rename accelerator_backend -> accelerator

CHANGELOG.md

@@ -240,6 +240,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `.get_model()` with explicit `.lightning_module` property ([#6035](https://github.com/PyTorchLightning/pytorch-lightning/pull/6035))
 
 
+- Deprecated Trainer attribute `accelerator_backend` in favor of `accelerator` ([#6034](https://github.com/PyTorchLightning/pytorch-lightning/pull/6034))
+
+
+
 ### Removed
 
 - Removed deprecated checkpoint argument `filepath` ([#5321](https://github.com/PyTorchLightning/pytorch-lightning/pull/5321))

pl_examples/basic_examples/conv_sequential_example.py

@@ -222,6 +222,6 @@ def instantiate_datamodule(args):
     trainer.fit(model, cifar10_dm)
     trainer.test(model, datamodule=cifar10_dm)
 
-    if trainer.accelerator_backend.rpc_enabled:
+    if trainer.accelerator.rpc_enabled:
         # Called at the end of trainer to ensure all processes are killed
         trainer.training_type_plugin.exit_rpc_process()

pytorch_lightning/core/lightning.py

@@ -41,7 +41,6 @@
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_to_tensors
 from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
-from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
 
@@ -448,11 +447,7 @@ def all_gather(
             the output will also be a collection with tensors of this shape.
         """
         group = group if group is not None else torch.distributed.group.WORLD
-        if self.trainer.accelerator_backend is not None:
-            all_gather = self.trainer.accelerator_backend.all_gather
-        else:
-            all_gather = all_gather_ddp_if_available
-
+        all_gather = self.trainer.accelerator.all_gather
         data = convert_to_tensors(data, device=self.device)
         all_gather = partial(all_gather, group=group, sync_grads=sync_grads)
         return apply_to_collection(data, torch.Tensor, all_gather)

pytorch_lightning/core/optimizer.py

@@ -132,7 +132,7 @@ def __optimizer_step(self, closure: Optional[Callable] = None, profiler_name: st
         model = trainer.lightning_module
 
         with trainer.profiler.profile(profiler_name):
-            trainer.accelerator_backend.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
+            trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
 
         if self._trainer.train_loop.automatic_optimization:
             trainer.train_loop.on_before_zero_grad(optimizer)

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -185,7 +185,7 @@ def init_deepspeed(self):
             self._format_config()
             self._config_initialized = True
 
-        precision = self.lightning_module.trainer.accelerator_backend.precision
+        precision = self.lightning_module.trainer.accelerator.precision
         model = LightningDeepSpeedModule(pl_module=self.model, precision=precision)
 
         if self.lightning_module.trainer.training:

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -90,7 +90,7 @@ def new_process(self, process_idx: int, trainer, mp_queue) -> None:
             trainer.progress_bar_callback.disable()
 
         self.model_to_device()
-        trainer.accelerator_backend.setup_optimizers(trainer)
+        trainer.accelerator.setup_optimizers(trainer)
         trainer.precision_plugin.connect(self._model, None, None)
 
         # replace trainer save_checkpoint to use `xm.save`

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -219,8 +219,7 @@ def hpc_save(self, folderpath: str, logger):
 
         model.on_hpc_save(checkpoint)
 
-        if self.trainer.accelerator_backend:
-            checkpoint = self.trainer.accelerator_backend.on_save(checkpoint)
+        checkpoint = self.trainer.accelerator.on_save(checkpoint)
 
         # do the actual save
         # TODO: fix for anything with multiprocess DP, DDP, DDP2
@@ -286,7 +285,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
             optimizer_states = []
             for i, optimizer in enumerate(self.trainer.optimizers):
                 # Rely on accelerator to dump optimizer state
-                optimizer_state = self.trainer.accelerator_backend.optimizer_state(optimizer)
+                optimizer_state = self.trainer.accelerator.optimizer_state(optimizer)
                 optimizer_states.append(optimizer_state)
 
             checkpoint['optimizer_states'] = optimizer_states

pytorch_lightning/trainer/data_loading.py

@@ -51,7 +51,7 @@ class TrainerDataLoadingMixin(ABC):
     limit_val_batches: Union[int, float]
     limit_test_batches: Union[int, float]
     replace_sampler_ddp: bool
-    accelerator_backend: Accelerator
+    accelerator: Accelerator
     num_nodes: int
     num_processes: int
     distributed_backend: Optional[str]
@@ -398,8 +398,7 @@ def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:
         dataloader = dataloader_fx()
         dataloader = self._flatten_dl_only(dataloader)
 
