use_gpu=True Error

[Mario-RC]

In Google Colab.

INSTALLED:
!pip install -qqq git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git

MY CODE:

from parrot import Parrot

def random_state(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
random_state(1234)

parrot_gpu = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5", use_gpu=True)

phrases = ['i drive a ford pickup truck.', 'i am very conservative.', 'my family lives down the street from me.',
'i go to church every sunday.', 'i have three guns and love hunting.']

para_phrases_gpu = parrot_gpu.augment(input_phrase=phrases[0], use_gpu=True, max_return_phrases = 10)

ERROR:

---

RuntimeError Traceback (most recent call last)
in ()
----> 1 para_phrases_gpu = parrot_gpu.augment(input_phrase=phrases[0], use_gpu=True, max_return_phrases = 10)

/usr/local/lib/python3.7/dist-packages/parrot/parrot.py in augment(self, input_phrase, use_gpu, diversity_ranker, do_diverse, max_return_phrases, max_length, adequacy_threshold, fluency_threshold)
128
129
--> 130 adequacy_filtered_phrases = self.adequacy_score.filter(input_phrase, paraphrases, adequacy_threshold, device )
131 if len(adequacy_filtered_phrases) > 0 :
132 fluency_filtered_phrases = self.fluency_score.filter(adequacy_filtered_phrases, fluency_threshold, device )

/usr/local/lib/python3.7/dist-packages/parrot/filters.py in filter(self, input_phrase, para_phrases, adequacy_threshold, device)
13 x = self.tokenizer(input_phrase, para_phrase, return_tensors='pt', max_length=128, truncation=True)
14 self.adequacy_model = self.adequacy_model.to(device)
---> 15 logits = self.adequacy_model(**x).logits
16 probs = logits.softmax(dim=1)
17 prob_label_is_true = probs[:,1]

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
1213 output_attentions=output_attentions,
1214 output_hidden_states=output_hidden_states,
-> 1215 return_dict=return_dict,
1216 )
1217 sequence_output = outputs[0]

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)
844 token_type_ids=token_type_ids,
845 inputs_embeds=inputs_embeds,
--> 846 past_key_values_length=past_key_values_length,
847 )
848 encoder_outputs = self.encoder(

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)
126
127 if inputs_embeds is None:
--> 128 inputs_embeds = self.word_embeddings(input_ids)
129 token_type_embeddings = self.token_type_embeddings(token_type_ids)
130

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/sparse.py in forward(self, input)
158 return F.embedding(
159 input, self.weight, self.padding_idx, self.max_norm,
--> 160 self.norm_type, self.scale_grad_by_freq, self.sparse)
161
162 def extra_repr(self) -> str:

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
2181 # remove once script supports set_grad_enabled
2182 no_grad_embedding_renorm(weight, input, max_norm, norm_type)
-> 2183 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
2184
2185

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper__index_select)

[PrithivirajDamodaran]

Try now

[Mario-RC]

Now it works perfectly, thank you very much for the quick fix!