Run on CPU not on gpu

[ElVictorious]

Hi,

First, congrats for the amazing job with this repo.

I experienced that the temporal fusion transformer do not works with GPU but it works well with cpus. I used 3 different computers and I had the same problem, even with colab. Same issue..

Thanks in advance for your help

model = TFTModel( input_chunk_length=INLEN,
output_chunk_length=N_FC,
hidden_size=HIDDEN,
lstm_layers=LSTMLAYERS,
num_attention_heads=ATTHEADS,
dropout=DROPOUT,
batch_size=BATCH,
n_epochs=EPOCHS,
likelihood=QuantileRegression(quantiles=QUANTILES),
# loss_fn=MSELoss(),
random_state=RAND,
force_reset=True)

model.fit( ts_ttrain,
future_covariates=tcov,
verbose=True)

RuntimeError Traceback (most recent call last)
in
15 model.fit( ts_ttrain,
16 future_covariates=tcov,
---> 17 verbose=True)

9 frames
/usr/local/lib/python3.7/dist-packages/darts/utils/torch.py in decorator(self, *args, **kwargs)
63 with fork_rng():
64 manual_seed(self._random_instance.randint(0, high=MAX_TORCH_SEED_VALUE))
---> 65 return decorated(self, *args, **kwargs)
66 return decorator

/usr/local/lib/python3.7/dist-packages/darts/models/forecasting/torch_forecasting_model.py in fit(self, series, past_covariates, future_covariates, val_series, val_past_covariates, val_future_covariates, verbose, epochs, max_samples_per_ts, num_loader_workers)
477 logger.info('Train dataset contains {} samples.'.format(len(train_dataset)))
478
--> 479 self.fit_from_dataset(train_dataset, val_dataset, verbose, epochs, num_loader_workers)
480
481 @Property

/usr/local/lib/python3.7/dist-packages/darts/utils/torch.py in decorator(self, *args, **kwargs)
63 with fork_rng():
64 manual_seed(self._random_instance.randint(0, high=MAX_TORCH_SEED_VALUE))
---> 65 return decorated(self, *args, **kwargs)
66 return decorator

/usr/local/lib/python3.7/dist-packages/darts/models/forecasting/torch_forecasting_model.py in fit_from_dataset(self, train_dataset, val_dataset, verbose, epochs, num_loader_workers)
591
592 # Train model
--> 593 self._train(train_loader, val_loader, tb_writer, verbose, train_num_epochs)
594
595 # Close tensorboard writer

/usr/local/lib/python3.7/dist-packages/darts/models/forecasting/torch_forecasting_model.py in _train(self, train_loader, val_loader, tb_writer, verbose, epochs)
886 self.model.train()
887 train_batch = self._batch_to_device(train_batch)
--> 888 output = self._produce_train_output(train_batch[:-1])
889 target = train_batch[-1] # By convention target is always the last element returned by datasets
890 loss = self._compute_loss(output, target)

/usr/local/lib/python3.7/dist-packages/darts/models/forecasting/tft_model.py in _produce_train_output(self, input_batch)
800
801 def _produce_train_output(self, input_batch: Tuple):
--> 802 return self.model(input_batch)
803
804 def predict(self, n, *args, **kwargs):

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/darts/models/forecasting/tft_model.py in forward(self, x)
447
448 # run local lstm encoder
--> 449 encoder_out, (hidden, cell) = self.lstm_encoder(input=embeddings_varying_encoder, hx=(input_hidden, input_cell))
450
451 # run local lstm decoder

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
690 if batch_sizes is None:
691 result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,
--> 692 self.dropout, self.training, self.bidirectional, self.batch_first)
693 else:
694 result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,

RuntimeError: rnn: hx is not contiguous

[dennisbader]

I could reproduce the error on colab.
This happens with lstm_layers > 1. Can you try with lstm_layers=1?

I assume the non-contiguity comes from expanding the hidden state tensors for the LSTM layers.
I will test if we can fix this with contiguous()