Skip to content

Learning Rate Scheduling

When training deep neural networks, it is often useful to reduce learning rate as the training progresses. This can be done by using pre-defined learning rate schedules or adaptive learning rate methods. Learning rate scheduling type is controlled by the training parameter lr_mode. From Trainer.train(...) docs:

`lr_mode` : Union[str, Mapping]

    When str:

    Learning rate scheduling policy, one of ['StepLRScheduler','PolyLRScheduler','CosineLRScheduler','FunctionLRScheduler'].

    'StepLRScheduler' refers to constant updates at epoch numbers passed through `lr_updates`. Each update decays the learning rate by `lr_decay_factor`.

    'CosineLRScheduler' refers to the Cosine Anealing policy as mentioned in https://arxiv.org/abs/1608.03983. The final learning rate ratio is controlled by `cosine_final_lr_ratio` training parameter.

    'PolyLRScheduler' refers to the polynomial decrease: in each epoch iteration `self.lr = self.initial_lr * pow((1.0 - (current_iter / max_iter)), 0.9)`

    'FunctionLRScheduler' refers to a user-defined learning rate scheduling function, that is passed through `lr_schedule_function`.

For example, the training code below will start with an initial learning rate of 0.1 and decay by 0.1 at epochs 100,150 and 200:

from super_gradients.training import Trainer


trainer = Trainer("my_custom_scheduler_training_experiment")

train_dataloader = ...
valid_dataloader = ...
model = ...
train_params = {
    "initial_lr": 0.1,
    "lr_mode":"StepLRScheduler",
    "lr_updates": [100, 150, 200],
    "lr_decay_factor": 0.1,
    ...,
}

trainer.train(model=model, training_params=train_params, train_loader=train_dataloader, valid_loader=valid_dataloader)
Equivalent in a .yaml configuration file:
training_hyperparams:
    initial_lr: 0.1
    lr_mode: StepLRScheduler
    user_lr_updates:
      - 100
      - 150
      - 200
    lr_decay_factor: 0.1
    ...

...

Using Custom LR Schedulers

Prerequisites: phase callbacks, training with configuration files.

In SG, learning rate schedulers are implemented as phase callbacks. They read the learning rate from the PhaseContext in their __call__ method, calculate the new learning rate according to the current state of training, and update the optimizer's param groups.

For example, the code snippet from the previous section translates "lr_mode":"StepLRScheduler" to a super_gradients.training.utils.callbacks.callbacks.StepLRScheduler instance, which is added to the phase callbacks list.

Implementing Your Own Scheduler

A custom learning rate scheduler should inherit from LRCallbackBase, so let's take a look at it:

class LRCallbackBase(PhaseCallback):
    """
    Base class for hard coded learning rate scheduling regimes, implemented as callbacks.
    """

    def __init__(self, phase, initial_lr, update_param_groups, train_loader_len, net, training_params, **kwargs):
        super(LRCallbackBase, self).__init__(phase)
        self.initial_lr = initial_lr
        self.lr = initial_lr
        self.update_param_groups = update_param_groups
        self.train_loader_len = train_loader_len
        self.net = net
        self.training_params = training_params

    def __call__(self, context: PhaseContext, **kwargs):
        if self.is_lr_scheduling_enabled(context):
            self.perform_scheduling(context)

    def is_lr_scheduling_enabled(self, context: PhaseContext):
        """
        Predicate that controls whether to perform lr scheduling based on values in context.

        @param context: PhaseContext: current phase's context.
        @return: bool, whether to apply lr scheduling or not.
        """
        raise NotImplementedError

    def perform_scheduling(self, context: PhaseContext):
        """
        Performs lr scheduling based on values in context.

        @param context: PhaseContext: current phase's context.
        """
        raise NotImplementedError

    def update_lr(self, optimizer, epoch, batch_idx=None):
        if self.update_param_groups:
            param_groups = self.net.module.update_param_groups(optimizer.param_groups, self.lr, epoch, batch_idx, self.training_params, self.train_loader_len)
            optimizer.param_groups = param_groups
        else:
            # UPDATE THE OPTIMIZERS PARAMETER
            for param_group in optimizer.param_groups:
                param_group["lr"] = self.lr

So when writing a custom scheduler, we need to override two methods:

  1. perform_scheduling: This is where the new learning rate is calculated. The lr attribute is updated according. Then, in order to update the optimizer's parameter groups a call for update_lr should also be done (or update the optimizers parameter groups with your own logic explicitly).
  2. is_lr_scheduling_enabled: Predicate that controls whether to perform lr scheduling based on values in context.

