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Merge branch 'master' into bump/python_3.9+

This commit is contained in:
Jirka Borovec 2024-11-13 16:04:49 +01:00 committed by GitHub
parent 5da2e4342e cae333575f
commit ce62f96c91
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8 changed files with 83 additions and 36 deletions
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20
.github/checkgroup.yml vendored
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@ -19,7 +19,7 @@ subprojects:
- "!*.md"
- "!**/*.md"
checks:
- "pl-cpu (macOS-13, lightning, 3.9, 2.1, oldest)"
- "pl-cpu (macOS-14, lightning, 3.9, 2.1, oldest)"
- "pl-cpu (macOS-14, lightning, 3.10, 2.1)"
- "pl-cpu (macOS-14, lightning, 3.11, 2.2.2)"
- "pl-cpu (macOS-14, lightning, 3.11, 2.3)"
@ -171,7 +171,7 @@ subprojects:
- "!*.md"
- "!**/*.md"
checks:
- "fabric-cpu (macOS-13, lightning, 3.9, 2.1, oldest)"
- "fabric-cpu (macOS-14, lightning, 3.9, 2.1, oldest)"
- "fabric-cpu (macOS-14, lightning, 3.10, 2.1)"
- "fabric-cpu (macOS-14, lightning, 3.11, 2.2.2)"
- "fabric-cpu (macOS-14, lightning, 3.11, 2.3)"
@ -266,14 +266,14 @@ subprojects:
- "install-pkg (ubuntu-22.04, lightning, 3.11)"
- "install-pkg (ubuntu-22.04, notset, 3.9)"
- "install-pkg (ubuntu-22.04, notset, 3.11)"
- "install-pkg (macOS-13, fabric, 3.9)"
- "install-pkg (macOS-13, fabric, 3.11)"
- "install-pkg (macOS-13, pytorch, 3.9)"
- "install-pkg (macOS-13, pytorch, 3.11)"
- "install-pkg (macOS-13, lightning, 3.9)"
- "install-pkg (macOS-13, lightning, 3.11)"
- "install-pkg (macOS-13, notset, 3.9)"
- "install-pkg (macOS-13, notset, 3.11)"
- "install-pkg (macOS-14, fabric, 3.9)"
- "install-pkg (macOS-14, fabric, 3.11)"
- "install-pkg (macOS-14, pytorch, 3.9)"
- "install-pkg (macOS-14, pytorch, 3.11)"
- "install-pkg (macOS-14, lightning, 3.9)"
- "install-pkg (macOS-14, lightning, 3.11)"
- "install-pkg (macOS-14, notset, 3.9)"
- "install-pkg (macOS-14, notset, 3.11)"
- "install-pkg (windows-2022, fabric, 3.9)"
- "install-pkg (windows-2022, fabric, 3.11)"
- "install-pkg (windows-2022, pytorch, 3.9)"

2
.github/workflows/ci-pkg-install.yml vendored
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@ -42,7 +42,7 @@ jobs:
strategy:
fail-fast: false
matrix:
os: ["ubuntu-22.04", "macOS-13", "windows-2022"]
os: ["ubuntu-22.04", "macOS-14", "windows-2022"]
pkg-name: ["fabric", "pytorch", "lightning", "notset"]
python-version: ["3.9", "3.11"]
steps:

7
.github/workflows/ci-tests-fabric.yml vendored
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@ -60,7 +60,7 @@ jobs:
- { os: "ubuntu-22.04", pkg-name: "fabric", python-version: "3.12", pytorch-version: "2.5.1" }
- { os: "windows-2022", pkg-name: "fabric", python-version: "3.12", pytorch-version: "2.5.1" }
# "oldest" versions tests, only on minimum Python
- { os: "macOS-13", pkg-name: "lightning", python-version: "3.9", pytorch-version: "2.1", requires: "oldest" }
- { os: "macOS-14", pkg-name: "lightning", python-version: "3.9", pytorch-version: "2.1", requires: "oldest" }
- {
os: "ubuntu-20.04",
pkg-name: "lightning",
@ -101,7 +101,10 @@ jobs:
- name: Set min. dependencies
if: ${{ matrix.requires == 'oldest' }}
run: python .actions/assistant.py replace_oldest_ver
run: |
python .actions/assistant.py replace_oldest_ver
pip install "cython<3.0" wheel
pip install "pyyaml==5.4" --no-build-isolation
- name: Adjust PyTorch versions in requirements files
if: ${{ matrix.requires != 'oldest' }}

