Expected object of device type cuda but got device type cpu for argument #3 'index' in call to _th_index_selec

[keherri]

Running into this error when trying to use a ml.p2.xlarge ec2 instance on aws for the Finetuning tutorial. It seems the tensors aren't automatically moved to the GPU.

RuntimeError                              Traceback (most recent call last)
<ipython-input-32-0bf1915c8e43> in <module>
----> 1 prediction = finder.get_answers(question=question, top_k_retriever=2, top_k_reader=5)

~/SageMaker/haystack/haystack/__init__.py in get_answers(self, question, top_k_reader, top_k_retriever, filters)
     49                                       paragrahps=paragraphs,
     50                                       meta_data_paragraphs=meta_data,
---> 51                                       top_k=top_k_reader)
     52 
     53         return results

~/SageMaker/haystack/haystack/reader/farm.py in predict(self, question, paragrahps, meta_data_paragraphs, top_k, max_processes)
    186         # get answers from QA model (Top 5 per input paragraph)
    187         predictions = self.inferencer.inference_from_dicts(
--> 188             dicts=input_dicts, rest_api_schema=True, max_processes=max_processes
    189         )
    190 

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/farm/infer.py in inference_from_dicts(self, dicts, rest_api_schema, max_processes)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/farm/infer.py in _get_predictions(self, dataset, tensor_names, baskets, rest_api_schema)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/farm/modeling/adaptive_model.py in forward(self, **kwargs)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/farm/modeling/language_model.py in forward(self, input_ids, padding_mask, **kwargs)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/transformers/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds)
    481 
    482         if inputs_embeds is None:
--> 483             inputs_embeds = self.embeddings(input_ids)  # (bs, seq_length, dim)
    484         tfmr_output = self.transformer(x=inputs_embeds, attn_mask=attention_mask, head_mask=head_mask)
    485         hidden_state = tfmr_output[0]

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/transformers/modeling_distilbert.py in forward(self, input_ids)
     87         position_ids = position_ids.unsqueeze(0).expand_as(input_ids)  # (bs, max_seq_length)
     88 
---> 89         word_embeddings = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)
     90         position_embeddings = self.position_embeddings(position_ids)  # (bs, max_seq_length, dim)
     91 

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/torch/nn/modules/sparse.py in forward(self, input)
    112         return F.embedding(
    113             input, self.weight, self.padding_idx, self.max_norm,
--> 114             self.norm_type, self.scale_grad_by_freq, self.sparse)
    115 
    116     def extra_repr(self):

~/anaconda3/envs/JupyterSystemEnv/lib/python3.6/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   1482         # remove once script supports set_grad_enabled
   1483         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1484     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   1485 
   1486 

RuntimeError: Expected object of device type cuda but got device type cpu for argument #3 'index' in call to _th_index_select

[keherri]

Retrained the model and now I can call finder.get_answers()