Conditionally include also train_dataloader in fit-eval checkpoints (#…

…918)

Summary: Pull Request resolved: #918

Differential Revision: D63919225

tests/framework/callbacks/test_checkpoint_utils.py

@@ -16,6 +16,7 @@
 from torchtnt.framework._test_utils import (
     DummyAutoUnit,
     DummyEvalUnit,
+    DummyFitUnit,
     DummyMeanMetric,
     DummyTrainUnit,
     generate_dummy_stateful_dataloader,
@@ -85,6 +86,33 @@ def test_get_app_state(self) -> None:
             ],
         )
 
+        # Test evaluate intra-epoch within train epoch on FIT (evaluate_every_n_steps)
+        my_unit = DummyFitUnit(input_dim=2)
+        my_unit.train_progress.increment_step()  # Simulate at least one step in each phase
+        my_unit.eval_progress.increment_step()
+
+        state = get_dummy_fit_state()
+        state._active_phase = ActivePhase.EVALUATE
+
+        train_dl = generate_dummy_stateful_dataloader(1, 1, 1)
+        eval_dl = generate_dummy_stateful_dataloader(1, 1, 1)
+        none_throws(state.train_state)._dataloader = train_dl
+        none_throws(state.eval_state)._dataloader = eval_dl
+
+        app_state = _prepare_app_state_for_checkpoint(state, my_unit, intra_epoch=True)
+        self.assertCountEqual(
+            app_state.keys(),
+            [
+                "module",
+                "optimizer",
+                "loss_fn",
+                "train_progress",
+                "eval_progress",
+                "train_dataloader",
+                "eval_dataloader",
+            ],
+        )
+
     def test_get_step_phase_mapping(self) -> None:
         unit = DummyAutoUnit(module=nn.Linear(2, 2))
         unit.train_progress._num_steps_completed = 5

torchtnt/framework/callbacks/_checkpoint_utils.py

@@ -9,8 +9,10 @@
 
 from typing import Any, cast, Dict, Union
 
+from pyre_extensions import none_throws
+
 from torchtnt.framework.callbacks.checkpointer_types import RestoreOptions
-from torchtnt.framework.state import EntryPoint, State
+from torchtnt.framework.state import ActivePhase, EntryPoint, State
 from torchtnt.framework.unit import AppStateMixin, TEvalUnit, TPredictUnit, TTrainUnit
 from torchtnt.utils.checkpoint import Phase
 
@@ -123,13 +125,27 @@ def _prepare_app_state_for_checkpoint(
             remove_lr_schedulers=True,
         )
 
+    if not intra_epoch:
+        return app_state
+
     # for intra-epoch checkpointing, include dataloader state of the current phase
-    phase_dl = state.active_phase_state().dataloader
-    if intra_epoch and isinstance(phase_dl, Stateful):
-        dataloader_state_key = _PHASE_DL_STATE_KEY_MAPPING[
-            state.active_phase.into_phase()
-        ]
-        app_state[dataloader_state_key] = phase_dl
+    active_dataloaders = {state.active_phase: state.active_phase_state().dataloader}
+
+    # Special case for FIT where eval is executed every n steps. We also need to save
+    # the train dataloader state. In this case, train epoch wouldn't be incremented yet.
+    if (
+        state.entry_point == EntryPoint.FIT
+        and state.active_phase == ActivePhase.EVALUATE
+        and cast(TTrainUnit, unit).train_progress.num_steps_completed_in_epoch != 0
+    ):
+        active_dataloaders[ActivePhase.TRAIN] = none_throws(
+            state.train_state
+        ).dataloader
+
+    for active_phase, dl in active_dataloaders.items():
+        if isinstance(dl, Stateful):
+            dl_key = _PHASE_DL_STATE_KEY_MAPPING[active_phase.into_phase()]
+            app_state[dl_key] = dl
 
     return app_state