mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
267 lines
9.0 KiB
C++
267 lines
9.0 KiB
C++
#include "llmodel.h"
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#include "dlhandle.h"
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#include "sysinfo.h"
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#include <cassert>
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#include <cstdlib>
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#include <filesystem>
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#include <fstream>
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#include <iostream>
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#include <memory>
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#include <optional>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <vector>
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#ifdef _MSC_VER
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#include <intrin.h>
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#endif
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std::string s_implementations_search_path = ".";
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#if !(defined(__x86_64__) || defined(_M_X64))
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// irrelevant on non-x86_64
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#define cpu_supports_avx() -1
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#define cpu_supports_avx2() -1
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#elif defined(_MSC_VER)
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// MSVC
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static int get_cpu_info(int func_id, int reg_id) {
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int info[4];
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__cpuid(info, func_id);
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return info[reg_id];
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}
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// AVX via EAX=1: Processor Info and Feature Bits, bit 28 of ECX
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#define cpu_supports_avx() !!(get_cpu_info(1, 2) & (1 << 28))
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// AVX2 via EAX=7, ECX=0: Extended Features, bit 5 of EBX
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#define cpu_supports_avx2() !!(get_cpu_info(7, 1) & (1 << 5))
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#else
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// gcc/clang
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#define cpu_supports_avx() !!__builtin_cpu_supports("avx")
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#define cpu_supports_avx2() !!__builtin_cpu_supports("avx2")
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#endif
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LLModel::Implementation::Implementation(Dlhandle &&dlhandle_)
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: m_dlhandle(new Dlhandle(std::move(dlhandle_))) {
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auto get_model_type = m_dlhandle->get<const char *()>("get_model_type");
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assert(get_model_type);
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m_modelType = get_model_type();
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auto get_build_variant = m_dlhandle->get<const char *()>("get_build_variant");
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assert(get_build_variant);
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m_buildVariant = get_build_variant();
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m_getFileArch = m_dlhandle->get<char *(const char *)>("get_file_arch");
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assert(m_getFileArch);
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m_isArchSupported = m_dlhandle->get<bool(const char *)>("is_arch_supported");
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assert(m_isArchSupported);
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m_construct = m_dlhandle->get<LLModel *()>("construct");
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assert(m_construct);
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}
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LLModel::Implementation::Implementation(Implementation &&o)
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: m_getFileArch(o.m_getFileArch)
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, m_isArchSupported(o.m_isArchSupported)
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, m_construct(o.m_construct)
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, m_modelType(o.m_modelType)
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, m_buildVariant(o.m_buildVariant)
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, m_dlhandle(o.m_dlhandle) {
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o.m_dlhandle = nullptr;
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}
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LLModel::Implementation::~Implementation() {
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delete m_dlhandle;
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}
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static bool isImplementation(const Dlhandle &dl) {
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return dl.get<bool(uint32_t)>("is_g4a_backend_model_implementation");
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}
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const std::vector<LLModel::Implementation> &LLModel::Implementation::implementationList() {
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if (cpu_supports_avx() == 0) {
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throw std::runtime_error("CPU does not support AVX");
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}
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// NOTE: allocated on heap so we leak intentionally on exit so we have a chance to clean up the
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// individual models without the cleanup of the static list interfering
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static auto* libs = new std::vector<Implementation>([] () {
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std::vector<Implementation> fres;
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std::string impl_name_re = "(gptj|llamamodel-mainline)";
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if (cpu_supports_avx2() == 0) {
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impl_name_re += "-avxonly";
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} else {
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impl_name_re += "-(default|metal)";
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}
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std::regex re(impl_name_re);
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auto search_in_directory = [&](const std::string& paths) {
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std::stringstream ss(paths);
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std::string path;
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// Split the paths string by the delimiter and process each path.
