;
|
|
invalid_param = !parse_cpu_range(range, params.cpuparams.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "--prio") {
|
|
CHECK_ARG
|
|
params.cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--cpu-strict") {
|
|
CHECK_ARG
|
|
params.cpuparams.strict_cpu = std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--poll") {
|
|
CHECK_ARG
|
|
params.cpuparams.poll = std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-tb" || arg == "--threads-batch") {
|
|
CHECK_ARG
|
|
params.cpuparams_batch.n_threads = std::stoi(argv[i]);
|
|
if (params.cpuparams_batch.n_threads <= 0) {
|
|
params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-Cb" || arg == "--cpu-mask-batch") {
|
|
CHECK_ARG
|
|
std::string mask = argv[i];
|
|
params.cpuparams_batch.mask_valid = true;
|
|
invalid_param = !parse_cpu_mask(mask, params.cpuparams_batch.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "-Crb" || arg == "--cpu-range_batch") {
|
|
CHECK_ARG
|
|
std::string range = argv[i];
|
|
params.cpuparams_batch.mask_valid = true;
|
|
invalid_param = !parse_cpu_range(range, params.cpuparams_batch.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "--prio-batch") {
|
|
CHECK_ARG
|
|
params.cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--cpu-strict-batch") {
|
|
params.cpuparams_batch.strict_cpu = true;
|
|
return true;
|
|
}
|
|
if (arg == "--poll-batch") {
|
|
CHECK_ARG
|
|
params.cpuparams_batch.poll = std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-td" || arg == "--threads-draft") {
|
|
CHECK_ARG
|
|
params.draft_cpuparams.n_threads = std::stoi(argv[i]);
|
|
if (params.draft_cpuparams.n_threads <= 0) {
|
|
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-Cd" || arg == "--cpu-mask-draft") {
|
|
CHECK_ARG
|
|
std::string mask = argv[i];
|
|
params.draft_cpuparams.mask_valid = true;
|
|
invalid_param = !parse_cpu_mask(mask, params.draft_cpuparams.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "-Crd" || arg == "--cpu-range-draft") {
|
|
CHECK_ARG
|
|
std::string range = argv[i];
|
|
params.draft_cpuparams.mask_valid = true;
|
|
invalid_param = !parse_cpu_range(range, params.draft_cpuparams.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "--prio-draft") {
|
|
CHECK_ARG
|
|
params.draft_cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--cpu-strict-draft") {
|
|
params.draft_cpuparams.strict_cpu = true;
|
|
return true;
|
|
}
|
|
if (arg == "--poll-draft") {
|
|
CHECK_ARG
|
|
params.draft_cpuparams.poll = std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-tbd" || arg == "--threads-batch-draft") {
|
|
CHECK_ARG
|
|
params.draft_cpuparams_batch.n_threads = std::stoi(argv[i]);
|
|
if (params.draft_cpuparams_batch.n_threads <= 0) {
|
|
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-Crbd" || arg == "--cpu-range-batch-draft") {
|
|
CHECK_ARG
|
|
std::string range = argv[i];
|
|
params.draft_cpuparams_batch.mask_valid = true;
|
|
invalid_param = !parse_cpu_range(range, params.draft_cpuparams_batch.cpumask);
|
|
return true;
|
|
}
|
|
if (arg == "--prio-batch-draft") {
|
|
CHECK_ARG
|
|
params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--cpu-strict-batch-draft") {
|
|
params.draft_cpuparams_batch.strict_cpu = true;
|
|
return true;
|
|
}
|
|
if (arg == "--poll-batch-draft") {
|
|
CHECK_ARG
|
|
params.draft_cpuparams_batch.poll = std::stoul(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-p" || arg == "--prompt") {
|
|
CHECK_ARG
|
|
params.prompt = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-e" || arg == "--escape") {
|
|
params.escape = true;
|
|
return true;
|
|
}
|
|
if (arg == "--no-escape") {
|
|
params.escape = false;
|
|
return true;
|
|
}
|
|
if (arg == "--prompt-cache") {
|
|
CHECK_ARG
|
|
params.path_prompt_cache = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--prompt-cache-all") {
|
|
params.prompt_cache_all = true;
|
|
return true;
|
|
}
|
|
if (arg == "--prompt-cache-ro") {
|
|
params.prompt_cache_ro = true;
|
|
return true;
|
|
}
|
|
if (arg == "-bf" || arg == "--binary-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i], std::ios::binary);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
// store the external file name in params
|
|
params.prompt_file = argv[i];
|
|
std::ostringstream ss;
|
|
ss << file.rdbuf();
|
|
params.prompt = ss.str();
|
|
fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-f" || arg == "--file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
// store the external file name in params
|
|
params.prompt_file = argv[i];
|
|
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
|
|
if (!params.prompt.empty() && params.prompt.back() == '\n') {
|
|
params.prompt.pop_back();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--in-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
params.in_files.push_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
|
|
CHECK_ARG
|
|
params.n_predict = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--top-k") {
|
|
CHECK_ARG
|
|
sparams.top_k = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-c" || arg == "--ctx-size") {
|
|
CHECK_ARG
|
|
params.n_ctx = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--grp-attn-n" || arg == "-gan") {
|
|
CHECK_ARG
|
|
params.grp_attn_n = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--grp-attn-w" || arg == "-gaw") {
|
|
CHECK_ARG
|
|
params.grp_attn_w = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--rope-freq-base") {
|
|
CHECK_ARG
|
|
params.rope_freq_base = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--rope-freq-scale") {
|
|
CHECK_ARG
|
|
params.rope_freq_scale = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--rope-scaling") {
|
|
CHECK_ARG
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
|
|
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
|
|
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
|
|
else { invalid_param = true; }
|
|
return true;
|
|
}
|
|
if (arg == "--rope-scale") {
|
|
CHECK_ARG
|
|
params.rope_freq_scale = 1.0f / std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--yarn-orig-ctx") {
|
|
CHECK_ARG
|
|
params.yarn_orig_ctx = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--yarn-ext-factor") {
|
|
CHECK_ARG
|
|
params.yarn_ext_factor = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--yarn-attn-factor") {
|
|
CHECK_ARG
|
|
params.yarn_attn_factor = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--yarn-beta-fast") {
|
|
CHECK_ARG
|
|
params.yarn_beta_fast = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--yarn-beta-slow") {
|
|
CHECK_ARG
|
|
params.yarn_beta_slow = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--pooling") {
|
|
CHECK_ARG
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
|
|
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
|
|
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
|
|
else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
|
|
else { invalid_param = true; }
|
|
return true;
|
|
}
|
|
if (arg == "--attention") {
|
|
CHECK_ARG
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
|
|
else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
|
|
else { invalid_param = true; }
|
|
return true;
|
|
}
|
|
if (arg == "--defrag-thold" || arg == "-dt") {
|
|
CHECK_ARG
|
|
params.defrag_thold = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--samplers") {
|
|
CHECK_ARG
|
|
const auto sampler_names = string_split(argv[i], ';');
|
|
sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
|
|
return true;
|
|
}
|
|
if (arg == "--sampling-seq") {
|
|
CHECK_ARG
|
|
sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--top-p") {
|
|
CHECK_ARG
|
|
sparams.top_p = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--min-p") {
|
|
CHECK_ARG
|
|
sparams.min_p = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--temp") {
|
|
CHECK_ARG
|
|
sparams.temp = std::stof(argv[i]);
|
|
sparams.temp = std::max(sparams.temp, 0.0f);
|
|
return true;
|
|
}
|
|
if (arg == "--tfs") {
|
|
CHECK_ARG
|
|
sparams.tfs_z = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--typical") {
|
|
CHECK_ARG
|
|
sparams.typical_p = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--repeat-last-n") {
|
|
CHECK_ARG
|
|
sparams.penalty_last_n = std::stoi(argv[i]);
|
|
sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
|
|
return true;
|
|
}
|
|
if (arg == "--repeat-penalty") {
|
|
CHECK_ARG
|
|
sparams.penalty_repeat = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--frequency-penalty") {
|
|
CHECK_ARG
|
|
sparams.penalty_freq = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--presence-penalty") {
|
|
CHECK_ARG
|
|
sparams.penalty_present = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--dynatemp-range") {
|
|
CHECK_ARG
|
|
sparams.dynatemp_range = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--dynatemp-exp") {
|
|
CHECK_ARG
|
|
sparams.dynatemp_exponent = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mirostat") {
|
|
CHECK_ARG
|
|
sparams.mirostat = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mirostat-lr") {
|
|
CHECK_ARG
|
|
sparams.mirostat_eta = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mirostat-ent") {
|
|
CHECK_ARG
|
|
sparams.mirostat_tau = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--cfg-negative-prompt") {
|
|
CHECK_ARG
|
|
sparams.cfg_negative_prompt = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--cfg-negative-prompt-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
|
|
if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
|
|
sparams.cfg_negative_prompt.pop_back();
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--cfg-scale") {
|
|
CHECK_ARG
|
|
sparams.cfg_scale = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-b" || arg == "--batch-size") {
|
|
CHECK_ARG
|
|
params.n_batch = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ub" || arg == "--ubatch-size") {
|
|
CHECK_ARG
|
|
params.n_ubatch = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--keep") {
|
|
CHECK_ARG
|
|
params.n_keep = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--draft") {
|
|
CHECK_ARG
|
|
params.n_draft = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--chunks") {
|
|
CHECK_ARG
|
|
params.n_chunks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-np" || arg == "--parallel") {
|
|
CHECK_ARG
|
|
params.n_parallel = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ns" || arg == "--sequences") {
|
|
CHECK_ARG
|
|
params.n_sequences = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--p-split" || arg == "-ps") {
|
|
CHECK_ARG
|
|
params.p_split = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-m" || arg == "--model") {
|
|
CHECK_ARG
|
|
params.model = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-md" || arg == "--model-draft") {
|
|
CHECK_ARG
|
|
params.model_draft = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-a" || arg == "--alias") {
|
|
CHECK_ARG
|
|
params.model_alias = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-mu" || arg == "--model-url") {
|
|
CHECK_ARG
|
|
params.model_url = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-hft" || arg == "--hf-token") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
params.hf_token = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-hfr" || arg == "--hf-repo") {
|
|
CHECK_ARG
|
|
params.hf_repo = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-hff" || arg == "--hf-file") {
|
|
CHECK_ARG
|
|
params.hf_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--lora") {
|
|
CHECK_ARG
|
|
