Add tracking of basic states in Trainer [wip - to-be-merged after v0.…

…9] (#2541)

* Add initial tracking of states in Trainer.

* Add INTERRUPTED state, improve tests, move state switching from callback to a trainer.

* Move part of a trainer state switching to a decorator.

* Add documentation.

* Fix docs, rename state enum, restore state to previous on exit if None, add tests for decorator only.

* Fix callback typing.

Co-authored-by: William Falcon <waf2107@columbia.edu>

pytorch_lightning/trainer/states.py

@@ -0,0 +1,49 @@
+from enum import Enum
+from functools import wraps
+from typing import Callable, Optional
+
+import pytorch_lightning
+
+
+class TrainerState(Enum):
+    """ State which is set in the :class:`~pytorch_lightning.trainer.trainer.Trainer`
+    to indicate what is currently or was executed. """
+    INITIALIZING = 'INITIALIZING'
+    RUNNING = 'RUNNING'
+    FINISHED = 'FINISHED'
+    INTERRUPTED = 'INTERRUPTED'
+
+
+def trainer_state(*, entering: Optional[TrainerState] = None, exiting: Optional[TrainerState] = None) -> Callable:
+    """ Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods
+    which changes state to `entering` before the function execution and `exiting`
+    after the function is executed. If `None` is passed to `entering`, the state is not changed.
+    If `None` is passed to `exiting`, the state is restored to the state before function execution.
+    If `INTERRUPTED` state is set inside a run function, the state remains `INTERRUPTED`.
+    """
+
+    def wrapper(fn) -> Callable:
+        @wraps(fn)
+        def wrapped_fn(self, *args, **kwargs):
+            if not isinstance(self, pytorch_lightning.Trainer):
+                return fn(self, *args, **kwargs)
+
+            state_before = self.state
+            if entering is not None:
+                self.state = entering
+            result = fn(self, *args, **kwargs)
+
+            # The INTERRUPTED state can be set inside the run function. To indicate that run was interrupted
+            # we retain INTERRUPTED state
+            if self.state == TrainerState.INTERRUPTED:
+                return result
+
+            if exiting is not None:
+                self.state = exiting
+            else:
+                self.state = state_before
+            return result
+
+        return wrapped_fn
+
+    return wrapper

pytorch_lightning/trainer/trainer.py

@@ -45,6 +45,7 @@
 from pytorch_lightning.trainer.lr_finder import TrainerLRFinderMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
 from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
+from pytorch_lightning.trainer.states import TrainerState, trainer_state
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.trainer.training_io import TrainerIOMixin
 from pytorch_lightning.trainer.training_loop import TrainerTrainLoopMixin
@@ -395,6 +396,7 @@ def __init__(
         self.interrupted = False
         self.should_stop = False
         self.running_sanity_check = False
+        self.state = TrainerState.INITIALIZING
 
         self._default_root_dir = default_root_dir or os.getcwd()
         self._weights_save_path = weights_save_path or self._default_root_dir
@@ -888,6 +890,7 @@ def weights_save_path(self) -> str:
     # -----------------------------
     # MODEL TRAINING
     # -----------------------------
+    @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)
     def fit(
         self,
         model: LightningModule,
@@ -1240,6 +1243,7 @@ def _run_sanity_check(self, ref_model, model):
             self.on_sanity_check_end()
             self.running_sanity_check = False
 
+    @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)
     def test(
         self,
         model: Optional[LightningModule] = None,

pytorch_lightning/trainer/training_loop.py

@@ -174,6 +174,7 @@ def training_step(self, batch, batch_idx):
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.core.step_result import EvalResult, Result
 from pytorch_lightning.loggers import LightningLoggerBase
+from pytorch_lightning.trainer.states import TrainerState
 from pytorch_lightning.trainer.supporters import TensorRunningAccum, Accumulator
 from pytorch_lightning.utilities import rank_zero_warn, AMPType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -253,6 +254,7 @@ class TrainerTrainLoopMixin(ABC):
     terminate_on_nan: bool
     tpu_id: int
     interactive_ddp_procs: ...
+    state: TrainerState
     amp_type: AMPType
     on_tpu: bool
 
@@ -418,6 +420,7 @@ def train(self):
             # user could press ctrl+c many times... only shutdown once
             if not self.interrupted:
                 self.interrupted = True
+                self.state = TrainerState.INTERRUPTED
                 self.on_keyboard_interrupt()
 
                 self.run_training_teardown()

