April 2025 monthly summary for pytorch/torchchat. Implemented a robust refactor of TokenizerArgs to use an Enum-based tokenizer_type, replacing boolean flags and simplifying configuration. This change ensures only one tokenizer type is active at a time and adds a check for the absence of any tokenizer, reducing misconfigurations and improving runtime reliability. The work lays groundwork for easier extension to additional tokenizer types and cleaner API usage.

In 2025-03 for pytorch/executorch, delivered runtime stability improvements during inference by hardening the view operator and XNNExecutor. This includes non-fatal input checks in the view operator and a null runtime check in XNNExecutor::prepare_args, with updated tests to verify robustness. The changes reduce crash risk and improve reliability in production inference workloads.