"element 0 of tensors does not require grad and does not have a grad_fn"

Hello PyTorch community,

Hello PyTorch community,

I hope you are all doing well. I am facing an issue while training my deep learning model using PyTorch Lightning, and I would appreciate some guidance on how to resolve it.

During the training process, I encountered the following error:

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Context: I am working on a text classification task and have implemented a custom dataset class UCC_Dataset, as well as a LightningDataModule subclass UCC_Data_Module. My model is defined as a subclass of pl.LightningModule called UCC_Comment_Classifier. The error occurs when calling trainer.fit(model, ucc_data_module).

Here is the relevant code snippet where the error occurs:

class UCC_Comment_Classifier(pl.LightningModule):

  def __init__(self):
    self.config = config
    self.pretrained_model = AutoModel.from_pretrained(config['model_name'], return_dict = True)
    self.hidden = torch.nn.Linear(self.pretrained_model.config.hidden_size, self.pretrained_model.config.hidden_size)
    self.classifier = torch.nn.Linear(self.pretrained_model.config.hidden_size, self.config['n_labels'])
    self.loss_func = nn.BCEWithLogitsLoss(reduction='mean')
    self.dropout = nn.Dropout()
  # Activer les gradients pour tous les paramètres du modèle
    for param in self.parameters():
        param.requires_grad = True
  def forward(self, input_ids, attention_mask, labels=None):
    # roberta layer
    output = self.pretrained_model(input_ids=input_ids, attention_mask=attention_mask)
    pooled_output = torch.mean(output.last_hidden_state, 1)
    # final logits
    pooled_output = self.dropout(pooled_output)
    pooled_output = self.hidden(pooled_output)
    pooled_output = F.relu(pooled_output)
    pooled_output = self.dropout(pooled_output)
    logits = self.classifier(pooled_output)
    # calculate loss
    loss = None
    if labels is not None:
      loss = self.loss_func(logits.view(-1, self.config['n_labels']), labels.view(-1, self.config['n_labels']))
    return loss, logits

  def training_step(self, batch, batch_index):
    loss, outputs = self(**batch)
    self.log("train loss ", loss, prog_bar = True, logger=True)
    return {"loss":loss, "predictions":outputs, "labels": batch["labels"]}

  def validation_step(self, batch, batch_index):
    loss, outputs = self(**batch)
    self.log("validation loss ", loss, prog_bar = True, logger=True)
    return {"val_loss": loss, "predictions":outputs, "labels": batch["labels"]}

  def predict_step(self, batch, batch_index , dataloader_idx: int = None):
    loss, outputs = self(**batch)
    return outputs

  def configure_optimizers(self):
    optimizer = AdamW(self.parameters(), lr=self.config['lr'], weight_decay=self.config['weight_decay'], no_deprecation_warning=True)
    total_steps = config['train_size'] * self.config['n_epochs']
    warmup_steps = math.floor(total_steps * self.config['warmup'])

    scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)
    return [optimizer],[scheduler]

I have verified that all the model parameters have requires_grad set to True, and I am unsure why this error is happening.

Any help or insights into resolving this issue would be highly appreciated. Thank you all in advance for your support!

Best regards,