Anyone have some idea of time series forecasting problem using DARTS strategy over the search space of Recurrent neural networks?

[franciszh0716]

Describe the issue:
I go through different 3 types of Neural Networks for the forecasting problem. All of them have the similar structure: Recurrent layer, and two dense layer. However when I tried with just modify the Recurrent layer to Layerchoice, there always have some problem like

File ~/Documents/GitHub/NAS/nni/nni/nas/experiment/experiment.py:270, in NasExperiment.start(self, port, debug, run_mode)
...
1079 f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor")
1080 hx = hx.unsqueeze(1)
1081 else:

RuntimeError: For unbatched 2-D input, hx should also be 2-D but got 3-D tensor.

Where

X_train, X_test, y_train, y_test = train_test_split(X_tensor, Y_tensor, random_state = 0)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

is

torch.Size([9656, 300]) torch.Size([3219, 300]) torch.Size([9656]) torch.Size([3219])
and the Dataloader is set as
train_dataset = TensorDataset(X_train, y_train.unsqueeze(1))
test_dataset = TensorDataset(X_test, y_test.unsqueeze(1))
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

I'm not sure what is the exact issue happening here.

Environment:

• NNI version: 3.0rc1
• Training service (local|remote|pai|aml|etc): local
• Client OS: macOS Sonoma 14.4 (23E214)
• Server OS (for remote mode only): N/A
• Python version: 3.11.8
• PyTorch/TensorFlow version: 2.2.1
• Is conda/virtualenv/venv used?: conda has been used
• Is running in Docker?: no

Configuration:

• Experiment config (remember to remove secrets!):
• Search space: RNN, GRU, and LSTM if the output problem can be solved

Log message:

• nnimanager.log:
• dispatcher.log:
• nnictl stdout and stderr:

How to reproduce it?: