Implement mlx.core.exporter()

[sydneyrenee]

🎯 Implement mlx.core.exporter()

Priority: low | Module: export | Type: other | Category: export

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📋 Quick Reference

Item	Value
Python Source	python/src/ops.cpp
Node Target	node/src/native/array.cc
Test File	node/test/ops.test.js
C++ Namespace	mlx::core

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🚀 Step-by-Step Implementation

Step 1: Review Python Implementation

# See the Python binding
grep -B 5 -A 30 '"exporter"' python/src/ops.cpp

Step 2: Implement in Node.js

File to edit: node/src/native/array.cc

Napi::Value Exporter(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::exporter(/* 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("exporter failed: ") + e.what())
        .ThrowAsJavaScriptException();
    return env.Null();
  }
}

Step 3: Register the Function

Add to the Init() function at bottom of node/src/native/array.cc:

core.Set("exporter", Napi::Function::New(env, Exporter, "exporter", &data));

Step 4: Add Tests

File: node/test/ops.test.js

const mx = require('..');

describe('mlx.core.exporter', () => {
  it('should work correctly', () => {
    // TODO: Add test based on Python tests
    // const a = mx.core.array([1, 2, 3]);
    // const result = mx.export.exporter(a);
    // expect(result).toBeDefined();
  });
});

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📚 Resources

Similar Implementations

Look at these in node/src/native/array.cc:

• Unary ops: Exp(), Log(), Sin(), Cos()
• Binary ops: Add(), Multiply(), Subtract()
• Reductions: Sum(), Mean(), Max(), Min()

Common Patterns

Parse Array:

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());
if (info.Length() > N) {
  stream = mlx::node::ParseStreamOrDevice(env, info[N], *addon);
}

Return Array:

return WrapArray(env, std::make_shared<mlx::core::array>(std::move(result)));

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✅ Completion Checklist

• Reviewed Python implementation
• Implemented function in node/src/native/array.cc
• Registered in Init()
• Added tests in node/test/ops.test.js
• Builds: cd node && npm run build
• Tests pass: npm test
• Updated docs/API_CHECKLIST.md

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Auto-generated for MLX Node.js API completeness

[sydneyrenee]

Status update: Core ops complete (220+ tests).

Export functions serialize computation graphs for deployment. These wrap mlx::core::export_function(), mlx::core::export_to_dot(), and the Exporter class. Lower priority — useful for model deployment pipelines but not needed for inference or training.

Depends on eval being implemented first, since export captures evaluated graph structure.