Add support for deepspeeds exclude_frozen_parameters

src/lightning/fabric/CHANGELOG.md

@@ -9,6 +9,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added `exclude_frozen_parameters` to `DeepSpeedStrategy` ([#21060](https://github.com/Lightning-AI/pytorch-lightning/pull/21060))
+
+
 -
 
 

src/lightning/fabric/strategies/deepspeed.py

@@ -100,6 +100,7 @@ def __init__(
         precision: Optional[Precision] = None,
         process_group_backend: Optional[str] = None,
         timeout: Optional[timedelta] = default_pg_timeout,
+        exclude_frozen_parameters: bool = False,
     ) -> None:
         """Provides capabilities to run training using the DeepSpeed library, with training optimizations for large
         billion parameter models. `For more information: https://pytorch-
@@ -229,6 +230,8 @@ def __init__(
                 when using ZeRO Stage 3. This differs from the DeepSpeed checkpoint which contains shards
                 per worker.
 
+            exclude_frozen_parameters: Exclude frozen parameters when saving checkpoints.
+
         """
         if not _DEEPSPEED_AVAILABLE:
             raise ImportError(
@@ -289,6 +292,7 @@ def __init__(
 
         self.remote_device = remote_device
         self.load_full_weights = load_full_weights
+        self.exclude_frozen_parameters = exclude_frozen_parameters
 
         # default FP16 parameters.
         self.loss_scale = loss_scale
@@ -444,7 +448,9 @@ def save_checkpoint(
         # there might be other stateful objects unrelated to the deepspeed engine - convert them to a state_dict
         state = self._convert_stateful_objects_in_state(state, filter={})
         # use deepspeed's internal checkpointing function to handle partitioned weights across processes
-        engine.save_checkpoint(path, client_state=state, tag="checkpoint")
+        engine.save_checkpoint(
+            path, client_state=state, tag="checkpoint", exclude_frozen_parameters=self.exclude_frozen_parameters
+        )
 
     @override
     def load_checkpoint(

src/lightning/pytorch/CHANGELOG.md

@@ -16,6 +16,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added Torch-Tensorrt integration with `LightningModule` ([#20808](https://github.com/Lightning-AI/pytorch-lightning/pull/20808))
 
 
+- Added `exclude_frozen_parameters` to `DeepSpeedStrategy` ([#21060](https://github.com/Lightning-AI/pytorch-lightning/pull/21060))
+
+
 ### Changed
 
 - Default to `RichProgressBar` and `RichModelSummary` if the rich package is available. Fallback to TQDMProgressBar and ModelSummary otherwise. ([#9580](https://github.com/Lightning-AI/pytorch-lightning/pull/9580))

src/lightning/pytorch/strategies/deepspeed.py

@@ -122,6 +122,7 @@ def __init__(
         precision_plugin: Optional[Precision] = None,
         process_group_backend: Optional[str] = None,
         timeout: Optional[timedelta] = default_pg_timeout,
+        exclude_frozen_parameters: bool = False,
     ) -> None:
         """Provides capabilities to run training using the DeepSpeed library, with training optimizations for large
         billion parameter models. `For more information: https://pytorch-
@@ -253,6 +254,8 @@ def __init__(
                 when using ZeRO Stage 3. This differs from the DeepSpeed checkpoint which contains shards
                 per worker.
 
+            exclude_frozen_parameters: Exclude frozen parameters when saving checkpoints.
+
         """
         if not _DEEPSPEED_AVAILABLE:
             raise MisconfigurationException(
@@ -311,6 +314,7 @@ def __init__(
 
         self.remote_device = remote_device
         self.load_full_weights = load_full_weights
+        self.exclude_frozen_parameters = exclude_frozen_parameters
 
         # default FP16 parameters.
         self.loss_scale = loss_scale
@@ -648,7 +652,12 @@ def save_checkpoint(self, checkpoint: dict, filepath: _PATH, storage_options: Op
         # dump states as a checkpoint dictionary object
         _exclude_keys = ["state_dict", "optimizer_states"]
         checkpoint = {k: v for k, v in checkpoint.items() if k not in _exclude_keys}
-        self.deepspeed_engine.save_checkpoint(filepath, client_state=checkpoint, tag="checkpoint")
+        self.deepspeed_engine.save_checkpoint(
+            filepath,
+            client_state=checkpoint,
+            tag="checkpoint",
+            exclude_frozen_parameters=self.exclude_frozen_parameters,
+        )
 
