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.
Problem:
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):
super().__init__()
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'])
torch.nn.init.xavier_uniform_(self.classifier.weight)
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,