Tensorflow 2.0 fit() is not recognizing batch_size
model.compile() only does configure the model for training and it doesn't have any memory allocation. 
Your bug is self-explained, you directly feed a large numpy array into the model. I would suggest coding a new data generator or keras.utils.Sequence to feed your input data. If so, you do not need specify the batch_size in fit method again, because your own generator or Sequence  will generate batches.
So I'm initializing a model as:
model = tf.keras.utils.multi_gpu_model(model, gpus=NUM_GPUS) and when I do model.compile() it runs perfectly fine.
But when I do history = model.fit(tf.cast(X_train, tf.float32), tf.cast(Y_train, tf.float32), validation_split=0.25, batch_size = 16, verbose=1, epochs=100), it gives me error: 

OOM when allocating tensor with shape[4760,256,256,3] and type float
  on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
  [Op:Cast] name: Cast/

This code worked perfectly fine previously but not anymore with Tensorflow 2.0. I have 4760 samples in my training set. I don't know why it's taking the entire set instead of the batch size.