     @override
     def load_checkpoint(self, checkpoint_path: _PATH) -> dict[str, Any]:

tests/tests_fabric/strategies/test_deepspeed.py

@@ -194,15 +194,19 @@ def test_deepspeed_save_checkpoint_client_state_separation(tmp_path):
     model.modules.return_value = [model]
     strategy.save_checkpoint(path=tmp_path, state={"model": model, "test": "data"})
     # the client_state should not contain any deepspeed engine or deepspeed optimizer
-    model.save_checkpoint.assert_called_with(tmp_path, client_state={"test": "data"}, tag="checkpoint")
+    model.save_checkpoint.assert_called_with(
+        tmp_path, client_state={"test": "data"}, tag="checkpoint", exclude_frozen_parameters=False
+    )
 
     # Model and optimizer
     optimizer = Mock()
     model = Mock(spec=DeepSpeedEngine, optimizer=optimizer)
     model.modules.return_value = [model]
     strategy.save_checkpoint(path=tmp_path, state={"model": model, "optimizer": optimizer, "test": "data"})
     # the client_state should not contain any deepspeed engine or deepspeed optimizer
-    model.save_checkpoint.assert_called_with(tmp_path, client_state={"test": "data"}, tag="checkpoint")
+    model.save_checkpoint.assert_called_with(
+        tmp_path, client_state={"test": "data"}, tag="checkpoint", exclude_frozen_parameters=False
+    )
 
 
 @RunIf(deepspeed=True)
@@ -219,6 +223,27 @@ def test_deepspeed_save_checkpoint_warn_colliding_keys(tmp_path):
         strategy.save_checkpoint(path=tmp_path, state={"model": model, "optimizer": optimizer, "mp_world_size": 2})
 
 
+@RunIf(deepspeed=True)
+@pytest.mark.parametrize("exclude_frozen_parameters", [True, False])
+def test_deepspeed_save_checkpoint_exclude_frozen_parameters(exclude_frozen_parameters):
+    """Test that the DeepSpeed strategy can save checkpoints with the `exclude_frozen_parameters` argument."""
+    from deepspeed import DeepSpeedEngine
+
+    strategy = DeepSpeedStrategy(exclude_frozen_parameters=exclude_frozen_parameters)
+    assert strategy.exclude_frozen_parameters is exclude_frozen_parameters
+
+    model = Mock(spec=DeepSpeedEngine, optimizer=None)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint(path="test_path", state={"model": model, "extra": "data"})
+
+    model.save_checkpoint.assert_called_with(
+        "test_path",
+        client_state={"extra": "data"},
+        tag="checkpoint",
+        exclude_frozen_parameters=exclude_frozen_parameters,
+    )
+
+
 @RunIf(deepspeed=True)
 def test_deepspeed_load_checkpoint_validate_path(tmp_path):
     """Test that we validate the checkpoint path for a DeepSpeed checkpoint and give suggestions for user error."""

tests/tests_pytorch/strategies/test_deepspeed.py

@@ -562,6 +562,46 @@ def test_deepspeed_multigpu_single_file(tmp_path):
     trainer.test(model, ckpt_path=checkpoint_path)
 
 
+@RunIf(min_cuda_gpus=1, standalone=True, deepspeed=True)
+def test_deepspeed_strategy_exclude_frozen_parameters_integration(tmp_path):
+    """Test end-to-end integration of exclude_frozen_parameters with actual model training and checkpointing."""
+
+    class TestModelWithFrozenParams(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.frozen_layer = torch.nn.Linear(32, 32)
+
+        def configure_model(self) -> None:
+            super().configure_model()
+            # Freeze the additional layer parameters
+            for param in self.frozen_layer.parameters():
+                param.requires_grad = False
+
+        def forward(self, x):
+            x = self.frozen_layer(x)
+            return super().forward(x)
+
+    model = TestModelWithFrozenParams()
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        strategy=DeepSpeedStrategy(exclude_frozen_parameters=True),
+        accelerator="gpu",
+        devices=1,
+        fast_dev_run=True,
+        precision="16-mixed",
+        enable_progress_bar=False,
+        enable_model_summary=False,
+    )
+
+    trainer.fit(model)
+    checkpoint_path = os.path.join(tmp_path, "checkpoint_exclude_frozen.ckpt")
+    trainer.save_checkpoint(checkpoint_path)
+
+    # Verify checkpoint was created
+    assert os.path.exists(checkpoint_path)
+
+
 class ModelParallelClassificationModel(LightningModule):
     def __init__(self, lr: float = 0.01, num_blocks: int = 5):
         super().__init__()