gpt4all/gpt4all-bindings/typescript/index.cc
Jacob Nguyen 0e866a0e8f
Refactor(typescript)/error handling (#1283)
* actually display error if it occurs while instantiating

* bump version
2023-07-26 20:06:16 -07:00

248 lines
9.9 KiB
C++

#include "index.h"
Napi::FunctionReference NodeModelWrapper::constructor;
Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
Napi::Function self = DefineClass(env, "LLModel", {
InstanceMethod("type", &NodeModelWrapper::getType),
InstanceMethod("isModelLoaded", &NodeModelWrapper::IsModelLoaded),
InstanceMethod("name", &NodeModelWrapper::getName),
InstanceMethod("stateSize", &NodeModelWrapper::StateSize),
InstanceMethod("raw_prompt", &NodeModelWrapper::Prompt),
InstanceMethod("setThreadCount", &NodeModelWrapper::SetThreadCount),
InstanceMethod("embed", &NodeModelWrapper::GenerateEmbedding),
InstanceMethod("threadCount", &NodeModelWrapper::ThreadCount),
InstanceMethod("getLibraryPath", &NodeModelWrapper::GetLibraryPath),
});
// Keep a static reference to the constructor
//
constructor = Napi::Persistent(self);
constructor.SuppressDestruct();
return self;
}
Napi::Value NodeModelWrapper::getType(const Napi::CallbackInfo& info)
{
if(type.empty()) {
return info.Env().Undefined();
}
return Napi::String::New(info.Env(), type);
}
NodeModelWrapper::NodeModelWrapper(const Napi::CallbackInfo& info) : Napi::ObjectWrap<NodeModelWrapper>(info)
{
auto env = info.Env();
fs::path model_path;
std::string full_weight_path;
//todo
std::string library_path = ".";
std::string model_name;
if(info[0].IsString()) {
model_path = info[0].As<Napi::String>().Utf8Value();
full_weight_path = model_path.string();
std::cout << "DEPRECATION: constructor accepts object now. Check docs for more.\n";
} else {
auto config_object = info[0].As<Napi::Object>();
model_name = config_object.Get("model_name").As<Napi::String>();
model_path = config_object.Get("model_path").As<Napi::String>().Utf8Value();
if(config_object.Has("model_type")) {
type = config_object.Get("model_type").As<Napi::String>();
}
full_weight_path = (model_path / fs::path(model_name)).string();
if(config_object.Has("library_path")) {
library_path = config_object.Get("library_path").As<Napi::String>();
} else {
library_path = ".";
}
}
llmodel_set_implementation_search_path(library_path.c_str());
llmodel_error e = {
.message="looks good to me",
.code=0,
};
inference_ = std::make_shared<llmodel_model>(llmodel_model_create2(full_weight_path.c_str(), "auto", &e));
if(e.code != 0) {
Napi::Error::New(env, e.message).ThrowAsJavaScriptException();
return;
}
if(GetInference() == nullptr) {
std::cerr << "Tried searching libraries in \"" << library_path << "\"" << std::endl;
std::cerr << "Tried searching for model weight in \"" << full_weight_path << "\"" << std::endl;
std::cerr << "Do you have runtime libraries installed?" << std::endl;
Napi::Error::New(env, "Had an issue creating llmodel object, inference is null").ThrowAsJavaScriptException();
return;
}
auto success = llmodel_loadModel(GetInference(), full_weight_path.c_str());
if(!success) {
Napi::Error::New(env, "Failed to load model at given path").ThrowAsJavaScriptException();
return;
}
name = model_name.empty() ? model_path.filename().string() : model_name;
};
//NodeModelWrapper::~NodeModelWrapper() {
//GetInference().reset();
//}
Napi::Value NodeModelWrapper::IsModelLoaded(const Napi::CallbackInfo& info) {
return Napi::Boolean::New(info.Env(), llmodel_isModelLoaded(GetInference()));
}
Napi::Value NodeModelWrapper::StateSize(const Napi::CallbackInfo& info) {
// Implement the binding for the stateSize method
return Napi::Number::New(info.Env(), static_cast<int64_t>(llmodel_get_state_size(GetInference())));
}
Napi::Value NodeModelWrapper::GenerateEmbedding(const Napi::CallbackInfo& info) {
auto env = info.Env();
std::string text = info[0].As<Napi::String>().Utf8Value();
size_t embedding_size = 0;
float* arr = llmodel_embedding(GetInference(), text.c_str(), &embedding_size);
if(arr == nullptr) {
Napi::Error::New(
env,
"Cannot embed. native embedder returned 'nullptr'"
).ThrowAsJavaScriptException();
return env.Undefined();
}
if(embedding_size == 0 && text.size() != 0 ) {
std::cout << "Warning: embedding length 0 but input text length > 0" << std::endl;
}
Napi::Float32Array js_array = Napi::Float32Array::New(env, embedding_size);
for (size_t i = 0; i < embedding_size; ++i) {
float element = *(arr + i);
js_array[i] = element;
}
