.. _optimization:
############
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:
*******************
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 <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`_. 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 <https://pytorch.org/docs/stable/optim.html#optimizer-step-closure>`_ 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:
.. code-block:: python
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:
.. code-block:: python
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``.
Gradient Accumulation
=====================
.. include:: ../common/gradient_accumulation.rst
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:
.. code-block:: python
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 <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`_.
One good example is `Timm Schedulers <https://github.com/rwightman/pytorch-image-models/blob/master/timm/scheduler/scheduler.py>`_. 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.
.. code-block:: python
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:
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
)
Total Stepping Batches
======================
You can use built-in trainer property :paramref:`~pytorch_lightning.trainer.trainer.Trainer.estimated_stepping_batches` to compute
total number of stepping batches for the complete training. The property is computed considering gradient accumulation factor and
distributed setting into consideration so you don't have to derive it manually. One good example where this can be helpful is while using
:class:`~torch.optim.lr_scheduler.OneCycleLR` scheduler, which requires pre-computed ``total_steps`` during initialization.
.. code-block:: python
def configure_optimizers(self):
optimizer = ...
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches
)
return [optimizer], [scheduler]