params.lora_adapters.push_back({
|
|
std::string(argv[i]),
|
|
1.0,
|
|
});
|
|
return true;
|
|
}
|
|
if (arg == "--lora-scaled") {
|
|
CHECK_ARG
|
|
std::string lora_adapter = argv[i];
|
|
CHECK_ARG
|
|
params.lora_adapters.push_back({
|
|
lora_adapter,
|
|
std::stof(argv[i]),
|
|
});
|
|
return true;
|
|
}
|
|
if (arg == "--lora-init-without-apply") {
|
|
params.lora_init_without_apply = true;
|
|
return true;
|
|
}
|
|
if (arg == "--control-vector") {
|
|
CHECK_ARG
|
|
params.control_vectors.push_back({ 1.0f, argv[i], });
|
|
return true;
|
|
}
|
|
if (arg == "--control-vector-scaled") {
|
|
CHECK_ARG
|
|
const char* fname = argv[i];
|
|
CHECK_ARG
|
|
params.control_vectors.push_back({ std::stof(argv[i]), fname, });
|
|
return true;
|
|
}
|
|
if (arg == "--control-vector-layer-range") {
|
|
CHECK_ARG
|
|
params.control_vector_layer_start = std::stoi(argv[i]);
|
|
CHECK_ARG
|
|
params.control_vector_layer_end = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--mmproj") {
|
|
CHECK_ARG
|
|
params.mmproj = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--image") {
|
|
CHECK_ARG
|
|
params.image.emplace_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-i" || arg == "--interactive") {
|
|
params.interactive = true;
|
|
return true;
|
|
}
|
|
if (arg == "-sp" || arg == "--special") {
|
|
params.special = true;
|
|
return true;
|
|
}
|
|
if (arg == "--embedding" || arg == "--embeddings") {
|
|
params.embedding = true;
|
|
return true;
|
|
}
|
|
if (arg == "--embd-normalize") {
|
|
CHECK_ARG
|
|
params.embd_normalize = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--embd-output-format") {
|
|
CHECK_ARG
|
|
params.embd_out = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--embd-separator") {
|
|
CHECK_ARG
|
|
params.embd_sep = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-if" || arg == "--interactive-first") {
|
|
params.interactive_first = true;
|
|
return true;
|
|
}
|
|
if (arg == "-cnv" || arg == "--conversation") {
|
|
params.conversation = true;
|
|
return true;
|
|
}
|
|
if (arg == "--infill") {
|
|
params.infill = true;
|
|
return true;
|
|
}
|
|
if (arg == "-dkvc" || arg == "--dump-kv-cache") {
|
|
params.dump_kv_cache = true;
|
|
return true;
|
|
}
|
|
if (arg == "-nkvo" || arg == "--no-kv-offload") {
|
|
params.no_kv_offload = true;
|
|
return true;
|
|
}
|
|
if (arg == "-ctk" || arg == "--cache-type-k") {
|
|
params.cache_type_k = argv[++i];
|
|
return true;
|
|
}
|
|
if (arg == "-ctv" || arg == "--cache-type-v") {
|
|
params.cache_type_v = argv[++i];
|
|
return true;
|
|
}
|
|
if (arg == "-mli" || arg == "--multiline-input") {
|
|
params.multiline_input = true;
|
|
return true;
|
|
}
|
|
if (arg == "--simple-io") {
|
|
params.simple_io = true;
|
|
return true;
|
|
}
|
|
if (arg == "-cb" || arg == "--cont-batching") {
|
|
params.cont_batching = true;
|
|
return true;
|
|
}
|
|
if (arg == "-nocb" || arg == "--no-cont-batching") {
|
|
params.cont_batching = false;
|
|
return true;
|
|
}
|
|
if (arg == "-fa" || arg == "--flash-attn") {
|
|
params.flash_attn = true;
|
|
return true;
|
|
}
|
|
if (arg == "-co" || arg == "--color") {
|
|
params.use_color = true;
|
|
return true;
|
|
}
|
|
if (arg == "--mlock") {
|
|
params.use_mlock = true;
|
|
return true;
|
|
}
|
|
if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
|
|
CHECK_ARG
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
if (!llama_supports_gpu_offload()) {
|
|
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
|
|
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
|
|
CHECK_ARG
|
|
params.n_gpu_layers_draft = std::stoi(argv[i]);
|
|
if (!llama_supports_gpu_offload()) {
|
|
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
|
|
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--main-gpu" || arg == "-mg") {
|
|
CHECK_ARG
|
|
params.main_gpu = std::stoi(argv[i]);
|
|
#ifndef GGML_USE_CUDA_SYCL_VULKAN
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
|
|
#endif // GGML_USE_CUDA_SYCL_VULKAN
|
|
return true;
|
|
}
|
|
if (arg == "--split-mode" || arg == "-sm") {
|
|
CHECK_ARG
|
|
std::string arg_next = argv[i];
|
|
if (arg_next == "none") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_NONE;
|
|
}
|
|
else if (arg_next == "layer") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
|
|
}
|
|
else if (arg_next == "row") {
|
|
#ifdef GGML_USE_SYCL
|
|
fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
|
|
exit(1);
|
|
#endif // GGML_USE_SYCL
|
|
params.split_mode = LLAMA_SPLIT_MODE_ROW;
|
|
}
|
|
else {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
#ifndef GGML_USE_CUDA_SYCL_VULKAN
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
|
|
#endif // GGML_USE_CUDA_SYCL_VULKAN
|
|
return true;
|
|
}
|
|
if (arg == "--tensor-split" || arg == "-ts") {
|
|
CHECK_ARG
|
|
std::string arg_next = argv[i];
|
|
|
|
// split string by , and /
|
|
const std::regex regex{ R"([,/]+)" };
|
|
std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
|
|
std::vector<std::string> split_arg{ it, {} };
|
|
if (split_arg.size() >= llama_max_devices()) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
for (size_t i = 0; i < llama_max_devices(); ++i) {
|
|
if (i < split_arg.size()) {
|
|
params.tensor_split[i] = std::stof(split_arg[i]);
|
|
}
|
|
else {
|
|
params.tensor_split[i] = 0.0f;
|
|
}
|
|
}
|
|
#ifndef GGML_USE_CUDA_SYCL_VULKAN
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
|
|
#endif // GGML_USE_CUDA_SYCL_VULKAN
|
|
return true;
|
|
}
|
|
if (arg == "--rpc") {
|
|
CHECK_ARG
|
|
params.rpc_servers = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--no-mmap") {
|
|
params.use_mmap = false;
|
|
return true;
|
|
}
|
|
if (arg == "--numa") {
|
|
CHECK_ARG
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
|
|
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
|
|
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
|
|
else { invalid_param = true; }
|
|
return true;
|
|
}
|
|
if (arg == "-v" || arg == "--verbose") {
|
|
params.verbosity = 1;
|
|
return true;
|
|
}
|
|
if (arg == "--verbosity") {
|
|
CHECK_ARG
|
|
params.verbosity = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--verbose-prompt") {
|
|
params.verbose_prompt = true;
|
|
return true;
|
|
}
|
|
if (arg == "--no-display-prompt") {
|
|
params.display_prompt = false;
|
|
return true;
|
|
}
|
|
if (arg == "-r" || arg == "--reverse-prompt") {
|
|
CHECK_ARG
|
|
params.antiprompt.emplace_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ld" || arg == "--logdir") {
|
|
CHECK_ARG
|
|
params.logdir = argv[i];
|
|
|
|
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
|
|
params.logdir += DIRECTORY_SEPARATOR;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-lcs" || arg == "--lookup-cache-static") {
|
|
CHECK_ARG
|
|
params.lookup_cache_static = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
|
|
CHECK_ARG
|
|
params.lookup_cache_dynamic = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
|
|
CHECK_ARG
|
|
params.logits_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--perplexity" || arg == "--all-logits") {
|
|
params.logits_all = true;
|
|
return true;
|
|
}
|
|
if (arg == "--ppl-stride") {
|
|
CHECK_ARG
|
|
params.ppl_stride = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--ppl-output-type") {
|
|
CHECK_ARG
|
|
params.ppl_output_type = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-ptc" || arg == "--print-token-count") {
|
|
CHECK_ARG
|
|
params.n_print = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--check-tensors") {
|
|
params.check_tensors = true;
|
|
return true;
|
|
}
|
|
if (arg == "--hellaswag") {
|
|
params.hellaswag = true;
|
|
return true;
|
|
}
|
|
if (arg == "--hellaswag-tasks") {
|
|
CHECK_ARG
|
|
params.hellaswag_tasks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--winogrande") {
|
|
params.winogrande = true;
|
|
return true;
|
|
}
|
|
if (arg == "--winogrande-tasks") {
|
|
CHECK_ARG
|
|
params.winogrande_tasks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--multiple-choice") {
|
|
params.multiple_choice = true;
|
|
return true;
|
|
}
|
|
if (arg == "--multiple-choice-tasks") {
|
|
CHECK_ARG
|
|
params.multiple_choice_tasks = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--kl-divergence") {
|
|
params.kl_divergence = true;
|
|
return true;
|
|
}
|
|
if (arg == "--ignore-eos") {
|
|
params.ignore_eos = true;
|
|
return true;
|
|
}
|
|
if (arg == "--penalize-nl") {
|
|
sparams.penalize_nl = true;
|
|
return true;
|
|
}
|
|
if (arg == "-l" || arg == "--logit-bias") {
|
|
CHECK_ARG
|
|
std::stringstream ss(argv[i]);
|
|
llama_token key;
|
|
char sign;
|
|
std::string value_str;
|
|
try {
|
|
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
|
|
sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
|
|
}
|
|
else {
|
|
throw std::exception();
|
|
}
|
|
}
|
|
catch (const std::exception&) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "-h" || arg == "--help" || arg == "--usage" ) {
|
|
params.usage = true;
|
|
return true;
|
|
}
|
|
if (arg == "--version") {
|
|
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
|
|
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
|
|
exit(0);
|
|
}
|
|
if (arg == "--in-prefix-bos") {
|
|
params.input_prefix_bos = true;
|
|
params.enable_chat_template = false;
|
|
return true;
|
|
}
|
|
if (arg == "--in-prefix") {
|
|
CHECK_ARG
|
|
params.input_prefix = argv[i];
|
|
params.enable_chat_template = false;
|
|
return true;
|
|
}
|
|
if (arg == "--in-suffix") {
|
|
CHECK_ARG
|
|
params.input_suffix = argv[i];
|
|
params.enable_chat_template = false;
|
|
return true;
|
|
}
|
|
if (arg == "--spm-infill") {
|
|
params.spm_infill = true;
|
|
return true;
|
|
}
|
|
if (arg == "--grammar") {
|
|
CHECK_ARG
|
|
sparams.grammar = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--grammar-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(sparams.grammar)
|
|
);
|
|
return true;
|
|
}
|
|
if (arg == "-j" || arg == "--json-schema") {
|
|
CHECK_ARG
|
|
sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
|
|
return true;
|
|
}
|
|
if (arg == "--override-kv") {
|
|
CHECK_ARG
|
|
if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
|
|
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--host") {
|
|
CHECK_ARG
|
|
params.hostname = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--port") {
|
|
CHECK_ARG
|
|
params.port = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--path") {
|
|
CHECK_ARG
|
|
params.public_path = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--api-key") {
|
|
CHECK_ARG
|
|
params.api_keys.push_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--api-key-file") {
|
|
CHECK_ARG
|
|
std::ifstream key_file(argv[i]);
|
|
if (!key_file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::string key;
|
|
while (std::getline(key_file, key)) {
|
|
if (!key.empty()) {
|
|
params.api_keys.push_back(key);
|
|
}
|
|
}
|
|
key_file.close();
|
|
return true;
|
|
}
|
|
if (arg == "--ssl-key-file") {
|
|
CHECK_ARG
|
|
params.ssl_file_key = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--ssl-cert-file") {
|
|
CHECK_ARG
|
|
params.ssl_file_cert = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--timeout" || arg == "-to") {
|
|
CHECK_ARG
|
|
params.timeout_read = std::stoi(argv[i]);
|
|
params.timeout_write = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--threads-http") {
|
|
CHECK_ARG
|
|
params.n_threads_http = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-spf" || arg == "--system-prompt-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
