Adds the `differentiable` argument, a method for updating parameters in an existing optimizer, and a template for testing the differentiability of multiple optimizers.
This is all based in discussions with @albanD & @jbschlosser
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80938
Approved by: https://github.com/albanD
What was happening is that when we have multiple learning rate schedulers, the order in which they are being initialized is not being taken into account. This is a problem if they were being initialized in sequential order (as one might intuitively do).
Each scheduler calls `step()` on initialization and sets the `lr` in its optimizer's `params_groups`. However, this means that step 0 will be using the `lr` that was set by the very last scheduler (in the case of initializing schedulers sequentially) instead of the first scheduler.
The fix in this PR, addresses the above bug by performing a call to the appropriate scheduler on initialization after decrementing the `last_epoch` values in order to keep them the same post-step. This will ensure that the correct scheduler is the one setting the `lr` values for the optimizer's `param_groups`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72856
Approved by: https://github.com/jbschlosser
Fixes#60265
The initial LR for this scheduler is not consistent when a new instance is created with `last_epoch != -1`
Maybe we can refactor the testing code to test `last_epoch != -1` in schedulers that can recreate their state from the current epoch?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60339
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69980
- Merged `torch/optim/adadelta.py` and `torch/optim/_multitensor/adadelta.py` into `torch/optim/adadelta.py`
- Moved adadelta functional forms from `torch/optim/_functional.py` and `torch/optim/_multi_tensor/_functional.py` to `torch/optim/adadelta.py`
- `torch/optim/_functional.py` just imports from `torch/optim/adadelta.py`
- Added a test `test_optimizers_foreach_flag` which replicates `test_multi_tensor_optimizers` in `test/test_optim.py`
- Add a test `test_adadelta_new` that replicates the behavior of `test_adadelta` but with `foreach` flag instead of using the multitensor adadleta class. If we delete `_multitensor/` we could replace `test_adadelta` with this
Remaining TODO:
- [ ] single_tensor adadelta supports complex but multitensor does not, need to integrate the singletensor logic in multitensor and switch the `test_adadelta_complex` to test for foreach in [True, False]
Test Plan: Imported from OSS
Reviewed By: VitalyFedyunin, albanD
Differential Revision: D33413059
Pulled By: mikaylagawarecki
fbshipit-source-id: 92a9fa98705762bb6bd464261671e49aef40070e
(cherry picked from commit a008227d227749d79367d7d592bcefcf51c22df5)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71333
Updated
- Adagrad
- Adamax
- Adam
- AdamW
- RAdam
make multi_tensor functionals take `state_steps: List[Tensor]` instead of taking `states: List[Dict]`
make `state_steps: List[int]s -> state_steps:List[Tensor]` where each is a Singleton tensor so step can be updated within the functional
(NAdam and ASGD) were updated in separate diffs to fold their handling of state into the functionals
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D33767872
Pulled By: mikaylagawarecki
fbshipit-source-id: 9baa7cafb6375eab839917df9287c65a437891f2
(cherry picked from commit 831c02b3d0f585f61165ead368213f94b97a99ee)
Summary:
In `TestLRScheduler._test()`, an unused variable `optimizers` is created. This PR is a minor refactoring that removes the variable and the loop block that populates the set.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70668
Reviewed By: wenleix
Differential Revision: D33586236
Pulled By: albanD
fbshipit-source-id: cabf870a8221f144df9d3e2f2b564cdc5c255f5a
Summary:
Solves the next most important use case in https://github.com/pytorch/pytorch/issues/68052.
I have kept the style as close to that in SGD as seemed reasonable, given the slight differences in their internal implementations.
All feedback welcome!
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68164
Reviewed By: VitalyFedyunin
Differential Revision: D32994129
Pulled By: albanD
fbshipit-source-id: 65c57c3f3dbbd3e3e5338d51def54482503e8850
Summary:
Fixes https://github.com/pytorch/pytorch/issues/67601.
As simple a fix as I could make it. I even managed to delete some testing code!
I checked calling `super()` and, as I had feared, it doesn't work out the box, so perhaps that ought to be revisited later.
As it stands, https://github.com/pytorch/pytorch/issues/20124, still applies to the chained scheduler, but I think this change is still an improvement.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68010
Reviewed By: zou3519
Differential Revision: D32278139
Pulled By: albanD
fbshipit-source-id: 4c6f9f1b2822affdf63a6d22ddfdbcb1c6afd579
Summary:
Fixes https://github.com/pytorch/pytorch/issues/46480 -- for SGD.
## Notes:
- I have modified the existing tests to take a new `constructor_accepts_maximize` flag. When this is set to true, the ` _test_basic_cases_template` function will test both maximizing and minimizing the sample function.
- This was the clearest way I could think of testing the changes -- I would appreciate feedback on this strategy.
