# RusTorch Refactoring Task List
# RusTorchリファクタリング・タスクリスト
## 🎯 Phase 1: Critical Infrastructure (v0.4.0)
### Task 1.1: Backend Abstraction Layer / バックエンド抽象化レイヤー
**Estimated Effort**: 40 hours / 推定工数: 40時間
**Dependencies**: None / 依存関係: なし
#### Subtasks / サブタスク:
- [ ] **1.1.1** Design `ComputeBackend` trait interface
- Define core operations (matmul, elementwise, convolution)
- Specify memory management interface
- Design error handling for backends
- **Files to modify**: Create `src/backends/mod.rs`
- [ ] **1.1.2** Implement CPU backend with SIMD
- Extract SIMD operations from existing code
- Implement `ComputeBackend` for CPU
- Add fallback implementations
- **Files to modify**: Create `src/backends/cpu.rs`
- [ ] **1.1.3** Create unified GPU backend interface
- Extract common GPU operations from existing kernels
- Implement shared GPU memory management
- Add device context management
- **Files to modify**: Create `src/backends/gpu/mod.rs`
- [ ] **1.1.4** Migrate CUDA backend
- Refactor `src/gpu/cuda_kernels.rs` to implement `ComputeBackend`
- Preserve existing CUDA kernel implementations
- Add CUDA-specific optimizations
- **Files to modify**: `src/backends/gpu/cuda.rs`
- [ ] **1.1.5** Migrate Metal backend
- Refactor `src/gpu/metal_kernels.rs` to implement `ComputeBackend`
- Preserve Metal shader implementations
- Add Metal-specific optimizations
- **Files to modify**: `src/backends/gpu/metal.rs`
- [ ] **1.1.6** Migrate OpenCL backend
- Refactor `src/gpu/opencl_kernels.rs` to implement `ComputeBackend`
- Preserve OpenCL kernel implementations
- Add OpenCL-specific optimizations
- **Files to modify**: `src/backends/gpu/opencl.rs`
- [ ] **1.1.7** Add backend selection and runtime
- Implement automatic backend selection logic
- Add manual backend override capability
- Create backend benchmarking utilities
- **Files to modify**: Create `src/backends/runtime.rs`
- [ ] **1.1.8** Update Tensor to use backends
- Modify `Tensor` to use `ComputeBackend` abstraction
- Maintain backward compatibility
- Add backend switching capabilities
- **Files to modify**: `src/tensor/core.rs`
### Task 1.2: Tensor Operations Split / テンソル演算分割
**Estimated Effort**: 32 hours / 推定工数: 32時間
**Dependencies**: Task 1.1 (Backend abstraction) / 依存関係: タスク1.1（バックエンド抽象化）
#### Subtasks / サブタスク:
- [ ] **1.2.1** Create operations module structure
- Design new module hierarchy
- Create module files and re-export structure
- Plan backward compatibility layer
- **Files to create**: `src/tensor/operations/mod.rs`
- [ ] **1.2.2** Extract arithmetic operations
- Move element-wise operations (+, -, *, /, %, ^)
- Implement using backend abstraction
- Add comprehensive tests
- **Files to create**: `src/tensor/operations/arithmetic.rs`
- [ ] **1.2.3** Extract linear algebra operations
- Move matrix operations (matmul, svd, qr, lu, eig)
- Add conditional compilation for linalg features
- Preserve PyTorch compatibility
- **Files to create**: `src/tensor/operations/linear_algebra.rs`
- [ ] **1.2.4** Extract reduction operations
- Move aggregation operations (sum, mean, max, min, var, std)
- Implement efficient reduction algorithms
- Add axis-specific reductions
- **Files to create**: `src/tensor/operations/reduction_ops.rs`
- [ ] **1.2.5** Extract shape operations
- Move shape manipulation (reshape, transpose, permute, squeeze, unsqueeze)
- Optimize memory layout operations
- Add broadcasting support
- **Files to create**: `src/tensor/operations/shape_ops.rs`
- [ ] **1.2.6** Extract statistical operations
- Move statistical functions (median, quantile, covariance, correlation)
- Add probability distribution support
- Optimize for large datasets
- **Files to create**: `src/tensor/operations/statistical.rs`