std::string system_prompt;
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(system_prompt)
|
|
);
|
|
params.system_prompt = system_prompt;
|
|
return true;
|
|
}
|
|
if (arg == "--log-format") {
|
|
CHECK_ARG
|
|
if (std::strcmp(argv[i], "json") == 0) {
|
|
params.log_json = true;
|
|
} else if (std::strcmp(argv[i], "text") == 0) {
|
|
params.log_json = false;
|
|
} else {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--no-slots") {
|
|
params.endpoint_slots = false;
|
|
return true;
|
|
}
|
|
if (arg == "--metrics") {
|
|
params.endpoint_metrics = true;
|
|
return true;
|
|
}
|
|
if (arg == "--slot-save-path") {
|
|
CHECK_ARG
|
|
params.slot_save_path = argv[i];
|
|
// if doesn't end with DIRECTORY_SEPARATOR, add it
|
|
if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
|
|
params.slot_save_path += DIRECTORY_SEPARATOR;
|
|
}
|
|
return true;
|
|
}
|
|
if (arg == "--chat-template") {
|
|
CHECK_ARG
|
|
if (!llama_chat_verify_template(argv[i])) {
|
|
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
|
|
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
params.chat_template = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--slot-prompt-similarity" || arg == "-sps") {
|
|
CHECK_ARG
|
|
params.slot_prompt_similarity = std::stof(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-pps") {
|
|
params.is_pp_shared = true;
|
|
return true;
|
|
}
|
|
if (arg == "-npp") {
|
|
CHECK_ARG
|
|
auto p = string_split<int>(argv[i], split_delim);
|
|
params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
|
|
return true;
|
|
}
|
|
if (arg == "-ntg") {
|
|
CHECK_ARG
|
|
auto p = string_split<int>(argv[i], split_delim);
|
|
params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
|
|
return true;
|
|
}
|
|
if (arg == "-npl") {
|
|
CHECK_ARG
|
|
auto p = string_split<int>(argv[i], split_delim);
|
|
params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
|
|
return true;
|
|
}
|
|
if (arg == "--context-file") {
|
|
CHECK_ARG
|
|
std::ifstream file(argv[i], std::ios::binary);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
params.context_files.push_back(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--chunk-size") {
|
|
CHECK_ARG
|
|
params.chunk_size = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--chunk-separator") {
|
|
CHECK_ARG
|
|
params.chunk_separator = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--junk") {
|
|
CHECK_ARG
|
|
params.n_junk = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--pos") {
|
|
CHECK_ARG
|
|
params.i_pos = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "-o" || arg == "--output" || arg == "--output-file") {
|
|
CHECK_ARG
|
|
params.out_file = argv[i];
|
|
params.cvector_outfile = argv[i];
|
|
params.lora_outfile = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "-ofreq" || arg == "--output-frequency") {
|
|
CHECK_ARG
|
|
params.n_out_freq = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--save-frequency") {
|
|
CHECK_ARG
|
|
params.n_save_freq = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--process-output") {
|
|
params.process_output = true;
|
|
return true;
|
|
}
|
|
if (arg == "--no-ppl") {
|
|
params.compute_ppl = false;
|
|
return true;
|
|
}
|
|
if (arg == "--chunk" || arg == "--from-chunk") {
|
|
CHECK_ARG
|
|
params.i_chunk = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
// cvector params
|
|
if (arg == "--positive-file") {
|
|
CHECK_ARG
|
|
params.cvector_positive_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--negative-file") {
|
|
CHECK_ARG
|
|
params.cvector_negative_file = argv[i];
|
|
return true;
|
|
}
|
|
if (arg == "--pca-batch") {
|
|
CHECK_ARG
|
|
params.n_pca_batch = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--pca-iter") {
|
|
CHECK_ARG
|
|
params.n_pca_iterations = std::stoi(argv[i]);
|
|
return true;
|
|
}
|
|
if (arg == "--method") {
|
|
CHECK_ARG
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
|
|
else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
|
|
else { invalid_param = true; }
|
|
return true;
|
|
}
|
|
if (arg == "--no-warmup") {
|
|
params.warmup = false;
|
|
return true;
|
|
}
|
|
#ifndef LOG_DISABLE_LOGS
|
|
// Parse args for logging parameters
|
|
if (log_param_single_parse(argv[i])) {
|
|
// Do nothing, log_param_single_parse automatically does it's thing
|
|
// and returns if a match was found and parsed.
|
|
return true;
|
|
}
|
|
if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
|
|
// We have a matching known parameter requiring an argument,
|
|
// now we need to check if there is anything after this argv
|
|
// and flag invalid_param or parse it.
|
|
CHECK_ARG
|
|
if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
|
|
invalid_param = true;
|
|
return true;
|
|
}
|
|
return true;
|
|
}
|
|
// End of Parse args for logging parameters
|
|
#endif // LOG_DISABLE_LOGS
|
|
|
|
return false;
|
|
}
|
|
|
|
#ifdef __GNUC__
|
|
#ifdef __MINGW32__
|
|
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
|
|
#else
|
|
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
|
|
#endif
|
|
#else
|
|
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
|
|
#endif
|
|
|
|
void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|
const llama_sampling_params & sparams = params.sparams;
|
|
|
|
std::string sampler_type_chars;
|
|
std::string sampler_type_names;
|
|
for (const auto sampler_type : sparams.samplers_sequence) {
|
|
sampler_type_chars += static_cast<char>(sampler_type);
|
|
sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
|
|
}
|
|
sampler_type_names.pop_back();
|
|
|
|
struct option_info {
|
|
LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
|
|
option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
|
|
va_list args_list;
|
|
va_start(args_list, desc);
|
|
char buffer[1024];
|
|
vsnprintf(buffer, sizeof(buffer), desc, args_list);
|
|
va_end(args_list);
|
|
this->desc = buffer;
|
|
}
|
|
|
|
option_info(const std::string & grp) : grp(grp) {}
|
|
|
|
std::string tags;
|
|
std::string args;
|
|
std::string desc;
|
|
std::string grp;
|
|
};
|
|
|
|
std::vector<option_info> options;
|
|
|
|
// TODO: filter by tags
|
|
|
|
options.push_back({ "general" });
|
|
options.push_back({ "*", "-h, --help, --usage", "print usage and exit" });
|
|
options.push_back({ "*", " --version", "show version and build info" });
|
|
options.push_back({ "*", "-v, --verbose", "print verbose information" });
|
|
options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity });
|
|
options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
|
|
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
|
|
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
|
|
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
|
|
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.cpuparams.n_threads });
|
|
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
|
|
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
|
|
options.push_back({ "speculative", "-tbd, --threads-batch-draft N","number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
|
|
|
|
#ifndef GGML_USE_OPENMP
|
|
// these options are available only with the internal threadpool
|
|
options.push_back({ "*", "-C, --cpu-mask M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")"});
|
|
options.push_back({ "*", "-Cr, --cpu-range lo-hi", "range of CPUs for affinity. Complements --cpu-mask"});
|
|
options.push_back({ "*", " --cpu-strict <0|1>", "use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu});
|
|
options.push_back({ "*", " --priority N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority});
|
|
options.push_back({ "*", " --poll <0...100>", "use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll});
|
|
|
|
options.push_back({ "*", "-Cb, --cpu-mask-batch M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)"});
|
|
options.push_back({ "*", "-Crb, --cpu-range-batch lo-hi", "ranges of CPUs for affinity. Complements --cpu-mask-batch"});
|
|
options.push_back({ "*", " --cpu-strict-batch <0|1>","use strict CPU placement (default: same as --cpu-strict)"});
|
|
options.push_back({ "*", " --priority-batch N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority)"});
|
|
options.push_back({ "*", " --poll-batch <0|1>", "use polling to wait for work (default: same as --poll"});
|
|
|
|
options.push_back({ "speculative", "-Cd, --cpu-mask-draft M", "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)"});
|
|
options.push_back({ "speculative", "-Crd, --cpu-range-draft lo-hi", "Ranges of CPUs for affinity. Complements --cpu-mask-draft"});
|
|
options.push_back({ "speculative", " --cpu-strict-draft <0|1>","Use strict CPU placement for draft model (default: same as --cpu-strict)"});
|
|
options.push_back({ "speculative", " --priority-draft N", "Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: same as --priority)"});
|
|
options.push_back({ "speculative", " --poll-draft <0|1>", "Use polling to wait for draft model work (default: same as --poll])"});
|
|
|
|
options.push_back({ "speculative", "-Cbd, --cpu-mask-batch-draft M","Draft model CPU affinity mask. Complements cpu-range-draft-batch (default: same as --cpu-mask-draft)"});
|
|
options.push_back({ "speculative", "-Crbd, --cpu-range-batch-draft lo-hi",
|
|
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)"});
|
|
options.push_back({ "speculative", " --cpu-strict-batch-draft <0|1>",
|
|
"Use strict CPU placement for draft model (default: --cpu-strict-draft)"});
|
|
options.push_back({ "speculative", " --priority-batch-draft N","Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority-draft)"});
|
|
options.push_back({ "speculative", " --poll-batch-draft <0|1>","Use polling to wait for draft model work (default: --poll-draft)"});
|
|
#endif // GGML_USE_OPENMP
|
|
|
|
options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
|
|
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
|
|
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
|
|
"path to static lookup cache to use for lookup decoding (not updated by generation)" });
|
|
options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME",
|
|
"path to dynamic lookup cache to use for lookup decoding (updated by generation)" });
|
|
|
|
options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
|
|
options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
|
|
options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch });
|
|
options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch });
|
|
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
|
|
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
|
|
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
|
|
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with\n"
|
|
"in conversation mode, this will be used as system prompt\n"
|
|
"(default: '%s')", params.prompt.c_str() });
|
|
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
|
|
options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" });
|
|
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
|
|
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
|
|
options.push_back({ "*", " --no-escape", "do not process escape sequences" });
|
|
options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print });
|
|
options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" });
|
|
options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n"
|
|
"not supported with --interactive or other interactive options" });
|
|
