diff --git a/tests/trainer/logging_/test_train_loop_logging.py b/tests/trainer/logging_/test_train_loop_logging.py
index c42c5a0f06bab..34c94e3ba63ea 100644
--- a/tests/trainer/logging_/test_train_loop_logging.py
+++ b/tests/trainer/logging_/test_train_loop_logging.py
@@ -747,3 +747,38 @@ def validation_epoch_end(self, *_) -> None:
     train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
     val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
     trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
+
+
+def test_no_batch_size_extraction_with_specifying_explictly(tmpdir):
+    batch_size = BoringModel().train_dataloader().batch_size + 10
+    fast_dev_run = 2
+    log_val = 7.0
+
+    class CustomBoringModel(BoringModel):
+        def on_before_batch_transfer(self, batch, *args, **kwargs):
+            # This is an ambiguous batch which have multiple potential batch sizes
+            if self.trainer.training:
+                batch = {"batch1": torch.randn(batch_size + 10, 10), "batch2": batch}
+            return batch
+
+        def training_step(self, batch, batch_idx):
+            self.log("step_log_val", log_val, on_epoch=False)
+            self.log("epoch_log_val", log_val, batch_size=batch_size, on_step=False, on_epoch=True)
+            self.log("epoch_sum_log_val", log_val, on_epoch=True, reduce_fx="sum")
+            return super().training_step(batch["batch2"], batch_idx)
+
+        def training_epoch_end(self, *args, **kwargs):
+            results = self.trainer._results
+            assert results["training_step.step_log_val"].value == log_val
+            assert results["training_step.step_log_val"].cumulated_batch_size == 0
+            assert results["training_step.epoch_log_val"].value == log_val * batch_size * fast_dev_run
+            assert results["training_step.epoch_log_val"].cumulated_batch_size == batch_size * fast_dev_run
+            assert results["training_step.epoch_sum_log_val"].value == log_val * fast_dev_run
+
+    model = CustomBoringModel()
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=fast_dev_run)
+
+    with pytest.warns(None) as record:
+        trainer.fit(model)
+
+    assert not any("Trying to infer the `batch_size`" in warn.message.args[0] for warn in record.list)