- [ ] **1.2.7** Extract FFT operations
- Move Fourier transform operations (fft, rfft, ifft, fftshift)
- Preserve existing FFT implementations
- Add 2D/ND FFT support planning
- **Files to create**: `src/tensor/operations/fft.rs`
- [ ] **1.2.8** Extract broadcasting operations
- Move broadcasting logic and utilities
- Optimize broadcasting performance
- Add shape compatibility checking
- **Files to create**: `src/tensor/operations/broadcasting.rs`
- [ ] **1.2.9** Update imports and tests
- Update all import statements across codebase
- Migrate tests to new module structure
- Ensure no functionality regression
- **Files to modify**: All files importing from `tensor/operations.rs`
### Task 1.3: GPU Kernel Consolidation / GPUカーネル統合
**Estimated Effort**: 24 hours / 推定工数: 24時間
**Dependencies**: Task 1.1 (Backend abstraction) / 依存関係: タスク1.1（バックエンド抽象化）
#### Subtasks / サブタスク:
- [ ] **1.3.1** Create unified kernel trait
- Design `KernelExecutor` trait for common operations
- Define kernel compilation and caching interface
- Specify performance profiling hooks
- **Files to create**: `src/backends/gpu/kernels/mod.rs`
- [ ] **1.3.2** Extract shared GPU memory management
- Consolidate GPU memory allocation logic
- Implement unified buffer management
- Add memory pooling for frequent allocations
- **Files to create**: `src/backends/gpu/memory.rs`
- [ ] **1.3.3** Create kernel compilation pipeline
- Implement shared kernel compilation infrastructure
- Add kernel caching and optimization
- Support runtime kernel generation
- **Files to create**: `src/backends/gpu/compiler.rs`
- [ ] **1.3.4** Add cross-backend performance benchmarking
- Create benchmarking framework for GPU operations
- Implement automatic backend selection based on performance
- Add performance regression detection
- **Files to create**: `src/backends/gpu/benchmarks.rs`
## 🎯 Phase 2: Module Organization (v0.5.0)
### Task 2.1: Neural Network Layer Traits / ニューラルネットワーク層トレイト
**Estimated Effort**: 36 hours / 推定工数: 36時間
**Dependencies**: Task 1.1, 1.2 (Backend and operations) / 依存関係: タスク1.1, 1.2（バックエンドと演算）
#### Subtasks / サブタスク:
- [ ] **2.1.1** Design layer trait hierarchy
- Create base `Layer` trait with forward pass
- Define `ParameterizedLayer` for layers with parameters
- Add specialized traits (ConvolutionLayer, RecurrentLayer, etc.)
- **Files to create**: `src/nn/traits/mod.rs`
- [ ] **2.1.2** Create parameter management traits
- Define parameter initialization strategies
- Implement parameter sharing and freezing
- Add gradient accumulation and zeroing
- **Files to create**: `src/nn/traits/parameters.rs`
- [ ] **2.1.3** Refactor convolution layers
- Extract shared convolution logic to base trait
- Implement Conv1d, Conv2d, Conv3d using shared base
- Add specialized convolution optimizations
- **Files to modify**: `src/nn/conv1d.rs`, `src/nn/conv2d.rs`, `src/nn/conv3d.rs`
- [ ] **2.1.4** Refactor recurrent layers
- Create shared RNN base trait
- Refactor RNN, LSTM, GRU to use shared implementation
- Optimize recurrent computation patterns
- **Files to modify**: `src/nn/rnn.rs`, `src/nn/lstm.rs`, `src/nn/gru.rs`
- [ ] **2.1.5** Standardize activation functions
- Create consistent activation function interface
- Add in-place and out-of-place variants
- Optimize activation implementations
- **Files to modify**: `src/nn/activation.rs`
- [ ] **2.1.6** Update layer creation and Module trait
- Enhance Module trait with new layer abstractions
- Add builder patterns for complex layers
- Implement automatic parameter discovery
- **Files to modify**: `src/nn/mod.rs`
### Task 2.2: Device Management Refactoring / デバイス管理リファクタリング
**Estimated Effort**: 20 hours / 推定工数: 20時間
**Dependencies**: Task 1.1 (Backend abstraction) / 依存関係: タスク1.1（バックエンド抽象化）
#### Subtasks / サブタスク:
- [ ] **2.2.1** Split device detection
