Problem handling of response masks

[mertsayar8]

Current handling of response_masks inside batch_forward_pass function does not take padding into consideration which results with shape unmatch during masking. I think response tokens should not be concatenated with a torch.zeros(query_length) and masking operation should be done without slicing.

An example with batch size of 2:

• First sample in the batch has a query size of 10 and response size of 9 (response mask has also size of 9).
• Second sample in the batch has a query size of 10 and response size of 5(response mask has also size of 5).
• With the concatenation, response_mask_batch[1] has the size of 15.
• start will be 14 for second sample(due to the padding) and end will be 19.
  Hence,
  The operation

masks[j, start:end] = masks[j, start:end] * response_masks_batch[j][start:end]

will yield a RuntimeError: The size of tensor a (5) must match the size of tensor b (1) at non-singleton dimension 0
as response_mask_batch[1][14:19] is same as response_mask_batch[1][14:15] which has length of 1.

Removing the concatenation of the response mask and removing the slicing from the response mask since response mask already has the length of end - start + 1, which is equal to length of masks[j, start:end] should be a better approach.