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while (std::getline(ss, path, ';')) {
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std::filesystem::path fs_path(path);
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// Iterate over all libraries
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for (const auto& f : std::filesystem::directory_iterator(fs_path)) {
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const std::filesystem::path& p = f.path();
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if (p.extension() != LIB_FILE_EXT) continue;
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if (!std::regex_search(p.stem().string(), re)) continue;
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// Add to list if model implementation
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try {
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Dlhandle dl(p.string());
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if (!isImplementation(dl))
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continue;
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fres.emplace_back(Implementation(std::move(dl)));
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} catch (...) {}
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}
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}
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};
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search_in_directory(s_implementations_search_path);
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return fres;
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}());
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// Return static result
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return *libs;
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}
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const LLModel::Implementation* LLModel::Implementation::implementation(const char *fname, const std::string& buildVariant) {
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bool buildVariantMatched = false;
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std::optional<std::string> archName;
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for (const auto& i : implementationList()) {
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if (buildVariant != i.m_buildVariant) continue;
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buildVariantMatched = true;
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char *arch = i.m_getFileArch(fname);
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if (!arch) continue;
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archName = arch;
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bool archSupported = i.m_isArchSupported(arch);
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free(arch);
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if (archSupported) return &i;
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}
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if (!buildVariantMatched)
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throw MissingImplementationError("Could not find any implementations for build variant: " + buildVariant);
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if (!archName)
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throw UnsupportedModelError("Unsupported file format");
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throw BadArchError(std::move(*archName));
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}
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LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::string buildVariant, int n_ctx) {
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// Get correct implementation
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const Implementation* impl = nullptr;
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#if defined(__APPLE__) && defined(__arm64__) // FIXME: See if metal works for intel macs
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if (buildVariant == "auto") {
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size_t total_mem = getSystemTotalRAMInBytes();
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try {
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impl = implementation(modelPath.c_str(), "metal");
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} catch (const std::exception &e) {
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// fall back to CPU
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}
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if(impl) {
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LLModel* metalimpl = impl->m_construct();
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metalimpl->m_implementation = impl;
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/* TODO(cebtenzzre): after we fix requiredMem, we should change this to happen at
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* load time, not construct time. right now n_ctx is incorrectly hardcoded 2048 in
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* most (all?) places where this is called, causing underestimation of required
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* memory. */
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size_t req_mem = metalimpl->requiredMem(modelPath, n_ctx, 100);
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float req_to_total = (float) req_mem / (float) total_mem;
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// on a 16GB M2 Mac a 13B q4_0 (0.52) works for me but a 13B q4_K_M (0.55) does not
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if (req_to_total >= 0.53) {
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delete metalimpl;
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impl = nullptr;
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} else {
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return metalimpl;
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}
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}
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}
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#else
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(void)n_ctx;
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#endif
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if (!impl) {
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//TODO: Auto-detect CUDA/OpenCL
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if (buildVariant == "auto") {
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if (cpu_supports_avx2() == 0) {
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buildVariant = "avxonly";
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} else {
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buildVariant = "default";
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}
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}
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impl = implementation(modelPath.c_str(), buildVariant);
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}
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// Construct and return llmodel implementation
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auto fres = impl->m_construct();
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fres->m_implementation = impl;
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return fres;
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}
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LLModel *LLModel::Implementation::constructDefaultLlama() {
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static std::unique_ptr<LLModel> llama([]() -> LLModel * {
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const std::vector<LLModel::Implementation> *impls;
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try {
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impls = &implementationList();
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} catch (const std::runtime_error &e) {
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std::cerr << __func__ << ": implementationList failed: " << e.what() << "\n";
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return nullptr;
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}
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const LLModel::Implementation *impl = nullptr;
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for (const auto &i: *impls) {
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if (i.m_buildVariant == "metal" || i.m_modelType != "LLaMA") continue;
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impl = &i;
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}
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if (!impl) {
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std::cerr << __func__ << ": could not find llama.cpp implementation\n";
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return nullptr;
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}
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auto fres = impl->m_construct();
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fres->m_implementation = impl;
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return fres;
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}());
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return llama.get();
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}
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std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices(size_t memoryRequired) {
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auto *llama = constructDefaultLlama();
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if (llama) { return llama->availableGPUDevices(memoryRequired); }
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return {};
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}
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int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath) {
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auto *llama = constructDefaultLlama();
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return llama ? llama->maxContextLength(modelPath) : -1;
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}
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int32_t LLModel::Implementation::layerCount(const std::string &modelPath) {
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auto *llama = constructDefaultLlama();
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return llama ? llama->layerCount(modelPath) : -1;
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}
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bool LLModel::Implementation::isEmbeddingModel(const std::string &modelPath) {
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auto *llama = constructDefaultLlama();
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return llama && llama->isEmbeddingModel(modelPath);
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}
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void LLModel::Implementation::setImplementationsSearchPath(const std::string& path) {
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s_implementations_search_path = path;
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}
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const std::string& LLModel::Implementation::implementationsSearchPath() {
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return s_implementations_search_path;
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}
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bool LLModel::Implementation::hasSupportedCPU() {
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return cpu_supports_avx() != 0;
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}
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int LLModel::Implementation::cpuSupportsAVX2() {
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return cpu_supports_avx2();
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}
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