Simplify enabling CPU offload in FSDP

docs/source-pytorch/advanced/model_parallel.rst

@@ -424,10 +424,9 @@ You can customize the strategy configuration by adjusting the arguments of :clas
 
     from pytorch_lightning import Trainer
     from pytorch_lightning.strategies import DDPFullyShardedNativeStrategy
-    from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
 
 
-    native_fsdp = DDPFullyShardedNativeStrategy(cpu_offload=CPUOffload(offload_params=True))
+    native_fsdp = DDPFullyShardedNativeStrategy(cpu_offload=True)
     trainer = pl.Trainer(strategy=native_fsdp, accelerator="gpu", device=4)
 
 

src/lightning_lite/strategies/fsdp.py

@@ -68,11 +68,10 @@ class FSDPStrategy(ParallelStrategy, _Sharded):
     `this tutorial <https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html>`__ for more information.
 
     Arguments:
-        cpu_offload: CPU offloading config. Currently, only parameter and gradient CPU offload is supported. It
-            can be enabled via passing in ``cpu_offload=CPUOffload(offload_params=True)``. Note that this currently
+        cpu_offload: Enable offloading parameters and gradients to CPU to save GPU memory at the cost of speed.
+            You can also pass a config: ``cpu_offload=CPUOffload(offload_params=True)``. Note that this currently
             implicitly enables gradient offloading to CPU in order for parameters and gradients to be on same device
-            to work with the optimizer. This API is subject to change. Default is ``None`` in which case there
-            will be no offloading.
+            to work with the optimizer. This API is subject to change. Default: no offoading
         backward_prefetch: This is an experimental feature that is subject to change in the near future. It allows
             users to enable two different backward prefetching algorithms to help backward communication and
             computation overlapping. The pros and cons of each algorithm is explained in the class ``BackwardPrefetch``.
@@ -91,7 +90,7 @@ def __init__(
         precision: Optional[Precision] = None,
         process_group_backend: Optional[str] = None,
         timeout: Optional[timedelta] = default_pg_timeout,
-        cpu_offload: Optional["CPUOffload"] = None,
+        cpu_offload: Union[bool, "CPUOffload", None] = None,
         backward_prefetch: Optional["BackwardPrefetch"] = None,
         mixed_precision: Optional["MixedPrecision"] = None,
         **kwargs: Any,
@@ -112,7 +111,7 @@ def __init__(
         self._backward_sync_control = _FSDPBackwardSyncControl()
         self._ddp_kwargs = kwargs
 
-        self.cpu_offload = cpu_offload
+        self.cpu_offload = _init_cpu_offload(cpu_offload)
         self.backward_prefetch = backward_prefetch
         self.mixed_precision = mixed_precision
 
@@ -258,7 +257,6 @@ def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
     def register_strategies(cls, strategy_registry: Dict) -> None:
         if not _TORCH_GREATER_EQUAL_1_12 or not torch.distributed.is_available():
             return
-        from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
 
         strategy_registry.register(
             "fsdp",
@@ -269,7 +267,7 @@ def register_strategies(cls, strategy_registry: Dict) -> None:
             "fsdp_full_shard_offload",
             cls,
             description="Native FSDP with Full Sharding and CPU Offloading",
-            cpu_offload=CPUOffload(offload_params=True),
+            cpu_offload=True,
         )
 
     def _setup_distributed(self) -> None:
@@ -308,6 +306,12 @@ def no_backward_sync(self, module: Module) -> Generator:
             yield
 
 
+def _init_cpu_offload(cpu_offload: Optional[Union[bool, "CPUOffload"]]) -> "CPUOffload":
+    from torch.distributed.fsdp import CPUOffload
+
+    return cpu_offload if isinstance(cpu_offload, CPUOffload) else CPUOffload(offload_params=bool(cpu_offload))
+
+
 def _optimizer_has_flat_params(optimizer: Optimizer) -> bool:
     from torch.distributed.fsdp import FlatParameter
 

src/pytorch_lightning/CHANGELOG.md

@@ -29,6 +29,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added a warning when `self.log(..., logger=True)` is called without a configured logger ([#15814](https://github.com/Lightning-AI/lightning/pull/15814))
 
+
+- Added the option to set `DDPFullyShardedNativeStrategy(cpu_offload=True|False)` via bool instead of needing to pass a configufation object ([#15832](https://github.com/Lightning-AI/lightning/pull/15832))
+
+
 ### Changed
 
 - Drop PyTorch 1.9 support ([#15347](https://github.com/Lightning-AI/lightning/pull/15347))

src/pytorch_lightning/strategies/fully_sharded_native.py

@@ -20,7 +20,7 @@
 
 import pytorch_lightning as pl
 from lightning_lite.plugins import CheckpointIO, ClusterEnvironment
-from lightning_lite.strategies.fsdp import _optimizer_has_flat_params
+from lightning_lite.strategies.fsdp import _init_cpu_offload, _optimizer_has_flat_params
 from lightning_lite.utilities.distributed import (
     _get_default_process_group_backend_for_device,
     _init_dist_connection,
@@ -83,14 +83,10 @@ class DDPFullyShardedNativeStrategy(ParallelStrategy):
     `this tutorial <https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html>`__ for more information.
 
