Pytorch-lightning: Error while training on multi gpus

[nrjvarshney]

I get the following error on training with multiple gpus. It works for single gpu training

avg_loss = torch.stack([x['val_loss'] for x in outputs_of_validation_steps]).mean()
RuntimeError: stack expects each tensor to be equal size, but got [2] at entry 0 and [1] at entry 343

 def validation_step(self, batch, batch_idx):                                
        logits, softmax_logits = self.forward(**batch)                          
        loss, prediction_label_count = self.loss_function(logits, batch["labels"])

        accuracy = self.compute_accuracy(logits, batch["labels"])               

        return {                                                                
            "val_loss": loss,                                                   
            "accuracy": accuracy,                                               
            "prediction_label_count": prediction_label_count,                   
        }                                                                       

    def validation_epoch_end(self, outputs_of_validation_steps):                
        avg_loss = torch.stack([x['val_loss'] for x in outputs_of_validation_steps]).mean()
        val_accuracy = torch.stack([x['accuracy'] for x in outputs_of_validation_steps]).mean()

        log = {'val_loss': avg_loss, "val_accuracy": val_accuracy}              

        return {'val_loss': avg_loss, "val_accuracy": val_accuracy, 'log': log} 

[nrjvarshney]

Using drop_last = True is not acceptable

[awaelchli]

Hi, I think I have solved that recently #3020. Which version are you on? Please try to upgrade and let me know.

[RahulSajnani]

Hi, I think I have solved that recently #3020. Which version are you on? Please try to upgrade and let me know.

This issue persists in Pytorch Lightning v0.9.0.

[awaelchli]

@RahulSajnani are you using results object or the same kind of manual reduction as shown in @nrjvarshney's code?
Because in the latter case, it is normal that this is a problem and becaues you do it manually, you need to choose torch.cat.
However, I recommend you use the Results api. https://pytorch-lightning.readthedocs.io/en/latest/results.html

[RahulSajnani]

@awaelchli I am using the same kind of manual reduction as @nrjvarshney . The reduction is as shown here:

epoch_train_loss = torch.stack([x['val_epoch_logger']['train_val_loss'] for x in outputs]).mean()

[awaelchli]

yes, then it makes sense that it fails, because for stacking, all tensors need to have the same shape. If the last tensor has different batch size, it fails.
Solution: use torch.cat or Results object to reduce. Example using code above:

 def validation_step(self, batch, batch_idx):                                
        logits, softmax_logits = self(**batch)                          
        loss, prediction_label_count = self.loss_function(logits, batch["labels"])

        accuracy = self.compute_accuracy(logits, batch["labels"])               

        result = EvalResult()
        result.log('val_accuracy', accuracy, reduce_fx=torch.mean)  # mean is also the default, we don't need to write it.
        result.log('val_loss', loss)

def validation_epoch_end(self, outputs_of_validation_steps):
       # not needed! everything will be done by results: collects all acc./losses, reduces, then logs.

Hope this helps.

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