# TODO - Future Implementation Tasks

## Python API - Core Functionality

### Execution Engine
[ ] Implement actual tensor execution in MLContext.compute()
    - Currently returns placeholder zeros
    - Need to integrate with ONNX/CoreML runtimes
    - Support actual input/output tensor data flow

[ ] Add MLTensor class for explicit tensor management
    - createTensor() for pre-allocating tensors
    - readTensor() for reading results
    - writeTensor() for setting input data

[ ] Implement async execution support
    - WebNN spec uses async/await
    - Python asyncio integration
    - Non-blocking compute operations

### Operations - Missing Implementations

[ ] Convolution operations
    - conv2d
    - convTranspose2d
    - depthwiseConv2d

[ ] Pooling operations
    - averagePool2d
    - maxPool2d
    - l2Pool2d
    - globalAveragePool
    - globalMaxPool

[ ] Normalization operations
    - batchNormalization
    - instanceNormalization
    - layerNormalization
    - localResponseNormalization

[ ] Reduction operations
    - reduceSum
    - reduceMean
    - reduceMax
    - reduceMin
    - reduceProduct
    - reduceL1
    - reduceL2
    - reduceLogSum
    - reduceLogSumExp

[ ] Element-wise operations
    - abs, ceil, floor, round
    - exp, log, sqrt, reciprocal
    - sin, cos, tan, asin, acos, atan
    - sinh, cosh, tanh, asinh, acosh, atanh
    - erf, identity, neg, sign

[ ] Logic operations
    - equal, greater, greaterOrEqual
    - lesser, lesserOrEqual, logicalNot
    - logicalAnd, logicalOr, logicalXor

[ ] Advanced operations
    - concat (concatenate tensors)
    - expand (broadcast dimensions)
    - gather, scatter
    - slice (extract sub-tensors)
    - split (split tensor into parts)
    - squeeze (remove dimensions of size 1)
    - tile (repeat tensor)
    - transpose
    - where (conditional selection)
    - pad (add padding)
    - prelu, elu, leakyRelu, hardSigmoid, hardSwish, gelu
    - softplus, softsign

[ ] Recurrent operations
    - gru, gruCell
    - lstm, lstmCell

[ ] Quantization operations
    - dequantizeLinear
    - quantizeLinear

[ ] Shape inference and broadcasting
    - Automatic shape computation for operations
    - Broadcasting rules for binary operations
    - Shape validation at graph build time

### CoreML Converter - Missing Operations

[ ] Add support for activation functions
    - relu (currently unsupported!)
    - sigmoid
    - tanh
    - softmax
    - leakyRelu, prelu, elu

[ ] Add support for element-wise operations
    - sub, mul, div (only add is supported)
    - power, sqrt, exp, log

[ ] Add support for shape operations
    - reshape (currently unsupported!)
    - transpose
    - concat, split

[ ] Add support for pooling operations
    - averagePool2d, maxPool2d

[ ] Add support for convolution operations
    - conv2d, convTranspose2d

[ ] Add support for normalization operations
    - batchNormalization, instanceNormalization

## Testing & Quality

### Python Tests
[ ] Comprehensive operation tests
    - Test each operation independently
    - Test with different data types
    - Test edge cases (empty tensors, scalars)
    - Test shape broadcasting

[ ] Integration tests
    - End-to-end graph building and conversion
    - Multi-layer network tests
    - Complex graph patterns

[ ] Property-based testing
    - Use hypothesis for generative testing
    - Random graph generation and validation

[ ] Performance benchmarks
    - Compilation time benchmarks
    - Conversion speed benchmarks
    - Memory usage profiling

[ ] Test coverage
    - Aim for >80% code coverage
    - Add coverage reporting to CI

### Type Checking & Linting
[ ] Add mypy for static type checking
    - Type check all Python bindings
    - Add mypy to CI pipeline

[ ] Add ruff/flake8 for Python linting
    - Enforce PEP 8 style
    - Add to pre-commit hooks

[ ] Add black for code formatting
    - Auto-format Python code
    - Check formatting in CI

### Rust Code Quality
[ ] Fix Rust 2024 edition warnings
    - Add unsafe blocks where needed
    - Update to new edition idioms

[ ] Add more Rust unit tests
    - Test converters with various graphs
    - Test validation edge cases

[ ] Reduce compiler warnings
    - Fix unused variable warnings
    - Address clippy suggestions

## Documentation

### API Documentation
[ ] Auto-generate API docs from docstrings
    - Add comprehensive docstrings to all Python classes
    - Use mkdocstrings to auto-generate reference docs
    - Add type hints throughout

[ ] Add more code examples
    - Real-world use cases (MNIST, ResNet, etc.)
    - Transfer learning examples
    - Model optimization examples

[ ] Video tutorials
    - Getting started video
    - Building complex models
    - Deployment guide

[ ] Interactive examples
    - Jupyter notebook examples
    - Google Colab notebooks
    - Try-it-live web interface

### Performance Documentation
[ ] Benchmarking guide
    - How to benchmark models
    - Performance comparison ONNX vs CoreML
    - Optimization tips

[ ] Memory usage guide
    - Understanding memory consumption
    - Reducing memory footprint
    - Float16 vs Float32 trade-offs

### Platform-Specific Guides
[ ] macOS Neural Engine guide
    - How to use ANE effectively
    - Performance characteristics
    - Supported operations

[ ] Windows DirectML guide (future)
    - DirectML integration
    - GPU acceleration on Windows

[ ] Linux GPU guide
    - CUDA/ROCm integration
    - CPU optimization flags

## CI/CD & Packaging

### PyPI Publishing
[ ] Create PyPI package publishing workflow
    - Build wheels for multiple platforms
    - manylinux wheels for Linux
    - macOS universal2 wheels
    - Windows wheels

