🧰 Tool fine-tuning support DPO

tests/test_dpo_trainer.py

@@ -1152,11 +1152,48 @@ def test_dpo_trainer_use_num_logits_to_keep(self):
 
             trainer.train()
 
-    def test_padding_free(self):
+    def test_dpo_trainer_with_tools(self):
         model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
         tokenizer = AutoTokenizer.from_pretrained(model_id)
         tokenizer.pad_token = tokenizer.eos_token
 
+        model = AutoModelForCausalLM.from_pretrained(model_id)
+
+        # Define dummy test tools
+        def get_current_temperature(location: str):
+            """
+            Gets the temperature at a given location.
+
+            Args:
+                location: The location to get the temperature for
+            """
+            return 22.0
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            training_args = DPOConfig(
+                output_dir=tmp_dir,
+                tools=[get_current_temperature],
+            )
+
+            dummy_dataset = load_dataset("trl-internal-testing/zen", "conversational_preference")
+
+            trainer = DPOTrainer(
+                model=model,
+                ref_model=None,
+                args=training_args,
+                processing_class=tokenizer,
+                train_dataset=dummy_dataset["train"],
+                eval_dataset=dummy_dataset["test"],
+            )
+            # We don't run the training, but at this stage, the dataset is supposed to be pre-processed. When
+            # pre-processing, we expect the available tools to be explicitly mentioned in the system prompt. That's
+            # what we're checking here
+            self.assertIn("get_current_temperature", tokenizer.decode(trainer.train_dataset["prompt_input_ids"][0]))
+
+    def test_padding_free(self):
+        model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        tokenizer.pad_token = tokenizer.eos_token
         # Normally, we need `attn_implementation="flash_attention_2"` to that the model returns correct logits.
         # Without it, the logits may be incorrect, but that's fine here. This test focuses only on the inner logic
         # of padding_free.

trl/trainer/dpo_config.py

@@ -15,7 +15,7 @@
 import warnings
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import Any, Optional
+from typing import Any, Callable, Optional, Union
 
 from transformers import TrainingArguments
 
@@ -93,6 +93,9 @@ class DPOConfig(TrainingArguments):
             Batch size to use when precomputing reference model log probabilities. This can be set higher than the
             training batch size to speed up preprocessing. If `None`, defaults to `per_device_train_batch_size` for
             training and `per_device_eval_batch_size` for evaluation.
+        tools (`Optional[list[Union[dict, Callable]]]`, *optional*, defaults to `None`):
+            List of tools (callable functions) that will be accessible to the model.
+            If the template does not support function calling, this argument will have no effect.
 
         > Parameters that control the training
 
@@ -261,6 +264,13 @@ class DPOConfig(TrainingArguments):
             "`per_device_train_batch_size` for training and `per_device_eval_batch_size` for evaluation."
         },
     )
+    tools: Optional[list[Union[dict, Callable]]] = field(
+        default=None,
+        metadata={
+            "help": "List of tools (callable functions) that will be accessible to the model. If the template does "
+            "not support function calling, this argument will have no effect."
+        },
+    )
 
     # Parameters that control the training
     learning_rate: float = field(

trl/trainer/dpo_trainer.py

@@ -543,7 +543,9 @@ def _prepare_dataset(
             # Apply the chat template if needed
             if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`
                 map_kwargs["desc"] = f"Applying chat template to {dataset_name} dataset"
-            dataset = dataset.map(maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, **map_kwargs)
+            dataset = dataset.map(
+                maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class, "tools": args.tools}, **map_kwargs
+            )
 
             # Tokenize the dataset
             if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`