Remove unviolated linting rules

pyproject.toml

@@ -93,9 +93,6 @@ ignore = [
     "S101",  # todo: Use of `assert` detected
     "S105", "S106", "S107",  # todo: Possible hardcoded password: ...
     "S113",  # todo: Probable use of requests call without timeout
-    "S301",  # todo: `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue
-    "S324",  # todo: Probable use of insecure hash functions in `hashlib`
-    "S403",  # todo: `pickle`, `cPickle`, `dill`, and `shelve` modules are possibly insecure
     "S404",  # todo: `subprocess` module is possibly insecure
     "S602",  # todo: `subprocess` call with `shell=True` identified, security issue
     "S603",  # todo: `subprocess` call: check for execution of untrusted input
@@ -106,20 +103,15 @@ ignore = [
 ]
 "tests/**" = [
     "S101",  # Use of `assert` detected
-    "S105", "S106",  # todo: Possible hardcoded password: ...
     "S301",  # `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue
-    "S113",  # todo: Probable use of requests call without timeout
     "S311",  # todo: Standard pseudo-random generators are not suitable for cryptographic purposes
     "S108",  # todo: Probable insecure usage of temporary file or directory: "/tmp/sys-customizations-sync"
-    "S202",  # Uses of `tarfile.extractall()`
     "S403",  # `pickle`, `cPickle`, `dill`, and `shelve` modules are possibly insecure
     "S404",  # `subprocess` module is possibly insecure
     "S602",  # todo: `subprocess` call with `shell=True` identified, security issue
     "S603",  # todo: `subprocess` call: check for execution of untrusted input
     "S605",  # todo: Starting a process with a shell: seems safe, but may be changed in the future; consider rewriting without `shell`
     "S607",  # todo: Starting a process with a partial executable path
-    "RET504",  # todo:Unnecessary variable assignment before `return` statement
-    "PT004",  # todo: Fixture `tmpdir_unittest_fixture` does not return anything, add leading underscore
     "PT012",  # todo: `pytest.raises()` block should contain a single simple statement
     "PT019",  # todo: Fixture `_` without value is injected as parameter, use `@pytest.mark.usefixtures` instead
 ]

tests/parity_fabric/models.py

@@ -60,8 +60,7 @@ def forward(self, x):
         x = torch.flatten(x, 1)  # flatten all dimensions except batch
         x = F.relu(self.fc1(x))
         x = F.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
+        return self.fc3(x)
 
     def get_optimizer(self):
         return torch.optim.SGD(self.parameters(), lr=0.0001)

tests/tests_fabric/strategies/test_model_parallel_integration.py

@@ -83,8 +83,7 @@ def _parallelize_feed_forward_fsdp2(model, device_mesh):
 
 def _parallelize_feed_forward_fsdp2_tp(model, device_mesh):
     model = _parallelize_feed_forward_tp(model, device_mesh)
-    model = _parallelize_feed_forward_fsdp2(model, device_mesh)
-    return model
+    return _parallelize_feed_forward_fsdp2(model, device_mesh)
 
 
 @RunIf(min_torch="2.4", standalone=True, min_cuda_gpus=4)

tests/tests_pytorch/accelerators/test_xla.py

@@ -46,8 +46,7 @@ def __init__(self):
     def forward(self, x):
         x = self.layer_1(x)
         x = self.layer_2(x)
-        x = self.layer_3(x)
-        return x
+        return self.layer_3(x)
 
 
 @RunIf(tpu=True, standalone=True)
@@ -230,8 +229,7 @@ def __init__(self):
     def forward(self, x):
         x = self.net_a(x)
         x = self.layer_2(x)
-        x = self.net_b(x)
-        return x
+        return self.net_b(x)
 
 
 @RunIf(tpu=True)

tests/tests_pytorch/callbacks/test_lr_monitor.py

@@ -428,8 +428,7 @@ def __init__(self):
 
         def forward(self, x):
             x = self.linear_a(x)
-            x = self.linear_b(x)
-            return x
+            return self.linear_b(x)
 
         def configure_optimizers(self):
             param_groups = [
@@ -603,8 +602,7 @@ def __init__(self, lr, momentum):
 
         def forward(self, x):
             x = self.linear_a(x)
-            x = self.linear_b(x)
-            return x
+            return self.linear_b(x)
 
         def configure_optimizers(self):
             param_groups = [

tests/tests_pytorch/callbacks/test_spike.py

@@ -29,8 +29,7 @@ def training_step(self, batch, batch_idx: int):
         if curr_loss_val is None:
             curr_loss_val = batch_idx
 
