ValueError: you tried to log -1 which is currently not supported. Try a dict or a scalar/tensor.

[kkckk1110]

What happened + What you expected to happen

I came across a bug when running the getting_started.ipynb in the following codes: the bug is:

ValueError: you tried to log -1 which is currently not supported. Try a dict or a scalar/tensor.

Also, I have a problem with the implementation of exogenous features. If I want to use Auto version of models, how can I add exogenous features to it? When I attempted to add futr_exog_list to AutoLSTM(h=h, config=config_lstm, loss=MQLoss(), num_samples=2), it raises an error that futr_exog_list argument cannot be found.

Versions / Dependencies

I have neuralforecast==1.6.4

Reproduction script

%%capture horizon = 12

models = [LSTM(h=horizon, # Forecast horizon max_steps=500, # Number of steps to train scaler_type='standard', # Type of scaler to normalize data encoder_hidden_size=64, # Defines the size of the hidden state of the LSTM decoder_hidden_size=64,), # Defines the number of hidden units of each layer of the MLP decoder NHITS(h=horizon, # Forecast horizon input_size=2 * horizon, # Length of input sequence max_steps=100, # Number of steps to train n_freq_downsample=[2, 1, 1]) # Downsampling factors for each stack output ] nf = NeuralForecast(models=models, freq='M') nf.fit(df=Y_df)

Issue Severity

None

[jmoralez]

Hey @kkckk1110, thanks for using neuralforecast. Can you provide the full stacktrace for the error you're getting? For the auto models you have to provide the exogenous features in the config.

[kkckk1110]

Thank you very much for your attention!