-        if self.accelerator_backend is not None:
-            self.accelerator_backend.barrier('get_dataloaders')
+        self.accelerator.barrier('get_dataloaders')
         return dataloader
 
     def _flatten_dl_only(self, dataloaders):

pytorch_lightning/trainer/deprecated_api.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from pytorch_lightning.accelerators import Accelerator
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.trainer.connectors.accelerator_connector import AcceleratorConnector
 from pytorch_lightning.trainer.states import RunningStage
@@ -133,10 +134,19 @@ def use_single_gpu(self, val: bool) -> None:
             self.accelerator_connector._device_type = DeviceType.GPU
 
 
-class DeprecatedModelAttributes:
+class DeprecatedTrainerAttributes:
 
+    accelerator: Accelerator
     lightning_module = LightningModule
 
+    @property
+    def accelerator_backend(self) -> Accelerator:
+        rank_zero_warn(
+            "The `Trainer.accelerator_backend` attribute is deprecated in favor of `Trainer.accelerator`"
+            " since 1.2 and will be removed in v1.4.", DeprecationWarning
+        )
+        return self.accelerator
+
     def get_model(self) -> LightningModule:
         rank_zero_warn(
             "The use of `Trainer.get_model()` is deprecated in favor of `Trainer.lightning_module`"

pytorch_lightning/trainer/evaluation_loop.py

@@ -157,11 +157,11 @@ def evaluation_step(self, batch, batch_idx, dataloader_idx):
         if self.testing:
             model_ref._current_fx_name = "test_step"
             with self.trainer.profiler.profile("test_step"):
-                output = self.trainer.accelerator_backend.test_step(args)
+                output = self.trainer.accelerator.test_step(args)
         else:
             model_ref._current_fx_name = "validation_step"
             with self.trainer.profiler.profile("validation_step"):
-                output = self.trainer.accelerator_backend.validation_step(args)
+                output = self.trainer.accelerator.validation_step(args)
 
         # capture any logged information
         self.trainer.logger_connector.cache_logged_metrics()

pytorch_lightning/trainer/predict_loop.py

@@ -74,7 +74,7 @@ def predict(self, batch, batch_idx, dataloader_idx):
         model_ref = self.trainer.lightning_module
 
         model_ref._current_fx_name = "predict"
-        predictions = self.trainer.accelerator_backend.predict(args)
+        predictions = self.trainer.accelerator.predict(args)
         self._predictions[dataloader_idx].append(predictions)
         self.trainer._progress_bar_callback.on_predict_batch_end(
             self.trainer, model_ref, predictions, batch, batch_idx, dataloader_idx

pytorch_lightning/trainer/properties.py

@@ -62,11 +62,6 @@ class TrainerProperties(ABC):
     def accelerator(self) -> Accelerator:
         return self.accelerator_connector.accelerator
 
-    @property
-    def accelerator_backend(self) -> Accelerator:
-        # for backward compatibility
-        return self.accelerator
-
     @property
     def distributed_backend(self) -> Optional[str]:
         # for backward compatibility
@@ -138,7 +133,7 @@ def log_dir(self) -> Optional[str]:
         else:
             dirpath = getattr(self.logger, 'log_dir' if isinstance(self.logger, TensorBoardLogger) else 'save_dir')
 
-        dirpath = self.accelerator_backend.broadcast(dirpath)
+        dirpath = self.accelerator.broadcast(dirpath)
         return dirpath
 
     @property
@@ -360,7 +355,7 @@ def lightning_optimizers(self) -> List[LightningOptimizer]:
 
     @property
     def lightning_module(self) -> LightningModule:
-        return self.accelerator_backend.lightning_module
+        return self.accelerator.lightning_module
 
     @property
     def optimizers(self) -> Optional[List[Optimizer]]:

pytorch_lightning/trainer/trainer.py

@@ -45,7 +45,7 @@
 from pytorch_lightning.trainer.connectors.slurm_connector import SLURMConnector
 from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
 from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
-from pytorch_lightning.trainer.deprecated_api import DeprecatedDistDeviceAttributes, DeprecatedModelAttributes
+from pytorch_lightning.trainer.deprecated_api import DeprecatedDistDeviceAttributes, DeprecatedTrainerAttributes
 from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
@@ -80,7 +80,7 @@ class Trainer(
     TrainerTrainingTricksMixin,
     TrainerDataLoadingMixin,
     DeprecatedDistDeviceAttributes,
-    DeprecatedModelAttributes,
+    DeprecatedTrainerAttributes,
 ):
 
