trunk/966b96f16c36ecc346ae84102b13071471b41a39: [dcp] Update save plan validation error messaging (#176728)

The PyTorch open-source team has merged a significant quality-of-life improvement into the framework's main development branch, aimed at developers working with large-scale model training. The commit, identified as `966b96f`, specifically targets the Distributed Checkpointing (DCP) API—a critical component for saving and loading the state of massive models trained across hundreds or thousands of GPUs. Previously, when a save plan validation failed, the system would only raise a generic `ValueError` stating "Failed to validate global plan," forcing engineers to dig through logs to find the root cause.

This update fundamentally changes the `_validate_global_plan` function's behavior. Instead of returning a simple boolean success/failure flag, it now collects detailed error messages during validation and returns them as a list. The calling code then incorporates these specific errors into the exception message, making the failure reason immediately apparent. To prevent overly long error strings, messages are truncated to 500 characters, though full details remain available via the standard Python logging system. This change, reviewed and approved by core maintainers, directly addresses a pain point in distributed training workflows, where opaque errors can lead to hours of wasted debugging time.

The impact is most felt in production environments where reliability and rapid iteration are key. For AI engineers and researchers training models like Llama or Stable Diffusion variants at scale, a clear error message can mean the difference between a quick configuration fix and a lengthy, frustrating investigation. This improvement exemplifies the ongoing refinement of PyTorch's developer experience, ensuring its ecosystem remains competitive and efficient for state-of-the-art AI development.