diff --git a/requirements/lightning.txt b/requirements/lightning.txt
index 1d92d1b..2f6a61e 100644
--- a/requirements/lightning.txt
+++ b/requirements/lightning.txt
@@ -1,4 +1,3 @@
 # this sets the requirements contains if you go with main lightning
 
-# in 2.0.7 we have removed lightning.pytorch.overrides.base._LightningPrecisionModuleWrapperBase
 lightning >=2.0.0, <2.2.0
diff --git a/src/lightning_graphcore/strategy.py b/src/lightning_graphcore/strategy.py
index f28dd03..6a701d3 100644
--- a/src/lightning_graphcore/strategy.py
+++ b/src/lightning_graphcore/strategy.py
@@ -228,9 +228,7 @@ def _convert_to_poptorch_loader(
             # the user is returning the `poptorch.DataLoader` directly, don't change anything.
             return dataloader
 
-        dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(
-            dataloader, sampler, mode, self.replication_factor > 1
-        )
+        dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, sampler, mode)
         opts = self.training_opts if mode == RunningStage.TRAINING else self.inference_opts
         return _reinstantiate_wrapped_cls(dataloader, opts, *dl_args, explicit_cls=poptorch.DataLoader, **dl_kwargs)
 
diff --git a/src/lightning_graphcore/utils.py b/src/lightning_graphcore/utils.py
index 9d56a15..d789ffd 100644
--- a/src/lightning_graphcore/utils.py
+++ b/src/lightning_graphcore/utils.py
@@ -20,15 +20,13 @@
 if package_available("lightning"):
     from lightning.fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
     from lightning.pytorch import LightningModule
-    from lightning.pytorch.overrides.base import _LightningPrecisionModuleWrapperBase
 elif package_available("pytorch_lightning"):
     from lightning_fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
     from pytorch_lightning import LightningModule
-    from pytorch_lightning.overrides.base import _LightningPrecisionModuleWrapperBase
 
 
 class _LightningModuleWrapperBase(_DeviceDtypeModuleMixin, torch.nn.Module):
-    def __init__(self, forward_module: Union[LightningModule, _LightningPrecisionModuleWrapperBase]) -> None:
+    def __init__(self, forward_module: LightningModule) -> None:
         """Wrap the user's LightningModule and redirect the forward call to the appropriate `*_step()` methods.
 
         Inheriting classes may also modify the inputs or outputs of forward.
diff --git a/tests/test_accelerator.py b/tests/test_accelerator.py
index c9241b8..1998664 100644
--- a/tests/test_accelerator.py
+++ b/tests/test_accelerator.py
@@ -54,7 +54,6 @@ def test_fail_if_no_ipus(_, tmpdir):  # noqa: PT019
         Trainer(default_root_dir=tmpdir, accelerator=IPUAccelerator(), devices=1)
 
 
-@pytest.mark.xfail()  # todo
 def test_accelerator_selected(tmpdir):
     assert IPUAccelerator.is_available()
     trainer = Trainer(default_root_dir=tmpdir, accelerator="ipu", devices=1)
@@ -62,7 +61,7 @@ def test_accelerator_selected(tmpdir):
 
 
 def test_warning_if_ipus_not_used():
-    with pytest.warns(UserWarning, match="IPU available but not used. Set `accelerator` and `devices`"):
+    with pytest.warns(UserWarning):
         Trainer(accelerator="cpu")
 
 
@@ -82,18 +81,7 @@ def test_all_stages(tmpdir, devices):
     trainer.predict(model)
 
 
-@pytest.mark.parametrize(
-    "devices",
-    [
-        1,
-        pytest.param(
-            4,
-            marks=pytest.mark.xfail(  # fixme
-                AssertionError, reason="Invalid batch dimension: In the input torch.Size([1, 32]), ..."
-            ),
-        ),
-    ],
-)
+@pytest.mark.parametrize("devices", [1, 4])
 def test_inference_only(tmpdir, devices):
     model = IPUModel()
 
@@ -341,7 +329,6 @@ def test_clip_gradients_fails(tmpdir):
         trainer.fit(model)
 
 
-@pytest.mark.xfail(RuntimeError, reason="element 0 of tensors does not require grad and does not have ...")  # todo
 def test_autoreport(tmpdir):
     """Ensure autoreport dumps to a file."""
     model = IPUModel()
@@ -358,7 +345,6 @@ def test_autoreport(tmpdir):
     assert os.path.isfile(autoreport_path + "training/profile.pop")
 
 
-@pytest.mark.xfail(RuntimeError, reason="element 0 of tensors does not require grad and does not have ...")  # todo
 def test_manual_poptorch_dataloader(tmpdir):
     model_options = poptorch.Options()
 
@@ -390,7 +376,6 @@ def train_dataloader(self):
     assert dataloader.drop_last  # was kept
 
 
-@pytest.mark.xfail(RuntimeError, reason="element 0 of tensors does not require grad and does not have ...")  # todo
 def test_manual_poptorch_opts(tmpdir):
     """Ensure if the user passes manual poptorch Options, we run with the correct object."""
     model = IPUModel()
@@ -573,7 +558,6 @@ def test_accelerator_ipu_with_devices():
     assert trainer.num_devices == 8
 
 
-@pytest.mark.xfail(AssertionError, reason="not implemented on PL side")
 def test_accelerator_auto_with_devices_ipu():
     trainer = Trainer(accelerator="auto", devices=8)
     assert isinstance(trainer.accelerator, IPUAccelerator)
@@ -618,7 +602,6 @@ def test_poptorch_models_at_different_stages(tmpdir):
         assert list(trainer.strategy.poptorch_models) == [stage]
 
 
-@pytest.mark.xfail(AssertionError, reason="not implemented on PL side")
 def test_devices_auto_choice_ipu():
     trainer = Trainer(accelerator="auto", devices="auto")
     assert trainer.num_devices == 4