[DRAFT] Refactor DPO

tests/test_data_utils.py

@@ -955,6 +955,20 @@ def test_with_extra_column(self):
         dataset = truncate_dataset(dataset, max_length)
         assert dataset.to_dict() == expected_output
 
+    def test_with_specified_columns(self):
+        examples = {
+            "prompt_ids": [[1, 2, 3], [6, 7], [12]],
+            "completion_ids": [[4, 5], [8, 9, 10, 11], [13, 14]],
+        }
+        dataset = Dataset.from_dict(examples)
+        max_length = 2
+        expected_output = {
+            "prompt_ids": [[1, 2], [6, 7], [12]],
+            "completion_ids": [[4, 5], [8, 9, 10, 11], [13, 14]],
+        }
+        dataset = truncate_dataset(dataset, max_length, columns=["prompt_ids"])
+        assert dataset.to_dict() == expected_output
+
 
 class TestMaybeConvertToChatML(TrlTestCase):
     def test_with_conversations_key(self):

trl/data_utils.py

@@ -714,7 +714,10 @@ def pack_dataset(
 
 
 def truncate_dataset(
-    dataset: DatasetType, max_length: int, map_kwargs: Optional[dict[str, Any]] = None
+    dataset: DatasetType,
+    max_length: int,
+    columns: Union[str, list[str]] = "all",
+    map_kwargs: Optional[dict[str, Any]] = None,
 ) -> DatasetType:
     r"""
     Truncate sequences in a dataset to a specified `max_length`.
@@ -724,6 +727,8 @@ def truncate_dataset(
             Dataset to truncate.
         max_length (`int`):
             Maximum sequence length to truncate to.
+        columns (`str` or `list[str]`, *optional*, defaults to `"all"`):
+            Which columns to truncate. If `"all"` (default), all columns are truncated.
         map_kwargs (`dict`, *optional*):
             Additional keyword arguments to pass to the dataset's map method when truncating examples.
 
@@ -749,32 +754,30 @@ def truncate_dataset(
         map_kwargs = {}
     if isinstance(dataset, Dataset):
         # Fast truncation with pyarrow
-        def truncate(examples):
+        def truncate(examples, columns):
             truncated_columns = []
             for column in examples.columns:
-                if pyarrow.types.is_list(column.type) or pyarrow.types.is_large_list(column.type):
-                    column = pc.list_slice(column, 0, max_length)
+                if columns == "all" or column._name in columns:
+                    if pyarrow.types.is_list(column.type) or pyarrow.types.is_large_list(column.type):
+                        column = pc.list_slice(column, 0, max_length)
                 truncated_columns.append(column)
             return pa.Table.from_arrays(truncated_columns, names=examples.column_names)
 
         dataset = dataset.with_format("arrow")
-        dataset = dataset.map(truncate, batched=True, **map_kwargs)
+        dataset = dataset.map(truncate, batched=True, **map_kwargs, fn_kwargs={"columns": columns})
         dataset = dataset.with_format(None)
     else:
 
-        def truncate(examples):
+        def truncate(examples, columns):
             truncated_examples = {}
             for key, column in examples.items():
-                if column and isinstance(column[0], list):
-                    column = [val[:max_length] for val in column]
+                if columns == "all" or key in columns:
+                    if column and isinstance(column[0], list):
+                        column = [val[:max_length] for val in column]
                 truncated_examples[key] = column
             return truncated_examples
 
-        dataset = dataset.map(
-            truncate,
-            batched=True,
-            **map_kwargs,
-        )
+        dataset = dataset.map(truncate, batched=True, **map_kwargs, fn_kwargs={"columns": columns})
     return dataset
 
 

trl/experimental/dpo/__init__.py

@@ -0,0 +1,16 @@
+# Copyright 2020-2025 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 .dpo_config import DPOConfig
+from .dpo_trainer import DPOTrainer

