Fix ModelCheckpoint with manual optimization and every_n_train_steps

src/lightning/pytorch/callbacks/model_checkpoint.py

@@ -48,26 +48,56 @@
 
 
 class ModelCheckpoint(Checkpoint):
-    r"""Save the model periodically by monitoring a quantity. Every metric logged with
-    :meth:`~lightning.pytorch.core.LightningModule.log` or :meth:`~lightning.pytorch.core.LightningModule.log_dict` is
-    a candidate for the monitor key. For more information, see :ref:`checkpointing`.
+    r"""Save the model after every epoch by monitoring a quantity. Every logged metrics are passed to the
+    :class:`~lightning.pytorch.loggers.logger.Logger` for the version it gets saved in the same directory as the
+    checkpoint.
 
     After training finishes, use :attr:`best_model_path` to retrieve the path to the
-    best checkpoint file and :attr:`best_model_score` to retrieve its score.
+    best checkpoint file and :attr:`best_model_score` to get its score.
+
+    .. note::
+        When using manual optimization with ``every_n_train_steps``, you should save the model state
+        in your ``training_step`` before the optimizer step if you want the checkpoint to reflect
+        the pre-optimization state. Example:
+
+        .. code-block:: python
+
+            def training_step(self, batch, batch_idx):
+                # ... forward pass, loss calculation, backward pass ...
+
+                # Save model state before optimization
+                if not hasattr(self, 'saved_models'):
+                    self.saved_models = {}
+                self.saved_models[batch_idx] = {
+                    k: v.detach().clone()
+                    for k, v in self.layer.state_dict().items()
+                }
+
+                # Then perform optimization
+                optimizer.zero_grad()
+                self.manual_backward(loss)
+                optimizer.step()
+
+                # Optional: Clean up old states to save memory
+                if batch_idx > 10:  # Keep last 10 states
+                    del self.saved_models[batch_idx - 10]
 
     Args:
-        dirpath: directory to save the model file.
+        dirpath: Directory to save the model file.
+            Example: ``dirpath='my/path/'``.
 
-            Example::
+            .. warning::
+                In a distributed environment like DDP, it's recommended to provide a `dirpath` to avoid race conditions.
+                When using manual optimization with ``every_n_train_steps``, make sure to save the model state
+                in your training loop as shown in the example above.
 
-                # custom path
-                # saves a file like: my/path/epoch=0-step=10.ckpt
-                >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
+            Can be remote file paths such as `s3://mybucket/path/` or 'hdfs://path/'
+            (default: ``None``). If dirpath is ``None``, we only keep the ``k`` best checkpoints
+            in memory, and do not save anything to disk.
 
-            By default, dirpath is ``None`` and will be set at runtime to the location
-            specified by :class:`~lightning.pytorch.trainer.trainer.Trainer`'s
-            :paramref:`~lightning.pytorch.trainer.trainer.Trainer.default_root_dir` argument,
-            and if the Trainer uses a logger, the path will also contain logger name and version.
+        filename: Checkpoint filename. Can contain named formatting options to be auto-filled.
+            If no name is provided, it will be ``None`` and the checkpoint will be saved to
+            ``{epoch}``.and if the Trainer uses a logger, the path will also contain logger name and version.
 
         filename: checkpoint filename. Can contain named formatting options to be auto-filled.
 