- Extract device enumeration logic
- Add device capability detection
- Implement device compatibility checking
- **Files to create**: `src/gpu/device/detection.rs`
- [ ] **2.2.2** Create context management
- Extract GPU context creation and management
- Add context pooling and reuse
- Implement context switching optimization
- **Files to create**: `src/gpu/device/context.rs`
- [ ] **2.2.3** Implement device selection
- Create intelligent device selection algorithms
- Add workload-based device assignment
- Implement load balancing across devices
- **Files to create**: `src/gpu/device/selection.rs`
- [ ] **2.2.4** Add capability management
- Implement feature detection per device
- Add capability-based operation fallbacks
- Create capability caching system
- **Files to create**: `src/gpu/device/capabilities.rs`
### Task 2.3: Model I/O Unification / モデルI/O統一
**Estimated Effort**: 28 hours / 推定工数: 28時間
**Dependencies**: None / 依存関係: なし
#### Subtasks / サブタスク:
- [ ] **2.3.1** Design unified model I/O interface
- Create common traits for model import/export
- Define format-agnostic model representation
- Add validation and conversion utilities
- **Files to create**: `src/model_io/mod.rs`
- [ ] **2.3.2** Consolidate PyTorch support
- Merge convert/ and model_import/ PyTorch functionality
- Implement comprehensive PyTorch compatibility
- Add state dict round-trip support
- **Files to create**: `src/model_io/pytorch/mod.rs`
- [ ] **2.3.3** Enhance ONNX support
- Consolidate ONNX import/export functionality
- Add comprehensive ONNX operator support
- Implement ONNX optimization passes
- **Files to create**: `src/model_io/onnx/mod.rs`
- [ ] **2.3.4** Improve Safetensors support
- Enhance Safetensors import/export
- Add metadata preservation
- Implement lazy loading for large models
- **Files to create**: `src/model_io/safetensors/mod.rs`
- [ ] **2.3.5** Add model validation utilities
- Create comprehensive model validation
- Add format conversion utilities
- Implement model comparison and diff tools
- **Files to create**: `src/model_io/common/validation.rs`
## 🎯 Phase 3: API Consistency (v0.6.0)
### Task 3.1: Error Handling Unification / エラーハンドリング統一
**Estimated Effort**: 16 hours / 推定工数: 16時間
**Dependencies**: All previous tasks / 依存関係: 全ての前タスク
#### Subtasks / サブタスク:
- [ ] **3.1.1** Design unified error types
- Create comprehensive RusTorchError enum
- Add error context and chaining
- Implement error conversion utilities
- **Files to create**: `src/error.rs` (enhanced)
- [ ] **3.1.2** Update all Result types
- Replace inconsistent Result types across codebase
- Add error context where appropriate
- Implement error propagation macros
- **Files to modify**: All files with Result returns
- [ ] **3.1.3** Enhance error messages
- Add detailed error descriptions
- Implement error context preservation
- Add suggestion systems for common errors
- **Files to modify**: Error-generating functions across codebase
### Task 3.2: SIMD Operations Consolidation / SIMD演算統合
**Estimated Effort**: 20 hours / 推定工数: 20時間
**Dependencies**: Task 1.1, 1.2 (Backend and operations) / 依存関係: タスク1.1, 1.2（バックエンドと演算）
#### Subtasks / サブタスク:
- [ ] **3.2.1** Create SIMD trait abstractions
- Design SIMD operation traits
- Add architecture-specific implementations
- Implement fallback strategies
- **Files to create**: `src/compute/simd/mod.rs`
- [ ] **3.2.2** Consolidate arithmetic operations
- Merge scattered vectorized math operations
- Add comprehensive SIMD arithmetic suite
- Optimize for different data types
- **Files to create**: `src/compute/simd/ops/arithmetic.rs`
- [ ] **3.2.3** Consolidate reduction operations