7
.github/workflows/ci-tests-pytorch.yml vendored
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@ -64,7 +64,7 @@ jobs:
- { os: "ubuntu-22.04", pkg-name: "pytorch", python-version: "3.12", pytorch-version: "2.5.1" }
- { os: "windows-2022", pkg-name: "pytorch", python-version: "3.12", pytorch-version: "2.5.1" }
# "oldest" versions tests, only on minimum Python
- { os: "macOS-13", pkg-name: "lightning", python-version: "3.9", pytorch-version: "2.1", requires: "oldest" }
- { os: "macOS-14", pkg-name: "lightning", python-version: "3.9", pytorch-version: "2.1", requires: "oldest" }
- {
os: "ubuntu-20.04",
pkg-name: "lightning",
@ -106,7 +106,10 @@ jobs:
- name: Set min. dependencies
if: ${{ matrix.requires == 'oldest' }}
run: python .actions/assistant.py replace_oldest_ver
run: |
python .actions/assistant.py replace_oldest_ver
pip install "cython<3.0" wheel
pip install "pyyaml==5.4" --no-build-isolation
- name: Adjust PyTorch versions in requirements files
if: ${{ matrix.requires != 'oldest' }}

2
src/lightning/pytorch/loggers/mlflow.py
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@ -300,7 +300,7 @@ class MLFlowLogger(Logger):
"""
if self._tracking_uri.startswith(LOCAL_FILE_URI_PREFIX):
return self._tracking_uri.lstrip(LOCAL_FILE_URI_PREFIX)
return self._tracking_uri[len(LOCAL_FILE_URI_PREFIX) :]
return None
@property

3
src/lightning/pytorch/utilities/data.py
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@ -350,7 +350,8 @@ def _is_dataloader_shuffled(dataloader: object) -> bool:
if not hasattr(dataloader, "sampler"):
# shuffling is enabled via a sampler. No sampler, no shuffling
return False
sampler = dataloader.sampler
batch_sampler = dataloader.batch_sampler
sampler = batch_sampler.sampler if batch_sampler is not None else dataloader.sampler
if isinstance(sampler, SequentialSampler):
return False
return isinstance(sampler, RandomSampler)

50
tests/tests_pytorch/test_cli.py
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@ -878,18 +878,27 @@ def test_lightning_cli_load_from_checkpoint_dependency_injection(cleandir):
hparams_path = Path(cli.trainer.log_dir) / "hparams.yaml"
assert hparams_path.is_file()
hparams = yaml.safe_load(hparams_path.read_text())
expected = {
"_instantiator": "lightning.pytorch.cli.instantiate_module",
"optimizer": "torch.optim.Adam",
"scheduler": "torch.optim.lr_scheduler.ConstantLR",
"activation": {"class_path": "torch.nn.LeakyReLU", "init_args": {"negative_slope": 0.05, "inplace": False}},
}
assert hparams == expected
expected_keys = ["_instantiator", "activation", "optimizer", "scheduler"]
expected_instantiator = "lightning.pytorch.cli.instantiate_module"
expected_activation = "torch.nn.LeakyReLU"
expected_optimizer = "torch.optim.Adam"
expected_scheduler = "torch.optim.lr_scheduler.ConstantLR"
assert sorted(hparams.keys()) == expected_keys
assert hparams["_instantiator"] == expected_instantiator
assert hparams["activation"]["class_path"] == expected_activation
assert hparams["optimizer"] == expected_optimizer or hparams["optimizer"]["class_path"] == expected_optimizer
assert hparams["scheduler"] == expected_scheduler or hparams["scheduler"]["class_path"] == expected_scheduler
checkpoint_path = next(Path(cli.trainer.log_dir, "checkpoints").glob("*.ckpt"), None)
assert checkpoint_path.is_file()
ckpt = torch.load(checkpoint_path, weights_only=True)
assert ckpt["hyper_parameters"] == expected
hparams = torch.load(checkpoint_path, weights_only=True)["hyper_parameters"]
assert sorted(hparams.keys()) == expected_keys
assert hparams["_instantiator"] == expected_instantiator
assert hparams["activation"]["class_path"] == expected_activation
assert hparams["optimizer"] == expected_optimizer or hparams["optimizer"]["class_path"] == expected_optimizer
assert hparams["scheduler"] == expected_scheduler or hparams["scheduler"]["class_path"] == expected_scheduler
model = TestModelSaveHparams.load_from_checkpoint(checkpoint_path)
assert isinstance(model, TestModelSaveHparams)
@ -905,18 +914,23 @@ def test_lightning_cli_load_from_checkpoint_dependency_injection_subclass_mode(c
cli = LightningCLI(TestModelSaveHparams, run=False, auto_configure_optimizers=False, subclass_mode_model=True)
cli.trainer.fit(cli.model)
expected = {
"_instantiator": "lightning.pytorch.cli.instantiate_module",
"_class_path": f"{__name__}.TestModelSaveHparams",
"optimizer": "torch.optim.Adam",
"scheduler": "torch.optim.lr_scheduler.ConstantLR",
"activation": {"class_path": "torch.nn.LeakyReLU", "init_args": {"negative_slope": 0.05, "inplace": False}},
}
expected_keys = ["_class_path", "_instantiator", "activation", "optimizer", "scheduler"]
expected_instantiator = "lightning.pytorch.cli.instantiate_module"
expected_class_path = f"{__name__}.TestModelSaveHparams"
expected_activation = "torch.nn.LeakyReLU"
expected_optimizer = "torch.optim.Adam"
expected_scheduler = "torch.optim.lr_scheduler.ConstantLR"
checkpoint_path = next(Path(cli.trainer.log_dir, "checkpoints").glob("*.ckpt"), None)
assert checkpoint_path.is_file()
ckpt = torch.load(checkpoint_path, weights_only=True)
assert ckpt["hyper_parameters"] == expected
hparams = torch.load(checkpoint_path, weights_only=True)["hyper_parameters"]
assert sorted(hparams.keys()) == expected_keys
assert hparams["_instantiator"] == expected_instantiator
assert hparams["_class_path"] == expected_class_path
assert hparams["activation"]["class_path"] == expected_activation
assert hparams["optimizer"] == expected_optimizer or hparams["optimizer"]["class_path"] == expected_optimizer
assert hparams["scheduler"] == expected_scheduler or hparams["scheduler"]["class_path"] == expected_scheduler
model = LightningModule.load_from_checkpoint(checkpoint_path)
assert isinstance(model, TestModelSaveHparams)