trl/experimental/dpo/dpo_config.py

@@ -0,0 +1,212 @@
+# Copyright 2020-2025 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 dataclasses import dataclass, field
+from typing import Any, Optional
+
+from transformers import TrainingArguments
+
+
+@dataclass
+class DPOConfig(TrainingArguments):
+    r"""
+    Configuration class for the [`DPOTrainer`].
+
+    This class includes only the parameters that are specific to DPO training. For a full list of training arguments,
+    please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may
+    differ from those in [`~transformers.TrainingArguments`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        > Parameters that control the model and reference model
+
+        model_init_kwargs (`dict[str, Any]`, *optional*):
+            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
+            argument of the [`DPOTrainer`] is provided as a string.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model and reference model.
+
+        > Parameters that control the data preprocessing
+
+        dataset_num_proc (`int`, *optional*):
+            Number of processes to use for processing the dataset.
+        pad_token (`str`, *optional*):
+            Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`,
+            it falls back to `processing_class.eos_token`.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt part of the sequence. If `None`, no truncation is applied.
+        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
+            Maximum length of the completion part of the sequence. If `None`, no truncation is applied.
+        max_length (`int` or `None`, *optional*, defaults to `1024`):
+            Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the right.
+            If `None`, no truncation is applied.
+        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
+            Truncation mode to use when the sequence exceeds `max_length`. Possible values are `"keep_end"` and
+            `"keep_start"`.
+        padding_free (`bool`, *optional*, defaults to `False`):
+            Whether to perform forward passes without padding by flattening all sequences in the batch into a single
+            continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only
+            supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch structure.
+        pad_to_multiple_of (`int`, *optional*):
+            If set, the sequences will be padded to a multiple of this value.
+        precompute_ref_log_probs (`bool`, *optional*, defaults to `True`):
+            Whether to precompute the reference model log probabilities for the entire training dataset before
+            training. This allows to save memory during training, as the reference model does not need to be kept in
+            memory.
+
+        > Parameters that control the training
+
+        loss_type (`str` or `list[str]`, *optional*, defaults to `"sigmoid"`):
+            Type of loss to use. Possible values are:
+
+                - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper.
+                - `"hinge"`: hinge loss on the normalized likelihood from the
+                [SLiC](https://huggingface.co/papers/2305.10425) paper.
+        beta (`float`, *optional*, defaults to `0.1`):
+            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
+            reference model.
+        activation_offloading (`bool`, *optional*, defaults to `False`):
+            Whether to offload the activations to the CPU.
+    """
+
+    _VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs"]
+
+    # Parameters whose default values are overridden from TrainingArguments
+    learning_rate: float = field(
+        default=1e-6,
+        metadata={"help": "The initial learning rate for AdamW."},
+    )
+    logging_steps: float = field(
+        default=10,
+        metadata={
+            "help": "Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, "
+            "will be interpreted as ratio of total training steps."
+        },
+    )
+    gradient_checkpointing: bool = field(
+        default=True,
+        metadata={
+            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
+        },
+    )
+    bf16: Optional[bool] = field(
+        default=None,
+        metadata={
+            "help": "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA "
+            "architecture or Intel XPU or using CPU (use_cpu) or Ascend NPU. If not set, it defaults to `True` if "
+            "`fp16` is not set."
+        },
+    )
+
+    # Parameters that control the model
+    model_init_kwargs: Optional[dict[str, Any]] = field(
+        default=None,
+        metadata={
+            "help": "Keyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `model` argument of "
+            "the `DPOTrainer` is provided as a string."
+        },
+    )
+    disable_dropout: bool = field(
+        default=True,
+        metadata={"help": "Whether to disable dropout in the model and reference model."},
+    )
+
+    # Parameters that control the data preprocessing
+    dataset_num_proc: Optional[int] = field(
+        default=None,
+        metadata={"help": "Number of processes to use for processing the dataset."},
+    )
+    pad_token: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that "
+            "is also `None`, it falls back to `processing_class.eos_token`."
+        },
+    )
+    max_prompt_length: Optional[int] = field(
+        default=512,
+        metadata={"help": "Maximum length of the prompt part of the sequence. If `None`, no truncation is applied."},
+    )
+    max_completion_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Maximum length of the completion part of the sequence. If `None`, no truncation is applied."
+        },
+    )
+    max_length: Optional[int] = field(
+        default=1024,
+        metadata={
+            "help": "Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from "
+            "the right. If `None`, no truncation is applied."
+        },
+    )
+    truncation_mode: str = field(
+        default="keep_end",
+        metadata={
+            "help": "Truncation mode to use when the sequence exceeds `max_length`. Possible values are `'keep_end'` "
+            "and `'keep_start'`.",
+            "choices": ["keep_end", "keep_start"],
+        },
+    )
+    padding_free: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to perform forward passes without padding by flattening all sequences in the batch into "
+            "a single continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this "
+            "is only supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch "
+            "structure."
+        },
+    )
+    pad_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={"help": "If set, the sequences will be padded to a multiple of this value."},
+    )
+    precompute_ref_log_probs: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to precompute the reference model log probabilities for the entire training dataset "
+            "before training. This allows to save memory during training, as the reference model does not need to be "
+            "kept in memory."
+        },
+    )
+
+    # Parameters that control the training
+    loss_type: list[str] = field(
+        default_factory=lambda: ["sigmoid"],
+        metadata={
+            "help": "Type of loss to use. Possible values are: `'sigmoid'`, `'hinge'`.",
+        },
+    )
+    beta: float = field(
+        default=0.1,
+        metadata={
+            "help": "Parameter controlling the deviation from the reference model. Higher β means less deviation from "
+            "the reference model."
+        },
+    )
+    activation_offloading: bool = field(
+        default=False,
+        metadata={"help": "Whether to offload the activations to the CPU."},
+    )
+
+    def __post_init__(self):
+        self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16
+
+        # Normalize loss_type to string format for internal use
+        if hasattr(self.loss_type, "__len__") and len(self.loss_type) == 1:
+            self.loss_type = self.loss_type[0]
+        super().__post_init__()