Optimization¶
Lightning offers two modes for managing the optimization process:
automatic optimization
manual 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 precision and accelerators 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 yourLightningModule
’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 stepself.manual_backward(loss)
instead ofloss.backward()
optimizer.step()
to update your model parameters
Here is a minimal example of manual optimization.
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)
.
Gradient accumulation¶
You can accumulate gradients over batches similarly to
accumulate_grad_batches
of automatic optimization.
To perform gradient accumulation with one optimizer, you can do as such.
# accumulate gradients over `n` batches
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) [manual]¶
Here is an example training a simple GAN with multiple optimizers.
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 configure_optimizers()
for all the available options
Learning rate scheduling [manual]¶
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
Before 1.3, 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"step"
and"interval"
, will be ignored even if they are provided in yourconfigure_optimizers()
during manual optimization.
Here is an example calling lr_scheduler.step()
every step.
# 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.
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:
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 torch.optim.LBFGS
.
See the PyTorch docs for more about the closure.
Here is an example using a closure function.
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 torch.optim.LBFGS
optimizer is not supported for apex AMP, native AMP, IPUs, or DeepSpeed.
Access your own optimizer [manual]¶
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 and precision for you.
def __init__(self):
super().__init__()
self.automatic_optimization = False
def training_step(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
...
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)
for lr_scheduler in lr_schedulers:
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
, zero_grad
and backward
for the optimizer to execute. 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. LBFGS
).
Warning
Before 1.2.2, Lightning internally calls backward
, step
and zero_grad
in the order.
From 1.2.2, 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
configure_optimizers()
.
# 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 optimizers at arbitrary intervals¶
To do more interesting things with your optimizers such as learning rate warm-up or odd scheduling,
override the 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()
,
backward()
are called in the closure function.
For example, here step optimizer A every batch and optimizer B every 2 batches.
# 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 learning rate warm-up.
# 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,
):
# 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
# update params
optimizer.step(closure=optimizer_closure)
Access your own optimizer¶
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 and precision for you.
# 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)
Configure gradient clipping¶
To configure custom gradient clipping, consider overriding
the configure_gradient_clipping()
method.
Attributes gradient_clip_val
and
gradient_clip_algorithm
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 clip_gradients()
method and pass
the arguments along with your optimizer.
Note
Make sure to not override clip_gradients()
method. If you want to customize gradient clipping, consider using
configure_gradient_clipping()
method.
For example, here we will apply gradient clipping only to the gradients associated with optimizer A.
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.
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
)