diff --git a/tests/operators/test_group_swiglu_with_masked.py b/tests/operators/test_group_swiglu_with_masked.py
new file mode 100644
index 000000000..bc01d2517
--- /dev/null
+++ b/tests/operators/test_group_swiglu_with_masked.py
@@ -0,0 +1,105 @@
+# 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
+import paddle.nn.functional as F
+
+from fastdeploy.model_executor.ops.gpu import group_swiglu_with_masked
+
+paddle.seed(2024)
+
+
+def group_swiglu_with_masked_paddle(fc1_out_tensor, token_nums_per_expert):
+    group_num, group_size, hidden_dim_x2 = fc1_out_tensor.shape
+
+    if token_nums_per_expert.dtype not in [paddle.int32, paddle.int64]:
+        raise ValueError(f"token_nums_per_expert must be int32 or int64, but receive {token_nums_per_expert.dtype}")
+
+    gate, up = paddle.chunk(fc1_out_tensor, chunks=2, axis=-1)
+
+    act_out = (F.silu(gate.to(paddle.float32)) * up.to(paddle.float32)).to(fc1_out_tensor.dtype)
+
+    # [0, 1, 2, ..., group_size-1]
+    range_tensor = paddle.arange(group_size, dtype=token_nums_per_expert.dtype)
+
+    mask = range_tensor < token_nums_per_expert.unsqueeze(1)
+
+    mask = mask.unsqueeze(-1)
+
+    output_tensor = act_out * mask.astype(act_out.dtype)
+
+    return output_tensor
+
+
+class TestGroupSwigluWithMasked(unittest.TestCase):
+    def get_input(self):
+        self.token_nums_tensor = paddle.to_tensor([5, 8, 0, 3], dtype=self.token_nums_per_expert_dtype)
+        self.input_tensor = paddle.randn([self.group_num, self.group_size, self.hidden_dim * 2], dtype="bfloat16")
+
+    def setUp(self) -> None:
+        self.group_num = 4
+        self.group_size = 8
+        self.hidden_dim = 16  # fc1_out_tensor.shape()[2] / 2
+        self.input_dtype = paddle.bfloat16
+        self.token_nums_per_expert_dtype = paddle.int64
+        self.get_input()
+
+    def test_group_swiglu_with_masked(self):
+        paddle_output = group_swiglu_with_masked_paddle(self.input_tensor, self.token_nums_tensor)
+        output = group_swiglu_with_masked(self.input_tensor, self.token_nums_tensor)
+
+        valid_token_mask = paddle.arange(
+            self.group_size, dtype=self.token_nums_per_expert_dtype
+        ) < self.token_nums_tensor.unsqueeze(1)
+
+        # Note(ooooo): Because GetEmptyTensor will random.
+        np.testing.assert_allclose(
+            paddle_output[valid_token_mask].astype("float32").numpy(),
+            output[valid_token_mask].astype("float32").numpy(),
+        )
+
+
+class TestGroupSwigluWithMaskedCase1(TestGroupSwigluWithMasked):
+    def setUp(self) -> None:
+        self.group_num = 4
+        self.group_size = 8
+        self.hidden_dim = 16  # fc1_out_tensor.shape()[2] / 2
+        self.input_dtype = paddle.bfloat16
+        self.token_nums_per_expert_dtype = paddle.int32
+        self.get_input()
+
+
+class TestGroupSwigluWithMaskedCase2(TestGroupSwigluWithMasked):
+    def setUp(self) -> None:
+        self.group_num = 4
+        self.group_size = 8
+        self.hidden_dim = 16  # fc1_out_tensor.shape()[2] / 2
+        self.input_dtype = paddle.bfloat16
+        self.token_nums_per_expert_dtype = paddle.int32
+        self.get_input()
+
+    def get_input(self):
+        self.token_nums_tensor = paddle.randint(
+            0, self.group_size + 1, shape=[self.group_num], dtype=self.token_nums_per_expert_dtype
+        )
+        self.input_tensor = paddle.randn(
+            [self.group_num, self.group_size, self.hidden_dim * 2], dtype=self.input_dtype
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()