.. automodule:: torch.optim
To use :mod:`torch.optim` you have to construct an optimizer object that will hold the current state and will update the parameters based on the computed gradients.
To construct an :class:`Optimizer` you have to give it an iterable containing the parameters (all should be :class:`~torch.autograd.Variable` s) to optimize. Then, you can specify optimizer-specific options such as the learning rate, weight decay, etc.
Example:
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) optimizer = optim.Adam([var1, var2], lr=0.0001)
:class:`Optimizer` s also support specifying per-parameter options. To do this, instead
of passing an iterable of :class:`~torch.autograd.Variable` s, pass in an iterable of
:class:`dict` s. Each of them will define a separate parameter group, and should contain
a params key, containing a list of parameters belonging to it. Other keys
should match the keyword arguments accepted by the optimizers, and will be used
as optimization options for this group.
Note
You can still pass options as keyword arguments. They will be used as defaults, in the groups that didn't override them. This is useful when you only want to vary a single option, while keeping all others consistent between parameter groups.
For example, this is very useful when one wants to specify per-layer learning rates:
optim.SGD([
{'params': model.base.parameters()},
{'params': model.classifier.parameters(), 'lr': 1e-3}
], lr=1e-2, momentum=0.9)
This means that model.base's parameters will use the default learning rate of 1e-2,
model.classifier's parameters will use a learning rate of 1e-3, and a momentum of
0.9 will be used for all parameters.
All optimizers implement a :func:`~Optimizer.step` method, that updates the parameters. It can be used in two ways:
This is a simplified version supported by most optimizers. The function can be called once the gradients are computed using e.g. :func:`~torch.autograd.Variable.backward`.
Example:
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
Some optimization algorithms such as Conjugate Gradient and LBFGS need to reevaluate the function multiple times, so you have to pass in a closure that allows them to recompute your model. The closure should clear the gradients, compute the loss, and return it.
Example:
for input, target in dataset:
def closure():
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
return loss
optimizer.step(closure)
.. autoclass:: Optimizer
.. autosummary::
:toctree: generated
:nosignatures:
Optimizer.add_param_group
Optimizer.load_state_dict
Optimizer.state_dict
Optimizer.step
Optimizer.zero_grad
.. autosummary::
:toctree: generated
:nosignatures:
Adadelta
Adagrad
Adam
AdamW
SparseAdam
Adamax
ASGD
LBFGS
NAdam
RAdam
RMSprop
Rprop
SGD
Many of our algorithms have various implementations optimized for performance, readability and/or generality, so we attempt to default to the generally fastest implementation for the current device if no particular implementation has been specified by the user.
We have 3 major categories of implementations: for-loop, foreach (multi-tensor), and fused. The most straightforward implementations are for-loops over the parameters with big chunks of computation. For-looping is usually slower than our foreach implementations, which combine parameters into a multi-tensor and run the big chunks of computation all at once, thereby saving many sequential kernel calls. A few of our optimizers have even faster fused implementations, which fuse the big chunks of computation into one kernel. We can think of foreach implementations as fusing horizontally and fused implementations as fusing vertically on top of that.
In general, the performance ordering of the 3 implementations is fused > foreach > for-loop. So when applicable, we default to foreach over for-loop. Applicable means the foreach implementation is available, the user has not specified any implementation-specific kwargs (e.g., fused, foreach, differentiable), and all tensors are native and on CUDA. Note that while fused should be even faster than foreach, the implementations are newer and we would like to give them more bake-in time before flipping the switch everywhere. You are welcome to try them out though!
