PyTorch Shape Validator

About

PyTorch Shape Validator helps developers tackle the frequent challenge of tensor shape mismatches by integrating standardized documentation and validation directly into the codebase. It provides a consistent notation system for dimensions like batch size, channels, and spatial resolution, transforming dense PyTorch code into a self-documenting resource. The skill offers patterns for inline comments, development-mode assertions that vanish in production, and reusable validation helpers to catch errors early. It is particularly effective for managing complex operations like broadcasting, reshaping, and multi-stage model architectures where tracking data flow is critical for stability.

Key Features

• Standardized shape notation system for consistent tensor documentation
• Reusable validation utility functions for spatial consistency and batch checking
• Custom type aliases for documenting domain-specific tensor structures
• Conditional runtime assertions that catch errors without impacting production performance
• 0 GitHub stars
• Implementation patterns for tracking shapes through complex operations like FFT and broadcasting

Use Cases

• Documenting research code to ensure model architectures are readable and reproducible
• Implementing robust broadcasting logic in custom PyTorch modules and loss functions
• Debugging runtime dimension mismatches in complex neural network layers