PoC: Revamp optimizer and scheduler experience using registries

CHANGELOG.md

@@ -32,9 +32,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Changed the default `num_workers` on linux to `0` (matching the default for other OS) ([#759](https://github.com/PyTorchLightning/lightning-flash/pull/759))
 
-
 - Changed `PreprocessTransform` to `InputTransform` ([#868](https://github.com/PyTorchLightning/lightning-flash/pull/868))
 
+- Optimizer and LR Scheduler registry are used to get the respective inputs to the Task using a string (or a callable). ([#777](https://github.com/PyTorchLightning/lightning-flash/pull/777))
+
 
 ### Fixed
 

README.md

@@ -175,6 +175,53 @@ In detail, the following methods are currently implemented:
 * **[metaoptnet](https://github.com/learnables/learn2learn/blob/master/learn2learn/algorithms/lightning/lightning_metaoptnet.py)** : from Lee *et al.* 2019, [Meta-Learning with Differentiable Convex Optimization](https://arxiv.org/abs/1904.03758)
 * **[anil](https://github.com/learnables/learn2learn/blob/master/learn2learn/algorithms/lightning/lightning_anil.py)** : from Raghu *et al.* 2020, [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://arxiv.org/abs/1909.09157)
 
+
+### Flash Optimizers / Schedulers
+
+With Flash, swapping among 40+ optimizers and 15 + schedulers recipes are simple. Find the list of available optimizers, schedulers as follows:
+
+```py
+ImageClassifier.available_optimizers()
+# ['A2GradExp', ..., 'Yogi']
+
+ImageClassifier.available_schedulers()
+# ['CosineAnnealingLR', 'CosineAnnealingWarmRestarts', ..., 'polynomial_decay_schedule_with_warmup']
+```
+
+Once you've chosen, create the model:
+
+```py
+#### The optimizer of choice can be passed as a
+# - String value
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler=None)
+
+# - Callable
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer=functools.partial(torch.optim.Adadelta, eps=0.5), lr_scheduler=None)
+
+# - Tuple[string, dict]: (The dict takes in the optimizer kwargs)
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer=("Adadelta", {"epa": 0.5}), lr_scheduler=None)
+
+#### The scheduler of choice can be passed as a
+# - String value
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler="constant_schedule")
+
+# - Callable
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler=functools.partial(CyclicLR, step_size_up=1500, mode='exp_range', gamma=0.5))
+
+# - Tuple[string, dict]: (The dict takes in the scheduler kwargs)
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler=("StepLR", {"step_size": 10]))
+```
+
+You can also register you own custom scheduler recipes beforeahand and use them shown as above:
+
+```py
+@ImageClassifier.lr_schedulers
+def my_steplr_recipe(optimizer):
+    return torch.optim.lr_scheduler.StepLR(optimizer, step_size=10)
+
+model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler="my_steplr_recipe")
+```
+
 ### Flash Transforms
 
 

