modeling_r.py

# 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.

import math
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np
import torch
from torch import nn

from transformers.activations import GELUActivation

from transformers.generation import GenerationMixin
from transformers.image_processing_utils import select_best_resolution
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.models.auto import AutoModel
from transformers.processing_utils import Unpack
from transformers.utils import (
    can_return_tuple,
    is_torchdynamo_compiling,
    logging,
)
from .configuration_r import RConfig


logger = logging.get_logger(__name__)


@dataclass
class RModelOutputWithPast(BaseModelOutputWithPast):
   

    image_hidden_states: Optional[torch.FloatTensor] = None


@dataclass
class RCausalLMOutputWithPast(ModelOutput):

    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None
    past_key_values: Optional[list[torch.FloatTensor]] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[torch.FloatTensor] = None


class RPooler(nn.Module):
    def __init__(self, config):
        super().__init__()

        mode = config.spatial_pool_mode
        stride = config.spatial_pool_stride
        out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
        self.image_size = (config.vision_config.image_size // config.vision_config.patch_size) ** 2

        if mode == "average":
            self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
        elif mode == "max":
            self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
        elif mode == "conv":
            self.pool = nn.Conv2d(
                in_channels=config.vision_config.hidden_size,
                out_channels=out_channels,
                kernel_size=stride,
                stride=stride,
            )
        else:
            raise ValueError(f"Unknown pooling mode: {mode}. Has to be one of [`average`, `max`, `conv`]")

    def forward(self, image_features):
        ori_width = int(math.sqrt(image_features.shape[1] * self.image_size // self.image_size))
        ori_height = int(ori_width * self.image_size // self.image_size)

        batch_size, _, dim = image_features.shape
        image_features_spatial = image_features.view(batch_size, ori_height, ori_height, dim).permute(0, 3, 1, 2)
        image_features_spatial_pool = self.pool(image_features_spatial)

        return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()


def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
    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):
    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):
    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


class RPreTrainedModel(PreTrainedModel):
    config_class = RConfig
    base_model_prefix = ""
    supports_gradient_checkpointing = True
    # _no_split_modules = ["LlamaDecoderLayer"]
    _no_split_modules = ["SiglipEncoderLayer", "Qwen3DecoderLayer", ]
    _skip_keys_device_placement = "past_key_values"
    _supports_cache_class = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_flex_attn = True
    _supports_attention_backend = True

    def _init_weights(self, module):
        std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)

        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, RModel):
            embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
            module.image_newline.data.normal_(mean=0.0, std=embed_std)


class RMultiModalProjector(nn.Module):
    def __init__(self, config):
        super().__init__()
        print("Using MultiModalProjector_withLayerNorm")

        self.pre_norm = torch.nn.LayerNorm(config.vision_config.hidden_size, eps=1e-06)
        self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
        self.act = GELUActivation()
        self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
        
        
    def forward(self, image_feature: torch.Tensor) -> torch.Tensor:
        image_feature = self.pre_norm(image_feature)
        hidden_states = self.linear_1(image_feature)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)

        return hidden_states

class RModel(RPreTrainedModel):
    _checkpoint_conversion_mapping = {"language_model.model": "language_model"}

    def __init__(self, config):
        super().__init__(config)
        self.vision_tower = AutoModel.from_config(config.vision_config)
        self.multi_modal_projector = RMultiModalProjector(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.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
        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, image_newline=None, vision_aspect_ratio="anyres"): 
        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
                if height * width != base_image_feature.shape[0]:
                    raise ValueError("The number of patches is not consistent with the image 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,
                )
                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])
                try:
                    max_num_patches = int(vision_aspect_ratio.strip("anyres_max_"))
                    channels, curr_height, curr_width = image_feature.shape
                    ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2))
                    if ratio > 1.1:
                        image_feature = image_feature[None]
                        image_feature = nn.functional.interpolate(
                            image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear"
                        )[0]
                except:
                    pass 
                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)
                image_feature = image_feature.flatten(0, 1)
            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

    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_sizes: torch.Tensor,
        vision_feature_layer: Optional[Union[int, list[int]]] = None,
        vision_feature_select_strategy: Optional[str] = None,
        vision_aspect_ratio: Optional[str] = None,
        batch_num_images: Optional[torch.LongTensor] = None,
    ):
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )
        vision_aspect_ratio = (
            vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
        )
    
        if batch_num_images is None:
            # treat this as a single-image case for backward compatibility
            need_patching = [True] * len(image_sizes)
        else:
            need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
        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,
            )
            if should_patch
            else 1
            for imsize, should_patch in zip(image_sizes, need_patching)
        ]