28
tests/tests_pytorch/utilities/test_data.py
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@ -12,6 +12,7 @@ from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
from lightning.pytorch.trainer.states import RunningStage
from lightning.pytorch.utilities.data import (
_get_dataloader_init_args_and_kwargs,
_is_dataloader_shuffled,
_update_dataloader,
extract_batch_size,
has_len_all_ranks,
@ -20,7 +21,7 @@ from lightning.pytorch.utilities.data import (
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning_utilities.test.warning import no_warning_call
from torch import Tensor
from torch.utils.data import BatchSampler, DataLoader, RandomSampler
from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
def test_extract_batch_size():
@ -304,6 +305,31 @@ def test_custom_batch_sampler_no_sampler():
_ = _update_dataloader(dataloader, dataloader.sampler, mode=RunningStage.PREDICTING)
def test_batch_sampler_shuffle_setting():
"""Test whether the `shuffle` state is correctly set in the `BatchSampler`."""
random_sampler = RandomSampler(range(10))
seq_sampler = SequentialSampler(range(10))
shuffled_dataloader = DataLoader(
range(10), batch_sampler=BatchSampler(random_sampler, batch_size=2, drop_last=False)
)
sequential_dataloader = DataLoader(
range(10), batch_sampler=BatchSampler(seq_sampler, batch_size=2, drop_last=False)
)
# if batch_size is 1, the pytorch init a default SequentialSampler and set BatchSampler to None
single_dataloader = DataLoader(range(10), batch_sampler=BatchSampler(seq_sampler, batch_size=1, drop_last=False))
assert _is_dataloader_shuffled(shuffled_dataloader)
assert not _is_dataloader_shuffled(sequential_dataloader)
assert not _is_dataloader_shuffled(single_dataloader)
# if batch_size is 1, and no batch_sampler is set, the pytorch will set BatchSampler to None
single_dataloader = DataLoader(range(10), batch_size=1)
shuffled_single_dataloader = DataLoader(range(10), batch_size=1, shuffle=True)
assert not _is_dataloader_shuffled(single_dataloader)
assert _is_dataloader_shuffled(shuffled_single_dataloader)
@pytest.mark.parametrize("mode", [RunningStage.TRAINING, RunningStage.PREDICTING, RunningStage.TESTING])
def test_dataloader_kwargs_replacement_with_iterable_dataset(mode):
"""Test that DataLoader kwargs are not replaced when using Iterable Dataset."""