Allow training_step in manual optimization to return general mappings

src/lightning/pytorch/CHANGELOG.md

@@ -10,7 +10,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
--
+- Added support for general mappings being returned from `training_step` when using manual optimization ([#21011](https://github.com/Lightning-AI/pytorch-lightning/pull/21011))
+
 
 
 ### Changed

src/lightning/pytorch/loops/optimization/manual.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections import OrderedDict
+from collections.abc import Mapping
 from contextlib import suppress
 from dataclasses import dataclass, field
 from typing import Any
@@ -45,7 +46,7 @@ class ManualResult(OutputResult):
     @classmethod
     def from_training_step_output(cls, training_step_output: STEP_OUTPUT) -> "ManualResult":
         extra = {}
-        if isinstance(training_step_output, dict):
+        if isinstance(training_step_output, Mapping):
             extra = training_step_output.copy()
         elif isinstance(training_step_output, Tensor):
             extra = {"loss": training_step_output}

tests/tests_pytorch/trainer/optimization/test_manual_optimization.py

@@ -304,8 +304,36 @@ def on_train_epoch_end(self, *_, **__):
     trainer.fit(model)
 
 
+class CustomMapping(collections.abc.Mapping):
+    """A custom implementation of Mapping for testing purposes."""
+
+    def __init__(self, *args, **kwargs):
+        self._store = dict(*args, **kwargs)
+
+    def __getitem__(self, key):
+        return self._store[key]
+
+    def __iter__(self):
+        return iter(self._store)
+
+    def __len__(self):
+        return len(self._store)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._store})"
+
+    def __copy__(self):
+        cls = self.__class__
+        new_obj = cls(self._store.copy())
+        return new_obj
+
+    def copy(self):
+        return self.__copy__()
+
+
 @RunIf(min_cuda_gpus=1)
-def test_multiple_optimizers_step(tmp_path):
+@pytest.mark.parametrize("dicttype", [dict, CustomMapping])
+def test_multiple_optimizers_step(tmp_path, dicttype):
     """Tests that `step` works with several optimizers."""
 
     class TestModel(ManualOptModel):
@@ -335,7 +363,7 @@ def training_step(self, batch, batch_idx):
             opt_b.step()
             opt_b.zero_grad()
 
-            return {"loss1": loss_1.detach(), "loss2": loss_2.detach()}
+            return dicttype(loss1=loss_1.detach(), loss2=loss_2.detach())
 
         # sister test: tests/plugins/test_amp_plugins.py::test_amp_gradient_unscale
         def on_after_backward(self) -> None: