o ۷i`J@sVddlmZmZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z dd lmZmZmZd d lmZmZd d lmZd d lmZd dlmZmZmZmZmZee Z!GdddeZ"GdddeZ#GdddeZ$GdddeZ%GdddeZ&GdddeZ'GdddeZ(GdddeZ)Gdd d eZ*gd!Z+dS)")OptionalUnionN)nn)Cache DynamicCache)GenerationConfig)FlashAttentionKwargs)Unpack)auto_docstringcan_return_tuplelogging)Idefics3ConfigIdefics3VisionConfig)Idefics3ImageProcessor)Idefics3ImageProcessorFast)Idefics3BaseModelOutputWithPast Idefics3ForConditionalGeneration Idefics3ModelIdefics3PreTrainedModelIdefics3VisionTransformerc@eZdZdZdZdS)SmolVLMVisionConfiga This is the configuration class to store the configuration of a [`SmolVLMVisionModel`]. It is used to instantiate a SmolVLM vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) used in SmolVLM [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1152): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input images. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer >>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig >>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration >>> configuration = SmolVLMVisionConfig() >>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration >>> model = SmolVLMVisionTransformer(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```smolvlm_visionN__name__ __module__ __qualname____doc__ model_typer!r!a/home/ubuntu/vllm_env/lib/python3.10/site-packages/transformers/models/smolvlm/modular_smolvlm.pyr)s3rc@ eZdZdS)SmolVLMPreTrainedModelNrrrr!r!r!r"r$ar$c@r#)SmolVLMVisionTransformerNr%r!r!r!r"r'er&r'c@r) SmolVLMConfiga This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a SmolVLM 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 model of the SmolVLM [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should cache the key/value pairs of the attention mechanism. Only relevant if `config.is_decoder=True`. image_token_id (`int`, *optional*, defaults to 128257): The id of the "image" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to tie the word embeddings with the token embeddings. vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`): Custom vision config or dict for the vision tower text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`): Custom text config or dict for the text model scale_factor (`int`, *optional*, defaults to 2): The scale factor for the image encoder. pad_token_id (`int`, *optional*, defaults to 128002): The id of the padding token. Example: ```python >>> from transformers import SmolVLMModel, SmolVLMConfig >>> # Initializing configuration >>> configuration = SmolVLMConfig() >>> # Initializing a model from the configuration >>> model = SmolVLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```smolvlmNrr!r!r!r"r(is%r(c@r#)SmolVLMImageProcessorNr%r!r!r!r"r*r&r*c@r#)SmolVLMImageProcessorFastNr%r!r!r!r"r+r&r+c@r#)SmolVLMBaseModelOutputWithPastNr%r!r!r!r"r,r&r,c#@s eZdZdZdejdejdejfddZ ddejd e ejfd d Z e e d d              dde ejde ejde ejde e de ejde ejd e ejde ejde ede ede ede ede ejdeedeeeffddZdS) SmolVLMModelz A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger in forward. Instead, we override inputs_merger here with custom logic. input_ids inputs_embedsimage_hidden_statescCs|j\}}}|dur!||tj|jjtj|jdk}|d}n||jjk}|jdd}t ||dks:t d||}tj j j |jddddd} | dd } |jd d} | d|} | d|} | d| }t|}|||| |ddf||<t|d ||}|S) Ndtypedevice).rdimrzCAt least one sample has tokens not divisible by patch_size.)r4r)value)shapeget_input_embeddingstorchtensorconfigimage_token_idlongr3sumall ValueErrorr functionalpadcumsum unsqueeze zeros_likewhere)selfr.r/r0_ patch_size image_masknum_image_tokensblocks_per_sampleoffsets block_offsetrow_cum chunk_idx local_idx block_idx image_embeds merged_embedsr!r!r" inputs_mergers*         zSmolVLMModel.inputs_mergerN pixel_valuespixel_attention_maskcs8j\}}}}}j|jdj||gjddRjdd}dkjdd|k} t| s:d| d <| |durVtj fd d d Dtj j d }n|j||g|jddR}|| }|j j j} |jd| | d} | jd| | d} | jddd k } |j| d} | j} || } | S)a Encodes images into continuous embeddings that can be forwarded to the language model. Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input images. pixel_attention_mask (`torch.LongTensor`, *optional*): The attention mask indicating padded regions in the image. )r2rNr4g)r8r5Trcsg|]}j|qSr!)r9).0irXr!r" sz3SmolVLMModel.get_image_features..)rrr)sizer2r3) dimensionr`step)r8rZ)rXpatch_attention_mask)r9tor2viewnumelr@any contiguousr;onesboolr3r= vision_configrKunfold vision_modellast_hidden_state connector)rIrXrY batch_size num_images num_channelsheightwidthnb_values_per_imagereal_images_indsrKpatches_subgridrcr0r!r^r"get_image_featuress0       zSmolVLMModel.get_image_featuresa Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where max_num_images is the maximum number of images among the batch_size samples in the batch. Padding images are not needed beyond padding the pixel_values at the entrance of the model. For efficiency, we only pass through the vision_model's forward the real images by discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3. ) custom_introattention_mask position_idspast_key_values use_cacheoutput_attentionsoutput_hidden_states return_dictcache_positionkwargsreturnc Ks~| dur| n|jj} | dur| n|jj} | dur| n|jj} | dur$| n|jj} |jr8|jjr8| r8t dd} |durB|j \}}n|durM|j \}}}nt d| r]|dur]t |jd}|durl|j ||j}|durx|durxt d|dur||||j}n |dur|j|j|jd}|dur|j|||d}|jd ||||| | | d| d |}t|j|j|j|j|d S) NzZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fz5You have to specify either input_ids or inputs_embeds)r=zMYou cannot specify both pixel_values and image_hidden_states at the same timer1)r.r/r0T) r/rzr{r|r}r~rrr)rnr| hidden_states attentionsr0r!)r=r~rr}use_return_dicttraining text_modelgradient_checkpointinglogger warning_oncer9rBrr:rdr3rxr2rWr,rnr|rr)rIr.rzr{r|r/rXrYr0r}r~rrrrrp seq_lengthrJoutputsr!r!r"forwardsf     zSmolVLMModel.forward)N) NNNNNNNNNNNNN)rrrrr; LongTensorTensorrW FloatTensorrrxr r r BoolTensorrjr r rtupler,rr!r!r!r"r-s~ ! 1       r-cs(eZdZfddZfddZZS)SmolVLMForConditionalGenerationcsLt|t||_t||jj_tj |j j |j j dd|_ |dS)NF)bias)super__init__r-modelrfrom_model_configrgeneration_configrLinear text_config hidden_size vocab_sizelm_head post_init)rIr= __class__r!r"rWs   z(SmolVLMForConditionalGeneration.__init__c stjdi|dS)a pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): Mask to avoid performing attention on padding pixel indices. image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The hidden states of the image encoder after modality projection. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or `model.image_token_id`. Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> import requests >>> import torch >>> from PIL import Image >>> from io import BytesIO >>> from transformers import AutoProcessor, AutoModelForImageTextToText >>> from transformers.image_utils import load_image >>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible >>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg") >>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg") >>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg") >>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct") >>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", dtype=torch.bfloat16, device_map="auto") >>> # Create inputs >>> messages = [ ... { ... "role": "user", ... "content": [ ... {"type": "video", "path": path/to/video}, ... {"type": "text", "text": "What is happening in this video?"}, ... ] ... } ... ] >>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True) >>> # Generate >>> generated_ids = model.generate(**inputs, max_new_tokens=256) >>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_texts) ```Nr!)rr)rI super_kwargsrr!r"r^s1z'SmolVLMForConditionalGeneration.forward)rrrrr __classcell__r!r!rr"rVs r)rr(r*r+rr$r-r'),typingrrr;r cache_utilsrr generationrmodeling_flash_attention_utilsr processing_utilsr utilsr r r idefics3.configuration_idefics3rr"idefics3.image_processing_idefics3r'idefics3.image_processing_idefics3_fastridefics3.modeling_idefics3rrrrr get_loggerrrrr$r'r(r*r+r,r-r__all__r!r!r!r"s0       8*8 <