---
title: Own your loop (advanced)
description: >-
  For advanced research topics like reinforcement learning, sparse coding, or
  GAN research, it may be desirable to
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/model/build_model_advanced'
---
# Own your loop (advanced) {#own-your-loop-advanced}

## Customize training loop {#customize-training-loop}

![Injecting custom code in a training loop](https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/custom_loop.png)

Inject custom code anywhere in the Training loop using any of the 20+ methods ([lightning_hooks](/docs/pytorch/2.6.6/core-api/lightning_module.md#hooks)) available in the LightningModule.

```python
import lightning as L

class LitModel(L.LightningModule):
    def backward(self, loss):
        loss.backward()
```

## 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, especially when dealing with multiple optimizers at the same time.

In this mode, Lightning will handle only accelerator, precision and strategy logic.
The users are left with `optimizer.zero_grad()`, gradient accumulation, optimizer 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
  * `self.toggle_optimizer()` and `self.untoggle_optimizer()`, or `self.toggled_optimizer()` if needed

Here is a minimal example of manual optimization.

```python
from lightning.pytorch 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()
```

> **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 {#access-your-own-optimizer}

The provided `optimizer` is a `LightningOptimizer` object wrapping your own optimizer
configured in your `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.

```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 {#gradient-accumulation}

You can accumulate gradients over batches similarly to `accumulate_grad_batches` argument in
[Trainer](/docs/pytorch/2.6.6/core-api/trainer.md) for automatic optimization. To perform gradient accumulation with one optimizer
after every `N` steps, you can do as such.

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

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

    # scale losses by 1/N (for N batches of gradient accumulation)
    loss = self.compute_loss(batch) / N
    self.manual_backward(loss)

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

### Gradient Clipping {#gradient-clipping}

You can clip optimizer gradients during manual optimization similar to passing the `gradient_clip_val` and
`gradient_clip_algorithm` argument in [Trainer](/docs/pytorch/2.6.6/core-api/trainer.md) during automatic optimization.
To perform gradient clipping with one optimizer with manual optimization, you can do as such.

```python
from lightning.pytorch import LightningModule

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

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

        # compute loss
        loss = self.compute_loss(batch)

        opt.zero_grad()
        self.manual_backward(loss)

        # clip gradients
        self.clip_gradients(opt, gradient_clip_val=0.5, gradient_clip_algorithm="norm")

        opt.step()
```

> **Warning**
>
> * Note that `configure_gradient_clipping()` won't be called in Manual Optimization. Instead consider using `self. clip_gradients()` manually like in the example above.

### Use Multiple Optimizers (like GANs) {#use-multiple-optimizers-like-gans}

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

```python
import torch
from torch import Tensor
from lightning.pytorch 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 {#learning-rate-scheduling}

Every optimizer you use can be paired with any
[Learning Rate Scheduler](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate). Please see the
documentation of `configure_optimizers` for all the available options

You can call `lr_scheduler.step()` at arbitrary intervals.
Use `self.lr_schedulers()` in  your `LightningModule` to access any learning rate schedulers
defined in your `configure_optimizers`.

> **Warning**
>
> * `lr_scheduler.step()` can be called at arbitrary intervals by the user in case of manual optimization, or by Lightning if `"interval"` is defined in `configure_optimizers` in case of automatic optimization.
> * Note that the `lr_scheduler_config` keys, such as `"frequency"` and `"interval"`, will be ignored even if they are provided in
>   your `configure_optimizers` during manual optimization.

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

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

def configure_optimizers(self):
    optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
    return [optimizer], [scheduler]

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.

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

def configure_optimizers(self):
    optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
    return [optimizer], [scheduler]

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:

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

def configure_optimizers(self):
    optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10)
    return [optimizer], [scheduler]

def on_train_epoch_end(self):
    sch = self.lr_schedulers()

    sch.step(self.trainer.callback_metrics["loss"])
```

> **Note**
>
> optimizers and learning rate schedulers. Regardless of the way you define them, `self.optimizers()` will always return
> either a single optimizer if you defined a single optimizer, or a list of optimizers if you defined multiple
> optimizers. The same applies to the `self.lr_schedulers()` method, which will return a single scheduler
> if you defined a single scheduler, or a list of schedulers if you defined multiple schedulers

### Optimizer Steps at Different Frequencies {#optimizer-steps-at-different-frequencies}

In manual optimization, you are free to `step()` one optimizer more often than another one.
For example, here we step the optimizer for the *discriminator* weights twice as often as the optimizer for the *generator*.

```python
# Alternating schedule for optimizer steps (e.g. GANs)
def training_step(self, batch, batch_idx):
    g_opt, d_opt = self.optimizers()
    ...

    # update discriminator every other step
    d_opt.zero_grad()
    self.manual_backward(errD)
    if (batch_idx + 1) % 2 == 0:
        d_opt.step()

    ...

    # update generator every step
    g_opt.zero_grad()
    self.manual_backward(errG)
    g_opt.step()
```

### Use Closure for LBFGS-like Optimizers {#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 `LBFGS`.

See [the PyTorch docs](https://pytorch.org/docs/stable/optim.html#optimizer-step-closure) for more about the closure.

Here is an example using a closure function.

```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 `LBFGS` optimizer is not supported for AMP or DeepSpeed.

---

**Navigation**

- Parent: [Model](/docs/pytorch/2.6.6/reference/model.md)