gpt4all/gpt4all-backend/llmodel.cpp
AT ca72428783
Remove support for GPT-J models. (#2676)
Signed-off-by: Adam Treat <treat.adam@gmail.com>
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-07-17 16:07:37 -04:00

348 lines
11 KiB
C++

#include "llmodel.h"
#include "dlhandle.h"
#include <cassert>
#include <cstdlib>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <iterator>
#include <memory>
#include <optional>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <vector>
#ifdef _WIN32
# define WIN32_LEAN_AND_MEAN
# ifndef NOMINMAX
# define NOMINMAX
# endif
# include <windows.h>
#endif
#ifdef _MSC_VER
# include <intrin.h>
#endif
#if defined(__APPLE__) && defined(__aarch64__)
# include "sysinfo.h" // for getSystemTotalRAMInBytes
#endif
namespace fs = std::filesystem;
#ifndef __APPLE__
static const std::string DEFAULT_BACKENDS[] = {"kompute", "cpu"};
#elif defined(__aarch64__)
static const std::string DEFAULT_BACKENDS[] = {"metal", "cpu"};
#else
static const std::string DEFAULT_BACKENDS[] = {"cpu"};
#endif
std::string s_implementations_search_path = ".";
#if !(defined(__x86_64__) || defined(_M_X64))
// irrelevant on non-x86_64
#define cpu_supports_avx() -1
#define cpu_supports_avx2() -1
#elif defined(_MSC_VER)
// MSVC
static int get_cpu_info(int func_id, int reg_id) {
int info[4];
__cpuid(info, func_id);
return info[reg_id];
}
// AVX via EAX=1: Processor Info and Feature Bits, bit 28 of ECX
#define cpu_supports_avx() !!(get_cpu_info(1, 2) & (1 << 28))
// AVX2 via EAX=7, ECX=0: Extended Features, bit 5 of EBX
#define cpu_supports_avx2() !!(get_cpu_info(7, 1) & (1 << 5))
#else
// gcc/clang
#define cpu_supports_avx() !!__builtin_cpu_supports("avx")
#define cpu_supports_avx2() !!__builtin_cpu_supports("avx2")
#endif
LLModel::Implementation::Implementation(Dlhandle &&dlhandle_)
: m_dlhandle(new Dlhandle(std::move(dlhandle_))) {
auto get_model_type = m_dlhandle->get<const char *()>("get_model_type");
assert(get_model_type);
m_modelType = get_model_type();
auto get_build_variant = m_dlhandle->get<const char *()>("get_build_variant");
assert(get_build_variant);
m_buildVariant = get_build_variant();
m_getFileArch = m_dlhandle->get<char *(const char *)>("get_file_arch");
assert(m_getFileArch);
m_isArchSupported = m_dlhandle->get<bool(const char *)>("is_arch_supported");
assert(m_isArchSupported);
m_construct = m_dlhandle->get<LLModel *()>("construct");
assert(m_construct);
}
LLModel::Implementation::Implementation(Implementation &&o)
: m_getFileArch(o.m_getFileArch)
, m_isArchSupported(o.m_isArchSupported)
, m_construct(o.m_construct)
, m_modelType(o.m_modelType)
, m_buildVariant(o.m_buildVariant)
, m_dlhandle(o.m_dlhandle) {
o.m_dlhandle = nullptr;
}
LLModel::Implementation::~Implementation()
{
delete m_dlhandle;
}
static bool isImplementation(const Dlhandle &dl)
{
return dl.get<bool(uint32_t)>("is_g4a_backend_model_implementation");
}
// Add the CUDA Toolkit to the DLL search path on Windows.
// This is necessary for chat.exe to find CUDA when started from Qt Creator.
static void addCudaSearchPath()
{
#ifdef _WIN32
if (const auto *cudaPath = _wgetenv(L"CUDA_PATH")) {
auto libDir = std::wstring(cudaPath) + L"\\bin";
if (!AddDllDirectory(libDir.c_str())) {
auto err = GetLastError();
std::wcerr << L"AddDllDirectory(\"" << libDir << L"\") failed with error 0x" << std::hex << err << L"\n";
}
}
#endif
}
const std::vector<LLModel::Implementation> &LLModel::Implementation::implementationList()
{
if (cpu_supports_avx() == 0) {
throw std::runtime_error("CPU does not support AVX");
}
// NOTE: allocated on heap so we leak intentionally on exit so we have a chance to clean up the
// individual models without the cleanup of the static list interfering
static auto* libs = new std::vector<Implementation>([] () {
std::vector<Implementation> fres;
addCudaSearchPath();
std::string impl_name_re = "llamamodel-mainline-(cpu|metal|kompute|vulkan|cuda)";
if (cpu_supports_avx2() == 0) {
impl_name_re += "-avxonly";
}
std::regex re(impl_name_re);
auto search_in_directory = [&](const std::string& paths) {
std::stringstream ss(paths);
std::string path;
// Split the paths string by the delimiter and process each path.
