Thank you for your timely reply. I read torch.sparse in PyTorch documents before posting but wasn’t aware of the github discussion.
Right now I have a solution as below, which is quite fast:

def spy_sparse2torch_sparse(data):
    """

    :param data: a scipy sparse csr matrix
    :return: a sparse torch tensor
    """
    samples=data.shape[0]
    features=data.shape[1]
    values=data.data
    coo_data=data.tocoo()
    indices=torch.LongTensor([coo_data.row,coo_data.col])
    t=torch.sparse.FloatTensor(indices,torch.from_numpy(values).float(),[samples,features])
    return t


But it is still not very helpful. When I print(t[0]), it says RuntimeError: Sparse tensors do not have strides. Then how should I extract a minibatch of it?
The .to_dense() method is impossible because it returns RuntimeError: $ Torch: not enough memory: you tried to allocate 141GB. Buy new RAM! at /pytorch/aten/src/TH/THGeneral.c:218