# Copyright 2024 the HuggingFace Inc. 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. """PyTorch Llava-NeXT model.""" import math from dataclasses import dataclass import numpy as np import torch from torch import nn from ... import initialization as init from ...activations import ACT2FN from ...cache_utils import Cache from ...generation import GenerationMixin from ...image_processing_utils import select_best_resolution from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput from ...modeling_utils import PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, logging, torch_compilable_check from ...utils.generic import can_return_tuple, merge_with_config_defaults from ..auto import AutoModel from .configuration_llava_next import LlavaNextConfig logger = logging.get_logger(__name__) def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): """ Calculate the shape of the image patch grid after the preprocessing for images of any resolution. Args: image_size (`tuple`): The size of the input image in the format (width, height). grid_pinpoints (`List`): A list containing possible resolutions. Each item in the list should be a tuple or list of the form `(height, width)`. patch_size (`int`): The size of each image patch. Returns: tuple: The shape of the image patch grid in the format (width, height). """ if not isinstance(grid_pinpoints, list): raise TypeError("grid_pinpoints should be a list of tuples or lists") # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate if not isinstance(image_size, (list, tuple)): if not isinstance(image_size, (torch.Tensor, np.ndarray)): raise TypeError( f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor" ) image_size = image_size.tolist() height, width = select_best_resolution(image_size, grid_pinpoints) return height // patch_size, width // patch_size def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int): """ Calculate the number of patches after the preprocessing for images of any resolution. Args: image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`): The size of the input image in the format (height, width). ? grid_pinpoints (`List`): A list containing possible resolutions. Each item in the list should be a tuple or list of the form `(height, width)`. patch_size (`int`): The size of each image patch. Returns: int: the number of patches """ if not isinstance(grid_pinpoints, list): raise TypeError("grid_pinpoints should be a list of tuples or lists") # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate if not isinstance(image_size, (list, tuple)): if not isinstance(image_size, (torch.Tensor, np.ndarray)): raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}") image_size = image_size.tolist() best_resolution = select_best_resolution(image_size, grid_pinpoints) height, width = best_resolution num_patches = 0 # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1 for i in range(0, height, patch_size): for j in range(0, width, patch_size): num_patches += 1 # add the base patch num_patches += 1 return num_patches def unpad_image(tensor, original_size): """ Unpads a PyTorch tensor of a padded and resized image. Args: tensor (`torch.Tensor`): The image tensor, assumed to be of shape (num_channels, height, width). original_size (`tuple`): The original size of the image (height, width). Returns: `torch.Tensor`: The unpadded image tensor. """ if not isinstance(original_size, (list, tuple)): if not isinstance(original_size, (torch.Tensor, np.ndarray)): raise TypeError( f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor" ) original_size = original_size.tolist() original_height, original_width = original_size current_height, current_width = tensor.shape[1:] original_aspect_ratio = original_width / original_height current_aspect_ratio = current_width / current_height if original_aspect_ratio > current_aspect_ratio: scale_factor = current_width / original_width new_height = int(round(original_height * scale_factor, 7)) padding = (current_height - new_height) // 2 unpadded_tensor = tensor[:, padding : current_height - padding, :] else: scale_factor = current_height / original_height new_width = int(round(original_width * scale_factor, 7)) padding = (current_width - new_width) // 2 unpadded_tensor = tensor[:, :, padding : current_width - padding] return unpadded_tensor @dataclass @auto_docstring( custom_intro=""" Base class for Llava outputs, with hidden states and attentions. """ ) class LlavaNextModelOutputWithPast(BaseModelOutputWithPast): r""" past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. """ image_hidden_states: torch.FloatTensor | None = None @dataclass @auto_docstring( custom_intro=""" Base class for LlavaNext causal language model (or autoregressive) outputs. """ ) class LlavaNextCausalLMOutputWithPast(ModelOutput): r""" loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. """ loss: torch.FloatTensor | None = None logits: torch.FloatTensor | None = None past_key_values: Cache | None = None hidden_states: tuple[torch.FloatTensor] | None = None attentions: tuple[torch.FloatTensor] | None = None image_hidden_states: torch.FloatTensor | None = None # Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->LlavaNext class LlavaNextMultiModalProjector(nn.Module): def __init__(self, config: LlavaNextConfig): super().__init__() # We have hidden_size * the number of vision feature layers num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer) self.linear_1 = nn.Linear( config.vision_config.hidden_size * num_feature_layers, config.text_config.hidden_size, bias=config.multimodal_projector_bias, ) self.act = ACT2FN[config.projector_hidden_act] self.linear_2 = nn.Linear( config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias ) def forward(self, image_features): hidden_states = self.linear_1(image_features) hidden_states = self.act(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states @auto_docstring class LlavaNextPreTrainedModel(PreTrainedModel): config: LlavaNextConfig base_model_prefix = "model" input_modalities = ("image", "text") supports_gradient_checkpointing = True _no_split_modules = ["LlamaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn = True _supports_sdpa = True _can_compile_fullgraph = True _supports_flex_attn = True _supports_attention_backend = True @torch.no_grad() def _init_weights(self, module): std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range) if isinstance(module, nn.Linear): init.normal_(module.weight, mean=0.0, std=std) if module.bias is not None: init.zeros_(module.bias) elif isinstance(module, LlavaNextModel): embed_std = 1 / math.sqrt(self.config.text_config.hidden_size) init.normal_(module.image_newline, mean=0.0, std=embed_std) @auto_docstring( custom_intro=""" The Llava-Next model which consists of a vision backbone and a language model without language modeling head. """ ) class LlavaNextModel(LlavaNextPreTrainedModel): _checkpoint_conversion_mapping = { r"^language_model.model": "language_model", } base_model_prefix = "model" def __init__(self, config: LlavaNextConfig): super().