# Input type Runtime Error in fine-tuning hf blog tutorial **URL:** \ **Category:** Beginners\ **Created:** [May 6, 2022, 4:15pm UTC](https://discuss.huggingface.co/t/input-type-runtime-error-in-fine-tuning-hf-blog-tutorial/17619 "2022-05-06T16:15:46Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![murdockthedude](https://avatars.discourse-cdn.com/v4/letter/m/51bf81/32.png) [@murdockthedude](https://discuss.huggingface.co/u/murdockthedude)\ **Post date:** [May 6, 2022, 4:15pm UTC](https://discuss.huggingface.co/t/input-type-runtime-error-in-fine-tuning-hf-blog-tutorial/17619/1 "2022-05-06T16:15:46Z")
Hi folks, I’m running through the hf Wav2Vec2 fine-tuning blog tutorial here: [Fine-Tune Wav2Vec2 for English ASR in Hugging Face with 🤗 Transformers](https://huggingface.co/blog/fine-tune-wav2vec2-english) On the training step I’m getting a RuntimeError appearing to indicate that not all my data is on the GPU. Unfortunately I’m not sure how I should modify the tutorial-provided code to resolve the error and ensure everything is on the GPU where it should be! It looks like the model is on the GPU but the training input isn’t. I expect there have been some framework updates in the time since the tutorial was written. Would appreciate some help! The Colab Notebook is located [here](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) and I’ve pasted the error stack below, which occurs when I run `trainer.train()` Thanks! Error Stack: ```auto --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) in () ----> 1 trainer.train() 13 frames /usr/local/lib/python3.7/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1314 tr_loss_step = self.training_step(model, inputs) 1315 else: -> 1316 tr_loss_step = self.training_step(model, inputs) 1317 1318 if ( /usr/local/lib/python3.7/dist-packages/transformers/trainer.py in training_step(self, model, inputs) 1845 if self.use_amp: 1846 with autocast(): -> 1847 loss = self.compute_loss(model, inputs) 1848 else: 1849 loss = self.compute_loss(model, inputs) /usr/local/lib/python3.7/dist-packages/transformers/trainer.py in compute_loss(self, model, inputs, return_outputs) 1879 else: 1880 labels = None -> 1881 outputs = model(**inputs) 1882 # Save past state if it exists 1883 # TODO: this needs to be fixed and made cleaner later. /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/wav2vec2/modeling_wav2vec2.py in forward(self, input_values, attention_mask, output_attentions, output_hidden_states, return_dict, labels) 1497 output_attentions=output_attentions, 1498 output_hidden_states=output_hidden_states, -> 1499 return_dict=return_dict, 1500 ) 1501 /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/wav2vec2/modeling_wav2vec2.py in forward(self, input_values, attention_mask, mask_time_indices, output_attentions, output_hidden_states, return_dict) 1062 return_dict = return_dict if return_dict is not None else self.config.use_return_dict 1063 -> 1064 extract_features = self.feature_extractor(input_values) 1065 extract_features = extract_features.transpose(1, 2) 1066 /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/wav2vec2/modeling_wav2vec2.py in forward(self, input_values) 335 hidden_states = input_values[:, None] 336 for conv_layer in self.conv_layers: --> 337 hidden_states = conv_layer(hidden_states) 338 339 return hidden_states /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/wav2vec2/modeling_wav2vec2.py in forward(self, hidden_states) 256 257 def forward(self, hidden_states): --> 258 hidden_states = self.conv(hidden_states) 259 hidden_states = self.layer_norm(hidden_states) 260 hidden_states = self.activation(hidden_states) /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/conv.py in forward(self, input) 300 301 def forward(self, input: Tensor) -> Tensor: --> 302 return self._conv_forward(input, self.weight, self.bias) 303 304 /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias) 297 _single(0), self.dilation, self.groups) 298 return F.conv1d(input, weight, bias, self.stride, --> 299 self.padding, self.dilation, self.groups) 300 301 def forward(self, input: Tensor) -> Tensor: RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.HalfTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor ``` ---
**Author:** ![tomboolean](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/tomboolean/32/4708_2.png) [@tomboolean](https://discuss.huggingface.co/u/tomboolean)\ **Post date:** [May 9, 2022, 5:36pm UTC](https://discuss.huggingface.co/t/input-type-runtime-error-in-fine-tuning-hf-blog-tutorial/17619/2 "2022-05-09T17:36:52Z")
I ran into the same issue. The solution described in [HugginFace dataset error: RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.HalfTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor](https://discuss.huggingface.co/t/hugginface-dataset-error-runtimeerror-input-type-torch-floattensor-and-weight-type-torch-cuda-halftensor-should-be-the-same-or-input-should-be-a-mkldnn-tensor-and-weight-is-a-dense-tensor/17516) is what I am trying next. I’ll let you know if I run into more issues after upgrading. ---
**Author:** ![Sandy1857](https://avatars.discourse-cdn.com/v4/letter/s/ecae2f/32.png) [@Sandy1857](https://discuss.huggingface.co/u/Sandy1857)\ **Post date:** [March 8, 2024, 8:55pm UTC](https://discuss.huggingface.co/t/input-type-runtime-error-in-fine-tuning-hf-blog-tutorial/17619/3 "2024-03-08T20:55:41Z")
What it means is that you have loaded your model to GPU using model.to(‘cuda’), but your inputs to the model are not on cuda yet. So just need to do inputs.to(‘cuda’)