Fix shuffle for distributed sampler

CHANGELOG.md

@@ -49,6 +49,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed data transfer to device when using `torchtext.data.Field` and `include_lengths is True` ([#2689](https://github.com/PyTorchLightning/pytorch-lightning/pull/2689)) 
 
+- Fixed shuffle argument for distributed sampler ([#2789](https://github.com/PyTorchLightning/pytorch-lightning/pull/2789))
+
 ## [0.8.5] - 2020-07-09
 
 ### Added

pytorch_lightning/trainer/data_loading.py

@@ -163,7 +163,7 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
                     ' `replace_sampler_ddp`=False if you want to use your custom sampler.')
 
             # replace with distributed sampler
-            sampler = self._get_distributed_sampler(dataloader)
+            sampler = self._get_distributed_sampler(dataloader, train)
             dataloader = self.replace_sampler(dataloader, sampler)
 
         return dataloader
@@ -179,7 +179,7 @@ def replace_sampler(self, dataloader, sampler):
         dataloader = type(dataloader)(**dl_args)
         return dataloader
 
-    def _get_distributed_sampler(self, dataloader):
+    def _get_distributed_sampler(self, dataloader, train):
         if self.use_tpu:
             kwargs = dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
         elif self.use_horovod:
@@ -193,6 +193,8 @@ def _get_distributed_sampler(self, dataloader):
             }
             assert self.distributed_backend is not None
             kwargs = dict(num_replicas=world_size[self.distributed_backend], rank=self.global_rank)
+
+        kwargs['shuffle'] = train
         sampler = DistributedSampler(dataloader.dataset, **kwargs)
         return sampler
 

tests/trainer/test_dataloaders.py

@@ -7,9 +7,10 @@
 from packaging.version import parse
 from torch.utils.data.dataloader import DataLoader
 from torch.utils.data.dataset import IterableDataset, Subset
+from torch.utils.data.distributed import DistributedSampler
 
 import tests.base.develop_pipelines as tpipes
-from pytorch_lightning import Trainer
+from pytorch_lightning import Trainer, Callback
 from pytorch_lightning.trainer.data_loading import _has_iterable_dataset, _has_len
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
@@ -640,6 +641,42 @@ class CustomSampler(torch.utils.data.Sampler):
             CustomDataLoader(list(range(1000)), sampler=CustomSampler(list(range(1000)))), train=True)
 
 
+class DistribSamplerCallback(Callback):
+
+    def on_train_start(self, trainer, pl_module):
+        train_sampler = trainer.train_dataloader.sampler
+        assert isinstance(train_sampler, DistributedSampler)
+        assert train_sampler.shuffle
+
+    def on_validation_start(self, trainer, pl_module):
+        val_sampler = trainer.val_dataloaders[0].sampler
+        assert isinstance(val_sampler, DistributedSampler)
+        assert not val_sampler.shuffle
+
+    def on_test_start(self, trainer, pl_module):
+        test_sampler = trainer.test_dataloaders[0].sampler
+        assert isinstance(test_sampler, DistributedSampler)
+        assert not test_sampler.shuffle
+
+
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Does not apply to Windows platform.')
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason='Test requires multiple GPUs')
+def test_dataloader_distributed_sampler(tmpdir):
+    """ Test DistributedSampler and it's arguments for DDP backend """
+
+    model = EvalModelTemplate()
+    trainer = Trainer(
+        gpus=[0, 1],
+        num_nodes=1,
+        distributed_backend='ddp_spawn',
+        default_root_dir=tmpdir,
+        max_steps=1,
+        callbacks=[DistribSamplerCallback()]
+    )
+    trainer.fit(model)
+    trainer.test(ckpt_path=None)
+
+
 @pytest.mark.skipif(torch.cuda.device_count() < 3, reason='Test requires multiple GPUs')
 def test_batch_size_smaller_than_num_gpus(tmpdir):
     # we need at least 3 gpus for this test