message: Update RNN/LSTM performance docs
diff: @@ -224,11 +224,7 @@ On NVIDIA GPUs, the use of @{tf.contrib.cudnn_rnn} should always be preferred
  unless you want layer normalization, which it doesn't support. It is often at
  least an order of magnitude faster than @{tf.contrib.rnn.BasicLSTMCell} and
  @{tf.contrib.rnn.LSTMBlockCell} and uses 3-4x less memory than
  -@{tf.contrib.rnn.BasicLSTMCell}. Unfortunately, @{tf.contrib.cudnn_rnn} is not
  -compatible with @{tf.train.SyncReplicasOptimizer} so you should either use a
  -different synchronization mechanism (consider an all-reduce based strategy) or
  -use the @{tf.contrib.rnn.LSTMBlockFusedCell} (at a significant performance
  -penalty).
  +@{tf.contrib.rnn.BasicLSTMCell}.
  If you need to run one step of the RNN at a time, as might be the case in
  reinforcement learning with a recurrent policy, then you should use the
  