mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-09-19 15:25:53 +00:00
a1f27072c2
* make runtime library backend universal searchable * corepack enable * fix * pass tests * simpler * add more jsdoc * fix testS * fix up circle ci * bump version * remove false positive warning * add disclaimer * update readme * revert * update ts docs --------- Co-authored-by: Matthew Nguyen <matthewpnguyen@Matthews-MacBook-Pro-7.local>
349 lines
14 KiB
C++
349 lines
14 KiB
C++
#include "index.h"
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Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
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Napi::Function self = DefineClass(env, "LLModel", {
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InstanceMethod("type", &NodeModelWrapper::getType),
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InstanceMethod("isModelLoaded", &NodeModelWrapper::IsModelLoaded),
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InstanceMethod("name", &NodeModelWrapper::getName),
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InstanceMethod("stateSize", &NodeModelWrapper::StateSize),
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InstanceMethod("raw_prompt", &NodeModelWrapper::Prompt),
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InstanceMethod("setThreadCount", &NodeModelWrapper::SetThreadCount),
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InstanceMethod("embed", &NodeModelWrapper::GenerateEmbedding),
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InstanceMethod("threadCount", &NodeModelWrapper::ThreadCount),
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InstanceMethod("getLibraryPath", &NodeModelWrapper::GetLibraryPath),
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InstanceMethod("initGpuByString", &NodeModelWrapper::InitGpuByString),
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InstanceMethod("hasGpuDevice", &NodeModelWrapper::HasGpuDevice),
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InstanceMethod("listGpu", &NodeModelWrapper::GetGpuDevices),
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InstanceMethod("memoryNeeded", &NodeModelWrapper::GetRequiredMemory),
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InstanceMethod("dispose", &NodeModelWrapper::Dispose)
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});
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// Keep a static reference to the constructor
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//
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Napi::FunctionReference* constructor = new Napi::FunctionReference();
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*constructor = Napi::Persistent(self);
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env.SetInstanceData(constructor);
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return self;
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}
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Napi::Value NodeModelWrapper::GetRequiredMemory(const Napi::CallbackInfo& info)
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{
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auto env = info.Env();
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return Napi::Number::New(env, static_cast<uint32_t>( llmodel_required_mem(GetInference(), full_model_path.c_str()) ));
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}
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Napi::Value NodeModelWrapper::GetGpuDevices(const Napi::CallbackInfo& info)
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{
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auto env = info.Env();
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int num_devices = 0;
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auto mem_size = llmodel_required_mem(GetInference(), full_model_path.c_str());
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llmodel_gpu_device* all_devices = llmodel_available_gpu_devices(GetInference(), mem_size, &num_devices);
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if(all_devices == nullptr) {
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Napi::Error::New(
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env,
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"Unable to retrieve list of all GPU devices"
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).ThrowAsJavaScriptException();
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return env.Undefined();
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}
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auto js_array = Napi::Array::New(env, num_devices);
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for(int i = 0; i < num_devices; ++i) {
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auto gpu_device = all_devices[i];
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/*
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*
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* struct llmodel_gpu_device {
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int index = 0;
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int type = 0; // same as VkPhysicalDeviceType
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size_t heapSize = 0;
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const char * name;
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const char * vendor;
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};
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*
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*/
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Napi::Object js_gpu_device = Napi::Object::New(env);
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js_gpu_device["index"] = uint32_t(gpu_device.index);
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js_gpu_device["type"] = uint32_t(gpu_device.type);
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js_gpu_device["heapSize"] = static_cast<uint32_t>( gpu_device.heapSize );
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js_gpu_device["name"]= gpu_device.name;
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js_gpu_device["vendor"] = gpu_device.vendor;
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js_array[i] = js_gpu_device;
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}
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return js_array;
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}
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Napi::Value NodeModelWrapper::getType(const Napi::CallbackInfo& info)
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{
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if(type.empty()) {
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return info.Env().Undefined();
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}
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return Napi::String::New(info.Env(), type);
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}
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Napi::Value NodeModelWrapper::InitGpuByString(const Napi::CallbackInfo& info)
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{
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auto env = info.Env();
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size_t memory_required = static_cast<size_t>(info[0].As<Napi::Number>().Uint32Value());
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std::string gpu_device_identifier = info[1].As<Napi::String>();
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size_t converted_value;
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if(memory_required <= std::numeric_limits<size_t>::max()) {
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converted_value = static_cast<size_t>(memory_required);
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} else {
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Napi::Error::New(
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env,
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"invalid number for memory size. Exceeded bounds for memory."
