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keye.py 57.3 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import math
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from abc import abstractmethod
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from collections.abc import Iterable, Mapping, Sequence
from functools import partial
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from typing import Annotated, Any, Literal, TypeAlias, TypeVar
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import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from transformers import PretrainedConfig
from transformers.activations import GELUActivation
from transformers.feature_extraction_utils import BatchFeature
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from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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from transformers.utils import torch_int

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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.logger import init_logger
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from vllm.model_executor.layers.attention import (
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    MMEncoderAttention,
)
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from vllm.model_executor.layers.conv import Conv2dLayer
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from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding.common import (
    ApplyRotaryEmb,
)
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from vllm.model_executor.model_loader.weight_utils import (
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    default_weight_loader,
    maybe_remap_kv_scale_name,
)
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    ImageItem,
    ModalityData,
    MultiModalDataDict,
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    MultiModalFeatureSpec,
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    MultiModalFieldConfig,
    MultiModalKwargsItems,
    VideoItem,
)
from vllm.multimodal.parse import (
    DictEmbeddingItems,
    ImageSize,
    ModalityDataItems,
    MultiModalDataItems,
    MultiModalDataParser,
)
from vllm.multimodal.processing import (
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    BaseDummyInputsBuilder,
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    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
)
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from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
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    SupportsMRoPE,
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    SupportsMultiModal,
    SupportsPP,
)
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from .siglip import SiglipMLP
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from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    init_vllm_registered_model,
    is_pp_missing_parameter,
    maybe_prefix,
)
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from .vision import is_vit_use_data_parallel
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logger = init_logger(__name__)


def smart_resize(
    height: int,
    width: int,
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    factor: int,
    min_pixels: int,
    max_pixels: int,
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):
    if height < factor:
        logger.warning(
            "smart_resize: height=%s < factor=%s, reset height=factor",
            height,
            factor,
        )
        width = round((width * factor) / height)
        height = factor

    if width < factor:
        logger.warning(
            "smart_resize: width=%s < factor=%s, reset width=factor",
            width,
            factor,
        )
        height = round((height * factor) / width)
        width = factor

    if max(height, width) / min(height, width) > 200:
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        raise ValueError(
            "absolute aspect ratio must be smaller than 200, got "
            "{max(height, width) / min(height, width)}"
        )
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    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = math.floor(height / beta / factor) * factor
        w_bar = math.floor(width / beta / factor) * factor
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar


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class KeyeImagePixelInputs(TensorSchema):
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    """
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    Dimensions:
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        - bnp: Batch size * Number of patches
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        - c: Number of channels
        - ps: Patch size
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        - ni: Number of images
        - g: Grid dimensions (3 for t, h, w)
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    """
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    type: Literal["pixel_values"]
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    pixel_values: Annotated[
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        torch.Tensor, TensorShape("bnp", 3, "ps", "ps", dynamic_dims={"bnp"})
    ]
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    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
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class KeyeImageEmbeddingInputs(TensorSchema):
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    """
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    Dimensions:
        - nf: Number of image features
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        - hs: Hidden size (must match the hidden size of language model
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          backbone)
        - ni: Number of images
        - g: Grid dimensions (3 for t, h, w)
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    """
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    type: Literal["image_embeds"]
    image_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
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KeyeImageInputs: TypeAlias = KeyeImagePixelInputs | KeyeImageEmbeddingInputs
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class KeyeVideoPixelInputs(TensorSchema):
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    """
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    Dimensions:
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        - bnp: Batch size * Number of patches
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        - c: Number of channels
        - ps: Patch size
        - ni: Number of images
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        - g: Grid dimensions (3 for t, h, w)
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    """
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    type: Literal["pixel_values_videos"]
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    pixel_values_videos: Annotated[
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        torch.Tensor, TensorShape("bnp", 3, "ps", "ps", dynamic_dims={"bnp"})
    ]
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    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]
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class KeyeVideoEmbeddingInputs(TensorSchema):
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    """
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    Dimensions:
        - nf: Number of video features
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        - hs: Hidden size (must match the hidden size of language model
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          backbone)
        - nv: Number of videos
        - g: Grid dimensions (3 for t, h, w)
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    """
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    type: Literal["video_embeds"]
    video_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]
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KeyeVideoInputs: TypeAlias = KeyeVideoPixelInputs | KeyeVideoEmbeddingInputs
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class KeyeVisionEmbeddings(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

