[feat] Add support for schedulers

CHANGELOG.md

@@ -15,6 +15,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Switch to use `torchmetrics` ([#169](https://github.com/PyTorchLightning/lightning-flash/pull/169))
 
+- Better support for `optimizer` and `schedulers` ([#232](https://github.com/PyTorchLightning/lightning-flash/pull/232))
+
+
 
 ### Fixed
 
@@ -28,7 +31,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 - Added `RetinaNet` & `backbones` to `ObjectDetector` Task ([#121](https://github.com/PyTorchLightning/lightning-flash/pull/121))
-- Added .csv image loading utils ([#116](https://github.com/PyTorchLightning/lightning-flash/pull/116), 
+- Added .csv image loading utils ([#116](https://github.com/PyTorchLightning/lightning-flash/pull/116),
     [#117](https://github.com/PyTorchLightning/lightning-flash/pull/117),
     [#118](https://github.com/PyTorchLightning/lightning-flash/pull/118))
 

flash/core/model.py

@@ -21,9 +21,13 @@
 from pytorch_lightning import LightningModule
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.trainer.states import RunningStage
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import nn
+from torch.optim.lr_scheduler import _LRScheduler
+from torch.optim.optimizer import Optimizer
 
 from flash.core.registry import FlashRegistry
+from flash.core.schedulers import _SCHEDULERS_REGISTRY
 from flash.core.utils import get_callable_dict
 from flash.data.data_pipeline import DataPipeline, Postprocess, Preprocess
 
@@ -64,11 +68,16 @@ class Task(LightningModule):
         postprocess: :class:`~flash.data.process.Postprocess` to use as the default for this task.
     """
 
+    schedulers: FlashRegistry = _SCHEDULERS_REGISTRY
+
     def __init__(
         self,
         model: Optional[nn.Module] = None,
         loss_fn: Optional[Union[Callable, Mapping, Sequence]] = None,
-        optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam,
+        optimizer: Union[Type[torch.optim.Optimizer], torch.optim.Optimizer] = torch.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+        scheduler: Optional[Union[Type[_LRScheduler], str, _LRScheduler]] = None,
+        scheduler_kwargs: Optional[Dict[str, Any]] = None,
         metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
         learning_rate: float = 5e-5,
         preprocess: Preprocess = None,
@@ -78,7 +87,11 @@ def __init__(
         if model is not None:
             self.model = model
         self.loss_fn = {} if loss_fn is None else get_callable_dict(loss_fn)
-        self.optimizer_cls = optimizer
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.optimizer_kwargs = optimizer_kwargs or {}
+        self.scheduler_kwargs = scheduler_kwargs or {}
+
         self.metrics = nn.ModuleDict({} if metrics is None else get_callable_dict(metrics))
         self.learning_rate = learning_rate
         # TODO: should we save more? Bug on some regarding yaml if we save metrics
@@ -168,8 +181,14 @@ def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> A
             batch = torch.stack(batch)
         return self(batch)
 
-    def configure_optimizers(self) -> torch.optim.Optimizer:
-        return self.optimizer_cls(filter(lambda p: p.requires_grad, self.parameters()), lr=self.learning_rate)
+    def configure_optimizers(self) -> Union[Optimizer, Tuple[List[Optimizer], List[_LRScheduler]]]:
+        optimizer = self.optimizer
+        if not isinstance(self.optimizer, Optimizer):
+            self.optimizer_kwargs["lr"] = self.learning_rate
+            optimizer = optimizer(filter(lambda p: p.requires_grad, self.parameters()), **self.optimizer_kwargs)
+        if self.scheduler:
+            return [optimizer], [self._instantiate_scheduler(optimizer)]
+        return optimizer
 