[ ] Automated version bumping
    - Semantic versioning
    - Changelog generation
    - Git tag automation

[ ] Release automation
    - GitHub Releases on tag push
    - Automated release notes
    - Asset uploading (wheels, docs)

### Multi-Platform Support
[ ] Test on multiple Python versions
    - Python 3.8, 3.9, 3.10, 3.11, 3.12
    - Matrix testing in CI

[ ] Test on multiple platforms
    - Ubuntu (latest, 20.04, 22.04)
    - macOS (Intel, Apple Silicon)
    - Windows (latest)

[ ] Platform-specific features
    - Conditional compilation for platform features
    - Feature detection at runtime

### Docker Images
[ ] Create Docker images
    - Python + Rust development image
    - Runtime-only image
    - GPU-enabled image

[ ] Docker Hub publishing
    - Automated image builds
    - Multi-architecture images
    - Version tagging

## Features & Enhancements

### Graph Optimization
[ ] Implement graph optimization passes
    - Constant folding
    - Dead code elimination
    - Operation fusion
    - Common subexpression elimination

[ ] Graph analysis tools
    - Visualize graphs (beyond Graphviz)
    - Memory usage estimation
    - Computational complexity analysis

### Model Import/Export
[ ] ONNX model import
    - Parse existing ONNX models
    - Convert ONNX → WebNN graph
    - Preserve metadata

[ ] PyTorch integration
    - Export PyTorch models to WebNN
    - torch.fx graph conversion
    - Maintain gradient information (future)

[ ] TensorFlow integration
    - Export TensorFlow models
    - SavedModel → WebNN conversion

[ ] Hugging Face integration
    - Export transformers models
    - Easy model hub integration

### Developer Experience
[ ] Better error messages
    - More descriptive validation errors
    - Suggestions for fixes
    - Error recovery hints

[ ] Debugging tools
    - Graph visualization in Jupyter
    - Intermediate value inspection
    - Step-by-step execution

[ ] Profiling tools
    - Operation-level timing
    - Memory profiling
    - Bottleneck identification

### WebNN Spec Compliance
[ ] Full WebNN API compliance
    - Implement all missing operations
    - Match behavior exactly
    - Pass WebNN conformance tests (if available)

[ ] Context options
    - Power preference enforcement
    - Device preference handling
    - Capability querying (opSupportLimits)

[ ] Graph execution modes
    - Sync vs async execution
    - Streaming execution for large inputs
    - Batch processing

## Ecosystem Integration

### NumPy Integration
[ ] Better NumPy interop
    - Zero-copy where possible
    - Support NumPy's __array_interface__
    - Proper dtype conversion

[ ] NumPy-like API
    - Operator overloading (+, -, *, /)
    - Slicing support
    - Pythonic indexing

### ML Framework Integration
[ ] JAX integration
    - Export JAX computations
    - jax.tree_util support

[ ] scikit-learn integration
    - Convert simple sklearn models
    - Pipeline integration

### Visualization
[ ] Netron support
    - Ensure exported models work in Netron
    - Add metadata for better visualization

[ ] TensorBoard integration
    - Graph visualization
    - Profiling data export

## Infrastructure

### Build System
[ ] Optimize build times
    - Incremental compilation
    - Build caching in CI
    - Parallel builds

[ ] Cross-compilation support
    - Build for different targets
    - Static linking options

### Security
[ ] Security audit
    - Dependency vulnerability scanning
    - SAST (Static Application Security Testing)
    - Regular security updates

[ ] Sandboxing
    - Restrict file system access
    - Memory limits
    - Timeout enforcement

### Monitoring
[ ] Usage analytics (opt-in)
    - Track which operations are used
    - Performance telemetry
    - Error reporting

[ ] Crash reporting
    - Automated crash reports (opt-in)
    - Stack trace collection
    - Issue auto-creation

## Community

### Examples & Templates
[ ] Example repository
    - Real-world examples
    - Template projects
    - Starter kits

[ ] Model zoo
    - Pre-built models
    - Optimized for WebNN
    - Various domains (CV, NLP, etc.)

### Documentation
[ ] Contributing guide
    - How to contribute
    - Development setup
    - Code review process

[ ] Architecture documentation
    - High-level design
    - Component interactions
    - Extension points

### Community Building
[ ] Discord/Slack channel
    - Community discussions
    - Support channel
    - Show & tell

[ ] Blog posts & tutorials
    - Getting started blog post
    - Technical deep dives
    - Performance case studies

## Priority Levels

HIGH PRIORITY (Next Session):
- [ ] Fix CoreML converter to support relu, sigmoid, tanh, softmax
- [ ] Implement actual compute() with ONNX runtime integration
- [ ] Add comprehensive Python tests
- [ ] Fix Rust 2024 edition warnings
- [ ] Add basic shape inference/validation

MEDIUM PRIORITY:
- [ ] Add more operations (conv2d, pooling, normalization)
- [ ] PyPI packaging and publishing
- [ ] Better error messages
- [ ] Performance benchmarks

LOW PRIORITY:
- [ ] Full WebNN spec compliance
- [ ] Advanced graph optimizations
- [ ] Multi-framework integration
- [ ] Community infrastructure

## Notes

- Most missing functionality is in the Rust backend (converters, executors)
- Python bindings are complete for the architecture - just need more operations
- CoreML converter is significantly behind ONNX converter in feature support
- Documentation is comprehensive and ready for community use
- Testing infrastructure needs expansion
- CI/CD for packaging and publishing not yet set up

Last Updated: 2024-12-05