We will demonstrate how this is done by implementing a simple scheduler that decays the learning rate by a user-defined rate at user-defined epoch numbers.

from super_gradients.training.utils.callbacks import LRCallbackBase, Phase
from super_gradients.common.abstractions.abstract_logger import get_logger

logger = get_logger(__name__)

class UserStepLRCallback(LRCallbackBase):
    def __init__(self, lr_updates: list, lr_decay_factors: list, **kwargs):
        super(UserStepLRCallback, self).__init__(Phase.TRAIN_EPOCH_END, **kwargs)
        assert len(lr_updates) == len(lr_decay_factors)
        self.lr_updates = lr_updates
        self.lr_decay_factors = lr_decay_factors

    def perform_scheduling(self, context):
        curr_lr = self.initial_lr
        for epoch_idx, epoch_decay_rate in zip(self.lr_updates, self.lr_decay_factors):
            if epoch_idx <= context.epoch:
                curr_lr *= epoch_decay_rate
        self.lr = curr_lr
        self.update_lr(context.optimizer, context.epoch, None)

    def is_lr_scheduling_enabled(self, context):
        return self.training_params.lr_warmup_epochs <= context.epoch

Notes

  • We specified that scheduling is enabled only after lr_warmup_epochs, this means that during lr warmup no updates will be done, even if such epoch is specifed!
  • Notice the Phase.TRAIN_EPOCH_END which we pass to the constructor, this means that our __call__ is triggered inside on_train_loader_end(self, context) (see new callbacks API mapping between Phase to Callback methods.)

Now, we need to register our new scheduler so we can pass it through the lr_mode training parameter. First we decorate our class with the register_lr_scheduler.

# myscheduler.py

from super_gradients.training.utils.callbacks import LRCallbackBase, Phase
from super_gradients.common.abstractions.abstract_logger import get_logger
from super_gradients.common.registry import register_lr_scheduler
logger = get_logger(__name__)

@register_lr_scheduler("user_step")
class UserStepLRCallback(LRCallbackBase):
    def __init__(self, user_lr_updates: list, user_lr_decay_factors: list, **kwargs):
        super(UserStepLRCallback, self).__init__(Phase.TRAIN_EPOCH_END, **kwargs)
        assert len(user_lr_updates) == len(user_lr_decay_factors)
        self.lr_updates = user_lr_updates
        self.lr_decay_factors = user_lr_decay_factors

    def perform_scheduling(self, context):
        curr_lr = self.initial_lr
        for epoch_idx, epoch_decay_rate in zip(self.lr_updates, self.lr_decay_factors):
            if epoch_idx <= context.epoch:
                curr_lr *= epoch_decay_rate
        self.lr = curr_lr
        self.update_lr(context.optimizer, context.epoch, None)

    def is_lr_scheduling_enabled(self, context):
        return self.training_params.lr_warmup_epochs <= context.epoch

Next, simply import it (even if the class itself isn't used on the training script code page) to trigger the registry.

# my_train_script.py

from super_gradients.training import Trainer
from myscheduler import UserStepLRCallback # triggers registry, now we can pass "lr_mode": "user_step"
...

And finally, use your new scheduler just as any other one supported by SG.

trainer = Trainer("my_custom_scheduler_training_experiment")

# The following code sections marked with '...' are placeholders 
# indicating additional necessary code that is not shown for simplicity.
train_dataloader = ...
valid_dataloader = ...
model = ...

train_params = {
    "initial_lr": 0.1,
    "lr_mode": "user_step",
    "user_lr_updates": [100, 150, 200], # WILL BE PASSED TO UserStepLRCallback CONSTRUCTOR
    "user_lr_decay_factors": [0.1, 0.01, 0.001], # WILL BE PASSED TO UserStepLRCallback CONSTRUCTOR
    ...
}

trainer.train(model=model, training_params=train_params, train_loader=train_dataloader, valid_loader=valid_dataloader)

Note that internally, Trainer unpacks training_params to the scheduler callback constructor, so we pass scheduler related parameters through training_params as well.

Equivalent in a .yaml configuration file:
training_hyperparams:
    initial_lr: 0.1
    lr_mode: user_step
    user_lr_updates: # WILL BE PASSED TO UserStepLRCallback CONSTRUCTOR
      - 100
      - 150
      - 200
    user_lr_decay_factors: # WILL BE PASSED TO UserStepLRCallback CONSTRUCTOR
      - 0.1
      - 0.01
      - 0.001
    ...

...

