Implement Mlx.core.SGD() For MLX Node.js

Hey guys! Let's dive into implementing mlx.core.SGD() for MLX in Node.js. This is a critical task to enhance our optimizer capabilities within the MLX framework. We'll be working on integrating the Stochastic Gradient Descent (SGD) optimizer, a fundamental algorithm in machine learning, into the Node.js bindings for MLX. This implementation will allow developers to leverage the power of SGD directly within their JavaScript and TypeScript projects, making it easier to train models on the client-side or in Node.js environments. This guide will walk you through the necessary steps, from reviewing the Python implementation to adding tests and ensuring everything works smoothly. Let's get started!

Quick Reference and Essential Files

Before we begin, let's get our bearings with a quick reference to the files we will be working with and their importance. Understanding these files is key to a smooth implementation process. Here's a quick overview:

• Python Source (python/src/ops.cpp): This file contains the Python binding for SGD. We will use this to understand the function signature, the arguments it takes, and how it behaves in the Python environment. Reviewing the Python implementation gives us a roadmap for how to implement the same functionality in Node.js.
• Node Target (node/src/native/array.cc): This is the core file where the Node.js bindings for MLX are defined. We will add our C++ code here to implement the SGD function. This file is the bridge between the MLX core and the Node.js environment.
• Test File (node/test/ops.test.js): The test file is where we will write unit tests to ensure that our SGD implementation works correctly. Testing is essential to verify that the function behaves as expected and to catch any potential issues before they cause problems.
• C++ Namespace (mlx::core): This is the namespace in the C++ code where the core MLX functions are defined. We'll be working within this namespace to correctly integrate SGD into the MLX environment.

Now, let's jump into the step-by-step implementation!

Step-by-Step Implementation Guide

Alright, let's get our hands dirty and implement the mlx.core.SGD() function. This part involves translating the Python implementation into C++ and Node.js-compatible code, ensuring that it interacts well with the existing MLX Node.js environment.

Step 1: Review the Python Implementation

First things first, we need to understand how SGD works in Python. This involves examining the arguments it takes, the computations it performs, and the output it generates. Use the following command to see the Python binding:

grep -B 5 -A 30 '"SGD"' python/src/ops.cpp

This command searches the ops.cpp file for the string "SGD" and provides a few lines before and after the match. This will give you context and show you how the function is defined and used in Python. Take note of the parameters and how they are handled. This information is vital for creating a similar function in Node.js.

Step 2: Implement in Node.js

Now, let's implement the SGD function in node/src/native/array.cc. Follow the code structure below, and we'll break it down.

Napi::Value Sgd(const Napi::CallbackInfo& info) {
  auto env = info.Env();
  auto* addon = static_cast<mlx::node::AddonData*>(info.Data());

  try {
    mlx::node::Runtime::Instance().EnsureMetalInit();
  } catch (const std::exception& e) {
    Napi::Error::New(env, e.what()).ThrowAsJavaScriptException();
    return env.Null();
  }

  // TODO: Parse arguments based on Python signature
  // Check python/src/python/src/ops.cpp for the exact signature

  // Example: Parse array argument
  auto* wrapper = UnwrapArray(env, info[0]);
  if (!wrapper) return env.Null();
  const auto& a = wrapper->tensor();

  // Parse stream
  auto stream = mlx::core::default_stream(mlx::core::default_device());
  // (adjust index based on number of args)
  if (info.Length() > 1) {
    stream = mlx::node::ParseStreamOrDevice(env, info[info.Length() - 1], *addon);
    if (env.IsExceptionPending()) return env.Null();
  }

  try {
    auto result = mlx::core::SGD(/* args */, stream);
    return WrapArray(env, std::make_shared<mlx::core::array>(std::move(result)));
  } catch (const std::exception& e) {
    Napi::Error::New(env, std::string("SGD failed: ") + e.what())
        .ThrowAsJavaScriptException();
    return env.Null();
  }
}

1. Environment Setup: We start by setting up the Node.js environment using info.Env(). We also retrieve the AddonData instance, which provides access to the MLX runtime.
2. Metal Initialization: We ensure that Metal is initialized using mlx::node::Runtime::Instance().EnsureMetalInit(). This is crucial for GPU operations.
3. Argument Parsing: This is where we parse the arguments passed to the SGD function. You must adapt the parsing logic to match the Python signature from Step 1. The example parses an array argument.
4. Stream Handling: A stream is set up to manage operations. If a stream is provided in the arguments, it's parsed accordingly. Otherwise, a default stream is used.
5. Core SGD Call: The mlx::core::SGD(/* args */, stream) is where the actual SGD function call happens. You need to replace /* args */ with the appropriate arguments you parsed earlier.
6. Return Value: The function wraps the result in an array and returns it. This ensures that the results can be used in the Node.js environment.

Step 3: Register the Function

After implementing the function, you must register it so that Node.js can find and use it. Add the following line to the Init() function at the bottom of node/src/native/array.cc:

core.Set("SGD", Napi::Function::New(env, Sgd, "SGD", &data));

This registers the Sgd function under the name `