Run plugin closure before on_before_optimizer_step [1/2]

CHANGELOG.md

@@ -285,6 +285,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an issue with logger outputs not being finalized correctly after prediction runs ([#8333](https://github.com/PyTorchLightning/pytorch-lightning/issues/8333))
 
 
+- Fixed the Apex and DeepSpeed plugin closure running after the `on_before_optimizer_step` hook ([#9288](https://github.com/PyTorchLightning/pytorch-lightning/issues/9288))
+
+
+- Fixed the Native AMP plugin closure not running with manual optimization ([#9288](https://github.com/PyTorchLightning/pytorch-lightning/issues/9288))
+
+
 - Fixed bug where data-loading functions where not getting the correct running stage passed ([#8858](https://github.com/PyTorchLightning/pytorch-lightning/pull/8858))
 
 

pytorch_lightning/core/lightning.py

@@ -630,10 +630,8 @@ def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
 
             - :class:`~torch.Tensor` - The loss tensor
             - ``dict`` - A dictionary. Can include any keys, but must include the key ``'loss'``
-            - ``None`` - Training will skip to the next batch
-
-        Note:
-            Returning ``None`` is currently not supported for multi-GPU or TPU, or with 16-bit precision enabled.
+            - ``None`` - Training will skip to the next batch. This is only for automatic optimization.
+                This is not supported for multi-GPU or TPU, or using ``DeepSpeed``.
 
         In this step you'd normally do the forward pass and calculate the loss for a batch.
         You can also do fancier things like multiple forward passes or something model specific.

pytorch_lightning/plugins/precision/apex_amp.py

@@ -97,10 +97,13 @@ def pre_optimizer_step(
         **kwargs: Any,
     ) -> bool:
         """Hook to do something before each optimizer step."""
+        result = lambda_closure()  # APEX amp does not support closures
         super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
-        # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
-        lambda_closure()  # APEX amp does not support closures
-        optimizer.step(**kwargs)
+        skipped_backward = result is None
+        # in manual optimization, the closure does not return a value
+        if not model.automatic_optimization or not skipped_backward:
+            # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
+            optimizer.step(**kwargs)
         return False
 
     def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:

pytorch_lightning/plugins/precision/deepspeed_precision.py

@@ -20,6 +20,7 @@
 import pytorch_lightning as pl
 from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
 from pytorch_lightning.utilities import GradClipAlgorithmType
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.warnings import WarningCache
 
@@ -42,9 +43,14 @@ def pre_optimizer_step(
         **kwargs: Any,
     ) -> bool:
         """Hook to do something before each optimizer step."""
+        result = lambda_closure()  # DeepSpeed does not support closures
         super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
+        # in manual optimization, the closure does not return a value
+        if model.automatic_optimization and result is None:
+            raise MisconfigurationException(
+                "Skipping backward by returning `None` from your `training_step` is not supported by `DeepSpeed`"
+            )
         # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
-        lambda_closure()  # DeepSpeed does not support closures
         deepspeed_engine = model.trainer.model
         deepspeed_engine.step()
         return False

pytorch_lightning/plugins/precision/native_amp.py

@@ -95,13 +95,13 @@ def pre_optimizer_step(
                 f"native PyTorch amp and lbfgs are not compatible (optimizer {optimizer_idx})."
                 " To request, please file a Github issue in PyTorch and tag @mcarilli"
             )
-        result = True
-        if model.automatic_optimization:
-            result = lambda_closure()
+        result = lambda_closure()  # native amp does not support closures
         self.scaler.unscale_(optimizer)
         super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
-        # lambda_closure returning None indicates that backward has been skipped
-        if result is not None:
+        skipped_backward = result is None
+        # in manual optimization, the closure does not return a value
+        if not model.automatic_optimization or not skipped_backward:
+            # note: the scaler will skip the `optimizer.step` if nonfinite gradients are found
             self.scaler.step(optimizer)
             self.scaler.update()
         return False