Stacktrace

```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) File ~/miniforge3/lib/python3.9/site-packages/lightning_fabric/loggers/tensorboard.py:202, in TensorBoardLogger.log_metrics(self, metrics, step) 201 try: --> 202 self.experiment.add_scalar(k, v, step) 203 # TODO(fabric): specify the possible exception File ~/miniforge3/lib/python3.9/site-packages/lightning_fabric/loggers/logger.py:117, in rank_zero_experiment..experiment(self) 115 return fn(self) --> 117 return get_experiment() or _DummyExperiment() File ~/miniforge3/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py:32, in rank_zero_only..wrapped_fn(*args, **kwargs) 31 if rank == 0: ---> 32 return fn(*args, **kwargs) 33 return None File ~/miniforge3/lib/python3.9/site-packages/lightning_fabric/loggers/logger.py:115, in rank_zero_experiment..experiment..get_experiment() 113 @rank_zero_only 114 def get_experiment() -> Callable: --> 115 return fn(self) File ~/miniforge3/lib/python3.9/site-packages/lightning_fabric/loggers/tensorboard.py:181, in TensorBoardLogger.experiment(self) 180 if _TENSORBOARD_AVAILABLE: --> 181 from torch.utils.tensorboard import SummaryWriter 182 else: File ~/miniforge3/lib/python3.9/site-packages/torch/utils/tensorboard/__init__.py:12, in 10 del tensorboard ---> 12 from .writer import FileWriter, SummaryWriter # noqa: F401 13 from tensorboard.summary.writer.record_writer import RecordWriter File ~/miniforge3/lib/python3.9/site-packages/torch/utils/tensorboard/writer.py:10, in 9 from tensorboard.compat import tf ---> 10 from tensorboard.compat.proto import event_pb2 11 from tensorboard.compat.proto.event_pb2 import Event, SessionLog File ~/miniforge3/lib/python3.9/site-packages/tensorboard/compat/proto/event_pb2.py:6, in 5 from google.protobuf.internal import enum_type_wrapper ----> 6 from google.protobuf import descriptor as _descriptor 7 from google.protobuf import descriptor_pool as _descriptor_pool File ~/miniforge3/lib/python3.9/site-packages/google/protobuf/descriptor.py:47, in 46 import os ---> 47 from google.protobuf.pyext import _message 48 _USE_C_DESCRIPTORS = True TypeError: bases must be types The above exception was the direct cause of the following exception: ValueError Traceback (most recent call last) Input In [16], in () ----> 1 model.fit(train) File ~/miniforge3/lib/python3.9/site-packages/neuralforecast/core.py:274, in NeuralForecast.fit(self, df, static_df, val_size, sort_df, use_init_models, verbose) 271 print("WARNING: Deleting previously fitted models.") 273 for model in self.models: --> 274 model.fit(self.dataset, val_size=val_size) 276 self._fitted = True File ~/miniforge3/lib/python3.9/site-packages/neuralforecast/common/_base_recurrent.py:633, in BaseRecurrent.fit(self, dataset, val_size, test_size, random_seed) 630 self.trainer_kwargs["check_val_every_n_epoch"] = None 632 trainer = pl.Trainer(**self.trainer_kwargs) --> 633 trainer.fit(self, datamodule=datamodule) File ~/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:520, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) 518 model = _maybe_unwrap_optimized(model) 519 self.strategy._lightning_module = model --> 520 call._call_and_handle_interrupt( 521 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path 522 ) File ~/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py:44, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs) 42 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs) 43 else: ---> 44 return trainer_fn(*args, **kwargs) 46 except _TunerExitException: 47 _call_teardown_hook(trainer) File ~/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:559, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) 549 self._data_connector.attach_data( 550 model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule 551 ) 553 ckpt_path = self._checkpoint_connector._select_ckpt_path( 554 self.state.fn, 555 ckpt_path, 556 model_provided=True, 557 model_connected=self.lightning_module is not None, 558 ) --> 559 self._run(model, ckpt_path=ckpt_path) 561 assert self.state.stopped 562 self.training = False File ~/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:918, in Trainer._run(self, model, ckpt_path) 915 call._call_callback_hooks(self, "on_fit_start") 916 call._call_lightning_module_hook(self, "on_fit_start") --> 918 _log_hyperparams(self) 920 if self.strategy.restore_checkpoint_after_setup: 921 log.debug(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}") File ~/miniforge3/lib/python3.9/site-packages/pytorch_lightning/loggers/utilities.py:94, in _log_hyperparams(trainer) 92 for logger in trainer.loggers: 93 if hparams_initial is not None: ---> 94 logger.log_hyperparams(hparams_initial) 95 logger.log_graph(pl_module) 96 logger.save() File ~/miniforge3/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py:32, in rank_zero_only..wrapped_fn(*args, **kwargs) 30 raise RuntimeError("The `rank_zero_only.rank` needs to be set before use") 31 if rank == 0: ---> 32 return fn(*args, **kwargs) 33 return None File ~/miniforge3/lib/python3.9/site-packages/pytorch_lightning/loggers/tensorboard.py:181, in TensorBoardLogger.log_hyperparams(self, params, metrics) 178 else: 179 self.hparams.update(params) --> 181 return super().log_hyperparams(params=params, metrics=metrics) File ~/miniforge3/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py:32, in rank_zero_only..wrapped_fn(*args, **kwargs) 30 raise RuntimeError("The `rank_zero_only.rank` needs to be set before use") 31 if rank == 0: ---> 32 return fn(*args, **kwargs) 33 return None File ~/miniforge3/lib/python3.9/site-packages/lightning_fabric/loggers/tensorboard.py:233, in TensorBoardLogger.log_hyperparams(self, params, metrics) 230 metrics = {"hp_metric": metrics} 232 if metrics: --> 233 self.log_metrics(metrics, 0) 235 if _TENSORBOARD_AVAILABLE: 236 from torch.utils.tensorboard.summary import hparams File ~/miniforge3/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py:32, in rank_zero_only..wrapped_fn(*args, **kwargs) 30 raise RuntimeError("The `rank_zero_only.rank` needs to be set before use") 31 if rank == 0: ---> 32 return fn(*args, **kwargs) 33 return None File ~/miniforge3/lib/python3.9/site-packages/lightning_fabric/loggers/tensorboard.py:206, in TensorBoardLogger.log_metrics(self, metrics, step) 204 except Exception as ex: 205 m = f"\n you tried to log {v} which is currently not supported. Try a dict or a scalar/tensor." --> 206 raise ValueError(m) from ex ValueError: you tried to log -1 which is currently not supported. Try a dict or a scalar/tensor. ```

[jmoralez]

Thanks. That seems to be a protobuf error, can you try the fix suggested here?

[kkckk1110]

That's really helpful! Thank you very much!