Unify checkpoint load paths [redo #9693]

CHANGELOG.md

@@ -224,6 +224,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `XLACheckpointIO` plugin ([#9972](https://github.com/PyTorchLightning/pytorch-lightning/pull/9972))
 
 
+- Added `ckpt_path` argument for `trainer.fit()` ([#10061](https://github.com/PyTorchLightning/pytorch-lightning/pull/10061))
+
 
 ### Changed
 
@@ -408,6 +410,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Deprecated `GPUStatsMonitor` and `XLAStatsMonitor` in favor of `DeviceStatsMonitor` callback ([#9924](https://github.com/PyTorchLightning/pytorch-lightning/pull/9924))
 
+
+- Deprecated passing `resume_from_checkpoint` to the `Trainer` constructor in favor of `trainer.fit(ckpt_path=)` ([#10061](https://github.com/PyTorchLightning/pytorch-lightning/pull/10061))
+
+
 ### Removed
 
 - Removed deprecated `metrics` ([#8586](https://github.com/PyTorchLightning/pytorch-lightning/pull/8586/))

docs/source/advanced/advanced_gpu.rst

@@ -622,7 +622,7 @@ After training using ZeRO Stage 3, you'll notice that your checkpoints are a dir
 
 .. warning::
 
-    This single file checkpoint does not include the optimizer/lr-scheduler states. This means we cannot restore training via the `resume_from_checkpoint` Trainer argument. Ensure to keep the sharded checkpoint directory if this is required.
+    This single file checkpoint does not include the optimizer/lr-scheduler states. This means we cannot restore training via the ``trainer.fit(ckpt_path=)`` call. Ensure to keep the sharded checkpoint directory if this is required.
 
 Custom DeepSpeed Config
 """""""""""""""""""""""

docs/source/common/trainer.rst

@@ -1347,6 +1347,10 @@ By setting to False, you have to add your own distributed sampler:
 resume_from_checkpoint
 ^^^^^^^^^^^^^^^^^^^^^^
 
+.. warning:: ``resume_from_checkpoint`` is deprecated in v1.5 and will be removed in v1.7.
+    Please pass ``trainer.fit(ckpt_path="some/path/to/my_checkpoint.ckpt")`` instead.
+
+
 .. raw:: html
 
     <video width="50%" max-width="400px" controls

docs/source/common/weights_loading.rst

@@ -212,7 +212,7 @@ do the following:
 .. code-block:: python
 
    model = LitModel()
-   trainer = Trainer(resume_from_checkpoint="some/path/to/my_checkpoint.ckpt")
+   trainer = Trainer()
 
    # automatically restores model, epoch, step, LR schedulers, apex, etc...
-   trainer.fit(model)
+   trainer.fit(model, ckpt_path="some/path/to/my_checkpoint.ckpt")

docs/source/extensions/loops_advanced.rst

@@ -30,7 +30,7 @@ The two hooks :class:`~pytorch_lightning.loops.base.Loop.on_save_checkpoint` and
     def on_load_checkpoint(self, state_dict):
         self.iteration = state_dict["iteration"]
 
-When the Trainer is restarting from a checkpoint (e.g., through :code:`Trainer(resume_from_checkpoint=...)`), the loop exposes a boolean attribute :attr:`~pytorch_lightning.loops.base.Loop.restarting`.
+When the Trainer is restarting from a checkpoint (e.g., through :code:`trainer.fit(ckpt_path=...)`), the loop exposes a boolean attribute :attr:`~pytorch_lightning.loops.base.Loop.restarting`.
 Based around the value of this variable, the user can write the loop in such a way that it can restart from an arbitrary point given the state loaded from the checkpoint.
 For example, the implementation of the :meth:`~pytorch_lightning.loops.base.Loop.reset` method could look like this given our previous example:
 

pytorch_lightning/trainer/callback_hook.py

@@ -279,8 +279,8 @@ def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         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."
+                "Be aware that when using `ckpt_path`,"
+                " callbacks used to create the checkpoint need to be provided during `Trainer` instantiation."
                 f" Please add the following callbacks: {list(difference)}.",
                 UserWarning,
             )

