a question about whether self.feature_index is in CPU or GPU

Hello,
I am relatively new to Pytorch. I have a question regarding the data in CPU/GPU, for example, in the source code here

https://github.com/shenweichen/DeepCTR-Torch/blob/bb6064343e8d271843bbd7efb9e8f3ecbd5d42b3/deepctr_torch/models/basemodel.py#L310

In particular,

sparse_embedding_list = [embedding_dict[feat.embedding_name]( X[:, self.feature_index[feat.name][0]:self.feature_index[feat.name][1]].long()) for feat in sparse_feature_columns]

It is pretty clear that X is the minibatch tensor data that has already been moved to GPU. embedding_dict is a Moduledict and feature_index is a plain Python Ordereddict. I had a few basic reading, it sounds like a dictionary object cannot directly be migrated into GPU, but only each tensor for each key can be copied one by one. So I am not sure for example, in the constructor init() of the BaseModel, what self.to(device) is going to do for its dictionary object?

That comes to my more specific question here, I am wondering when calling to build sparse_embedding_list, how Pytorch handles this, is everything here already in GPU when calling init() or some runtime copy of the value self.feature_index[feat.name] into GPU, does it incur any efficiency issue?

Thank you for your help!

Start with deepctr_torch/models/basemodel.py at the linked BaseModel constructor and the sparse_embedding_list expression. Read how self.to(device) is applied and compare the ModuleDict with the plain feature_index mapping. Done means documenting which values move to the device, which remain Python objects, and whether this path adds runtime copying.