Claude/refactor pytorch codegen zr oi f#46
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- Add LayerCodeSpec dataclass for structured code specifications - Create TemplateManager for Jinja2 template loading and caching - Add get_pytorch_code_spec() and get_tensorflow_code_spec() methods to NodeDefinition - Create template directory structure for PyTorch and TensorFlow - Add base layer templates for both frameworks
- Create layer templates for conv2d, linear, maxpool, flatten, relu, softmax, dropout, batchnorm, attention, add, concat - Create file templates for model.py, train.py, dataset.py, config.py - Templates preserve good coding practices: reusable classes, comprehensive documentation, proper shape annotations
- Add base utilities (topological sort, edge map builder) - Create PyTorchCodeOrchestrator class - Implement template-driven code generation - Support skip connections in forward pass - Preserve all existing features (adaptive hyperparameters, test code generation) - Return same API format as original codegen
- Modify generate_pytorch_code() to delegate to PyTorchCodeOrchestrator - Preserve exact same function signature and return type - Maintain 100% backward compatibility with views - Mark legacy code as preserved for reference - Complete refactor: template-based, extensible, non-monolithic
- Create layer templates for conv2d, linear, maxpool, flatten, dropout, batchnorm, add, concat - Create file templates for model.py, train.py, dataset.py, config.py - Adapt templates for TensorFlow/Keras API and NHWC format - Maintain same documentation quality as PyTorch
- Add get_tensorflow_code_spec() to Conv2D, Linear, MaxPool2D, Flatten, Dropout, BatchNorm2D, Add, Concat - Adapt for TensorFlow/Keras API (filters vs channels, NHWC format) - Mirror PyTorch implementation pattern for consistency
- Add TensorFlowCodeOrchestrator class mirroring PyTorch structure - Implement template-driven code generation for TensorFlow/Keras - Support NHWC format and TensorFlow-specific patterns - Handle skip connections in forward pass - Return same API format as original TensorFlow codegen
- Modify generate_tensorflow_code() to delegate to TensorFlowCodeOrchestrator - Preserve exact same function signature and return type - Maintain 100% backward compatibility with views - Mark legacy code as preserved for reference - Complete TensorFlow refactor: template-based, extensible, non-monolithic
- Fix: Remove explicit .forward() and .call() method calls (use __call__ instead) - Fix: PyTorch layers should be called directly, not via .forward() - Fix: TensorFlow layers should be called directly, not via .call() - Add: Base orchestrator class to eliminate WET code (DRY principle) - Prepare: Foundation for refactoring both orchestrators to inherit from base
- Fix: Change import from .pytorch_codegen to .enhanced_pytorch_codegen - The actual file is named enhanced_pytorch_codegen.py, not pytorch_codegen.py
- Remove imports of classes that don't exist (GroupBlockShapeComputer, safe_get_shape_data, etc.) - These were only used in legacy code that never executes due to early delegation - Fixes ModuleNotFoundError on server startup
…age and enhance edge mapping for skip connections
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Pull request overview
This PR refactors PyTorch code generation to use a template-based architecture, adds new PyTorch layer nodes (LSTM, GRU, Embedding, Conv1D, Conv3D, pooling layers), implements corresponding TensorFlow nodes, and updates the Gemini service import.
- Migrates PyTorch and TensorFlow code generation from string concatenation to Jinja2 templates
- Adds recurrent (LSTM, GRU), embedding, and additional convolution/pooling layers for PyTorch
- Introduces template-based code generation infrastructure with orchestrators for both frameworks
- Updates Gemini AI service import path
Reviewed changes
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Show a summary per file
| File | Description |
|---|---|
| project/frontend/src/lib/nodes/definitions/pytorch/*.ts | New PyTorch layer definitions (LSTM, GRU, Embedding, Conv1D/3D, pooling) |
| project/frontend/src/lib/nodes/definitions/pytorch/index.ts | Exports for new layer nodes |
| project/block_manager/services/tensorflow_codegen.py | Delegates to new orchestrator, removes legacy code |
| project/block_manager/services/nodes/tensorflow/*.py | TensorFlow node definitions with code spec methods |
| project/block_manager/services/nodes/pytorch/*.py | PyTorch node definitions with code spec methods |
| project/block_manager/services/nodes/templates/**/*.jinja2 | Jinja2 templates for layer code generation |
| project/block_manager/services/nodes/templates/manager.py | Template loading and caching infrastructure |
| project/block_manager/services/nodes/base.py | Adds LayerCodeSpec dataclass and code spec methods |
| project/block_manager/services/gemini_service.py | Updates import path for Gemini library |
| project/block_manager/services/enhanced_pytorch_codegen.py | Delegates to new orchestrator |
| project/block_manager/services/codegen/*.py | New orchestrators and generators for template-based code generation |
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