Commit 939ae80
Change schedulers to chainable form (#24352)
Summary:
Pull Request resolved: #24352
Enable chainable schedulers as requested in #13022 by implementing the changes mentioned below from [comment](#21800 (comment)).
* Changing the behavior of schedulers to the chainable formula when available
* Using the closed form whenever epoch is different from None until the next release with a deprecation warning
* Making `get_computed_values` the supported way of obtaining the last computed learning rate by the scheduler (see [comment](#21800 (comment)) for new syntax)
* Returning a deprecation warning when invoking the undocumented get_lr function (see [comment](#21800 (comment))) referring to `get_computed_values`, and deprecating it in the next release.
* `CosineAnnealingWarmRestart` still takes an epoch parameter as it is the only one with a mechanic relying on fractional epoch
* `MultiplicativeLR` is consumes a function providing the multiplicative factor at each epoch. It mimics `LambdaLR` in its syntax.
# #20527
### Before
The user calls scheduler with a constant epoch either across loops or in the same loop.
```
import torch.optim as optim
from torch import nn
conv = nn.Conv2d(3,3,3)
optimizer = optim.Adam(conv.parameters())
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 2)
# Scheduler with sometimes-constant epoch number
for epoch in [0, 0, 1, 1, 2, 2, 3, 3]:
lr_scheduler.step(epoch)
print(optimizer.param_groups[0]['lr'])
```
### After
If the user wants to step
```
import torch.optim as optim
from torch import nn
conv = nn.Conv2d(3,3,3)
optimizer = optim.Adam(conv.parameters())
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 2)
last_epoch = -1
for epoch in [0, 0, 1, 1, 2, 2, 3, 3]:
# Check if epoch number has changed manually
if epoch-last_epoch > 0:
lr_scheduler.step()
last_epoch = epoch
print(epoch, scheduler.get_computed_values())
```
# #22107
### Before
```
import torch
from torchvision.models import resnet18
net = resnet18()
optimizer = torch.optim.SGD(net.parameters(), 0.1)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[3, 6, 9], gamma=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 3, gamma=0.1)
for i in range(10):
# Scheduler computes and returns new learning rate, leading to unexpected behavior
print(i, scheduler.get_lr())
scheduler.step()
```
### After
```
import torch
from torchvision.models import resnet18
net = resnet18()
optimizer = torch.optim.SGD(net.parameters(), 0.1)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[3, 6, 9], gamma=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 3, gamma=0.1)
for i in range(10):
# Returns last computed learning rate by scheduler
print(i, lr_scheduler.get_computed_values())
lr_scheduler.step()
```
Test Plan: Imported from OSS
Differential Revision: D17349760
Pulled By: vincentqb
fbshipit-source-id: 0a6ac01e2a6b45000bc6f9df732033dd81f0d89f1 parent 2503fdc commit 939ae80
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