# Buggy code
def step(
    self, x: Dict[str, torch.Tensor], y: torch.Tensor, batch_idx: int, label="train"
):
    """
    Run for each train/val step.
    """
    # pack y sequence if different encoder lengths exist
    if (x["decoder_lengths"] < x["decoder_lengths"].max()).any():
        y = rnn.pack_padded_sequence(
            y, lengths=x["decoder_lengths"], batch_first=True, enforce_sorted=False
        )

    if label == "train" and len(self.hparams.monotone_constaints) > 0:
        # calculate gradient with respect to continous decoder features
        x["decoder_cont"].requires_grad_(True)
        assert not torch._C._get_cudnn_enabled(), (
            "To use monotone constraints, wrap model and training in context "
            "`torch.backends.cudnn.flags(enable=False)`"
        )
        out = self(x)
        out["prediction"] = self.transform_output(out)
        prediction = out["prediction"]

        gradient = torch.autograd.grad(
            outputs=prediction,
            inputs=x["decoder_cont"],
            grad_outputs=torch.ones_like(prediction),  # t
            create_graph=True,  # allows usage in graph
            allow_unused=True,
        )[0]

        # select relevant features
        indices = torch.tensor(
            [
                self.hparams.x_reals.index(name)
                for name in self.hparams.monotone_constaints.keys()
            ]
        )
        monotonicity = torch.tensor(
            [val for val in self.hparams.monotone_constaints.values()],
            dtype=gradient.dtype,
            device=gradient.device,
        )
        # add additionl loss if gradient points in wrong direction
        gradient = gradient[..., indices] * monotonicity[None, None]
        monotinicity_loss = gradient.clamp_max(0).mean()
        # multiply monotinicity loss by large number to ensure relevance and take to the power of 2
        # for smoothness of loss function
        monotinicity_loss = 10 * torch.pow(monotinicity_loss, 2)
        if isinstance(self.loss, MASE):
            loss = self.loss(
                prediction,
                y,
                encoder_target=x["encoder_target"],
                encoder_lengths=x["encoder_lengths"],
            )
        else:
            loss = self.loss(prediction, y)

        loss = loss * (1 + monotinicity_loss)
    else:
        out = self(x)
        out["prediction"] = self.transform_output(out)

        # calculate loss
        prediction = out["prediction"]
        if isinstance(self.loss, MASE):
            loss = self.loss(
                prediction,
                y,
                encoder_target=x["encoder_target"],
                encoder_lengths=x["encoder_lengths"],
            )
        else:
            loss = self.loss(prediction, y)

    # logging losses
    y_hat_detached = prediction.detach()
    y_hat_point_detached = self.loss.to_prediction(y_hat_detached)
    for metric in self.logging_metrics:
        if isinstance(metric, MASE):
            loss_value = metric(
                y_hat_point_detached,
                y,
                encoder_target=x["encoder_target"],
                encoder_lengths=x["encoder_lengths"],
            )
        else:
            loss_value = metric(y_hat_point_detached, y)
        self.log(
            f"{label}_{metric.name}",
            loss_value,
            on_step=label == "train",
            on_epoch=True,
        )
    log = {"loss": loss, "n_samples": x["decoder_lengths"].size(0)}
    if self.log_interval(label == "train") > 0:
        self._log_prediction(x, out, batch_idx, label=label)
    return log, out


# Traceback (RuntimeError)
RuntimeError                              Traceback (most recent call last)
<ipython-input-11-a92b5627800b> in <module>
5     val_dataloaders=val_dataloader,
6     max_lr=10.0,
----> 7     min_lr=1e-6,
8 )
9

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/tuner/tuning.py in lr_find(self, model, train_dataloader, val_dataloaders, min_lr, max_lr, num_training, mode, early_stop_threshold, datamodule)
128             mode,
129             early_stop_threshold,
--> 130             datamodule,
131         )
132

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/tuner/lr_finder.py in lr_find(trainer, model, train_dataloader, val_dataloaders, min_lr, max_lr, num_training, mode, early_stop_threshold, datamodule)
173                 train_dataloader=train_dataloader,
174                 val_dataloaders=val_dataloaders,
--> 175                 datamodule=datamodule)
176
177     # Prompt if we stopped early

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
437         self.call_hook('on_fit_start')
438
--> 439         results = self.accelerator_backend.train()
440         self.accelerator_backend.teardown()
441

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in train(self)
52
53         # train or test
---> 54         results = self.train_or_test()
55         return results
56

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in train_or_test(self)
64             results = self.trainer.run_test()
65         else:
---> 66             results = self.trainer.train()
67         return results
68

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in train(self)
459
460     def train(self):
--> 461         self.run_sanity_check(self.get_model())
462
463         # enable train mode

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)
645
646             # run eval step
--> 647             _, eval_results = self.run_evaluation(test_mode=False, max_batches=self.num_sanity_val_batches)
648
649             # allow no returns from eval

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, test_mode, max_batches)
565
566                 # lightning module methods
--> 567                 output = self.evaluation_loop.evaluation_step(test_mode, batch, batch_idx, dataloader_idx)
568                 output = self.evaluation_loop.evaluation_step_end(output)
569

