`self.log` 在数据加载器数量不一致时发生错误

Hi all, I posted a discussion in Lightning.ai forum here and @awaelchli suggested me reporting an issue. There might be a bug in the way self.log records dataloader_idx. If we have two validation dataloaders, say A and B. We use A every epoch, but only use B every 2 epoch. However, while using self.log, in validation_step(), an error would show up: You called self.log({name}, ...) twice in {fx} with different arguments. This is not allowed (see here The way I implement it was by switching the available dataloaders in val_dataloader() and reload dataloader every epoch. def val_dataloader(): if self.should_run_B(): return [loader_A, loader_B] else: return [loader_A] I notice that when we are at the epoch that only use one validation dataloader, dataloader_idx is always None. On the other hand, when we have two validation dataloaders, dataloader_idx would be sequentially presented as 0 or 1 in validation_step. If I understand correctly, this is the main reason causing the error. Another interesting finding is that if we set add_dataloader_idx=True for all self.log, the program would run without error. But the tensorboard logging would be wrong that shows and c_0 andc_0/dataloader_idx_0 together (See snapshot below). These two were meant to remain in the same figure but somehow it got split into two figures. Probably it is because of the alternating dataloader design. ### How to reproduce the bug import torch from torch.utils.data import DataLoader from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset from pytorch_lightning import Trainer class TestModel(BoringModel): def training_step(self, batch, batch_idx): out = super().training_step(batch, batch_idx) self.log("a", out["loss"]) self.log("b", out["loss"], on_step=True, on_epoch=True) return out def validation_step(self, batch, batch_idx, dataloader_idx=0): out = super().validation_step(batch, batch_idx) if dataloader_idx == 0: self.log("c_0", out["x"], add_dataloader_idx=False) self.log("d_0", out["x"], on_step=True, on_epoch=True, add_dataloader_idx=False) elif dataloader_idx == 1: self.log("c_1", out["x"], add_dataloader_idx=False) self.log("d_1", out["x"], on_step=True, on_epoch=True, add_dataloader_idx=False) return out def validation_epoch_end(self, outputs): self.log("g", torch.tensor(2, device=self.device), on_epoch=True) …