remove redundant test

tests/trainer/test_dataloaders.py

@@ -19,7 +19,7 @@
 import torch
 from torch.utils.data import RandomSampler
 from torch.utils.data.dataloader import DataLoader
-from torch.utils.data.dataset import Dataset, IterableDataset, Subset
+from torch.utils.data.dataset import Dataset, IterableDataset
 from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import SequentialSampler
 
@@ -855,53 +855,6 @@ def test_dataloader_distributed_sampler_already_attached(tmpdir):
  assert trainer.state.finished, "DDP Training failed"
 
 
-@RunIf(min_gpus=3)
-def test_batch_size_smaller_than_num_gpus(tmpdir):
- # we need at least 3 gpus for this test
- num_gpus = 3
- batch_size = 3
-
- class CurrentTestModel(BoringModel):
- def __init__(self, batch_size) -> None:
- super().__init__()
- self.save_hyperparameters()
- # batch norm doesn't work with batch size 1, we replace it
- self.c_d1_bn = torch.nn.ReLU()
-
- def training_step(self, *args, **kwargs):
- output = super().training_step(*args, **kwargs)
- loss = output["loss"]
- # we make sure to add some metrics to the output dict,
- # this is essential for this test
- output["progress_bar"] = {"train_loss": loss}
- return output
-
- def train_dataloader(self):
- dataset = RandomDataset(32, 64)
- # construct a dataset with a size that is not divisible by num_gpus
- # therefore the last batch will have a size < num_gpus
- size = num_gpus * self.hparams.batch_size + (num_gpus - 1)
- dataset = Subset(dataset, range(size))
- dataloader = DataLoader(dataset, batch_size=self.hparams.batch_size, drop_last=False)
- return dataloader
-
- model = CurrentTestModel(batch_size=batch_size)
-
- trainer = Trainer(
- default_root_dir=tmpdir,
- max_epochs=1,
- limit_train_batches=0.1,
- limit_val_batches=0,
- accelerator="gpu",
- devices=num_gpus,
- )
-
- # we expect the reduction for the metrics also to happen on the last batch
- # where we will get fewer metrics than gpus
- trainer.fit(model)
- assert trainer.state.finished, f"Training failed with {trainer.state}"
-
-
 @pytest.mark.parametrize(
  ["multiple_trainloader_mode", "num_training_batches"],
  [("min_size", 16), ("max_size_cycle", 64)],