Include chat_template_kwargs in apply_chat_template

tests/test_data_utils.py

@@ -396,6 +396,29 @@ def test_maybe_apply_chat_template(self, tokenizer_id, example):
             assert isinstance(result["label"], bool)
             assert result["label"] == example["label"]
 
+    def test_apply_chat_template_with_chat_template_kwargs(self):
+        tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen3ForCausalLM")
+
+        example = {
+            "prompt": [{"role": "user", "content": "What color is the sky?"}],
+            # with this tokenizer, when you pass enable_thinking=False, it will add "<think>\n\n</think>\n\n"
+            "chat_template_kwargs": {"enable_thinking": False},
+        }
+        result = apply_chat_template(example, tokenizer)
+
+        # docstyle-ignore
+        expected = textwrap.dedent("""\
+        <|im_start|>user
+        What color is the sky?<|im_end|>
+        <|im_start|>assistant
+        <think>
+
+        </think>
+
+        """)
+
+        assert result["prompt"] == expected
+
     def test_apply_chat_template_with_tools(self):
         tokenizer = AutoProcessor.from_pretrained("trl-internal-testing/tiny-LlamaForCausalLM-3.2")
 

trl/data_utils.py

@@ -143,7 +143,13 @@ def apply_chat_template(
 
     # Apply the chat template to the whole conversation
     if "messages" in example:
-        messages = tokenizer.apply_chat_template(example["messages"], tools=tools, tokenize=False, **template_kwargs)
+        messages = tokenizer.apply_chat_template(
+            example["messages"],
+            tools=tools,
+            tokenize=False,
+            **example.get("chat_template_kwargs", {}),
+            **template_kwargs,
+        )
 
     # Apply the chat template to the prompt, adding the generation prompt
     if "prompt" in example:
@@ -162,14 +168,19 @@ def apply_chat_template(
             continue_final_message=continue_final_message,
             tokenize=False,
             add_generation_prompt=add_generation_prompt,
+            **example.get("chat_template_kwargs", {}),
             **template_kwargs,
         )
 
     # Apply the chat template to the entire prompt + completion
     if "prompt" in example:  # explicit prompt and prompt-completion case
         if "chosen" in example:
             prompt_chosen = tokenizer.apply_chat_template(
-                example["prompt"] + example["chosen"], tools=tools, tokenize=False, **template_kwargs
+                example["prompt"] + example["chosen"],
+                tools=tools,
+                tokenize=False,
+                **example.get("chat_template_kwargs", {}),
+                **template_kwargs,
             )
             # DeepSeek-R1 inserts a <tool_call> token when using `add_generation_prompt`, which can cause discrepancies
             # between the prompt alone and the combined prompt+completion. To ensure consistency, we extract the
@@ -179,24 +190,42 @@ def apply_chat_template(
             chosen = prompt_chosen[len(prompt) :]
         if "rejected" in example and "prompt" in example:  # explicit prompt
             prompt_rejected = tokenizer.apply_chat_template(
-                example["prompt"] + example["rejected"], tools=tools, tokenize=False, **template_kwargs
+                example["prompt"] + example["rejected"],
+                tools=tools,
+                tokenize=False,
+                **example.get("chat_template_kwargs", {}),
+                **template_kwargs,
             )
             # Handle DeepSeek-R1 <tool_call> token, see the above comment for details
             prompt = "".join(x for x, _ in takewhile(lambda x: x[0] == x[1], zip(prompt, prompt_rejected)))
             rejected = prompt_rejected[len(prompt) :]
         if "completion" in example:
             prompt_completion = tokenizer.apply_chat_template(
-                example["prompt"] + example["completion"], tools=tools, tokenize=False, **template_kwargs
+                example["prompt"] + example["completion"],
+                tools=tools,
+                tokenize=False,
+                **example.get("chat_template_kwargs", {}),
+                **template_kwargs,
             )
             # Handle DeepSeek-R1 <tool_call> token, see the above comment for details
             prompt = "".join(x for x, _ in takewhile(lambda x: x[0] == x[1], zip(prompt, prompt_completion)))
             completion = prompt_completion[len(prompt) :]
     else:  # implicit prompt case
         if "chosen" in example:
-            chosen = tokenizer.apply_chat_template(example["chosen"], tools=tools, tokenize=False, **template_kwargs)
+            chosen = tokenizer.apply_chat_template(
+                example["chosen"],
+                tools=tools,
+                tokenize=False,
+                **example.get("chat_template_kwargs", {}),
+                **template_kwargs,
+            )
         if "rejected" in example:
             rejected = tokenizer.apply_chat_template(
-                example["rejected"], tools=tools, tokenize=False, **template_kwargs
+                example["rejected"],
+                tools=tools,
+                tokenize=False,
+                **example.get("chat_template_kwargs", {}),
+                **template_kwargs,
             )
 
     # Extract the completion by removing the prompt part from the prompt-completion string
@@ -239,7 +268,9 @@ def maybe_apply_chat_template(
                 - Unpaired preference dataset: `"prompt"`, `"completion"`, and `"label"`.
 
             For keys `"messages"`, `"prompt"`, `"chosen"`, `"rejected"`, and `"completion"`, the values are lists of
-            messages, where each message is a dictionary with keys `"role"` and `"content"`.
+            messages, where each message is a dictionary with keys `"role"` and `"content"`. Additionally, the example
+            may contain a `"chat_template_kwargs"` key, which is a dictionary of additional keyword arguments to pass
+            to the chat template renderer.
         tokenizer (`PreTrainedTokenizerBase`):
             Tokenizer to apply the chat template with.
         tools (`list[Union[dict, Callable]]`, *optional*):