Configure mypy to install dependencies in CI and update pyproject.toml (#10682)

* mypy install deps

* fix deps

* add examples

* fix type errors

* fix type error

* fix

* fix

* update pyproject.toml

Author: daniellepintz

.github/workflows/code-checks.yml

@@ -14,8 +14,8 @@ jobs:
       - uses: actions/setup-python@v2
         with:
           python-version: 3.9
-      - name: Install mypy
+      - name: Install dependencies
         run: |
-          grep mypy requirements/test.txt | xargs -0 pip install
+          pip install '.[dev]'
           pip list
       - run: mypy --install-types --non-interactive

pyproject.toml

@@ -36,41 +36,31 @@ disable_error_code = "attr-defined"
 # style choices
 warn_no_return = "False"
 
-# TODO: Fix typing for these modules
+# Changes mypy default to ignore all errors
 [[tool.mypy.overrides]]
 module = [
-    "pytorch_lightning.callbacks.*",
-    "pytorch_lightning.core.*",
-    "pytorch_lightning.loggers.*",
-    "pytorch_lightning.loops.*",
-    "pytorch_lightning.overrides.*",
-    "pytorch_lightning.plugins.environments.*",
-    "pytorch_lightning.plugins.training_type.*",
-    "pytorch_lightning.profiler.*",
-    "pytorch_lightning.trainer.*",
-    "pytorch_lightning.distributed.*",
-    "pytorch_lightning.tuner.*",
-    "pytorch_lightning.utilities.*",
+    "pytorch_lightning.*",
 ]
 ignore_errors = "True"
 
+# Override the default for files where we would like to enable type checking
+# TODO: Bring more files into this section
 [[tool.mypy.overrides]]
 module = [
     "pytorch_lightning.callbacks.device_stats_monitor",
     "pytorch_lightning.callbacks.early_stopping",
     "pytorch_lightning.callbacks.gpu_stats_monitor",
     "pytorch_lightning.callbacks.gradient_accumulation_scheduler",
-    "pytorch_lightning.callbacks.lr_monitor",
     "pytorch_lightning.callbacks.model_summary",
     "pytorch_lightning.callbacks.progress",
     "pytorch_lightning.callbacks.pruning",
     "pytorch_lightning.callbacks.rich_model_summary",
     "pytorch_lightning.core.optimizer",
-    "pytorch_lightning.lite.*",
-    "pytorch_lightning.loops.optimization.*",
+    "pytorch_lightning.loops.optimization.closure.py",
+    "pytorch_lightning.loops.optimization.manual_loop.py",
     "pytorch_lightning.loops.evaluation_loop",
-    "pytorch_lightning.trainer.connectors.checkpoint_connector",
-    "pytorch_lightning.trainer.connectors.logger_connector.*",
+    "pytorch_lightning.trainer.connectors.logger_connector.py",
+    "pytorch_lightning.trainer.connectors.logger_connector.fx_validator.py",
     "pytorch_lightning.trainer.connectors.signal_connector",
     "pytorch_lightning.trainer.progress.*",
     "pytorch_lightning.tuner.auto_gpu_select",
@@ -80,8 +70,6 @@ module = [
     "pytorch_lightning.utilities.cloud_io",
     "pytorch_lightning.utilities.device_dtype_mixin",
     "pytorch_lightning.utilities.device_parser",
-    "pytorch_lightning.utilities.distributed",
-    "pytorch_lightning.utilities.memory",
     "pytorch_lightning.utilities.model_summary",
     "pytorch_lightning.utilities.parameter_tying",
     "pytorch_lightning.utilities.parsing",

pytorch_lightning/trainer/connectors/logger_connector/result.py

@@ -280,8 +280,8 @@ def wrapped_func(*args: Any, **kwargs: Any) -> Optional[Any]:
                 )
 
             # return cached value
-            if self._computed is not None:  # type: ignore
-                return self._computed  # type: ignore
+            if self._computed is not None:
+                return self._computed
             self._computed = compute(*args, **kwargs)
             return self._computed
 

pytorch_lightning/utilities/parameter_tying.py

@@ -19,7 +19,6 @@
 from typing import Dict, List, Optional
 
 from torch import nn
-from torch.nn import Parameter
 
 
 def find_shared_parameters(module: nn.Module) -> List[str]:
@@ -64,7 +63,7 @@ def _get_module_by_path(module: nn.Module, path: str) -> nn.Module:
     return module
 
 
-def _set_module_by_path(module: nn.Module, path: str, value: Parameter) -> None:
+def _set_module_by_path(module: nn.Module, path: str, value: nn.Module) -> None:
     path = path.split(".")
     for name in path[:-1]:
         module = getattr(module, name)