Make chat templates part of ProcessorMixin (#30744)

* Let's try moving chat templates out of IDEFICS and into the generic ProcessorMixin

* Chat templates should not be mandatory

* Chat templates should not be mandatory

* Not all classes will have default chat templates

* stash commit

* Add chat template docstring

* Clean up docstring

* Add chat templates to LLaVA/LLaVA-next

* Docstring fixup

* Quick IDEFICS2 fixup

* Remove some old references to the Conversation class

* make fixup

src/transformers/models/idefics2/processing_idefics2.py

@@ -16,7 +16,7 @@
 Processor class for IDEFICS2.
 """
 
-from typing import TYPE_CHECKING, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, load_image
@@ -56,13 +56,15 @@ class Idefics2Processor(ProcessorMixin):
             The length of the image sequence i.e. the number of <image> tokens per image in the input.
             This parameter is used to build the string from the input prompt and image tokens and should match the
             config.perceiver_config.resampler_n_latents value for the model used.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
     """
 
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "Idefics2ImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor, tokenizer=None, image_seq_len: int = 64, **kwargs):
+    def __init__(self, image_processor, tokenizer=None, image_seq_len: int = 64, chat_template: str = None, **kwargs):
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`.")
         if tokenizer is None:
@@ -78,10 +80,7 @@ class Idefics2Processor(ProcessorMixin):
         }
         tokenizer.add_special_tokens(tokens_to_add)
 
-        # Stores a Jinja template that formats chat histories into tokenizable strings
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
-        self.chat_template = kwargs.pop("chat_template", None)
-
-        super().__init__(image_processor, tokenizer)
 
     def _extract_images_from_prompts(self, prompts):
         prompt_images = []
@@ -252,49 +251,6 @@ class Idefics2Processor(ProcessorMixin):
         image_processor_input_names = self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
 
-    def apply_chat_template(
-        self,
-        conversation: Union[List[Dict[str, str]]],
-        chat_template: Optional[str] = None,
-        tokenize: bool = False,
-        **kwargs,
-    ) -> str:
-        """
-        Overrides the tokenizer's `apply_chat_template` method to apply the IDEFICS2 chat template by default
-        if no chat template is provided.
-
-        By default, the output isn't tokenized. This is because the IDEFICS2 chat template is designed to insert
-        the image token <image> into the sequence according to the message, but does not handle expanding the image
-        tokens to the sequence length or adding the surrounding tokens e.g. <fake_image_token>.
-
-        Args:
-            conversation (`Union[List[Dict, str, str]]`):
-                The conversation to format.
-            chat_template (`Optional[str]`, *optional*):
-                The Jinja template to use for formatting the conversation. If not provided, the default chat template
-                is used.
-            tokenize (`bool`, *optional*, defaults to `False`):
-                Whether to tokenize the output or not.
-            **kwargs:
-                Additional keyword arguments for the tokenizer's `apply_chat_template` method.
-        """
-
-        if chat_template is None:
-            if self.chat_template is not None:
-                chat_template = self.chat_template
-            else:
-                logger.warning_once(
-                    "No chat template is set for this processor, falling back to a default class-level template. This is "
-                    "very error-prone, because models are often trained with templates different from the class default! "
-                    "Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which "
-                    "point any code depending on them will stop working. We recommend setting a valid chat template before "
-                    "then to ensure that this model continues working without issues."
-                )
-                chat_template = self.default_chat_template
-        return self.tokenizer.apply_chat_template(
-            conversation, chat_template=chat_template, tokenize=tokenize, **kwargs
-        )
-
     @property
     def default_chat_template(self):
         """

src/transformers/models/llava/processing_llava.py

@@ -37,14 +37,16 @@ class LlavaProcessor(ProcessorMixin):
             The image processor is a required input.
         tokenizer ([`LlamaTokenizerFast`], *optional*):
             The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
     """
 
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "AutoImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor=None, tokenizer=None):
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None):
-        super().__init__(image_processor, tokenizer)
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
         self,

src/transformers/models/llava_next/processing_llava_next.py

@@ -37,14 +37,16 @@ class LlavaNextProcessor(ProcessorMixin):
             The image processor is a required input.
         tokenizer ([`LlamaTokenizerFast`], *optional*):
             The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
     """
 
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "AutoImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor=None, tokenizer=None):
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None):
-        super().__init__(image_processor, tokenizer)
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
         self,

src/transformers/processing_utils.py

@@ -22,7 +22,7 @@ import json
 import os
 import warnings
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 from .dynamic_module_utils import custom_object_save
 from .tokenization_utils_base import PreTrainedTokenizerBase
@@ -60,6 +60,7 @@ class ProcessorMixin(PushToHubMixin):
     """
 
     attributes = ["feature_extractor", "tokenizer"]
+    optional_attributes = ["chat_template"]
     # Names need to be attr_class for attr in attributes
     feature_extractor_class = None
     tokenizer_class = None
@@ -67,6 +68,10 @@ class ProcessorMixin(PushToHubMixin):
 
     # args have to match the attributes class attribute
     def __init__(self, *args, **kwargs):
+        # First, extract optional attributes from kwargs if present
+        # Optional attributes can never be positional arguments
+        for optional_attribute in self.optional_attributes:
+            setattr(self, optional_attribute, kwargs.pop(optional_attribute, None))
         # Sanitize args and kwargs
         for key in kwargs:
             if key not in self.attributes:
@@ -522,6 +527,51 @@ class ProcessorMixin(PushToHubMixin):
         first_attribute = getattr(self, self.attributes[0])
         return getattr(first_attribute, "model_input_names", None)
 
+    def apply_chat_template(
+        self,
+        conversation: Union[List[Dict[str, str]]],
+        chat_template: Optional[str] = None,
+        tokenize: bool = False,
+        **kwargs,
+    ) -> str:
+        """
+        Similar to the `apply_chat_template` method on tokenizers, this method applies a Jinja template to input
+        conversations to turn them into a single tokenizable string.
+
+        Args:
+            conversation (`List[Dict, str, str]`):
+                The conversation to format.
+            chat_template (`Optional[str]`, *optional*):
+                The Jinja template to use for formatting the conversation. If not provided, the default chat template
+                is used.
+            tokenize (`bool`, *optional*, defaults to `False`):
+                Whether to tokenize the output or not.
+            **kwargs:
+                Additional keyword arguments
+        """
+
+        if chat_template is None:
+            if self.chat_template is not None:
+                chat_template = self.chat_template
+            elif getattr(self, "default_chat_template", None) is not None:
+                logger.warning_once(
+                    "No chat template is set for this processor, falling back to a default class-level template. This is "
+                    "very error-prone, because models are often trained with templates different from the class default! "
+                    "Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which "
+                    "point any code depending on them will stop working. We recommend setting a valid chat template before "
+                    "then to ensure that this model continues working without issues."
+                )
+                chat_template = self.default_chat_template
+            else:
+                raise ValueError(
+                    "No chat template is set for this processor. Please either set the `chat_template` attribute, "
+                    "or provide a chat template as an argument. See "
+                    "https://huggingface.co/docs/transformers/main/en/chat_templating for more information."
+                )
+        return self.tokenizer.apply_chat_template(
+            conversation, chat_template=chat_template, tokenize=tokenize, **kwargs
+        )
+
 
 ProcessorMixin.push_to_hub = copy_func(ProcessorMixin.push_to_hub)
 if ProcessorMixin.push_to_hub.__doc__ is not None: