New checkpoint saving strategy that keeps every k steps and additionally the n most recent checkpoints.

docs/components/components.md

@@ -88,6 +88,7 @@ The composed initializer supports seeded weight initialization for reproducibili
 | checkpoint_saving | default | [CheckpointSaving](../../src/modalities/checkpointing/checkpoint_saving.py)| [CheckpointSavingConfig](s../../src/modalities/config/config.py) | -- | Component for saving checkpoints based on a savig and execution strategy. |
 | checkpoint_saving_strategy | save_every_k_steps_checkpointing_strategy | [SaveEveryKStepsCheckpointingStrategy](../../src/modalities/checkpointing/checkpoint_saving_strategies.py)| [SaveEveryKStepsCheckpointingStrategyConfig](../../src/modalities/config/config.py) | [CheckpointSavingStrategyIF](../../src/modalities/checkpointing/checkpoint_saving_strategies.py) | Checkpointing strategy saving a checkpoint every k steps |
 | checkpoint_saving_strategy | save_k_most_recent_checkpoints_strategy | [SaveKMostRecentCheckpointsStrategy](../../src/modalities/checkpointing/checkpoint_saving_strategies.py)| [SaveKMostRecentCheckpointsStrategyConfig](../../src/modalities/config/config.py) | [CheckpointSavingStrategyIF](../../src/modalities/checkpointing/checkpoint_saving_strategies.py) | Checkpointing strategy saving only the last k checkpoints and deleting the previous ones |
+| checkpoint_saving_strategy | keep_every_k_steps_and_m_most_recent_checkpointing_strategy | [KeepEveryKStepsAndMMostRecentCheckpointingStrategy](../../src/modalities/checkpointing/checkpoint_saving_strategies.py)| [KeepEveryKStepsAndMMostRecentCheckpointingStrategyConfig](../../src/modalities/config/config.py) | [CheckpointSavingStrategyIF](../../src/modalities/checkpointing/checkpoint_saving_strategies.py) | Checkpointing strategy saving a checkpoint every k steps and keeping the m most recent checkpoints |
 | checkpoint_saving_execution | fsdp | [FSDPCheckpointSaving](../../src/modalities/checkpointing/fsdp/fsdp_checkpoint_saving.py)| [FSDPCheckpointSavingConfig](../../src/modalities/config/config.py) | [CheckpointSavingExecutionABC](../../src/modalities/checkpointing/checkpoint_saving_execution.py) | FSDPCheckpointSaving class for saving checkpoints of FSDP models and optimizers. |
 | checkpoint_loading | fsdp | [FSDPCheckpointLoading](../../src/modalities/checkpointing/fsdp/fsdp_checkpoint_loading.py)| [FSDPCheckpointLoadingConfig](../../src/modalities/config/config.py) | [CheckpointLoadingIF](../../src/modalities/checkpointing/checkpoint_loading.py) | Component for loading FSDP checkpoints|
 | checkpoint_loading | torch | [TorchCheckpointLoading](../../src/modalities/checkpointing/torch/torch_checkpoint_loading.py)| [TorchCheckpointLoadingConfig](../../src/modalities/config/config.py) | [CheckpointLoadingIF](../../src/modalities/checkpointing/checkpoint_loading.py) | Component for loading PyTorch checkpoints|

