add test_set_value_by_flags_and_idx.py (#4186)

Author: Echo-Nie

tests/operators/test_set_value_by_flags_and_idx.py

@@ -0,0 +1,218 @@
+# 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 set_value_by_flags_and_idx
+
+
+def set_value_by_flags_and_idx_numpy(
+    pre_ids_all, input_ids, seq_lens_this_time, seq_lens_encoder, seq_lens_decoder, step_idx, stop_flags
+):
+    """Numpy reference implementation"""
+    result = pre_ids_all.copy()
+    bs = seq_lens_this_time.shape[0]
+    for i in range(bs):
+        if stop_flags[i]:
+            continue
+        seq_len_enc = seq_lens_encoder[i]
+        seq_len_dec = seq_lens_decoder[i]
+        current_step_idx = step_idx[i]
+        if seq_len_enc == 0 and seq_len_dec == 0:
+            continue
+        if current_step_idx >= 0:
+            if seq_len_enc > 0:
+                token_idx = seq_len_enc - 1
+                token_to_assign = input_ids[i, token_idx]
+            else:
+                token_to_assign = input_ids[i, 0]
+            result[i, current_step_idx] = token_to_assign
+    return result
+
+
+class TestSetValueByFlagsAndIdxRandom(unittest.TestCase):
+    """Random case testing"""
+
+    def setUp(self):
+        paddle.seed(42)
+        np.random.seed(42)
+        batch_size = 10
+        max_length = 10
+        max_input_length = 15
+
+        # Generate random inputs
+        self.pre_ids_all_np = np.random.randint(0, 1000, size=(batch_size, max_length), dtype="int64")
+        self.input_ids_np = np.random.randint(0, 1000, size=(batch_size, max_input_length), dtype="int64")
+        self.seq_lens_this_time_np = np.random.randint(0, max_input_length, size=(batch_size,), dtype="int32")
+        self.seq_lens_encoder_np = np.random.randint(0, max_input_length, size=(batch_size,), dtype="int32")
+        self.seq_lens_decoder_np = np.random.randint(0, max_input_length, size=(batch_size,), dtype="int32")
+        self.step_idx_np = np.random.randint(0, max_length, size=(batch_size,), dtype="int64")
+        self.stop_flags_np = np.random.choice([True, False], size=(batch_size,), p=[0.1, 0.9])
+
+    def test_set_value_by_flags_and_idx(self):
+        # NumPy baseline
+        numpy_out = set_value_by_flags_and_idx_numpy(
+            self.pre_ids_all_np,
+            self.input_ids_np,
+            self.seq_lens_this_time_np,
+            self.seq_lens_encoder_np,
+            self.seq_lens_decoder_np,
+            self.step_idx_np,
+            self.stop_flags_np,
+        )
+        # custom op
+        pre_ids_all = paddle.to_tensor(self.pre_ids_all_np)
+        set_value_by_flags_and_idx(
+            pre_ids_all,
+            paddle.to_tensor(self.input_ids_np),
+            paddle.to_tensor(self.seq_lens_this_time_np),
+            paddle.to_tensor(self.seq_lens_encoder_np),
+            paddle.to_tensor(self.seq_lens_decoder_np),
+            paddle.to_tensor(self.step_idx_np),
+            paddle.to_tensor(self.stop_flags_np),
+        )
+        # Ensure outputs match exactly
+        np.testing.assert_array_equal(numpy_out, pre_ids_all.numpy())
+
+
+class TestSetValueByFlagsAndIdxCornerCases(unittest.TestCase):
+    """Cover corner cases"""
+
+    def test_encoder_update(self):
+        # encoder case: seq_lens_encoder > 0, use last token
+        pre_ids_all = np.zeros((1, 5), dtype="int64")
+        input_ids = np.array([[11, 12, 13]], dtype="int64")
+        seq_lens_this_time = np.array([3], dtype="int32")
+        seq_lens_encoder = np.array([3], dtype="int32")
+        seq_lens_decoder = np.array([0], dtype="int32")
+        step_idx = np.array([0], dtype="int64")
+        stop_flags = np.array([False], dtype="bool")
+
+        expected = set_value_by_flags_and_idx_numpy(
+            pre_ids_all, input_ids, seq_lens_this_time, seq_lens_encoder, seq_lens_decoder, step_idx, stop_flags
+        )
+        pre_ids_all_tensor = paddle.to_tensor(pre_ids_all)
+        set_value_by_flags_and_idx(
+            pre_ids_all_tensor,
+            paddle.to_tensor(input_ids),
+            paddle.to_tensor(seq_lens_this_time),
+            paddle.to_tensor(seq_lens_encoder),
+            paddle.to_tensor(seq_lens_decoder),
+            paddle.to_tensor(step_idx),
+            paddle.to_tensor(stop_flags),
+        )
+        np.testing.assert_array_equal(expected, pre_ids_all_tensor.numpy())
+
