krishnab:

Or can I just keep the (year, month, week number, and day) as the matrix that I input into the embedding layer? In other words, does the pytorch implementation of Embedding layers handle having these multiple columns as represented by a single output embedding matrix?


You could feed the data as a single tensor to the nn.Embedding layer.
However, I would use separate embeddings, as your input data would have completely different ranges.
nn.Embedding layers accept inputs containing values in [0, nb_embeddings-1].
If your year data contains values in e.g. [1988, 2020], you would waste a lot of embedding vectors, as they are never used.
However, if you normalize the data (subtract the min. value), you would have overlapping indices for all data attributes, i.e. the year, week, day etc. would all index the same embeddings.