While downsampling training data should we also downsample the validation data or retain validation split as it is?
I would recommend not to downsample the validation set. In the end you care about performance on the test set with the skewed class distribution. Therefore your validation set (used for hyperparameter selection, early stopping etc.) should have the same distribution in my opinion.

Have you considered upsampling the minority class? By downsampling you loose training data, which might contain valuable information and therefore might harm the learning process.