【Hackathon 9th No.68】supplementary unit test for ngram_match

tests/operators/test_ngram_match.py

@@ -0,0 +1,123 @@
+# 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 paddle
+
+from fastdeploy.model_executor.ops.gpu import ngram_match
+
+
+class TestNgramMatchOp(unittest.TestCase):
+
+    def setUp(self):
+        paddle.set_device("cpu")
+
+    def test_basic_match(self):
+        """
+        Case 1: input_ids overlaps with pre_ids, and can extract draft tokens.
+        """
+        batch_size = 1
+        seq_len = 6
+
+        # Input IDs
+        input_ids = paddle.to_tensor([[10, 20, 30, 40, 50, 60]], dtype="int64")
+        # Length of input IDs
+        input_ids_len = paddle.to_tensor([6], dtype="int64")
+        # Previous IDs
+        pre_ids = paddle.to_tensor([[10, 20, 30, 40, 0, 0]], dtype="int64")
+        # Current step index
+        step_idx = paddle.to_tensor([3], dtype="int64")
+        # Number of draft tokens
+        draft_token_num = paddle.to_tensor([3], dtype="int32")
+        # Placeholder for draft tokens
+        draft_tokens = paddle.zeros([batch_size, seq_len], dtype="int64")
+
+        # Sequence lengths for this time step
+        seq_lens_this_time = paddle.zeros([batch_size], dtype="int32")
+        # Sequence lengths for encoder
+        seq_lens_encoder = paddle.zeros([batch_size], dtype="int32")
+        # Sequence lengths for decoder
+        seq_lens_decoder = paddle.ones([batch_size], dtype="int32")
+        # Maximum decoding length
+        max_dec_len = paddle.to_tensor([10], dtype="int64")
+
+        ngram_match(
+            input_ids,
+            input_ids_len,
+            pre_ids,
+            step_idx,
+            draft_token_num,
+            draft_tokens,
+            seq_lens_this_time,
+            seq_lens_encoder,
+            seq_lens_decoder,
+            max_dec_len,
+            3,
+            4,
+        )
+
+        # print("step_idx:", step_idx.numpy())
+        # print("draft_tokens_out:", draft_tokens.numpy())
+        # print("seq_lens_this_time_out:", seq_lens_this_time.numpy())
+
+        # Extract non-zero tokens and assert the results.
+        nonzero_tokens = draft_tokens.numpy()[0][draft_tokens.numpy()[0] != 0]
+        expected_tokens = [50, 60]
+        self.assertTrue((nonzero_tokens == expected_tokens).all())
+
+        # Check length
+        self.assertEqual(seq_lens_this_time.numpy()[0], 3)
+
+    def test_no_match(self):
+        """
+        Case 2: pre_ids does not match input_ids, should only keep the current token.
+        """
+        batch_size = 1
+        input_ids = paddle.to_tensor([[100, 200, 300, 400]], dtype="int64")
+        input_ids_len = paddle.to_tensor([4], dtype="int64")
+        pre_ids = paddle.to_tensor([[1, 2, 3, 4]], dtype="int64")
+        step_idx = paddle.to_tensor([3], dtype="int64")
+        draft_token_num = paddle.to_tensor([2], dtype="int32")
+        draft_tokens = paddle.zeros([batch_size, 4], dtype="int64")
+
+        seq_lens_this_time = paddle.zeros([batch_size], dtype="int32")
+        seq_lens_encoder = paddle.zeros([batch_size], dtype="int32")
+        seq_lens_decoder = paddle.ones([batch_size], dtype="int32")
+        max_dec_len = paddle.to_tensor([6], dtype="int64")
+
+        ngram_match(
+            input_ids,
+            input_ids_len,
+            pre_ids,
+            step_idx,
+            draft_token_num,
+            draft_tokens,
+            seq_lens_this_time,
+            seq_lens_encoder,
+            seq_lens_decoder,
+            max_dec_len,
+            3,
+            3,
+        )
+
+        print("draft_tokens_out:", draft_tokens.numpy())
+        print("seq_lens_this_time_out:", seq_lens_this_time.numpy())
+
+        # No match → should only keep 1 token
+        self.assertEqual(seq_lens_this_time.numpy()[0], 1)
+
+
+if __name__ == "__main__":
+    unittest.main()