oneflow.optim¶
oneflow.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can be also easily integrated in the future.
How to use an optimizer¶
To use oneflow.optim
you have to construct an optimizer object, that will hold
the current state and will update the parameters based on the computed gradients.
Constructing it¶
To construct an Optimizer
you have to give it an iterable containing the
parameters (all should be Variable
s) to optimize. Then,
you can specify optimizer-specific options such as the learning rate, weight decay, etc.
Note
If you need to move a model to GPU via .cuda()
, please do so before
constructing optimizers for it. Parameters of a model after .cuda()
will be different objects with those before the call.
In general, you should make sure that optimized parameters live in consistent locations when optimizers are constructed and used.
Example:
import oneflow
import oneflow.nn as nn
import oneflow.optim as optim
model = nn.Linear(16, 3)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
Per-parameter options¶
Optimizer
also support specifying per-parameter options. To do this, instead
of passing an iterable of Variable
, pass in an iterable of
dict
. 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:
import oneflow.nn as nn
import oneflow.optim as optim
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.base = nn.Linear(64, 32)
self.classifier = nn.Linear(32, 10)
def forward(self, x):
out = self.base(x)
out = self.classifier(out)
return out
model = Model()
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.
Taking an optimization step¶
All optimizers implement a step()
method, that updates the
parameters. It can be used in two ways:
optimizer.step()
¶
This is a simplified version supported by most optimizers. The function can be
called once the gradients are computed using e.g.
backward()
.
Example:
import oneflow
import oneflow.nn as nn
import oneflow.nn.functional as F
import oneflow.optim as optim
from oneflow.utils.data import Dataset, DataLoader
class CustomDataset(Dataset):
def __init__(self, num):
self.inputs = oneflow.randn(num, 1)
self.targets = oneflow.sin(self.inputs)
def __len__(self):
return self.inputs.shape[0]
def __getitem__(self, index):
return self.inputs[index], self.targets[index]
class Model(nn.Module):
def __init__(self, input_size):
super(Model, self).__init__()
self.linear1 = nn.Linear(input_size, 64)
self.linear2 = nn.Linear(64, input_size)
def forward(self, x):
out = self.linear1(x)
return self.linear2(F.relu(out))
dataset = CustomDataset(10000)
dataloader = DataLoader(dataset, batch_size=10)
model = Model(1)
loss_fn = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-3)
for epoch in range(100):
for input, target in dataloader:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
Base class¶
-
class
oneflow.optim.
Optimizer
(parameters, options)¶
Add a param group to the |
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Load the state of the optimizer which is created by state_dict function. |
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Returns the state of the optimizer as a |
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Performs a single optimization step (parameter update). |
|
Sets the gradients of all optimized |
Algorithms¶
Adjust Learning Rate¶
oneflow.optim.lr_scheduler
provides several methods to adjust the learning
rate based on the number of epochs. oneflow.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:
import oneflow
import oneflow.nn as nn
import oneflow.nn.functional as F
import oneflow.optim as optim
from oneflow.utils.data import Dataset, DataLoader
class CustomDataset(Dataset):
def __init__(self, num):
self.inputs = oneflow.randn(num, 1)
self.targets = oneflow.sin(self.inputs)
def __len__(self):
return self.inputs.shape[0]
def __getitem__(self, index):
return self.inputs[index], self.targets[index]
class Model(nn.Module):
def __init__(self, input_size):
super(Model, self).__init__()
self.linear1 = nn.Linear(input_size, 64)
self.linear2 = nn.Linear(64, input_size)
def forward(self, x):
out = self.linear1(x)
return self.linear2(F.relu(out))
dataset = CustomDataset(10000)
dataloader = DataLoader(dataset, batch_size=10)
model = Model(1)
loss_fn = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-3)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
for epoch in range(20):
for input, target in dataloader:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
scheduler.step()
Most learning rate schedulers can be chained (also referred to as chaining schedulers).
Example:
import oneflow
import oneflow.nn as nn
import oneflow.nn.functional as F
import oneflow.optim as optim
from oneflow.utils.data import Dataset, DataLoader
class CustomDataset(Dataset):
def __init__(self, num):
self.inputs = oneflow.randn(num, 1)
self.targets = oneflow.sin(self.inputs)
def __len__(self):
return self.inputs.shape[0]
def __getitem__(self, index):
return self.inputs[index], self.targets[index]
class Model(nn.Module):
def __init__(self, input_size):
super(Model, self).__init__()
self.linear1 = nn.Linear(input_size, 64)
self.linear2 = nn.Linear(64, input_size)
def forward(self, x):
out = self.linear1(x)
return self.linear2(F.relu(out))
dataset = CustomDataset(10000)
dataloader = DataLoader(dataset, batch_size=10)
model = Model(1)
loss_fn = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-3)
scheduler1 = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
scheduler2 = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5, 10], gamma=0.1)
for epoch in range(20):
for input, target in dataloader:
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
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. Please
check if you are calling scheduler.step()
at the wrong time.
Set the learning rate of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial lr and \(T_{cur}\) is the number of epochs since the last restart in SGDR: |
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This operator creates a Cosine decayed learning rate scheduler. |
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Decays the learning rate of each parameter group by gamma every epoch. |
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Sets the learning rate of each parameter group to the initial lr times a given function. |
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Decays the learning rate of each parameter group by gamma once the number of step reaches one of the milestones. |
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This operator creates a polynomial decayed learning rate scheduler. |
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Reduce learning rate when a metric has stopped improving. |
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Decays the learning rate of each parameter group by gamma every step_size steps. |
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Decays the learning rate of each parameter group by a small constant factor until the number of step reaches a pre-defined milestone: total_iters. |
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Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of step reaches a pre-defined milestone: total_iters. |
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Chains list of learning rate schedulers. |
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Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact intervals to reflect which scheduler is supposed to be called at a given step. |
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Set the learning rate of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial lr, \(T_{cur}\) is the number of steps since the last restart and \(T_{i}\) is the number of steps between two warm restarts in SGDR: |