Hugging Face - Time Series Transformer Error
Author: modiparv
Pull request: no

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_27/4172075850.py in <module>
      8     static_real_features=batch1["static_real_features"],
      9     future_values=batch1["future_values"],
---> 10     future_time_features=batch1["future_time_features"]
     11 )

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.7/site-packages/transformers/models/time_series_transformer/modeling_time_series_transformer.py in forward(self, past_values, past_time_features, past_observed_mask, static_categorical_features, static_real_features, future_values, future_time_features, future_observed_mask, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, output_hidden_states, output_attentions, use_cache, return_dict)
   1611             output_attentions=output_attentions,
   1612             use_cache=use_cache,
-> 1613             return_dict=return_dict,
   1614         )
   1615 

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.7/site-packages/transformers/models/time_series_transformer/modeling_time_series_transformer.py in forward(self, past_values, past_time_features, past_observed_mask, static_categorical_features, static_real_features, future_values, future_time_features, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, output_hidden_states, output_attentions, use_cache, return_dict)
   1422             static_real_features=static_real_features,
   1423             future_values=future_values,
-> 1424             future_time_features=future_time_features,
   1425         )
   1426 

/opt/conda/lib/python3.7/site-packages/transformers/models/time_series_transformer/modeling_time_series_transformer.py in create_network_inputs(self, past_values, past_time_features, static_categorical_features, static_real_features, past_observed_mask, future_values, future_time_features)
   1322             static_feat = torch.cat((static_real_features, static_feat), dim=1)
   1323         if static_categorical_features is not None:
-> 1324             embedded_cat = self.embedder(static_categorical_features)
   1325             static_feat = torch.cat((embedded_cat, static_feat), dim=1)
   1326         expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
   1264                 return modules[name]
   1265         raise AttributeError("'{}' object has no attribute '{}'".format(
-> 1266             type(self).__name__, name))
   1267 
   1268     def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:


Comments:
![image](https://user-images.githubusercontent.com/93371014/235079818-1f3f888d-33ba-40be-ad4a-a557e8b54792.png)


cc @kashif 

@modiparv I believe you have configured the model with `num_static_categorical_features=0` and yet you are feeding the model static categorical covariates as the `static_categorical_features` input... perhaps kindly test without it and I can add an extra check there... 

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