options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" });
|
|
options.push_back({ "main", "-r, --reverse-prompt PROMPT",
|
|
"halt generation at PROMPT, return control in interactive mode\n"
|
|
"can be specified more than once for multiple prompts" });
|
|
options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
|
|
options.push_back({ "main", "-cnv, --conversation", "run in conversation mode, does not print special tokens and suffix/prefix\n"
|
|
"if suffix/prefix are not specified, default chat template will be used\n"
|
|
"(default: %s)", params.conversation ? "true" : "false" });
|
|
options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
|
|
options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
|
|
options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" });
|
|
options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" });
|
|
options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" });
|
|
options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" });
|
|
options.push_back({ "main", " --no-warmup", "skip warming up the model with an empty run" });
|
|
options.push_back({ "server infill",
|
|
" --spm-infill", "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });
|
|
|
|
options.push_back({ "sampling" });
|
|
options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n"
|
|
"(default: %s)", sampler_type_names.c_str() });
|
|
options.push_back({ "*", " --sampling-seq SEQUENCE",
|
|
"simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
|
|
options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
|
|
options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
|
|
options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp });
|
|
options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
|
|
options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
|
|
options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
|
|
options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
|
|
options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
|
|
options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
|
|
options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
|
|
options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
|
|
options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
|
|
options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
|
|
options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
|
|
options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n"
|
|
"Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
|
|
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
|
|
options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
|
|
options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
|
|
options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n"
|
|
"i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
|
|
"or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
|
|
options.push_back({ "main", " --cfg-negative-prompt PROMPT",
|
|
"negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
|
|
options.push_back({ "main", " --cfg-negative-prompt-file FNAME",
|
|
"negative prompt file to use for guidance" });
|
|
options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
|
|
options.push_back({ "main", " --chat-template JINJA_TEMPLATE",
|
|
"set custom jinja chat template (default: template taken from model's metadata)\n"
|
|
"if suffix/prefix are specified, template will be disabled\n"
|
|
"only commonly used templates are accepted:\n"
|
|
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
|
|
options.push_back({ "grammar" });
|
|
options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
|
|
options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" });
|
|
options.push_back({ "*", "-j, --json-schema SCHEMA",
|
|
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
|
|
"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
|
|
|
|
options.push_back({ "embedding" });
|
|
options.push_back({ "embedding", " --pooling {none,mean,cls,last}",
|
|
"pooling type for embeddings, use model default if unspecified" });
|
|
options.push_back({ "embedding", " --attention {causal,non-causal}",
|
|
"attention type for embeddings, use model default if unspecified" });
|
|
|
|
options.push_back({ "context hacking" });
|
|
options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
|
|
"RoPE frequency scaling method, defaults to linear unless specified by the model" });
|
|
options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" });
|
|
options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
|
|
options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" });
|
|
options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
|
|
options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
|
|
options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
|
|
options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
|
|
options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
|
|
options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n });
|
|
options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w });
|
|
options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" });
|
|
options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" });
|
|
options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
|
|
options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
|
|
|
|
options.push_back({ "perplexity" });
|
|
options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
|
|
options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" });
|
|
options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
|
|
options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" });
|
|
options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
|
|
options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" });
|
|
options.push_back({ "perplexity", " --multiple-choice-tasks N",
|
|
"number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
|
|
options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" });
|
|
options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride });
|
|
options.push_back({ "perplexity", " --ppl-output-type {0,1}",
|
|
"output type for perplexity calculation (default: %d)", params.ppl_output_type });
|
|
|
|
options.push_back({ "parallel" });
|
|
options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
|
|
options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel });
|
|
options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences });
|
|
options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
|
|
options.push_back({ "*", "-nocb, --no-cont-batching", "disable continuous batching" });
|
|
|
|
options.push_back({ "multi-modality" });
|
|
options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
|
|
options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" });
|
|
|
|
options.push_back({ "backend" });
|
|
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });
|
|
|
|
if (llama_supports_mlock()) {
|
|
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
|
|
}
|
|
if (llama_supports_mmap()) {
|
|
options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
|
|
}
|
|
options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n"
|
|
" - distribute: spread execution evenly over all nodes\n"
|
|
" - isolate: only spawn threads on CPUs on the node that execution started on\n"
|
|
" - numactl: use the CPU map provided by numactl\n"
|
|
"if run without this previously, it is recommended to drop the system page cache before using this\n"
|
|
"see https://github.com/ggerganov/llama.cpp/issues/1437" });
|
|
|
|
if (llama_supports_gpu_offload()) {
|
|
options.push_back({ "*", "-ngl, --gpu-layers N",
|
|
"number of layers to store in VRAM" });
|
|
options.push_back({ "*", "-ngld, --gpu-layers-draft N",
|
|
"number of layers to store in VRAM for the draft model" });
|
|
options.push_back({ "*", "-sm, --split-mode SPLIT_MODE",
|
|
"how to split the model across multiple GPUs, one of:\n"
|
|
" - none: use one GPU only\n"
|
|
" - layer (default): split layers and KV across GPUs\n"
|
|
" - row: split rows across GPUs" });
|
|
options.push_back({ "*", "-ts, --tensor-split SPLIT",
|
|
"fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
|
|
options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n"
|
|
"or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
|
|
}
|
|
|
|
options.push_back({ "model" });
|
|
options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
|
|
options.push_back({ "*", " --override-kv KEY=TYPE:VALUE",
|
|
"advanced option to override model metadata by key. may be specified multiple times.\n"
|
|
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
|
|
options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (can be repeated to use multiple adapters)" });
|
|
options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
|
|
options.push_back({ "*", " --control-vector FNAME", "add a control vector\n"
|
|
"note: this argument can be repeated to add multiple control vectors" });
|
|
options.push_back({ "*", " --control-vector-scaled FNAME SCALE",
|
|
"add a control vector with user defined scaling SCALE\n"
|
|
"note: this argument can be repeated to add multiple scaled control vectors" });
|
|
options.push_back({ "*", " --control-vector-layer-range START END",
|
|
"layer range to apply the control vector(s) to, start and end inclusive" });
|
|
options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n"
|
|
"or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
|
|
options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" });
|
|
options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" });
|
|
options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" });
|
|
options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" });
|
|
options.push_back({ "*", "-hft, --hf-token TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)" });
|
|
|
|
options.push_back({ "retrieval" });
|
|
options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" });
|
|
options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size });
|
|
options.push_back({ "retrieval", " --chunk-separator STRING",
|
|
"separator between chunks (default: '%s')", params.chunk_separator.c_str() });
|
|
|
|
options.push_back({ "passkey" });
|
|
options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk });
|
|
options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos });
|
|
|
|
options.push_back({ "imatrix" });
|
|
options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() });
|
|
options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq });
|
|
options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
|
|
options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
|
|
options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
|
|
options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk });
|
|
|
|
options.push_back({ "bench" });
|
|
options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
|
|
options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" });
|
|
options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" });
|
|
options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" });
|
|
|
|
options.push_back({ "embedding" });
|
|
options.push_back({ "embedding", " --embd-normalize", "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
|
|
options.push_back({ "embedding", " --embd-output-format", "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
