57d602b4d889632a55aaf6f9cb13cc24d11ef10c,torch_geometric/nn/data_parallel.py,DataParallel,scatter,#DataParallel#Any#Any#,58

Before Change

        device_id = (device_id[:-1] + device_id[1:]) / 2.0
        device_id = device_id.to(torch.long)  // round.
        split = device_id.bincount().cumsum(0)
        split = torch.cat([split.new_zeros(1), split], dim=0).tolist()

        return [
            Batch.from_data_list(data_list[split[i]:split[i + 1]]).to(
                torch.device("cuda:{}".format(i))) for i in range(num_devices)

After Change

        device_id = device_id.to(torch.long)  // round.
        split = device_id.bincount().cumsum(0)
        split = torch.cat([split.new_zeros(1), split], dim=0)
        split = torch.unique(split, sorted=True)
        split = split.tolist()

        return [
            Batch.from_data_list(data_list[split[i]:split[i + 1]]).to(
                torch.device("cuda:{}".format(device_ids[i])))

Instances