diff --git a/python/tvm/relax/frontend/torch/exported_program_translator.py b/python/tvm/relax/frontend/torch/exported_program_translator.py
index c8d9d12505c6..335de7a24016 100644
--- a/python/tvm/relax/frontend/torch/exported_program_translator.py
+++ b/python/tvm/relax/frontend/torch/exported_program_translator.py
@@ -285,6 +285,7 @@ def create_convert_map(
             # tensor creation
             "_to_copy.default": self._to_copy,
             "arange.start": self._arange,
+            "contiguous.default": lambda node: self.env[node.args[0]],  # no-op
             "clone.default": lambda node: self.env[node.args[0]],
             "empty.memory_format": self._empty,
             "fill.Scalar": self._fill,
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index 8ca335c2fe7a..6406610bf53e 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -2930,6 +2930,26 @@ def main(
     verify_model(Arange(), example_args, {}, Expected)
 
 
+def test_contiguous():
+    class Contiguous(Module):
+        def forward(self, input):
+            return input.contiguous()
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main(
+            input: R.Tensor((10, 10), dtype="float32"),
+        ) -> R.Tuple(R.Tensor((10, 10), dtype="float32")):
+            with R.dataflow():
+                gv: R.Tuple(R.Tensor((10, 10), dtype="float32")) = (input,)
+                R.output(gv)
+            return gv
+
+    example_args = (torch.randn(10, 10, dtype=torch.float32),)
+    verify_model(Contiguous(), example_args, {}, Expected)
+
+
 def test_clone():
     class Clone(Module):
         def forward(self, input):