How does PyTea handle loops in PyTorch code

1. Constant-bound Loops: PyTea unrolls constant-bound loops, which means it analyzes the loop body as if it were a straight-line code. This approach simplifies the analysis by treating the loop as a sequence of operations without the complexity of loop control logic[1].

2. Unknown-bound Loops: For loops with unknown bounds, such as those iterating over datasets, PyTea analyzes the loop body under two specific conditions. It considers both the case of a fixed-sized minibatch and a smaller, residual minibatch. This ensures that PyTea can handle loops that depend on dataset sizes without needing to know the exact number of iterations in advance[1].

3. Path Pruning: PyTea uses path pruning to limit the number of execution paths it needs to analyze. This technique helps prevent an explosion of paths, which can occur when dealing with complex conditional logic or loops. By focusing on the most relevant paths, PyTea maintains efficiency and scalability[1].

Overall, PyTea's approach to handling loops is designed to balance precision with performance, ensuring that it can effectively detect tensor shape errors in PyTorch applications without becoming overly complex or time-consuming.