|
|
options.push_back({ "embedding", " --embd-separator", "separator of embendings (default \\n) for example \"<#sep#>\"" });
|
|
|
|
options.push_back({ "server" });
|
|
options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() });
|
|
options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port });
|
|
options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() });
|
|
options.push_back({ "server", " --embedding(s)", "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
|
|
options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" });
|
|
options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" });
|
|
options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" });
|
|
options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" });
|
|
options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read });
|
|
options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
|
|
options.push_back({ "server", " --system-prompt-file FNAME",
|
|
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
|
|
options.push_back({ "server", " --log-format {text,json}",
|
|
"log output format: json or text (default: json)" });
|
|
options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
|
|
options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
|
|
options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" });
|
|
options.push_back({ "server", " --chat-template JINJA_TEMPLATE",
|
|
"set custom jinja chat template (default: template taken from model's metadata)\n"
|
|
"only commonly used templates are accepted:\n"
|
|
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
|
|
options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY",
|
|
"how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
|
|
options.push_back({ "server", " --lora-init-without-apply", "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});
|
|
|
|
#ifndef LOG_DISABLE_LOGS
|
|
options.push_back({ "logging" });
|
|
options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" });
|
|
options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" });
|
|
options.push_back({ "logging", " --log-test", "Run simple logging test" });
|
|
options.push_back({ "logging", " --log-disable", "Disable trace logs" });
|
|
options.push_back({ "logging", " --log-enable", "Enable trace logs" });
|
|
options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" });
|
|
options.push_back({ "logging", " --log-new", "Create a separate new log file on start. "
|
|
"Each log file will have unique name: \"<name>.<ID>.log\"" });
|
|
options.push_back({ "logging", " --log-append", "Don't truncate the old log file." });
|
|
#endif // LOG_DISABLE_LOGS
|
|
|
|
options.push_back({ "cvector" });
|
|
options.push_back({ "cvector", "-o, --output FNAME", "output file (default: '%s')", params.cvector_outfile.c_str() });
|
|
options.push_back({ "cvector", " --positive-file FNAME", "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
|
|
options.push_back({ "cvector", " --negative-file FNAME", "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
|
|
options.push_back({ "cvector", " --pca-batch N", "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
|
|
options.push_back({ "cvector", " --pca-iter N", "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
|
|
options.push_back({ "cvector", " --method {pca,mean}", "dimensionality reduction method to be used (default: pca)" });
|
|
|
|
options.push_back({ "export-lora" });
|
|
options.push_back({ "export-lora", "-m, --model", "model path from which to load base model (default '%s')", params.model.c_str() });
|
|
options.push_back({ "export-lora", " --lora FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)" });
|
|
options.push_back({ "export-lora", " --lora-scaled FNAME S", "path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
|
|
options.push_back({ "export-lora", "-o, --output FNAME", "output file (default: '%s')", params.lora_outfile.c_str() });
|
|
|
|
printf("usage: %s [options]\n", argv[0]);
|
|
|
|
for (const auto & o : options) {
|
|
if (!o.grp.empty()) {
|
|
printf("\n%s:\n\n", o.grp.c_str());
|
|
continue;
|
|
}
|
|
printf(" %-32s", o.args.c_str());
|
|
if (o.args.length() > 30) {
|
|
printf("\n%34s", "");
|
|
}
|
|
|
|
const auto desc = o.desc;
|
|
size_t start = 0;
|
|
size_t end = desc.find('\n');
|
|
while (end != std::string::npos) {
|
|
printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
|
|
start = end + 1;
|
|
end = desc.find('\n', start);
|
|
}
|
|
|
|
printf("%s\n", desc.substr(start).c_str());
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
std::string gpt_params_get_system_info(const gpt_params & params) {
|
|
std::ostringstream os;
|
|
|
|
os << "system_info: n_threads = " << params.cpuparams.n_threads;
|
|
if (params.cpuparams_batch.n_threads != -1) {
|
|
os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
|
|
}
|
|
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
|
|
// TODO: windows + arm64 + mingw64
|
|
DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS);
|
|
os << " / " << logicalProcessorCount << " | " << llama_print_system_info();
|
|
#else
|
|
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
|
|
#endif
|
|
|
|
return os.str();
|
|
}
|
|
|
|
//
|
|
// String utils
|
|
//
|
|
|
|
std::vector<std::string> string_split(std::string input, char separator) {
|
|
std::vector<std::string> parts;
|
|
size_t separator_pos = input.find(separator);
|
|
while (separator_pos != std::string::npos) {
|
|
std::string part = input.substr(0, separator_pos);
|
|
parts.emplace_back(part);
|
|
input = input.substr(separator_pos + 1);
|
|
separator_pos = input.find(separator);
|
|
}
|
|
parts.emplace_back(input);
|
|
return parts;
|
|
}
|
|
|
|
std::string string_strip(const std::string & str) {
|
|
size_t start = 0;
|
|
size_t end = str.size();
|
|
while (start < end && std::isspace(str[start])) {
|
|
start++;
|
|
}
|
|
while (end > start && std::isspace(str[end - 1])) {
|
|
end--;
|
|
}
|
|
return str.substr(start, end - start);
|
|
}
|
|
|
|
std::string string_get_sortable_timestamp() {
|
|
using clock = std::chrono::system_clock;
|
|
|
|
const clock::time_point current_time = clock::now();
|
|
const time_t as_time_t = clock::to_time_t(current_time);
|
|
char timestamp_no_ns[100];
|
|
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
|
|
|
|
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
|
|
current_time.time_since_epoch() % 1000000000).count();
|
|
char timestamp_ns[11];
|
|
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
|
|
|
|
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
|
|
}
|
|
|
|
void string_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);
|
|
}
|
|
|
|
void string_process_escapes(std::string & input) {
|
|
std::size_t input_len = input.length();
|
|
std::size_t output_idx = 0;
|
|
|
|
for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
|
|
if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
|
|
switch (input[++input_idx]) {
|
|
case 'n': input[output_idx++] = '\n'; break;
|
|
case 'r': input[output_idx++] = '\r'; break;
|
|
case 't': input[output_idx++] = '\t'; break;
|
|
case '\'': input[output_idx++] = '\''; break;
|
|
case '\"': input[output_idx++] = '\"'; break;
|
|
case '\\': input[output_idx++] = '\\'; break;
|
|
case 'x':
|
|
// Handle \x12, etc
|
|
if (input_idx + 2 < input_len) {
|
|
const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
|
|
char *err_p = nullptr;
|
|
const long val = std::strtol(x, &err_p, 16);
|
|
if (err_p == x + 2) {
|
|
input_idx += 2;
|
|
input[output_idx++] = char(val);
|
|
break;
|
|
}
|
|
}
|
|
// fall through
|
|
default: input[output_idx++] = '\\';
|
|
input[output_idx++] = input[input_idx]; break;
|
|
}
|
|
} else {
|
|
input[output_idx++] = input[input_idx];
|
|
}
|
|
}
|
|
|
|
input.resize(output_idx);
|
|
}
|
|
|
|
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
|
|
const char * sep = strchr(data, '=');
|
|
if (sep == nullptr || sep - data >= 128) {
|
|
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
|
|
return false;
|
|
}
|
|
llama_model_kv_override kvo;
|
|
std::strncpy(kvo.key, data, sep - data);
|
|
kvo.key[sep - data] = 0;
|
|
sep++;
|
|
if (strncmp(sep, "int:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
|
|
kvo.val_i64 = std::atol(sep);
|
|
} else if (strncmp(sep, "float:", 6) == 0) {
|
|
sep += 6;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
|
|
kvo.val_f64 = std::atof(sep);
|
|
} else if (strncmp(sep, "bool:", 5) == 0) {
|
|
sep += 5;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
|
|
if (std::strcmp(sep, "true") == 0) {
|
|
kvo.val_bool = true;
|
|
} else if (std::strcmp(sep, "false") == 0) {
|
|
kvo.val_bool = false;
|
|
} else {
|
|
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
|
|
return false;
|
|
}
|
|
} else if (strncmp(sep, "str:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
|
|
if (strlen(sep) > 127) {
|
|
fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
|
|
return false;
|
|
}
|
|
strncpy(kvo.val_str, sep, 127);
|
|
kvo.val_str[127] = '\0';
|
|
} else {
|
|
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
|
|
return false;
|
|
}
|
|
overrides.emplace_back(std::move(kvo));
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// Filesystem utils
|
|
//
|
|
|
|
// Validate if a filename is safe to use
|
|
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
|
|
bool fs_validate_filename(const std::string & filename) {
|
|
if (!filename.length()) {
|
|
// Empty filename invalid
|
|
return false;
|
|
}
|
|
if (filename.length() > 255) {
|
|
// Limit at common largest possible filename on Linux filesystems
|
|
// to avoid unnecessary further validation
|
|
// (On systems with smaller limits it will be caught by the OS)
|
|
return false;
|
|
}
|
|
|
|
std::u32string filename_utf32;
|
|
try {
|
|
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
|
|
filename_utf32 = converter.from_bytes(filename);
|
|
|
|
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
|
|
// or invalid encodings were encountered. Reject such attempts
|
|
std::string filename_reencoded = converter.to_bytes(filename_utf32);
|
|
if (filename_reencoded != filename) {
|
|
return false;
|
|
}
|
|
} catch (const std::exception &) {
|
|
return false;
|
|
}
|
|
|
|
// Check for forbidden codepoints:
|
|
// - Control characters
|
|
// - Unicode equivalents of illegal characters
|
|
// - UTF-16 surrogate pairs
|
|
// - UTF-8 replacement character
|
|
// - Byte order mark (BOM)
|
|
// - Illegal characters: / \ : * ? " < > |
|
|
for (char32_t c : filename_utf32) {
|
|
if (c <= 0x1F // Control characters (C0)
|
|
|| c == 0x7F // Control characters (DEL)
|
|
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|
|
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|
|
|| c == 0x2215 // Division Slash (forward slash equivalent)
|
|
|| c == 0x2216 // Set Minus (backslash equivalent)
|
|
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|
|
|| c == 0xFFFD // Replacement Character (UTF-8)
|
|
|| c == 0xFEFF // Byte Order Mark (BOM)
|
|
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|
|
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
|
|
// Unicode and other whitespace is not affected, only 0x20 space
|
|
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
|
|
return false;
|
|
}
|
|
|
|
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
|
|
if (filename.find("..") != std::string::npos) {
|
|
return false;
|
|
}
|
|
|
|
// Reject "."
|
|
if (filename == ".") {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// returns true if successful, false otherwise
|
|
bool fs_create_directory_with_parents(const std::string & path) {
|
|
#ifdef _WIN32
|
|
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
|
|
std::wstring wpath = converter.from_bytes(path);
|
|
|
|
// if the path already exists, check whether it's a directory
|
|
const DWORD attributes = GetFileAttributesW(wpath.c_str());
|
|
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
|
|
return true;
|
|
}
|
|
|
|
size_t pos_slash = 0;
|
|
|
|
// process path from front to back, procedurally creating directories
|
|
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
|
|
const std::wstring subpath = wpath.substr(0, pos_slash);
|
|
const wchar_t * test = subpath.c_str();
|
|
|
|
const bool success = CreateDirectoryW(test, NULL);
|
|
if (!success) {
|
|
const DWORD error = GetLastError();
|
|
|
|
// if the path already exists, ensure that it's a directory
|
|
if (error == ERROR_ALREADY_EXISTS) {
|
|
const DWORD attributes = GetFileAttributesW(subpath.c_str());
|
|
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
pos_slash += 1;
|
|
}
|
|
|
|
return true;
|
|
#else
|
|
// if the path already exists, check whether it's a directory
|
|
struct stat info;
|
|
if (stat(path.c_str(), &info) == 0) {
|
|
return S_ISDIR(info.st_mode);
|
|
}
|
|
|
|
size_t pos_slash = 1; // skip leading slashes for directory creation
|
|
|
|
// process path from front to back, procedurally creating directories
|
|
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
|
|
const std::string subpath = path.substr(0, pos_slash);
|
|
struct stat info;
|
|
|
|
// if the path already exists, ensure that it's a directory
|
|
if (stat(subpath.c_str(), &info) == 0) {
|
|
if (!S_ISDIR(info.st_mode)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
// create parent directories
|
|
const int ret = mkdir(subpath.c_str(), 0755);
|
|
if (ret != 0) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
pos_slash += 1;
|
|
}
|
|
|
|
return true;
|
|
#endif // _WIN32
|
|
}
|
|
|
|
std::string fs_get_cache_directory() {
|
|
std::string cache_directory = "";
|
|
auto ensure_trailing_slash = [](std::string p) {
|
|
// Make sure to add trailing slash
|
|
if (p.back() != DIRECTORY_SEPARATOR) {
|
|
p += DIRECTORY_SEPARATOR;
|
|
}
|
|
return p;
|
|
};
|
|
if (getenv("LLAMA_CACHE")) {
|
|
cache_directory = std::getenv("LLAMA_CACHE");
|
|
} else {
|
|
#ifdef __linux__
|
|
if (std::getenv("XDG_CACHE_HOME")) {
|
|
cache_directory = std::getenv("XDG_CACHE_HOME");
|
|
} else {
|
|
cache_directory = std::getenv("HOME") + std::string("/.cache/");
|
|
}
|
|
#elif defined(__APPLE__)
|
|
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
|
|
#elif defined(_WIN32)
|
|
cache_directory = std::getenv("LOCALAPPDATA");
|
|
#endif // __linux__
|
|
cache_directory = ensure_trailing_slash(cache_directory);
|
|
cache_directory += "llama.cpp";
|
|
}
|
|
return ensure_trailing_slash(cache_directory);
|
|
}
|
|
|
|
std::string fs_get_cache_file(const std::string & filename) {
|
|
GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
|
|
std::string cache_directory = fs_get_cache_directory();
|
|
const bool success = fs_create_directory_with_parents(cache_directory);
|
|
if (!success) {
|
|
throw std::runtime_error("failed to create cache directory: " + cache_directory);
|
|
}
|
|
return cache_directory + filename;
|
|
}
|
|
|
|
|
|
//
|
|
// Model utils
|
|
//
|
|
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
|
|
llama_init_result iparams;
|
|
auto mparams = llama_model_params_from_gpt_params(params);
|
|
|
|
llama_model * model = nullptr;
|
|
|
|
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
|
|
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
|
|
} else if (!params.model_url.empty()) {
|
|
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
|
|
} else {
|
|
model = llama_load_model_from_file(params.model.c_str(), mparams);
|
|
}
|
|
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
|
|
return iparams;
|
|
}
|
|
|
|
auto cparams = llama_context_params_from_gpt_params(params);
|
|
|
|
llama_context * lctx = llama_new_context_with_model(model, cparams);
|
|
if (lctx == NULL) {
|
|
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
|
|
llama_free_model(model);
|
|
return iparams;
|
|
}
|
|
|
|
if (!params.control_vectors.empty()) {
|
|
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
|
|
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
|
|
|
|
const auto cvec = llama_control_vector_load(params.control_vectors);
|
|
if (cvec.n_embd == -1) {
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return iparams;
|
|
}
|
|
|
|
int err = llama_control_vector_apply(lctx,
|
|
cvec.data.data(),
|
|
cvec.data.size(),
|
|
cvec.n_embd,
|
|
params.control_vector_layer_start,
|
|
params.control_vector_layer_end);
|
|
if (err) {
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return iparams;
|
|
}
|
|
}
|
|
|
|
// load and optionally apply lora adapters
|
|
for (auto & la : params.lora_adapters) {
|
|
llama_lora_adapter_container loaded_la;
|
|
loaded_la.path = la.path;
|
|
loaded_la.scale = la.scale;
|
|
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
|
|
if (loaded_la.adapter == nullptr) {
|
|
fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return iparams;
|
|
}
|
|
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
|
|
}
|
|
if (!params.lora_init_without_apply) {
|
|
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
|
|
}
|
|
|
|
if (params.ignore_eos) {
|
|
params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
if (params.warmup) {
|
|
LOG("warming up the model with an empty run\n");
|
|
|
|
std::vector<llama_token> tmp;
|
|
llama_token bos = llama_token_bos(model);
|
|
llama_token eos = llama_token_eos(model);
|
|
// some models (e.g. T5) don't have a BOS token
|
|
if (bos != -1) {
|
|
tmp.push_back(bos);
|
|
}
|
|
tmp.push_back(eos);
|
|
|
|
if (llama_model_has_encoder(model)) {
|
|
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
|
|
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
|
|
if (decoder_start_token_id == -1) {
|
|
decoder_start_token_id = bos;
|
|
}
|
|
tmp.clear();
|
|
tmp.push_back(decoder_start_token_id);
|
|
}
|
|
if (llama_model_has_decoder(model)) {
|
|
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
|
|
}
|
|
llama_kv_cache_clear(lctx);
|
|
llama_synchronize(lctx);
|
|
llama_reset_timings(lctx);
|
|
}
|
|
|
|
iparams.model = model;
|
|
iparams.context = lctx;
|
|
return iparams;
|
|
}
|
|
|
|
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
|
|
llama_lora_adapter_clear(ctx);
|
|
for (auto & la : lora_adapters) {
|
|
if (la.scale != 0.0f) {
|
|
llama_lora_adapter_set(ctx, la.adapter, la.scale);
|
|
}
|
|
}
|
|
}
|
|
|
|
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
|
|
auto mparams = llama_model_default_params();
|
|
|
|
if (params.n_gpu_layers != -1) {
|
|
mparams.n_gpu_layers = params.n_gpu_layers;
|
|
}
|
|
mparams.rpc_servers = params.rpc_servers.c_str();
|
|
mparams.main_gpu = params.main_gpu;
|
|
mparams.split_mode = params.split_mode;
|
|
mparams.tensor_split = params.tensor_split;
|
|
mparams.use_mmap = params.use_mmap;
|
|
mparams.use_mlock = params.use_mlock;
|
|
mparams.check_tensors = params.check_tensors;
|
|
if (params.kv_overrides.empty()) {
|
|
mparams.kv_overrides = NULL;
|
|
} else {
|
|
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
|
|
mparams.kv_overrides = params.kv_overrides.data();
|
|
}
|
|
|
|
return mparams;
|
|
}
|
|
|
|
static ggml_type kv_cache_type_from_str(const std::string & s) {
|
|
if (s == "f32") {
|
|
return GGML_TYPE_F32;
|
|
}
|
|
if (s == "f16") {
|
|
return GGML_TYPE_F16;
|
|
}
|
|
if (s == "q8_0") {
|
|
return GGML_TYPE_Q8_0;
|
|
}
|
|
if (s == "q4_0") {
|
|
return GGML_TYPE_Q4_0;
|
|
}
|
|
if (s == "q4_1") {
|
|
return GGML_TYPE_Q4_1;
|
|
}
|
|
if (s == "iq4_nl") {
|
|
return GGML_TYPE_IQ4_NL;
|
|
}
|
|
if (s == "q5_0") {
|
|
return GGML_TYPE_Q5_0;
|
|
}
|
|
if (s == "q5_1") {
|
|
return GGML_TYPE_Q5_1;
|
|
}
|
|
|
|
throw std::runtime_error("Invalid cache type: " + s);
|
|
}
|
|
|
|
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
|
|
auto cparams = llama_context_default_params();
|
|
|
|
cparams.n_ctx = params.n_ctx;
|
|
cparams.n_seq_max = params.n_parallel;
|
|
cparams.n_batch = params.n_batch;
|
|
cparams.n_ubatch = params.n_ubatch;
|
|
cparams.n_threads = params.cpuparams.n_threads;
|
|
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
|
|
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
|
|
cparams.seed = params.seed;
|
|
cparams.logits_all = params.logits_all;
|
|
cparams.embeddings = params.embedding;
|
|
cparams.rope_scaling_type = params.rope_scaling_type;
|
|
cparams.rope_freq_base = params.rope_freq_base;
|
|
cparams.rope_freq_scale = params.rope_freq_scale;
|
|
cparams.yarn_ext_factor = params.yarn_ext_factor;
|
|
cparams.yarn_attn_factor = params.yarn_attn_factor;
|
|
cparams.yarn_beta_fast = params.yarn_beta_fast;
|
|
cparams.yarn_beta_slow = params.yarn_beta_slow;
|
|
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
|
|
cparams.pooling_type = params.pooling_type;
|
|
cparams.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.type_k = kv_cache_type_from_str(params.cache_type_k);
|
|
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
|
|
|
|
return cparams;
|
|
}
|
|
|
|
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
|
|
struct ggml_threadpool_params tpp;
|
|
|
|
ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
|
|
|
|
if (params.mask_valid) {
|
|
std::memcpy(&tpp.cpumask, ¶ms.cpumask, GGML_MAX_N_THREADS);
|
|
}
|
|
|
|
tpp.prio = params.priority;
|
|
tpp.poll = params.poll;
|
|
tpp.strict_cpu = params.strict_cpu;
|
|
|
|
return tpp;
|
|
}
|
|
|
|
#ifdef LLAMA_USE_CURL
|
|
|
|
static bool starts_with(const std::string & str, const std::string & prefix) {
|
|
// While we wait for C++20's std::string::starts_with...
|
|
return str.rfind(prefix, 0) == 0;
|
|
}
|
|
|
|
static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
|
|
|
|
// Initialize libcurl
|
|
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
|
|
if (!curl) {
|
|
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
bool force_download = false;
|
|
|
|
// Set the URL, allow to follow http redirection
|
|
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
|
|
|
|
// Check if hf-token or bearer-token was specified
|
|
if (!hf_token.empty()) {
|
|
std::string auth_header = "Authorization: Bearer ";
|
|
auth_header += hf_token.c_str();
|
|
struct curl_slist *http_headers = NULL;
|
|
http_headers = curl_slist_append(http_headers, auth_header.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
|
|
// operating system. Currently implemented under MS-Windows.
|
|
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
|
|
#endif
|
|
|
|
// Check if the file already exists locally
|
|
struct stat model_file_info;
|
|
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
|
|
|
|
// If the file exists, check its JSON metadata companion file.
|
|
std::string metadata_path = path + ".json";
|
|
nlohmann::json metadata;
|
|
std::string etag;
|
|
std::string last_modified;
|
|
|
|
if (file_exists) {
|
|
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
|
|
std::ifstream metadata_in(metadata_path);
|
|
if (metadata_in.good()) {
|
|
try {
|
|
metadata_in >> metadata;
|
|
fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
|
|
if (metadata.contains("url") && metadata.at("url").is_string()) {
|
|
auto previous_url = metadata.at("url").get<std::string>();
|
|
if (previous_url != url) {
|
|
fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
|
|
etag = metadata.at("etag");
|
|
}
|
|
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
|
|
last_modified = metadata.at("lastModified");
|
|
}
|
|
} catch (const nlohmann::json::exception & e) {
|
|
fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
|
|
return false;
|
|
}
|
|
}
|
|
} else {
|
|
fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
|
|
}
|
|
|
|
// Send a HEAD request to retrieve the etag and last-modified headers
|
|
struct llama_load_model_from_url_headers {
|
|
std::string etag;
|
|
std::string last_modified;
|
|
};
|
|
llama_load_model_from_url_headers headers;
|
|
{
|
|
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
|
|
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
|
|
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
|
|
|
|
static std::regex header_regex("([^:]+): (.*)\r\n");
|
|
static std::regex etag_regex("ETag", std::regex_constants::icase);
|
|
static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
|
|
|
|
std::string header(buffer, n_items);
|
|
std::smatch match;
|
|
if (std::regex_match(header, match, header_regex)) {
|
|
const std::string & key = match[1];
|
|
const std::string & value = match[2];
|
|
if (std::regex_match(key, match, etag_regex)) {
|
|
headers->etag = value;
|
|
} else if (std::regex_match(key, match, last_modified_regex)) {
|
|
headers->last_modified = value;
|
|
}
|
|
}
|
|
return n_items;
|
|
};
|
|
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
|
|
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
|
|
|
|
CURLcode res = curl_easy_perform(curl.get());
|
|
if (res != CURLE_OK) {
|
|
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code != 200) {
|
|
// HEAD not supported, we don't know if the file has changed
|
|
// force trigger downloading
|
|
force_download = true;
|
|
fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
|
|
}
|
|
}
|
|
|
|
bool should_download = !file_exists || force_download;
|
|
if (!should_download) {
|
|
if (!etag.empty() && etag != headers.etag) {
|
|
fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
|
|
should_download = true;
|
|
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
|
|
fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
|
|
should_download = true;
|
|
}
|
|
}
|
|
if (should_download) {
|
|
std::string path_temporary = path + ".downloadInProgress";
|
|
if (file_exists) {
|
|
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
|
|
if (remove(path.c_str()) != 0) {
|
|
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Set the output file
|
|
|
|
struct FILE_deleter {
|
|
void operator()(FILE * f) const {
|
|
fclose(f);
|
|
}
|
|
};
|
|
|
|
std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
|
|
if (!outfile) {
|
|
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
|
|
return false;
|
|
}
|
|
|
|
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
|
|
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
|
|
return fwrite(data, size, nmemb, (FILE *)fd);
|
|
};
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
|
|
|
|
// display download progress
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
|
|
|
|
// helper function to hide password in URL
|
|
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
|
|
std::size_t protocol_pos = url.find("://");
|
|
if (protocol_pos == std::string::npos) {
|
|
return url; // Malformed URL
|
|
}
|
|
|
|
std::size_t at_pos = url.find('@', protocol_pos + 3);
|
|
if (at_pos == std::string::npos) {
|
|
return url; // No password in URL
|
|
}
|
|
|
|
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
|
|
};
|
|
|
|
// start the download
|
|
fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
|
|
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
|
|
auto res = curl_easy_perform(curl.get());
|
|
if (res != CURLE_OK) {
|
|
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
|
|
return false;
|
|
}
|
|
|
|
long http_code = 0;
|
|
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
|
if (http_code < 200 || http_code >= 400) {
|
|
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
|
|
return false;
|
|
}
|
|
|
|
// Causes file to be closed explicitly here before we rename it.
