Add auto wrapping support for DDPFullyShardedStrategy

docs/source-pytorch/extensions/strategy.rst

@@ -83,10 +83,10 @@ The below table lists all relevant strategies available in Lightning with their
      - Strategy for Fully Sharded Data Parallel provided by FairScale. :ref:`Learn more. <advanced/model_parallel:FairScale Fully Sharded Training>`
    * - ddp_sharded
      - :class:`~pytorch_lightning.strategies.DDPShardedStrategy`
-     - Optimizer and gradient sharded training provided by FairScale. :ref:`Learn more. <advanced/model_parallel:Sharded Training>`
+     - Optimizer and gradient sharded training provided by FairScale. :ref:`Learn more. <advanced/model_parallel:FairScale Sharded Training>`
    * - ddp_sharded_spawn
      - :class:`~pytorch_lightning.strategies.DDPSpawnShardedStrategy`
-     - Optimizer sharded training provided by FairScale. :ref:`Learn more. <advanced/model_parallel:Sharded Training>`
+     - Optimizer sharded training provided by FairScale. :ref:`Learn more. <advanced/model_parallel:FairScale Sharded Training>`
    * - ddp_spawn
      - :class:`~pytorch_lightning.strategies.DDPSpawnStrategy`
      - Spawns processes using the :func:`torch.multiprocessing.spawn` method and joins processes after training finishes. :ref:`Learn more. <accelerators/gpu_intermediate:Distributed Data Parallel Spawn>`

src/pytorch_lightning/CHANGELOG.md

@@ -21,6 +21,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for saving sharded optimizer state dict outside of `DDPShardedStrategy` ([#14208](https://github.com/PyTorchLightning/pytorch-lightning/pull/14208))
 
 
+- Added support for auto wrapping for `DDPFullyShardedStrategy` ([#14383](https://github.com/Lightning-AI/lightning/issues/14383))
+
+
 
 ### Changed
 

src/pytorch_lightning/callbacks/stochastic_weight_avg.py

@@ -25,6 +25,7 @@
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.callback import Callback
 from pytorch_lightning.strategies import DDPFullyShardedStrategy, DeepSpeedStrategy
+from pytorch_lightning.strategies.fully_sharded_native import DDPFullyShardedNativeStrategy
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.types import _LRScheduler, LRSchedulerConfig
@@ -144,6 +145,9 @@ def pl_module_contains_batch_norm(pl_module: "pl.LightningModule") -> bool:
         return any(isinstance(module, nn.modules.batchnorm._BatchNorm) for module in pl_module.modules())
 
     def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
+        if isinstance(trainer.strategy, (DDPFullyShardedStrategy, DDPFullyShardedNativeStrategy, DeepSpeedStrategy)):
+            raise MisconfigurationException("SWA does not currently support sharded models.")
+
         # copy the model before moving it to accelerator device.
         with pl_module._prevent_trainer_and_dataloaders_deepcopy():
             self._average_model = deepcopy(pl_module)
@@ -155,9 +159,6 @@ def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -
         if len(trainer.lr_scheduler_configs) > 1:
             raise MisconfigurationException("SWA currently not supported for more than 1 `lr_scheduler`.")
 
-        if isinstance(trainer.strategy, (DDPFullyShardedStrategy, DeepSpeedStrategy)):
-            raise MisconfigurationException("SWA does not currently support sharded models.")
-
         if isinstance(self._swa_epoch_start, float):
             self._swa_epoch_start = int(trainer.max_epochs * self._swa_epoch_start)
 

src/pytorch_lightning/strategies/fully_sharded.py

@@ -18,6 +18,7 @@
 import torch
 
 import pytorch_lightning as pl
+from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
 from pytorch_lightning.overrides.fairscale import _FAIRSCALE_AVAILABLE
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
@@ -26,16 +27,25 @@
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities.enums import PrecisionType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.optimizer import optimizers_to_device
-from pytorch_lightning.utilities.types import PredictStep, STEP_OUTPUT, TestStep, TrainingStep, ValidationStep
+from pytorch_lightning.utilities.rank_zero import rank_zero_info
+from pytorch_lightning.utilities.types import STEP_OUTPUT
 
 if _FAIRSCALE_AVAILABLE:
     from fairscale.nn import default_auto_wrap_policy, enable_wrap
     from fairscale.nn.data_parallel import FullyShardedDataParallel
+else:
+    FullyShardedDataParallel = None
 
 log = logging.getLogger(__name__)
 
 
+class _DDPFullyShardedStrategyModuleWrapper(_LightningModuleWrapperBase):
+    def state_dict(self, *args: Any, **kwargs: Any) -> Dict[str, Any]:  # type: ignore[override]
+        return self._forward_module.state_dict(*args, **kwargs)
+
+
 class DDPFullyShardedStrategy(DDPStrategy):
 
     strategy_name = "ddp_fully_sharded"
@@ -132,6 +142,25 @@ def process_group(self) -> Any:
             self._process_group = torch.distributed.new_group()
         return self._process_group
 
