Doing some tests with all Optimizer and LRScheduler classes in optim package, I noticed a couple of mistakes in type annotations, so created a pull request to fix them.
- In Optimizer class, incorrectly named parameter `default` instead of `defaults` in pyi file
- In SGD class, type for `maximize` and `differentiable` not available in either py or pyi files
I don't know if there is a plan to move all types from pyi to py files, so wasn't too sure where to fix what.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90216
Approved by: https://github.com/janeyx99
Fixes#84053
As described in the issue, the AveragedModel will deep copy the model during initialization, which means that the buffers in the averaged model cannot be updated together with the model.
One solution is to make the buffers equal to the source model every time when calling `update_parameters`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84054
Approved by: https://github.com/samdow
Hi, we noticed in our team that by using CyclicLR, there is a problem with memory clearance on GPU (probably it will be the case without the GPU as well, but that was our use case) After initializing CyclicLR, GPU memory is not cleared even after the model, optimizer and scheduler are out of scope (e.g. reference count is zero). This is because `__init__` method inside `CyclicLR` creates reference to its own methods and it will not get removed until `gc.collect()` is called manually. This is a problem if people want to test multiple models in one run of a script, after testing the first model, second one will fail on `CUDA out of memory error` because the first one is not cleared from the memory.
I propose a simple fix by using `weakref`, similarly as in `_LRScheduler` base class, but if you have any comments I am happy to change it.
Here is the code to reproduce the bug:
```
import torch
import weakref
from transformers import DetrForObjectDetection
class X:
def __init__(self, optimizer):
self.optimizer = optimizer
# Will cause cyclic reference.
self.func = self.dummy
# Will work as expected, memory cleared after instance count is zero.
# self.func = weakref.WeakMethod(self.dummy)
def dummy(self, x):
return 1.
def test():
model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
model.to('cuda')
optimizer = torch.optim.Adam(model.parameters())
x = X(optimizer)
test()
print(f'{torch.cuda.memory_reserved()}, {torch.cuda.memory_allocated()}') # Should print (<some memory>, 0), but with cyclic reference, it will print (<some memory>, <some memory>).
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85462
Approved by: https://github.com/albanD
Hello there 👋
As discussed in #84485, this PR enables more flexibility on the optimizers that are wrapped by LR schedulers in PyTorch. Currently, it is incompatible with optimizers that have a number of betas different than 2. This PR fixes that with minimal modifications.
Fixes#84485
Any feedback is welcome!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84486
Approved by: https://github.com/Lezcano, https://github.com/soulitzer
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
### Description
Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function.
### Testing
There shouldn't be any testing required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487
Approved by: https://github.com/albanD
### Description
PR #80336 introduced a new parameter to the Sparse Adam optimizer. The new parameter is accessed inside the `step` method of the optimizer. If we try to deserialize and run an older version of the optimizer before this change was introduced, it fails in the step that tries to access the missing parameter.
I have added a workaround to set a default value in case the parameter is unavailable in the optimizer.
### Issue
<!-- Link to Issue ticket or RFP -->
### Testing
* Testing on PyTorch CI
* Manual validation against existing serialized models to make sure they continue to work
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82273
Approved by: https://github.com/mehtanirav, https://github.com/albanD
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
