File "/Users/franciscotafur/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/models/nhits.py", line 43, in forward
    forecast = F.interpolate(
  File "/Users/franciscotafur/anaconda3/envs/Merck2/lib/python3.10/site-packages/torch/nn/functional.py", line 3954, in interpolate
    return torch._C._nn.upsample_linear1d(input, output_size, align_corners, scale_factors)
NotImplementedError: The operator 'aten::upsample_linear1d.out' is not currently implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on <https://github.com/pytorch/pytorch/issues/77764>. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
[W 2023-10-20 14:23:25,600] Trial 0 failed with value None.
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[45], line 1
----> 1 nf.fit(df=df_final)

File ~/anaconda3/envs/Merck2/lib/python3.10/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 ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:375, in BaseAuto.fit(self, dataset, val_size, test_size, random_seed)
    373     best_config = results.get_best_result().config
    374 else:
--> 375     results = self._optuna_tune_model(
    376         cls_model=self.cls_model,
    377         dataset=dataset,
    378         val_size=val_size,
    379         test_size=test_size,
    380         verbose=self.verbose,
    381         num_samples=self.num_samples,
    382         search_alg=search_alg,
    383         config=self.config,
    384     )
    385     best_config = results.best_trial.user_attrs["ALL_PARAMS"]
    386 self.model = self._fit_model(
    387     cls_model=self.cls_model,
    388     config=best_config,
   (...)
    391     test_size=test_size,
    392 )

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:327, in BaseAuto._optuna_tune_model(self, cls_model, dataset, val_size, test_size, verbose, num_samples, search_alg, config)
    324     sampler = None
    326 study = optuna.create_study(sampler=sampler, direction="minimize")
--> 327 study.optimize(
    328     objective,
    329     n_trials=num_samples,
    330     show_progress_bar=verbose,
    331 )
    332 return study

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/optuna/study/study.py:442, in Study.optimize(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)
    339 def optimize(
    340     self,
    341     func: ObjectiveFuncType,
   (...)
    348     show_progress_bar: bool = False,
    349 ) -> None:
    350     """Optimize an objective function.
    351 
    352     Optimization is done by choosing a suitable set of hyperparameter values from a given
   (...)
    440             If nested invocation of this method occurs.
    441     """
--> 442     _optimize(
    443         study=self,
    444         func=func,
    445         n_trials=n_trials,
    446         timeout=timeout,
    447         n_jobs=n_jobs,
    448         catch=tuple(catch) if isinstance(catch, Iterable) else (catch,),
    449         callbacks=callbacks,
    450         gc_after_trial=gc_after_trial,
    451         show_progress_bar=show_progress_bar,
    452     )

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/optuna/study/_optimize.py:66, in _optimize(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)
     64 try:
     65     if n_jobs == 1:
---> 66         _optimize_sequential(
     67             study,
     68             func,
     69             n_trials,
     70             timeout,
     71             catch,
     72             callbacks,
     73             gc_after_trial,
     74             reseed_sampler_rng=False,
     75             time_start=None,
     76             progress_bar=progress_bar,
     77         )
     78     else:
     79         if n_jobs == -1:

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/optuna/study/_optimize.py:163, in _optimize_sequential(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)
    160         break
    162 try:
--> 163     frozen_trial = _run_trial(study, func, catch)
    164 finally:
    165     # The following line mitigates memory problems that can be occurred in some
    166     # environments (e.g., services that use computing containers such as GitHub Actions).
    167     # Please refer to the following PR for further details:
    168     # <https://github.com/optuna/optuna/pull/325>.
    169     if gc_after_trial:

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/optuna/study/_optimize.py:251, in _run_trial(study, func, catch)
    244         assert False, "Should not reach."
    246 if (
    247     frozen_trial.state == TrialState.FAIL
    248     and func_err is not None
    249     and not isinstance(func_err, catch)
    250 ):
--> 251     raise func_err
    252 return frozen_trial