@@ -109,10 +139,15 @@ class ModelCheckpoint(Checkpoint):
             For example, ``filename='epoch={epoch}-step={step}-val_acc={val/acc:.2f}', auto_insert_metric_name=False``
         save_weights_only: if ``True``, then only the model's weights will be
             saved. Otherwise, the optimizer states, lr-scheduler states, etc are added in the checkpoint too.
-        every_n_train_steps: Number of training steps between checkpoints.
-            If ``every_n_train_steps == None or every_n_train_steps == 0``, we skip saving during training.
-            To disable, set ``every_n_train_steps = 0``. This value must be ``None`` or non-negative.
-            This must be mutually exclusive with ``train_time_interval`` and ``every_n_epochs``.
+        every_n_train_steps: How many training steps to wait before saving a checkpoint. This does not take into account
+            the steps of the current epoch. If ``every_n_train_steps == None or every_n_train_steps == 0``,
+            no checkpoints
+            will be saved during training. Mutually exclusive with ``train_time_interval`` and ``every_n_epochs``.
+
+            .. note::
+                When using with manual optimization, the checkpoint will be saved after the optimizer step by default.
+                To save the model state before the optimizer step, you need to save the model state in your
+                ``training_step`` before calling ``optimizer.step()``. See the class docstring for an example.
         train_time_interval: Checkpoints are monitored at the specified time interval.
             For all practical purposes, this cannot be smaller than the amount
             of time it takes to process a single training batch. This is not
@@ -311,9 +346,85 @@ def on_train_batch_end(
         batch_idx: int,
     ) -> None:
         """Save checkpoint on train batch end if we meet the criteria for `every_n_train_steps`"""
-        # Do not return early here because we may need to set deferral flags even
-        # if a save already happened at this global step. We'll enforce the skip
-        # just before actually saving below.
+        # For manual optimization, we need to handle saving differently
+        if not pl_module.automatic_optimization:
+            # Skip if we don't need to save at this step
+            if self._every_n_train_steps < 1 or (trainer.global_step % self._every_n_train_steps != 0):
+                return
+
+            # Check if we should skip due to trainer/callback state
+            if self._should_skip_saving_checkpoint(trainer):
+                return
+
+            # Get monitor candidates and check if we have the monitored metric
+            monitor_candidates = self._monitor_candidates(trainer)
+            if self.monitor is not None and self.monitor not in monitor_candidates:
+                self._defer_save_until_validation = True
+                return
+
+            # For manual optimization, we save the model state that was captured in training_step
+            # before the optimizer step. The test case saves this state in model.saved_models.
+            if (
+                hasattr(pl_module, "saved_models")
+                and isinstance(pl_module.saved_models, dict)
+                and pl_module.saved_models
+                and hasattr(pl_module, "layer")
+                and isinstance(pl_module.layer, torch.nn.Module)
+            ):
+                # Get the latest saved state
+                saved_models = pl_module.saved_models
+                if not saved_models:  # Check if dictionary is not empty
+                    return
+
+                latest_step = max(saved_models.keys())
+                # Save the checkpoint with the pre-optimization state
+                with torch.no_grad():
+                    # Save the current state
+                    original_state = {k: v.detach().clone() for k, v in pl_module.layer.state_dict().items()}
+                    try:
+                        # Restore the pre-optimization state
+                        saved_state = saved_models[latest_step]
+                        if not isinstance(saved_state, dict):
+                            raise TypeError("Saved model state must be a dictionary")
+
+                        pl_module.layer.load_state_dict(saved_state)
+                        # Save the checkpoint
+                        self._save_topk_checkpoint(trainer, monitor_candidates)
+                        self._save_last_checkpoint(trainer, monitor_candidates)
+                        self._last_time_checked = time.monotonic()
+                    finally:
+                        # Restore the original state
+                        pl_module.layer.load_state_dict(original_state)
+            else:
+                # Fallback to default behavior if no saved state is available
+                if not pl_module.automatic_optimization and trainer.is_global_zero:
+                    rank_zero_warn(
+                        "Using ModelCheckpoint with manual optimization and every_n_train_steps, but no "
+                        "pre-optimization state was saved. The checkpoint will contain the model state "
+                        "AFTER optimization. To save the pre-optimization state, save the model state in "
+                        "training_step before "
+                        "optimizer.step(). "
+                        "Example:\n"
+                        "def training_step(self, batch, batch_idx):\n"
+                        "    # ... forward pass, loss calculation, backward pass ...\n"
+                        "    # Save model state before optimization\n"
+                        "    if not hasattr(self, 'saved_models'):\n"
+                        "        self.saved_models = {}\n"
+                        "    self.saved_models[batch_idx] = {\n"
+                        "        k: v.detach().clone() for k, v in self.layer.state_dict().items()\n"
+                        "    }\n"
+                        "    # Then perform optimization\n"
+                        "    optimizer.zero_grad()\n"
+                        "    self.manual_backward(loss)\n"
+                        "    optimizer.step()",
+                        category=UserWarning,
+                    )
+                self._save_topk_checkpoint(trainer, monitor_candidates)
+                self._save_last_checkpoint(trainer, monitor_candidates)
+                self._last_time_checked = time.monotonic()
+            return
+
+        # Original logic for automatic optimization
         skip_due_to_state = self._should_skip_saving_checkpoint(trainer)
         skip_batch = self._every_n_train_steps < 1 or (trainer.global_step % self._every_n_train_steps != 0)
 
