# TODO: Framework Implementation and Testing Status

This document tracks the implementation status and TODO items for each framework.

## Legend

- ✅ **Implemented** - Feature is fully implemented and tested
- 🚧 **Partial** - Feature is partially implemented but needs completion
- ❌ **Missing** - Feature is not yet implemented
- 🧪 **Needs Tests** - Implemented but needs more comprehensive testing

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## Implementation Summary

| Feature | PyTorch | TensorFlow | Keras | Flax NNX | Flax Linen |
|---------|:-------:|:----------:|:-----:|:--------:|:----------:|
| **YatNMN** | ✅ | ✅ | ✅ | ✅ | ✅ |
| **YatConv1D** | ✅ | ✅ | ✅ | ✅ | ✅ |
| **YatConv2D** | ✅ | ✅ | ✅ | ✅ | ✅ |
| **YatConv3D** | ✅ | ✅ | ✅ | ✅ | ✅ |
| **YatConvTranspose1D** | ✅ | ✅ | ✅ | ✅ | ❌ |
| **YatConvTranspose2D** | ✅ | ✅ | ✅ | ✅ | ❌ |
| **YatConvTranspose3D** | ✅ | ✅ | ❌ | ✅ | ❌ |
| **MultiHeadAttention** | ❌ | ❌ | ❌ | ✅ | ❌ |
| **RNN Cells** | ❌ | ❌ | ❌ | ✅ | ❌ |
| **DropConnect** | ❌ | ❌ | ❌ | ✅ | ❌ |
| **Custom Activations** | ❌ | ❌ | ❌ | ✅ | ❌ |

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## Flax NNX (Most Feature-Complete)

### Implemented ✅
- **YatNMN** (Dense layer) - Full implementation with alpha scaling and DropConnect
- **YatConv** (1D, 2D, 3D) - Full implementation with all padding modes
- **YatConvTranspose** - Full implementation for 1D, 2D, and 3D
- **MultiHeadAttention** - Full attention implementation with softermax option
- **YatSimpleCell, YatLSTMCell, YatGRUCell** - RNN cells with Yat operations
- **Custom Activations** - softermax, softer_sigmoid, soft_tanh
- **DropConnect** - Regularization support for all layers
- **Alpha Scaling** - Learnable scaling parameter

### TODO Items

1. **Testing** 🧪
   - [x] Basic functionality tests
   - [x] Comprehensive test suite (attention, RNN, transpose, DropConnect)
   - [x] Edge case testing (shapes, dtypes)
   - [ ] Performance benchmarks vs standard Flax layers

2. **Documentation** 📚
   - [x] Usage examples in EXAMPLES.md
   - [ ] Add examples for RNN sequence processing
   - [ ] Document DropConnect best practices

3. **Optimization** ⚡
   - [ ] JIT compilation optimization
   - [ ] Memory efficiency improvements for large models
   - [ ] Batch processing optimizations

---

## Flax Linen

### Implemented ✅
- **YatNMN** - Full dense layer implementation
- **YatConv1D, YatConv2D, YatConv3D** - Full convolutional layers

### Missing ❌
- **YatConvTranspose** - Not implemented
- **YatAttention** - Not implemented
- **RNN Cells** - Not implemented
- **DropConnect** - Not implemented

### TODO Items

1. **Core Implementation** 🔨
   - [x] Implement YatConv1D, YatConv2D, YatConv3D
   - [ ] Implement YatConvTranspose layers
   - [ ] Add DropConnect support to YatNMN
   - [ ] Consider if Linen needs attention/RNN layers (lower priority - use NNX)

2. **Testing** 🧪
   - [x] Basic YatNMN tests
   - [x] Comprehensive YatNMN tests (alpha, bias, epsilon)
   - [x] YatConv tests
   - [x] Cross-framework consistency tests

3. **Documentation** 📚
   - [x] Linen usage examples in EXAMPLES.md
   - [ ] Document differences from NNX implementation

---

## PyTorch

### Implemented ✅
- **YatNMN** - Full dense layer implementation
- **YatConv1d/2d/3d** - Full convolutional implementation
- **YatConvTranspose1d/2d/3d** - Full transpose convolution implementation
- **Standard Conv layers** - Conv1d/2d/3d (non-Yat variants)
- **Lazy Conv layers** - LazyConv1d/2d/3d variants

### Missing ❌
- **YatAttention** - Not implemented
- **RNN Cells** - Not implemented
- **DropConnect** - Not implemented for PyTorch layers

### TODO Items

1. **Core Implementation** 🔨
   - [ ] Implement YatMultiHeadAttention
   - [ ] Implement YatRNN cells (Simple, LSTM, GRU)
   - [ ] Add DropConnect support to PyTorch layers
   - [ ] Consider adding custom activation functions

2. **Testing** 🧪
   - [x] Comprehensive test coverage for all conv layers
   - [x] Test coverage for transpose conv layers
   - [x] Test coverage for YatNMN
   - [x] YAT math validation tests
   - [x] Cross-framework consistency tests
   - [ ] Add performance benchmarks vs standard PyTorch layers

3. **Documentation** 📚
   - [x] PyTorch examples in EXAMPLES.md
   - [ ] Add PyTorch-specific examples for attention (when implemented)
   - [ ] Document device/dtype handling best practices

4. **Features** ✨
   - [ ] Add support for mixed precision (FP16/BF16)
   - [ ] Optimize for PyTorch compilation (torch.compile)