     Arguments:
-        cpu_offload:
-            CPU offloading config. Currently, only parameter and gradient CPU
-            offload is supported. It can be enabled via passing in
-            ``cpu_offload=CPUOffload(offload_params=True)``. Note that this
-            currently implicitly enables gradient offloading to CPU in order for
-            params and grads to be on same device to work with optimizer. This
-            API is subject to change. Default is ``None`` in which case there
-            will be no offloading.
+        cpu_offload: Enable offloading parameters and gradients to CPU to save GPU memory at the cost of speed.
+            You can also pass a config: ``cpu_offload=CPUOffload(offload_params=True)``. Note that this currently
+            implicitly enables gradient offloading to CPU in order for parameters and gradients to be on same device
+            to work with the optimizer. This API is subject to change. Default: no offoading
         backward_prefetch:
             This is an experimental feature that is subject to change in the
             the near future. It allows users to enable two different backward_prefetch
@@ -115,7 +111,7 @@ def __init__(
         checkpoint_io: Optional[CheckpointIO] = None,
         precision_plugin: Optional[PrecisionPlugin] = None,
         process_group_backend: Optional[str] = None,
-        cpu_offload: Optional[CPUOffload] = None,
+        cpu_offload: Union[bool, "CPUOffload", None] = None,
         backward_prefetch: Optional[BackwardPrefetch] = None,
         mixed_precision: Optional[MixedPrecision] = None,
         **kwargs: Any,
@@ -135,7 +131,7 @@ def __init__(
         self._process_group = None
         self.num_nodes = 1
         self._process_group_backend = process_group_backend
-        self.cpu_offload = cpu_offload
+        self.cpu_offload = _init_cpu_offload(cpu_offload)
         self.backward_prefetch = backward_prefetch
         self.mixed_precision = mixed_precision
         self._rank_0_will_call_children_scripts: bool = False
@@ -386,6 +382,6 @@ def register_strategies(cls, strategy_registry: Dict) -> None:
                 "fsdp_native_full_shard_offload",
                 cls,
                 description="Native FSDP with Full Sharding and CPU Offloading",
-                cpu_offload=CPUOffload(offload_params=True),
+                cpu_offload=True,
             )
             cls._registered_strategies.append("fsdp_native_full_shard_offload")

tests/tests_lite/strategies/test_fsdp.py

@@ -26,7 +26,7 @@
 from lightning_lite.utilities.imports import _TORCH_GREATER_EQUAL_1_12
 
 if _TORCH_GREATER_EQUAL_1_12:
-    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel, MixedPrecision
+    from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, FullyShardedDataParallel, MixedPrecision
 
 
 @mock.patch("lightning_lite.strategies.fsdp._TORCH_GREATER_EQUAL_1_12", False)
@@ -36,13 +36,26 @@ def test_fsdp_support(*_):
 
 
 @RunIf(min_torch="1.12")
-def test_fsdp_custom_mixed_precision(*_):
+def test_fsdp_custom_mixed_precision():
     """Test that passing a custom mixed precision config works."""
     config = MixedPrecision()
     strategy = FSDPStrategy(mixed_precision=config)
     assert strategy.mixed_precision_config == config
 
 
+@RunIf(min_torch="1.12")
+def test_fsdp_cpu_offload():
+    """Test the different ways cpu offloading can be enabled."""
+    # bool
+    strategy = FSDPStrategy(cpu_offload=True)
+    assert strategy.cpu_offload == CPUOffload(offload_params=True)
+
+    # dataclass
+    config = CPUOffload()
+    strategy = FSDPStrategy(cpu_offload=config)
+    assert strategy.cpu_offload == config
+
+
 @RunIf(min_torch="1.12")
 def test_fsdp_setup_optimizer_validation():
     """Test that `setup_optimizer()` validates the param groups and reference to FSDP parameters."""

tests/tests_pytorch/strategies/test_ddp_fully_sharded_native.py

@@ -14,7 +14,7 @@
 from tests_pytorch.helpers.runif import RunIf
 
 if _TORCH_GREATER_EQUAL_1_12:
-    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel, MixedPrecision
+    from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, FullyShardedDataParallel, MixedPrecision
     from torch.distributed.fsdp.wrap import wrap
 
 
@@ -259,3 +259,16 @@ def configure_optimizers(self):
     model = NoFlatParametersModel()
     with pytest.raises(ValueError, match="The optimizer does not seem to reference any FSDP parameters"):
         trainer.fit(model)
+
+
+@RunIf(min_torch="1.12")
+def test_fully_sharded_native_strategy_cpu_offload():
+    """Test the different ways cpu offloading can be enabled."""
+    # bool
+    strategy = DDPFullyShardedNativeStrategy(cpu_offload=True)
+    assert strategy.cpu_offload == CPUOffload(offload_params=True)
+
+    # dataclass
+    config = CPUOffload()
+    strategy = DDPFullyShardedNativeStrategy(cpu_offload=config)
+    assert strategy.cpu_offload == config