- Merge vectorized reduction implementations
- Add parallel reduction strategies
- Optimize for different architectures
- **Files to create**: `src/compute/simd/ops/reduction.rs`
- [ ] **3.2.4** Add architecture-specific implementations
- Implement AVX2/AVX512 optimizations
- Add ARM NEON implementations
- Create automatic architecture detection
- **Files to create**: `src/compute/simd/arch/`
### Task 3.3: Memory Management Strategy / メモリ管理戦略
**Estimated Effort**: 24 hours / 推定工数: 24時間
**Dependencies**: Task 1.1 (Backend abstraction) / 依存関係: タスク1.1（バックエンド抽象化）
#### Subtasks / サブタスク:
- [ ] **3.3.1** Design memory allocator traits
- Create `MemoryAllocator` trait interface
- Add alignment and pooling support
- Implement allocator statistics
- **Files to create**: `src/memory/allocator.rs`
- [ ] **3.3.2** Implement specialized allocators
- Create system, pool, and aligned allocators
- Add GPU memory allocator
- Implement allocator selection logic
- **Files to create**: `src/memory/allocators/`
- [ ] **3.3.3** Add memory pooling
- Implement efficient memory pools
- Add pool size optimization
- Create pool garbage collection
- **Files to create**: `src/memory/pool.rs`
- [ ] **3.3.4** Integrate with tensor system
- Update Tensor to use new allocators
- Add memory usage tracking
- Implement memory optimization hints
- **Files to modify**: `src/tensor/core.rs`
## 📊 Task Dependencies and Timeline / タスク依存関係とタイムライン
### Critical Path / クリティカルパス:
1. Task 1.1 (Backend Abstraction) → **40 hours**
2. Task 1.2 (Operations Split) → **32 hours**
3. Task 2.1 (Layer Traits) → **36 hours**
4. Task 3.1 (Error Handling) → **16 hours**
**Total Critical Path**: 124 hours / 総クリティカルパス: 124時間
### Parallel Work Opportunities / 並行作業機会:
- Task 1.3 can run parallel with Task 1.2 (after Task 1.1)
- Task 2.2 and 2.3 can run parallel (independent)
- Task 3.2 and 3.3 can run parallel (after Task 1.1/1.2)
### Resource Allocation / リソース配分:
- **Phase 1**: 96 hours (3-4 weeks with 1 developer)
- **Phase 2**: 84 hours (4-5 weeks with 1 developer)
- **Phase 3**: 60 hours (2-3 weeks with 1 developer)
- **Total**: 240 hours (10-12 weeks)
## ✅ Definition of Done / 完了の定義
### For Each Task / 各タスクについて:
- [ ] Implementation completed according to specification
- [ ] All existing tests pass
- [ ] New tests added for new functionality (80%+ coverage)
- [ ] Documentation updated (including examples)
- [ ] Performance benchmarks show no regression
- [ ] Code review completed
- [ ] Integration tests pass
### For Each Phase / 各フェーズについて:
- [ ] All tasks in phase completed
- [ ] Full test suite passes (647+ tests)
- [ ] Performance benchmarks meet targets
- [ ] Documentation generated and reviewed
- [ ] Migration guide updated
- [ ] Backward compatibility verified
- [ ] Release notes prepared
## 🔍 Risk Mitigation / リスク緩和
### High-Risk Areas / 高リスク領域:
1. **Backend Migration**: Breaking existing GPU functionality
- **Mitigation**: Extensive testing on multiple GPU types
- **Fallback**: Keep old implementation during transition
2. **Operations Split**: Breaking tensor API compatibility
- **Mitigation**: Comprehensive re-export layer
- **Fallback**: Staged migration with feature flags
3. **Performance Regression**: New abstractions may introduce overhead
- **Mitigation**: Continuous benchmarking during development
- **Fallback**: Zero-cost abstraction principles
### Contingency Planning / 緊急計画:
- Each task has 20% time buffer for unexpected issues
- Critical path tasks have priority for resource allocation
- Regular checkpoint reviews at 25%, 50%, 75% completion
- Rollback plans for each major refactoring
This comprehensive task list provides clear, actionable steps for the complete RusTorch refactoring initiative.