Docs/fixes (#5914)

* wip

* ..

* ...

* Apply suggestions from code review

Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>

Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>

Makefile

@@ -3,7 +3,7 @@
 # to imitate SLURM set only single node
 export SLURM_LOCALID=0
 # assume you have installed need packages
-export SPHINX_MOCK_REQUIREMENTS=0
+export SPHINX_MOCK_REQUIREMENTS=1
 
 clean:
 	# clean all temp runs

docs/.build_docs.sh

@@ -1,3 +0,0 @@
-rm -rf source/generated
-make clean
-make html --debug --jobs 2 SPHINXOPTS="-W"

pytorch_lightning/callbacks/swa.py

@@ -23,10 +23,10 @@ from torch import nn
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_6_0, rank_zero_warn
+from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_6, rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
-if _TORCH_GREATER_EQUAL_1_6_0:
+if _TORCH_GREATER_EQUAL_1_6:
     from torch.optim.swa_utils import SWALR
 
 _AVG_FN = Callable[[torch.Tensor, torch.Tensor, torch.LongTensor], torch.FloatTensor]

pytorch_lightning/utilities/__init__.py

@@ -36,7 +36,7 @@ from pytorch_lightning.utilities.imports import (  # noqa: F401
     _NATIVE_AMP_AVAILABLE,
     _OMEGACONF_AVAILABLE,
     _RPC_AVAILABLE,
-    _TORCH_GREATER_EQUAL_1_6_0,
+    _TORCH_GREATER_EQUAL_1_6,
     _TORCHTEXT_AVAILABLE,
     _TORCHVISION_AVAILABLE,
     _XLA_AVAILABLE,

pytorch_lightning/utilities/imports.py

@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """General utilities"""
+import operator
 import platform
 from distutils.version import LooseVersion
 from importlib.util import find_spec
 
-import pkg_resources
 import torch
+from pkg_resources import DistributionNotFound, get_distribution
 
 
 def _module_available(module_path: str) -> bool:
@@ -39,19 +40,21 @@ def _module_available(module_path: str) -> bool:
         return False
 
 
-def _get_version(package: str) -> LooseVersion:
-    return LooseVersion(pkg_resources.get_distribution(package).version)
+def _compare_version(package: str, op, version) -> bool:
+    try:
+        pkg_version = LooseVersion(get_distribution(package).version)
+        return op(pkg_version, LooseVersion(version))
+    except DistributionNotFound:
+        return False
 
 
 _IS_WINDOWS = platform.system() == "Windows"
-_TORCH_GREATER_EQUAL_1_6_0 = _get_version("torch") >= LooseVersion("1.6.0")
+_TORCH_GREATER_EQUAL_1_6 = _compare_version("torch", operator.ge, "1.6.0")
 
 _APEX_AVAILABLE = _module_available("apex.amp")
 _BOLTS_AVAILABLE = _module_available('pl_bolts')
 _FAIRSCALE_AVAILABLE = not _IS_WINDOWS and _module_available('fairscale.nn.data_parallel')
-_FAIRSCALE_PIPE_AVAILABLE = (
-    _FAIRSCALE_AVAILABLE and _TORCH_GREATER_EQUAL_1_6_0 and _get_version('fairscale') <= LooseVersion("0.1.3")
-)
+_FAIRSCALE_PIPE_AVAILABLE = _TORCH_GREATER_EQUAL_1_6 and _compare_version("fairscale", operator.le, "0.1.3")
 _GROUP_AVAILABLE = not _IS_WINDOWS and _module_available('torch.distributed.group')
 _HOROVOD_AVAILABLE = _module_available("horovod.torch")
 _HYDRA_AVAILABLE = _module_available("hydra")

tests/callbacks/test_swa.py

@@ -21,11 +21,11 @@ from torch import nn
 from torch.utils.data import DataLoader
 
 from pytorch_lightning import Trainer
-from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_6_0
+from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_6
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel, RandomDataset
 
-if _TORCH_GREATER_EQUAL_1_6_0:
+if _TORCH_GREATER_EQUAL_1_6:
     from pytorch_lightning.callbacks import StochasticWeightAveraging
 
     class SwaTestModel(BoringModel):
@@ -114,7 +114,7 @@ def train_with_swa(tmpdir, batchnorm=True, accelerator=None, gpus=None, num_proc
     assert trainer.get_model() == model
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6_0, reason="SWA available from PyTorch 1.6.0")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 @pytest.mark.skipif(
     not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
@@ -123,31 +123,31 @@ def test_swa_callback_ddp(tmpdir):
     train_with_swa(tmpdir, accelerator="ddp", gpus=2)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6_0, reason="SWA available from PyTorch 1.6.0")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 def test_swa_callback_ddp_spawn(tmpdir):
     train_with_swa(tmpdir, accelerator="ddp_spawn", gpus=2)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6_0, reason="SWA available from PyTorch 1.6.0")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
 @pytest.mark.skipif(platform.system() == "Windows", reason="ddp_cpu is not available on Windows")
 def test_swa_callback_ddp_cpu(tmpdir):
     train_with_swa(tmpdir, accelerator="ddp_cpu", num_processes=2)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6_0, reason="SWA available from PyTorch 1.6.0")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires a GPU machine")
 def test_swa_callback_1_gpu(tmpdir):
     train_with_swa(tmpdir, gpus=1)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6_0, reason="SWA available from PyTorch 1.6.0")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
 @pytest.mark.parametrize("batchnorm", (True, False))
 def test_swa_callback(tmpdir, batchnorm):
     train_with_swa(tmpdir, batchnorm=batchnorm)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6_0, reason="SWA available from PyTorch 1.6.0")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
 def test_swa_raises():
     with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
         StochasticWeightAveraging(swa_epoch_start=0, swa_lrs=0.1)