        if isinstance(pixel_values, torch.Tensor):
            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")
        elif isinstance(pixel_values, list):
            # list of [(batch_size, num_patches, num_channels, height, width)]
            assert len(pixel_values) == len(image_num_patches), (
                f"pixel_values is a list of {len(pixel_values)} tensors, but image_num_patches is of length {len(image_num_patches)}"
            )
            _pixel_values_list = [pix_val.squeeze(0)[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
            
            pixel_values = torch.cat(_pixel_values_list, dim=0)
            
        image_features = self.vision_tower(pixel_values, output_hidden_states=True)
        # 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_features.hidden_states[vision_feature_layer]
        else:
            hs_pool = [image_features.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:]
        elif vision_feature_select_strategy == "full":
            selected_image_feature = selected_image_feature
        image_features = self.multi_modal_projector(selected_image_feature)

        image_features = torch.split(image_features, image_num_patches, dim=0)

        image_features, feature_lens = self.pack_image_features(
            image_features,
            image_sizes,
            image_newline=self.image_newline,
            vision_aspect_ratio=vision_aspect_ratio,
        )
    
        return image_features

    @can_return_tuple
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        image_sizes: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        vision_feature_layer: Optional[Union[int, list[int]]] = None,
        vision_feature_select_strategy: Optional[str] = None,
        vision_aspect_ratio: Optional[str] = None,
        batch_num_images: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Union[tuple, RModelOutputWithPast]:

  
        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
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )
        vision_aspect_ratio = (
            vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
        )

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if pixel_values is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both `pixel_values` and `inputs_embeds` at the same time, "
                "and must specify either one"
            )
        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        # Images are processed with Anyres
 
        if pixel_values is not None:
            image_features = self.get_image_features(
                pixel_values,
                image_sizes,
                vision_feature_layer=vision_feature_layer,
                vision_feature_select_strategy=vision_feature_select_strategy,
                batch_num_images=batch_num_images,
            )
            image_features = torch.cat(image_features, dim=0)

            special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
            if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
                n_image_tokens = (input_ids == self.config.image_token_id).sum()
                n_image_features = image_features.shape[0]
                raise ValueError(
                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
                )
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            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 RModelOutputWithPast(
            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,
        )

    def apply_pooling(self, image_features):
        height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
        batch_frames, seq_len, dim = image_features.shape
        image_features = image_features.view(batch_frames, height, width, -1)
        image_features = image_features.permute(0, 3, 1, 2).contiguous()

        height, width = image_features.shape[2:]
        scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)]
        image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear")

        image_features = image_features.permute(0, 2, 3, 1)
        image_features = image_features.view(batch_frames, -1, dim)
        return image_features

class RForConditionalGeneration(RPreTrainedModel, GenerationMixin):
    _checkpoint_conversion_mapping = {
        "^language_model.model": "model.language_model",
        "^vision_tower": "model.vision_tower",
        "^multi_modal_projector": "model.multi_modal_projector",
        "^image_newline": "model.image_newline",
        "^language_model.lm_head": "lm_head",
    }
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: RConfig):
        super().__init__(config)
        self.model = RModel(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 set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    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,
        )

    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_sizes: torch.Tensor,
        vision_feature_layer: Optional[Union[int, list[int]]] = None,
        vision_feature_select_strategy: Optional[str] = None,
    ):
        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,
        )

    # Make modules available throught conditional class for BC
    @property
    def language_model(self):
        return self.model.language_model

    @property
    def vision_tower(self):
        return self.model.vision_tower

    @property
    def multi_modal_projector(self):
        return self.model.multi_modal_projector

    @can_return_tuple
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        image_sizes: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        vision_feature_layer: Optional[Union[int, list[int]]] = None,
        vision_feature_select_strategy: Optional[str] = None,
        vision_aspect_ratio: Optional[str] = None,
        batch_num_images: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs,
    ) -> Union[tuple, RCausalLMOutputWithPast]:

        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
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )
        vision_aspect_ratio = (
            vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
        )

        
        
        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            image_sizes=image_sizes,
            vision_aspect_ratio=vision_aspect_ratio,
            vision_feature_layer=vision_feature_layer,
            vision_feature_select_strategy=vision_feature_select_strategy,
            batch_num_images=batch_num_images,
            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,
            logits_to_keep=logits_to_keep,
            **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 RCausalLMOutputWithPast(
            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,
        **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,
            **kwargs,
        )

        if cache_position[0] == 0:
            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
            # Otherwise we need pixel values to be passed to model
            model_inputs["pixel_values"] = pixel_values
            model_inputs["image_sizes"] = image_sizes

        return model_inputs

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        cache_position: torch.Tensor,
        batch_size: int,
        **kwargs,
    ):

        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )

        return causal_mask


__all__ = ["RModel", "RForConditionalGeneration", "RPreTrainedModel"]