Uniformize kwargs for chameleon processor

src/transformers/models/auto/tokenization_auto.py

@@ -109,10 +109,7 @@
             ("canine", ("CanineTokenizer", None)),
             (
                 "chameleon",
-                (
-                    "LlamaTokenizer" if is_sentencepiece_available() else None,
-                    "LlamaTokenizerFast" if is_tokenizers_available() else None,
-                ),
+                (None, "LlamaTokenizerFast" if is_tokenizers_available() else None),
             ),
             ("chinese_clip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
             (

src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py

@@ -24,7 +24,7 @@
 
 from transformers import (
     ChameleonConfig,
-    ChameleonForCausalLM,
+    ChameleonForConditionalGeneration,
     ChameleonImageProcessor,
     ChameleonProcessor,
 )
@@ -49,10 +49,10 @@
 Thereafter, models can be loaded via:
 
 ```py
-from transformers import ChameleonForCausalLM, LlamaTokenizer
+from transformers import ChameleonForConditionalGeneration, LlamaTokenizerFast
 
-model = ChameleonForCausalLM.from_pretrained("/output/path")
-tokenizer = LlamaTokenizer.from_pretrained("/output/path")
+model = ChameleonForConditionalGeneration.from_pretrained("/output/path")
+tokenizer = LlamaTokenizerFast.from_pretrained("/output/path")
 ```
 
 Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
@@ -372,7 +372,7 @@ def permute(w, n_heads, dim1=dim, dim2=dim):
         vocabulary_map=vocabulary_map,
     )
     with init_empty_weights():
-        model = ChameleonForCausalLM(config)
+        model = ChameleonForConditionalGeneration(config)
 
     model.load_state_dict(state_dict, assign=True, strict=False)
     model.save_pretrained(model_path, safe_serialization=True)
@@ -397,7 +397,7 @@ def permute(w, n_heads, dim1=dim, dim2=dim):
     # taken from https://github.com/facebookresearch/chameleon/blob/7a72f40aa5f462965c8374f25257f55b65b25ff4/data/prompts_for_human_evaluations.jsonl
     print("Loading the checkpoint in a Chameleon model...")
     print("*" * 100)
-    model = ChameleonForCausalLM.from_pretrained(
+    model = ChameleonForConditionalGeneration.from_pretrained(
         model_path, attn_implementation="eager", torch_dtype=torch.bfloat16, device_map="auto"
     )
     processor = ChameleonProcessor.from_pretrained(model_path)

src/transformers/models/chameleon/processing_chameleon.py

@@ -16,13 +16,36 @@
 Processor class for Chameleon.
 """
 
+import sys
 from typing import List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
-from ...utils import TensorType
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
+
+
+class ChameleonTextKwargs(TextKwargs, total=False):
+    return_for_text_completion: bool
+
+
+class ChameleonProcessorKwargs(ProcessingKwargs, total=False):
+    text_kwargs: ChameleonTextKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_for_text_completion": False,
+        },
+        "common_kwargs": {
+            "return_tensors": "pt",
+        },
+    }
 
 
 class ChameleonProcessor(ProcessorMixin):
@@ -45,7 +68,7 @@ class ChameleonProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
-    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    tokenizer_class = "LlamaTokenizerFast"
     image_processor_class = "ChameleonImageProcessor"
 
     def __init__(self, image_processor, tokenizer, image_seq_length: int = 1024, image_token: str = "<image>"):
@@ -57,13 +80,9 @@ def __init__(self, image_processor, tokenizer, image_seq_length: int = 1024, ima
 
     def __call__(
         self,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        images: ImageInput = None,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: int = None,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
-        return_for_text_completion: bool = False,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        images: Optional[ImageInput] = None,
+        **kwargs: Unpack[ChameleonProcessorKwargs],
     ) -> BatchFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
@@ -80,26 +99,6 @@ def __call__(
             images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                 The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                 tensor. Both channels-first and channels-last formats are supported.
-            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
-                Select a strategy to pad the returned sequences (according to the model's padding side and padding
-                index) among:
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            max_length (`int`, *optional*):
-                Maximum length of the returned list and optionally padding length (see above).
-            truncation (`bool`, *optional*):
-                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors of a particular framework. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return NumPy `np.ndarray` objects.
-                - `'jax'`: Return JAX `jnp.ndarray` objects.
 
         Returns:
             [`BatchFeature`]: A [`BatchFeature`] with the following fields:
@@ -114,6 +113,15 @@ def __call__(
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
             raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+        if text is None and images is None:
+            raise ValueError("You must provide either text or images")
+
+        output_kwargs = self._merge_kwargs(
+            ChameleonProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        return_for_text_completion = output_kwargs["text_kwargs"].pop("return_for_text_completion", False)
 
         # Replace the image token with the expanded image token sequence
         prompt_strings = []
@@ -124,19 +132,12 @@ def __call__(
                 sample += self.tokenizer.sep_token  # special Chameleon treatment to add sep for chat mode
             prompt_strings.append(sample)
 
-        data = self.tokenizer(
-            prompt_strings,
-            return_tensors=return_tensors,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-        )
+        data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
 
         if images is not None:
-            pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
-            data["pixel_values"] = pixel_values
+            data["pixel_values"] = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+        return BatchFeature(data=data, tensor_type=output_kwargs["common_kwargs"]["return_tensors"])
 