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py in evaluation_step(self, test_mode, batch, batch_idx, dataloader_idx)
169             output = self.trainer.accelerator_backend.test_step(args)
170         else:
--> 171             output = self.trainer.accelerator_backend.validation_step(args)
172
173         # track batch size for weighted average

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in validation_step(self, args)
76                 output = self.__validation_step(args)
77         else:
---> 78             output = self.__validation_step(args)
79
80         return output

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in __validation_step(self, args)
84         batch = self.to_device(batch)
85         args[0] = batch
---> 86         output = self.trainer.model.validation_step(*args)
87         return output
88

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/base_model.py in validation_step(self, batch, batch_idx)
138     def validation_step(self, batch, batch_idx):
139         x, y = batch
--> 140         log, _ = self.step(x, y, batch_idx, label="val")  # log loss
141         self.log("val_loss", log["loss"], on_step=False, on_epoch=True, prog_bar=True)
142         return log

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py in step(self, x, y, batch_idx, label)
566         """
567         # extract data and run model
--> 568         log, out = super().step(x, y, batch_idx, label=label)
569         # calculate interpretations etc for latter logging
570         if self.log_interval(label == "train") > 0:

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/base_model.py in step(self, x, y, batch_idx, label)
194             loss = loss * (1 + monotinicity_loss)
195         else:
--> 196             out = self(x)
197             out["prediction"] = self.transform_output(out)
198

~/repo/emart-promo/env/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725             result = self._slow_forward(*input, **kwargs)
726         else:
--> 727             result = self.forward(*input, **kwargs)
728         for hook in itertools.chain(
729                 _global_forward_hooks.values(),

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py in forward(self, x)
489         encoder_output, (hidden, cell) = self.lstm_encoder(
490             rnn.pack_padded_sequence(
--> 491                 embeddings_varying_encoder, lstm_encoder_lengths, enforce_sorted=False, batch_first=True
492             ),
493             (input_hidden, input_cell),

~/repo/emart-promo/env/lib/python3.7/site-packages/torch/nn/utils/rnn.py in pack_padded_sequence(input, lengths, batch_first, enforce_sorted)
242
243     data, batch_sizes = \
--> 244         _VF._pack_padded_sequence(input, lengths, batch_first)
245     return _packed_sequence_init(data, batch_sizes, sorted_indices, None)
246

RuntimeError: 'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor

# Fixed code
def step(
    self,
    x: Dict[str, torch.Tensor],
    y: torch.Tensor,
    batch_idx: int,
    label="train",
    **kwargs,
):
    """
    Run for each train/val step.
    """
    # pack y sequence if different encoder lengths exist
    if (x["decoder_lengths"] < x["decoder_lengths"].max()).any():
        y = rnn.pack_padded_sequence(
            y, lengths=x["decoder_lengths"], batch_first=True, enforce_sorted=False
        )

    if label == "train" and len(self.hparams.monotone_constaints) > 0:
        # calculate gradient with respect to continous decoder features
        x["decoder_cont"].requires_grad_(True)
        assert not torch._C._get_cudnn_enabled(), (
            "To use monotone constraints, wrap model and training in context "
            "`torch.backends.cudnn.flags(enable=False)`"
        )
        out = self(x, **kwargs)
        out["prediction"] = self.transform_output(out)
        prediction = out["prediction"]

        gradient = torch.autograd.grad(
            outputs=prediction,
            inputs=x["decoder_cont"],
            grad_outputs=torch.ones_like(prediction),  # t
            create_graph=True,  # allows usage in graph
            allow_unused=True,
        )[0]

        # select relevant features
        indices = torch.tensor(
            [
                self.hparams.x_reals.index(name)
                for name in self.hparams.monotone_constaints.keys()
            ]
        )
        monotonicity = torch.tensor(
            [val for val in self.hparams.monotone_constaints.values()],
            dtype=gradient.dtype,
            device=gradient.device,
        )
        # add additionl loss if gradient points in wrong direction
        gradient = gradient[..., indices] * monotonicity[None, None]
        monotinicity_loss = gradient.clamp_max(0).mean()
        # multiply monotinicity loss by large number to ensure relevance and take to the power of 2
        # for smoothness of loss function
        monotinicity_loss = 10 * torch.pow(monotinicity_loss, 2)
        if isinstance(self.loss, MASE):
            loss = self.loss(
                prediction,
                y,
                encoder_target=x["encoder_target"],
                encoder_lengths=x["encoder_lengths"],
            )
        else:
            loss = self.loss(prediction, y)

        loss = loss * (1 + monotinicity_loss)
    else:
        out = self(x, **kwargs)
        out["prediction"] = self.transform_output(out)

        # calculate loss
        prediction = out["prediction"]
        if isinstance(self.loss, MASE):
            loss = self.loss(
                prediction,
                y,
                encoder_target=x["encoder_target"],
                encoder_lengths=x["encoder_lengths"],
            )
        else:
            loss = self.loss(prediction, y)

    # log
    self._log_metrics(x, y, out, label=label)
    if self.log_interval(label == "train") > 0:
        self._log_prediction(x, out, batch_idx, label=label)
    log = {"loss": loss, "n_samples": x["decoder_lengths"].size(0)}

    return log, out