refactor reading env defaults (#6510)

* change tests

* fix

* test

* _defaults_from_env_vars

Co-authored-by: Carlos Mocholí <[email protected]>

Author: Borda

pytorch_lightning/trainer/connectors/env_vars_connector.py

@@ -18,27 +18,24 @@
 from pytorch_lightning.utilities.argparse import get_init_arguments_and_types, parse_env_variables
 
 
-def overwrite_by_env_vars(fn: Callable) -> Callable:
+def _defaults_from_env_vars(fn: Callable) -> Callable:
     """
     Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods for which
     input arguments should be moved automatically to the correct device.
-
     """
-
     @wraps(fn)
-    def overwrite_by_env_vars(self, *args, **kwargs):
-        # get the class
-        cls = self.__class__
+    def insert_env_defaults(self, *args, **kwargs):
+        cls = self.__class__  # get the class
         if args:  # inace any args passed move them to kwargs
             # parse only the argument names
             cls_arg_names = [arg[0] for arg in get_init_arguments_and_types(cls)]
             # convert args to kwargs
             kwargs.update({k: v for k, v in zip(cls_arg_names, args)})
+        env_variables = vars(parse_env_variables(cls))
         # update the kwargs by env variables
-        # todo: maybe add a warning that some init args were overwritten by Env arguments
-        kwargs.update(vars(parse_env_variables(cls)))
+        kwargs = dict(list(env_variables.items()) + list(kwargs.items()))
 
         # all args were already moved to kwargs
         return fn(self, **kwargs)
 
-    return overwrite_by_env_vars
+    return insert_env_defaults

pytorch_lightning/trainer/trainer.py

@@ -38,7 +38,7 @@
 from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
 from pytorch_lightning.trainer.connectors.data_connector import DataConnector
 from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
-from pytorch_lightning.trainer.connectors.env_vars_connector import overwrite_by_env_vars
+from pytorch_lightning.trainer.connectors.env_vars_connector import _defaults_from_env_vars
 from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
 from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
 from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerConnector
@@ -84,7 +84,7 @@ class Trainer(
     DeprecatedTrainerAttributes,
 ):
 
-    @overwrite_by_env_vars
+    @_defaults_from_env_vars
     def __init__(
         self,
         logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,

pytorch_lightning/utilities/argparse.py

@@ -107,7 +107,7 @@ def parse_env_variables(cls, template: str = "PL_%(cls_name)s_%(cls_argument)s")
 
 
 def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:
-    r"""Scans the Trainer signature and returns argument names, types and default values.
+    r"""Scans the class signature and returns argument names, types and default values.
 
     Returns:
         List with tuples of 3 values:
@@ -119,11 +119,11 @@ def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:
         >>> args = get_init_arguments_and_types(Trainer)
 
     """
-    trainer_default_params = inspect.signature(cls).parameters
+    cls_default_params = inspect.signature(cls).parameters
     name_type_default = []
-    for arg in trainer_default_params:
-        arg_type = trainer_default_params[arg].annotation
-        arg_default = trainer_default_params[arg].default
+    for arg in cls_default_params:
+        arg_type = cls_default_params[arg].annotation
+        arg_default = cls_default_params[arg].default
         try:
             arg_types = tuple(arg_type.__args__)
         except AttributeError:

tests/trainer/flags/test_env_vars.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+from unittest import mock
 
 from pytorch_lightning import Trainer
 
 
-def test_passing_env_variables(tmpdir):
+def test_passing_no_env_variables():
     """Testing overwriting trainer arguments """
     trainer = Trainer()
     assert trainer.logger is not None
@@ -25,17 +26,29 @@ def test_passing_env_variables(tmpdir):
     assert trainer.logger is None
     assert trainer.max_steps == 42
 
-    os.environ['PL_TRAINER_LOGGER'] = 'False'
-    os.environ['PL_TRAINER_MAX_STEPS'] = '7'
+
+@mock.patch.dict(os.environ, {"PL_TRAINER_LOGGER": "False", "PL_TRAINER_MAX_STEPS": "7"})
+def test_passing_env_variables_only():
+    """Testing overwriting trainer arguments """
     trainer = Trainer()
     assert trainer.logger is None
     assert trainer.max_steps == 7
 
-    os.environ['PL_TRAINER_LOGGER'] = 'True'
+
+@mock.patch.dict(os.environ, {"PL_TRAINER_LOGGER": "True", "PL_TRAINER_MAX_STEPS": "7"})
+def test_passing_env_variables_defaults():
+    """Testing overwriting trainer arguments """
     trainer = Trainer(False, max_steps=42)
-    assert trainer.logger is not None
-    assert trainer.max_steps == 7
+    assert trainer.logger is None
+    assert trainer.max_steps == 42
+
 
-    # this has to be cleaned
-    del os.environ['PL_TRAINER_LOGGER']
-    del os.environ['PL_TRAINER_MAX_STEPS']
+@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "0,1", "PL_TRAINER_GPUS": "2"})
+@mock.patch('torch.cuda.device_count', return_value=2)
+@mock.patch('torch.cuda.is_available', return_value=True)
+def test_passing_env_variables_gpus(cuda_available_mock, device_count_mock):
+    """Testing overwriting trainer arguments """
+    trainer = Trainer()
+    assert trainer.gpus == 2
+    trainer = Trainer(gpus=1)
+    assert trainer.gpus == 1