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):

tests/models/chameleon/test_processor_chameleon.py

@@ -0,0 +1,16 @@
+import tempfile
+import unittest
+
+from transformers import ChameleonProcessor
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    from_pretrained_id = "leloy/Anole-7b-v0.1-hf"
+    processor_class = ChameleonProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        processor = self.processor_class.from_pretrained(self.from_pretrained_id)
+        processor.save_pretrained(self.tmpdirname)

tests/test_processing_common.py

@@ -49,6 +49,8 @@
 @require_torch
 class ProcessorTesterMixin:
     processor_class = None
+    text_data_arg_name = "input_ids"
+    images_data_arg_name = "pixel_values"
 
     def prepare_processor_dict(self):
         return {}
@@ -136,14 +138,14 @@ def test_tokenizer_defaults_preserved_by_kwargs(self):
         image_input = self.prepare_image_inputs()
 
         inputs = processor(text=input_str, images=image_input, return_tensors="pt")
-        self.assertEqual(len(inputs["input_ids"][0]), 117)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 117)
 
     @require_torch
     @require_vision
     def test_image_processor_defaults_preserved_by_image_kwargs(self):
         if "image_processor" not in self.processor_class.attributes:
             self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor", size=(234, 234))
+        image_processor = self.get_component("image_processor", size=(234, 234), crop_size=(234, 234))
         tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
 
         processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
@@ -153,7 +155,7 @@ def test_image_processor_defaults_preserved_by_image_kwargs(self):
         image_input = self.prepare_image_inputs()
 
         inputs = processor(text=input_str, images=image_input)
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 234)
+        self.assertEqual(len(inputs[self.images_data_arg_name][0][0]), 234)
 
     @require_vision
     @require_torch
@@ -171,7 +173,7 @@ def test_kwargs_overrides_default_tokenizer_kwargs(self):
         inputs = processor(
             text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
         )
-        self.assertEqual(len(inputs["input_ids"][0]), 112)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 112)
 
     @require_torch
     @require_vision
@@ -187,8 +189,8 @@ def test_kwargs_overrides_default_image_processor_kwargs(self):
         input_str = "lower newer"
         image_input = self.prepare_image_inputs()
 
-        inputs = processor(text=input_str, images=image_input, size=[224, 224])
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
+        inputs = processor(text=input_str, images=image_input, size=[224, 224], crop_size=(224, 224))
+        self.assertEqual(len(inputs[self.images_data_arg_name][0][0]), 224)
 
     @require_torch
     @require_vision
@@ -208,12 +210,13 @@ def test_unstructured_kwargs(self):
             images=image_input,
             return_tensors="pt",
             size={"height": 214, "width": 214},
+            crop_size={"height": 214, "width": 214},
             padding="max_length",
             max_length=76,
         )
 
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
+        self.assertEqual(inputs[self.images_data_arg_name].shape[2], 214)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 76)
 
     @require_torch
     @require_vision
@@ -233,13 +236,14 @@ def test_unstructured_kwargs_batched(self):
             images=image_input,
             return_tensors="pt",
             size={"height": 214, "width": 214},
+            crop_size={"height": 214, "width": 214},
             padding="longest",
             max_length=76,
         )
 
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(inputs[self.images_data_arg_name].shape[2], 214)
 
-        self.assertEqual(len(inputs["input_ids"][0]), 6)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 6)
 
     @require_torch
     @require_vision
@@ -260,6 +264,7 @@ def test_doubly_passed_kwargs(self):
                 images=image_input,
                 images_kwargs={"size": {"height": 222, "width": 222}},
                 size={"height": 214, "width": 214},
+                crop_size={"height": 214, "width": 214},
             )
 
     @require_torch
@@ -279,16 +284,19 @@ def test_structured_kwargs_nested(self):
         # Define the kwargs for each modality
         all_kwargs = {
             "common_kwargs": {"return_tensors": "pt"},
-            "images_kwargs": {"size": {"height": 214, "width": 214}},
+            "images_kwargs": {
+                "size": {"height": 214, "width": 214},
+                "crop_size": {"height": 214, "width": 214},
+            },
             "text_kwargs": {"padding": "max_length", "max_length": 76},
         }
 
         inputs = processor(text=input_str, images=image_input, **all_kwargs)
         self.skip_processor_without_typed_kwargs(processor)
 
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(inputs[self.images_data_arg_name].shape[2], 214)
 
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 76)
 
     @require_torch
     @require_vision
@@ -307,14 +315,17 @@ def test_structured_kwargs_nested_from_dict(self):
         # Define the kwargs for each modality
         all_kwargs = {
             "common_kwargs": {"return_tensors": "pt"},
-            "images_kwargs": {"size": {"height": 214, "width": 214}},
+            "images_kwargs": {
+                "size": {"height": 214, "width": 214},
+                "crop_size": {"height": 214, "width": 214},
+            },
             "text_kwargs": {"padding": "max_length", "max_length": 76},
         }
 
         inputs = processor(text=input_str, images=image_input, **all_kwargs)
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(inputs[self.images_data_arg_name].shape[2], 214)
 
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 76)
 
 
 class MyProcessor(ProcessorMixin):