optimizers.rst

Optimization

Lightning offers two modes for managing the optimization process:

• Manual Optimization
• Automatic Optimization

For the majority of research cases, automatic optimization will do the right thing for you and it is what most users should use.

For advanced/expert users who want to do esoteric optimization schedules or techniques, use manual optimization.

Manual Optimization

For advanced research topics like reinforcement learning, sparse coding, or GAN research, it may be desirable to manually manage the optimization process.

This is only recommended for experts who need ultimate flexibility. Lightning will handle only accelerator, precision and strategy logic. The users are left with optimizer.zero_grad(), gradient accumulation, model toggling, etc..

To manually optimize, do the following:

• Set self.automatic_optimization=False in your LightningModule's __init__.
• Use the following functions and call them manually:
  • self.optimizers() to access your optimizers (one or multiple)
  • optimizer.zero_grad() to clear the gradients from the previous training step
  • self.manual_backward(loss) instead of loss.backward()
  • optimizer.step() to update your model parameters

Here is a minimal example of manual optimization.

.. testcode:: python

    from pytorch_lightning import LightningModule


    class MyModel(LightningModule):
        def __init__(self):
            super().__init__()
            # Important: This property activates manual optimization.
            self.automatic_optimization = False

        def training_step(self, batch, batch_idx):
            opt = self.optimizers()
            opt.zero_grad()
            loss = self.compute_loss(batch)
            self.manual_backward(loss)
            opt.step()

Warning

Before 1.2, optimizer.step() was calling optimizer.zero_grad() internally. From 1.2, it is left to the user's expertise.

Tip

Be careful where you call optimizer.zero_grad(), or your model won't converge. It is good practice to call optimizer.zero_grad() before self.manual_backward(loss).

Access your Own Optimizer

The provided optimizer is a :class:`~pytorch_lightning.core.optimizer.LightningOptimizer` object wrapping your own optimizer configured in your :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers`. You can access your own optimizer with optimizer.optimizer. However, if you use your own optimizer to perform a step, Lightning won't be able to support accelerators, precision and profiling for you.

.. testcode:: python

   class Model(LightningModule):
       def __init__(self):
           super().__init__()
           self.automatic_optimization = False
           ...

       def training_step(self, batch, batch_idx):
           optimizer = self.optimizers()

           # `optimizer` is a `LightningOptimizer` wrapping the optimizer.
           # To access it, do the following.
           # However, it won't work on TPU, AMP, etc...
           optimizer = optimizer.optimizer
           ...

Gradient Accumulation

You can accumulate gradients over batches similarly to accumulate_grad_batches argument in :ref:`Trainer <trainer>` for automatic optimization. To perform gradient accumulation with one optimizer after every N steps, you can do as such.

.. testcode:: python

    def __init__(self):
        super().__init__()
        self.automatic_optimization = False


    def training_step(self, batch, batch_idx):
        opt = self.optimizers()

        loss = self.compute_loss(batch)
        self.manual_backward(loss)

        # accumulate gradients of N batches
        if (batch_idx + 1) % N == 0:
            opt.step()
            opt.zero_grad()

Use Multiple Optimizers (like GANs)

Here is an example training a simple GAN with multiple optimizers using manual optimization.

.. testcode:: python

    import torch
    from torch import Tensor
    from pytorch_lightning import LightningModule


    class SimpleGAN(LightningModule):
        def __init__(self):
            super().__init__()
            self.G = Generator()
            self.D = Discriminator()

            # Important: This property activates manual optimization.
            self.automatic_optimization = False

        def sample_z(self, n) -> Tensor:
            sample = self._Z.sample((n,))
            return sample

        def sample_G(self, n) -> Tensor:
            z = self.sample_z(n)
            return self.G(z)

        def training_step(self, batch, batch_idx):
            # Implementation follows the PyTorch tutorial:
            # https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
            g_opt, d_opt = self.optimizers()

