Why is the data type not unified for custom training loops (dlarray) and internal training loops (array) in deep learning?

[XTrain,TTrain] = japaneseVowelsTrainData;

inputSize = 12;

numHead = 10;

numHiddenUnits = 100;

numClasses = 9;

embeddingDimension = 50; %

numWords = 200 ;

layers = [

sequenceInputLayer(inputSize)

batchNormalizationLayer

peepholeLSTMLayer(numHiddenUnits,inputSize,OutputMode="last")

% lstmLayer(numHiddenUnits,'OutputMode','last')

batchNormalizationLayer

fullyConnectedLayer(numClasses)

softmaxLayer

classificationLayer];

for lstmLayer, the data type of forward function is array:

for peepholeLSTMLayer which is a custom defined layer, the data type of forward (predict) function is dlarray:

Why is the data type not unified for custom training loops (dlarray) and internal training loops (array) in deep learning?

It brings some trouble and inconvenience and I think it leads to corpulent as well.

What is puzzling is that: for internal layers (lstmLayer), there is no layer validating with auto-generated example inputs and forward function is used during training, however for user-defined layers, there is layer validating with auto-generated example inputs and predict but not forward function is used. Why is there the difference?

I think the deep learning tolbox of matlab is over-staffed, it is inconvenient and complicated for implementing deep leaning functions but should be concise and plain.