Add special logic for 'step' in _optimizer_to_device (Lightning-AI#20019

)

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

src/lightning/fabric/CHANGELOG.md

@@ -38,7 +38,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an attribute error when loading a checkpoint into a quantized model using the `_lazy_load()` function ([#20121](https://github.com/Lightning-AI/lightning/pull/20121))
 
 
--
+- Fixed `_optimizer_to_device` logic for special 'step' key in optimizer state causing performance regression ([#20019](https://github.com/Lightning-AI/lightning/pull/20019))
 
 
 

src/lightning/fabric/utilities/optimizer.py

@@ -12,13 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections.abc import MutableMapping
 from typing import Iterable
 
-from lightning_utilities.core.apply_func import apply_to_collection
 from torch import Tensor
 from torch.optim import Optimizer
 
-from lightning.fabric.utilities.apply_func import move_data_to_device
+from lightning.fabric.utilities.apply_func import apply_to_collection, move_data_to_device
 from lightning.fabric.utilities.types import _DEVICE
 
 
@@ -31,4 +31,12 @@ def _optimizers_to_device(optimizers: Iterable[Optimizer], device: _DEVICE) -> N
 def _optimizer_to_device(optimizer: Optimizer, device: _DEVICE) -> None:
     """Moves the state of a single optimizer to the device."""
     for p, v in optimizer.state.items():
-        optimizer.state[p] = apply_to_collection(v, Tensor, move_data_to_device, device, allow_frozen=True)
+        if not isinstance(v, MutableMapping):
+            # Support for custom optimizers
+            optimizer.state[p] = apply_to_collection(v, Tensor, move_data_to_device, device, allow_frozen=True)
+            continue
+        for key, val in v.items():
+            # The 'step' parameter needs to remain unmoved (possibly on the CPU) since that is where the optimizer
+            # needs it. See https://github.com/pytorch/pytorch/issues/74424
+            if key != "step":
+                v[key] = move_data_to_device(val, device)

src/lightning/pytorch/CHANGELOG.md

@@ -49,6 +49,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `_LoggerConnector`'s `_ResultMetric` to move all registered keys to the device of the logged value if needed ([#19814](https://github.com/Lightning-AI/pytorch-lightning/issues/19814))
 
+- Fixed `_optimizer_to_device` logic for special 'step' key in optimizer state causing performance regression ([#20019](https://github.com/Lightning-AI/lightning/pull/20019))
+
 - Fixed parameter counts in `ModelSummary` when model has distributed parameters (DTensor) ([#20163](https://github.com/Lightning-AI/pytorch-lightning/pull/20163))
 
 

tests/tests_fabric/utilities/test_optimizer.py

@@ -1,36 +1,86 @@
-import collections
 import dataclasses
 
+import pytest
 import torch
 from lightning.fabric.utilities.optimizer import _optimizer_to_device
 from torch import Tensor
 
+from tests_fabric.helpers.runif import RunIf
 
-def test_optimizer_to_device():
-    @dataclasses.dataclass(frozen=True)
+
+@pytest.mark.parametrize(
+    "optimizer_class",
+    [
+        torch.optim.Adam,
+        torch.optim.AdamW,
+        torch.optim.SGD,
+        torch.optim.RMSprop,
+        torch.optim.Adagrad,
+        torch.optim.Adadelta,
+        torch.optim.Adamax,
+    ],
+)
+@pytest.mark.parametrize(
+    "src_device",
+    [
+        torch.device("cpu"),
+        pytest.param(torch.device("cuda"), marks=RunIf(min_cuda_gpus=1)),
+    ],
+)
+@pytest.mark.parametrize(
+    "dst_device",
+    [
+        torch.device("cpu"),
+        pytest.param(torch.device("cuda"), marks=RunIf(min_cuda_gpus=1)),
+    ],
+)
+def test_optimizer_to_device(optimizer_class, src_device, dst_device):
+    # Optimizer with no state initialized
+    model = torch.nn.Linear(2, 2, device=src_device)
+    optimizer = optimizer_class(model.parameters(), lr=0.1)
+    _optimizer_to_device(optimizer, dst_device)
+    _assert_opt_parameters_on_device(optimizer, dst_device)
+
+    # Optimizer with state initialized
+    model = torch.nn.Linear(2, 2, device=src_device)
+    optimizer = optimizer_class(model.parameters(), lr=0.1)
+    model(torch.randn(2, 2, device=src_device)).sum().backward()
+    optimizer.step()
+    _optimizer_to_device(optimizer, dst_device)
+    _assert_opt_parameters_on_device(optimizer, dst_device)
+
+
+def _assert_opt_parameters_on_device(opt, device):
+    for _, v in opt.state.items():
+        for key, item in v.items():
+            if not isinstance(item, Tensor):
+                continue
+            if key == "step":
+                # The "step" tensor needs to remain on CPU
+                assert item.device.type == "cpu"
+            else:
+                assert item.device.type == device.type
+
+
+@RunIf(min_cuda_gpus=1)
+@pytest.mark.parametrize("frozen", [True, False])
+def test_optimizer_to_device_with_dataclass_in_state(frozen):
+    src_device = torch.device("cpu")
+    dst_device = torch.device("cuda")
+    model = torch.nn.Linear(32, 2, device=src_device)
+
+    @dataclasses.dataclass(frozen=frozen)
     class FooState:
-        bar: int
+        integer: int
+        tensor: Tensor
 
     class TestOptimizer(torch.optim.SGD):
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
-            self.state["dummy"] = torch.tensor(0)
-            self.state["frozen"] = FooState(0)
-
-    layer = torch.nn.Linear(32, 2)
-    opt = TestOptimizer(layer.parameters(), lr=0.1)
-    _optimizer_to_device(opt, "cpu")
-    if torch.cuda.is_available():
-        _optimizer_to_device(opt, "cuda")
-        assert_opt_parameters_on_device(opt, "cuda")
-
-
-def assert_opt_parameters_on_device(opt, device: str):
-    for param in opt.state.values():
-        # Not sure there are any global tensors in the state dict
-        if isinstance(param, Tensor):
-            assert param.data.device.type == device
-        elif isinstance(param, collections.abc.Mapping):
-            for subparam in param.values():
-                if isinstance(subparam, Tensor):
-                    assert param.data.device.type == device
+            self.state[model.weight] = {"dummy": torch.tensor(0)}
+            self.state[model.bias] = FooState(0, torch.tensor(0))
+
+    optimizer = TestOptimizer(model.parameters(), lr=0.1)
+    _optimizer_to_device(optimizer, dst_device)
+    assert optimizer.state[model.weight]["dummy"].device.type == dst_device.type
+    assert optimizer.state[model.bias].tensor.device.type == ("cpu" if frozen else dst_device.type)