confusions about torchmetrics in pytorch_lightning

[KasuganoLove]

According to:

https://lightning.ai/docs/torchmetrics/stable/pages/lightning.html

We are recommended to instantiate three torchmetrics (including test) when logging the metric object and letting Lightning take care of when to reset the metric etc. Here is the official code (without test):

class MyModule(LightningModule):

    def __init__(self):
        ...
        self.train_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
        self.valid_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)

    def training_step(self, batch, batch_idx):
        x, y = batch
        preds = self(x)
        ...
        batch_value = self.train_acc(preds, y)
        self.log('train_acc_step', batch_value)

    def on_train_epoch_end(self):
        self.train_acc.reset()

    def validation_step(self, batch, batch_idx):
        logits = self(x)
        ...
        self.valid_acc.update(logits, y)

    def on_validation_epoch_end(self, outputs):
        self.log('valid_acc_epoch', self.valid_acc.compute())
        self.valid_acc.reset()

My question is:

1. since we call the torchmetrics.reset() on_train_epoch_end, on_validation_epoch_end and on_test_epoch_end, we only need one torchmetric to calculate all, is that right?
2. When we just use torchmetrics.forward() to calculate the metrics of the inputs, the internal state doen't matter (even torchmetrics.reset() is redundant), is that right?
3. When calculating torchmetrics with the internal state (like FID scores), since question 1, we only need one torchmetric to calculate all, is that right?
4. Is there a potential risk of doing this? Will torchmetrics.compute() in my second paragraph of code still works properly under ddp mode ?

Here is my code for torchmetrics which just use torchmetrics.forward() to calculate the metrics of the inputs:

class mylightningmodule(pl.LightningModule):
    def __init__(self, metric=None, **kwargs):
        super().__init__()
        # lightning code
        self.save_hyperparameters(ignore=['metric'])
        self.mIoU = metric

    def training_step(self, batch, batch_idx):
        mIoU = self.mIoU(preds=predict_masks, target=masks)
        self.log('train_mIoU', mIoU, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
        return loss

    def validation_step(self, batch, batch_idx):
        mIoU = self.mIoU(preds=predict_masks, target=masks)
        self.log('val_mIoU', mIoU, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)

    def test_step(self, batch, batch_idx):
        mIoU = self.mIoU(preds=predict_masks, target=masks)
        self.log('test_mIoU', mIoU, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)

    def on_train_epoch_end(self):
        self.mIoU.reset()

    def on_validation_epoch_end(self):
        self.mIoU.reset()

    def on_test_epoch_end(self):
        self.mIoU.reset()

# train, validate and test code
metric = MulticlassJaccardIndex(num_classes=5)
trainer.fit(model=mylightningmodule, datamodule=data_module)
trainer.validate(model=mylightningmodule, datamodule=data_module)
trainer.test(model=mylightningmodule, datamodule=data_module)

Here is my code for torchmetrics with useful internal state (like FID scores):

class mylightningmodule(pl.LightningModule):
    def __init__(self, metric=None, **kwargs):
        super().__init__()
        # lightning code
        self.save_hyperparameters(ignore=['metric'])
        # FrechetInceptionDistance
        self.FID = metric

    def validation_step(self, batch, batch_idx):
        # calculate FID
        self.FID.update(X_0, real=True)
        self.FID.update(pred_x_0, real=False)

    def test_step(self, batch, batch_idx):
        # calculate FID
        self.FID.update(X_0, real=True)
        self.FID.update(pred_x_0, real=False)

    def on_validation_epoch_end(self):
        FID = self.FID.compute()
        self.log('val_FID', FID, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
        self.FID.reset()

    def on_test_epoch_end(self):
        FID = self.FID.compute()
        self.log('test_FID', FID, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
        self.FID.reset()

# train, validate and test code
metric = FrechetInceptionDistance()
trainer.fit(model=mylightningmodule, datamodule=data_module)
trainer.validate(model=mylightningmodule, datamodule=data_module)
trainer.test(model=mylightningmodule, datamodule=data_module)