deprecate hpc_load() and integrate it with restore()

CHANGELOG.md

@@ -186,6 +186,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated default value of `monitor` argument in EarlyStopping callback to enforce `monitor` as a required argument ([#7907](https://github.com/PyTorchLightning/pytorch-lightning/pull/7907))
 
 
+- Deprecated the use of `CheckpointConnector.hpc_load()` in favor of `CheckpointConnector.restore()` ([#7652](https://github.com/PyTorchLightning/pytorch-lightning/pull/7652))
+
+
 ### Removed
 
 - Removed `ProfilerConnector` ([#7654](https://github.com/PyTorchLightning/pytorch-lightning/pull/7654))

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -21,9 +21,14 @@
 
 import pytorch_lightning
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, DeviceType, rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities import (
+    _OMEGACONF_AVAILABLE,
+    DeviceType,
+    rank_zero_deprecation,
+    rank_zero_info,
+    rank_zero_warn,
+)
 from pytorch_lightning.utilities.cloud_io import atomic_save, get_filesystem
-from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.upgrade_checkpoint import KEYS_MAPPING as DEPRECATED_CHECKPOINT_KEYS
 
@@ -45,7 +50,7 @@ def hpc_resume_path(self) -> Optional[str]:
         dir_path_hpc = str(self.trainer.weights_save_path)
         max_version = self.max_ckpt_version_in_folder(dir_path_hpc, "hpc_ckpt_")
         if max_version is not None:
-            return f"{dir_path_hpc}/hpc_ckpt_{max_version}.ckpt"
+            return os.path.join(dir_path_hpc, f"hpc_ckpt_{max_version}.ckpt")
 
     def resume_start(self) -> None:
         """
@@ -129,6 +134,10 @@ def restore_model_state(self, model: LightningModule, checkpoint) -> None:
         # hook: give user access to checkpoint if needed.
         model.on_load_checkpoint(checkpoint)
 
+        # call hpc specific hook
+        if self.hpc_resume_path is not None:
+            model.on_hpc_load(self._loaded_checkpoint)
+
         # restore model state_dict
         self.trainer.training_type_plugin.load_model_state_dict(checkpoint)
 
@@ -248,6 +257,7 @@ def restore_lr_schedulers(self) -> None:
     # ----------------------------------
     # PRIVATE OPS
     # ----------------------------------
+
     def hpc_save(self, folderpath: str, logger):
         # make sure the checkpoint folder exists
         folderpath = str(folderpath)  # because the tests pass a path object
@@ -365,29 +375,18 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
 
         return checkpoint
 
-    def hpc_load(self, checkpoint_path: str, on_gpu: bool):
-        """
-        Load model/training states from a 'PyTorch-Lightning checkpoint' file for hpc.
-        All restored states are listed in return value description of `dump_checkpoint`.
+    def hpc_load(self, checkpoint_path: str) -> None:
         """
+        Attempts to restore the full training and model state from a HPC checkpoint file.
 
-        # read a checkpoint dictionary object from the 'PyTorch-Lightning checkpoint' file at `checkpoint_path`
-        checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)
-
-        # acquire the model
-        model = self.trainer.lightning_module
-
-        # restore model and datamodule state
-        self.restore_model_state(model, checkpoint)
-
-        if self.trainer.root_gpu is not None:
-            model.cuda(self.trainer.root_gpu)
-
-        # restore training state
-        self.restore_training_state(checkpoint)
-
-        # call hpc specific hook
-        model.on_hpc_load(checkpoint)
+        .. deprecated::v1.4
+            Will be removed in v1.6. Use :meth:`restore` instead.
+        """
+        rank_zero_deprecation(
+            "`CheckpointConnector.hpc_load()` was deprecated in v1.4 and will be removed in v1.6."
+            " Use `CheckpointConnector.restore()` instead."
+        )
+        self.restore(checkpoint_path)
 
     def max_ckpt_version_in_folder(self, dir_path: Union[str, Path], name_key: str = 'ckpt_') -> Optional[int]:
         """List up files in `dir_path` with `name_key`, then yield maximum suffix number.

tests/deprecated_api/test_remove_1-4.py

@@ -66,3 +66,16 @@ def training_step(self, batch, batch_idx):
 
     with pytest.deprecated_call(match=r"Relying on.*is deprecated in v1.2 and will be removed in v1.4"):
         trainer.fit(TestModel())
+
+
+def test_v1_4_0_deprecated_hpc_load(tmpdir):
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+    )
+    trainer.fit(model)
+    trainer.checkpoint_connector.hpc_save(tmpdir, trainer.logger)
+    checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder(str(tmpdir))
+    with pytest.deprecated_call(match=r"`CheckpointConnector.hpc_load\(\)` was deprecated in v1.4"):
+        trainer.checkpoint_connector.hpc_load(checkpoint_path)

tests/helpers/pipelines.py

@@ -91,7 +91,7 @@ def run_model_test(
         trainer.checkpoint_connector.hpc_save(save_dir, logger)
         # test HPC loading
         checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder(save_dir)
-        trainer.checkpoint_connector.hpc_load(checkpoint_path, on_gpu=on_gpu)
+        trainer.checkpoint_connector.restore(checkpoint_path)
 