|
|
outfile.reset();
|
|
|
|
// Write the updated JSON metadata file.
|
|
metadata.update({
|
|
{"url", url},
|
|
{"etag", headers.etag},
|
|
{"lastModified", headers.last_modified}
|
|
});
|
|
std::ofstream(metadata_path) << metadata.dump(4);
|
|
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
|
|
|
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
|
|
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_url(
|
|
const char * model_url,
|
|
const char * path_model,
|
|
const char * hf_token,
|
|
const struct llama_model_params & params) {
|
|
// Basic validation of the model_url
|
|
if (!model_url || strlen(model_url) == 0) {
|
|
fprintf(stderr, "%s: invalid model_url\n", __func__);
|
|
return NULL;
|
|
}
|
|
|
|
if (!llama_download_file(model_url, path_model, hf_token)) {
|
|
return NULL;
|
|
}
|
|
|
|
// check for additional GGUFs split to download
|
|
int n_split = 0;
|
|
{
|
|
struct gguf_init_params gguf_params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ NULL,
|
|
};
|
|
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
|
|
if (!ctx_gguf) {
|
|
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
|
|
return NULL;
|
|
}
|
|
|
|
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
|
|
if (key_n_split >= 0) {
|
|
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
|
|
}
|
|
|
|
gguf_free(ctx_gguf);
|
|
}
|
|
|
|
if (n_split > 1) {
|
|
char split_prefix[PATH_MAX] = {0};
|
|
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
|
|
// Verify the first split file format
|
|
// and extract split URL and PATH prefixes
|
|
{
|
|
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
|
|
fprintf(stderr, "\n%s: unexpected model file name: %s"
|
|
" n_split=%d\n", __func__, path_model, n_split);
|
|
return NULL;
|
|
}
|
|
|
|
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
|
|
fprintf(stderr, "\n%s: unexpected model url: %s"
|
|
" n_split=%d\n", __func__, model_url, n_split);
|
|
return NULL;
|
|
}
|
|
}
|
|
|
|
// Prepare download in parallel
|
|
std::vector<std::future<bool>> futures_download;
|
|
for (int idx = 1; idx < n_split; idx++) {
|
|
futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
|
|
char split_path[PATH_MAX] = {0};
|
|
llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
|
|
|
|
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
|
|
|
|
return llama_download_file(split_url, split_path, hf_token);
|
|
}, idx));
|
|
}
|
|
|
|
// Wait for all downloads to complete
|
|
for (auto & f : futures_download) {
|
|
if (!f.get()) {
|
|
return NULL;
|
|
}
|
|
}
|
|
}
|
|
|
|
return llama_load_model_from_file(path_model, params);
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_hf(
|
|
const char * repo,
|
|
const char * model,
|
|
const char * path_model,
|
|
const char * hf_token,
|
|
const struct llama_model_params & params) {
|
|
// construct hugging face model url:
|
|
//
|
|
// --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
|
|
// https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
|
|
//
|
|
// --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
|
|
// https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
|
|
//
|
|
|
|
std::string model_url = "https://huggingface.co/";
|
|
model_url += repo;
|
|
model_url += "/resolve/main/";
|
|
model_url += model;
|
|
|
|
return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
|
|
}
|
|
|
|
#else
|
|
|
|
struct llama_model * llama_load_model_from_url(
|
|
const char * /*model_url*/,
|
|
const char * /*path_model*/,
|
|
const char * /*hf_token*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_hf(
|
|
const char * /*repo*/,
|
|
const char * /*model*/,
|
|
const char * /*path_model*/,
|
|
const char * /*hf_token*/,
|
|
const struct llama_model_params & /*params*/) {
|
|
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
#endif // LLAMA_USE_CURL
|
|
|
|
//
|
|
// Batch utils
|
|
//
|
|
|
|
void llama_batch_clear(struct llama_batch & batch) {
|
|
batch.n_tokens = 0;
|
|
}
|
|
|
|
void llama_batch_add(
|
|
struct llama_batch & batch,
|
|
llama_token id,
|
|
llama_pos pos,
|
|
const std::vector<llama_seq_id> & seq_ids,
|
|
bool logits) {
|
|
batch.token [batch.n_tokens] = id;
|
|
batch.pos [batch.n_tokens] = pos;
|
|
batch.n_seq_id[batch.n_tokens] = seq_ids.size();
|
|
for (size_t i = 0; i < seq_ids.size(); ++i) {
|
|
batch.seq_id[batch.n_tokens][i] = seq_ids[i];
|
|
}
|
|
batch.logits [batch.n_tokens] = logits;
|
|
|
|
batch.n_tokens++;
|
|
}
|
|
|
|
//
|
|
// Vocab utils
|
|
//
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_context * ctx,
|
|
const std::string & text,
|
|
bool add_special,
|
|
bool parse_special) {
|
|
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
|
|
}
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_model * model,
|
|
const std::string & text,
|
|
bool add_special,
|
|
bool parse_special) {
|
|
// upper limit for the number of tokens
|
|
int n_tokens = text.length() + 2 * add_special;
|
|
std::vector<llama_token> result(n_tokens);
|
|
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
|
|
GGML_ASSERT(check == -n_tokens);
|
|
} else {
|
|
result.resize(n_tokens);
|
|
}
|
|
return result;
|
|
}
|
|
|
|
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
|
|
std::string piece;
|
|
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
|
|
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
|
|
if (n_chars < 0) {
|
|
piece.resize(-n_chars);
|
|
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
|
|
GGML_ASSERT(check == -n_chars);
|
|
}
|
|
else {
|
|
piece.resize(n_chars);
|
|
}
|
|
|
|
return piece;
|
|
}
|
|
|
|
std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
|
|
std::string text;
|
|
text.resize(std::max(text.capacity(), tokens.size()));
|
|
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
|
if (n_chars < 0) {
|
|
text.resize(-n_chars);
|
|
n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
|
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
|
|
}
|
|
|
|
text.resize(n_chars);
|
|
|
|
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
|
|
return text;
|
|
}
|
|
|
|
//
|
|
// Chat template utils
|
|
//
|
|
|
|
bool llama_chat_verify_template(const std::string & tmpl) {
|
|
llama_chat_message chat[] = {{"user", "test"}};
|
|
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
|
|
return res >= 0;
|
|
}
|
|
|
|
std::string llama_chat_apply_template(const struct llama_model * model,
|
|
const std::string & tmpl,
|
|
const std::vector<llama_chat_msg> & msgs,
|
|
bool add_ass) {
|
|
int alloc_size = 0;
|
|
bool fallback = false; // indicate if we must fallback to default chatml
|
|
std::vector<llama_chat_message> chat;
|
|
for (auto & msg : msgs) {
|
|
chat.push_back({msg.role.c_str(), msg.content.c_str()});
|
|
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
|
|
}
|
|
|
|
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
|
|
std::vector<char> buf(alloc_size);
|
|
|
|
// run the first time to get the total output length
|
|
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
|
|
|
|
// error: chat template is not supported
|
|
if (res < 0) {
|
|
if (ptr_tmpl != nullptr) {
|
|
// if the custom "tmpl" is not supported, we throw an error
|
|
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
|
|
throw std::runtime_error("this custom template is not supported");
|
|
} else {
|
|
// If the built-in template is not supported, we default to chatml
|
|
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
|
|
fallback = true;
|
|
}
|
|
}
|
|
|
|
// if it turns out that our buffer is too small, we resize it
|
|
if ((size_t) res > buf.size()) {
|
|
buf.resize(res);
|
|
res = llama_chat_apply_template(
|
|
fallback ? nullptr : model,
|
|
fallback ? "chatml" : ptr_tmpl,
|
|
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
|
|
}
|
|
|
|
std::string formatted_chat(buf.data(), res);
|
|
return formatted_chat;
|
|
}
|
|
|
|
std::string llama_chat_format_single(const struct llama_model * model,
|
|
const std::string & tmpl,
|
|
const std::vector<llama_chat_msg> & past_msg,
|
|
const llama_chat_msg & new_msg,
|
|
bool add_ass) {
|
|
std::ostringstream ss;
|
|
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
|
|
std::vector<llama_chat_msg> chat_new(past_msg);
|
|
// if the past_msg ends with a newline, we must preserve it in the formatted version
|
|
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
|
|
ss << "\n";
|
|
};
|
|
// format chat with new_msg
|
|
chat_new.push_back(new_msg);
|
|
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
|
|
// get the diff part
|
|
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
|
|
return ss.str();
|
|
}
|
|
|
|
std::string llama_chat_format_example(const struct llama_model * model,
|
|
const std::string & tmpl) {
|
|
std::vector<llama_chat_msg> msgs = {
|
|
{"system", "You are a helpful assistant"},
|
|
{"user", "Hello"},
|
|
{"assistant", "Hi there"},
|
|
{"user", "How are you?"},
|
|
};
|
|
return llama_chat_apply_template(model, tmpl, msgs, true);
|
|
}
|
|
|
|
//
|
|
// KV cache utils
|
|
//
|
|
|
|
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
|
|
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
|
|
|
|
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
|
|
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
|
|
|
|
llama_kv_cache_view_cell * c_curr = view.cells;
|
|
llama_seq_id * cs_curr = view.cells_sequences;
|
|
|
|
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
|
|
if (i % row_size == 0) {
|
|
printf("\n%5d: ", i);
|
|
}
|
|
int seq_count = 0;
|
|
for (int j = 0; j < view.n_seq_max; j++) {
|
|
if (cs_curr[j] >= 0) { seq_count++; }
|
|
}
|
|
putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
|
|
}
|
|
|
|
printf("\n=== Done dumping\n");
|
|
}
|
|
|
|
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
|
|
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
|
|
|
|
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
|
|
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
|
|
|
|
std::unordered_map<llama_seq_id, size_t> seqs;
|
|
llama_kv_cache_view_cell * c_curr = view.cells;
|
|
llama_seq_id * cs_curr = view.cells_sequences;
|
|
|
|
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
|
|
for (int j = 0; j < view.n_seq_max; j++) {
|
|
if (cs_curr[j] < 0) { continue; }
|
|
if (seqs.find(cs_curr[j]) == seqs.end()) {
|
|
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
|
|
const size_t sz = seqs.size();
|
|
seqs[cs_curr[j]] = sz;
|
|
}
|
|
}
|
|
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
|
|
}
|
|
|
|
printf("=== Sequence legend: ");
|
|
for (const auto & it : seqs) {
|
|
printf("%zu=%d, ", it.second, it.first);
|
|
}
|
|
printf("'+'=other sequence ids");