while (std::getline(ss, path, ';')) {
std::u8string u8_path(path.begin(), path.end());
// Iterate over all libraries
for (const auto &f : fs::directory_iterator(u8_path)) {
const fs::path &p = f.path();
if (p.extension() != LIB_FILE_EXT) continue;
if (!std::regex_search(p.stem().string(), re)) continue;
// Add to list if model implementation
Dlhandle dl;
try {
dl = Dlhandle(p);
} catch (const Dlhandle::Exception &e) {
std::cerr << "Failed to load " << p.filename().string() << ": " << e.what() << "\n";
continue;
}
if (!isImplementation(dl)) {
std::cerr << "Not an implementation: " << p.filename().string() << "\n";
continue;
}
fres.emplace_back(Implementation(std::move(dl)));
}
}
};
search_in_directory(s_implementations_search_path);
return fres;
}());
// Return static result
return *libs;
}
static std::string applyCPUVariant(const std::string &buildVariant)
{
if (buildVariant != "metal" && cpu_supports_avx2() == 0) {
return buildVariant + "-avxonly";
}
return buildVariant;
}
const LLModel::Implementation* LLModel::Implementation::implementation(const char *fname, const std::string& buildVariant)
{
bool buildVariantMatched = false;
std::optional<std::string> archName;
for (const auto& i : implementationList()) {
if (buildVariant != i.m_buildVariant) continue;
buildVariantMatched = true;
char *arch = i.m_getFileArch(fname);
if (!arch) continue;
archName = arch;
bool archSupported = i.m_isArchSupported(arch);
free(arch);
if (archSupported) return &i;
}
if (!buildVariantMatched)
return nullptr;
if (!archName)
throw UnsupportedModelError("Unsupported file format");
throw BadArchError(std::move(*archName));
}
LLModel *LLModel::Implementation::construct(const std::string &modelPath, const std::string &backend, int n_ctx)
{
std::vector<std::string> desiredBackends;
if (backend != "auto") {
desiredBackends.push_back(backend);
} else {
desiredBackends.insert(desiredBackends.end(), DEFAULT_BACKENDS, std::end(DEFAULT_BACKENDS));
}
for (const auto &desiredBackend: desiredBackends) {
const auto *impl = implementation(modelPath.c_str(), applyCPUVariant(desiredBackend));
if (impl) {
// Construct llmodel implementation
auto *fres = impl->m_construct();
fres->m_implementation = impl;
#if defined(__APPLE__) && defined(__aarch64__) // FIXME: See if metal works for intel macs
/* TODO(cebtenzzre): after we fix requiredMem, we should change this to happen at
* load time, not construct time. right now n_ctx is incorrectly hardcoded 2048 in
* most (all?) places where this is called, causing underestimation of required
* memory. */
if (backend == "auto" && desiredBackend == "metal") {
// on a 16GB M2 Mac a 13B q4_0 (0.52) works for me but a 13B q4_K_M (0.55) does not
size_t req_mem = fres->requiredMem(modelPath, n_ctx, 100);
if (req_mem >= size_t(0.53f * getSystemTotalRAMInBytes())) {
delete fres;
continue;
}
}
#else
(void)n_ctx;
#endif
return fres;
}
}
throw MissingImplementationError("Could not find any implementations for backend: " + backend);
}
LLModel *LLModel::Implementation::constructGlobalLlama(const std::optional<std::string> &backend)
{
static std::unordered_map<std::string, std::unique_ptr<LLModel>> implCache;
const std::vector<Implementation> *impls;
try {
impls = &implementationList();
} catch (const std::runtime_error &e) {
std::cerr << __func__ << ": implementationList failed: " << e.what() << "\n";
return nullptr;
}
std::vector<std::string> desiredBackends;
if (backend) {
desiredBackends.push_back(backend.value());
} else {
desiredBackends.insert(desiredBackends.end(), DEFAULT_BACKENDS, std::end(DEFAULT_BACKENDS));
}
const Implementation *impl = nullptr;
for (const auto &desiredBackend: desiredBackends) {
auto cacheIt = implCache.find(desiredBackend);
if (cacheIt != implCache.end())
return cacheIt->second.get(); // cached
for (const auto &i: *impls) {
if (i.m_modelType == "LLaMA" && i.m_buildVariant == applyCPUVariant(desiredBackend)) {
impl = &i;
break;
}
}
if (impl) {
auto *fres = impl->m_construct();
fres->m_implementation = impl;
implCache[desiredBackend] = std::unique_ptr<LLModel>(fres);
return fres;
}
}
std::cerr << __func__ << ": could not find Llama implementation for backend: " << backend.value_or("default") << "\n";
return nullptr;
}
std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices(size_t memoryRequired)
{
std::vector<LLModel::GPUDevice> devices;
#ifndef __APPLE__
static const std::string backends[] = {"kompute", "cuda"};
for (const auto &backend: backends) {
auto *llama = constructGlobalLlama(backend);
if (llama) {
auto backendDevs = llama->availableGPUDevices(memoryRequired);
devices.insert(devices.end(), backendDevs.begin(), backendDevs.end());
}
}
#endif
return devices;
}
int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath)
{
auto *llama = constructGlobalLlama();
return llama ? llama->maxContextLength(modelPath) : -1;
}
int32_t LLModel::Implementation::layerCount(const std::string &modelPath)
{
auto *llama = constructGlobalLlama();
return llama ? llama->layerCount(modelPath) : -1;
}
bool LLModel::Implementation::isEmbeddingModel(const std::string &modelPath)
{
auto *llama = constructGlobalLlama();
return llama && llama->isEmbeddingModel(modelPath);
}
void LLModel::Implementation::setImplementationsSearchPath(const std::string& path)
{
s_implementations_search_path = path;
}
const std::string& LLModel::Implementation::implementationsSearchPath()
{
return s_implementations_search_path;
}
bool LLModel::Implementation::hasSupportedCPU()
{
return cpu_supports_avx() != 0;
}
int LLModel::Implementation::cpuSupportsAVX2()
{
return cpu_supports_avx2();
}