tests/models/test_hooks.py

@@ -275,6 +275,7 @@ def _train_batch(self, *args, **kwargs):
     def _auto_train_batch(trainer, model, batches, device=torch.device("cpu"), current_epoch=0, **kwargs):
         using_native_amp = kwargs.get("amp_backend") == "native"
         using_deepspeed = kwargs.get("plugins") == "deepspeed"
+        using_plugin = kwargs.get("amp_backend") or kwargs.get("plugins")
         out = []
         on_before_optimizer_step = [
             dict(name="Callback.on_before_optimizer_step", args=(trainer, model, ANY, 0)),
@@ -290,10 +291,8 @@ def _auto_train_batch(trainer, model, batches, device=torch.device("cpu"), curre
                     dict(name="Callback.on_batch_start", args=(trainer, model)),
                     dict(name="Callback.on_train_batch_start", args=(trainer, model, ANY, i, 0)),
                     dict(name="on_train_batch_start", args=(ANY, i, 0)),
-                    # these are before the training step because
-                    # they are not part of the `training_step_and_backward` closure, however,
-                    # with native amp, the closure is run first and then the optimizer step.
-                    *(on_before_optimizer_step if not using_native_amp else []),
+                    # without a precision plugin, we execute the closure inside the `optimizer.step`
+                    *([] if using_plugin else on_before_optimizer_step),
                     dict(name="forward", args=(ANY,)),
                     dict(name="training_step", args=(ANY, i)),
                     dict(name="training_step_end", args=(dict(loss=ANY),)),
@@ -306,7 +305,7 @@ def _auto_train_batch(trainer, model, batches, device=torch.device("cpu"), curre
                     *([dict(name="backward", args=(ANY, ANY, 0))] if not using_deepspeed else []),
                     dict(name="Callback.on_after_backward", args=(trainer, model)),
                     dict(name="on_after_backward"),
-                    *(on_before_optimizer_step if using_native_amp else []),
+                    *(on_before_optimizer_step if using_plugin else []),
                     dict(
                         name="optimizer_step",
                         args=(current_epoch, i, ANY, 0, ANY),
@@ -322,6 +321,7 @@ def _auto_train_batch(trainer, model, batches, device=torch.device("cpu"), curre
     @staticmethod
     def _manual_train_batch(trainer, model, batches, device=torch.device("cpu"), **kwargs):
         using_deepspeed = kwargs.get("plugins") == "deepspeed"
+        using_plugin = kwargs.get("amp_backend") or kwargs.get("plugins")
         out = []
         for i in range(batches):
             out.extend(
@@ -342,8 +342,11 @@ def _manual_train_batch(trainer, model, batches, device=torch.device("cpu"), **k
                     dict(name="on_after_backward"),
                     # `manual_backward` calls the previous 3
                     dict(name="manual_backward", args=(ANY,)),
+                    *([dict(name="closure")] if using_plugin else []),
                     dict(name="Callback.on_before_optimizer_step", args=(trainer, model, ANY, 0)),
                     dict(name="on_before_optimizer_step", args=(ANY, 0)),
+                    # without a precision plugin, we execute the closure inside the `optimizer.step`
+                    *([] if using_plugin else [dict(name="closure")]),
                     dict(name="training_step", args=(ANY, i)),
                     dict(name="training_step_end", args=(dict(loss=ANY),)),
                     dict(name="Callback.on_train_batch_end", args=(trainer, model, dict(loss=ANY), ANY, i, 0)),
@@ -439,7 +442,7 @@ def training_step(self, batch, batch_idx):
             opt = self.optimizers()
             opt.zero_grad()
             self.manual_backward(loss)
-            opt.step()
+            opt.step(lambda: called.append({"name": "closure"}))
             return {"loss": loss}
 
     model = TestModel(called)

tests/plugins/test_deepspeed_plugin.py

@@ -791,3 +791,15 @@ def test_deepspeed_multigpu_no_schedulers(tmpdir):
     trainer.fit(model)
 
     _assert_save_model_is_equal(model, tmpdir, trainer)
+
+
+@RunIf(min_gpus=1, deepspeed=True)
+def test_deepspeed_skip_backward_raises(tmpdir):
+    class TestModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            return None
+
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir, plugins=[DeepSpeedPlugin()], gpus=1, fast_dev_run=True, precision=16)
+    with pytest.raises(MisconfigurationException, match="returning `None` .* is not supported"):
+        trainer.fit(model)

tests/trainer/loops/test_training_loop.py

@@ -191,7 +191,7 @@ def training_epoch_end(self, outputs) -> None:
 
 
 def test_batch_loop_releases_loss(tmpdir):
-    """Test that loss/graph is released so that it can be garbage collected before the next training step"""
+    """Test that loss/graph is released so that it can be garbage collected before the next training step."""
 
     class TestModel(BoringModel):
         def training_step(self, batch, batch_idx):

tests/trainer/optimization/test_manual_optimization.py

@@ -64,17 +64,50 @@ def configure_optimizers(self):
         return optimizer, optimizer_2
 
