[3 / 3] improvements to saving and loading callback state

pytorch_lightning/callbacks/model_checkpoint.py

@@ -265,16 +265,16 @@ def state_key(self) -> str:
             save_on_train_epoch_end=self._save_on_train_epoch_end,
         )
 
-    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        """
-        When pretrain routine starts we build the ckpt dir on the fly
-        """
-        self.__resolve_ckpt_dir(trainer)
+    def on_init_end(self, trainer: "pl.Trainer") -> None:
         if self._save_on_train_epoch_end is None:
             # if the user runs validation multiple times per training epoch, we try to save checkpoint after
             # validation instead of on train epoch end
             self._save_on_train_epoch_end = trainer.val_check_interval == 1.0
 
+    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """When pretrain routine starts we build the ckpt dir on the fly."""
+        self.__resolve_ckpt_dir(trainer)
+
     def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self._last_time_checked = time.monotonic()
 

pytorch_lightning/trainer/callback_hook.py

@@ -11,12 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 from abc import ABC
 from copy import deepcopy
 from typing import Any, Dict, List, Optional, Type, Union
 
 import torch
+from packaging.version import Version
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks import Callback
@@ -255,14 +255,14 @@ def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         if callback_states is None:
             return
 
-        current_callbacks_type = {type(cb) for cb in self.callbacks}
-        saved_callbacks_type = set(callback_states.keys())
-        difference = saved_callbacks_type.difference(current_callbacks_type)
+        is_legacy_ckpt = Version(checkpoint["pytorch-lightning_version"]) < Version("1.5.0dev")
+        current_callbacks_keys = {cb._legacy_state_key if is_legacy_ckpt else cb.state_key for cb in self.callbacks}
+        difference = callback_states.keys() - current_callbacks_keys
         if difference:
             rank_zero_warn(
-                "Be aware that when using ``resume_from_checkpoint``, "
-                "callbacks used to create the checkpoint need to be provided. "
-                f"Please, add the following callbacks: {list(difference)}. ",
+                "Be aware that when using `resume_from_checkpoint`,"
+                " callbacks used to create the checkpoint need to be provided."
+                f" Please add the following callbacks: {list(difference)}.",
                 UserWarning,
             )
 

tests/callbacks/test_callbacks.py

@@ -12,10 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from pathlib import Path
+from re import escape
 from unittest.mock import call, Mock
 
+import pytest
+
 from pytorch_lightning import Callback, Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
 from tests.helpers import BoringModel
+from tests.helpers.utils import no_warning_call
 
 
 def test_callbacks_configured_in_model(tmpdir):
@@ -132,3 +137,34 @@ def test_resume_callback_state_saved_by_type(tmpdir):
     trainer = Trainer(default_root_dir=tmpdir, max_steps=2, callbacks=[callback], resume_from_checkpoint=ckpt_path)
     trainer.fit(model)
     assert callback.state == 111
+
+
+def test_resume_incomplete_callbacks_list_warning(tmpdir):
+    model = BoringModel()
+    callback0 = ModelCheckpoint(monitor="epoch")
+    callback1 = ModelCheckpoint(monitor="global_step")
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        callbacks=[callback0, callback1],
+    )
+    trainer.fit(model)
+    ckpt_path = trainer.checkpoint_callback.best_model_path
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        callbacks=[callback1],  # one callback is missing!
+        resume_from_checkpoint=ckpt_path,
+    )
+    with pytest.warns(UserWarning, match=escape(f"Please add the following callbacks: [{repr(callback0.state_key)}]")):
+        trainer.fit(model)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        callbacks=[callback1, callback0],  # all callbacks here, order switched
+        resume_from_checkpoint=ckpt_path,
+    )
+    with no_warning_call(UserWarning, match="Please add the following callbacks:"):
+        trainer.fit(model)