diff --git a/tests/operators/test_fused_get_rotary_embedding.py b/tests/operators/test_fused_get_rotary_embedding.py
new file mode 100644
index 000000000..f6f07f86d
--- /dev/null
+++ b/tests/operators/test_fused_get_rotary_embedding.py
@@ -0,0 +1,81 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+
+from fastdeploy.model_executor.ops.gpu import fused_get_rotary_embedding
+
+
+def numpy_rope(position_ids, head_dim, prompt_num=0, seq_len=None):
+    """Numpy reference implementation"""
+    batch_size, max_seq_len = position_ids.shape
+    if seq_len is None:
+        seq_len = max_seq_len - prompt_num
+
+    inv_head_dim = 1.0 / float(head_dim)
+    rope_embedding = np.empty((2, batch_size, 1, seq_len, head_dim), dtype=np.float32)
+
+    for b in range(batch_size):
+        for s in range(seq_len):
+            pos = position_ids[b, s + prompt_num]
+            for h in range(0, head_dim, 2):
+                exponent_factor = -float(h) * inv_head_dim
+                inv_freq = np.power(10000.0, exponent_factor)
+                val = pos * inv_freq
+                cos_val, sin_val = np.cos(val), np.sin(val)
+                rope_embedding[0, b, 0, s, h : h + 2] = cos_val
+                rope_embedding[1, b, 0, s, h : h + 2] = sin_val
+    return rope_embedding
+
+
+class TestFusedGetRotaryEmbedding(unittest.TestCase):
+    def setUp(self):
+        paddle.set_device("gpu")
+        np.random.seed(42)
+        self.batch_size = 2
+        self.seq_len = 4
+        self.head_dim = 8
+
+    def _run_and_check(self, batch_size, seq_len, head_dim, prompt_num=0):
+        input_ids = paddle.randint(0, 100, [batch_size, seq_len], dtype="int32")
+        position_ids = paddle.arange(seq_len + 2 * prompt_num).tile([batch_size, 1]).astype("float32")
+
+        head_dim_tensor = paddle.arange(head_dim, dtype="int32")
+
+        out = fused_get_rotary_embedding(input_ids, position_ids, head_dim_tensor, prompt_num)
+        out_np = out.numpy()
+        ref = numpy_rope(position_ids.numpy(), head_dim, prompt_num, seq_len=seq_len)
+
+        # check shape
+        expect_shape = (2, batch_size, 1, seq_len, head_dim)
+        self.assertEqual(tuple(out.shape), expect_shape)
+
+        # check values
+        np.testing.assert_allclose(out_np, ref, rtol=1e-5, atol=1e-6)
+
+    def test_basic_case(self):
+        self._run_and_check(self.batch_size, self.seq_len, self.head_dim)
+
+    def test_minimal_head_dim(self):
+        self._run_and_check(batch_size=1, seq_len=2, head_dim=2)
+
+    def test_with_prompt_num(self):
+        self._run_and_check(self.batch_size, self.seq_len, self.head_dim, prompt_num=3)
+
+
+if __name__ == "__main__":
+    unittest.main()