__init__(config) self.vision_tower = AutoModel.from_config(config.vision_config) self.multi_modal_projector = LlavaNextMultiModalProjector(config) embed_std = 1 / math.sqrt(config.text_config.hidden_size) self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std) self.vocab_size = config.text_config.vocab_size self.language_model = AutoModel.from_config(config.text_config) self.post_init() def get_input_embeddings(self): return self.language_model.get_input_embeddings() def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value) def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None): """ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors. Args: image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) List of image feature tensor, each contains all the visual feature of all patches. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). vision_feature_select_strategy (`str`) The feature selection strategy used to select the vision feature from the vision backbone. image_newline (`torch.Tensor` of shape `(embed_dim)`) New line embedding vector. Returns: image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`) feature_lens (`list[int]`) token length of each image in image_features """ new_image_features = [] feature_lens = [] for image_idx, image_feature in enumerate(image_features): if image_feature.shape[0] > 1: base_image_feature = image_feature[0] image_feature = image_feature[1:] height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size num_patch_height, num_patch_width = get_anyres_image_grid_shape( image_sizes[image_idx], self.config.image_grid_pinpoints, self.config.vision_config.image_size, ) if ( np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0 and vision_feature_select_strategy == "default" ): logger.warning_once( "Image feature shape does not line up with the provided patch size. " "You may be using the `default` vision_feature_select_strategy with a" " visual encoder that does not have CLS." ) image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() image_feature = image_feature.flatten(1, 2).flatten(2, 3) image_feature = unpad_image(image_feature, image_sizes[image_idx]) if image_newline is not None: image_feature = torch.cat( ( image_feature, image_newline[:, None, None] .expand(*image_feature.shape[:-1], 1) .to(image_feature.device, image_feature.dtype), ), dim=-1, ) image_feature = image_feature.flatten(1, 2).transpose(0, 1) image_feature = torch.cat((base_image_feature, image_feature), dim=0) else: image_feature = image_feature[0] if image_newline is not None: image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0) new_image_features.append(image_feature) feature_lens.append(image_feature.size(0)) feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device) return new_image_features, feature_lens @can_return_tuple @merge_with_config_defaults @auto_docstring( custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." ) def get_image_features( self, pixel_values: torch.FloatTensor, image_sizes: torch.Tensor, vision_feature_layer: int | list[int] | None = None, vision_feature_select_strategy: str | None = None, output_hidden_states: bool | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple | BaseModelOutputWithPooling: r""" pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) The tensors corresponding to the input images. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). vision_feature_layer (`Union[int, list[int]]`, *optional*): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`, *optional*): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` """ # ! infer image_num_patches from image_sizes image_num_patches = [ image_size_to_num_patches( image_size=imsize, grid_pinpoints=self.config.image_grid_pinpoints, patch_size=self.config.vision_config.image_size, ) for imsize in image_sizes ] if pixel_values.dim() == 5: # stacked if input is (batch_size, num_patches, num_channels, height, width) _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)] pixel_values = torch.cat(_pixel_values_list, dim=0) elif pixel_values.dim() != 4: # otherwise has to be stacked from list of (num_patches, num_channels, height, width) raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions") image_outputs = self.vision_tower( pixel_values, output_hidden_states=True, # Ignore arg on purpose return_dict=True, **kwargs, ) # If we have one vision feature layer, return the corresponding hidden states, # otherwise, select the hidden states of each feature layer and concatenate them if isinstance(vision_feature_layer, int): selected_image_feature = image_outputs.hidden_states[vision_feature_layer] else: hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer] selected_image_feature = torch.cat(hs_pool, dim=-1) if vision_feature_select_strategy == "default": selected_image_feature = selected_image_feature[:, 1:] image_features = self.multi_modal_projector(selected_image_feature) image_features = torch.split(image_features, image_num_patches, dim=0) # NOTE we only support multimodal_patch_merge_type == "spatial_unpad" image_features, feature_lens = self.pack_image_features( image_features, image_sizes, vision_feature_select_strategy=vision_feature_select_strategy, image_newline=self.image_newline, ) image_outputs.pooler_output = image_features return image_outputs def