Feat: Add support for APO-zero in KTOTrainer

examples/scripts/kto.py

@@ -59,7 +59,7 @@
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
 
-from trl import KTOConfig, KTOTrainer, ModelConfig, get_peft_config, setup_chat_format
+from trl import KTOConfig, KTOTrainer, ModelConfig, get_peft_config, maybe_reformat_dpo_to_kto, setup_chat_format
 
 
 # Define and parse arguments.
@@ -97,10 +97,17 @@ class ScriptArguments:
     # Load the dataset
     dataset = load_dataset(script_args.dataset_name)
 
+    # If needed, reformat a DPO-formatted dataset (prompt, chosen, rejected) to a KTO-format (prompt, completion, label)
+    dataset = maybe_reformat_dpo_to_kto(dataset, num_proc=kto_args.dataset_num_proc)
+
     # Apply chat template
     def format_dataset(example):
-        example["prompt"] = tokenizer.apply_chat_template(example["prompt"], tokenize=False)
-        example["completion"] = tokenizer.apply_chat_template(example["completion"], tokenize=False)
+        if isinstance(example["completion"], str):
+            example["prompt"] = tokenizer.apply_chat_template(example["prompt"], tokenize=False)
+            example["completion"] = tokenizer.apply_chat_template(example["completion"], tokenize=False)
+        else:
+            example["prompt"] = tokenizer.apply_chat_template(example["completion"][:-1], tokenize=False)
+            example["completion"] = tokenizer.apply_chat_template([example["completion"][-1]], tokenize=False)
         return example
 
     # Compute that only on the main process for faster data processing.

tests/test_dataset_reformat.py

@@ -0,0 +1,71 @@
+# 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
+
+from datasets import Dataset, DatasetDict
+
+from trl.data_utils import maybe_reformat_dpo_to_kto
+
+
+class MaybeReformatDPOToKTOTester(unittest.TestCase):
+    def setUp(self):
+        # Create a sample DPO-formatted dataset for testing
+        self.dpo_data = {
+            "prompt": ["What is AI?", "Define machine learning."],
+            "chosen": ["AI is artificial intelligence.", "Machine learning is a subset of AI."],
+            "rejected": ["AI is a computer.", "Machine learning is a program."],
+        }
+        self.dpo_dataset = DatasetDict({"train": Dataset.from_dict(self.dpo_data)})
+
+        # Create a sample KTO-formatted dataset for testing
+        self.kto_data = {
+            "prompt": ["What is AI?", "Define machine learning.", "What is AI?", "Define machine learning."],
+            "completion": [
+                "AI is artificial intelligence.",
+                "Machine learning is a subset of AI.",
+                "AI is a computer.",
+                "Machine learning is a program.",
+            ],
+            "label": [True, True, False, False],
+        }
+        self.kto_dataset = DatasetDict({"train": Dataset.from_dict(self.kto_data)})
+
+    def test_dpo_to_kto_conversion(self):
+        # Test that a DPO-formatted dataset is correctly reformatted to KTO format
+        reformatted_dataset = maybe_reformat_dpo_to_kto(self.dpo_dataset)
+        self.assertEqual(
+            reformatted_dataset["train"].to_dict(),
+            self.kto_dataset["train"].to_dict(),
+            "The DPO-formatted dataset was not correctly reformatted to KTO format.",
+        )
+
+    def test_already_kto_format(self):
+        # Test that a KTO-formatted dataset remains unchanged
+        reformatted_dataset = maybe_reformat_dpo_to_kto(self.kto_dataset)
+        self.assertEqual(
+            reformatted_dataset["train"].to_dict(),
+            self.kto_dataset["train"].to_dict(),
+            "The KTO-formatted dataset should remain unchanged.",
+        )
+
+    def test_invalid_format(self):
+        # Test that a dataset with an incompatible format raises a ValueError
+        invalid_data = {
+            "input": ["What is AI?", "Define machine learning."],
+            "output": ["AI is artificial intelligence.", "Machine learning is a subset of AI."],
+        }
+        invalid_dataset = DatasetDict({"train": Dataset.from_dict(invalid_data)})
+
+        with self.assertRaises(ValueError):
+            maybe_reformat_dpo_to_kto(invalid_dataset)

tests/test_kto_trainer.py

@@ -75,15 +75,17 @@ def _init_dummy_dataset(self):
 
     @parameterized.expand(
         [
-            ["gpt2", True, True],
-            ["gpt2", True, False],
-            # ["t5", True],
-            ["gpt2", False, True],
-            ["gpt2", False, False],
-            # ["t5", False],
+            ["gpt2", "kto", True, True],
+            ["gpt2", "kto", True, False],
+            ["gpt2", "kto", False, True],
+            ["gpt2", "kto", False, False],
+            ["gpt2", "apo_zero_unpaired", True, True],
+            ["gpt2", "apo_zero_unpaired", True, False],
+            ["gpt2", "apo_zero_unpaired", False, True],
+            ["gpt2", "apo_zero_unpaired", False, False],
         ]
     )
-    def test_kto_trainer(self, name, pre_compute, eval_dataset):
+    def test_kto_trainer(self, name, loss_type, pre_compute, eval_dataset):
         with tempfile.TemporaryDirectory() as tmp_dir:
             training_args = KTOConfig(
                 output_dir=tmp_dir,
@@ -95,6 +97,7 @@ def test_kto_trainer(self, name, pre_compute, eval_dataset):
                 eval_strategy="steps",
                 beta=0.1,
                 precompute_ref_log_probs=pre_compute,
+                loss_type=loss_type,
                 report_to="none",
             )
 