Below is a table showing the available and default implementations of each algorithm:
| Algorithm | Default | Has foreach? | Has fused? |
|---|---|---|---|
| :class:`Adadelta` | foreach | yes | no |
| :class:`Adagrad` | foreach | yes | no |
| :class:`Adam` | foreach | yes | yes |
| :class:`AdamW` | foreach | yes | yes |
| :class:`SparseAdam` | for-loop | no | no |
| :class:`Adamax` | foreach | yes | no |
| :class:`ASGD` | foreach | yes | no |
| :class:`LBFGS` | for-loop | no | no |
| :class:`NAdam` | foreach | yes | no |
| :class:`RAdam` | foreach | yes | no |
| :class:`RMSprop` | foreach | yes | no |
| :class:`Rprop` | foreach | yes | no |
| :class:`SGD` | foreach | yes | no |
:mod:`torch.optim.lr_scheduler` provides several methods to adjust the learning rate based on the number of epochs. :class:`torch.optim.lr_scheduler.ReduceLROnPlateau` allows dynamic learning rate reducing based on some validation measurements.
Learning rate scheduling should be applied after optimizer's update; e.g., you should write your code this way:
Example:
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler = ExponentialLR(optimizer, gamma=0.9)
for epoch in range(20):
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
scheduler.step()
Most learning rate schedulers can be called back-to-back (also referred to as chaining schedulers). The result is that each scheduler is applied one after the other on the learning rate obtained by the one preceding it.
Example:
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler1 = ExponentialLR(optimizer, gamma=0.9)
scheduler2 = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
for epoch in range(20):
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
scheduler1.step()
scheduler2.step()
In many places in the documentation, we will use the following template to refer to schedulers algorithms.
>>> scheduler = ... >>> for epoch in range(100): >>> train(...) >>> validate(...) >>> scheduler.step()
Warning
Prior to PyTorch 1.1.0, the learning rate scheduler was expected to be called before
the optimizer's update; 1.1.0 changed this behavior in a BC-breaking way. If you use
the learning rate scheduler (calling scheduler.step()) before the optimizer's update
(calling optimizer.step()), this will skip the first value of the learning rate schedule.
If you are unable to reproduce results after upgrading to PyTorch 1.1.0, please check
if you are calling scheduler.step() at the wrong time.
.. autosummary::
:toctree: generated
:nosignatures:
lr_scheduler.LambdaLR
lr_scheduler.MultiplicativeLR
lr_scheduler.StepLR
lr_scheduler.MultiStepLR
lr_scheduler.ConstantLR
lr_scheduler.LinearLR
lr_scheduler.ExponentialLR
lr_scheduler.PolynomialLR
lr_scheduler.CosineAnnealingLR
lr_scheduler.ChainedScheduler
lr_scheduler.SequentialLR
lr_scheduler.ReduceLROnPlateau
lr_scheduler.CyclicLR
lr_scheduler.OneCycleLR
lr_scheduler.CosineAnnealingWarmRestarts
:mod:`torch.optim.swa_utils` implements Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). In particular, the :class:`torch.optim.swa_utils.AveragedModel` class implements SWA and EMA models, :class:`torch.optim.swa_utils.SWALR` implements the SWA learning rate scheduler and :func:`torch.optim.swa_utils.update_bn` is a utility function used to update SWA/EMA batch normalization statistics at the end of training.
SWA has been proposed in Averaging Weights Leads to Wider Optima and Better Generalization.
EMA is a widely known technique to reduce the training time by reducing the number of weight updates needed. It is a variation of Polyak averaging, but using exponential weights instead of equal weights across iterations.
The AveragedModel class serves to compute the weights of the SWA or EMA model.
You can create an SWA averaged model by running:
>>> averaged_model = AveragedModel(model)EMA models are constructed by specifying the multi_avg_fn argument as follows:
>>> decay = 0.999
>>> averaged_model = AveragedModel(model, multi_avg_fn=get_ema_multi_avg_fn(decay))Decay is a parameter between 0 and 1 that controls how fast the averaged parameters are decayed. If not provided to get_ema_multi_avg_fn, the default is 0.999.
get_ema_multi_avg_fn returns a function that applies the following EMA equation to the weights:
W^\textrm{EMA}_{t+1} = \alpha W^\textrm{EMA}_{t} + (1 - \alpha) W^\textrm{model}_t
where alpha is the EMA decay.
