Upload processor

added_tokens.json

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+{
+  "<image>": 32000
+}

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoProcessor": "processing_taivisionlm.TaiVisionProcessor"
+  },
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_seq_length": 196,
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "TaiVisionProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

processing_taivisionlm.py

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+"""
+Processor class for TraVisionLM.
+"""
+import transformers
+import logging
+from typing import List, Optional, Union
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput, is_valid_image
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils import (
+    AddedToken,
+    PaddingStrategy,
+    PreTokenizedInput,
+    TextInput,
+    TruncationStrategy,  
+)
+from transformers.utils import TensorType
+from .configuration_taivisionlm import TaiVisionLMConfig
+
+logger = logging.getLogger(__name__)
+
+IMAGE_TOKEN = "<image>"
+
+# Copied from transformers.models.idefics2.processing_idefics2.is_url
+def is_url(val) -> bool:
+    return isinstance(val, str) and val.startswith("http")
+
+
+# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
+def is_image_or_image_url(elem):
+    return is_url(elem) or is_valid_image(elem)
+
+# Copied from transformers.models.paligemma.processing_paligemma._is_str_or_image
+def _is_str_or_image(elem):
+    return isinstance(elem, (str)) or is_image_or_image_url(elem)
+
+
+def build_string_from_input(image_seq_len, image_token):
+    """
+    Builds a string from the input prompt and image tokens.
+    For example, for the call:
+    build_string_from_input(
+        image_seq_len=3,
+        image_token="<im>",
+    )
+    The output will be:
+    "<im><im><im>"
+    Args:
+        image_seq_len (`int`): The length of the image sequence.
+        image_token (`str`): The image token.
+    """
+    return f"{image_token * image_seq_len}"
+
+
+class TaiVisionProcessor(ProcessorMixin):
+    r"""
+    Constructs a TraVision processor which wraps a SigLIP image processor and a GPT2 tokenizer into a single processor.
+
+    [`TaiVisionProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`LlamaTokenizerFast`]. See the
+    [`~TaiVisionProcessor.__call__`] and [`~TaiVisionProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`SiglipImageProcessor`], *optional*):
+            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"]
+    valid_kwargs = ["chat_template"]
+    image_processor_class = "SiglipImageProcessor"
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        if image_processor is None:
+            raise ValueError("You need to specify an `image_processor`.")
+        if tokenizer is None:
+            raise ValueError("You need to specify a `tokenizer`.")
+        if not hasattr(image_processor, "image_seq_length"):
+            raise ValueError("Image processor is missing an `image_seq_length` attribute.")
+
+        self.image_seq_length = image_processor.image_seq_length
+
+        image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True)
+        tokens_to_add = {"additional_special_tokens": [image_token]}
+        tokenizer.add_special_tokens(tokens_to_add)
+        self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
+        tokenizer.add_bos_token = False
+        tokenizer.add_eos_token = False
+
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+        
+    def __call__(
+        self,
+        prompts: 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=None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+        do_resize: bool = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        data_format: Optional["ChannelDimension"] = "channels_first",  # noqa: F821
+        input_data_format: Optional[
+            Union[str, "ChannelDimension"]  # noqa: F821
+        ] = None,
+        resample: "PILImageResampling" = None,  # noqa: F821
+        do_convert_rgb: bool = None,
+        do_thumbnail: bool = None,
+        do_align_long_axis: bool = None,
+        do_rescale: bool = None,
+        labels: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to GPT2TokenizerFast's [`~GPT2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+
+        The usage for TraVisionLM fine-tuning preparation follows a standard 4D causal mask where only the prompt and label tokens
+        are attended in an auto-regressive manner. The label in `text` are to be passed separately to the __call__ function and 
+        will be placed after the prompt, which is the instruction to steer the model generation. 
+
+        Args:
+            prompts (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
+                number of channels, H and W are \image height and width.
+            tokenize_newline_separately (`bool`, defaults to `False`):
+                Adds a separately tokenized '\n' at the end of the prompt. 
+            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.
+            labels (`str`, `List[str]`, `List[List[str]]`):
+                The label or batch of labels to be encoded. Only necessary for training. 
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The text or batch of text to be encoded. If provided, the prompt and label should be 
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `label`
+              is provided, the `input_ids` will also contain the label input ids.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **labels** -- Labels compatible with training if `label` is not None
+        """
+
+        # return_token_type_ids = True if labels is not None else False
+        return_token_type_ids = True
+
+        if images is None:
+            raise ValueError("`images` are expected as arguments to a `TraVisionProcessor` instance.")