docs/source/general/optimization.rst

@@ -0,0 +1,197 @@
+
+.. _optimization:
+
+########################################
+Optimization (Optimizers and Schedulers)
+########################################
+
+Using optimizers and learning rate schedulers with Flash has become easier and cleaner than ever.
+
+With the use of :ref:`registry`, instantiation of an optimzer or a learning rate scheduler can done with just a string.
+
+Setting an optimizer to a task
+==============================
+
+Each task has an inbuilt method :func:`~flash.core.model.Task.available_optimizers` which will list all the optimizers
+registered with Flash.
+
+    >>> from flash.core.classification import ClassificationTask
+    >>> ClassificationTask.available_optimizers()
+    ['adadelta', ..., 'sgd']
+
+To train / finetune a :class:`~flash.core.model.Task` of your choice, just pass on a string.
+
+.. code-block:: python
+
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4)
+
+
+In order to customize specific parameters of the Optimizer, pass along a dictionary of kwargs with the string as a tuple.
+
+.. code-block:: python
+
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=("Adam", {"amsgrad": True}), learning_rate=1e-4)
+
+
+An alternative to customizing an optimizer using a tuple is to pass it as a callable.
+
+.. code-block:: python
+
+    from functools import partial
+    from torch.optim import Adam
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=partial(Adam, amsgrad=True), learning_rate=1e-4)
+
+
+Setting a Learning Rate Scheduler
+=================================
+
+Each task has an inbuilt method :func:`~flash.core.model.Task.available_lr_schedulers` which will list all the learning
+rate schedulers registered with Flash.
+
+    >>> from flash.core.classification import ClassificationTask
+    >>> ClassificationTask.available_lr_schedulers()
+    ['lambdalr', ..., 'cosineannealingwarmrestarts']
+
+To train / finetune a :class:`~flash.core.model.Task` of your choice, just pass on a string.
+
+.. code-block:: python
+
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(
+        num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4, lr_scheduler="constant_schedule"
+    )
+
+.. note:: ``"constant_schedule"`` and a few other lr schedulers will be available only if you have installed the ``transformers`` library from Hugging Face.
+
+
+In order to customize specific parameters of the LR Scheduler, pass along a dictionary of kwargs with the string as a tuple.
+
+.. code-block:: python
+
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(
+        num_classes=10,
+        backbone="resnet18",
+        optimizer="Adam",
+        learning_rate=1e-4,
+        lr_scheduler=("StepLR", {"step_size": 10}),
+    )
+
+
+An alternative to customizing the LR Scheduler using a tuple is to pass it as a callable.
+
+.. code-block:: python
+
+    from functools import partial
+    from torch.optim.lr_scheduler import CyclicLR
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(
+        num_classes=10,
+        backbone="resnet18",
+        optimizer="Adam",
+        learning_rate=1e-4,
+        lr_scheduler=partial(CyclicLR, step_size_up=1500, mode="exp_range", gamma=0.5),
+    )
+
+
+Additionally, the ``lr_scheduler`` parameter also accepts the Lightning Scheduler configuration which can be passed on using a tuple.
+
+The Lightning Scheduler configuration is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.
+
+.. code-block:: python
+
+    lr_scheduler_config = {
+        # REQUIRED: The scheduler instance
+        "scheduler": lr_scheduler,
+        # The unit of the scheduler's step size, could also be 'step'.
+        # 'epoch' updates the scheduler on epoch end whereas 'step'
+        # updates it after a optimizer update.
+        "interval": "epoch",
+        # How many epochs/steps should pass between calls to
+        # `scheduler.step()`. 1 corresponds to updating the learning
+        # rate after every epoch/step.
+        "frequency": 1,
+        # Metric to to monitor for schedulers like `ReduceLROnPlateau`
+        "monitor": "val_loss",
+        # If set to `True`, will enforce that the value specified 'monitor'
+        # is available when the scheduler is updated, thus stopping
+        # training if not found. If set to `False`, it will only produce a warning
+        "strict": True,
+        # If using the `LearningRateMonitor` callback to monitor the
+        # learning rate progress, this keyword can be used to specify
+        # a custom logged name
+        "name": None,
+    }
+
+When there are schedulers in which the ``.step()`` method is conditioned on a value, such as the ``torch.optim.lr_scheduler.ReduceLROnPlateau`` scheduler,
+Flash requires that the Lightning Scheduler configuration contains the keyword ``"monitor"`` set to the metric name that the scheduler should be conditioned on.
+Below is an example for this:
+
+.. code-block:: python
+
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(
+        num_classes=10,
+        backbone="resnet18",
+        optimizer="Adam",
+        learning_rate=1e-4,
+        lr_scheduler=("reducelronplateau", {"mode": "max"}, {"monitor": "val_accuracy"}),
+    )
+
+
+.. note:: Do not set the ``"scheduler"`` key in the Lightning Scheduler configuration, it will overriden with an instance of the provided scheduler key.
+
+
+Pre-Registering optimizers and scheduler recipes
+================================================
+
+Flash registry also provides the flexiblty of registering functions. This feature is also provided in the Optimizer and Scheduler registry.
+
+Using the ``optimizers`` and ``lr_schedulers`` decorator pertaining to each :class:`~flash.core.model.Task`, custom optimizer and LR scheduler recipes can be pre-registered.
+
+.. code-block:: python
+
+    import torch
+    from flash.image import ImageClassifier
+
+
+    @ImageClassifier.lr_schedulers
+    def my_flash_steplr_recipe(optimizer):
+        return torch.optim.lr_scheduler.StepLR(optimizer, step_size=10)
+
+
+    model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler="my_flash_steplr_recipe")
+
+
+Provider specific requirements
+==============================
+
+Schedulers
+**********
+
+Certain LR Schedulers provided by Hugging Face require both ``num_training_steps`` and ``num_warmup_steps``.
+
+In order to use them in Flash, just provide ``num_warmup_steps`` as float between 0 and 1 which indicates the fraction of the training steps
+that will be used as warmup steps. Flash's :class:`~flash.core.trainer.Trainer` will take care of computing the number of training steps and
+number of warmup steps based on the flags that are set in the Trainer.
+
+.. code-block:: python
+
+    from flash.image import ImageClassifier
+
+    model = ImageClassifier(
+        backbone="resnet18",
+        num_classes=2,
+        optimizer="Adam",
+        lr_scheduler=("cosine_schedule_with_warmup", {"num_warmup_steps": 0.1}),
+    )

