diff --git a/tests/callbacks/test_quantization.py b/tests/callbacks/test_quantization.py
index 092eb108cb..23d0cbc9d5 100644
--- a/tests/callbacks/test_quantization.py
+++ b/tests/callbacks/test_quantization.py
@@ -16,7 +16,7 @@ from typing import Callable, Union
 
 import pytest
 import torch
-from torchmetrics.functional import mean_relative_error
+from torchmetrics.functional import mean_absolute_percentage_error as mape
 
 from pytorch_lightning import seed_everything, Trainer
 from pytorch_lightning.callbacks import QuantizationAwareTraining
@@ -42,7 +42,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
     trainer = Trainer(**trainer_args)
     trainer.fit(model, datamodule=dm)
     org_size = get_model_size_mb(model)
-    org_score = torch.mean(torch.tensor([mean_relative_error(model(x), y) for x, y in dm.test_dataloader()]))
+    org_score = torch.mean(torch.tensor([mape(model(x), y) for x, y in dm.test_dataloader()]))
 
     fusing_layers = [(f"layer_{i}", f"layer_{i}a") for i in range(3)] if fuse else None
     qcb = QuantizationAwareTraining(observer_type=observe, modules_to_fuse=fusing_layers, quantize_on_fit_end=convert)
@@ -51,7 +51,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
 
     quant_calls = qcb._forward_calls
     assert quant_calls == qcb._forward_calls
-    quant_score = torch.mean(torch.tensor([mean_relative_error(qmodel(x), y) for x, y in dm.test_dataloader()]))
+    quant_score = torch.mean(torch.tensor([mape(qmodel(x), y) for x, y in dm.test_dataloader()]))
     # test that the test score is almost the same as with pure training
     assert torch.allclose(org_score, quant_score, atol=0.45)
     model_path = trainer.checkpoint_callback.best_model_path
@@ -70,7 +70,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
 
     # todo: make it work also with strict loading
     qmodel2 = RegressionModel.load_from_checkpoint(model_path, strict=False)
-    quant2_score = torch.mean(torch.tensor([mean_relative_error(qmodel2(x), y) for x, y in dm.test_dataloader()]))
+    quant2_score = torch.mean(torch.tensor([mape(qmodel2(x), y) for x, y in dm.test_dataloader()]))
     assert torch.allclose(org_score, quant2_score, atol=0.45)