Refactor Checkpointer

Several bugs fixes, refactors, and feature improvement for the next PR (integration with TorchFT)

1. Code refactor for better readbility
2. Remove the time based checkpoint condiation, this is not used and can cause deadlocks when integrating with TorchFT. This will also make code simplier.
3. Fixes a async_with_pinned_memory bug.
4. The original keep_last_k implementation can cause exceptions in certain case and is also slow. Fixes the bugs and use a thread to purge checkpoints.

ghstack-source-id: b629a710860f63c07e7137c86ca5832742221901
Pull Request resolved: #871

docs/checkpoint.md

@@ -20,7 +20,6 @@ In your torchtitan training config, ensure that `enable_checkpoint` is set to Tr
 [checkpoint]
 enable_checkpoint = true
 folder = "checkpoint"
-interval_type = "steps"
 interval = 500
 ```
 
@@ -47,7 +46,6 @@ export_dtype = "bfloat16"
 [checkpoint]
 enable_checkpoint = true
 folder = "checkpoint"
-interval_type = "steps"
 interval = 10
 load_step = 5
 model_weights_only = true

tests/unit_tests/test_checkpoint.py

@@ -0,0 +1,279 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import shutil
+import tempfile
+import time
+import unittest
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass, field
+from unittest import mock
+
+import torch
+
+from torchtitan.components.checkpoint import CheckpointManager
+
+
+def fake_dcp_save(state, checkpoint_id):
+    state = {k: v.state_dict() for k, v in state.items()}
+    os.makedirs(checkpoint_id, exist_ok=True)
+    torch.save(state, os.path.join(checkpoint_id, "state.pt"))
+
+
+def fake_dcp_load(state, checkpoint_id):
+    state["trainer"].dcp_load_is_called = 7312
+
+
+def fake_async_save(state, checkpoint_id, process_group):
+    def run_save():
+        fake_dcp_save(state, checkpoint_id)
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        f = executor.submit(run_save)
+
+    mock_future = mock.Mock()
+    mock_future.result = mock.Mock(side_effect=f.result)
+    return mock_future
+
+
+def fake_get_model_state_dict(model, *args, **kwargs):
+    return model.state_dict()
+
+
+@dataclass
+class DummyCheckpointConfig:
+    enable_checkpoint: bool = True
+    folder: str = "dummy_folder"
+    interval: int = 10
+    async_mode: str = "disabled"
+    keep_latest_k: int = 0
+    model_weights_only: bool = False
+    export_dtype: str = "float32"
+    exclude_from_loading = []
+
+
+@dataclass
+class DummyJob:
+    dump_folder: str = "dummy_folder"
+
+
+@dataclass
+class DummyJobConfig:
+    checkpoint: DummyCheckpointConfig = field(default_factory=DummyCheckpointConfig)
+    job: DummyJob = field(default_factory=DummyJob)
+
+
+# Dummy instances to supply as constructor arguments.
+dummy_dataloader = mock.Mock()
+dummy_dataloader.state_dict = mock.Mock(side_effect=lambda: {"dataloader": 1})
+dummy_model_parts = [mock.Mock()]
+dummy_model_parts[0].state_dict = mock.Mock(side_effect=lambda: {"model": 2})
+dummy_optimizers = mock.Mock()
+dummy_optimizers.state_dict = mock.Mock(side_effect=lambda: {"optimizer": 3})
+dummy_lr_schedulers = mock.Mock()
+dummy_lr_schedulers.state_dict = mock.Mock(side_effect=lambda: {"lr_scheduler": 4})
+
+
+class TestCheckpointManager(unittest.TestCase):
+    def setUp(self):
+        self.temp_dir = tempfile.mkdtemp()
+
+        self.dummy_job = DummyJob(dump_folder=self.temp_dir)
+        self.job_config = DummyJobConfig(job=self.dummy_job)
+        self.checkpoint_folder = os.path.join(
+            self.dummy_job.dump_folder, self.job_config.checkpoint.folder
+        )
+        os.makedirs(self.checkpoint_folder, exist_ok=True)
+        self.trainer_state = mock.Mock()
+        self.trainer_state.state_dict = mock.Mock(side_effect=lambda: {"my_state": 765})
+
+    def tearDown(self):
+        # Remove the temporary directory after each test.
+        shutil.rmtree(self.temp_dir)
+
+    @mock.patch(
+        "torchtitan.components.checkpoint.get_model_state_dict",
+        side_effect=fake_get_model_state_dict,
+    )
+    @mock.patch("torchtitan.components.checkpoint.dcp.save", side_effect=fake_dcp_save)
+    def test_save(self, *_):
+        """Test that calling save() writes a checkpoint file to disk."""
+        job_config = DummyJobConfig(job=self.dummy_job)
+        manager = CheckpointManager(
+            dummy_dataloader,
+            dummy_model_parts,
+            dummy_optimizers,
+            dummy_lr_schedulers,
+            {"trainer": self.trainer_state},
+            job_config,
+        )
+        step = 20
+        manager.save(curr_step=step, force=True)
+        state_file = self._checkpoint_id(step)
+        self.assertTrue(
+            os.path.exists(state_file), "The checkpoint file was not created on disk."
+        )
+        loaded_state = torch.load(state_file, weights_only=False)
+        self.assertEqual(
+            loaded_state["trainer"]["my_state"],
+            765,
+            "Saved state does not match expected value.",
+        )
+
+    @mock.patch(
+        "torchtitan.components.checkpoint.get_model_state_dict",
+        side_effect=fake_get_model_state_dict,
+    )
+    @mock.patch("torchtitan.components.checkpoint.dcp.load", side_effect=fake_dcp_load)
+    @mock.patch("torchtitan.components.checkpoint.dcp.save", side_effect=fake_dcp_save)
+    def test_load(self, *_):
+        """Test that load() properly reads the checkpoint file from disk and restores state."""
+        job_config = DummyJobConfig(job=self.dummy_job)