            X, _ = batch
            batch_size = X.shape[0]

            real_label = torch.ones((batch_size, 1), device=self.device)
            fake_label = torch.zeros((batch_size, 1), device=self.device)

            g_X = self.sample_G(batch_size)

            ##########################
            # Optimize Discriminator #
            ##########################
            d_x = self.D(X)
            errD_real = self.criterion(d_x, real_label)

            d_z = self.D(g_X.detach())
            errD_fake = self.criterion(d_z, fake_label)

            errD = errD_real + errD_fake

            d_opt.zero_grad()
            self.manual_backward(errD)
            d_opt.step()

            ######################
            # Optimize Generator #
            ######################
            d_z = self.D(g_X)
            errG = self.criterion(d_z, real_label)

            g_opt.zero_grad()
            self.manual_backward(errG)
            g_opt.step()

            self.log_dict({"g_loss": errG, "d_loss": errD}, prog_bar=True)

        def configure_optimizers(self):
            g_opt = torch.optim.Adam(self.G.parameters(), lr=1e-5)
            d_opt = torch.optim.Adam(self.D.parameters(), lr=1e-5)
            return g_opt, d_opt

Learning Rate Scheduling

Every optimizer you use can be paired with any Learning Rate Scheduler. Please see the documentation of :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers` for all the available options

You can call lr_scheduler.step() at arbitrary intervals. Use self.lr_schedulers() in your :class:`~pytorch_lightning.core.lightning.LightningModule` to access any learning rate schedulers defined in your :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers`.

Warning

• Before v1.3, Lightning automatically called lr_scheduler.step() in both automatic and manual optimization. From 1.3, lr_scheduler.step() is now for the user to call at arbitrary intervals.
• Note that the lr_scheduler_config keys, such as "frequency" and "interval", will be ignored even if they are provided in your :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers` during manual optimization.

Here is an example calling lr_scheduler.step() every step.

.. testcode:: python

    # step every batch
    def __init__(self):
        super().__init__()
        self.automatic_optimization = False


    def training_step(self, batch, batch_idx):
        # do forward, backward, and optimization
        ...

        # single scheduler
        sch = self.lr_schedulers()
        sch.step()

        # multiple schedulers
        sch1, sch2 = self.lr_schedulers()
        sch1.step()
        sch2.step()

If you want to call lr_scheduler.step() every N steps/epochs, do the following.

.. testcode:: python

    def __init__(self):
        super().__init__()
        self.automatic_optimization = False


    def training_step(self, batch, batch_idx):
        # do forward, backward, and optimization
        ...

        sch = self.lr_schedulers()

        # step every N batches
        if (batch_idx + 1) % N == 0:
            sch.step()

        # step every N epochs
        if self.trainer.is_last_batch and (self.trainer.current_epoch + 1) % N == 0:
            sch.step()

If you want to call schedulers that require a metric value after each epoch, consider doing the following:

.. testcode::

    def __init__(self):
        super().__init__()
        self.automatic_optimization = False


    def training_epoch_end(self, outputs):
        sch = self.lr_schedulers()

        # If the selected scheduler is a ReduceLROnPlateau scheduler.
        if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau):
            sch.step(self.trainer.callback_metrics["loss"])

Use Closure for LBFGS-like Optimizers

It is a good practice to provide the optimizer with a closure function that performs a forward, zero_grad and backward of your model. It is optional for most optimizers, but makes your code compatible if you switch to an optimizer which requires a closure, such as :class:`~torch.optim.LBFGS`.

See the PyTorch docs for more about the closure.

Here is an example using a closure function.

.. testcode:: python

    def __init__(self):
        super().__init__()
        self.automatic_optimization = False


    def configure_optimizers(self):
        return torch.optim.LBFGS(...)


    def training_step(self, batch, batch_idx):
        opt = self.optimizers()

        def closure():
            loss = self.compute_loss(batch)
            opt.zero_grad()
            self.manual_backward(loss)
            return loss

        opt.step(closure=closure)

Warning

The :class:`~torch.optim.LBFGS` optimizer is not supported for apex AMP, native AMP, IPUs, or DeepSpeed.