docs/source/general/registry.rst

@@ -1,9 +1,10 @@
+
+.. _registry:
+
 ########
 Registry
 ########
 
-.. _registry:
-
 ********************
 Available Registries
 ********************

docs/source/index.rst

@@ -33,6 +33,7 @@ Lightning Flash
    general/registry
    general/serve
    general/backbones
+   general/optimization
 
 .. toctree::
    :maxdepth: 1

flash/audio/speech_recognition/model.py

@@ -13,11 +13,10 @@
 # limitations under the License.
 import os
 import warnings
-from typing import Any, Dict, Mapping, Optional, Type, Union
+from typing import Any, Callable, Dict, Mapping, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-from torch.optim.lr_scheduler import _LRScheduler
 
 from flash.audio.speech_recognition.backbone import SPEECH_RECOGNITION_BACKBONES
 from flash.audio.speech_recognition.collate import DataCollatorCTCWithPadding
@@ -39,11 +38,9 @@ class SpeechRecognition(Task):
     Args:
         backbone: Any speech recognition model from `HuggingFace/transformers
             <https://huggingface.co/models?pipeline_tag=automatic-speech-recognition>`_.
+        learning_rate: Learning rate to use for training, defaults to ``1e-5``.
         optimizer: Optimizer to use for training.
-        optimizer_kwargs: Additional kwargs to use when creating the optimizer (if not passed as an instance).
-        scheduler: The scheduler or scheduler class to use.
-        scheduler_kwargs: Additional kwargs to use when creating the scheduler (if not passed as an instance).
-        learning_rate: Learning rate to use for training, defaults to ``1e-3``.
+        lr_scheduler: The LR scheduler to use during training.
         serializer: The :class:`~flash.core.data.process.Serializer` to use when serializing prediction outputs.
     """
 
@@ -54,10 +51,10 @@ class SpeechRecognition(Task):
     def __init__(
         self,
         backbone: str = "facebook/wav2vec2-base-960h",
-        optimizer: Type[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,
+        optimizer: Union[str, Callable, Tuple[str, Dict[str, Any]]] = "Adam",
+        lr_scheduler: Optional[
+            Union[str, Callable, Tuple[str, Dict[str, Any]], Tuple[str, Dict[str, Any], Dict[str, Any]]]
+        ] = None,
         learning_rate: float = 1e-5,
         serializer: Optional[Union[Serializer, Mapping[str, Serializer]]] = None,
     ):
@@ -71,9 +68,7 @@ def __init__(
         super().__init__(
             model=model,
             optimizer=optimizer,
-            optimizer_kwargs=optimizer_kwargs,
-            scheduler=scheduler,
-            scheduler_kwargs=scheduler_kwargs,
+            lr_scheduler=lr_scheduler,
             learning_rate=learning_rate,
             serializer=serializer,
         )