Using PyTorchs Native LR Schedulers (torch.optim.lr_scheduler)

PyTorch offers a wide variety of learning rate schedulers. They can all be easily used by passing a Mapping through the lr_mode parameter, following aa simple API. From Trainer.train(...) docs:

When Mapping, refers to a torch.optim.lr_scheduler._LRScheduler, following the below API:

    lr_mode = {LR_SCHEDULER_CLASS_NAME: {**LR_SCHEDULER_KWARGS, "phase": XXX, "metric_name": XXX)

    Where "phase" (of Phase type) controls when to call torch.optim.lr_scheduler._LRScheduler.step().
    For instance, in order to:
    - Update LR on each batch: Use phase: Phase.TRAIN_BATCH_END
    - Update LR after each epoch: Use phase: Phase.TRAIN_EPOCH_END

    The "metric_name" refers to the metric to watch (See docs for "metric_to_watch" in train(...)
     https://docs.deci.ai/super-gradients/docstring/training/sg_trainer.html) when using
      ReduceLROnPlateau. In any other case this kwarg is ignored.

    **LR_SCHEDULER_KWARGS are simply passed to the torch scheduler's __init__.



    For example:
        lr_mode = {"StepLR": {"gamma": 0.1, "step_size": 1, "phase": Phase.TRAIN_EPOCH_END}}
        is equivalent to following training code:

            from torch.optim.lr_scheduler import StepLR
            ...
            optimizer = ....
            scheduler = StepLR(optimizer=optimizer, gamma=0.1, step_size=1)

            for epoch in num_epochs:
                train_epoch(...)
                scheduler.step()
                ....

Examples

Using StepLR

trainer = Trainer("torch_Scheduler_example")

# The following code sections marked with '...' are placeholders 
# indicating additional necessary code that is not shown for simplicity.
train_dataloader = ...
valid_dataloader = ...
model = ...

train_params = {
    "max_epochs": 2,
    "lr_mode": {"StepLR": {"gamma": 0.1, "step_size": 1, "phase": Phase.TRAIN_EPOCH_END}},
    "lr_warmup_epochs": 0,
    "initial_lr": 0.1,
    "loss": torch.nn.CrossEntropyLoss(),
    "optimizer": "SGD",

    "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
    "train_metrics_list": [Accuracy()],
    "valid_metrics_list": [Accuracy()],
    "metric_to_watch": "Accuracy",
    "greater_metric_to_watch_is_better": True,
}
trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)
Equivalent in a .yaml configuration file:
training_hyperparams:
    # Setting up LR Scheduler
    lr_mode:
      StepLR:
        gamma: 0.1
        step_size: 1
        phase: TRAIN_EPOCH_END

    # Setting up other parameters
    max_epochs: 2
    lr_warmup_epochs: 0
    initial_lr: 0.1
    loss: CrossEntropyLoss
    optimizer: SGD
    optimizer_params:
      weight_decay: 1e-4
      momentum: 0.9
    train_metrics_list:
      - Accuracy
    valid_metrics_list:
      - Accuracy
    metric_to_watch: Accuracy
    greater_metric_to_watch_is_better: true

...

Using ReduceLROnPlateau

If you choose to use ReduceLROnPlateau as the learning rate scheduler, you need to specify a metric_name. This parameter follows the same guidelines as metric_to_watch.

For an in-depth understanding of these metrics, see the metrics guide.

trainer = Trainer("torch_ROP_Scheduler_example")
train_dataloader = ...
valid_dataloader = ...
model = ...
train_params = {
    "max_epochs": 2,
    "lr_decay_factor": 0.1,
    "lr_mode": {
        "ReduceLROnPlateau": {"patience": 0, "phase": Phase.TRAIN_EPOCH_END, "metric_name": "DummyMetric"}},
    "lr_warmup_epochs": 0,
    "initial_lr": 0.1,
    "loss": torch.nn.CrossEntropyLoss(),
    "optimizer": "SGD",

    "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
    "train_metrics_list": [Accuracy()],
    "valid_metrics_list": [Accuracy()],
    "metric_to_watch": "DummyMetric",
    "greater_metric_to_watch_is_better": True,
}
trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)

The scheduler's state_dict is saved under torch_scheduler_state_dict entry inside the checkpoint during training, allowing us to resume from the same state of the scheduling.

Equivalent in a .yaml configuration file:
training_hyperparams:
    # Setting up LR Scheduler
    lr_mode:
      ReduceLROnPlateau:
        patience: 0
        phase: TRAIN_EPOCH_END
        metric_name: DummyMetric

    # Setting up other parameters
    max_epochs: 2
    lr_decay_factor: 0.1
    lr_warmup_epochs: 0
    initial_lr: 0.1
    loss: CrossEntropyLoss
    optimizer: SGD
    optimizer_params:
      weight_decay: 1e-4
      momentum: 0.9
    train_metrics_list:
      - Accuracy
    valid_metrics_list:
      - Accuracy
    metric_to_watch: DummyMetric
    greater_metric_to_watch_is_better: true

...