-        loss = self.layer(torch.tensor(curr_loss_val, device=self.device, dtype=self.dtype).view(1, 1))
-        return loss
+        return self.layer(torch.tensor(curr_loss_val, device=self.device, dtype=self.dtype).view(1, 1))
 
     def configure_optimizers(self):
         return torch.optim.SGD(self.parameters(), lr=1e-3)

tests/tests_pytorch/callbacks/test_stochastic_weight_avg.py

@@ -266,8 +266,7 @@ def __init__(self):
 
         def forward(self, x):
             x = self.layer1(x)
-            x = self.layer2(x)
-            return x
+            return self.layer2(x)
 
         def configure_optimizers(self):
             params = [{"params": self.layer1.parameters(), "lr": 0.1}, {"params": self.layer2.parameters(), "lr": 0.2}]

tests/tests_pytorch/helpers/advanced_models.py

@@ -46,8 +46,7 @@ def block(in_feat, out_feat, normalize=True):
 
     def forward(self, z):
         img = self.model(z)
-        img = img.view(img.size(0), *self.img_shape)
-        return img
+        return img.view(img.size(0), *self.img_shape)
 
 
 class Discriminator(nn.Module):
@@ -204,8 +203,7 @@ def forward(self, x):
         x = torch.tanh(x)
         x = self.c_d1_bn(x)
         x = self.c_d1_drop(x)
-        x = self.c_d2(x)
-        return x
+        return self.c_d2(x)
 
     def training_step(self, batch, batch_nb):
         x, y = batch

tests/tests_pytorch/helpers/simple_models.py

@@ -100,8 +100,7 @@ def forward(self, x):
         x = self.layer_1a(x)
         x = self.layer_2(x)
         x = self.layer_2a(x)
-        x = self.layer_end(x)
-        return x
+        return self.layer_end(x)
 
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=0.01)

tests/tests_pytorch/models/test_hparams.py

@@ -417,8 +417,7 @@ def _raw_checkpoint_path(trainer) -> str:
     raw_checkpoint_paths = [x for x in raw_checkpoint_paths if ".ckpt" in x]
     assert raw_checkpoint_paths
     raw_checkpoint_path = raw_checkpoint_paths[0]
-    raw_checkpoint_path = os.path.join(trainer.checkpoint_callback.dirpath, raw_checkpoint_path)
-    return raw_checkpoint_path
+    return os.path.join(trainer.checkpoint_callback.dirpath, raw_checkpoint_path)
 
 
 @pytest.mark.parametrize("base_class", [HyperparametersMixin, LightningModule, LightningDataModule])

tests/tests_pytorch/plugins/test_amp_plugins.py

@@ -165,8 +165,7 @@ def __init__(self):
 
         def forward(self, x: Tensor):
             x = self.layer1(x)
-            x = self.layer2(x)
-            return x
+            return self.layer2(x)
 
         def training_step(self, batch, batch_idx):
             _, opt2 = self.optimizers()

tests/tests_pytorch/strategies/test_model_parallel_integration.py

@@ -74,8 +74,7 @@ def _parallelize_feed_forward_fsdp2(model, device_mesh):
 
 def _parallelize_feed_forward_fsdp2_tp(model, device_mesh):
     model = _parallelize_feed_forward_tp(model, device_mesh)
-    model = _parallelize_feed_forward_fsdp2(model, device_mesh)
-    return model
+    return _parallelize_feed_forward_fsdp2(model, device_mesh)
 
 
 def _parallelize_with_compile(parallelize):

tests/tests_pytorch/trainer/optimization/test_manual_optimization.py

@@ -324,8 +324,7 @@ def __repr__(self):
 
     def __copy__(self):
         cls = self.__class__
-        new_obj = cls(self._store.copy())
-        return new_obj
+        return cls(self._store.copy())
 
     def copy(self):
         return self.__copy__()

tests/tests_pytorch/tuner/test_lr_finder.py

@@ -652,8 +652,7 @@ def training_step(self, batch: Any, batch_idx: int) -> STEP_OUTPUT:
             x, y = batch
             z = self.encoder(x)
             x_hat = self.decoder(z)
-            loss = F.mse_loss(x_hat, y)
-            return loss
+            return F.mse_loss(x_hat, y)
 
         def configure_optimizers(self):
             return torch.optim.Adam(self.parameters(), lr=self.hparams.lr)

tests/tests_pytorch/utilities/test_model_summary.py

@@ -81,8 +81,7 @@ def forward(self, x, y):
         out1 = self.layer1(x)
         out2 = self.layer2(y)
         out = self.relu(torch.cat((out1, out2), 1))
-        out = self.combine(out)
-        return out
+        return self.combine(out)
 
 
 class MixedDtypeModel(LightningModule):

tests/tests_pytorch/utilities/test_parameter_tying.py

@@ -30,8 +30,7 @@ def __init__(self):
     def forward(self, x):
         x = self.layer_1(x)
         x = self.layer_2(x)
-        x = self.layer_3(x)
-        return x
+        return self.layer_3(x)
 
 
 @pytest.mark.parametrize(
@@ -67,8 +66,7 @@ def __init__(self):
         def forward(self, x):
             x = self.net_a(x)
             x = self.layer_2(x)
-            x = self.net_b(x)
-            return x
+            return self.net_b(x)
 
     model = NestedModule()
     set_shared_parameters(model, [["layer.weight", "net_a.layer.weight", "net_b.layer.weight"]])