gpt4all/gpt4all-backend/llmodel.cpp
Jared Van Bortel 061d1969f8
expose n_gpu_layers parameter of llama.cpp (#1890)
Also dynamically limit the GPU layers and context length fields to the maximum supported by the model.

Signed-off-by: Jared Van Bortel <jared@nomic.ai>
2024-01-31 14:17:44 -05:00

238 lines
7.9 KiB
C++

#include "llmodel.h"
#include "dlhandle.h"
#include "sysinfo.h"
#include <cassert>
#include <cstdlib>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <memory>
#include <regex>
#include <sstream>
#include <string>
#include <vector>
#ifdef _MSC_VER
#include <intrin.h>
#endif
std::string s_implementations_search_path = ".";
static bool has_at_least_minimal_hardware() {
#if defined(__x86_64__) || defined(_M_X64)
#ifndef _MSC_VER
return __builtin_cpu_supports("avx");
#else
int cpuInfo[4];
__cpuid(cpuInfo, 1);
return cpuInfo[2] & (1 << 28);
#endif
#else
return true; // Don't know how to handle non-x86_64
#endif
}
static bool requires_avxonly() {
#if defined(__x86_64__) || defined(_M_X64)
#ifndef _MSC_VER
return !__builtin_cpu_supports("avx2");
#else
int cpuInfo[4];
__cpuidex(cpuInfo, 7, 0);
return !(cpuInfo[1] & (1 << 5));
#endif
#else
return false; // Don't know how to handle non-x86_64
#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_magicMatch = m_dlhandle->get<bool(const char*)>("magic_match");
assert(m_magicMatch);
m_construct = m_dlhandle->get<LLModel *()>("construct");
assert(m_construct);
}
LLModel::Implementation::Implementation(Implementation &&o)
: m_magicMatch(o.m_magicMatch)
, 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() {
if (m_dlhandle) delete m_dlhandle;
}
bool LLModel::Implementation::isImplementation(const Dlhandle &dl) {
return dl.get<bool(uint32_t)>("is_g4a_backend_model_implementation");
}
const std::vector<LLModel::Implementation> &LLModel::Implementation::implementationList() {
// 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;
std::string impl_name_re = "(bert|gptj|llamamodel-mainline)";
if (requires_avxonly()) {
impl_name_re += "-avxonly";
} else {
impl_name_re += "-(default|metal)";
}
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::filesystem::path fs_path(path);
// Iterate over all libraries
for (const auto& f : std::filesystem::directory_iterator(fs_path)) {
const std::filesystem::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
try {
Dlhandle dl(p.string());
if (!Implementation::isImplementation(dl)) {
continue;
}
fres.emplace_back(Implementation(std::move(dl)));
} catch (...) {}
}
}
};
search_in_directory(s_implementations_search_path);
return fres;
}());
// Return static result
return *libs;
}
const LLModel::Implementation* LLModel::Implementation::implementation(const char *fname, const std::string& buildVariant) {
bool buildVariantMatched = false;
for (const auto& i : implementationList()) {
if (buildVariant != i.m_buildVariant) continue;
buildVariantMatched = true;
if (!i.m_magicMatch(fname)) continue;
return &i;
}
if (!buildVariantMatched) {
std::cerr << "LLModel ERROR: Could not find any implementations for build variant: " << buildVariant << "\n";
}
return nullptr;
}
LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::string buildVariant, int n_ctx) {
if (!has_at_least_minimal_hardware()) {
std::cerr << "LLModel ERROR: CPU does not support AVX\n";
return nullptr;
}
// Get correct implementation
const Implementation* impl = nullptr;
#if defined(__APPLE__) && defined(__arm64__) // FIXME: See if metal works for intel macs
if (buildVariant == "auto") {
size_t total_mem = getSystemTotalRAMInBytes();
impl = implementation(modelPath.c_str(), "metal");
if(impl) {
LLModel* metalimpl = impl->m_construct();
metalimpl->m_implementation = impl;
/* 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. */
size_t req_mem = metalimpl->requiredMem(modelPath, n_ctx, 100);
float req_to_total = (float) req_mem / (float) total_mem;
// on a 16GB M2 Mac a 13B q4_0 (0.52) works for me but a 13B q4_K_M (0.55) does not
if (req_to_total >= 0.53) {
delete metalimpl;
impl = nullptr;
} else {
return metalimpl;
}
}
}
#else
(void)n_ctx;
#endif
if (!impl) {
//TODO: Auto-detect CUDA/OpenCL
if (buildVariant == "auto") {
if (requires_avxonly()) {
buildVariant = "avxonly";
} else {
buildVariant = "default";
}
}
impl = implementation(modelPath.c_str(), buildVariant);
if (!impl) return nullptr;
}
// Construct and return llmodel implementation
auto fres = impl->m_construct();
fres->m_implementation = impl;
return fres;
}
LLModel *LLModel::Implementation::constructDefaultLlama() {
static std::unique_ptr<LLModel> llama([]() -> LLModel * {
const LLModel::Implementation *impl = nullptr;
for (const auto &i : implementationList()) {
if (i.m_buildVariant == "metal" || i.m_modelType != "LLaMA") continue;
impl = &i;
}
if (!impl) {
std::cerr << "LLModel ERROR: Could not find CPU LLaMA implementation\n";
return nullptr;
}
auto fres = impl->m_construct();
fres->m_implementation = impl;
return fres;
}());
return llama.get();
}
std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices() {
auto * llama = constructDefaultLlama();
if (llama) { return llama->availableGPUDevices(0); }
return {};
}
int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath) {
auto * llama = constructDefaultLlama();
return llama ? llama->maxContextLength(modelPath) : -1;
}
int32_t LLModel::Implementation::layerCount(const std::string &modelPath) {
auto * llama = constructDefaultLlama();
return llama ? llama->layerCount(modelPath) : -1;
}
void LLModel::Implementation::setImplementationsSearchPath(const std::string& path) {
s_implementations_search_path = path;
}
const std::string& LLModel::Implementation::implementationsSearchPath() {
return s_implementations_search_path;
}