Thunder gives you PyTorch models superpowers for training and inference. Unlock out-of-the-box optimizations for performance, memory and parallelism, or roll out your own.

1 Open Issue Need Help Last updated: Jul 2, 2025

Open Issues Need Help

View All on GitHub
AI/ML Deep Learning Frameworks

AI Summary: The task is to fix a bug in the Thunder compiler's interpreter. The bug causes a `TypeError` when tracing code that uses type annotations like `list[int]`, because the interpreter incorrectly uses `__getitem__` instead of `__class_getitem__`. The fix involves modifying the `_binary_subscr_handler` in `thunder/core/interpreter.py` to correctly handle type annotations and adding a corresponding test case in `test_interpreter.py`.

Complexity: 4/5
good first issue high priority interpreter huggingface

Thunder gives you PyTorch models superpowers for training and inference. Unlock out-of-the-box optimizations for performance, memory and parallelism, or roll out your own.

Python