|
|
|
|
c_curr = view.cells;
|
|
cs_curr = view.cells_sequences;
|
|
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
|
|
if (i % row_size == 0) {
|
|
printf("\n%5d: ", i);
|
|
}
|
|
for (int j = 0; j < view.n_seq_max; j++) {
|
|
if (cs_curr[j] >= 0) {
|
|
const auto & it = seqs.find(cs_curr[j]);
|
|
putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
|
|
} else {
|
|
putchar('.');
|
|
}
|
|
}
|
|
putchar(' ');
|
|
}
|
|
|
|
printf("\n=== Done dumping\n");
|
|
}
|
|
|
|
//
|
|
// Embedding utils
|
|
//
|
|
|
|
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
|
|
double sum = 0.0;
|
|
|
|
switch (embd_norm) {
|
|
case -1: // no normalisation
|
|
sum = 1.0;
|
|
break;
|
|
case 0: // max absolute
|
|
for (int i = 0; i < n; i++) {
|
|
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
|
|
}
|
|
sum /= 32760.0; // make an int16 range
|
|
break;
|
|
case 2: // euclidean
|
|
for (int i = 0; i < n; i++) {
|
|
sum += inp[i] * inp[i];
|
|
}
|
|
sum = std::sqrt(sum);
|
|
break;
|
|
default: // p-norm (euclidean is p-norm p=2)
|
|
for (int i = 0; i < n; i++) {
|
|
sum += std::pow(std::abs(inp[i]), embd_norm);
|
|
}
|
|
sum = std::pow(sum, 1.0 / embd_norm);
|
|
break;
|
|
}
|
|
|
|
const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
out[i] = inp[i] * norm;
|
|
}
|
|
}
|
|
|
|
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
|
|
double sum = 0.0;
|
|
double sum1 = 0.0;
|
|
double sum2 = 0.0;
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
sum += embd1[i] * embd2[i];
|
|
sum1 += embd1[i] * embd1[i];
|
|
sum2 += embd2[i] * embd2[i];
|
|
}
|
|
|
|
// Handle the case where one or both vectors are zero vectors
|
|
if (sum1 == 0.0 || sum2 == 0.0) {
|
|
if (sum1 == 0.0 && sum2 == 0.0) {
|
|
return 1.0f; // two zero vectors are similar
|
|
}
|
|
return 0.0f;
|
|
}
|
|
|
|
return sum / (sqrt(sum1) * sqrt(sum2));
|
|
}
|
|
|
|
//
|
|
// Control vector utils
|
|
//
|
|
|
|
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
|
|
llama_control_vector_data result = { -1, {} };
|
|
|
|
ggml_context * ctx = nullptr;
|
|
struct gguf_init_params meta_gguf_params = {
|
|
/* .no_alloc = */ false,
|
|
/* .ctx = */ &ctx,
|
|
};
|
|
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
|
|
if (!ctx_gguf) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
|
|
}
|
|
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
std::string name = gguf_get_tensor_name(ctx_gguf, i);
|
|
|
|
int layer_idx = -1;
|
|
|
|
// split on '.'
|
|
size_t dotpos = name.find('.');
|
|
if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
|
|
try {
|
|
layer_idx = std::stoi(name.substr(dotpos + 1));
|
|
} catch (...) {
|
|
layer_idx = -1;
|
|
}
|
|
}
|
|
if (layer_idx < 0) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
|
|
result.data.clear();
|
|
}
|
|
|
|
gguf_free(ctx_gguf);
|
|
ggml_free(ctx);
|
|
|
|
return result;
|
|
}
|
|
|
|
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
|
|
llama_control_vector_data result = { -1, {} };
|
|
|
|
for (const auto & info : load_infos) {
|
|
auto cur = llama_control_vector_load_one(info);
|
|
|
|
if (cur.n_embd == -1) {
|
|
result.n_embd = -1;
|
|
break;
|
|
}
|
|
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
|
|
result.data.clear();
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
//
|
|
// YAML utils
|
|
//
|
|
|
|
void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
|
|
if (data.empty()) {
|
|
fprintf(stream, "%s:\n", prop_name);
|
|
return;
|
|
}
|
|
|
|
fprintf(stream, "%s: [", prop_name);
|
|
for (size_t i = 0; i < data.size() - 1; ++i) {
|
|
fprintf(stream, "%e, ", data[i]);
|
|
}
|
|
fprintf(stream, "%e]\n", data.back());
|
|
}
|
|
|
|
void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
|
|
if (data.empty()) {
|
|
fprintf(stream, "%s:\n", prop_name);
|
|
return;
|
|
}
|
|
|
|
fprintf(stream, "%s: [", prop_name);
|
|
for (size_t i = 0; i < data.size() - 1; ++i) {
|
|
fprintf(stream, "%d, ", data[i]);
|
|
}
|
|
fprintf(stream, "%d]\n", data.back());
|
|
}
|
|
|
|
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
|
|
std::string data_str(data == NULL ? "" : data);
|
|
|
|
if (data_str.empty()) {
|
|
fprintf(stream, "%s:\n", prop_name);
|
|
return;
|
|
}
|
|
|
|
size_t pos_start = 0;
|
|
size_t pos_found = 0;
|
|
|
|
if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
|
|
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
|
|
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
|
|
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
|
|
data_str = "\"" + data_str + "\"";
|
|
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
|
|
return;
|
|
}
|
|
|
|
if (data_str.find('\n') == std::string::npos) {
|
|
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
|
|
return;
|
|
}
|
|
|
|
fprintf(stream, "%s: |\n", prop_name);
|
|
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
|
|
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
|
|
pos_start = pos_found + 1;
|
|
}
|
|
}
|
|
|
|
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
|
|
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
|
|
const llama_sampling_params & sparams = params.sparams;
|
|
|
|
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
|
|
fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
|
|
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
|
|
|
|
#ifdef NDEBUG
|
|
fprintf(stream, "debug: false\n");
|
|
#else
|
|
fprintf(stream, "debug: true\n");
|
|
#endif // NDEBUG
|
|
|
|
fprintf(stream, "model_desc: %s\n", model_desc);
|
|
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
|
|
|
|
#ifdef __OPTIMIZE__
|
|
fprintf(stream, "optimize: true\n");
|
|
#else
|
|
fprintf(stream, "optimize: false\n");
|
|
#endif // __OPTIMIZE__
|
|
|
|
fprintf(stream, "time: %s\n", timestamp.c_str());
|
|
|
|
fprintf(stream, "\n");
|
|
fprintf(stream, "###############\n");
|
|
fprintf(stream, "# User Inputs #\n");
|
|
fprintf(stream, "###############\n");
|
|
fprintf(stream, "\n");
|
|
|
|
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
|
|
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
|
|
yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
|
|
fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
|
|
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
|
|
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
|
|
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
|
|
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
|
|
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
|
|
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
|
|
yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
|
|
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
|
|
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
|
|
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
|
|
|
|
const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
|
|
const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
|
|
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
|
|
|
|
yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
|
|
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
|
|
yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
|
|
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
|
|
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
|
|
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
|
|
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
|
|
|
|
fprintf(stream, "logit_bias:\n");
|
|
for (std::pair<llama_token, float> lb : sparams.logit_bias) {
|
|
if (ignore_eos && lb.first == logit_bias_eos->first) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " %d: %f", lb.first, lb.second);
|
|
}
|
|
|
|
fprintf(stream, "lora:\n");
|
|
for (auto & la : params.lora_adapters) {
|
|
if (la.scale == 1.0f) {
|
|
fprintf(stream, " - %s\n", la.path.c_str());
|
|
}
|
|
}
|
|
fprintf(stream, "lora_scaled:\n");
|
|
for (auto & la : params.lora_adapters) {
|
|
if (la.scale != 1.0f) {
|
|
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
|
|
}
|
|
}
|
|
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
|
|
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
|
|
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
|
|
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
|
|
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
|
|
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
|
|
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
|
|
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
|
|
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
|
|
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
|
|
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
|
|
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
|
|
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
|
|
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
|
|
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
|
|
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
|
|
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
|
|
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
|
|
yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
|
|
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
|
|
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
|
|
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
|
|
yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
|
|
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
|
|
|
|
fprintf(stream, "reverse_prompt:\n");
|
|
for (std::string ap : params.antiprompt) {
|
|
size_t pos = 0;
|
|
while ((pos = ap.find('\n', pos)) != std::string::npos) {
|
|
ap.replace(pos, 1, "\\n");
|
|
pos += 1;
|
|
}
|
|
|
|
fprintf(stream, " - %s\n", ap.c_str());
|
|
}
|
|
|
|
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
|
|
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
|
|
fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
|
|
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
|
|
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
|
|
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
|
|
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
|
|
|
|
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
|
|
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
|
|
|
|
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
|
|
fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency());
|
|
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
|
|
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
|
|
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
|
|
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
|
|
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
|
|
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
|
|
}
|