trl/__init__.py

@@ -81,6 +81,7 @@
         "MultitaskPromptTuningConfig",
         "MultitaskPromptTuningInit",
     ],
+    "data_utils": ["maybe_reformat_dpo_to_kto"],
 }
 
 try:
@@ -162,6 +163,7 @@
     from .trainer.callbacks import RichProgressCallback, SyncRefModelCallback
     from .trainer.utils import get_kbit_device_map, get_peft_config, get_quantization_config
     from .commands.cli_utils import init_zero_verbose, SFTScriptArguments, DPOScriptArguments, TrlParser
+    from .data_utils import maybe_reformat_dpo_to_kto
 
     try:
         if not is_diffusers_available():

trl/commands/cli.py

@@ -21,7 +21,7 @@
 from rich.console import Console
 
 
-SUPPORTED_COMMANDS = ["sft", "dpo", "chat"]
+SUPPORTED_COMMANDS = ["sft", "dpo", "chat", "kto"]
 
 
 def main():

trl/data_utils.py

@@ -0,0 +1,74 @@
+# Copyright 2022 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.
+from copy import deepcopy
+
+from datasets import DatasetDict
+
+
+def _reformat_row_dpo_to_kto(row: dict):
+    """Turn a DPO-formatted dataset row into two KTO-formatted rows."""
+
+    chosen_row = {"prompt": row["prompt"], "completion": row["chosen"], "label": [True] * len(row["chosen"])}
+    rejected_row = {
+        "prompt": row["prompt"],
+        "completion": row["rejected"],
+        "label": [False] * len(row["chosen"]),
+    }
+    new_rows = {k: chosen_row[k] + rejected_row[k] for k in chosen_row.keys()}
+    return new_rows
+
+
+def maybe_reformat_dpo_to_kto(dataset: DatasetDict, num_proc: int = None):
+    """
+    Reformat a dataset from the DPO format to the KTO format if necessary.
+
+    This function checks whether the input dataset is already in the KTO format (containing "prompt", "completion", and "label" fields).
+    If the dataset is in DPO format (with "prompt", "chosen", and "rejected" fields), it converts it to KTO format by:
+    - Removing any unnecessary columns.
+    - Reformatting each row to create a unified format suitable for KTO training.
+
+    Args:
+        dataset (DatasetDict): The dataset to potentially reformat.
+        num_proc (int, optional): The number of processes to use for multiprocessing during dataset transformation. Defaults to None.
+
+    Returns:
+        DatasetDict: The reformatted dataset, if conversion was needed; otherwise, the original dataset.
+
+    Raises:
+        ValueError: If the dataset format is not compatible with KTO or DPO.
+    """
+    keys = list(dataset["train"].features.keys())
+
+    # check if the dataset is in the KTO format or needs to be reformatted
+    if "prompt" in keys and "completion" in keys and "label" in keys:
+        return dataset
+    elif "prompt" in keys and "rejected" in keys and "chosen" in keys:
+        # remove unnecessary fields
+        keys_to_remove = deepcopy(keys)
+        keys_to_remove.remove("prompt")
+        keys_to_remove.remove("chosen")
+        keys_to_remove.remove("rejected")
+        dataset = dataset.remove_columns(keys_to_remove)
+
+        # turn each DPO-formatted row into two KTO-formatted rows.
+        dataset = dataset.map(
+            _reformat_row_dpo_to_kto,
+            num_proc=num_proc,
+            batched=True,
+            remove_columns=["chosen", "rejected"],
+            desc="Reformatting Dataset from DPO format to KTO format.",
+        )
+        return dataset
+    else:
+        raise ValueError("Dataset format not compatible with KTO.")

trl/trainer/kto_config.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from dataclasses import dataclass
-from typing import Dict, Optional
+from typing import Dict, Literal, Optional
 
 from transformers import TrainingArguments
 
@@ -27,6 +27,11 @@ class KTOConfig(TrainingArguments):
     command line.
 
     Parameters:
+        loss_type (`str`, *optional*, defaults to `"kto"`):
+                The type of unpaired loss to use. Possible values are:
+
+                    - `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper.
+                    - `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper.
         max_length (`int`, *optional*, defaults to `None`):
             The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator.
         max_prompt_length (`int`, *optional*, defaults to `None`):
@@ -60,6 +65,10 @@ class KTOConfig(TrainingArguments):
             Number of processes to use for processing the datasets.
     """
 
+    loss_type: Literal[
+        "kto",
+        "apo_zero_unpaired",
+    ] = "kto"
     max_length: Optional[int] = None
     """The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator."""
     max_prompt_length: Optional[int] = None