Support deprecated attribute usage

pl_bolts/models/rl/double_dqn_model.py

@@ -58,7 +58,7 @@ def training_step(self, batch: Tuple[Tensor, Tensor], _) -> OrderedDict:
         # calculates training loss
         loss = double_dqn_loss(batch, self.net, self.target_net, self.gamma)
 
-        if self.trainer.use_dp or self.trainer.use_ddp2:
+        if self._use_dp_or_ddp2(self.trainer):
             loss = loss.unsqueeze(0)
 
         # Soft update of target network

pl_bolts/models/rl/dqn_model.py

@@ -20,7 +20,7 @@
 from pl_bolts.models.rl.common.gym_wrappers import make_environment
 from pl_bolts.models.rl.common.memory import MultiStepBuffer
 from pl_bolts.models.rl.common.networks import CNN
-from pl_bolts.utils import _GYM_AVAILABLE
+from pl_bolts.utils import _GYM_AVAILABLE, _PL_GREATER_EQUAL_1_4
 from pl_bolts.utils.warnings import warn_missing_pkg
 
 if _GYM_AVAILABLE:
@@ -272,7 +272,7 @@ def training_step(self, batch: Tuple[Tensor, Tensor], _) -> OrderedDict:
         # calculates training loss
         loss = dqn_loss(batch, self.net, self.target_net, self.gamma)
 
-        if self.trainer.use_dp or self.trainer.use_ddp2:
+        if self._use_dp_or_ddp2(self.trainer):
             loss = loss.unsqueeze(0)
 
         # Soft update of target network
@@ -404,6 +404,13 @@ def add_model_specific_args(arg_parser: argparse.ArgumentParser, ) -> argparse.A
 
         return arg_parser
 
+    @staticmethod
+    def _use_dp_or_ddp2(trainer: Trainer) -> bool:
+        # for backwards compatibility
+        if _PL_GREATER_EQUAL_1_4:
+            return trainer.accelerator_connector.use_dp or trainer.accelerator_connector.use_ddp2
+        return trainer.use_dp or trainer.use_ddp2
+
 
 def cli_main():
     parser = argparse.ArgumentParser(add_help=False)

pl_bolts/models/rl/per_dqn_model.py

@@ -116,7 +116,7 @@ def training_step(self, batch, _) -> OrderedDict:
         # calculates training loss
         loss, batch_weights = per_dqn_loss(samples, weights, self.net, self.target_net, self.gamma)
 
-        if self.trainer.use_dp or self.trainer.use_ddp2:
+        if self._use_dp_or_ddp2(self.trainer):
             loss = loss.unsqueeze(0)
 
         # update priorities in buffer

pl_bolts/models/self_supervised/moco/moco2_module.py

@@ -26,7 +26,7 @@
     Moco2TrainImagenetTransforms,
     Moco2TrainSTL10Transforms,
 )
-from pl_bolts.utils import _TORCHVISION_AVAILABLE
+from pl_bolts.utils import _PL_GREATER_EQUAL_1_4, _TORCHVISION_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
 if _TORCHVISION_AVAILABLE:
@@ -150,7 +150,7 @@ def _momentum_update_key_encoder(self):
     @torch.no_grad()
     def _dequeue_and_enqueue(self, keys, queue_ptr, queue):
         # gather keys before updating queue
-        if self.trainer.use_ddp or self.trainer.use_ddp2:
+        if self._use_ddp_or_ddp2(self.trainer):
             keys = concat_all_gather(keys)
 
         batch_size = keys.shape[0]
@@ -229,14 +229,14 @@ def forward(self, img_q, img_k, queue):
         with torch.no_grad():  # no gradient to keys
 
             # shuffle for making use of BN
-            if self.trainer.use_ddp or self.trainer.use_ddp2:
+            if self._use_ddp_or_ddp2(self.trainer):
                 img_k, idx_unshuffle = self._batch_shuffle_ddp(img_k)
 
             k = self.encoder_k(img_k)  # keys: NxC
             k = nn.functional.normalize(k, dim=1)
 
             # undo shuffle
-            if self.trainer.use_ddp or self.trainer.use_ddp2:
+            if self._use_ddp_or_ddp2(self.trainer):
                 k = self._batch_unshuffle_ddp(k, idx_unshuffle)
 
         # compute logits
@@ -335,6 +335,13 @@ def add_model_specific_args(parent_parser):
 
         return parser
 
+    @staticmethod
+    def _use_ddp_or_ddp2(trainer: Trainer) -> bool:
+        # for backwards compatibility
+        if _PL_GREATER_EQUAL_1_4:
+            return trainer.accelerator_connector.use_ddp or trainer.accelerator_connector.use_ddp2
+        return trainer.use_ddp or trainer.use_ddp2
+
 
 # utils
 @torch.no_grad()

pl_bolts/utils/__init__.py

@@ -38,5 +38,6 @@ def _compare_version(package: str, op, version) -> bool:
 _WANDB_AVAILABLE: bool = _module_available("wandb")
 _MATPLOTLIB_AVAILABLE: bool = _module_available("matplotlib")
 _TORCHVISION_LESS_THAN_0_9_1: bool = _compare_version("torchvision", operator.lt, "0.9.1")
+_PL_GREATER_EQUAL_1_4 = _compare_version("pytorch_lightning", operator.ge, "1.4.0")
 
 __all__ = ["BatchGradientVerification"]