+        manager = CheckpointManager(
+            dummy_dataloader,
+            dummy_model_parts,
+            dummy_optimizers,
+            dummy_lr_schedulers,
+            {"trainer": self.trainer_state},
+            job_config,
+        )
+        step = 30
+        manager.save(curr_step=step, force=True)
+        # Simulate a state change.
+        manager.states["test"] = 999
+        success = manager.load(step=step)
+        self.assertTrue(
+            success,
+            "The load() method should have returned True for an existing checkpoint.",
+        )
+        self.assertTrue(hasattr(manager.states["trainer"], "dcp_load_is_called"))
+
+        self.assertEqual(
+            manager.states["trainer"].dcp_load_is_called,
+            7312,
+            "The state was not correctly restored after loading.",
+        )
+
+    @mock.patch("torchtitan.components.checkpoint.dist.get_rank", return_value=0)
+    @mock.patch(
+        "torchtitan.components.checkpoint.get_model_state_dict",
+        side_effect=fake_get_model_state_dict,
+    )
+    @mock.patch("torchtitan.components.checkpoint.dcp.save", side_effect=fake_dcp_save)
+    def test_purge_stale_checkpoints_rank_zero(self, *_):
+        """
+        Test that when keep_latest_k is 3 and dist.get_rank() returns 0, stale checkpoints
+        are purged by placing the correct paths into the purge queue.
+        """
+        job_config = DummyJobConfig(job=self.dummy_job)
+        job_config.checkpoint.keep_latest_k = 3
+        manager = CheckpointManager(
+            dummy_dataloader,
+            dummy_model_parts,
+            dummy_optimizers,
+            dummy_lr_schedulers,
+            {"trainer": self.trainer_state},
+            job_config,
+        )
+        steps = [10, 20, 30, 40, 50]
+        for s in steps:
+            manager.save(curr_step=s, force=False)
+        while not manager.purge_queue.empty():
+            time.sleep(1)
+        time.sleep(1)
+        os.sync()
+        expected_paths = [
+            os.path.join(self.checkpoint_folder, "step-30"),
+            os.path.join(self.checkpoint_folder, "step-40"),
+            os.path.join(self.checkpoint_folder, "step-50"),
+        ]
+        for step in [10, 20]:
+            self.assertFalse(
+                os.path.exists(self._checkpoint_id(step)),
+                "The checkpoint is not purged.",
+            )
+
+        for step in [30, 40, 50]:
+            self.assertTrue(
+                os.path.exists(self._checkpoint_id(step)), "The checkpointis purged."
+            )
+
+    @mock.patch("torchtitan.components.checkpoint.dist.get_rank", return_value=1)
+    @mock.patch(
+        "torchtitan.components.checkpoint.get_model_state_dict",
+        side_effect=fake_get_model_state_dict,
+    )
+    @mock.patch("torchtitan.components.checkpoint.dcp.save", side_effect=fake_dcp_save)
+    def test_purge_stale_checkpoints_rank_nonzero(self, *_):
+        """
+        Test that when dist.get_rank() returns a non-zero value, the purge logic does not
+        place any paths in the purge queue.
+        """
+        job_config = DummyJobConfig(job=self.dummy_job)
+        job_config.checkpoint.keep_latest_k = 3
+        manager = CheckpointManager(
+            dummy_dataloader,
+            dummy_model_parts,
+            dummy_optimizers,
+            dummy_lr_schedulers,
+            {"trainer": self.trainer_state},
+            job_config,
+        )
+        steps = [10, 20, 30, 40, 50]
+        for s in steps:
+            manager.save(curr_step=s, force=False)
+        while not manager.purge_queue.empty():
+            time.sleep(1)
+        time.sleep(1)
+        os.sync()
+
+        for step in [10, 20, 30, 40, 50]:
+            self.assertTrue(
+                os.path.exists(self._checkpoint_id(step)), "The checkpointis purged."
+            )
+
+    @mock.patch("torchtitan.components.checkpoint.dist.new_group")
+    @mock.patch(
+        "torchtitan.components.checkpoint.get_model_state_dict",
+        side_effect=fake_get_model_state_dict,
+    )
+    @mock.patch(
+        "torchtitan.components.checkpoint.dcp.async_save", side_effect=fake_async_save
+    )
+    def test_async_save_calls_async_wait(self, *_):
+        """
+        Test that in async mode (AsyncMode.ASYNC), calling save() twice correctly waits
+        on the previous async future via _async_wait().
+        """
+        # Set async_mode to "async" in the job configuration.
+        job_config = DummyJobConfig(job=self.dummy_job)
+        job_config.checkpoint.async_mode = "async"
+        manager = CheckpointManager(
+            dummy_dataloader,
+            dummy_model_parts,
+            dummy_optimizers,
+            dummy_lr_schedulers,
+            {"trainer": self.trainer_state},
+            job_config,
+        )
+        # First save: should schedule an async save.
+        manager.save(curr_step=10, force=False)
+        f = manager.async_future
+        f.result.assert_not_called()
+        manager.save(curr_step=20, force=False)
+        f.result.assert_called_once()
+        f = manager.async_future
+        f.result.assert_not_called()
+
+    def _checkpoint_id(self, step):
+        checkpoint_id = os.path.join(self.checkpoint_folder, f"step-{step}")
+        state_file = os.path.join(checkpoint_id, "state.pt")
+        return state_file
+
+
+if __name__ == "__main__":
+    unittest.main()