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).ThrowAsJavaScriptException();
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return env.Undefined();
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}
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auto result = llmodel_gpu_init_gpu_device_by_string(GetInference(), converted_value, gpu_device_identifier.c_str());
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return Napi::Boolean::New(env, result);
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}
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Napi::Value NodeModelWrapper::HasGpuDevice(const Napi::CallbackInfo& info)
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{
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return Napi::Boolean::New(info.Env(), llmodel_has_gpu_device(GetInference()));
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}
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NodeModelWrapper::NodeModelWrapper(const Napi::CallbackInfo& info) : Napi::ObjectWrap<NodeModelWrapper>(info)
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{
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auto env = info.Env();
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fs::path model_path;
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std::string full_weight_path,
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library_path = ".",
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model_name,
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device;
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if(info[0].IsString()) {
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model_path = info[0].As<Napi::String>().Utf8Value();
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full_weight_path = model_path.string();
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std::cout << "DEPRECATION: constructor accepts object now. Check docs for more.\n";
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} else {
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auto config_object = info[0].As<Napi::Object>();
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model_name = config_object.Get("model_name").As<Napi::String>();
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model_path = config_object.Get("model_path").As<Napi::String>().Utf8Value();
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if(config_object.Has("model_type")) {
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type = config_object.Get("model_type").As<Napi::String>();
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}
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full_weight_path = (model_path / fs::path(model_name)).string();
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if(config_object.Has("library_path")) {
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library_path = config_object.Get("library_path").As<Napi::String>();
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} else {
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library_path = ".";
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}
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device = config_object.Get("device").As<Napi::String>();
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}
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llmodel_set_implementation_search_path(library_path.c_str());
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const char* e;
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inference_ = llmodel_model_create2(full_weight_path.c_str(), "auto", &e);
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if(!inference_) {
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Napi::Error::New(env, e).ThrowAsJavaScriptException();
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return;
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}
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if(GetInference() == nullptr) {
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std::cerr << "Tried searching libraries in \"" << library_path << "\"" << std::endl;
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std::cerr << "Tried searching for model weight in \"" << full_weight_path << "\"" << std::endl;
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std::cerr << "Do you have runtime libraries installed?" << std::endl;
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Napi::Error::New(env, "Had an issue creating llmodel object, inference is null").ThrowAsJavaScriptException();
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return;
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}
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if(device != "cpu") {
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size_t mem = llmodel_required_mem(GetInference(), full_weight_path.c_str());
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std::cout << "Initiating GPU\n";
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auto success = llmodel_gpu_init_gpu_device_by_string(GetInference(), mem, device.c_str());
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if(success) {
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std::cout << "GPU init successfully\n";
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} else {
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//https://github.com/nomic-ai/gpt4all/blob/3acbef14b7c2436fe033cae9036e695d77461a16/gpt4all-bindings/python/gpt4all/pyllmodel.py#L215
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//Haven't implemented this but it is still open to contribution
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std::cout << "WARNING: Failed to init GPU\n";
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}
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}
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auto success = llmodel_loadModel(GetInference(), full_weight_path.c_str());
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if(!success) {
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Napi::Error::New(env, "Failed to load model at given path").ThrowAsJavaScriptException();
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return;
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}
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name = model_name.empty() ? model_path.filename().string() : model_name;
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full_model_path = full_weight_path;
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};
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// NodeModelWrapper::~NodeModelWrapper() {
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// if(GetInference() != nullptr) {
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// std::cout << "Debug: deleting model\n";
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// llmodel_model_destroy(inference_);
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// std::cout << (inference_ == nullptr);
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// }
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// }
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// void NodeModelWrapper::Finalize(Napi::Env env) {
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// if(inference_ != nullptr) {
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// std::cout << "Debug: deleting model\n";
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//
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// }
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// }
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Napi::Value NodeModelWrapper::IsModelLoaded(const Napi::CallbackInfo& info) {
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return Napi::Boolean::New(info.Env(), llmodel_isModelLoaded(GetInference()));
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}
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Napi::Value NodeModelWrapper::StateSize(const Napi::CallbackInfo& info) {
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// Implement the binding for the stateSize method
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return Napi::Number::New(info.Env(), static_cast<int64_t>(llmodel_get_state_size(GetInference())));
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}
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Napi::Value NodeModelWrapper::GenerateEmbedding(const Napi::CallbackInfo& info) {
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auto env = info.Env();
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std::string text = info[0].As<Napi::String>().Utf8Value();
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size_t embedding_size = 0;
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float* arr = llmodel_embedding(GetInference(), text.c_str(), &embedding_size);
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if(arr == nullptr) {
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Napi::Error::New(
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env,
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"Cannot embed. native embedder returned 'nullptr'"
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).ThrowAsJavaScriptException();
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return env.Undefined();
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}
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if(embedding_size == 0 && text.size() != 0 ) {
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std::cout << "Warning: embedding length 0 but input text length > 0" << std::endl;
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}
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Napi::Float32Array js_array = Napi::Float32Array::New(env, embedding_size);
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for (size_t i = 0; i < embedding_size; ++i) {
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float element = *(arr + i);
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js_array[i] = element;
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}
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llmodel_free_embedding(arr);
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return js_array;
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}
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/**
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* Generate a response using the model.
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* @param model A pointer to the llmodel_model instance.
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* @param prompt A string representing the input prompt.
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* @param prompt_callback A callback function for handling the processing of prompt.
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* @param response_callback A callback function for handling the generated response.
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* @param recalculate_callback A callback function for handling recalculation requests.
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* @param ctx A pointer to the llmodel_prompt_context structure.
