How can I use PyTea to detect shape errors in my PyTorch code

1. Install PyTea: First, ensure you have the necessary dependencies installed. You need `node.js >= 12.x`, `python >= 3.8`, and `z3-solver >= 4.8`. You can install these using the following commands:

bash
   sudo apt-get install nodejs
   pip install z3-solver
   

2. Download PyTea: Download the PyTea package from its GitHub repository:

bash
   wget https://github.com/ropas/pytea/releases/download/v0.1.0/pytea.zip
   unzip pytea.zip
   

3. Run PyTea: Navigate to the unzipped directory and run PyTea on your PyTorch code. For example, if your PyTorch code is in a file named `source.py`, you can analyze it using:

bash
   python bin/pytea.py path/to/source.py
   

4. Interpret Results: PyTea will analyze your code and report any shape errors it finds. The results are categorized into several types:
- Potential Success Path: No shape mismatch found, but further analysis by Z3 might reveal issues.
- Potential Unreachable Path: Shape mismatch or API misuse detected, but the path might be unreachable due to unresolved branch conditions.
- Immediate Failed Path: Error found, analysis stopped immediately.
- Valid Path (Offline Analysis): No errors found by the SMT solver.
- Invalid Path (Offline Analysis): SMT solver found conditions that could violate constraints.
- Undecidable Path (Offline Analysis): SMT solver timed out due to unsolvable constraints.

5. Fix Errors: Based on the feedback from PyTea, adjust your code to resolve any reported shape errors. This typically involves ensuring that tensor operations are performed on tensors with compatible shapes.

PyTea is designed to help you catch shape errors before running your code, saving time and resources by avoiding runtime errors during training or evaluation phases.