---

Automatic Optimization

With Lightning, most users don't have to think about when to call .zero_grad(), .backward() and .step() since Lightning automates that for you.

Under the hood, Lightning does the following:

for epoch in epochs:
    for batch in data:

        def closure():
            loss = model.training_step(batch, batch_idx, ...)
            optimizer.zero_grad()
            loss.backward()
            return loss

        optimizer.step(closure)

    lr_scheduler.step()

In the case of multiple optimizers, Lightning does the following:

for epoch in epochs:
    for batch in data:
        for opt in optimizers:

            def closure():
                loss = model.training_step(batch, batch_idx, optimizer_idx)
                opt.zero_grad()
                loss.backward()
                return loss

            opt.step(closure)

    for lr_scheduler in lr_schedulers:
        lr_scheduler.step()

As can be seen in the code snippet above, Lightning defines a closure with training_step(), optimizer.zero_grad() and loss.backward() for the optimization. This mechanism is in place to support optimizers which operate on the output of the closure (e.g. the loss) or need to call the closure several times (e.g. :class:`~torch.optim.LBFGS`).

Warning

Before v1.2.2, Lightning internally calls backward, step and zero_grad in the order. From v1.2.2, the order is changed to zero_grad, backward and step.

Use Multiple Optimizers (like GANs)

To use multiple optimizers (optionally with learning rate schedulers), return two or more optimizers from :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers`.

.. testcode:: python

    # two optimizers, no schedulers
    def configure_optimizers(self):
        return Adam(...), SGD(...)


    # two optimizers, one scheduler for adam only
    def configure_optimizers(self):
        opt1 = Adam(...)
        opt2 = SGD(...)
        optimizers = [opt1, opt2]
        lr_schedulers = {"scheduler": ReduceLROnPlateau(opt1, ...), "monitor": "metric_to_track"}
        return optimizers, lr_schedulers


    # two optimizers, two schedulers
    def configure_optimizers(self):
        opt1 = Adam(...)
        opt2 = SGD(...)
        return [opt1, opt2], [StepLR(opt1, ...), OneCycleLR(opt2, ...)]

Under the hood, Lightning will call each optimizer sequentially:

for epoch in epochs:
    for batch in data:
        for opt in optimizers:
            loss = train_step(batch, batch_idx, optimizer_idx)
            opt.zero_grad()
            loss.backward()
            opt.step()

    for lr_scheduler in lr_schedulers:
        lr_scheduler.step()

Step Optimizeres at Arbitrary Intervals

To do more interesting things with your optimizers such as learning rate warm-up or odd scheduling, override the :meth:`~pytorch_lightning.core.lightning.LightningModule.optimizer_step` function.

Warning

If you are overriding this method, make sure that you pass the optimizer_closure parameter to optimizer.step() function as shown in the examples because training_step(), optimizer.zero_grad(), loss.backward() are called in the closure function.

For example, here step optimizer A every batch and optimizer B every 2 batches.

.. testcode:: python

    # Alternating schedule for optimizer steps (e.g. GANs)
    def optimizer_step(
        self,
        epoch,
        batch_idx,
        optimizer,
        optimizer_idx,
        optimizer_closure,
        on_tpu=False,
        using_native_amp=False,
        using_lbfgs=False,
    ):
        # update generator every step
        if optimizer_idx == 0:
            optimizer.step(closure=optimizer_closure)

        # update discriminator every 2 steps
        if optimizer_idx == 1:
            if (batch_idx + 1) % 2 == 0:
                # the closure (which includes the `training_step`) will be executed by `optimizer.step`
                optimizer.step(closure=optimizer_closure)
            else:
                # call the closure by itself to run `training_step` + `backward` without an optimizer step
                optimizer_closure()

        # ...
        # add as many optimizers as you want

Here we add a manual learning rate warm-up without an lr scheduler.

