Ensure accelerator is valid if running interactively (#5970)

Co-authored-by: chaton <[email protected]>
Co-authored-by: Adrian Wälchli <[email protected]>
Co-authored-by: Carlos Mocholi <[email protected]>

Author: 4 people

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -517,6 +517,9 @@ def set_distributed_mode(self, distributed_backend: Optional[str] = None):
                 rank_zero_warn('You are running on single node with no parallelization, so distributed has no effect.')
                 self._distrib_type = None
 
+        # finished configuring self._distrib_type, check ipython environment
+        self.check_interactive_compatibility()
+
         # for DDP overwrite nb processes by requested GPUs
         if (
             self._device_type == DeviceType.GPU
@@ -558,6 +561,19 @@ def _set_horovod_backend(self):
         else:
             self.num_processes = hvd.local_size()
 
+    def check_interactive_compatibility(self):
+        """
+        Raises a `MisconfigurationException` if the accelerator and/or plugin
+        is not compatible with an interactive environment
+        """
+        from pytorch_lightning.utilities import _IS_INTERACTIVE
+        if _IS_INTERACTIVE and self._distrib_type is not None and not self._distrib_type.is_interactive_compatible():
+            raise MisconfigurationException(
+                f"Selected distributed backend {self._distrib_type} is not compatible with an interactive"
+                " environment. Run your code as a script, or choose one of the compatible backends:"
+                f" {', '.join(DistributedType.interactive_compatible_types())}"
+            )
+
     def check_horovod(self):
         """Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
         if not _HOROVOD_AVAILABLE:

pytorch_lightning/utilities/__init__.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """General utilities"""
-
 import numpy
 
 from pytorch_lightning.utilities.apply_func import move_data_to_device  # noqa: F401
@@ -33,6 +32,7 @@
     _HOROVOD_AVAILABLE,
     _HYDRA_AVAILABLE,
     _HYDRA_EXPERIMENTAL_AVAILABLE,
+    _IS_INTERACTIVE,
     _module_available,
     _NATIVE_AMP_AVAILABLE,
     _OMEGACONF_AVAILABLE,

pytorch_lightning/utilities/enums.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 """Enumerated utilities"""
 from enum import Enum
-from typing import Union
+from typing import List, Optional, Union
 
 
 class LightningEnum(str, Enum):
     """ Type of any enumerator with allowed comparison to string invariant to cases. """
 
     @classmethod
-    def from_str(cls, value: str) -> 'LightningEnum':
+    def from_str(cls, value: str) -> Optional['LightningEnum']:
         statuses = [status for status in dir(cls) if not status.startswith('_')]
         for st in statuses:
             if st.lower() == value.lower():
@@ -31,7 +31,7 @@ def __eq__(self, other: Union[str, Enum]) -> bool:
         other = other.value if isinstance(other, Enum) else str(other)
         return self.value.lower() == other.lower()
 
-    def __hash__(self):
+    def __hash__(self) -> int:
         # re-enable hashtable so it can be used as a dict key or in a set
         # example: set(LightningEnum)
         return hash(self.name)
@@ -58,6 +58,16 @@ class DistributedType(LightningEnum):
     >>> DistributedType.DDP2 in ('ddp2', )
     True
     """
+
+    @staticmethod
+    def interactive_compatible_types() -> List['DistributedType']:
+        """Returns a list containing interactive compatible DistributeTypes"""
+        return [DistributedType.DP, DistributedType.DDP_SPAWN, DistributedType.DDP_SHARDED_SPAWN]
+
+    def is_interactive_compatible(self) -> bool:
+        """Returns whether self is interactive compatible"""
+        return self in DistributedType.interactive_compatible_types()
+
     DP = 'dp'
     DDP = 'ddp'
     DDP2 = 'ddp2'

pytorch_lightning/utilities/imports.py

@@ -14,6 +14,7 @@
 """General utilities"""
 import operator
 import platform
+import sys
 from distutils.version import LooseVersion
 from importlib.util import find_spec
 
@@ -49,10 +50,11 @@ def _compare_version(package: str, op, version) -> bool:
 
 
 _IS_WINDOWS = platform.system() == "Windows"
+_IS_INTERACTIVE = hasattr(sys, "ps1")  # https://stackoverflow.com/a/64523765
 _TORCH_LOWER_EQUAL_1_4 = _compare_version("torch", operator.le, "1.5.0")
 _TORCH_GREATER_EQUAL_1_6 = _compare_version("torch", operator.ge, "1.6.0")
 _TORCH_GREATER_EQUAL_1_7 = _compare_version("torch", operator.ge, "1.7.0")
-_TORCH_QUANTIZE_AVAILABLE = bool([eg for eg in torch.backends.quantized.supported_engines if eg != 'none'])
+
 _APEX_AVAILABLE = _module_available("apex.amp")
 _BOLTS_AVAILABLE = _module_available('pl_bolts')
 _DEEPSPEED_AVAILABLE = not _IS_WINDOWS and _module_available('deepspeed')
@@ -65,6 +67,7 @@ def _compare_version(package: str, op, version) -> bool:
 _NATIVE_AMP_AVAILABLE = _module_available("torch.cuda.amp") and hasattr(torch.cuda.amp, "autocast")
 _OMEGACONF_AVAILABLE = _module_available("omegaconf")
 _RPC_AVAILABLE = not _IS_WINDOWS and _module_available('torch.distributed.rpc')
+_TORCH_QUANTIZE_AVAILABLE = bool([eg for eg in torch.backends.quantized.supported_engines if eg != 'none'])
 _TORCHTEXT_AVAILABLE = _module_available("torchtext")
 _TORCHVISION_AVAILABLE = _module_available('torchvision')
 _XLA_AVAILABLE = _module_available("torch_xla")

tests/accelerators/test_accelerator_connector.py

@@ -32,6 +32,7 @@
     SingleDevicePlugin,
 )
 from pytorch_lightning.plugins.environments import ClusterEnvironment, SLURMEnvironment, TorchElasticEnvironment
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers.boring_model import BoringModel
 
 
@@ -387,6 +388,19 @@ def on_fit_start(self, trainer, pl_module):
         trainer.fit(model)
 
 
+@mock.patch("pytorch_lightning.utilities._IS_INTERACTIVE", return_value=True)
+@mock.patch('torch.cuda.device_count', return_value=2)
+def test_ipython_incompatible_backend_error(*_):
+    with pytest.raises(MisconfigurationException, match="backend ddp is not compatible"):
+        Trainer(accelerator="ddp", gpus=2)
+
+    with pytest.raises(MisconfigurationException, match="backend ddp is not compatible"):
+        Trainer(accelerator="ddp_cpu", num_processes=2)
+
+    with pytest.raises(MisconfigurationException, match="backend ddp2 is not compatible"):
+        Trainer(accelerator="ddp2", gpus=2)
+
+
 @pytest.mark.parametrize(
     ["accelerator", "plugin"],
     [('ddp_spawn', 'ddp_sharded'), (None, 'ddp_sharded')],