【Hackathon 9th No.30】add test_tritonmoe_preprocess (#3891)

* add test_tritonmoe_preprocess

* add value check

* del test_support_all...

tests/operators/test_tritonmoe_preprocess.py

@@ -0,0 +1,120 @@
+# 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 tritonmoe_preprocess
+
+
+class TestTritonMOEPreprocess(unittest.TestCase):
+    def setUp(self):
+        paddle.set_device("gpu")
+        np.random.seed(42)
+
+    def _run_op(self, topk_ids_np, num_experts, GEMM_BLOCK_SIZE_M):
+        """Convert numpy to Paddle Tensor and run operator"""
+        topk_ids = paddle.to_tensor(topk_ids_np, dtype="int64")
+        sorted_ids, expert_ids, num_tokens_post_pad = tritonmoe_preprocess(topk_ids, num_experts, GEMM_BLOCK_SIZE_M)
+        return sorted_ids.numpy(), expert_ids.numpy(), num_tokens_post_pad.numpy()
+
+    def _check_output_shapes(
+        self, sorted_ids, expert_ids, num_tokens_post_pad, topk_ids_np, num_experts, GEMM_BLOCK_SIZE_M
+    ):
+        """Check output shapes and dtypes"""
+        expected_max_num_tokens_padded = topk_ids_np.size + num_experts * (GEMM_BLOCK_SIZE_M - 1)
+        self.assertEqual(sorted_ids.shape[0], expected_max_num_tokens_padded)
+
+        expected_max_num_m_blocks = expected_max_num_tokens_padded // GEMM_BLOCK_SIZE_M
+        self.assertEqual(expert_ids.shape[0], expected_max_num_m_blocks)
+
+        self.assertEqual(num_tokens_post_pad.shape[0], 1)
+        self.assertTrue(sorted_ids.dtype == np.int32)
+        self.assertTrue(expert_ids.dtype == np.int32)
+        self.assertTrue(num_tokens_post_pad.dtype == np.int32)
+
+    def _check_output_values_basic(self, sorted_ids, expert_ids, num_tokens_post_pad):
+        """Check expected values for the fixed example"""
+        expected_sorted_ids = np.array(
+            [
+                8,
+                12,
+                16,
+                16,
+                4,
+                9,
+                15,
+                16,
+                5,
+                10,
+                14,
+                16,
+                6,
+                11,
+                13,
+                16,
+                3,
+                7,
+                16,
+                16,
+                2,
+                16,
+                16,
+                16,
+                1,
+                16,
+                16,
+                16,
+                0,
+                16,
+                16,
+                16,
+            ],
+            dtype=np.int32,
+        )
+        np.testing.assert_array_equal(sorted_ids[: len(expected_sorted_ids)], expected_sorted_ids)
+
+        expected_expert_ids = np.array([0, 1, 2, 3, 4, 5, 6, 7], dtype=np.int32)
+        np.testing.assert_array_equal(expert_ids[: len(expected_expert_ids)], expected_expert_ids)
+
+        self.assertTrue(num_tokens_post_pad[0] % 4 == 0)
+
+    def test_basic_case(self):
+        """Basic fixed example test"""
+        num_experts = 8
+        GEMM_BLOCK_SIZE_M = 4
+        topk_ids_np = np.array([[7, 6, 5, 4], [1, 2, 3, 4], [0, 1, 2, 3], [0, 3, 2, 1]], dtype=np.int64)
+
+        sorted_ids, expert_ids, num_tokens_post_pad = self._run_op(topk_ids_np, num_experts, GEMM_BLOCK_SIZE_M)
+        self._check_output_shapes(
+            sorted_ids, expert_ids, num_tokens_post_pad, topk_ids_np, num_experts, GEMM_BLOCK_SIZE_M
+        )
+        self._check_output_values_basic(sorted_ids, expert_ids, num_tokens_post_pad)
+
+    def test_unsupported_num_experts(self):
+        """Test unsupported num_experts raises OSError"""
+        topk_ids_np = np.array([[0, 1], [1, 0]], dtype=np.int64)
+        unsupported_experts = [3, 9, 65, 129]
+        GEMM_BLOCK_SIZE_M = 4
+
+        for num_experts in unsupported_experts:
+            with self.subTest(num_experts=num_experts):
+                with self.assertRaises(OSError):
+                    self._run_op(topk_ids_np, num_experts, GEMM_BLOCK_SIZE_M)
+
+
+if __name__ == "__main__":
+    unittest.main()