Don't take final ckpt if no more training was done in FIT (pytorch#846)

Summary: Pull Request resolved: pytorch#846

Reviewed By: JKSenthil

Differential Revision: D58397317

fbshipit-source-id: 31b8f7382059f04cd35f26eafbd957bd53e7f3f0

tests/framework/callbacks/test_base_checkpointer.py

@@ -500,6 +500,44 @@ def test_save_on_train_end(self) -> None:
                     ],
                 )
 
+    def test_save_on_train_end_on_fit(self) -> None:
+        input_dim = 2
+        dataset_len = 10
+        batch_size = 2
+        max_epochs = 6
+
+        for save_every_n_eval_epochs, expected_last_ckpt in [
+            (None, "epoch_6_train_step_30_eval_step_25"),
+            (2, "epoch_6_train_step_30_eval_step_30"),
+        ]:
+            my_unit = DummyAutoUnit(module=nn.Linear(input_dim, 2))
+            train_dataloader = generate_random_dataloader(
+                dataset_len, input_dim, batch_size
+            )
+            eval_dataloader = generate_random_dataloader(
+                dataset_len, input_dim, batch_size
+            )
+            with tempfile.TemporaryDirectory() as temp_dir:
+                checkpoint_cb = BaseCheckpointSaver(
+                    temp_dir,
+                    save_every_n_epochs=2,
+                    save_every_n_eval_epochs=save_every_n_eval_epochs,
+                )
+                fit(
+                    my_unit,
+                    train_dataloader=train_dataloader,
+                    eval_dataloader=eval_dataloader,
+                    max_epochs=max_epochs,
+                    evaluate_every_n_epochs=1,
+                    callbacks=[checkpoint_cb],
+                )
+                expected_path = os.path.join(temp_dir, expected_last_ckpt)
+                self.assertTrue(os.path.exists(expected_path))
+                self.assertEqual(
+                    checkpoint_cb._checkpoint_manager._ckpt_paths[-1].path,
+                    expected_path,
+                )
+
     @skip_if_not_distributed
     def test_directory_sync_collective(self) -> None:
         spawn_multi_process(

torchtnt/framework/callbacks/base_checkpointer.py

@@ -16,7 +16,7 @@
 from torchtnt.framework.callback import Callback
 from torchtnt.framework.callbacks._checkpoint_utils import _get_step_phase_mapping
 from torchtnt.framework.callbacks.checkpointer_types import RestoreOptions
-from torchtnt.framework.state import State
+from torchtnt.framework.state import EntryPoint, State
 from torchtnt.framework.unit import AppStateMixin, TEvalUnit, TTrainData, TTrainUnit
 from torchtnt.utils.checkpoint import (
     BestCheckpointConfig,
@@ -25,6 +25,7 @@
     get_best_checkpoint_path,
     get_latest_checkpoint_path,
     MetricData,
+    Phase,
 )
 from torchtnt.utils.distributed import PGWrapper
 from torchtnt.utils.rank_zero_log import rank_zero_info, rank_zero_warn
@@ -177,12 +178,28 @@ def _generate_checkpoint_and_upkeep(
         if not self._checkpoint_manager.should_save_checkpoint(checkpoint_path):
             return False
 
-        # 2.1) Make sure that last checkpoint does not already exist
-        if hook == "on_train_end" and self._does_checkpoint_exist(
-            checkpoint_path, self._process_group
-        ):
-            rank_zero_warn("Final checkpoint already exists, skipping.", logger=logger)
-            return False
+        if hook == "on_train_end":
+            # 2.1) Make sure that last checkpoint does not already exist
+            if self._does_checkpoint_exist(checkpoint_path, self._process_group):
+                rank_zero_warn(
+                    "Final checkpoint already exists, skipping.", logger=logger
+                )
+                return False
+
+            # 2.2) If doing fit without eval checkpointing, only consider training progress when
+            # checking if last checkpoint exists.
+            if (
+                state.entry_point == EntryPoint.FIT
+                and self._save_every_n_eval_epochs is None
+                and self._checkpoint_manager._ckpt_paths
+                and self._checkpoint_manager._ckpt_paths[-1].step[Phase.TRAIN]
+                == cast(TTrainUnit, unit).train_progress.num_steps_completed
+            ):
+                rank_zero_info(
+                    "Omitting final checkpoint since train progress is unchanged, and eval checkpointing is not configured.",
+                    logger=logger,
+                )
+                return False
 
         # 3) try to save checkpoint
         if not self._checkpoint_impl(