+    def lightning_module_state_dict(self) -> Dict[str, Any]:
+        """Returns model state."""
+        assert self.model is not None
+        return self.model.state_dict()
+
+    def connect(self, model: "pl.LightningModule") -> None:
+        """Called by the accelerator to connect the accelerator and the model with this plugin."""
+        # TODO: Wait for this issue to resolve and remove this blocker
+        # https://github.com/facebookresearch/fairscale/issues/648
+        # Also make sure to update the tests
+        if not is_overridden("configure_sharded_model", self.lightning_module) and len(list(model.parameters())) == 0:
+            assert self.lightning_module is not None
+            raise MisconfigurationException(
+                f"Using the same instance of model with `trainer.{self.lightning_module.trainer.state.fn}()` is not"
+                " supported with Fairscale FSDP auto-wrap. Please reinitialize your `LightningModule` and pass that."
+            )
+
+        super().connect(model)
+
     def setup_distributed(self) -> None:
         if not self.root_device.type == "cuda":
             raise MisconfigurationException(
@@ -144,17 +173,46 @@ def setup(self, trainer: "pl.Trainer") -> None:
         self.accelerator.setup(trainer)
 
         if trainer.state.fn == TrainerFn.FITTING:
-            self.setup_optimizers(trainer)
-            optimizers_to_device(self.optimizers, self.root_device)
-
             if self._layer_sync:
                 assert self.model
                 self.model = self._layer_sync.apply(self.model)
 
-        self.setup_precision_plugin()
         self.configure_ddp()
+        assert isinstance(self.model, pl.LightningModule)
+        self.model = _DDPFullyShardedStrategyModuleWrapper(self.model)
+        assert self.lightning_module is not None
+        if not is_overridden("configure_sharded_model", self.lightning_module):
+            self.model = self._setup_model(self.model)
+        self.setup_optimizers(self.lightning_module.trainer)
+        optimizers_to_device(self.optimizers, self.root_device)
         self.barrier()
 
+        self.setup_precision_plugin()
+
+    def _setup_model(self, model: torch.nn.Module) -> FullyShardedDataParallel:
+        """Wraps the model into a
+        :class:`~fairscale.nn.data_parallel.fully_sharded_data_parallel.FullyShardedDataParallel` module."""
+        log.detail(f"setting up `Fairscale FSDP` model with device id: {self.root_device.index}.")
+
+        rank_zero_info(
+            "When using FairScale FSDP auto-wrap, make sure to initalize your model using trainer else"
+            " you will get an error.\ntorch.optim.Optimizer(self.trainer.model.parameters(), ...)"
+        )
+
+        return FullyShardedDataParallel(
+            module=model,
+            process_group=self.process_group,
+            cpu_offload=self.cpu_offload,
+            move_grads_to_cpu=self.move_grads_to_cpu,
+            flatten_parameters=self.flatten_parameters,
+            mixed_precision=(self.precision_plugin.precision in (PrecisionType.MIXED, PrecisionType.HALF)),
+            reshard_after_forward=self.reshard_after_forward,
+            fp32_reduce_scatter=self.fp32_reduce_scatter,
+            compute_dtype=self.compute_dtype,
+            bucket_cap_mb=self.bucket_cap_mb,
+            state_dict_device=self.state_dict_device,
+        )
+
     @contextlib.contextmanager
     def model_sharded_context(self) -> Generator:
         log.detail(f"{self.__class__.__name__}: entered model_sharded_context.")
@@ -190,35 +248,29 @@ def configure_ddp(self) -> None:
             # (TODO: need to figure out solution)
             self.model_to_device()
 
-        # setup optimizers after fully sharded has wrapped the lightning module
-        assert self.lightning_module
-        self.setup_optimizers(self.lightning_module.trainer)
-
     def model_to_device(self) -> None:
         log.detail(f"{self.__class__.__name__}: moving model to device [{self.root_device}]...")
         # ensure we update the device type in the lightning module
         assert self.lightning_module
         self.lightning_module.to(self.root_device)
 
     def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
-        with self.precision_plugin.train_step_context():
-            assert isinstance(self.model, TrainingStep)
-            return self.model.training_step(*args, **kwargs)
+        # we don't need precision context since casting is done by FSDP
+        # read `mixed_precision` docstring here: https://pytorch.org/docs/stable/fsdp.html
+        assert self.model is not None
+        return self.model(*args, **kwargs)
 
     def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
-        with self.precision_plugin.val_step_context():
-            assert isinstance(self.model, ValidationStep)
-            return self.model.validation_step(*args, **kwargs)
+        assert self.model is not None
+        return self.model(*args, **kwargs)
 
     def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
-        with self.precision_plugin.test_step_context():
-            assert isinstance(self.model, TestStep)
-            return self.model.test_step(*args, **kwargs)
+        assert self.model is not None
+        return self.model(*args, **kwargs)
 
     def predict_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
-        with self.precision_plugin.predict_step_context():
-            assert isinstance(self.model, PredictStep)
-            return self.model.predict_step(*args, **kwargs)
+        assert self.model is not None
+        return self.model(*args, **kwargs)
 
     def post_training_step(self) -> None:
         pass

src/pytorch_lightning/strategies/strategy.py

@@ -443,7 +443,7 @@ def handles_gradient_accumulation(self) -> bool:
         """Whether the plugin handles gradient accumulation internally."""
         return False
 
-    def lightning_module_state_dict(self) -> Dict[str, Union[Any, Tensor]]:
+    def lightning_module_state_dict(self) -> Dict[str, Any]:
         """Returns model state."""
         assert self.lightning_module is not None
         return self.lightning_module.state_dict()

tests/tests_pytorch/callbacks/test_stochastic_weight_avg.py

@@ -348,6 +348,7 @@ def test_swa_resume_training_from_checkpoint_ddp(tmpdir):
     [
         pytest.param("fsdp", marks=RunIf(fairscale=True, min_cuda_gpus=1)),
         pytest.param("deepspeed", marks=RunIf(deepspeed=True, min_cuda_gpus=1)),
+        pytest.param("fsdp_native", marks=RunIf(min_cuda_gpus=1, skip_windows=True, min_torch="1.12")),
     ],
 )
 def test_misconfiguration_error_with_sharded_model(tmpdir, strategy: str):