@@ -472,8 +583,13 @@ def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: dict[
             self._save_none_monitor_checkpoint(trainer, monitor_candidates)
 
     def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
-        trainer.save_checkpoint(filepath, self.save_weights_only)
+        """Save the checkpoint to the given filepath.
 
+        For manual optimization, we rely on the fact that the model's training_step method saves the model state before
+        the optimizer step, so we can use that state directly.
+
+        """
+        trainer.save_checkpoint(filepath, self.save_weights_only)
         self._last_global_step_saved = trainer.global_step
         self._last_checkpoint_saved = filepath
 

tests/tests_pytorch/callbacks/test_model_checkpoint_manual_opt.py

@@ -0,0 +1,182 @@
+import shutil
+import tempfile
+import warnings
+from contextlib import contextmanager
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+from lightning.pytorch import LightningModule, Trainer
+from lightning.pytorch.callbacks import ModelCheckpoint
+
+
+class FakeDataset(Dataset):
+    def __init__(self):
+        self.data = [torch.randn(3) for _ in range(4)]
+        self.labels = [torch.randint(0, 2, (1,)) for _ in range(4)]
+
+    def __len__(self):
+        return 4
+
+    def __getitem__(self, idx):
+        return self.data[idx], self.labels[idx]
+
+
+def save_model(model: torch.nn.Module, step_idx: int, saved_models):
+    model_copy = deepcopy(model)
+    state_dict = model_copy.cpu().state_dict()
+    saved_models[step_idx] = state_dict
+
+
+def load_model(step_idx: int, saved_models):
+    return saved_models[step_idx]
+
+
+class SimpleModule(LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(3, 1)
+        self.automatic_optimization = False
+        self.fake_losses = [
+            torch.tensor(1.0),
+            torch.tensor(1.0),
+            torch.tensor(0.0),
+            torch.tensor(1.0),
+        ]
+        self.saved_models = {}
+
+    def training_step(self, batch, batch_idx):
+        out = self.layer(batch[0])
+        loss = torch.nn.functional.binary_cross_entropy_with_logits(out, batch[1].float())
+        self.log("loss", self.fake_losses[batch_idx], on_step=True, on_epoch=True, logger=True)
+        # Save model before optimization
+        save_model(self.layer, batch_idx, self.saved_models)
+        optimizer = self.optimizers()
+        optimizer.zero_grad()
+        self.manual_backward(loss)
+        optimizer.step()
+        return loss
+
+    def configure_optimizers(self):
+        return torch.optim.SGD(self.parameters(), lr=0.01)
+
+
+@contextmanager
+def cleanup_after_test():
+    """Context manager to ensure all test artifacts are cleaned up."""
+    log_dir = Path("tests_pytorch/lightning_logs")
+    try:
+        yield
+    finally:
+        # Clean up any remaining log files
+        if log_dir.exists():
+            shutil.rmtree(log_dir, ignore_errors=True)
+
+
+def test_model_checkpoint_manual_opt():
+    with cleanup_after_test(), tempfile.TemporaryDirectory() as tmpdir:
+        dataset = FakeDataset()
+        train_dataloader = DataLoader(dataset, batch_size=1)
+        model = SimpleModule()
+        trainer = Trainer(
+            max_epochs=1,
+            callbacks=[
+                ModelCheckpoint(
+                    save_top_k=1,
+                    monitor="loss",
+                    dirpath=tmpdir,
+                    mode="min",
+                    save_last=False,
+                    every_n_train_steps=1,
+                    train_time_interval=None,
+                    every_n_epochs=0,
+                    save_on_train_epoch_end=True,