 
-def test_multiple_optimizers_manual_no_return(tmpdir):
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {},
+        pytest.param({"gpus": 1, "precision": 16, "amp_backend": "native"}, marks=RunIf(amp_native=True, min_gpus=1)),
+        pytest.param(
+            {"gpus": 1, "precision": 16, "amp_backend": "apex", "amp_level": "O2"},
+            marks=RunIf(amp_apex=True, min_gpus=1),
+        ),
+    ],
+)
+def test_multiple_optimizers_manual_no_return(tmpdir, kwargs):
+    apex_optimizer_patches = []
+    apex_optimizer_steps = []
+
     class TestModel(ManualOptModel):
         def training_step(self, batch, batch_idx):
             # avoid returning a value
             super().training_step(batch, batch_idx)
 
-        def training_epoch_end(self, outputs) -> None:
+        def training_epoch_end(self, outputs):
             # outputs is empty as training_step does not return
             # and it is not automatic optimization
             assert not outputs
 
+        def on_train_start(self):
+            if kwargs.get("amp_backend") != "apex":
+                return
+            # extremely ugly. APEX patches all the native torch optimizers on `_initialize` which we call on
+            # `ApexMixedPrecisionPlugin.dispatch`. Additionally, their replacement `new_step` functions are locally
+            # defined so can't even patch those, thus we need to create the mock after APEX has been initialized
+            nonlocal apex_optimizer_patches, apex_optimizer_steps
+            for opt in self.trainer.optimizers:
+                # `amp.scale_loss` will also patch the step to avoid it when gradient overflow happens. avoid it
+                opt._amp_stash.already_patched = True
+                patch = mock.patch.object(opt, "step")
+                apex_optimizer_patches.append(patch)
+                apex_optimizer_steps.append(patch.start())
+
+        def on_train_end(self):
+            if kwargs.get("amp_backend") == "apex":
+                for p in apex_optimizer_patches:
+                    p.stop()
+
     model = TestModel()
     model.val_dataloader = None
 
@@ -86,12 +119,26 @@ def training_epoch_end(self, outputs) -> None:
         max_epochs=1,
         log_every_n_steps=1,
         weights_summary=None,
+        **kwargs,
     )
 
+    if kwargs.get("amp_backend") == "native":
+        # mock the scaler instead of the optimizer step because it can be skipped with NaNs
+        scaler_step_patch = mock.patch.object(
+            trainer.precision_plugin.scaler, "step", wraps=trainer.precision_plugin.scaler.step
+        )
+        scaler_step = scaler_step_patch.start()
+
     with mock.patch.object(Accelerator, "backward", wraps=trainer.accelerator.backward) as bwd_mock:
         trainer.fit(model)
     assert bwd_mock.call_count == limit_train_batches * 3
 
+    if kwargs.get("amp_backend") == "native":
+        scaler_step_patch.stop()
+        assert scaler_step.call_count == len(model.optimizers()) * limit_train_batches
+    if kwargs.get("amp_backend") == "apex":
+        assert [s.call_count for s in apex_optimizer_steps] == [len(model.optimizers())] * limit_train_batches
+
 
 def test_multiple_optimizers_manual_return(tmpdir):
     class TestModel(ManualOptModel):
@@ -171,40 +218,6 @@ def test_multiple_optimizers_manual_native_amp(tmpdir):
     assert bwd_mock.call_count == limit_train_batches * 3
 
 
-@RunIf(min_gpus=1, amp_apex=True)
-def test_multiple_optimizers_manual_apex_no_return(tmpdir):
-    class TestModel(ManualOptModel):
-        def training_step(self, batch, batch_idx):
-            # avoid returning a value
-            super().training_step(batch, batch_idx)
-
-        def training_epoch_end(self, outputs) -> None:
-            # outputs is empty as training_step does not return
-            # and it is not automatic optimization
-            assert len(outputs) == 0
-
-    model = TestModel()
-    model.val_dataloader = None
-
-    limit_train_batches = 2
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        limit_train_batches=limit_train_batches,
-        limit_val_batches=2,
-        max_epochs=1,
-        log_every_n_steps=1,
-        weights_summary=None,
-        precision=16,
-        amp_level="O2",
-        amp_backend="apex",
-        gpus=1,
-    )
-
-    with mock.patch.object(Accelerator, "backward", wraps=trainer.accelerator.backward) as bwd_mock:
-        trainer.fit(model)
-    assert bwd_mock.call_count == limit_train_batches * 3
-
-
 class ManualOptimizationExtendedModel(BoringModel):
 
     count = 0