Introduce checkpoint migration (#15237)

Co-authored-by: Carlos Mocholí <[email protected]>

src/lightning_lite/utilities/device_dtype_mixin.py

@@ -12,15 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Optional, Union
+from typing import Any, List, Optional, Union
 
 import torch
 from torch.nn import Module
 from typing_extensions import Self
 
 
 class _DeviceDtypeModuleMixin(Module):
-    __jit_unused_properties__ = ["device", "dtype"]
+    __jit_unused_properties__: List[str] = ["device", "dtype"]
 
     def __init__(self) -> None:
         super().__init__()

src/pytorch_lightning/CHANGELOG.md

@@ -10,7 +10,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
--
+- Added utilities to migrate checkpoints from one Lightning version to another ([#15237](https://github.com/Lightning-AI/lightning/pull/15237))
 
 -
 
@@ -19,7 +19,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
--
+- From now on, Lightning Trainer and `LightningModule.load_from_checkpoint` automatically upgrade the loaded checkpoint if it was produced in an old version of Lightning ([#15237](https://github.com/Lightning-AI/lightning/pull/15237))
 
 -
 
@@ -57,7 +57,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [1.8.0] - 2022-11-01
 
-
 ### Added
 
 - Added support for requeueing slurm array jobs ([#15040](https://github.com/Lightning-AI/lightning/pull/15040))

src/pytorch_lightning/core/mixins/hparams_mixin.py

@@ -15,15 +15,15 @@
 import inspect
 import types
 from argparse import Namespace
-from typing import Any, MutableMapping, Optional, Sequence, Union
+from typing import Any, List, MutableMapping, Optional, Sequence, Union
 
 from pytorch_lightning.core.saving import ALLOWED_CONFIG_TYPES, PRIMITIVE_TYPES
 from pytorch_lightning.utilities.parsing import AttributeDict, save_hyperparameters
 
 
 class HyperparametersMixin:
 
-    __jit_unused_properties__ = ["hparams", "hparams_initial"]
+    __jit_unused_properties__: List[str] = ["hparams", "hparams_initial"]
 
     def __init__(self) -> None:
         super().__init__()

src/pytorch_lightning/core/module.py

@@ -75,7 +75,7 @@ class LightningModule(
 ):
     # Below is for property support of JIT
     # since none of these are important when using JIT, we are going to ignore them.
-    __jit_unused_properties__ = (
+    __jit_unused_properties__: List[str] = (
         [
             "example_input_array",
             "on_gpu",

src/pytorch_lightning/core/saving.py

@@ -20,6 +20,7 @@
 from argparse import Namespace
 from copy import deepcopy
 from enum import Enum
+from pathlib import Path
 from typing import Any, Callable, cast, Dict, IO, MutableMapping, Optional, Type, Union
 from warnings import warn
 
@@ -32,6 +33,7 @@
 from lightning_lite.utilities.types import _MAP_LOCATION_TYPE, _PATH
 from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE
 from pytorch_lightning.utilities.migration import pl_legacy_patch
+from pytorch_lightning.utilities.migration.utils import _pl_migrate_checkpoint
 from pytorch_lightning.utilities.parsing import AttributeDict, parse_class_init_keys
 from pytorch_lightning.utilities.rank_zero import rank_zero_warn
 
@@ -156,6 +158,11 @@ def _load_from_checkpoint(
     with pl_legacy_patch():
         checkpoint = pl_load(checkpoint_path, map_location=map_location)
 
+    # convert legacy checkpoints to the new format
+    checkpoint = _pl_migrate_checkpoint(
+        checkpoint, checkpoint_path=(checkpoint_path if isinstance(checkpoint_path, (str, Path)) else None)
+    )
+
     if hparams_file is not None:
         extension = str(hparams_file).split(".")[-1]
         if extension.lower() == "csv":
@@ -168,6 +175,7 @@ def _load_from_checkpoint(
         # overwrite hparams by the given file
         checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = hparams
 
