[RNNCell vs RNN]
What is the better way when implementing RNN decoder?
I used to work with tensorflow, so I am familiar with implementing RNN decoder by calling RNNCells for each unrolling step.
However, it seems many implementation calls RNN with input whose seq_len size is 1 for each time step, including official seq2seq tutorial 108.
As I see, RNN corresponds to tf.nn.dynamic_rnn or tf.nn.static_rnn of tensorflow, which internally call RNNCell for number of given unrolling steps.
I am curious if calling RNN for multiple times causes unrolling slower than calling RNNCell for multiple times.
I found OpenNMT-py 50 used RNNCell for decoding.
Do people even use RNNCells in PyTorch?