---

## Keras

### Implemented ✅
- **YatNMN** - Full dense layer implementation
- **YatConv1D, YatConv2D, YatConv3D** - Full convolutional implementation
- **YatConvTranspose1D, YatConvTranspose2D** - Transpose convolution (1D, 2D)

### Missing ❌
- **YatConvTranspose3D** - Not implemented
- **YatAttention** - Not implemented
- **RNN Cells** - Not implemented
- **DropConnect** - Not implemented

### TODO Items

1. **Core Implementation** 🔨
   - [x] Implement YatConv3D
   - [x] Implement YatConvTranspose1D, 2D
   - [ ] Implement YatConvTranspose3D
   - [ ] Add DropConnect support
   - [ ] Consider implementing attention layers

2. **Testing** 🧪
   - [x] Basic functionality tests
   - [x] Comprehensive tests (gradients, all dimensions)
   - [x] Cross-framework consistency tests
   - [ ] Add integration tests with Keras Model API
   - [ ] Test with different Keras backends

3. **Documentation** 📚
   - [x] Keras examples in EXAMPLES.md
   - [ ] Add Keras Sequential/Functional API examples
   - [ ] Add examples showing Keras callbacks integration

---

## TensorFlow

### Implemented ✅
- **YatNMN** - Full dense layer implementation
- **YatConv1D, YatConv2D, YatConv3D** - Full convolutional implementation
- **YatConvTranspose1D, YatConvTranspose2D, YatConvTranspose3D** - Full transpose convolution

### Missing ❌
- **YatAttention** - Not implemented
- **RNN Cells** - Not implemented
- **DropConnect** - Not implemented

### TODO Items

1. **Core Implementation** 🔨
   - [x] Implement YatConvTranspose1D, 2D, 3D
   - [ ] Add DropConnect support
   - [ ] Consider implementing attention layers

2. **Testing** 🧪
   - [x] Basic functionality tests
   - [x] Comprehensive tests (gradients, all dimensions)
   - [x] Cross-framework consistency tests
   - [ ] Add tests for TensorFlow 2.x eager execution
   - [ ] Test with tf.function decorator

3. **Documentation** 📚
   - [x] TensorFlow examples in EXAMPLES.md
   - [ ] Add examples for custom training loops

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## Cross-Framework Items

### Testing Infrastructure 🧪
- [x] Basic test structure for all frameworks
- [x] Framework availability detection
- [x] Cross-framework consistency tests (`test_cross_framework_consistency.py`)
- [x] Numerical equivalence verification across frameworks
- [x] Comprehensive test suites for each framework
- [ ] Performance benchmarking suite

### Documentation 📚
- [x] README with quick start
- [x] Framework comparison table
- [x] EXAMPLES.md with comprehensive usage guides
- [x] Framework imports reference
- [ ] API reference documentation (auto-generated)
- [ ] Tutorial notebooks for each framework
- [ ] Best practices guide
- [ ] Migration guide (switching between frameworks)

### CI/CD 🔄
- [x] GitHub Actions for testing
- [x] Coverage reporting (Codecov)
- [ ] Automated testing on multiple Python versions
- [ ] Automated testing on multiple OS (Linux, macOS, Windows)
- [ ] Automated documentation generation

### Performance ⚡
- [ ] Benchmark suite comparing Yat layers vs standard layers
- [ ] Memory profiling
- [ ] Optimization guides for each framework
- [ ] JIT compilation examples

### Features ✨
- [ ] Model zoo with pre-trained models
- [ ] Transfer learning examples
- [ ] Mixed precision support (where applicable)
- [ ] Distributed training examples
- [ ] Model quantization support

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## Priority Rankings

### High Priority 🔴
1. ~~Complete Keras YatConv3D implementation~~ ✅
2. ~~Implement YatConvTranspose for Keras/TensorFlow~~ ✅
3. ~~Add comprehensive tests for all implemented features~~ ✅
4. ~~Cross-framework consistency tests~~ ✅
5. Add DropConnect support to PyTorch, Keras, TensorFlow

### Medium Priority 🟡
1. Implement attention layers for PyTorch, Keras, TensorFlow
2. Add RNN cells for PyTorch, Keras, TensorFlow
3. Implement YatConvTranspose for Linen
4. Performance optimization and benchmarking
5. Keras YatConvTranspose3D

### Low Priority 🟢
1. Advanced features (ternary networks, quantization)
2. Model zoo and pre-trained models
3. Additional documentation and tutorials
4. Multi-OS CI testing

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## Recent Completions ✅

### December 2025
- ✅ Implemented YatConv1D/2D/3D for Flax Linen
- ✅ Implemented YatConvTranspose1D/2D for Keras
- ✅ Implemented YatConvTranspose1D/2D/3D for TensorFlow
- ✅ Created comprehensive test suites for all frameworks
- ✅ Added cross-framework consistency tests
- ✅ Created EXAMPLES.md with detailed usage guides
- ✅ Improved README.md structure and readability
- ✅ Fixed YAT math in PyTorch transpose convolutions
- ✅ Verified numerical equivalence across all frameworks

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## Notes

- **NNX** is the most feature-complete implementation and should be used as a reference for other frameworks
- **Linen** has lower priority for additional features, as NNX is the recommended Flax API going forward
- **PyTorch** has excellent coverage for conv layers but is missing attention and RNN support
- **Keras/TensorFlow** are nearly at feature parity for convolution layers
- **Cross-framework consistency** is verified with < 1e-6 error tolerance

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*Last Updated: December 2025*
