# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Union from ...configuration_utils import PretrainedConfig from ...image_processing_utils import BatchFeature from ...image_utils import ( ImageInput, make_flat_list_of_images, ) from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import ( PreTokenizedInput, TextInput, ) from ...utils import ( auto_docstring, is_torch_available, logging, ) from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel from ..idefics.modeling_idefics import IdeficsBaseModelOutputWithPast, IdeficsCausalLMOutputWithPast from ..janus.image_processing_janus import JanusImageProcessor from ..janus.image_processing_janus_fast import JanusImageProcessorFast from ..janus.modeling_janus import JanusForConditionalGeneration, JanusModel, JanusPreTrainedModel if is_torch_available(): import torch import torch.nn as nn logger = logging.get_logger(__name__) class DeepseekVLConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DeepseekVLModel`]. It is used to instantiate a DeepseekVL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DeepseekVL [deepseek-community/deepseek-vl-1.3b-chat](https://huggingface.co/deepseek-community/deepseek-vl-1.3b-chat) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`): The config object or dictionary of the text backbone. vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SiglipVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 100015): The index representing image tokens in the model's token vocabulary. Example: ```python >>> from transformers import DeepseekVLConfig, DeepseekVLModel >>> # Initializing a DeepseekVL deepseek-community/deepseek-vl-1.3b-chat style configuration >>> configuration = DeepseekVLConfig() >>> # Initializing a model (with random weights) from the deepseek-community/deepseek-vl-1.3b-chat style configuration >>> model = DeepseekVLModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "deepseek_vl" sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig} def __init__( self, text_config: AutoConfig = None, vision_config: AutoConfig = None, image_token_id: int = 100015, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `LlamaConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. Initializing the `SiglipVisionConfig` with default values.") if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "llama") text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) if isinstance(vision_config, dict): vision_config["model_type"] = vision_config.get("model_type", "siglip_vision_model") vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) self.text_config = text_config self.vision_config = vision_config self.image_token_id = image_token_id class DeepseekVLBaseModelOutputWithPast(IdeficsBaseModelOutputWithPast): pass class DeepseekVLCausalLMOutputWithPast(IdeficsCausalLMOutputWithPast): pass class DeepseekVLAligner(nn.Module): def __init__(self, config): super().__init__() self.config = config in_features = config.vision_config.hidden_size out_features = config.text_config.hidden_size self.linear1 = nn.Linear(in_features, out_features) self.activation = nn.GELU() self.linear2 = nn.Linear(out_features, out_features) def forward(self, vision_encodings: torch.Tensor) -> torch.Tensor: x = self.linear1(vision_encodings) x = self.activation(x) x = self.linear2(x) return x class DeepseekVLPreTrainedModel(JanusPreTrainedModel): _no_split_modules = ["LlamaDecoderLayer"] def _init_weights(self, module): """Initialize the weights""" # Required only for Linear layer in DeepseekVLAligner if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.text_config.initializer_range) if module.bias is not None: module.bias.data.zero_() @auto_docstring class DeepseekVLModel(JanusModel): def __init__(self, config): super().__init__(config) self.config = config self.vision_model = AutoModel.from_config(config.vision_config) self.aligner = DeepseekVLAligner(config) self.language_model = AutoModel.from_config(config=config.text_config) self.gradient_checkpointing = False # Initialize weights and apply final processing. self.post_init() del self.vqmodel del self.generation_embeddings del self.generation_aligner del self.generation_head class DeepseekVLForConditionalGeneration(JanusForConditionalGeneration): def prepare_embeddings_for_image_generation(self): raise AttributeError("Not needed for DeepseekVL") def decode_image_tokens(self): raise AttributeError("Not needed for DeepseekVL") def generate(self): raise AttributeError("Not needed for DeepseekVL") class DeepseekVLImageProcessor(JanusImageProcessor): def __init__(self, **super_kwargs): super().__init__(**super_kwargs) def postprocess(self): raise AttributeError("Not needed for DeepseekVL") def unnormalize(self): raise AttributeError("Not needed for DeepseekVL") class DeepseekVLImageProcessorFast(JanusImageProcessorFast): def __init__(self, **super_kwargs): super().__init__(**super_kwargs) def postprocess(self): raise AttributeError("Not needed for DeepseekVL") class DeepseekVLProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": {"padding": False}, "common_kwargs": {"return_tensors": "pt"}, } class DeepseekVLProcessor(ProcessorMixin): r""" Constructs a DeepseekVL processor which wraps a DeepseekVL Image Processor and a Llama tokenizer into a single processor. [`DeepseekVLProcessor`] offers all the functionalities of [`DeepseekVLImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~DeepseekVLProcessor.__call__`] and [`~DeepseekVLProcessor.decode`] for more information. Args: image_processor ([`DeepseekVLImageProcessor`]): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`]): 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. num_image_tokens (`int`, *optional*, defaults to 576): The number of special image tokens used as placeholders for visual content in text sequences. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = ["chat_template", "num_image_tokens"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, image_processor, tokenizer, chat_template=None, num_image_tokens=576, ): self.image_token = tokenizer.image_token self.num_image_tokens = num_image_tokens super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None, images: ImageInput = None, **kwargs: Unpack[DeepseekVLProcessorKwargs], ) -> 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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to DeepseekVLImageProcessor's [`~DeepseekVLImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: text (`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. Both channels-first and channels-last formats are supported. 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: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **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`. """ output_kwargs = self._merge_kwargs( DeepseekVLProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs ) if text is None and images is None: raise ValueError("You must specify either text or images.") if text is not None: if isinstance(text, str): text = [text] elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)): raise ValueError("Invalid input text. Please provide a string, or a list of strings") prompt_strings = [] one_img_tokens = self.image_token * self.num_image_tokens for prompt in text: prompt = prompt.replace(self.image_token, one_img_tokens) prompt_strings.append(prompt) data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"]) # process images if pixel_values are provided if images is not None: images = make_flat_list_of_images(images) data["pixel_values"] = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"] return BatchFeature(data=data) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property 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)) __all__ = [ "DeepseekVLConfig", "DeepseekVLPreTrainedModel", "DeepseekVLModel", "DeepseekVLForConditionalGeneration", "DeepseekVLImageProcessor", "DeepseekVLImageProcessorFast", "DeepseekVLProcessor", ]