Hi,
I noticed that on Android, the NNAPI pytorch backend might decide to dispatch some operations to the GPU:
 I ExecutionPlan: Device qti-default can't do operation RESIZE_NEAREST_NEIGHBOR
 I ExecutionPlan: Device qti-dsp can't do operation RESIZE_NEAREST_NEIGHBOR
 I ExecutionPlan: Device qti-gpu can't do operation RESIZE_NEAREST_NEIGHBOR
 I ExecutionPlan: Device qti-hta can't do operation RESIZE_NEAREST_NEIGHBOR
 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(RESIZE_NEAREST_NEIGHBOR) = 4 (nnapi-reference)
 I ExecutionPlan: Device qti-dsp can't do operation CONCATENATION
 I ExecutionPlan: Device qti-hta can't do operation CONCATENATION
 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-default)


My understanding is that for the concatenation operation, the qti-gpu will be used through qti-default. What are the benefits of using Vulkan over NNAPI and vice-versa for running inference on GPU?
Thanks,
Julien