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/optuna/study/_optimize.py:200, in _run_trial(study, func, catch)
    198 with get_heartbeat_thread(trial._trial_id, study._storage):
    199     try:
--> 200         value_or_values = func(trial)
    201     except exceptions.TrialPruned as e:
    202         # TODO(mamu): Handle multi-objective cases.
    203         state = TrialState.PRUNED

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:311, in BaseAuto._optuna_tune_model.<locals>.objective(trial)
    309 def objective(trial):
    310     cfg = config(trial)
--> 311     fitted_model = self._fit_model(
    312         cls_model=cls_model,
    313         config=cfg,
    314         dataset=dataset,
    315         val_size=val_size,
    316         test_size=test_size,
    317     )
    318     trial.set_user_attr("ALL_PARAMS", cfg)
    319     return fitted_model.trainer.callback_metrics["valid_loss"].item()

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:336, in BaseAuto._fit_model(self, cls_model, config, dataset, val_size, test_size)
    334 def _fit_model(self, cls_model, config, dataset, val_size, test_size):
    335     model = cls_model(**config)
--> 336     model.fit(dataset, val_size=val_size, test_size=test_size)
    337     return model

FTrainer._run_stage(self)
   1032 if self.training:
   1033     with isolate_rng():
-> 1034         self._run_sanity_check()
   1035     with torc
    387     self._build_step_args_from_hook_kwargs(hook_kwargs, hook_name)
    
    567     insample_mask=insample_mask,  # [Ws, L]
   (...)
    570     stat_exog=stat_exog,
    571 )  # [Ws, 1]
    573 # Model Predictions
--> 574 output_batch = self(windows_batch)
    575 valid_loss_batch = self._compute_valid_loss(
    576     outsample_y=original_outsample_y,
    577     output=output_batch,
    578     outsample_mask=outsample_mask,
    579     temporal_cols=batch["temporal_cols"],
    580 )
    581 valid_losses.append(valid_loss_batch)

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/models/nhits.py:392, in NHITS.forward(self, windows_batch)
    390 block_forecasts = [forecast.repeat(1, self.h, 1)]
    391 for i, block in enumerate(self.blocks):
--> 392     backcast, block_forecast = block(
    393         insample_y=residuals,
    394         futr_exog=futr_exog,
    395         hist_exog=hist_exog,
    396         stat_exog=stat_exog,
    397     )
    398     residuals = (residuals - backcast) * insample_mask
    399     forecast = forecast + block_forecast

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/models/nhits.py:179, in NHITSBlock.forward(self, insample_y, futr_exog, hist_exog, stat_exog)
    177 # Compute local projection weights and projection
    178 theta = self.layers(insample_y)
--> 179 backcast, forecast = self.basis(theta)
    180 return backcast, forecast

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/neuralforecast/models/nhits.py:43, in _IdentityBasis.forward(self, theta)
     40 knots = knots.reshape(len(knots), self.out_features, -1)
     41 if self.interpolation_mode in ["nearest", "linear"]:
     42     # knots = knots[:,None,:]
---> 43     forecast = F.interpolate(
     44         knots, size=self.forecast_size, mode=self.interpolation_mode
     45     )
     46     # forecast = forecast[:,0,:]
     47 elif "cubic" in self.interpolation_mode:

File ~/anaconda3/envs/Merck2/lib/python3.10/site-packages/torch/nn/functional.py:3954, in interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias)
   3952 if input.dim() == 3 and mode == "linear":
   3953     assert align_corners is not None
-> 3954     return torch._C._nn.upsample_linear1d(input, output_size, align_corners, scale_factors)
   3955 if input.dim() == 4 and mode == "bilinear":
   3956     assert align_corners is not None

NotImplementedError: The operator 'aten::upsample_linear1d.out' is not currently implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on <https://github.com/pytorch/pytorch/issues/77764>. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.