.. testcode:: python

    # learning rate warm-up
    def optimizer_step(
        self,
        epoch,
        batch_idx,
        optimizer,
        optimizer_idx,
        optimizer_closure,
        on_tpu=False,
        using_native_amp=False,
        using_lbfgs=False,
    ):
        # update params
        optimizer.step(closure=optimizer_closure)

        # skip the first 500 steps
        if self.trainer.global_step < 500:
            lr_scale = min(1.0, float(self.trainer.global_step + 1) / 500.0)
            for pg in optimizer.param_groups:
                pg["lr"] = lr_scale * self.hparams.learning_rate

Access your Own Optimizer

The provided optimizer is a :class:`~pytorch_lightning.core.optimizer.LightningOptimizer` object wrapping your own optimizer configured in your :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers`. You can access your own optimizer with optimizer.optimizer. However, if you use your own optimizer to perform a step, Lightning won't be able to support accelerators, precision and profiling for you.

.. testcode:: python

    # function hook in LightningModule
    def optimizer_step(
        self,
        epoch,
        batch_idx,
        optimizer,
        optimizer_idx,
        optimizer_closure,
        on_tpu=False,
        using_native_amp=False,
        using_lbfgs=False,
    ):
        optimizer.step(closure=optimizer_closure)


    # `optimizer` is a `LightningOptimizer` wrapping the optimizer.
    # To access it, do the following.
    # However, it won't work on TPU, AMP, etc...
    def optimizer_step(
        self,
        epoch,
        batch_idx,
        optimizer,
        optimizer_idx,
        optimizer_closure,
        on_tpu=False,
        using_native_amp=False,
        using_lbfgs=False,
    ):
        optimizer = optimizer.optimizer
        optimizer.step(closure=optimizer_closure)

Bring your own Custom Learning Rate Schedulers

Lightning allows using custom learning rate schedulers that aren't available in PyTorch natively. One good example is Timm Schedulers. When using custom learning rate schedulers relying on a different API from Native PyTorch ones, you should override the :meth:`~pytorch_lightning.core.lightning.LightningModule.lr_scheduler_step` with your desired logic. If you are using native PyTorch schedulers, there is no need to override this hook since Lightning will handle it automatically by default.

from timm.scheduler import TanhLRScheduler


def configure_optimizers(self):
    optimizer = ...
    scheduler = TanhLRScheduler(optimizer, ...)
    return [optimizer], [{"scheduler": scheduler, "interval": "epoch"}]


def lr_scheduler_step(self, scheduler, optimizer_idx, metric):
    scheduler.step(epoch=self.current_epoch)  # timm's scheduler need the epoch value

Configure Gradient Clipping

To configure custom gradient clipping, consider overriding the :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_gradient_clipping` method. Attributes gradient_clip_val and gradient_clip_algorithm from Trainer will be passed in the respective arguments here and Lightning will handle gradient clipping for you. In case you want to set different values for your arguments of your choice and let Lightning handle the gradient clipping, you can use the inbuilt :meth:`~pytorch_lightning.core.lightning.LightningModule.clip_gradients` method and pass the arguments along with your optimizer.

Warning

Make sure to not override :meth:`~pytorch_lightning.core.lightning.LightningModule.clip_gradients` method. If you want to customize gradient clipping, consider using :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_gradient_clipping` method.

For example, here we will apply gradient clipping only to the gradients associated with optimizer A.

.. testcode:: python

    def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm):
        if optimizer_idx == 0:
            # Lightning will handle the gradient clipping
            self.clip_gradients(
                optimizer, gradient_clip_val=gradient_clip_val, gradient_clip_algorithm=gradient_clip_algorithm
            )

Here we configure gradient clipping differently for optimizer B.

.. testcode:: python

    def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm):
        if optimizer_idx == 0:
            # Lightning will handle the gradient clipping
            self.clip_gradients(
                optimizer, gradient_clip_val=gradient_clip_val, gradient_clip_algorithm=gradient_clip_algorithm
            )
        elif optimizer_idx == 1:
            self.clip_gradients(
                optimizer, gradient_clip_val=gradient_clip_val * 2, gradient_clip_algorithm=gradient_clip_algorithm
            )