     @overwrite_by_env_vars
@@ -470,7 +470,7 @@ def fit(
         # ----------------------------
         self.call_setup_hook(model)
         self.call_hook("on_before_accelerator_backend_setup", model)
-        self.accelerator_backend.setup(self, model)
+        self.accelerator.setup(self, model)
         self.setup_trainer(model)
 
         # ----------------------------
@@ -533,24 +533,24 @@ def fit(
 
         self._set_running_stage(None, model)
 
-        return self.accelerator_backend.results or 1
+        return self.accelerator.results or 1
 
     def pre_dispatch(self):
-        self.accelerator_backend.pre_dispatch()
+        self.accelerator.pre_dispatch()
 
     def post_dispatch(self):
-        self.accelerator_backend.post_dispatch()
-        self.accelerator_backend.teardown()
+        self.accelerator.post_dispatch()
+        self.accelerator.teardown()
 
     def dispatch(self):
         if self.testing:
-            self.accelerator_backend.start_testing(self)
+            self.accelerator.start_testing(self)
 
         elif self.predicting:
-            self.accelerator_backend.start_predicting(self)
+            self.accelerator.start_predicting(self)
 
         else:
-            self.accelerator_backend.start_training(self)
+            self.accelerator.start_training(self)
 
     def train_or_test_or_predict(self):
         if self.testing:
@@ -949,7 +949,7 @@ def __test_using_best_weights(self, ckpt_path, test_dataloaders):
                 )
                 return {}
             if not self._device_type == DeviceType.TPU:
-                self.accelerator_backend.barrier()
+                self.accelerator.barrier()
 
             ckpt = pl_load(ckpt_path, map_location=lambda storage, loc: storage)
             model.load_state_dict(ckpt['state_dict'])
@@ -1109,8 +1109,8 @@ def call_hook(self, hook_name, *args, **kwargs):
 
             # if the PL module doesn't have the hook then call the accelerator
             # used to auto-reduce things for the user with Results obj
-            elif hasattr(self.accelerator_backend, hook_name):
-                accelerator_hook = getattr(self.accelerator_backend, hook_name)
+            elif hasattr(self.accelerator, hook_name):
+                accelerator_hook = getattr(self.accelerator, hook_name)
                 output = accelerator_hook(*args, **kwargs)
 
         if not skip:

pytorch_lightning/trainer/training_loop.py

@@ -290,8 +290,8 @@ def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
             model_ref._current_fx_name = 'training_step'
             model_ref._results = Result()
             with self.trainer.profiler.profile("training_step"):
-                training_step_output = self.trainer.accelerator_backend.training_step(args)
-                self.trainer.accelerator_backend.post_training_step()
+                training_step_output = self.trainer.accelerator.training_step(args)
+                self.trainer.accelerator.post_training_step()
 
             self.trainer.logger_connector.cache_logged_metrics()
 
@@ -438,14 +438,14 @@ def on_before_zero_grad(self, optimizer):
         self.trainer.call_hook('on_before_zero_grad', optimizer)
 
     def optimizer_zero_grad(self, batch_idx, optimizer, opt_idx):
-        self.trainer.accelerator_backend.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx)
+        self.trainer.accelerator.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx)
 
     def track_and_norm_grad(self, optimizer):
         # track gradient norms
         grad_norm_dic = self._track_gradient_norm()
 
         # clip gradients
-        self.trainer.accelerator_backend.clip_gradients(optimizer, self.trainer.gradient_clip_val)
+        self.trainer.accelerator.clip_gradients(optimizer, self.trainer.gradient_clip_val)
         self._cur_grad_norm_dict = grad_norm_dic
 
     def _track_gradient_norm(self):
@@ -769,9 +769,9 @@ def backward(self, result, optimizer, opt_idx, *args, **kwargs):
 
         # backward can be called manually in the training loop
         if isinstance(result, torch.Tensor):
-            self.trainer.accelerator_backend.backward(result, optimizer, opt_idx, should_accumulate, *args, **kwargs)
+            self.trainer.accelerator.backward(result, optimizer, opt_idx, should_accumulate, *args, **kwargs)
         else:
-            result.closure_loss = self.trainer.accelerator_backend.backward(
+            result.closure_loss = self.trainer.accelerator.backward(
                 result.closure_loss, optimizer, opt_idx, should_accumulate, *args, **kwargs
             )