I’ve found the problem while creating an example executable code.
The problem actually is not related to those two operations at all, even though the memory seems to accumulate after those operations.
In fact, within my original training code, I concatenated the loss tensors instead of adding the loss, thus increasing the memory size required.  Specifically, I had

total_gen_loss += gen_loss

instead of

total_gen_loss += gen_loss.item()

where gen_loss is an outputted torch.tensor (e.g., gen_loss == tensor(2.9921, grad_fn=))
For some reason, the memory accumulation occurs during the Linear and Relu layer operations.  However, fixing the loss addition stopped the memory accumulation.
Thank you for your help!