Here the model model can be an arbitrary :class:`torch.nn.Module` object. averaged_model
will keep track of the running averages of the parameters of the model. To update these
averages, you should use the :func:`update_parameters` function after the optimizer.step():
>>> averaged_model.update_parameters(model)For SWA and EMA, this call is usually done right after the optimizer step(). In the case of SWA, this is usually skipped for some numbers of steps at the beginning of the training.
By default, :class:`torch.optim.swa_utils.AveragedModel` computes a running equal average of
the parameters that you provide, but you can also use custom averaging functions with the
avg_fn or multi_avg_fn parameters:
avg_fnallows defining a function operating on each parameter tuple (averaged parameter, model parameter) and should return the new averaged parameter.multi_avg_fnallows defining more efficient operations acting on a tuple of parameter lists, (averaged parameter list, model parameter list), at the same time, for example using thetorch._foreach*functions. This function must update the averaged parameters in-place.
In the following example ema_model computes an exponential moving average using the avg_fn parameter:
>>> ema_avg = lambda averaged_model_parameter, model_parameter, num_averaged:\
>>> 0.9 * averaged_model_parameter + 0.1 * model_parameter
>>> ema_model = torch.optim.swa_utils.AveragedModel(model, avg_fn=ema_avg)In the following example ema_model computes an exponential moving average using the more efficient multi_avg_fn parameter:
>>> ema_model = AveragedModel(model, multi_avg_fn=get_ema_multi_avg_fn(0.9))Typically, in SWA the learning rate is set to a high constant value. :class:`SWALR` is a learning rate scheduler that anneals the learning rate to a fixed value, and then keeps it constant. For example, the following code creates a scheduler that linearly anneals the learning rate from its initial value to 0.05 in 5 epochs within each parameter group:
>>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, \
>>> anneal_strategy="linear", anneal_epochs=5, swa_lr=0.05)You can also use cosine annealing to a fixed value instead of linear annealing by setting
anneal_strategy="cos".
:func:`update_bn` is a utility function that allows to compute the batchnorm statistics for the SWA model
on a given dataloader loader at the end of training:
>>> torch.optim.swa_utils.update_bn(loader, swa_model):func:`update_bn` applies the swa_model to every element in the dataloader and computes the activation
statistics for each batch normalization layer in the model.
Warning
:func:`update_bn` assumes that each batch in the dataloader loader is either a tensors or a list of
tensors where the first element is the tensor that the network swa_model should be applied to.
If your dataloader has a different structure, you can update the batch normalization statistics of the
swa_model by doing a forward pass with the swa_model on each element of the dataset.
In the example below, swa_model is the SWA model that accumulates the averages of the weights.
We train the model for a total of 300 epochs and we switch to the SWA learning rate schedule
and start to collect SWA averages of the parameters at epoch 160:
>>> loader, optimizer, model, loss_fn = ...
>>> swa_model = torch.optim.swa_utils.AveragedModel(model)
>>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=300)
>>> swa_start = 160
>>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
>>>
>>> for epoch in range(300):
>>> for input, target in loader:
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
>>> if epoch > swa_start:
>>> swa_model.update_parameters(model)
>>> swa_scheduler.step()
>>> else:
>>> scheduler.step()
>>>
>>> # Update bn statistics for the swa_model at the end
>>> torch.optim.swa_utils.update_bn(loader, swa_model)
>>> # Use swa_model to make predictions on test data
>>> preds = swa_model(test_input)In the example below, ema_model is the EMA model that accumulates the exponentially-decayed averages of the weights with a decay rate of 0.999.
We train the model for a total of 300 epochs and start to collect EMA averages immediately.
>>> loader, optimizer, model, loss_fn = ...
>>> ema_model = torch.optim.swa_utils.AveragedModel(model, \
>>> multi_avg_fn=torch.optim.swa_utils.get_ema_multi_avg_fn(0.999))
>>>
>>> for epoch in range(300):
>>> for input, target in loader:
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
>>> ema_model.update_parameters(model)
>>>
>>> # Update bn statistics for the ema_model at the end
>>> torch.optim.swa_utils.update_bn(loader, ema_model)
>>> # Use ema_model to make predictions on test data
>>> preds = ema_model(test_input)