llmodel_free_embedding(arr);
return js_array;
}
/**
* Generate a response using the model.
* @param model A pointer to the llmodel_model instance.
* @param prompt A string representing the input prompt.
* @param prompt_callback A callback function for handling the processing of prompt.
* @param response_callback A callback function for handling the generated response.
* @param recalculate_callback A callback function for handling recalculation requests.
* @param ctx A pointer to the llmodel_prompt_context structure.
*/
Napi::Value NodeModelWrapper::Prompt(const Napi::CallbackInfo& info) {
auto env = info.Env();
std::string question;
if(info[0].IsString()) {
question = info[0].As<Napi::String>().Utf8Value();
} else {
Napi::Error::New(info.Env(), "invalid string argument").ThrowAsJavaScriptException();
return info.Env().Undefined();
}
//defaults copied from python bindings
llmodel_prompt_context promptContext = {
.logits = nullptr,
.tokens = nullptr,
.n_past = 0,
.n_ctx = 1024,
.n_predict = 128,
.top_k = 40,
.top_p = 0.9f,
.temp = 0.72f,
.n_batch = 8,
.repeat_penalty = 1.0f,
.repeat_last_n = 10,
.context_erase = 0.5
};
if(info[1].IsObject())
{
auto inputObject = info[1].As<Napi::Object>();
// Extract and assign the properties
if (inputObject.Has("logits") || inputObject.Has("tokens")) {
Napi::Error::New(info.Env(), "Invalid input: 'logits' or 'tokens' properties are not allowed").ThrowAsJavaScriptException();
return info.Env().Undefined();
}
// Assign the remaining properties
if(inputObject.Has("n_past"))
promptContext.n_past = inputObject.Get("n_past").As<Napi::Number>().Int32Value();
if(inputObject.Has("n_ctx"))
promptContext.n_ctx = inputObject.Get("n_ctx").As<Napi::Number>().Int32Value();
if(inputObject.Has("n_predict"))
promptContext.n_predict = inputObject.Get("n_predict").As<Napi::Number>().Int32Value();
if(inputObject.Has("top_k"))
promptContext.top_k = inputObject.Get("top_k").As<Napi::Number>().Int32Value();
if(inputObject.Has("top_p"))
promptContext.top_p = inputObject.Get("top_p").As<Napi::Number>().FloatValue();
if(inputObject.Has("temp"))
promptContext.temp = inputObject.Get("temp").As<Napi::Number>().FloatValue();
if(inputObject.Has("n_batch"))
promptContext.n_batch = inputObject.Get("n_batch").As<Napi::Number>().Int32Value();
if(inputObject.Has("repeat_penalty"))
promptContext.repeat_penalty = inputObject.Get("repeat_penalty").As<Napi::Number>().FloatValue();
if(inputObject.Has("repeat_last_n"))
promptContext.repeat_last_n = inputObject.Get("repeat_last_n").As<Napi::Number>().Int32Value();
if(inputObject.Has("context_erase"))
promptContext.context_erase = inputObject.Get("context_erase").As<Napi::Number>().FloatValue();
}
//copy to protect llmodel resources when splitting to new thread
llmodel_prompt_context copiedPrompt = promptContext;
std::string copiedQuestion = question;
PromptWorkContext pc = {
copiedQuestion,
std::ref(inference_),
copiedPrompt,
};
auto threadSafeContext = new TsfnContext(env, pc);
threadSafeContext->tsfn = Napi::ThreadSafeFunction::New(
env, // Environment
info[2].As<Napi::Function>(), // JS function from caller
"PromptCallback", // Resource name
0, // Max queue size (0 = unlimited).
1, // Initial thread count
threadSafeContext, // Context,
FinalizerCallback, // Finalizer
(void*)nullptr // Finalizer data
);
threadSafeContext->nativeThread = std::thread(threadEntry, threadSafeContext);
return threadSafeContext->deferred_.Promise();
}
void NodeModelWrapper::SetThreadCount(const Napi::CallbackInfo& info) {
if(info[0].IsNumber()) {
llmodel_setThreadCount(GetInference(), info[0].As<Napi::Number>().Int64Value());
} else {
Napi::Error::New(info.Env(), "Could not set thread count: argument 1 is NaN").ThrowAsJavaScriptException();
return;
}
}
Napi::Value NodeModelWrapper::getName(const Napi::CallbackInfo& info) {
return Napi::String::New(info.Env(), name);
}
Napi::Value NodeModelWrapper::ThreadCount(const Napi::CallbackInfo& info) {
return Napi::Number::New(info.Env(), llmodel_threadCount(GetInference()));
}
Napi::Value NodeModelWrapper::GetLibraryPath(const Napi::CallbackInfo& info) {
return Napi::String::New(info.Env(),
llmodel_get_implementation_search_path());
}
llmodel_model NodeModelWrapper::GetInference() {
return *inference_;
}
//Exports Bindings
Napi::Object Init(Napi::Env env, Napi::Object exports) {
exports["LLModel"] = NodeModelWrapper::GetClass(env);
return exports;
}
NODE_API_MODULE(NODE_GYP_MODULE_NAME, Init)