Local cache dir not fully clearing in DDP multi-node training.

Nobody has claimed this yet.

## 🐛 Bug

At the moment it seems that a significant portion of data stored in the cache (~40-60%) ends up not removed over the course of training, and then remains in the cache dir upon completion of training. I suspect this

### To Reproduce

Currently training a few models over 8 nodes with a large source dataset (only 1 epoch), and the cache dir size accumulates indefinitely. Over the process, it seems that the lock counts for these files are much greater than 0, so I wonder if it has to do with `force_download`-related behavior?

### Expected behavior

The cache dir does not expand significantly above its target size, and files aren't being locked as many times.

### Additional context

Environment detail

- PyTorch Version (e.g., 1.0):
- OS (e.g., Linux):
- How you installed PyTorch (`conda`, `pip`, source):
- Build command you used (if compiling from source):
- Python version:
- CUDA/cuDNN version:
- GPU models and configuration:
- Any other relevant information:

Start by reproducing the cache growth during the reported 8-node, one-epoch DDP training run and inspect the cache locking and force_download behavior. The issue mentions no source files or tests, so locate the cache cleanup and lock-management entry points first. Done means the cache stays near its target size after training and files do not retain excessive lock counts.

Written by the indexing model from the issue text.