get_placeholder_mask( self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor ): """ Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is equal to the length of multimodal features. If the lengths are different, an error is raised. """ if input_ids is None: special_image_mask = inputs_embeds == self.get_input_embeddings()( torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) ) special_image_mask = special_image_mask.all(-1) else: special_image_mask = input_ids == self.config.image_token_id n_image_tokens = special_image_mask.sum() special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) torch_compilable_check( inputs_embeds[special_image_mask].numel() == image_features.numel(), f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}", ) return special_image_mask @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, pixel_values: torch.FloatTensor | None = None, image_sizes: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, vision_feature_layer: int | list[int] | None = None, vision_feature_select_strategy: str | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple | LlavaNextModelOutputWithPast: r""" vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) if pixel_values is not None and pixel_values.size(0) > 0: image_features = self.get_image_features( pixel_values, image_sizes, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, return_dict=True, ).pooler_output image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) special_image_mask = self.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, image_features=image_features ) inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) outputs = self.language_model( attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, cache_position=cache_position, **kwargs, ) return LlavaNextModelOutputWithPast( last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=image_features if pixel_values is not None else None, ) @auto_docstring( custom_intro=""" The LLAVA-NeXT model which consists of a vision backbone and a language model. """ ) class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixin): _checkpoint_conversion_mapping = { r"^language_model.model": "model.language_model", r"^vision_tower": "model.vision_tower", r"^multi_modal_projector": "model.multi_modal_projector", r"^image_newline": "model.image_newline", r"^language_model.lm_head": "lm_head", } _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} def __init__(self, config: LlavaNextConfig): super().__init__(config) self.model = LlavaNextModel(config) self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) self.post_init() def get_input_embeddings(self): return self.model.get_input_embeddings() def set_input_embeddings(self, value): self.model.set_input_embeddings(value) def get_output_embeddings(self) -> nn.Module: return self.lm_head def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None): return self.model.pack_image_features( image_features=image_features, image_sizes=image_sizes, vision_feature_select_strategy=vision_feature_select_strategy, image_newline=image_newline, ) @auto_docstring def get_image_features( self, pixel_values: torch.FloatTensor, image_sizes: torch.Tensor, vision_feature_layer: int | list[int] | None = None, vision_feature_select_strategy: str | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple | BaseModelOutputWithPooling: r""" pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) The tensors corresponding to the input images. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). vision_feature_layer (`Union[int, list[int]]`, *optional*): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`, *optional*): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` """ return self.model.get_image_features( pixel_values=pixel_values, image_sizes=image_sizes, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, **kwargs, ) @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, pixel_values: torch.FloatTensor | None = None, image_sizes: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, vision_feature_layer: int | list[int] | None = None, vision_feature_select_strategy: str | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, cache_position: torch.LongTensor | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> tuple | LlavaNextCausalLMOutputWithPast: r""" vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used. 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 -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import AutoProcessor, LlavaNextForConditionalGeneration >>> model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") >>> prompt = "[INST] \nWhat is shown in this image? [/INST]" >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> inputs = processor(images=image, text=prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs, max_length=30) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)" ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.model( input_ids, pixel_values=pixel_values, image_sizes=image_sizes, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, cache_position=cache_position, **kwargs, ) hidden_states = outputs[0] # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function( logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs ) return LlavaNextCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=outputs.image_hidden_states, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, image_sizes=None, attention_mask=None, cache_position=None, logits_to_keep=None, is_first_iteration=False, **kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, logits_to_keep=logits_to_keep, is_first_iteration=is_first_iteration, **kwargs, ) # Pixel values are used only in the first iteration if available # In subsequent iterations, they are already merged with text and cached # NOTE: first iteration doesn't have to be prefill, it can be the first # iteration with a question and cached system prompt (continue generate from cache) if is_first_iteration or not kwargs.get("use_cache", True): model_inputs["pixel_values"] = pixel_values model_inputs["image_sizes"] = image_sizes return model_inputs __all__ = ["LlavaNextForConditionalGeneration", "LlavaNextPreTrainedModel", "LlavaNextModel"]