mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-16 06:13:09 +00:00
453 lines
18 KiB
C++
453 lines
18 KiB
C++
#include "index.h"
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#include "napi.h"
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Napi::Function NodeModelWrapper::GetClass(Napi::Env env)
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{
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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("infer", &NodeModelWrapper::Infer),
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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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// 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(
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env, static_cast<uint32_t>(llmodel_required_mem(GetInference(), full_model_path.c_str(), nCtx, nGpuLayers)));
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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(), nCtx, nGpuLayers);
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llmodel_gpu_device *all_devices = llmodel_available_gpu_devices(mem_size, &num_devices);
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if (all_devices == nullptr)
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{
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Napi::Error::New(env, "Unable to retrieve list of all GPU devices").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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{
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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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{
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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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{
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converted_value = static_cast<size_t>(memory_required);
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}
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else
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{
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Napi::Error::New(env, "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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auto config_object = info[0].As<Napi::Object>();
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// sets the directory where models (gguf files) are to be searched
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llmodel_set_implementation_search_path(
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config_object.Has("library_path") ? config_object.Get("library_path").As<Napi::String>().Utf8Value().c_str()
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: ".");
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std::string model_name = config_object.Get("model_name").As<Napi::String>();
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fs::path model_path = config_object.Get("model_path").As<Napi::String>().Utf8Value();
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std::string full_weight_path = (model_path / fs::path(model_name)).string();
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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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nCtx = config_object.Get("nCtx").As<Napi::Number>().Int32Value();
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nGpuLayers = config_object.Get("ngl").As<Napi::Number>().Int32Value();
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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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{
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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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{
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std::cerr << "Tried searching libraries in \"" << llmodel_get_implementation_search_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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std::string device = config_object.Get("device").As<Napi::String>();
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if (device != "cpu")
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{
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size_t mem = llmodel_required_mem(GetInference(), full_weight_path.c_str(), nCtx, nGpuLayers);
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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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{
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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(), nCtx, nGpuLayers);
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if (!success)
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{
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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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// optional
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if (config_object.Has("model_type"))
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{
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type = config_object.Get("model_type").As<Napi::String>();
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}
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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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{
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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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{
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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::Array ChunkedFloatPtr(float *embedding_ptr, int embedding_size, int text_len, Napi::Env const &env)
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{
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auto n_embd = embedding_size / text_len;
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// std::cout << "Embedding size: " << embedding_size << std::endl;
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// std::cout << "Text length: " << text_len << std::endl;
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// std::cout << "Chunk size (n_embd): " << n_embd << std::endl;
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Napi::Array result = Napi::Array::New(env, text_len);
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auto count = 0;
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for (int i = 0; i < embedding_size; i += n_embd)
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{
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int end = std::min(i + n_embd, embedding_size);
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// possible bounds error?
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// Constructs a container with as many elements as the range [first,last), with each element emplace-constructed
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// from its corresponding element in that range, in the same order.
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std::vector<float> chunk(embedding_ptr + i, embedding_ptr + end);
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Napi::Float32Array fltarr = Napi::Float32Array::New(env, chunk.size());
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// I know there's a way to emplace the raw float ptr into a Napi::Float32Array but idk how and
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// im too scared to cause memory issues
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// this is goodenough
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for (int j = 0; j < chunk.size(); j++)
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{
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fltarr.Set(j, chunk[j]);
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}
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result.Set(count++, fltarr);
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}
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return result;
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}
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Napi::Value NodeModelWrapper::GenerateEmbedding(const Napi::CallbackInfo &info)
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{
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auto env = info.Env();
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auto prefix = info[1];
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auto dimensionality = info[2].As<Napi::Number>().Int32Value();
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auto do_mean = info[3].As<Napi::Boolean>().Value();
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auto atlas = info[4].As<Napi::Boolean>().Value();
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size_t embedding_size;
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size_t token_count = 0;
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// This procedure can maybe be optimized but its whatever, i have too many intermediary structures
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std::vector<std::string> text_arr;
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bool is_single_text = false;
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if (info[0].IsString())
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{
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is_single_text = true;
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text_arr.push_back(info[0].As<Napi::String>().Utf8Value());
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}
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else
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{
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auto jsarr = info[0].As<Napi::Array>();
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size_t len = jsarr.Length();
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text_arr.reserve(len);
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for (size_t i = 0; i < len; ++i)
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{
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std::string str = jsarr.Get(i).As<Napi::String>().Utf8Value();
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text_arr.push_back(str);
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}
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}
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std::vector<const char *> str_ptrs;
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str_ptrs.reserve(text_arr.size() + 1);
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for (size_t i = 0; i < text_arr.size(); ++i)
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str_ptrs.push_back(text_arr[i].c_str());
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str_ptrs.push_back(nullptr);
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const char *_err = nullptr;
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float *embeds = llmodel_embed(GetInference(), str_ptrs.data(), &embedding_size,
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prefix.IsUndefined() ? nullptr : prefix.As<Napi::String>().Utf8Value().c_str(),
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dimensionality, &token_count, do_mean, atlas, nullptr, &_err);
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if (!embeds)
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{
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// i dont wanna deal with c strings lol
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std::string err(_err);
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Napi::Error::New(env, err == "(unknown error)" ? "Unknown error: sorry bud" : err).ThrowAsJavaScriptException();
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return env.Undefined();
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}
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auto embedmat = ChunkedFloatPtr(embeds, embedding_size, text_arr.size(), env);
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llmodel_free_embedding(embeds);
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auto res = Napi::Object::New(env);
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res.Set("n_prompt_tokens", token_count);
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if(is_single_text) {
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res.Set("embeddings", embedmat.Get(static_cast<uint32_t>(0)));
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} else {
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res.Set("embeddings", embedmat);
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}
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return res;
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}
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/**
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* Generate a response using the model.