tests/trainer/test_states.py

@@ -0,0 +1,210 @@
+import pytest
+
+from pytorch_lightning import Trainer, Callback
+from pytorch_lightning.trainer.states import TrainerState, trainer_state
+from tests.base import EvalModelTemplate
+
+
+class StateSnapshotCallback(Callback):
+    """ Allows to shapshot the state inside a particular trainer method. """
+
+    def __init__(self, snapshot_method: str):
+        super().__init__()
+        assert snapshot_method in ['on_batch_start', 'on_test_batch_start']
+        self.snapshot_method = snapshot_method
+        self.trainer_state = None
+
+    def on_batch_start(self, trainer, pl_module):
+        if self.snapshot_method == 'on_batch_start':
+            self.trainer_state = trainer.state
+
+    def on_test_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+        if self.snapshot_method == 'on_test_batch_start':
+            self.trainer_state = trainer.state
+
+
+def test_state_decorator_nothing_passed(tmpdir):
+    """ Test that state is not changed if nothing is passed to a decorator"""
+
+    @trainer_state()
+    def test_method(self):
+        return self.state
+
+    trainer = Trainer(default_root_dir=tmpdir)
+    trainer.state = TrainerState.INITIALIZING
+
+    snapshot_state = test_method(trainer)
+
+    assert snapshot_state == TrainerState.INITIALIZING
+    assert trainer.state == TrainerState.INITIALIZING
+
+
+def test_state_decorator_entering_only(tmpdir):
+    """ Tests that state is set to entering inside a run function and restored to the previous value after. """
+
+    @trainer_state(entering=TrainerState.RUNNING)
+    def test_method(self):
+        return self.state
+
+    trainer = Trainer(default_root_dir=tmpdir)
+    trainer.state = TrainerState.INITIALIZING
+
+    snapshot_state = test_method(trainer)
+
+    assert snapshot_state == TrainerState.RUNNING
+    assert trainer.state == TrainerState.INITIALIZING
+
+
+def test_state_decorator_exiting_only(tmpdir):
+    """ Tests that state is not changed inside a run function and set to `exiting` after. """
+
+    @trainer_state(exiting=TrainerState.FINISHED)
+    def test_method(self):
+        return self.state
+
+    trainer = Trainer(default_root_dir=tmpdir)
+    trainer.state = TrainerState.INITIALIZING
+
+    snapshot_state = test_method(trainer)
+
+    assert snapshot_state == TrainerState.INITIALIZING
+    assert trainer.state == TrainerState.FINISHED
+
+
+def test_state_decorator_entering_and_exiting(tmpdir):
+    """ Tests that state is set to `entering` inside a run function and set ot `exiting` after. """
+
+    @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)
+    def test_method(self):
+        return self.state
+
+    trainer = Trainer(default_root_dir=tmpdir)
+    trainer.state = TrainerState.INITIALIZING
+
+    snapshot_state = test_method(trainer)
+
+    assert snapshot_state == TrainerState.RUNNING
+    assert trainer.state == TrainerState.FINISHED
+
+
+def test_state_decorator_interrupt(tmpdir):
+    """ Tests that state remains `INTERRUPTED` is its set in run function. """
+
+    @trainer_state(exiting=TrainerState.FINISHED)
+    def test_method(self):
+        self.state = TrainerState.INTERRUPTED
+
+    trainer = Trainer(default_root_dir=tmpdir)
+    trainer.state = TrainerState.INITIALIZING
+
+    test_method(trainer)
+    assert trainer.state == TrainerState.INTERRUPTED
+
+
+def test_initialize_state(tmpdir):
+    """ Tests that state is INITIALIZE after Trainer creation """
+    trainer = Trainer(default_root_dir=tmpdir)
+    assert trainer.state == TrainerState.INITIALIZING
+
+
+@pytest.mark.parametrize("extra_params", [
+    pytest.param(dict(fast_dev_run=True), id='Fast-Run'),
+    pytest.param(dict(max_steps=1), id='Single-Step'),
+])
+def test_running_state_during_fit(tmpdir, extra_params):
+    """ Tests that state is set to RUNNING during fit """
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    snapshot_callback = StateSnapshotCallback(snapshot_method='on_batch_start')
+
+    trainer = Trainer(
+        callbacks=[snapshot_callback],
+        default_root_dir=tmpdir,
+        **extra_params
+    )
+
+    trainer.fit(model)
+
+    assert snapshot_callback.trainer_state == TrainerState.RUNNING
+
+
+@pytest.mark.parametrize("extra_params", [
+    pytest.param(dict(fast_dev_run=True), id='Fast-Run'),
+    pytest.param(dict(max_steps=1), id='Single-Step'),
+])
+def test_finished_state_after_fit(tmpdir, extra_params):
+    """ Tests that state is FINISHED after fit """
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        **extra_params
+    )
+
+    trainer.fit(model)
+
+    assert trainer.state == TrainerState.FINISHED
+
+
+def test_running_state_during_test(tmpdir):
+    """ Tests that state is set to RUNNING during test """
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    snapshot_callback = StateSnapshotCallback(snapshot_method='on_test_batch_start')
+
+    trainer = Trainer(
+        callbacks=[snapshot_callback],
+        default_root_dir=tmpdir,
+        fast_dev_run=True,
+    )
+
+    trainer.test(model)
+
+    assert snapshot_callback.trainer_state == TrainerState.RUNNING
+
+
+def test_finished_state_after_test(tmpdir):
+    """ Tests that state is FINISHED after fit """
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        fast_dev_run=True,
+    )
+
+    trainer.test(model)
+
+    assert trainer.state == TrainerState.FINISHED
+
+
+@pytest.mark.parametrize("extra_params", [
+    pytest.param(dict(fast_dev_run=True), id='Fast-Run'),
+    pytest.param(dict(max_steps=1), id='Single-Step'),
+])
+def test_interrupt_state_on_keyboard_interrupt(tmpdir, extra_params):
+    """ Tests that state is set to INTERRUPTED on KeyboardInterrupt """
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    class InterruptCallback(Callback):
+        def __init__(self):
+            super().__init__()
+
+        def on_batch_start(self, trainer, pl_module):
+            raise KeyboardInterrupt
+
+    trainer = Trainer(
+        callbacks=[InterruptCallback()],
+        default_root_dir=tmpdir,
+        **extra_params
+    )
+
+    trainer.fit(model)
+
+    assert trainer.state == TrainerState.INTERRUPTED