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=Falsein 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.
.. 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).
The provided optimizer is a :class:`~pytorch_lightning.core.optimizer.LightningOptimizer` object wrapping your own optimizer
configured in your :meth:`~pytorch_lightning.core.module.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
...
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()
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
Every optimizer you use can be paired with any Learning Rate Scheduler. Please see the documentation of :meth:`~pytorch_lightning.core.module.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.module.LightningModule` to access any learning rate schedulers
defined in your :meth:`~pytorch_lightning.core.module.LightningModule.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 :meth:`~pytorch_lightning.core.module.LightningModule.configure_optimizers` in case of automatic optimization.- Note that the
lr_scheduler_configkeys, such as"frequency"and"interval", will be ignored even if they are provided in your :meth:`~pytorch_lightning.core.module.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"])
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.