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* @param prompt A string representing the input prompt.
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* @param options Inference options.
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*/
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Napi::Value NodeModelWrapper::Infer(const Napi::CallbackInfo &info)
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{
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auto env = info.Env();
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std::string prompt;
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if (info[0].IsString())
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{
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prompt = info[0].As<Napi::String>().Utf8Value();
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}
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else
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{
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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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if (!info[1].IsObject())
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{
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Napi::Error::New(info.Env(), "Missing Prompt Options").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 = {.logits = nullptr,
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.tokens = nullptr,
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.n_past = 0,
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.n_ctx = nCtx,
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.n_predict = 4096,
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.top_k = 40,
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.top_p = 0.9f,
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.min_p = 0.0f,
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.temp = 0.1f,
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.n_batch = 8,
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.repeat_penalty = 1.2f,
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.repeat_last_n = 10,
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.context_erase = 0.75};
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PromptWorkerConfig promptWorkerConfig;
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auto inputObject = info[1].As<Napi::Object>();
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if (inputObject.Has("logits") || inputObject.Has("tokens"))
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{
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Napi::Error::New(info.Env(), "Invalid input: 'logits' or 'tokens' properties are not allowed")
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.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("nPast") && inputObject.Get("nPast").IsNumber())
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{
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promptContext.n_past = inputObject.Get("nPast").As<Napi::Number>().Int32Value();
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}
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if (inputObject.Has("nPredict") && inputObject.Get("nPredict").IsNumber())
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{
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promptContext.n_predict = inputObject.Get("nPredict").As<Napi::Number>().Int32Value();
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}
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if (inputObject.Has("topK") && inputObject.Get("topK").IsNumber())
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{
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promptContext.top_k = inputObject.Get("topK").As<Napi::Number>().Int32Value();
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}
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if (inputObject.Has("topP") && inputObject.Get("topP").IsNumber())
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{
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promptContext.top_p = inputObject.Get("topP").As<Napi::Number>().FloatValue();
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}
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if (inputObject.Has("minP") && inputObject.Get("minP").IsNumber())
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{
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promptContext.min_p = inputObject.Get("minP").As<Napi::Number>().FloatValue();
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}
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if (inputObject.Has("temp") && inputObject.Get("temp").IsNumber())
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{
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promptContext.temp = inputObject.Get("temp").As<Napi::Number>().FloatValue();
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}
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if (inputObject.Has("nBatch") && inputObject.Get("nBatch").IsNumber())
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{
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promptContext.n_batch = inputObject.Get("nBatch").As<Napi::Number>().Int32Value();
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}
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if (inputObject.Has("repeatPenalty") && inputObject.Get("repeatPenalty").IsNumber())
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{
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promptContext.repeat_penalty = inputObject.Get("repeatPenalty").As<Napi::Number>().FloatValue();
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}
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if (inputObject.Has("repeatLastN") && inputObject.Get("repeatLastN").IsNumber())
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{
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promptContext.repeat_last_n = inputObject.Get("repeatLastN").As<Napi::Number>().Int32Value();
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}
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if (inputObject.Has("contextErase") && inputObject.Get("contextErase").IsNumber())
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{
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promptContext.context_erase = inputObject.Get("contextErase").As<Napi::Number>().FloatValue();
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}
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if (inputObject.Has("onPromptToken") && inputObject.Get("onPromptToken").IsFunction())
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{
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promptWorkerConfig.promptCallback = inputObject.Get("onPromptToken").As<Napi::Function>();
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promptWorkerConfig.hasPromptCallback = true;
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}
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if (inputObject.Has("onResponseToken") && inputObject.Get("onResponseToken").IsFunction())
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{
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promptWorkerConfig.responseCallback = inputObject.Get("onResponseToken").As<Napi::Function>();
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promptWorkerConfig.hasResponseCallback = true;
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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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promptWorkerConfig.context = promptContext;
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promptWorkerConfig.model = GetInference();
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promptWorkerConfig.mutex = &inference_mutex;
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promptWorkerConfig.prompt = prompt;
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promptWorkerConfig.result = "";
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promptWorkerConfig.promptTemplate = inputObject.Get("promptTemplate").As<Napi::String>();
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if (inputObject.Has("special"))
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{
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promptWorkerConfig.special = inputObject.Get("special").As<Napi::Boolean>();
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}
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if (inputObject.Has("fakeReply"))
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{
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// this will be deleted in the worker
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promptWorkerConfig.fakeReply = new std::string(inputObject.Get("fakeReply").As<Napi::String>().Utf8Value());
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}
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auto worker = new PromptWorker(env, promptWorkerConfig);
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worker->Queue();
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return worker->GetPromise();
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}
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void NodeModelWrapper::Dispose(const Napi::CallbackInfo &info)
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{
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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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{
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if (info[0].IsNumber())
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{
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llmodel_setThreadCount(GetInference(), info[0].As<Napi::Number>().Int64Value());
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}
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else
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{
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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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{
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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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{
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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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{
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return Napi::String::New(info.Env(), llmodel_get_implementation_search_path());
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}
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llmodel_model NodeModelWrapper::GetInference()
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{
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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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{
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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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