     def configure_finetune_callback(self) -> List[Callback]:
         return []
@@ -323,3 +342,63 @@ def available_models(cls) -> List[str]:
         if registry is None:
             return []
         return registry.available_keys()
+
+    @classmethod
+    def available_schedulers(cls) -> List[str]:
+        registry: Optional[FlashRegistry] = getattr(cls, "schedulers", None)
+        if registry is None:
+            return []
+        return registry.available_keys()
+
+    def get_num_training_steps(self) -> int:
+        """Total training steps inferred from datamodule and devices."""
+        if not getattr(self, "trainer", None):
+            raise MisconfigurationException("The LightningModule isn't attached to the trainer yet.")
+        if isinstance(self.trainer.limit_train_batches, int) and self.trainer.limit_train_batches != 0:
+            dataset_size = self.trainer.limit_train_batches
+        elif isinstance(self.trainer.limit_train_batches, float):
+            # limit_train_batches is a percentage of batches
+            dataset_size = len(self.train_dataloader())
+            dataset_size = int(dataset_size * self.trainer.limit_train_batches)
+        else:
+            dataset_size = len(self.train_dataloader())
+
+        num_devices = max(1, self.trainer.num_gpus, self.trainer.num_processes)
+        if self.trainer.tpu_cores:
+            num_devices = max(num_devices, self.trainer.tpu_cores)
+
+        effective_batch_size = self.trainer.accumulate_grad_batches * num_devices
+        max_estimated_steps = (dataset_size // effective_batch_size) * self.trainer.max_epochs
+
+        if self.trainer.max_steps and self.trainer.max_steps < max_estimated_steps:
+            return self.trainer.max_steps
+        return max_estimated_steps
+
+    def _compute_warmup(self, num_training_steps: int, num_warmup_steps: Union[int, float]) -> int:
+        if not isinstance(num_warmup_steps, float) or (num_warmup_steps > 1 or num_warmup_steps < 0):
+            raise MisconfigurationException(
+                "`num_warmup_steps` should be provided as float between 0 and 1 in `scheduler_kwargs`"
+            )
+        if isinstance(num_warmup_steps, float):
+            # Convert float values to percentage of training steps to use as warmup
+            num_warmup_steps *= num_training_steps
+        return round(num_warmup_steps)
+
+    def _instantiate_scheduler(self, optimizer: Optimizer) -> _LRScheduler:
+        scheduler = self.scheduler
+        if isinstance(scheduler, _LRScheduler):
+            return scheduler
+        if isinstance(scheduler, str):
+            scheduler_fn = self.schedulers.get(self.scheduler)
+            num_training_steps: int = self.get_num_training_steps()
+            num_warmup_steps: int = self._compute_warmup(
+                num_training_steps=num_training_steps,
+                num_warmup_steps=self.scheduler_kwargs.get("num_warmup_steps"),
+            )
+            return scheduler_fn(optimizer, num_warmup_steps, num_training_steps)
+        elif issubclass(scheduler, _LRScheduler):
+            return scheduler(optimizer, **self.scheduler_kwargs)
+        raise MisconfigurationException(
+            "scheduler can be a scheduler, a scheduler type with `scheduler_kwargs` "
+            f"or a built-in scheduler in {self.available_schedulers()}"
+        )

flash/core/schedulers.py

@@ -0,0 +1,14 @@
+from typing import Callable, List
+
+from flash.core.registry import FlashRegistry
+from flash.utils.imports import _TRANSFORMERS_AVAILABLE
+
+_SCHEDULERS_REGISTRY = FlashRegistry("scheduler")
+
+if _TRANSFORMERS_AVAILABLE:
+    from transformers import optimization
+    functions: List[Callable] = [
+        getattr(optimization, n) for n in dir(optimization) if ("get_" in n and n != 'get_scheduler')
+    ]
+    for fn in functions:
+        _SCHEDULERS_REGISTRY(fn, name=fn.__name__[4:])

flash/data/data_pipeline.py

@@ -16,6 +16,7 @@
 import weakref
 from typing import Any, Callable, Dict, Iterable, Optional, Sequence, Set, Tuple, Type, TYPE_CHECKING, Union
 
