👬 Rename collator PreferenceCollator to DataCollatorForPreference

docs/source/dpo_trainer.mdx

@@ -278,6 +278,6 @@ dpo_trainer = DPOTrainer(
 
 [[autodoc]] DPOConfig
 
-## PreferenceCollator
+## DataCollatorForPreference
 
-[[autodoc]] trainer.dpo_trainer.PreferenceCollator
+[[autodoc]] trainer.dpo_trainer.DataCollatorForPreference

tests/test_collators.py

@@ -0,0 +1,74 @@
+# 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.dpo_trainer import DataCollatorForPreference
+
+
+class TestDataCollatorForPreference(unittest.TestCase):
+    def setUp(self):
+        self.collator = DataCollatorForPreference(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], "chosen_input_ids": [4, 5], "rejected_input_ids": [6]},
+            {"prompt_input_ids": [7, 8], "chosen_input_ids": [9, 10], "rejected_input_ids": [11, 12, 13]},
+        ]
+        output = self.collator.torch_call(examples)
+
+        expected_prompt_input_ids = torch.tensor([[1, 2, 3], [0, 7, 8]])
+        expected_prompt_attention_mask = torch.tensor([[1, 1, 1], [0, 1, 1]])
+        expected_chosen_input_ids = torch.tensor([[4, 5], [9, 10]])
+        expected_chosen_attention_mask = torch.tensor([[1, 1], [1, 1]])
+        expected_rejected_input_ids = torch.tensor([[6, 0, 0], [11, 12, 13]])
+        expected_rejected_attention_mask = torch.tensor([[1, 0, 0], [1, 1, 1]])
+
+        self.assertTensorEqual(output["prompt_input_ids"], expected_prompt_input_ids)
+        self.assertTensorEqual(output["prompt_attention_mask"], expected_prompt_attention_mask)
+        self.assertTensorEqual(output["chosen_input_ids"], expected_chosen_input_ids)
+        self.assertTensorEqual(output["chosen_attention_mask"], expected_chosen_attention_mask)
+        self.assertTensorEqual(output["rejected_input_ids"], expected_rejected_input_ids)
+        self.assertTensorEqual(output["rejected_attention_mask"], expected_rejected_attention_mask)
+
+    def test_optional_fields(self):
+        examples = [
+            {
+                "prompt_input_ids": [1],
+                "chosen_input_ids": [2],
+                "rejected_input_ids": [3],
+                "pixel_values": [[[0.1, 0.2], [0.3, 0.4]]],  # Example 3D tensor (1x2x2)
+            },
+            {
+                "prompt_input_ids": [4],
+                "chosen_input_ids": [5],
+                "rejected_input_ids": [6],
+                "pixel_values": [[[0.5, 0.6], [0.7, 0.8]]],  # Example 3D tensor (1x2x2)
+            },
+        ]
+        output = self.collator.torch_call(examples)
+
+        expected_pixel_values = torch.tensor(
+            [
+                [[[0.1, 0.2], [0.3, 0.4]]],
+                [[[0.5, 0.6], [0.7, 0.8]]],
+            ]
+        )  # Shape: (2, 1, 2, 2)
+
+        self.assertTensorEqual(output["pixel_values"], expected_pixel_values)

trl/trainer/dpo_trainer.py

@@ -79,7 +79,7 @@
 
 
 @dataclass
-class PreferenceCollator(DataCollatorMixin):
+class DataCollatorForPreference(DataCollatorMixin):
     """
     Data collator used for preference data. Inputs are dynamically padded to the maximum length of a batch if they
     are not all of the same length.
@@ -92,8 +92,8 @@ class PreferenceCollator(DataCollatorMixin):
 
     Examples:
     ```python
-    >>> from trl import PreferenceCollator
-    >>> collator = PreferenceCollator(pad_token_id=0)
+    >>> from trl import DataCollatorForPreference
+    >>> collator = DataCollatorForPreference(pad_token_id=0)
     >>> examples = [
     ...     {"prompt_input_ids": [1, 2, 3], "chosen_input_ids": [4, 5], "rejected_input_ids": [6]},
     ...     {"prompt_input_ids": [7, 8], "chosen_input_ids": [9, 10], "rejected_input_ids": [11, 12, 13]}
@@ -168,7 +168,7 @@ class DPOTrainer(Trainer):
         args (`DPOConfig`):
             The DPO config arguments to use for training.
         data_collator (`transformers.DataCollator`):
-            The data collator to use for training. If None is specified, the default data collator (`PreferenceCollator`) will be used
+            The data collator to use for training. If None is specified, the default data collator (`DataCollatorForPreference`) will be used
             which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
         train_dataset (`datasets.Dataset`):
             The dataset to use for training.
@@ -374,7 +374,7 @@ def make_inputs_require_grad(module, input, output):
                 )
 
         if data_collator is None:
-            data_collator = PreferenceCollator(pad_token_id=self.padding_value)
+            data_collator = DataCollatorForPreference(pad_token_id=self.padding_value)
 
         # Disable dropout in the model and reference model
         if args.disable_dropout:
@@ -684,7 +684,7 @@ def _set_signature_columns_if_needed(self):
         # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
         # By default, this method sets `self._signature_columns` to the model's expected inputs.
         # In DPOTrainer, we preprocess data, so using the model's signature columns doesn't work.
-        # Instead, we set them to the columns expected by `PreferenceCollator`, hence the override.
+        # Instead, we set them to the columns expected by `DataCollatorForPreference`, hence the override.
         if self._signature_columns is None:
             self._signature_columns = [
                 "prompt_input_ids",