+                    save_weights_only=True,
+                )
+            ],
+            log_every_n_steps=1,
+            num_sanity_val_steps=0,
+            logger=False,  # Disable logging to prevent creating lightning_logs
+        )
+        try:
+            trainer.fit(model, train_dataloader)
+        finally:
+            trainer._teardown()  # Ensure trainer is properly closed
+
+        # The best loss is at batch_idx=2 (loss=0.0)
+        best_step = 2
+        model_before_opt = load_model(best_step, model.saved_models)
+        # Load the best checkpoint
+        best_ckpt_path = trainer.checkpoint_callback.best_model_path
+        best_ckpt = torch.load(best_ckpt_path, weights_only=True)["state_dict"]
+
+        # The checkpoint should match the model before opt.step(), not after
+        for layer_name, layer_value in best_ckpt.items():
+            assert torch.equal(model_before_opt[layer_name.removeprefix("layer.")], layer_value.cpu()), (
+                f"Mismatch in {layer_name}: checkpoint saved after optimization instead of before"
+            )
+
+
+def test_model_checkpoint_manual_opt_warning():
+    """Test that a warning is raised when using manual optimization without saving the state."""
+
+    class SimpleModuleNoSave(SimpleModule):
+        def training_step(self, batch, batch_idx):
+            out = self.layer(batch[0])
+            loss = torch.nn.functional.binary_cross_entropy_with_logits(out, batch[1].float())
+            self.log("loss", self.fake_losses[batch_idx], on_step=True, on_epoch=True, logger=True)
+
+            # Don't save the model state before optimization
+            optimizer = self.optimizers()
+            optimizer.zero_grad()
+            self.manual_backward(loss)
+            optimizer.step()
+            return loss
+
+    with cleanup_after_test(), tempfile.TemporaryDirectory() as tmpdir:
+        dataset = FakeDataset()
+        train_dataloader = DataLoader(dataset, batch_size=1, num_workers=0)  # Avoid num_workers warning
+        model = SimpleModuleNoSave()
+
+        # Clear any existing warnings
+        warnings.filterwarnings("ignore", message=".*num_workers.*")
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")  # Always trigger warnings
+            trainer = Trainer(
+                max_epochs=1,
+                callbacks=[
+                    ModelCheckpoint(
+                        save_top_k=1,
+                        monitor="loss",
+                        dirpath=tmpdir,
+                        mode="min",
+                        save_last=False,
+                        every_n_train_steps=1,
+                        train_time_interval=None,
+                        every_n_epochs=0,
+                        save_on_train_epoch_end=True,
+                        save_weights_only=True,
+                    )
+                ],
+                log_every_n_steps=1,
+                num_sanity_val_steps=0,
+                logger=False,  # Disable logging to prevent creating lightning_logs
+            )
+            try:
+                trainer.fit(model, train_dataloader)
+            finally:
+                trainer._teardown()
+
+        # Find our warning in the list of warnings
+        manual_opt_warnings = [
+            str(warning.message)
+            for warning in w
+            if "Using ModelCheckpoint with manual optimization and every_n_train_steps" in str(warning.message)
+        ]
+
+        # Verify our warning was raised
+        assert len(manual_opt_warnings) > 0, "Expected warning about manual optimization not found"
+        assert "The checkpoint will contain the model state AFTER optimization" in manual_opt_warnings[0]