## Work to be done:
[] I need to update the docs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67847
Reviewed By: H-Huang
Differential Revision: D32252631
Pulled By: albanD
fbshipit-source-id: 27915a3cc2d18b7e4d17bfc2d666fe7d2cfdf9a4
Summary:
Catches deprecation warnings when we call `scheduler.step(epoch)`
in tests.
Removes duplicate parameters to optimizers unless we are specifically
testing for that
Fixes https://github.com/pytorch/pytorch/issues/67696
There is one warning remaining when I run this locally -- however that is due to the implementation of the `SequentialLR` Scheduler. I will open a new issue relating to that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67954
Reviewed By: H-Huang
Differential Revision: D32244056
Pulled By: albanD
fbshipit-source-id: 2ab3086a58e10c8d29809ccbaab80606a1ec61d8
Summary:
Fixes part of https://github.com/pytorch/pytorch/issues/67696 by adding calls to `optimizer.step()` in various places.
## Notes for reviewers:
- It is not entirely clear which is the right optimizer to step in each case. I have favoured the more explicit approach of creating a set of optimizers and calling step on each of them.
- At the time of writing, the only Scheduler without an `optimizer` instance variable is `ChainedScheduler` which I need to deal with once. I use `hasattr` to do this check. Let me know if this ought to be changed.
- I am opening this PR for review when it only solve part of the issue, as I'd rather get feedback sooner. I think it is fine to fix the issue in several PRs too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67756
Reviewed By: jbschlosser
Differential Revision: D32187864
Pulled By: albanD
fbshipit-source-id: fd0d133bcaa3a24588e5a997ad198fdf5879ff5a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66587
Made some changes in the step function of the non-vectorized Adadelta optimizer to handle complex numbers as two real numbers as per 65711 on github
ghstack-source-id: 141484731
Test Plan:
buck test mode/dev caffe2/test:optim -- 'test_adadelta_complex'
https://pxl.cl/1R7kJ
Reviewed By: albanD
Differential Revision: D31630069
fbshipit-source-id: 2741177b837960538ce39772897af36bbce7b7d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66671
Made changes in the step function of the vectorized and non-vectorized adagrad optimizers to handle complex numbers as two real numbers as per 65711 on github
ghstack-source-id: 141442350
Test Plan:
buck test mode/dev caffe2/test:optim -- 'test_adagrad_complex'
https://pxl.cl/1Rd44
Reviewed By: albanD
Differential Revision: D31673503
fbshipit-source-id: 90a0d0c69b556716e2d17c59ce80f09c750fc464
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66501
Add testing for the Adagrad optimizer to ensure that it behaves as if complex numbers are two real numbers in R^2 as per issue 65711 on github
ghstack-source-id: 140414042
Test Plan:
buck test mode/dev caffe2/test:optim -- 'test_adagrad_complex'
https://pxl.cl/1R27M
Reviewed By: albanD
Differential Revision: D31584240
fbshipit-source-id: 5c9938084566b8ea49cc8ff002789731f62fe87e
Summary: Adding test to ensure non-Vanilla SGD behaves as if complex numbers are two real numbers in R^2 as per issue 65711 on github
Test Plan:
```buck test mode/dev caffe2/test:optim -- 'test_sgd_complex'```
https://pxl.cl/1QLxw
Reviewed By: albanD
Differential Revision: D31477212
fbshipit-source-id: 500678e561a05ac96759223b4c87a37cab26c6a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66230
Adding test to ensure Vanilla SGD behaves as if complex numbers are two real numbers in R^2 as per issue 65711 on github
https://github.com/pytorch/pytorch/issues/65711
ghstack-source-id: 139918862
Test Plan:
```buck test mode/dev caffe2/test:optim -- 'test_sgd_complex'```
https://pxl.cl/1QHvX
Reviewed By: albanD
Differential Revision: D31449289
fbshipit-source-id: da8b00421085796a23b643e73f96b19b5b560a32
Summary:
Partially resolves https://github.com/pytorch/vision/issues/4281
In this PR we are proposing a new scheduler --SequentialLR-- which enables list of different schedulers called in different periods of the training process.
The main motivation of this scheduler is recently gained popularity of warming up phase in the training time. It has been shown that having a small steps in initial stages of training can help convergence procedure get faster.
With the help of SequentialLR we mainly enable to call a small constant (or linearly increasing) learning rate followed by actual target learning rate scheduler.
```PyThon
scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
scheduler2 = ExponentialLR(optimizer, gamma=0.9)
scheduler = SequentialLR(optimizer, schedulers=[scheduler1, scheduler2], milestones=[5])
for epoch in range(100):
train(...)
validate(...)
scheduler.step()
```
which this code snippet will call `ConstantLR` in the first 5 epochs and will follow up with `ExponentialLR` in the following epochs.