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -38,7 +38,14 @@
 class CheckpointConnector:
     def __init__(self, trainer: "pl.Trainer", resume_from_checkpoint: Optional[_PATH] = None) -> None:
         self.trainer = trainer
-        self.resume_checkpoint_path = resume_from_checkpoint
+        self.resume_checkpoint_path: Optional[_PATH] = None
+        # TODO: remove resume_from_checkpoint_fit_path in v1.7
+        self.resume_from_checkpoint_fit_path: Optional[_PATH] = resume_from_checkpoint
+        if resume_from_checkpoint is not None:
+            rank_zero_deprecation(
+                "Setting `Trainer(resume_from_checkpoint=)` is deprecated in v1.5 and"
+                " will be removed in v1.7. Please pass `Trainer.fit(ckpt_path=)` directly instead."
+            )
         self._loaded_checkpoint: Dict[str, Any] = {}
 
     @property
@@ -53,14 +60,14 @@ def hpc_resume_path(self) -> Optional[str]:
         if os.path.exists(auto_save_checkpoint):
             return auto_save_checkpoint
 
-    def resume_start(self) -> None:
+    def resume_start(self, checkpoint_path: Optional[_PATH] = None) -> None:
         """Attempts to pre-load the checkpoint file to memory, with the source path determined in this priority:
 
         1. from HPC weights if found
-        2. from `resume_from_checkpoint` file if provided
+        2. from `checkpoint_path` file if provided
         3. don't restore
         """
-        self.resume_checkpoint_path = self.hpc_resume_path or self.resume_checkpoint_path
+        self.resume_checkpoint_path = self.hpc_resume_path or checkpoint_path
         checkpoint_path = self.resume_checkpoint_path
         if not checkpoint_path:
             return
@@ -83,8 +90,18 @@ def _load_and_validate_checkpoint(self, checkpoint_path: _PATH) -> Dict[str, Any
     def resume_end(self) -> None:
         """Signal the connector that all states have resumed and memory for the checkpoint object can be
         released."""
+        assert self.trainer.state.fn is not None
         if self.resume_checkpoint_path:
-            rank_zero_info(f"Restored all states from the checkpoint file at {self.resume_checkpoint_path}")
+            if self.trainer.state.fn == TrainerFn.FITTING:
+                rank_zero_info(f"Restored all states from the checkpoint file at {self.resume_checkpoint_path}")
+            elif self.trainer.state.fn in (TrainerFn.VALIDATING, TrainerFn.TESTING, TrainerFn.PREDICTING):
+                rank_zero_info(f"Loaded model weights from checkpoint at {self.resume_checkpoint_path}")
+        # TODO: remove resume_from_checkpoint_fit_path in v1.7
+        if (
+            self.trainer.state.fn == TrainerFn.FITTING
+            and self.resume_checkpoint_path == self.resume_from_checkpoint_fit_path
+        ):
+            self.resume_from_checkpoint_fit_path = None
         self.resume_checkpoint_path = None
         self._loaded_checkpoint = {}
 
@@ -99,16 +116,15 @@ def restore(self, checkpoint_path: Optional[_PATH] = None) -> None:
         state-restore, in this priority:
 
         1. from HPC weights if found
-        2. from `resume_from_checkpoint` file if provided
+        2. from `checkpoint_path` file if provided
         3. don't restore
 
         All restored states are listed in return value description of `dump_checkpoint`.
 
         Args:
             checkpoint_path: Path to a PyTorch Lightning checkpoint file.
         """
-        self.resume_checkpoint_path = checkpoint_path
-        self.resume_start()
+        self.resume_start(checkpoint_path)
 
         # restore module states
         self.restore_datamodule()
@@ -157,15 +173,6 @@ def restore_model(self) -> None:
                 if isinstance(module, Metric):
                     module.reset()
 
-    def restore_model_weights(self, checkpoint_path: Optional[_PATH]) -> None:
-        """Restore only the model weights."""
-        checkpoint = self._loaded_checkpoint
-        if checkpoint_path is not None:
-            checkpoint = self._load_and_validate_checkpoint(checkpoint_path)
-
-        self.trainer.lightning_module.on_load_checkpoint(checkpoint)
-        self.trainer.training_type_plugin.load_model_state_dict(checkpoint)
-
     def restore_training_state(self) -> None:
         """Restore the trainer state from the pre-loaded checkpoint.
 

pytorch_lightning/trainer/trainer.py

@@ -362,6 +362,10 @@ def __init__(
                 no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
                 training will start from the beginning of the next epoch.
 
+                .. deprecated:: v1.5
+                    ``resume_from_checkpoint`` is deprecated in v1.5 and will be removed in v1.7.
+                    Please pass the path to ``Trainer.fit(..., ckpt_path=...)`` instead.
+
             strategy: Supports different training strategies with aliases
                 as well custom training type plugins.
 
