Skip replacing dataloader sampler if it's already a distributed sampler

CHANGELOG.md

@@ -20,6 +20,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Improved error messages for invalid `configure_optimizers` returns ([#3587](https://github.com/PyTorchLightning/pytorch-lightning/pull/3587))
 
 - Allow changing the logged step value in `validation_step` ([#4130](https://github.com/PyTorchLightning/pytorch-lightning/pull/4130))
+- Allow setting `replace_sampler_ddp=True` with a distributed sampler already added ([#4273](https://github.com/PyTorchLightning/pytorch-lightning/pull/4273))
 
 ### Deprecated
 
@@ -122,7 +123,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Fixed
 
 - Fixed `current_epoch` property update to reflect true epoch number inside `LightningDataModule`, when `reload_dataloaders_every_epoch=True`. ([#3974](https://github.com/PyTorchLightning/pytorch-lightning/pull/3974))
-- Fixed to print scaler value in progress bar ([#4053](https://github.com/PyTorchLightning/pytorch-lightning/pull/4053)) 
+- Fixed to print scaler value in progress bar ([#4053](https://github.com/PyTorchLightning/pytorch-lightning/pull/4053))
 - Fixed mismatch between docstring and code regarding when `on_load_checkpoint` hook is called ([#3996](https://github.com/PyTorchLightning/pytorch-lightning/pull/3996))
 
 
@@ -467,7 +468,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed adding val step argument to metrics ([#2986](https://github.com/PyTorchLightning/pytorch-lightning/pull/2986))
 - Fixed an issue that caused `Trainer.test()` to stall in ddp mode ([#2997](https://github.com/PyTorchLightning/pytorch-lightning/pull/2997))
 - Fixed gathering of results with tensors of varying shape ([#3020](https://github.com/PyTorchLightning/pytorch-lightning/pull/3020))
-- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043)) 
+- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043))
 - Fixed automatic batch scaling not working with half precision ([#3045](https://github.com/PyTorchLightning/pytorch-lightning/pull/3045))
 - Fixed setting device to root gpu ([#3042](https://github.com/PyTorchLightning/pytorch-lightning/pull/3042))
 

pytorch_lightning/trainer/__init__.py

@@ -1278,6 +1278,8 @@ def world_size(self):
 Enables auto adding of distributed sampler. By default it will add ``shuffle=True``
 for train sampler and ``shuffle=False`` for val/test sampler. If you want to customize
 it, you can set ``replace_sampler_ddp=False`` and add your own distributed sampler.
+If ``replace_sampler_ddp=True`` and a distributed sampler was already added,
+Lightning will not replace the existing one.
 
 .. testcode::
 

pytorch_lightning/trainer/data_loading.py

@@ -103,7 +103,7 @@ def _worker_check(self, dataloader: DataLoader, name: str) -> None:
                                f' (try {num_cpus} which is the number of cpus on this machine)'
                                ' in the `DataLoader` init to improve performance.')
 
-    def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
+    def auto_add_sampler(self, dataloader: DataLoader, shuffle: bool) -> DataLoader:
 
         # don't do anything if it's not a dataloader
         is_dataloader = isinstance(dataloader, DataLoader)
@@ -112,8 +112,9 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
 
         if not is_dataloader or is_iterable_ds:
             return dataloader
-        need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu)
 
+        is_in_dist = self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu
+        need_dist_sampler = is_in_dist and not isinstance(dataloader.sampler, DistributedSampler)
         if self.replace_sampler_ddp and need_dist_sampler:
             if not isinstance(dataloader.sampler, (SequentialSampler, RandomSampler)):
                 raise MisconfigurationException(
@@ -123,7 +124,7 @@ 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, train)
+            sampler = self._get_distributed_sampler(dataloader, shuffle)
             dataloader = self.replace_sampler(dataloader, sampler)
 
         return dataloader
@@ -136,10 +137,11 @@ def replace_sampler(self, dataloader, sampler):
         }
 
         dl_args['sampler'] = sampler
+        dl_args['shuffle'] = False
         dataloader = type(dataloader)(**dl_args)
         return dataloader
 
-    def _get_distributed_sampler(self, dataloader, train):
+    def _get_distributed_sampler(self, dataloader, shuffle):
         if self.use_tpu:
             kwargs = dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
         elif self.use_horovod:
@@ -154,7 +156,7 @@ def _get_distributed_sampler(self, dataloader, train):
             assert self.distributed_backend is not None
             kwargs = dict(num_replicas=world_size[self.distributed_backend], rank=self.global_rank)
 
-        kwargs['shuffle'] = train and not self.overfit_batches
+        kwargs['shuffle'] = shuffle and not self.overfit_batches
         sampler = DistributedSampler(dataloader.dataset, **kwargs)
         return sampler
 
@@ -179,7 +181,7 @@ def reset_train_dataloader(self, model: LightningModule) -> None:
         self.num_training_batches = 0
 
         # automatically add samplers
-        self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)
+        self.train_dataloader = self.auto_add_sampler(self.train_dataloader, shuffle=True)
 
         self.num_training_batches = len(self.train_dataloader) if has_len(self.train_dataloader) else float('inf')
         self._worker_check(self.train_dataloader, 'train dataloader')
@@ -267,7 +269,7 @@ def _reset_eval_dataloader(
             rank_zero_warn("One of given dataloaders is None and it will be skipped.")
 
         # add samplers
-        dataloaders = [self.auto_add_sampler(dl, train=False) for dl in dataloaders if dl is not None]
+        dataloaders = [self.auto_add_sampler(dl, shuffle=False) for dl in dataloaders if dl is not None]
 
         loader_num_batches = []
 

tests/trainer/test_dataloaders.py

@@ -686,17 +686,17 @@ def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None,
     class CustomDummyObj:
         sampler = None
 
-    result = trainer.auto_add_sampler(CustomDummyObj(), train=True)
+    result = trainer.auto_add_sampler(CustomDummyObj(), shuffle=True)
     assert isinstance(result, CustomDummyObj), "Wrongly reinstantiated data loader"
 
     dataset = list(range(1000))
-    result = trainer.auto_add_sampler(CustomDataLoader(dataset), train=True)
+    result = trainer.auto_add_sampler(CustomDataLoader(dataset), shuffle=True)
     assert isinstance(result, torch.utils.data.DataLoader)
     assert isinstance(result, CustomDataLoader)
     assert hasattr(result, 'dummy_kwarg')
 
     # Shuffled DataLoader should also work
-    result = trainer.auto_add_sampler(CustomDataLoader(list(range(1000)), shuffle=True), train=True)
+    result = trainer.auto_add_sampler(CustomDataLoader(list(range(1000)), shuffle=True), shuffle=True)
     assert isinstance(result, torch.utils.data.DataLoader)
     assert isinstance(result, CustomDataLoader)
     assert hasattr(result, 'dummy_kwarg')
@@ -707,7 +707,7 @@ class CustomSampler(torch.utils.data.Sampler):
     # Should raise an error if existing sampler is being replaced
     with pytest.raises(MisconfigurationException, match='DistributedSampler'):
         trainer.auto_add_sampler(
-            CustomDataLoader(list(range(1000)), sampler=CustomSampler(list(range(1000)))), train=True)
+            CustomDataLoader(list(range(1000)), sampler=CustomSampler(list(range(1000)))), shuffle=True)
 
 
 class DistribSamplerCallback(Callback):
@@ -746,6 +746,39 @@ def test_dataloader_distributed_sampler(tmpdir):
     trainer.test(ckpt_path=None)
 
 
+class ModelWithDataLoaderDistributedSampler(EvalModelTemplate):
+
+    def train_dataloader(self):
+        dataloader = super().train_dataloader()
+        dist_sampler = DistributedSampler(dataloader.dataset, shuffle=True)
+        return DataLoader(
+            dataloader.dataset,
+            batch_size=self.batch_size,
+            drop_last=False,
+            sampler=dist_sampler,
+            shuffle=False
+        )
+
+
+@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_already_attached(tmpdir):
+    """ Test DistributedSampler and it's arguments for DDP backend when DistSampler already included on dataloader """
+
+    model = ModelWithDataLoaderDistributedSampler()
+    trainer = Trainer(
+        gpus=[0, 1],
+        num_nodes=1,
+        distributed_backend='ddp_spawn',
+        default_root_dir=tmpdir,
+        max_steps=100,
+        callbacks=[DistribSamplerCallback()],
+        replace_sampler_ddp=True,
+    )
+    result = trainer.fit(model)
+    assert result == 1, "DDP Training failed"
+
+
 @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