Fix/mismatched toggle optimizer (#7563)

* fix: avoid potential mismatched toggling of optimzier
Refs #7405

chore: update CHANGELOG

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fix: resolve a confict

chore: update changelog

* feat: add a test that fails in master

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* fix typo in tests/trainer/optimization/test_multiple_optimizers.py

Co-authored-by: ananthsub <[email protected]>

* Polish tests/trainer/optimization/test_multiple_optimizers.py

Co-authored-by: Carlos Mocholí <[email protected]>

* Polish tests/trainer/optimization/test_multiple_optimizers.py

Co-authored-by: Carlos Mocholí <[email protected]>

* fix: change placeholder in optimizer_step from positional args to keyword args

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: ananthsub <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>

Author: 4 people

CHANGELOG.md

@@ -40,6 +40,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Changed calling of `untoggle_optimizer(opt_idx)` out of the closure function ([#7563](https://github.com/PyTorchLightning/pytorch-lightning/pull/7563)
 
 - Changed the `Trainer`'s `checkpoint_callback` argument to allow only boolean values ([#7539](https://github.com/PyTorchLightning/pytorch-lightning/pull/7539))
 

pytorch_lightning/trainer/training_loop.py

@@ -724,7 +724,6 @@ def _run_optimization(self, batch_idx, split_idx, split_batch, opt_idx=0, optimi
             # -------------------
             # calculate loss (train step + train step end)
             # -------------------
-
             # automatic_optimization=True: perform ddp sync only when performing optimizer_step
             # automatic_optimization=False: don't block synchronization here
             with self.block_ddp_sync_behaviour():
@@ -737,6 +736,9 @@ def _run_optimization(self, batch_idx, split_idx, split_batch, opt_idx=0, optimi
         else:
             if self.trainer.lightning_module.automatic_optimization:
                 self.optimizer_step(optimizer, opt_idx, batch_idx, closure)
+                if len(self.trainer.optimizers) > 1:
+                    # revert back to previous state
+                    self.trainer.lightning_module.untoggle_optimizer(opt_idx)
             else:
                 result = self.training_step(split_batch, batch_idx, opt_idx, self._hiddens)
 
@@ -837,10 +839,6 @@ def training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer,
                         "training_step returned None. If this was on purpose, ignore this warning..."
                     )
 
-                if len(self.trainer.optimizers) > 1:
-                    # revert back to previous state
-                    self.trainer.lightning_module.untoggle_optimizer(opt_idx)
-
         return result
 
     def _check_finite(self, loss: torch.Tensor) -> None:

tests/trainer/optimization/test_multiple_optimizers.py

@@ -168,3 +168,68 @@ def training_step(self, batch, batch_idx):
 
     with pytest.raises(ValueError, match='`training_step` is missing the `optimizer_idx`'):
         trainer.fit(TestModel())
+
+
+def test_custom_optimizer_step_with_multiple_optimizers(tmpdir):
+    """
+    This tests ensures custom optimizer_step works,
+    even when optimizer.step is not called for a particular optimizer
+    """
+
+    class TestModel(BoringModel):
+        training_step_called = [0, 0]
+        optimizer_step_called = [0, 0]
+
+        def __init__(self):
+            super().__init__()
+            self.layer_a = torch.nn.Linear(32, 2)
+            self.layer_b = torch.nn.Linear(32, 2)
+
+        def configure_optimizers(self):
+            opt_a = torch.optim.SGD(self.layer_a.parameters(), lr=0.001)
+            opt_b = torch.optim.SGD(self.layer_b.parameters(), lr=0.001)
+            return opt_a, opt_b
+
+        def training_step(self, batch, batch_idx, optimizer_idx):
+            self.training_step_called[optimizer_idx] += 1
+            x = self.layer_a(batch[0]) if (optimizer_idx == 0) else self.layer_b(batch[0])
+            loss = torch.nn.functional.mse_loss(x, torch.ones_like(x))
+            return loss
+
+        def training_epoch_end(self, outputs) -> None:
+            # outputs should be an array with an entry per optimizer
+            assert len(outputs) == 2
+
+        def optimizer_step(
+            self,
+            epoch,
+            batch_idx,
+            optimizer,
+            optimizer_idx,
+            optimizer_closure,
+            **_,
+        ):
+            # update first optimizer every step
+            if optimizer_idx == 0:
+                self.optimizer_step_called[optimizer_idx] += 1
+                optimizer.step(closure=optimizer_closure)
+
+            # update second optimizer every 2 steps
+            if optimizer_idx == 1:
+                if batch_idx % 2 == 0:
+                    self.optimizer_step_called[optimizer_idx] += 1
+                    optimizer.step(closure=optimizer_closure)
+
+    model = TestModel()
+    model.val_dataloader = None
+
+    trainer = pl.Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=4,
+        max_epochs=1,
+        log_every_n_steps=1,
+        weights_summary=None,
+    )
+    trainer.fit(model)
+    assert model.training_step_called == [4, 2]
+    assert model.optimizer_step_called == [4, 2]