+        
+        images = [images] if not isinstance(images, list) else images
+        
+        if prompts is None:
+            logger.warning_once(
+                "You are using TaiVisionLM without a text prefix. It will perform as a picture-captioning model."
+            )
+            prompts = "描述這張圖片"  # default prompt if it is not provided as an argument
+            if len(images) != 1:
+                prompts = [prompts] * len(images)
+                
+        if isinstance(prompts, List) and isinstance(images, List):
+            if len(images) < len(text):
+                raise ValueError(
+                    f"Received {len(images)} images for {len(prompts)} prompts. Each prompt should be associated with an image."
+                )
+        if _is_str_or_image(prompts):
+            prompts = [prompts]
+        elif isinstance(prompts, list) and _is_str_or_image(prompts[0]):
+            pass
+        
+        # add \n after image tokens
+        prompts = [f"\n<|user|>\n{prompt}{self.tokenizer.eos_token}\n" for prompt in prompts]
+        # TODO: tokenize the prompt twice, and check if the prompt is too long
+        prompt_length = [len(self.tokenizer.tokenize(prompt)) + self.image_seq_length for prompt in prompts]
+        
+        
+        if labels is not None:
+            if _is_str_or_image(labels):
+                labels = [labels] # convert it to list if it is a string
+            labels = [f"<|assistant|>\n{label}{self.tokenizer.eos_token}" for label in labels]
+
+            text = [f"{prompt}{label}" for prompt, label in zip(prompts, labels)]
+            
+        else:
+            text = prompts
+        
+        assert len(images) == len(text), "The number of images and text should be the same."
+        
+        input_strings = [
+            build_string_from_input(
+                image_seq_len=self.image_seq_length,
+                image_token=IMAGE_TOKEN,
+            )            
+            for _ in text
+        ]
+        
+        # this will do some image processing, like resizing, normalizing, etc.
+        pixel_values = self.image_processor(
+            images,
+            do_resize=do_resize,
+            do_normalize=do_normalize,
+            return_tensors=return_tensors,
+            image_mean=image_mean,
+            image_std=image_std,
+            input_data_format=input_data_format,
+            data_format=data_format,
+            resample=resample,
+            do_convert_rgb=do_convert_rgb,
+        )["pixel_values"]
+
+        if max_length is not None:
+            max_length += self.image_seq_length  # max_length has to account for the image tokens
+
+        inputs = self.tokenizer(
+            input_strings,
+            text_pair=text,
+            return_tensors=return_tensors,
+            padding=padding,
+            max_length=max_length,
+            truncation=truncation,
+            return_token_type_ids=return_token_type_ids,
+        )
+
+        return_data = {**inputs, "pixel_values": pixel_values}
+
+        # we are doing training, so we need to return the labels
+        if labels is not None:
+            # fill the labels with -100 where we don't have to compute the loss
+            # mask the padding part
+            labels = inputs["input_ids"].masked_fill(inputs["attention_mask"] == 0, -100)
+            # mask the image + prompt part, so that we don't train the model to predict the image tokens
+            import torch
+            prompt_length_tensor = torch.tensor(prompt_length)
+            labels = labels.masked_fill(torch.arange(labels.size(1)).unsqueeze(0) < prompt_length_tensor.unsqueeze(1), -100)
+            return_data.update({"labels": labels})
+            
+        return BatchFeature(data=return_data)
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->GPT2
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to GPT2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->GPT2
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to GPT2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->TraVision
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+    
+
+# if __name__ == '__main__':
+    # config = TaiVisionLMConfig.from_pretrained("./")
+    # preprocessor = transformers.SiglipImageProcessor.from_pretrained("google/siglip-base-patch16-224")
+    # preprocessor.image_seq_length = config.num_image_tokens
+    # tokenizer = transformers.AutoTokenizer.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-chat")
+    # processor = TaiVisionProcessor(tokenizer=tokenizer, image_processor=preprocessor)
+    # processor.save_pretrained("./")
+    
+    # from PIL import Image
+    # import requests
+    # processor = TaiVisionProcessor.from_pretrained("./")
+    # url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
+    # image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+    # prompt = "Hello< what is your name?"
+    # label = "I am fine, thank you."
+    # inputs = processor(prompts=prompt, labels=label,images=image, return_tensors="pt",padding="max_length",max_length=512)
+    # for key, value in inputs.items():
+    #     print(f"{key}: {value}")
+    # print(processor.decode(inputs.input_ids.tolist()[0]))

processor_config.json

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+{
+  "auto_map": {
+    "AutoProcessor": "processing_taivisionlm.TaiVisionProcessor"
+  },
+  "chat_template": null,
+  "processor_class": "TaiVisionProcessor"
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<image>"
+  ],
+  "auto_map": {
+    "AutoProcessor": "processing_taivisionlm.TaiVisionProcessor"
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<unk>",
+  "padding_side": "right",
+  "processor_class": "TaiVisionProcessor",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}