+    # TODO: make this a migration:
     # for past checkpoint need to add the new key
     checkpoint.setdefault(cls.CHECKPOINT_HYPER_PARAMS_KEY, {})
     # override the hparams with values that were passed in
@@ -198,6 +206,7 @@ def _load_state(
     if cls.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
 
         if issubclass(cls, pl.LightningModule):
+            # TODO: make this a migration:
             # 1. (backward compatibility) Try to restore model hparams from checkpoint using old/past keys
             for _old_hparam_key in CHECKPOINT_PAST_HPARAMS_KEYS:
                 cls_kwargs_loaded.update(checkpoint.get(_old_hparam_key, {}))

src/pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -33,8 +33,8 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
 from pytorch_lightning.utilities.migration import pl_legacy_patch
+from pytorch_lightning.utilities.migration.utils import _pl_migrate_checkpoint
 from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
-from pytorch_lightning.utilities.upgrade_checkpoint import KEYS_MAPPING as DEPRECATED_CHECKPOINT_KEYS
 
 if _OMEGACONF_AVAILABLE:
     from omegaconf import Container
@@ -81,19 +81,9 @@ def resume_start(self, checkpoint_path: Optional[_PATH] = None) -> None:
             return
 
         rank_zero_info(f"Restoring states from the checkpoint path at {checkpoint_path}")
-        self._loaded_checkpoint = self._load_and_validate_checkpoint(checkpoint_path)
-
-    def _load_and_validate_checkpoint(self, checkpoint_path: _PATH) -> Dict[str, Any]:
         with pl_legacy_patch():
             loaded_checkpoint = self.trainer.strategy.load_checkpoint(checkpoint_path)
-        if any(key in loaded_checkpoint for key in DEPRECATED_CHECKPOINT_KEYS):
-            raise ValueError(
-                "The checkpoint you're attempting to load follows an"
-                " outdated schema. You can upgrade to the current schema by running"
-                " `python -m pytorch_lightning.utilities.upgrade_checkpoint --file model.ckpt`"
-                " where `model.ckpt` is your checkpoint file."
-            )
-        return loaded_checkpoint
+        self._loaded_checkpoint = _pl_migrate_checkpoint(loaded_checkpoint, checkpoint_path)
 
     def _set_ckpt_path(
         self, state_fn: TrainerFn, ckpt_path: Optional[str], model_provided: bool, model_connected: bool

src/pytorch_lightning/utilities/migration/__init__.py

@@ -0,0 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 pytorch_lightning.utilities.migration.utils import migrate_checkpoint  # noqa: F401
+from pytorch_lightning.utilities.migration.utils import pl_legacy_patch  # noqa: F401

src/pytorch_lightning/utilities/migration/migration.py

@@ -0,0 +1,69 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Contains migration functions to upgrade legacy checkpoints to the format of the current Lightning version.
+
+When Lightning loads a checkpoint, these migrations will be applied on the loaded checkpoint dictionary sequentially,
+see :func:`~pytorch_lightning.utilities.migration.utils.migrate_checkpoint`.
+
+For the Lightning developer: How to add a new migration?
+
+1. Create a new function with a descriptive name and docstring that explains the details of this migration. Include
+   version information as well as the specific commit or PR where the breaking change happened.
+2. Add the function to the `_migration_index()` below. The key in the index is the version of Lightning in which the
+   change happened. Any checkpoint with a version greater or equal to that version will apply the given function.
+   Multiple migrations per version get executed in the provided list order.
+3. You can test the migration on a checkpoint (backup your files first) by running:
+
+   cp model.ckpt model.ckpt.backup
+   python -m pytorch_lightning.utilities.upgrade_checkpoint --file model.ckpt
+"""
+
+from typing import Any, Callable, Dict, List
+
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
+
+_CHECKPOINT = Dict[str, Any]
+
+
+def _migration_index() -> Dict[str, List[Callable[[_CHECKPOINT], _CHECKPOINT]]]:
+    """Migration functions returned here will get executed in the order they are listed."""
+    return {
+        "0.10.0": [_migrate_model_checkpoint_early_stopping],
+    }
+
+
+def _migrate_model_checkpoint_early_stopping(checkpoint: _CHECKPOINT) -> _CHECKPOINT:
+    """The checkpoint and early stopping keys were renamed.
+
+    Version: 0.10.0
+    Commit: a5d1176
+    """
+    keys_mapping = {
+        "checkpoint_callback_best_model_score": (ModelCheckpoint, "best_model_score"),
+        "checkpoint_callback_best_model_path": (ModelCheckpoint, "best_model_path"),
+        "checkpoint_callback_best": (ModelCheckpoint, "best_model_score"),
+        "early_stop_callback_wait": (EarlyStopping, "wait_count"),
+        "early_stop_callback_patience": (EarlyStopping, "patience"),
+    }
+    checkpoint["callbacks"] = checkpoint.get("callbacks") or {}
+
+    for key, new_path in keys_mapping.items():
+        if key in checkpoint:
+            value = checkpoint[key]
+            callback_type, callback_key = new_path
+            checkpoint["callbacks"][callback_type] = checkpoint["callbacks"].get(callback_type) or {}
+            checkpoint["callbacks"][callback_type][callback_key] = value
+            del checkpoint[key]
+    return checkpoint