diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index 1d734bca03..13191db936 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -693,6 +693,20 @@ def aten_ops_softmax(
     )
 
 
+@dynamo_tensorrt_converter(
+    torch.ops.aten._log_softmax.default, supports_dynamic_shapes=True
+)
+def aten_ops_log_softmax(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    softmax = aten_ops_softmax(ctx, target, args, kwargs, name)
+    return impl.unary.log(ctx, target, SourceIR.ATEN, name, softmax)
+
+
 @dynamo_tensorrt_converter(
     torch.ops.aten.split.Tensor,
     capability_validator=has_static_shapes_in_args([1]),
diff --git a/py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py b/py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py
index ae3e7e1ffa..7552b16685 100644
--- a/py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py
+++ b/py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py
@@ -77,7 +77,6 @@
     aten.logit_backward,
     aten.log_sigmoid_backward,
     aten.log_sigmoid_forward,
-    aten._log_softmax,
     aten._log_softmax_backward_data,
     aten.logspace,
     aten.logsumexp.default,
diff --git a/tests/py/dynamo/conversion/test_log_softmax_aten.py b/tests/py/dynamo/conversion/test_log_softmax_aten.py
new file mode 100644
index 0000000000..306d225fb1
--- /dev/null
+++ b/tests/py/dynamo/conversion/test_log_softmax_aten.py
@@ -0,0 +1,34 @@
+import torch
+from torch.testing._internal.common_utils import run_tests
+from torch_tensorrt import Input
+
+from .harness import DispatchTestCase
+
+
+class TestLogSoftmaxConverter(DispatchTestCase):
+    def test_log_softmax(self):
+        class TestModule(torch.nn.Module):
+            def forward(self, x):
+                return torch.ops.aten._log_softmax.default(x, 1, False)
+
+        inputs = [torch.randn(1, 3, 5, 7)]
+        self.run_test(TestModule(), inputs)
+
+    def test_log_softmax_with_dynamic_shape(self):
+        class TestModule(torch.nn.Module):
+            def forward(self, x):
+                return torch.ops.aten._log_softmax.default(x, 2, False)
+
+        input_specs = [
+            Input(
+                min_shape=(1, 1, 1, 1),
+                opt_shape=(2, 4, 6, 8),
+                max_shape=(8, 8, 8, 8),
+                dtype=torch.float32,
+            ),
+        ]
+        self.run_test_with_dynamic_shape(TestModule(), input_specs)
+
+
+if __name__ == "__main__":
+    run_tests()