src/modalities/checkpointing/checkpoint_saving_strategies.py

@@ -119,3 +119,60 @@ def get_checkpoint_instruction(
         """
         save_current = training_progress.num_seen_steps_total % self.k == 0
         return CheckpointingInstruction(save_current=save_current, checkpoints_to_delete=[])
+
+
+class KeepEveryKStepsAndMMostRecentCheckpointingStrategy(CheckpointSavingStrategyIF):
+    """Strategy for keeping every k steps permanently and additionally the most recent checkpoints."""
+
+    def __init__(self, k: int, num_recent_checkpoints_to_keep: int = 2):
+        """
+        Initializes the CheckpointSavingStrategy object.
+
+        Args:
+            k (int): The interval of steps to keep.
+            num_recent_checkpoints_to_keep (int, optional): The number of recent checkpoints to keep.
+                This includes all checkpoints but only the ones not divisible by k will actually be deleted.
+                Defaults to 2.
+
+        Returns:
+            None
+        """
+        super().__init__()
+        self._k = k
+        self._num_recent_checkpoints_to_keep = num_recent_checkpoints_to_keep
+        self._saved_recent_checkpoints: list[TrainingProgress] = []
+        assert self._k > 0, "k must be greater than 0"
+        assert self._num_recent_checkpoints_to_keep >= 1, "num_recent_checkpoints_to_keep must be at least 1"
+
+    def get_checkpoint_instruction(
+        self,
+        training_progress: TrainingProgress,
+        evaluation_result: dict[str, EvaluationResultBatch] | None = None,
+        early_stopping_criterion_fulfilled: bool = False,
+    ) -> CheckpointingInstruction:
+        """
+        Returns a CheckpointingInstruction object.
+
+        Args:
+            training_progress (TrainingProgress): The training progress.
+            evaluation_result (dict[str, EvaluationResultBatch] | None, optional):
+            The evaluation result. Defaults to None.
+            early_stopping_criterion_fulfilled (bool, optional):
+            Whether the early stopping criterion is fulfilled. Defaults to False.
+
+        Returns:
+            CheckpointingInstruction: The checkpointing instruction object.
+        """
+        self._saved_recent_checkpoints.append(dataclasses.replace(training_progress))
+        checkpoints_to_delete, self._saved_recent_checkpoints = (
+            (
+                self._saved_recent_checkpoints[: -self._num_recent_checkpoints_to_keep],
+                self._saved_recent_checkpoints[-self._num_recent_checkpoints_to_keep :],
+            )
+            if len(self._saved_recent_checkpoints) > self._num_recent_checkpoints_to_keep
+            else ([], self._saved_recent_checkpoints)
+        )
+        # Do not delete checkpoints that are divisible by k.
+        checkpoints_to_delete = [cp for cp in checkpoints_to_delete if cp.num_seen_steps_current_run % self._k != 0]
+
+        return CheckpointingInstruction(save_current=True, checkpoints_to_delete=checkpoints_to_delete)

src/modalities/config/config.py

@@ -92,6 +92,11 @@ class SaveKMostRecentCheckpointsStrategyConfig(BaseModel):
     k: Annotated[int, Field(strict=True, ge=-1)]
 
 
+class KeepEveryKStepsAndMMostRecentCheckpointingStrategyConfig(BaseModel):
+    k: Annotated[int, Field(strict=True, gt=0)]
+    num_recent_checkpoints_to_keep: Annotated[int, Field(strict=True, ge=1)] = 2
+
+
 class TorchCheckpointLoadingConfig(BaseModel):
     device: PydanticPytorchDeviceType
     precision: Optional[PrecisionEnum] = None

src/modalities/registry/components.py

@@ -10,6 +10,7 @@
 
 from modalities.checkpointing.checkpoint_saving import CheckpointSaving
 from modalities.checkpointing.checkpoint_saving_strategies import (
+    KeepEveryKStepsAndMMostRecentCheckpointingStrategy,
     SaveEveryKStepsCheckpointingStrategy,
     SaveKMostRecentCheckpointsStrategy,
 )
@@ -47,6 +48,7 @@
     GPT2LLMCollateFnConfig,
     GPT2MFUCalculatorConfig,
     GPT2ModelTPConfig,
+    KeepEveryKStepsAndMMostRecentCheckpointingStrategyConfig,
     LinearLRSchedulerConfig,
     LinearWarmupCosineAnnealingLRSchedulerConfig,
     LLMDataLoaderConfig,
@@ -353,6 +355,12 @@ class ComponentEntity:
         SaveKMostRecentCheckpointsStrategy,
         SaveKMostRecentCheckpointsStrategyConfig,
     ),
+    ComponentEntity(
+        "checkpoint_saving_strategy",
+        "keep_every_k_steps_and_m_most_recent_checkpointing_strategy",
+        KeepEveryKStepsAndMMostRecentCheckpointingStrategy,
+        KeepEveryKStepsAndMMostRecentCheckpointingStrategyConfig,
+    ),
     # checkpoint saving execution
     ComponentEntity("checkpoint_saving_execution", "fsdp1", FSDP1CheckpointSaving, FSDP1CheckpointSavingConfig),
     ComponentEntity("checkpoint_saving_execution", "dcp", DCPCheckpointSaving, DCPCheckpointSavingConfig),

