Handle `set_to_none` when using DeepSpeed optimizer in Lite (#16275)

src/lightning_fabric/CHANGELOG.md

@@ -31,6 +31,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for managing callbacks via `Fabric(callbacks=...)` and emitting events through `Fabric.call()` ([#16074](https://github.com/Lightning-AI/lightning/issues/16074))
 
 
+- Added support for a consistent `.zero_grad(set_to_none=...)` on the wrapped optimizer regardless of which strategy is used ([#16275](https://github.com/Lightning-AI/lightning/issues/16275))
+
+
 ### Changed
 
 - Renamed the class `LightningLite` to `Fabric` ([#15932](https://github.com/Lightning-AI/lightning/issues/15932), [#15938](https://github.com/Lightning-AI/lightning/issues/15938))

src/lightning_fabric/wrappers.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.
+import inspect
 from typing import Any, Callable, Dict, Generator, Iterator, Mapping, Optional, overload, TypeVar, Union
 
 import torch
@@ -44,7 +45,9 @@ class _FabricOptimizer:
         """
         # `__del__` is skipped in case the optimizer has implemented custom destructor logic which we would
         # not want to call on destruction of the `_FabricOptimizer
-        self.__dict__ = {k: v for k, v in optimizer.__dict__.items() if k not in ("state_dict", "step", "__del__")}
+        self.__dict__ = {
+            k: v for k, v in optimizer.__dict__.items() if k not in ("state_dict", "step", "zero_grad", "__del__")
+        }
         self.__class__ = type("Fabric" + optimizer.__class__.__name__, (self.__class__, optimizer.__class__), {})
         self._optimizer = optimizer
         self._strategy = strategy
@@ -68,6 +71,10 @@ class _FabricOptimizer:
             **kwargs,
         )
 
+    def zero_grad(self, **kwargs: Any) -> None:
+        kwargs = _process_optimizer_zero_grad_kwargs(self.optimizer, kwargs)
+        self.optimizer.zero_grad(**kwargs)
+
 
 class _FabricModule(_DeviceDtypeModuleMixin):
     def __init__(
@@ -175,3 +182,10 @@ class _FabricDataLoader:
 
         for item in iterator:
             yield move_data_to_device(item, self._device)
+
+
+def _process_optimizer_zero_grad_kwargs(optimizer: Optimizer, kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    if "set_to_none" in kwargs and "set_grads_to_None" in inspect.signature(optimizer.zero_grad).parameters:
+        # Some optimizers out there, for example DeepSpeedZeroOptimizer, use a different name than PyTorch
+        kwargs["set_grads_to_None"] = kwargs.pop("set_to_none")
+    return kwargs

tests/tests_fabric/test_wrappers.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 unittest import mock
 from unittest.mock import call, Mock
 
 import pytest
@@ -291,3 +292,34 @@ def test_lite_optimizer_steps():
     lite_optimizer = _FabricOptimizer(optimizer=optimizer, strategy=strategy)
     lite_optimizer.step()
     strategy.optimizer_step.assert_called_once_with(strategy.model)
+
+
+def test_fabric_optimizer_zero_grad_kwargs():
+    """Test that Fabric can adapt the `.zero_grad()` arguments to the underlying optimizer."""
+
+    # Test PyTorch's standard `.zero_grad()` signature
+    with mock.patch("torch.optim.SGD.zero_grad") as zero_grad_mock:
+        optimizer = torch.optim.SGD(torch.nn.Linear(1, 1).parameters(), 0.1)
+        fabric_optimizer = _FabricOptimizer(optimizer=optimizer, strategy=Mock())
+        fabric_optimizer.zero_grad()
+        zero_grad_mock.assert_called_with()
+        fabric_optimizer.zero_grad(set_to_none=False)
+        zero_grad_mock.assert_called_with(set_to_none=False)
+        fabric_optimizer.zero_grad(set_to_none=True)
+        zero_grad_mock.assert_called_with(set_to_none=True)
+
+    # Test weird `.zero_grad()` signatures from other libraries
+    custom_zero_grad = Mock()
+
+    class CustomSGD(torch.optim.SGD):
+        def zero_grad(self, set_grads_to_None=False):
+            custom_zero_grad(set_grads_to_None=set_grads_to_None)
+
+    optimizer = CustomSGD(torch.nn.Linear(1, 1).parameters(), 0.1)
+    fabric_optimizer = _FabricOptimizer(optimizer=optimizer, strategy=Mock())
+    fabric_optimizer.zero_grad()
+    custom_zero_grad.assert_called_with(set_grads_to_None=False)
+    fabric_optimizer.zero_grad(set_to_none=False)
+    custom_zero_grad.assert_called_with(set_grads_to_None=False)
+    fabric_optimizer.zero_grad(set_to_none=True)
+    custom_zero_grad.assert_called_with(set_grads_to_None=True)