 
 @torch.no_grad()

tests/models/data/horovod/train_default_model.py

@@ -87,7 +87,7 @@ def training_epoch_end(self, outputs) -> None:
     trainer.checkpoint_connector.hpc_save(ckpt_path, trainer.logger)
     # test HPC loading
     checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder(ckpt_path)
-    trainer.checkpoint_connector.hpc_load(checkpoint_path, on_gpu=on_gpu)
+    trainer.checkpoint_connector.restore(checkpoint_path)
 
     if on_gpu:
         trainer = Trainer(gpus=1, accelerator='horovod', max_epochs=1)

tests/trainer/connectors/test_callback_connector.py

@@ -1,3 +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.
 import logging
 from unittest.mock import Mock
 

tests/trainer/connectors/test_checkpoint_connector.py

@@ -0,0 +1,155 @@
+# 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.
+import os
+from unittest.mock import Mock
+
+import torch
+
+from pytorch_lightning import Trainer
+from tests.helpers import BoringModel
+
+
+class HPCHookdedModel(BoringModel):
+
+    def __init__(self):
+        super().__init__()
+        self.hpc_save_called = 0
+        self.hpc_load_called = 0
+
+    def on_hpc_save(self, checkpoint):
+        assert "state_dict" in checkpoint
+        self.hpc_save_called += 1
+
+    def on_hpc_load(self, checkpoint):
+        assert "state_dict" in checkpoint
+        self.hpc_load_called += 1
+
+
+def test_hpc_hook_calls(tmpdir):
+    model = HPCHookdedModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        checkpoint_callback=False,
+        logger=False,
+    )
+    trainer.fit(model)
+    connector = trainer.checkpoint_connector
+    connector.hpc_save(tmpdir, logger=Mock())
+    assert model.hpc_save_called == 1
+    assert model.hpc_load_called == 0
+
+    # new training run, restore from hpc checkpoint file automatically
+    assert set(os.listdir(tmpdir)) == {"hpc_ckpt_1.ckpt"}
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        checkpoint_callback=False,
+        logger=False,
+    )
+    trainer.fit(model)
+    assert model.hpc_save_called == 1
+    assert model.hpc_load_called == 1
+
+
+def test_preloaded_checkpoint_lifecycle(tmpdir):
+    """ Tests that the preloaded checkpoint contents gets cleared from memory when it is not required anymore. """
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+    )
+    trainer.fit(model)
+
+    connector = trainer.checkpoint_connector
+
+    assert not trainer.resume_from_checkpoint
+    assert not connector.resume_checkpoint_path
+    assert not connector._loaded_checkpoint
+
+    connector.resume_start()
+    assert not connector.resume_checkpoint_path
+    assert not connector._loaded_checkpoint
+    connector.resume_end()
+    assert not connector.resume_checkpoint_path
+    assert not connector._loaded_checkpoint
+
+    ckpt_path = trainer.checkpoint_callback.best_model_path
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, resume_from_checkpoint=ckpt_path)
+    connector = trainer.checkpoint_connector
+    connector.resume_start()
+    assert connector.resume_checkpoint_path == ckpt_path
+    assert connector._loaded_checkpoint
+    assert isinstance(connector._loaded_checkpoint, dict)
+    connector.resume_end()
+    assert not connector.resume_checkpoint_path
+    assert not connector._loaded_checkpoint
+
+
+def test_hpc_restore_attempt(tmpdir):
+    """ Test that restore() attempts to restore the hpc_ckpt with highest priority. """
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        checkpoint_callback=False,
+        logger=False,
+    )
+    trainer.fit(model)
+
+    hpc_ckpt_path = tmpdir / "hpc_ckpt_3.ckpt"
+    trainer.save_checkpoint(hpc_ckpt_path)
+    assert os.listdir(tmpdir) == ["hpc_ckpt_3.ckpt"]
+
+    # set weights to zero
+    for param in model.parameters():
+        torch.nn.init.constant_(param, 0)
+
+    # case 1: restore hpc first, no explicit resume path provided
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=2,
+        checkpoint_callback=False,
+        logger=False,
+    )
+    trainer.fit(model)
+
+    for param in model.parameters():
+        assert param.abs().sum() > 0
+        torch.nn.init.constant_(param, 0)
+
+    # case 2: explicit resume path provided, restore hpc anyway
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=3, resume_from_checkpoint="not existing")
+    trainer.fit(model)
+
+    for param in model.parameters():
+        assert param.abs().sum() > 0
+
+
+def test_hpc_max_ckpt_version(tmpdir):
+    """ Test that the CheckpointConnector is able to find the hpc checkpoint file with the highest version. """
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+    )
+    trainer.fit(model)
+    trainer.save_checkpoint(tmpdir / "hpc_ckpt.ckpt")
+    trainer.save_checkpoint(tmpdir / "hpc_ckpt_0.ckpt")
+    trainer.save_checkpoint(tmpdir / "hpc_ckpt_3.ckpt")
+    trainer.save_checkpoint(tmpdir / "hpc_ckpt_33.ckpt")
+
+    assert trainer.checkpoint_connector.hpc_resume_path == str(tmpdir / "hpc_ckpt_33.ckpt")
+    assert trainer.checkpoint_connector.max_ckpt_version_in_folder(tmpdir) == 33
+    assert trainer.checkpoint_connector.max_ckpt_version_in_folder(tmpdir / "not" / "existing") is None