How does PyTea handle complex execution paths in PyTorch code

1. Conservative Pruning: PyTea uses a conservative pruning technique to reduce the number of execution paths. This method helps prevent path explosion, which can occur when analyzing code with numerous conditional branches. By pruning less relevant paths, PyTea focuses on the most critical execution paths that are likely to contain tensor shape errors[1][2].

2. Symbolic Abstraction: PyTea relies on symbolic abstraction to simplify the analysis of loops and conditional statements. This abstraction allows PyTea to analyze the code without needing to execute it, making it more efficient for handling complex paths[1][2].

3. Loop Handling: For loops, PyTea unrolls constant-bound loops and analyzes their straight-line code version. For loops with unknown bounds, such as those iterating over datasets, PyTea analyzes the loop body for two cases: a fixed-sized minibatch and a smaller residual minibatch. This approach simplifies the analysis of loops that might otherwise complicate the execution path analysis[1][2].

4. Timeout Mechanism: If the number of execution paths still becomes too large despite pruning, PyTea uses a timeout mechanism. This ensures that the analysis does not take an excessively long time, even if it means not fully exploring all possible paths[1][2].

By combining these strategies, PyTea efficiently handles complex execution paths in PyTorch code, allowing it to detect tensor shape errors quickly and accurately.