diff --git a/src/lightning/fabric/CHANGELOG.md b/src/lightning/fabric/CHANGELOG.md
index 750001f527954..a1b491528b7cd 100644
--- a/src/lightning/fabric/CHANGELOG.md
+++ b/src/lightning/fabric/CHANGELOG.md
@@ -150,6 +150,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed FSDP re-applying activation checkpointing when the user had manually applied it already ([#18006](https://github.com/Lightning-AI/lightning/pull/18006))
 
 
+- Fixed `TensorBoardLogger.log_graph` not unwrapping the `_FabricModule` ([#17844](https://github.com/Lightning-AI/lightning/pull/17844))
+
+
 ## [2.0.5] - 2023-07-07
 
 ### Added
diff --git a/src/lightning/fabric/loggers/tensorboard.py b/src/lightning/fabric/loggers/tensorboard.py
index 7cd15042635c0..a3f84315c9185 100644
--- a/src/lightning/fabric/loggers/tensorboard.py
+++ b/src/lightning/fabric/loggers/tensorboard.py
@@ -27,6 +27,7 @@
 from lightning.fabric.utilities.logger import _sanitize_params as _utils_sanitize_params
 from lightning.fabric.utilities.rank_zero import rank_zero_only, rank_zero_warn
 from lightning.fabric.utilities.types import _PATH
+from lightning.fabric.wrappers import _unwrap_objects
 
 log = logging.getLogger(__name__)
 
@@ -246,6 +247,7 @@ def log_hyperparams(  # type: ignore[override]
     def log_graph(self, model: Module, input_array: Optional[Tensor] = None) -> None:
         model_example_input = getattr(model, "example_input_array", None)
         input_array = model_example_input if input_array is None else input_array
+        model = _unwrap_objects(model)
 
         if input_array is None:
             rank_zero_warn(
@@ -262,8 +264,10 @@ def log_graph(self, model: Module, input_array: Optional[Tensor] = None) -> None
             getattr(model, "_apply_batch_transfer_handler", None)
         ):
             # this is probably is a LightningModule
-            input_array = model._on_before_batch_transfer(input_array)  # type: ignore[operator]
-            input_array = model._apply_batch_transfer_handler(input_array)  # type: ignore[operator]
+            input_array = model._on_before_batch_transfer(input_array)
+            input_array = model._apply_batch_transfer_handler(input_array)
+            self.experiment.add_graph(model, input_array)
+        else:
             self.experiment.add_graph(model, input_array)
 
     @rank_zero_only
diff --git a/tests/tests_fabric/loggers/test_tensorboard.py b/tests/tests_fabric/loggers/test_tensorboard.py
index 05889452ceeb2..243f08cd144bf 100644
--- a/tests/tests_fabric/loggers/test_tensorboard.py
+++ b/tests/tests_fabric/loggers/test_tensorboard.py
@@ -23,6 +23,7 @@
 
 from lightning.fabric.loggers import TensorBoardLogger
 from lightning.fabric.loggers.tensorboard import _TENSORBOARD_AVAILABLE
+from lightning.fabric.wrappers import _FabricModule
 from tests_fabric.test_fabric import BoringModel
 
 
@@ -153,8 +154,18 @@ def test_tensorboard_log_graph(tmpdir, example_input_array):
     if example_input_array is not None:
         model.example_input_array = None
 
-    logger = TensorBoardLogger(tmpdir, log_graph=True)
+    logger = TensorBoardLogger(tmpdir)
+    logger._experiment = Mock()
     logger.log_graph(model, example_input_array)
+    if example_input_array is not None:
+        logger.experiment.add_graph.assert_called_with(model, example_input_array)
+    logger._experiment.reset_mock()
+
+    # model wrapped in `FabricModule`
+    wrapped = _FabricModule(model, precision=Mock())
+    logger.log_graph(wrapped, example_input_array)
+    if example_input_array is not None:
+        logger.experiment.add_graph.assert_called_with(model, example_input_array)
 
 
 @pytest.mark.skipif(not _TENSORBOARD_AVAILABLE, reason=str(_TENSORBOARD_AVAILABLE))