@@ -616,6 +620,7 @@ def fit(
         val_dataloaders: Optional[EVAL_DATALOADERS] = None,
         datamodule: Optional[LightningDataModule] = None,
         train_dataloader=None,  # TODO: remove with 1.6
+        ckpt_path: Optional[str] = None,
     ) -> None:
         r"""
         Runs the full optimization routine.
@@ -629,6 +634,10 @@ def fit(
 
             val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
 
+            ckpt_path: Path/URL of the checkpoint from which training is resumed. If there is
+                no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
+                training will start from the beginning of the next epoch.
+
             datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
         """
         if train_dataloader is not None:
@@ -637,14 +646,17 @@ def fit(
                 " Use `trainer.fit(train_dataloaders)` instead. HINT: added 's'"
             )
             train_dataloaders = train_dataloader
-        self._call_and_handle_interrupt(self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule)
+        self._call_and_handle_interrupt(
+            self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
+        )
 
     def _fit_impl(
         self,
         model: "pl.LightningModule",
         train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
         val_dataloaders: Optional[EVAL_DATALOADERS] = None,
         datamodule: Optional[LightningDataModule] = None,
+        ckpt_path: Optional[str] = None,
     ) -> None:
         Trainer._log_api_event("fit")
 
@@ -667,7 +679,9 @@ def _fit_impl(
             model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
         )
 
-        self._run(model)
+        # TODO: ckpt_path only in v1.7
+        ckpt_path = ckpt_path or self.resume_from_checkpoint
+        self._run(model, ckpt_path=ckpt_path)
 
         assert self.state.stopped
         self.training = False
@@ -754,7 +768,7 @@ def _validate_impl(
         )
 
         # run validate
-        results = self._run(model)
+        results = self._run(model, ckpt_path=self.validated_ckpt_path)
 
         assert self.state.stopped
         self.validating = False
@@ -844,7 +858,7 @@ def _test_impl(
         )
 
         # run test
-        results = self._run(model)
+        results = self._run(model, ckpt_path=self.tested_ckpt_path)
 
         assert self.state.stopped
         self.testing = False
@@ -927,7 +941,7 @@ def _predict_impl(
             ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
         )
 
-        results = self._run(model)
+        results = self._run(model, ckpt_path=self.predicted_ckpt_path)
 
         assert self.state.stopped
         self.predicting = False
@@ -996,24 +1010,18 @@ def tune(
 
         return result
 
-    def _restore_modules_and_callbacks(self) -> None:
-        if self.state.fn != TrainerFn.FITTING:
-            return
-
-        self.checkpoint_connector.restore_datamodule()
+    def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None) -> None:
+        # restore modules after setup
+        self.checkpoint_connector.resume_start(checkpoint_path)
         self.checkpoint_connector.restore_model()
-        # restore callback states
-        self.checkpoint_connector.restore_callbacks()
-
-    def _load_checkpoint_weights(self):
-        # only one process running at this point for TPUs, as spawn isn't triggered yet
-        # todo: move this logic internally within the barrier.
-        if not self._device_type == DeviceType.TPU:
-            self.training_type_plugin.barrier()
-        rank_zero_info(f"Loading model weights from checkpoint at {self._ckpt_path}")
-        self.checkpoint_connector.restore_model_weights(self._ckpt_path)
-
-    def _run(self, model: "pl.LightningModule") -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
+        if self.state.fn == TrainerFn.FITTING:
+            self.checkpoint_connector.restore_datamodule()
+            # restore callback states
+            self.checkpoint_connector.restore_callbacks()
+
+    def _run(
+        self, model: "pl.LightningModule", ckpt_path: Optional[str] = None
+    ) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
         # clean hparams
         if hasattr(model, "hparams"):
             parsing.clean_namespace(model.hparams)
@@ -1030,9 +1038,6 @@ def _run(self, model: "pl.LightningModule") -> Optional[Union[_EVALUATE_OUTPUT,
         self.data_connector.prepare_data()
         self.callback_connector._attach_model_callbacks()
 
-        if self._ckpt_path and not self.training_type_plugin.restore_checkpoint_after_pre_dispatch:
-            self._load_checkpoint_weights()
-
         # ----------------------------
         # SET UP TRAINING
         # ----------------------------
@@ -1041,9 +1046,8 @@ def _run(self, model: "pl.LightningModule") -> Optional[Union[_EVALUATE_OUTPUT,
         self._call_setup_hook()  # allow user to setup lightning_module in accelerator environment
 
