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Summary:
This adds a periodic call to `torch.cuda.empty_cache()` in order to
mitigate memory fragmentation in the PyTorch CUDA cached allocator
that can cause OOMs on models approaching GPU memory limit.
By default, this will occur every 64 updates.

Performance considerations:

- I've benchmarked this on a reasonably large model with memory
  footprint 16 GB, and the overhead with the default setting is <0.2%.
  With `update-freq > 1`, the cost is mitigated even further.
- This behavior can be disabled with a value of zero.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/882

Differential Revision: D17742386

Pulled By: jma127

fbshipit-source-id: 68d8f93f798d6818b5efc3d67d43b52dfb8b2865