+    def test_decoder_update(self):
+        # decoder case: seq_lens_encoder=0, use first token
+        pre_ids_all = np.zeros((1, 4), dtype="int64")
+        input_ids = np.array([[101, 102]], dtype="int64")
+        seq_lens_this_time = np.array([2], dtype="int32")
+        seq_lens_encoder = np.array([0], dtype="int32")
+        seq_lens_decoder = np.array([2], dtype="int32")
+        step_idx = np.array([2], dtype="int64")
+        stop_flags = np.array([False], dtype="bool")
+
+        expected = set_value_by_flags_and_idx_numpy(
+            pre_ids_all, input_ids, seq_lens_this_time, seq_lens_encoder, seq_lens_decoder, step_idx, stop_flags
+        )
+        pre_ids_all_tensor = paddle.to_tensor(pre_ids_all)
+        set_value_by_flags_and_idx(
+            pre_ids_all_tensor,
+            paddle.to_tensor(input_ids),
+            paddle.to_tensor(seq_lens_this_time),
+            paddle.to_tensor(seq_lens_encoder),
+            paddle.to_tensor(seq_lens_decoder),
+            paddle.to_tensor(step_idx),
+            paddle.to_tensor(stop_flags),
+        )
+        np.testing.assert_array_equal(expected, pre_ids_all_tensor.numpy())
+
+    def test_stop_flag(self):
+        # stop_flags=True, no update
+        pre_ids_all = np.zeros((1, 3), dtype="int64")
+        input_ids = np.array([[5, 6, 7]], dtype="int64")
+        seq_lens_this_time = np.array([3], dtype="int32")
+        seq_lens_encoder = np.array([3], dtype="int32")
+        seq_lens_decoder = np.array([0], dtype="int32")
+        step_idx = np.array([1], dtype="int64")
+        stop_flags = np.array([True], dtype="bool")
+
+        expected = set_value_by_flags_and_idx_numpy(
+            pre_ids_all, input_ids, seq_lens_this_time, seq_lens_encoder, seq_lens_decoder, step_idx, stop_flags
+        )
+        pre_ids_all_tensor = paddle.to_tensor(pre_ids_all)
+        set_value_by_flags_and_idx(
+            pre_ids_all_tensor,
+            paddle.to_tensor(input_ids),
+            paddle.to_tensor(seq_lens_this_time),
+            paddle.to_tensor(seq_lens_encoder),
+            paddle.to_tensor(seq_lens_decoder),
+            paddle.to_tensor(step_idx),
+            paddle.to_tensor(stop_flags),
+        )
+        np.testing.assert_array_equal(expected, pre_ids_all_tensor.numpy())
+
+    def test_skip_when_both_len_zero(self):
+        # seq_lens_encoder=0 and seq_lens_decoder=0, skip
+        pre_ids_all = np.zeros((1, 3), dtype="int64")
+        input_ids = np.array([[8, 9, 10]], dtype="int64")
+        seq_lens_this_time = np.array([3], dtype="int32")
+        seq_lens_encoder = np.array([0], dtype="int32")
+        seq_lens_decoder = np.array([0], dtype="int32")
+        step_idx = np.array([0], dtype="int64")
+        stop_flags = np.array([False], dtype="bool")
+
+        expected = pre_ids_all.copy()
+        pre_ids_all_tensor = paddle.to_tensor(pre_ids_all)
+        set_value_by_flags_and_idx(
+            pre_ids_all_tensor,
+            paddle.to_tensor(input_ids),
+            paddle.to_tensor(seq_lens_this_time),
+            paddle.to_tensor(seq_lens_encoder),
+            paddle.to_tensor(seq_lens_decoder),
+            paddle.to_tensor(step_idx),
+            paddle.to_tensor(stop_flags),
+        )
+        np.testing.assert_array_equal(expected, pre_ids_all_tensor.numpy())
+
+    def test_step_idx_negative(self):
+        # step_idx < 0, skip
+        pre_ids_all = np.zeros((1, 3), dtype="int64")
+        input_ids = np.array([[42, 43, 44]], dtype="int64")
+        seq_lens_this_time = np.array([3], dtype="int32")
+        seq_lens_encoder = np.array([2], dtype="int32")
+        seq_lens_decoder = np.array([1], dtype="int32")
+        step_idx = np.array([-1], dtype="int64")
+        stop_flags = np.array([False], dtype="bool")
+
+        expected = pre_ids_all.copy()
+        pre_ids_all_tensor = paddle.to_tensor(pre_ids_all)
+        set_value_by_flags_and_idx(
+            pre_ids_all_tensor,
+            paddle.to_tensor(input_ids),
+            paddle.to_tensor(seq_lens_this_time),
+            paddle.to_tensor(seq_lens_encoder),
+            paddle.to_tensor(seq_lens_decoder),
+            paddle.to_tensor(step_idx),
+            paddle.to_tensor(stop_flags),
+        )
+        np.testing.assert_array_equal(expected, pre_ids_all_tensor.numpy())
+
+
+if __name__ == "__main__":
+    unittest.main()