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 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 parametersself.toggle_optimizer()
andself.untoggle_optimizer()
if needed
Here is a minimal example of manual optimization.
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¶
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.
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
Trainer for automatic optimization. To perform gradient accumulation with one optimizer
after every N
steps, you can do as such.
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()
Gradient Clipping¶
You can clip optimizer gradients during manual optimization similar to passing the gradient_clip_val
and
gradient_clip_algorithm
argument in Trainer during automatic optimization.
To perform gradient clipping with one optimizer with manual optimization, you can do as such.
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 usingself. clip_gradients()
manually like in the example above.
Use Multiple Optimizers (like GANs)¶
Here is an example training a simple GAN with multiple optimizers using manual optimization.
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¶
Every optimizer you use can be paired with any
Learning Rate Scheduler. 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 inconfigure_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 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 on_train_epoch_end(self):
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"])
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.
# 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¶
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 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 LBFGS
optimizer is not supported for AMP, IPUs, or DeepSpeed.