build(deps): update pytorch-lightning requirement from <2.0.0,>=1.5 t…

…o >=1.5,<3.0.0 in /requirements (#1636)

* build(deps): update pytorch-lightning requirement in /requirements

Updates the requirements on [pytorch-lightning](https://github.com/Lightning-AI/lightning) to permit the latest version.
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@1.5.0...2.0.0)

---
updated-dependencies:
- dependency-name: pytorch-lightning
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

* update
* 1.6
* .0
* pl
* lai

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>

docs/source/conf.py

@@ -301,7 +301,6 @@ def package_list_from_file(file):
 # define mapping from PyPI names to python imports
 PACKAGE_MAPPING = {
  "PyYAML": "yaml",
- "pytorch-lightning": "pytorch_lightning",
 }
 MOCK_PACKAGES = []
 if SPHINX_MOCK_REQUIREMENTS:

docs/source/pages/lightning.rst

@@ -2,7 +2,7 @@
 
  import torch
  from torch.nn import Module
- from pytorch_lightning.core.lightning import LightningModule
+ from lightning import LightningModule
  from torchmetrics import Metric
 
 #################################

docs/source/pages/overview.rst

@@ -1,7 +1,7 @@
 .. testsetup:: *
 
  import torch
- from pytorch_lightning.core.lightning import LightningModule
+ from lightning import LightningModule
 
 ##################
 Structure Overview
@@ -96,7 +96,7 @@ be moved to the same device as the input of the metric:
  print(out.device) # cuda:0
 
 However, when **properly defined** inside a :class:`~torch.nn.Module` or
-:class:`~pytorch_lightning.core.lightning.LightningModule` the metric will be automatically moved
+:class:`~lightning.LightningModule` the metric will be automatically moved
 to the same device as the module when using ``.to(device)``. Being
 **properly defined** means that the metric is correctly identified as a child module of the
 model (check ``.children()`` attribute of the model). Therefore, metrics cannot be placed

requirements/docs.txt

@@ -11,6 +11,8 @@ sphinx-paramlinks>=0.5.1
 sphinx-togglebutton>=0.2
 sphinx-copybutton>=0.3
 
+lightning>=1.8.0, <3.0.0
+
 # integrations
 -r integrate.txt
 -r visual.txt

requirements/integrate.txt

@@ -1 +1 @@
-pytorch-lightning>=1.6.0, <2.0.0
+pytorch-lightning >=1.6.0, <3.0.0

tests/integrations/lightning/boring_model.py

@@ -13,9 +13,14 @@
 # limitations under the License.
 
 import torch
-from pytorch_lightning import LightningModule
+from lightning_utilities import module_available
 from torch.utils.data import Dataset
 
+if module_available("lightning"):
+ from lightning import LightningModule
+else:
+ from pytorch_lightning import LightningModule
+
 
 class RandomDictStringDataset(Dataset):
  """Class for creating a dictionary of random strings."""

tests/integrations/test_lightning.py

@@ -14,13 +14,17 @@
 from unittest import mock
 
 import torch
-from pytorch_lightning import LightningModule, Trainer
+from lightning_utilities import module_available
 from torch import tensor
 from torch.nn import Linear
-from torch.utils.data import DataLoader
+
+if module_available("lightning"):
+ from lightning import LightningModule, Trainer
+else:
+ from pytorch_lightning import LightningModule, Trainer
 
 from integrations.helpers import no_warning_call
-from integrations.lightning.boring_model import BoringModel, RandomDataset
+from integrations.lightning.boring_model import BoringModel
 from torchmetrics import MetricCollection, SumMetric
 from torchmetrics.classification import BinaryAccuracy, BinaryAveragePrecision
 
@@ -40,7 +44,7 @@ class TestModel(BoringModel):
  def __init__(self) -> None:
  super().__init__()
  self.metric = SumMetric()
- self.sum = 0.0
+ self.register_buffer("sum", torch.tensor(0.0))
 
  def training_step(self, batch, batch_idx):
  x = batch
@@ -176,11 +180,11 @@ def __init__(self) -> None:
  super().__init__()
  self.metric_step = SumMetric()
  self.metric_epoch = SumMetric()
- self.sum = torch.tensor(0.0)
+ self.register_buffer("sum", torch.tensor(0.0))
  self.outs = []
 
  def on_train_epoch_start(self):
- self.sum = torch.tensor(0.0)
+ self.sum = torch.tensor(0.0, device=self.sum.device)
 
  def training_step(self, batch, batch_idx):
  x = batch
@@ -221,8 +225,8 @@ class TestModel(BoringModel):
  def __init__(self) -> None:
  super().__init__()
  self.metric = MetricCollection([SumMetric(), DiffMetric()])
- self.sum = torch.tensor(0.0)
- self.diff = torch.tensor(0.0)
+ self.register_buffer("sum", torch.tensor(0.0))
+ self.register_buffer("diff", torch.tensor(0.0))
 
  def training_step(self, batch, batch_idx):
  x = batch
@@ -265,7 +269,7 @@ def __init__(self) -> None:
  # the metric is not used in the module's `forward`
  # so the module should be exportable to TorchScript
  self.metric = SumMetric()
- self.sum = torch.tensor(0.0)
+ self.register_buffer("sum", torch.tensor(0.0))
 
  def training_step(self, batch, batch_idx):
  x = batch