         # check if we should delay restoring checkpoint till later
-        if not self.training_type_plugin.restore_checkpoint_after_pre_dispatch:
-            self.checkpoint_connector.resume_start()
-            self._restore_modules_and_callbacks()
+        if not self.accelerator.restore_checkpoint_after_pre_dispatch:
+            self._restore_modules_and_callbacks(ckpt_path)
 
         self._call_configure_sharded_model()  # allow user to setup in model sharded environment
         self.accelerator.setup(self)
@@ -1091,16 +1095,14 @@ def _run(self, model: "pl.LightningModule") -> Optional[Union[_EVALUATE_OUTPUT,
         # plugin will setup fitting (e.g. ddp will launch child processes)
         self._pre_dispatch()
 
-        if self.training_type_plugin.restore_checkpoint_after_pre_dispatch:
-            if self._ckpt_path:
-                self._load_checkpoint_weights()
-
-            self.checkpoint_connector.resume_start()
-            self._restore_modules_and_callbacks()
+        if self.accelerator.restore_checkpoint_after_pre_dispatch:
+            self._restore_modules_and_callbacks(ckpt_path)
 
         # restore optimizers, etc.
         self.checkpoint_connector.restore_training_state()
 
+        self.checkpoint_connector.resume_end()
+
         # dispatch `start_training` or `start_evaluating` or `start_predicting`
         self._dispatch()
 
@@ -1200,9 +1202,6 @@ def _pre_training_routine(self):
         # register signals
         self.signal_connector.register_signal_handlers()
 
-        if self.state.fn != TrainerFn.TUNING:
-            self.checkpoint_connector.resume_end()
-
         # --------------------------
         # Pre-train
         # --------------------------
@@ -1803,7 +1802,11 @@ def checkpoint_callbacks(self) -> List[ModelCheckpoint]:
 
     @property
     def resume_from_checkpoint(self) -> Optional[Union[str, Path]]:
-        return self.checkpoint_connector.resume_checkpoint_path
+        rank_zero_deprecation(
+            "`trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7."
+            " Specify the fit checkpoint path with `trainer.fit(ckpt_path=)` instead."
+        )
+        return self.checkpoint_connector.resume_from_checkpoint_fit_path
 
     def save_checkpoint(self, filepath: _PATH, weights_only: bool = False) -> None:
         self.checkpoint_connector.save_checkpoint(filepath, weights_only)
@@ -2028,15 +2031,6 @@ def _active_loop(self) -> Optional[Union[FitLoop, EvaluationLoop, PredictionLoop
         if self.predicting:
             return self.predict_loop
 
-    @property
-    def _ckpt_path(self) -> Optional[str]:
-        if self.state.fn == TrainerFn.VALIDATING:
-            return self.validated_ckpt_path
-        if self.state.fn == TrainerFn.TESTING:
-            return self.tested_ckpt_path
-        if self.state.fn == TrainerFn.PREDICTING:
-            return self.predicted_ckpt_path
-
     """
     Logging properties
     """

tests/accelerators/test_cpu.py

@@ -80,10 +80,8 @@ def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:
     assert accelerator.training_type_plugin.restore_checkpoint_after_pre_dispatch == restore_after_pre_dispatch
     assert plugin.restore_checkpoint_after_pre_dispatch == restore_after_pre_dispatch
 
-    trainer = Trainer(
-        default_root_dir=tmpdir, accelerator=accelerator, fast_dev_run=True, resume_from_checkpoint=checkpoint_path
-    )
-    trainer.fit(model)
+    trainer = Trainer(default_root_dir=tmpdir, accelerator=accelerator, fast_dev_run=True)
+    trainer.fit(model, ckpt_path=checkpoint_path)
     for func in (trainer.test, trainer.validate, trainer.predict):
         accelerator.training_type_plugin.predispatched_called = False
         func(model, ckpt_path=checkpoint_path)

tests/accelerators/test_tpu.py

@@ -62,8 +62,8 @@ def test_resume_training_on_cpu(tmpdir):
     assert weight_tensor.device == torch.device("cpu")
 
     # Verify that training is resumed on CPU
-    trainer = Trainer(resume_from_checkpoint=model_path, max_epochs=1, default_root_dir=tmpdir)
-    trainer.fit(model)
+    trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
+    trainer.fit(model, ckpt_path=model_path)
     assert trainer.state.finished, f"Training failed with {trainer.state}"