+import torch
 from pytorch_lightning.trainer.connectors.data_connector import _PatchDataLoader
 from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.utilities import imports
@@ -285,6 +286,8 @@ def _set_loader(model: 'Task', loader_name: str, new_loader: DataLoader) -> None
     def _attach_preprocess_to_model(
         self, model: 'Task', stage: Optional[RunningStage] = None, device_transform_only: bool = False
     ) -> None:
+        device_collate_fn = torch.nn.Identity()
+
         if not stage:
             stages = [RunningStage.TRAINING, RunningStage.VALIDATING, RunningStage.TESTING, RunningStage.PREDICTING]
 

flash/utils/imports.py

@@ -5,3 +5,4 @@
 _COCO_AVAILABLE = _module_available("pycocotools")
 _TIMM_AVAILABLE = _module_available("timm")
 _TORCHVISION_AVAILABLE = _module_available("torchvision")
+_TRANSFORMERS_AVAILABLE = _module_available("transformers")

tests/core/test_model.py

@@ -20,12 +20,16 @@
 import pytorch_lightning as pl
 import torch
 from PIL import Image
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import nn, Tensor
 from torch.nn import functional as F
+from torch.utils.data import DataLoader
 
+import flash
 from flash.core.classification import ClassificationTask
 from flash.tabular import TabularClassifier
 from flash.text import SummarizationTask, TextClassifier
+from flash.utils.imports import _TRANSFORMERS_AVAILABLE
 from flash.vision import ImageClassificationData, ImageClassifier
 
 # ======== Mock functions ========
@@ -160,3 +164,64 @@ class Foo(ImageClassifier):
         backbones = None
 
     assert Foo.available_backbones() == []
+
+
+def test_optimization(tmpdir):
+
+    model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10), nn.LogSoftmax())
+    optim = torch.optim.Adam(model.parameters())
+    task = ClassificationTask(model, optimizer=optim, scheduler=None)
+
+    optimizer = task.configure_optimizers()
+    assert optimizer == optim
+
+    task = ClassificationTask(model, optimizer=torch.optim.Adadelta, optimizer_kwargs={"eps": 0.5}, scheduler=None)
+    optimizer = task.configure_optimizers()
+    assert isinstance(optimizer, torch.optim.Adadelta)
+    assert optimizer.defaults["eps"] == 0.5
+
+    task = ClassificationTask(
+        model,
+        optimizer=torch.optim.Adadelta,
+        scheduler=torch.optim.lr_scheduler.StepLR,
+        scheduler_kwargs={"step_size": 1}
+    )
+    optimizer, scheduler = task.configure_optimizers()
+    assert isinstance(optimizer[0], torch.optim.Adadelta)
+    assert isinstance(scheduler[0], torch.optim.lr_scheduler.StepLR)
+
+    optim = torch.optim.Adadelta(model.parameters())
+    task = ClassificationTask(model, optimizer=optim, scheduler=torch.optim.lr_scheduler.StepLR(optim, step_size=1))
+    optimizer, scheduler = task.configure_optimizers()
+    assert isinstance(optimizer[0], torch.optim.Adadelta)
+    assert isinstance(scheduler[0], torch.optim.lr_scheduler.StepLR)
+
+    if _TRANSFORMERS_AVAILABLE:
+        from transformers.optimization import get_linear_schedule_with_warmup
+
+        assert task.available_schedulers() == [
+            'constant_schedule', 'constant_schedule_with_warmup', 'cosine_schedule_with_warmup',
+            'cosine_with_hard_restarts_schedule_with_warmup', 'linear_schedule_with_warmup',
+            'polynomial_decay_schedule_with_warmup'
+        ]
+
+        optim = torch.optim.Adadelta(model.parameters())
+        with pytest.raises(MisconfigurationException, match="The LightningModule isn't attached to the trainer yet."):
+            task = ClassificationTask(model, optimizer=optim, scheduler="linear_schedule_with_warmup")
+            optimizer, scheduler = task.configure_optimizers()
+
+        task = ClassificationTask(
+            model,
+            optimizer=optim,
+            scheduler="linear_schedule_with_warmup",
+            scheduler_kwargs={"num_warmup_steps": 0.1},
+            loss_fn=F.nll_loss,
+        )
+        trainer = flash.Trainer(max_epochs=1, limit_train_batches=2)
+        ds = DummyDataset()
+        trainer.fit(task, train_dataloader=DataLoader(ds))
+        optimizer, scheduler = task.configure_optimizers()
+        assert isinstance(optimizer[0], torch.optim.Adadelta)
+        assert isinstance(scheduler[0], torch.optim.lr_scheduler.LambdaLR)
+        expected = get_linear_schedule_with_warmup.__name__
+        assert scheduler[0].lr_lambdas[0].__qualname__.split('.')[0] == expected