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*/
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Napi::Value NodeModelWrapper::Prompt(const Napi::CallbackInfo& info) {
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auto env = info.Env();
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std::string question;
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if(info[0].IsString()) {
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question = info[0].As<Napi::String>().Utf8Value();
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} else {
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Napi::Error::New(info.Env(), "invalid string argument").ThrowAsJavaScriptException();
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return info.Env().Undefined();
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}
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//defaults copied from python bindings
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llmodel_prompt_context promptContext = {
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.logits = nullptr,
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.tokens = nullptr,
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.n_past = 0,
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.n_ctx = 1024,
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.n_predict = 128,
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.top_k = 40,
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.top_p = 0.9f,
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.temp = 0.72f,
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.n_batch = 8,
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.repeat_penalty = 1.0f,
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.repeat_last_n = 10,
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.context_erase = 0.5
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};
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if(info[1].IsObject())
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{
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auto inputObject = info[1].As<Napi::Object>();
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// Extract and assign the properties
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if (inputObject.Has("logits") || inputObject.Has("tokens")) {
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Napi::Error::New(info.Env(), "Invalid input: 'logits' or 'tokens' properties are not allowed").ThrowAsJavaScriptException();
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return info.Env().Undefined();
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}
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// Assign the remaining properties
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if(inputObject.Has("n_past"))
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promptContext.n_past = inputObject.Get("n_past").As<Napi::Number>().Int32Value();
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if(inputObject.Has("n_ctx"))
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promptContext.n_ctx = inputObject.Get("n_ctx").As<Napi::Number>().Int32Value();
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if(inputObject.Has("n_predict"))
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promptContext.n_predict = inputObject.Get("n_predict").As<Napi::Number>().Int32Value();
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if(inputObject.Has("top_k"))
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promptContext.top_k = inputObject.Get("top_k").As<Napi::Number>().Int32Value();
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if(inputObject.Has("top_p"))
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promptContext.top_p = inputObject.Get("top_p").As<Napi::Number>().FloatValue();
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if(inputObject.Has("temp"))
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promptContext.temp = inputObject.Get("temp").As<Napi::Number>().FloatValue();
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if(inputObject.Has("n_batch"))
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promptContext.n_batch = inputObject.Get("n_batch").As<Napi::Number>().Int32Value();
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if(inputObject.Has("repeat_penalty"))
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promptContext.repeat_penalty = inputObject.Get("repeat_penalty").As<Napi::Number>().FloatValue();
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if(inputObject.Has("repeat_last_n"))
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promptContext.repeat_last_n = inputObject.Get("repeat_last_n").As<Napi::Number>().Int32Value();
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if(inputObject.Has("context_erase"))
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promptContext.context_erase = inputObject.Get("context_erase").As<Napi::Number>().FloatValue();
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}
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//copy to protect llmodel resources when splitting to new thread
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llmodel_prompt_context copiedPrompt = promptContext;
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std::string copiedQuestion = question;
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PromptWorkContext pc = {
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copiedQuestion,
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inference_,
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copiedPrompt,
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""
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};
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auto threadSafeContext = new TsfnContext(env, pc);
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threadSafeContext->tsfn = Napi::ThreadSafeFunction::New(
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env, // Environment
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info[2].As<Napi::Function>(), // JS function from caller
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"PromptCallback", // Resource name
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0, // Max queue size (0 = unlimited).
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1, // Initial thread count
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threadSafeContext, // Context,
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FinalizerCallback, // Finalizer
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(void*)nullptr // Finalizer data
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);
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threadSafeContext->nativeThread = std::thread(threadEntry, threadSafeContext);
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return threadSafeContext->deferred_.Promise();
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}
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void NodeModelWrapper::Dispose(const Napi::CallbackInfo& info) {
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llmodel_model_destroy(inference_);
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}
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void NodeModelWrapper::SetThreadCount(const Napi::CallbackInfo& info) {
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if(info[0].IsNumber()) {
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llmodel_setThreadCount(GetInference(), info[0].As<Napi::Number>().Int64Value());
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} else {
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Napi::Error::New(info.Env(), "Could not set thread count: argument 1 is NaN").ThrowAsJavaScriptException();
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return;
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}
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}
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Napi::Value NodeModelWrapper::getName(const Napi::CallbackInfo& info) {
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return Napi::String::New(info.Env(), name);
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}
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Napi::Value NodeModelWrapper::ThreadCount(const Napi::CallbackInfo& info) {
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return Napi::Number::New(info.Env(), llmodel_threadCount(GetInference()));
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}
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Napi::Value NodeModelWrapper::GetLibraryPath(const Napi::CallbackInfo& info) {
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return Napi::String::New(info.Env(),
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llmodel_get_implementation_search_path());
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}
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llmodel_model NodeModelWrapper::GetInference() {
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return inference_;
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}
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//Exports Bindings
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Napi::Object Init(Napi::Env env, Napi::Object exports) {
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exports["LLModel"] = NodeModelWrapper::GetClass(env);
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return exports;
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}
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NODE_API_MODULE(NODE_GYP_MODULE_NAME, Init)
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