SOLVED:
When using an embedding layer, num_embeddings parameter should be the size of your vocabulary +1. Should you accidentally provide an index to the embedding layer outside of this range-1 (e.g. your num_embeddings is set to 111 and you provide 111 as your last index rather than 110), you will be met with an unhelpful CUDA error as shown above.
A helpful ‘index provided out of bounds’ error from the nn.Embedding layer would be nice if possible.