The compute_groups may lead to unexpected behavior about torchmetrics

[github-actions]

Hi! thanks for your contribution!, great first issue!

[SkafteNicki]

Hi @YicunDuanUMich, sorry for the late reply.
I am pretty sure that the bug you are seeing was fixes in later versions of torchmetrics. In particular I think this PR probably contains the solution for your issues: #2571.
Just as an example, here is a script that initializes the classification metric precision with both average="macro" and average="weighted" e.g. two metric where there states are the same and they only differ how values are aggregated in the end.

import torchmetrics
import torch

collection = torchmetrics.MetricCollection({
    "micro_precision": torchmetrics.Precision(task="multiclass", average='weighted', num_classes=3),
    "macro_precision": torchmetrics.Precision(task="multiclass", average='macro', num_classes=3),
})

for _ in range(3):
    x = torch.randn(10, 3).softmax(dim=1)
    y = torch.randint(0, 3, (10,))
    collection.update(x, y)
    out = collection.compute()
    print(out)

In v0.11.3 of torchmetrics I am seeing somewhat the same behavior you are describing, that the second metric is not being updated correctly. However in the newest version of torchmetrics things are working as expected.
Therefore, please update to the newest version of torchmetrics.
Closing issue.