tests/checkpointing/test_checkpoint_strategies.py

@@ -1,6 +1,12 @@
+import dataclasses
+
 import pytest
 
-from modalities.checkpointing.checkpoint_saving_strategies import SaveKMostRecentCheckpointsStrategy
+from modalities.checkpointing.checkpoint_saving_instruction import CheckpointingInstruction
+from modalities.checkpointing.checkpoint_saving_strategies import (
+    KeepEveryKStepsAndMMostRecentCheckpointingStrategy,
+    SaveKMostRecentCheckpointsStrategy,
+)
 from modalities.training.training_progress import TrainingProgress
 
 
@@ -43,3 +49,59 @@ def test_checkpoint_strategy_k(
     if k != 0 and save_current:
         training_progress.num_seen_steps_current_run = 100
         assert checkpoint_strategy.saved_step_checkpoints[0].num_seen_steps_current_run == num_seen_steps_current_run
+
+
+@pytest.mark.parametrize(
+    "k, num_recent_checkpoints_to_keep, num_steps",
+    [
+        (3, 2, 11),
+        (2, 1, 10),
+        (4, 3, 15),
+    ],
+)
+def test_keep_every_k_steps_keeps_every_k_steps(k: int, num_recent_checkpoints_to_keep: int, num_steps: int) -> None:
+    checkpoint_strategy = KeepEveryKStepsAndMMostRecentCheckpointingStrategy(
+        k=k, num_recent_checkpoints_to_keep=num_recent_checkpoints_to_keep
+    )
+    training_progress = TrainingProgress(
+        num_seen_steps_current_run=0,
+        num_seen_tokens_current_run=0,
+        num_target_steps=20,
+        num_target_tokens=40,
+    )
+
+    # Simulate training progress and checkpointing
+    simulator = _CheckpointSavingSimulator()
+    for step in range(1, num_steps + 1):
+        training_progress.num_seen_steps_current_run = step
+        checkpoint_instruction = checkpoint_strategy.get_checkpoint_instruction(training_progress=training_progress)
+        simulator.simulate_training_step(training_progress, checkpoint_instruction)
+
+    for ckpt in simulator.saved_checkpoints:
+        # Check that only checkpoints that are divisible by k or the most recent ones are kept.
+        last_checkpoints = set(range(num_steps - num_recent_checkpoints_to_keep + 1, num_steps + 1))
+        assert ckpt.num_seen_steps_current_run % k == 0 or ckpt.num_seen_steps_current_run in last_checkpoints
+
+
+def test_keep_every_k_steps_checkpointing_strategy_invalid_arguments() -> None:
+    with pytest.raises(AssertionError):
+        KeepEveryKStepsAndMMostRecentCheckpointingStrategy(k=0, num_recent_checkpoints_to_keep=1)
+    with pytest.raises(AssertionError):
+        KeepEveryKStepsAndMMostRecentCheckpointingStrategy(k=-1, num_recent_checkpoints_to_keep=1)
+    with pytest.raises(AssertionError):
+        KeepEveryKStepsAndMMostRecentCheckpointingStrategy(k=2, num_recent_checkpoints_to_keep=0)
+    with pytest.raises(AssertionError):
+        KeepEveryKStepsAndMMostRecentCheckpointingStrategy(k=2, num_recent_checkpoints_to_keep=-1)
+
+
+class _CheckpointSavingSimulator:
+    def __init__(self):
+        self.saved_checkpoints: list[TrainingProgress] = []
+
+    def simulate_training_step(
+        self, training_progress: TrainingProgress, ckpt_instruction: CheckpointingInstruction
+    ) -> None:
+        if ckpt_instruction.save_current:
+            self.saved_checkpoints.append(dataclasses.replace(training_progress))
+        for checkpoint_to_delete in ckpt_instruction.checkpoints_to_delete:
+            self.saved_checkpoints = [cp for cp in self.saved_checkpoints if cp != checkpoint_to_delete]