test

tests/test_collators.py

@@ -0,0 +1,46 @@
+# Copyright 2024 The HuggingFace Team. 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 torch
+
+from trl.trainer.kto_trainer import DataCollatorForUnpairedPreference
+
+
+class TestDataCollatorForUnpairedPreference(unittest.TestCase):
+    def setUp(self):
+        self.collator = DataCollatorForUnpairedPreference(pad_token_id=0)
+
+    def assertTensorEqual(self, tensor1, tensor2):
+        self.assertTrue(torch.equal(tensor1, tensor2), f"Tensors are not equal:\n{tensor1}\n{tensor2}")
+
+    def test_padding_behavior(self):
+        examples = [
+            {"prompt_input_ids": [1, 2, 3], "completion_input_ids": [4, 5], "label": True},
+            {"prompt_input_ids": [6, 7], "completion_input_ids": [8, 9, 10], "label": False},
+        ]
+        output = self.collator.torch_call(examples)
+
+        expected_prompt_input_ids = torch.tensor([[1, 2, 3], [0, 6, 7]])
+        expected_prompt_attention_mask = torch.tensor([[1, 1, 1], [0, 1, 1]])
+        expected_completion_input_ids = torch.tensor([[4, 5, 0], [8, 9, 10]])
+        expected_completion_attention_mask = torch.tensor([[1, 1, 0], [1, 1, 1]])
+        expected_labels = torch.tensor([True, False])
+
+        self.assertTensorEqual(output["prompt_input_ids"], expected_prompt_input_ids)
+        self.assertTensorEqual(output["prompt_attention_mask"], expected_prompt_attention_mask)
+        self.assertTensorEqual(output["completion_input_ids"], expected_completion_input_ids)
+        self.assertTensorEqual(output["completion_attention_mask"], expected_completion_attention_mask)
+        self.assertTensorEqual(output["labels"], expected_labels)