This scheduler could be used to provide call of any group of schedulers next to each other. The main consideration we should make is every time we switch to a new scheduler we assume that new scheduler starts from the beginning- zeroth epoch.
We also add Chained Scheduler to `optim.rst` and `lr_scheduler.pyi` files here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64037
Reviewed By: albanD
Differential Revision: D30841099
Pulled By: iramazanli
fbshipit-source-id: 94f7d352066ee108eef8cda5f0dcb07f4d371751
Summary:
Partially unblocks https://github.com/pytorch/vision/issues/4281
Previously we have added WarmUp Schedulers to PyTorch Core in the PR : https://github.com/pytorch/pytorch/pull/60836 which had two mode of execution - linear and constant depending on warming up function.
In this PR we are changing this interface to more direct form, as separating linear and constant modes to separate Schedulers. In particular
```Python
scheduler1 = WarmUpLR(optimizer, warmup_factor=0.1, warmup_iters=5, warmup_method="constant")
scheduler2 = WarmUpLR(optimizer, warmup_factor=0.1, warmup_iters=5, warmup_method="linear")
```
will look like
```Python
scheduler1 = ConstantLR(optimizer, warmup_factor=0.1, warmup_iters=5)
scheduler2 = LinearLR(optimizer, warmup_factor=0.1, warmup_iters=5)
```
correspondingly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64395
Reviewed By: datumbox
Differential Revision: D30753688
Pulled By: iramazanli
fbshipit-source-id: e47f86d12033f80982ddf1faf5b46873adb4f324
Summary:
In this PR we are introducing ChainedScheduler which initially proposed in the discussion https://github.com/pytorch/pytorch/pull/26423#discussion_r329976246 .
The idea is to provide a user friendly chaining method for schedulers, especially for the cases many of them are involved and we want to have a clean and easy to read interface for schedulers. This method will be even more crucial once CompositeSchedulers and Schedulers for different type of parameters are involved.
The immediate application of Chained Scheduler is expected to happen in TorchVision Library to combine WarmUpLR and MultiStepLR https://github.com/pytorch/vision/blob/master/references/video_classification/scheduler.py#L5 . However, it can be expected that in many other use cases also this method could be applied.
### Example
The usage is as simple as below:
```python
sched=ChainedScheduler([ExponentialLR(self.opt, gamma=0.9),
WarmUpLR(self.opt, warmup_factor=0.2, warmup_iters=4, warmup_method="constant"),
StepLR(self.opt, gamma=0.1, step_size=3)])
```
Then calling
```python
sched.step()
```
would trigger step function for all three schedulers consecutively
Partially resolves https://github.com/pytorch/vision/issues/4281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63491
Reviewed By: datumbox, mruberry
Differential Revision: D30576180
Pulled By: iramazanli
fbshipit-source-id: b43f0749f55faab25079641b7d91c21a891a87e4
Summary:
It has been discussed in the https://github.com/pytorch/pytorch/pull/60836#issuecomment-899084092 that we have observed an obstacle to chain some type of learning rate schedulers. In particular we observed
* some of the learning rate schedulers returns initial learning rates at epoch 0 as
```
return self.base_lrs`
```
* This can be a problem when two schedulers called as chained as
```
scheduler1.step()
scheduler2.step()
```
in particular, we completely ignore the effect of scheduler1 at epoch 0. This could not be an issue if at epoch 0, scheduler1 was ineffective as in many schedulers, however for schedulers as WarmUp Schedulers, where at epoch 0 schedulers multiplicative value is smaller than 1 this could lead to undesired behaviors.
The following code snippet illustrates the problem better
## Reproducing the bug
```python
import torch
from torch.nn import Parameter
from torch.optim import SGD
from torch.optim.lr_scheduler import WarmUpLR, ExponentialLR
model = [Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = SGD(model, 1.0)
scheduler1 = WarmUpLR(optimizer, warmup_factor=0.1, warmup_iters=5, warmup_method="constant")
scheduler2 = ExponentialLR(optimizer, gamma=0.9)
for epoch in range(10):
print(epoch, scheduler2.get_last_lr()[0])
optimizer.step()
scheduler1.step()
scheduler2.step()
```
### Current Result
```
0 1.0
1 0.9
2 0.81
3 0.7290000000000001
4 0.6561000000000001
5 5.904900000000001
6 5.314410000000001
7 4.782969000000001
8 4.304672100000001
9 3.874204890000001
```
### Expected Result
```
0 1.0
1 0.9
2 0.81
3 0.7290000000000001
4 0.6561000000000001
5 0.5904900000000001
6 0.5314410000000001
7 0.4782969000000001
8 0.4304672100000001
9 0.3874204890000001
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63457
Reviewed By: datumbox
Differential Revision: D30424160
Pulled By: iramazanli
fbshipit-source-id: 3e15af8d278c872cd6f53406b55f4d3ce5002867
