fix switch_optimizer callback

Author: GiovanniCanali

pina/callback/optimizer_callback.py

@@ -21,45 +21,52 @@ def __init__(self, new_optimizers, epoch_switch):
             single :class:`torch.optim.Optimizer` instance or a list of them
             for multiple model solver.
         :type new_optimizers: pina.optim.TorchOptimizer | list
-        :param epoch_switch: The epoch at which the optimizer switch occurs.
-        :type epoch_switch: int
+        :param int epoch_switch: The epoch at which the optimizer switch occurs.
 
         Example:
-            >>> switch_callback = SwitchOptimizer(new_optimizers=optimizer,
-            >>>                                  epoch_switch=10)
+            >>> optimizer = TorchOptimizer(torch.optim.Adam, lr=0.01)
+            >>> switch_callback = SwitchOptimizer(
+            >>>     new_optimizers=optimizer, epoch_switch=10
+            >>> )
         """
         super().__init__()
 
+        # Check if epoch_switch is greater than 1
         if epoch_switch < 1:
             raise ValueError("epoch_switch must be greater than one.")
 
+        # If new_optimizers is not a list, convert it to a list
         if not isinstance(new_optimizers, list):
             new_optimizers = [new_optimizers]
 
-        # check type consistency
+        # Check consistency
+        check_consistency(epoch_switch, int)
         for optimizer in new_optimizers:
             check_consistency(optimizer, TorchOptimizer)
-        check_consistency(epoch_switch, int)
-        # save new optimizers
+
+        # Store the new optimizers and epoch switch
         self._new_optimizers = new_optimizers
         self._epoch_switch = epoch_switch
 
     def on_train_epoch_start(self, trainer, __):
         """
         Switch the optimizer at the start of the specified training epoch.
 
-        :param trainer: The trainer object managing the training process.
-        :type trainer: pytorch_lightning.Trainer
+        :param lightning.pytorch.Trainer trainer: The trainer object managing
+            the training process.
         :param _: Placeholder argument (not used).
-
-        :return: None
-        :rtype: None
         """
+        # Check if the current epoch matches the switch epoch
         if trainer.current_epoch == self._epoch_switch:
             optims = []
 
+            # Hook the new optimizers to the model parameters
             for idx, optim in enumerate(self._new_optimizers):
                 optim.hook(trainer.solver._pina_models[idx].parameters())
                 optims.append(optim)
 
+            # Update the solver's optimizers
             trainer.solver._pina_optimizers = optims
+
+            # Update the trainer's strategy optimizers
+            trainer.strategy.optimizers = [o.instance for o in optims]

tests/test_callback/test_optimizer_callback.py

@@ -1,45 +1,63 @@
-from pina.callback import SwitchOptimizer
 import torch
 import pytest
 
 from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 from pina.optim import TorchOptimizer
+from pina.callback import SwitchOptimizer
+from pina.problem.zoo import Poisson2DSquareProblem as Poisson
+
+
+# Define the problem
+problem = Poisson()
+problem.discretise_domain(10)
+model = FeedForward(len(problem.input_variables), len(problem.output_variables))
+
+# Define the optimizer
+optimizer = TorchOptimizer(torch.optim.Adam)
 
-# make the problem
-poisson_problem = Poisson()
-boundaries = ["g1", "g2", "g3", "g4"]
-n = 10
-poisson_problem.discretise_domain(n, "grid", domains=boundaries)
-poisson_problem.discretise_domain(n, "grid", domains="D")
-model = FeedForward(
-    len(poisson_problem.input_variables), len(poisson_problem.output_variables)
-)
+# Initialize the solver
+solver = PINN(problem=problem, model=model, optimizer=optimizer)
 
-# make the solver
-solver = PINN(problem=poisson_problem, model=model)
+# Define new optimizers for testing
+lbfgs = TorchOptimizer(torch.optim.LBFGS, lr=1.0)
+adamW = TorchOptimizer(torch.optim.AdamW, lr=0.01)
 
-adam = TorchOptimizer(torch.optim.Adam, lr=0.01)
-lbfgs = TorchOptimizer(torch.optim.LBFGS, lr=0.001)
 
+@pytest.mark.parametrize("epoch_switch", [5, 10])
+@pytest.mark.parametrize("new_opt", [lbfgs, adamW])
+def test_switch_optimizer_constructor(new_opt, epoch_switch):
 
-def test_switch_optimizer_constructor():
-    SwitchOptimizer(adam, epoch_switch=10)
+    # Constructor
+    SwitchOptimizer(new_optimizers=new_opt, epoch_switch=epoch_switch)
 
+    # Should fail if epoch_switch is less than 1
+    with pytest.raises(ValueError):
+        SwitchOptimizer(new_optimizers=new_opt, epoch_switch=0)
 
-def test_switch_optimizer_routine():
-    # check initial optimizer
+
+@pytest.mark.parametrize("epoch_switch", [5, 10])
+@pytest.mark.parametrize("new_opt", [lbfgs, adamW])
+def test_switch_optimizer_routine(new_opt, epoch_switch):
+
+    # Check if the optimizer is initialized correctly
     solver.configure_optimizers()
-    assert solver.optimizer.instance.__class__ == torch.optim.Adam
-    # make the trainer
-    switch_opt_callback = SwitchOptimizer(lbfgs, epoch_switch=3)
+
+    # Initialize the trainer
+    switch_opt_callback = SwitchOptimizer(
+        new_optimizers=new_opt, epoch_switch=epoch_switch
+    )
     trainer = Trainer(
         solver=solver,
-        callbacks=[switch_opt_callback],
+        callbacks=switch_opt_callback,
         accelerator="cpu",
-        max_epochs=5,
+        max_epochs=epoch_switch + 2,
     )
     trainer.train()
-    assert solver.optimizer.instance.__class__ == torch.optim.LBFGS
+
+    # Check that the trainer strategy optimizers have been updated
+    assert solver.optimizer.instance.__class__ == new_opt.instance.__class__
+    assert (
+        trainer.strategy.optimizers[0].__class__ == new_opt.instance.__class__
+    )