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        self.patch_embedding = Conv2dLayer(
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            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            padding="valid",
        )

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        self.num_patches = (self.image_size // self.patch_size) ** 2
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        self.num_positions = self.num_patches
        self.cache_position_embedding = dict()
        self.cache_position_count = dict()
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        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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        self.packing_position_embedding = nn.Embedding(32768, self.embed_dim)

        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions).expand((1, -1)),
            persistent=False,
        )

    def interpolate_pos_encoding(
        self,
        embeddings: torch.Tensor,
        height: int,
        width: int,
        is_after_patchify: bool = False,
    ) -> torch.Tensor:
        num_positions = self.position_embedding.weight.shape[0]

        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)

        dim = embeddings.shape[-1]

        if is_after_patchify:
            new_height = height
            new_width = width
        else:
            new_height = height // self.patch_size
            new_width = width // self.patch_size

        sqrt_num_positions = torch_int(num_positions**0.5)
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        patch_pos_embed = patch_pos_embed.reshape(
            1, sqrt_num_positions, sqrt_num_positions, dim
        )
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        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bilinear",
            align_corners=False,
        )

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

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    def fetch_position_embedding_lfu_cache(self, embeddings, h, w, max_cache: int = 20):
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        grid = (h, w)
        if grid in self.cache_position_embedding:
            self.cache_position_count[grid] += 1
            return self.cache_position_embedding[grid]

        if len(self.cache_position_embedding) >= max_cache:
            min_hit_grid = min(
                self.cache_position_count,
                key=self.cache_position_count.get,
            )
            self.cache_position_count.pop(min_hit_grid)
            self.cache_position_embedding.pop(min_hit_grid)

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        position_embedding = self.interpolate_pos_encoding(embeddings, h, w, True)
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        self.cache_position_count[grid] = 1
        self.cache_position_embedding[grid] = position_embedding
        return position_embedding

    def forward(
        self,
        pixel_values: torch.FloatTensor,
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        position_ids: torch.Tensor | None = None,
        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
        | None = None,
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        interpolate_pos_encoding=False,
    ) -> torch.Tensor:
        if pixel_values.dim() == 4:
            pixel_values = pixel_values.unsqueeze(0)
        if pixel_values.dim() == 5:
            if position_ids is None:
                raise ValueError(
                    "position_ids cannot be None when pixel_values.dim() is 5."
                )
            (
                batch_size,
                squence_len,
                channel,
                height,
                width,
            ) = pixel_values.shape
            target_dtype = self.patch_embedding.weight.dtype
            pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w")
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            patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
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            embeddings = patch_embeds.flatten(-2).squeeze(-1)

            if interpolate_pos_encoding and image_grid_thw is not None:
                start = 0
                tmp_embeddings = list()
                for image_grid in image_grid_thw:
                    t, h, w = image_grid
                    end = start + t * h * w
                    image_embeddings = embeddings[start:end, :]
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                    position_embedding = (
                        self.interpolate_pos_encoding(image_embeddings, h, w, True)
                        .squeeze(0)
                        .repeat(t, 1)
                    )
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                    image_embeddings = image_embeddings + position_embedding
                    tmp_embeddings.append(image_embeddings)
                    start = end
                embeddings = torch.concat(tmp_embeddings, dim=0).unsqueeze(0)
            else:
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                embeddings = embeddings + self.packing_position_embedding(position_ids)
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            return embeddings
        else:
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            raise ValueError(
                "Unsupported pixel_values dimension:"
                f" {pixel_values.dim()}. Expected 4 or 5."
            )
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def apply_rotary_pos_emb_flashatt(
    q: torch.Tensor,
    k: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
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    apply_rotary_emb: ApplyRotaryEmb,
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) -> tuple[torch.Tensor, torch.Tensor]:
    cos = cos.chunk(2, dim=-1)[0].contiguous()
    sin = sin.chunk(2, dim=-1)[0].contiguous()

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    q_embed = apply_rotary_emb(q, cos, sin)
    k_embed = apply_rotary_emb(k, cos, sin)
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    return q_embed, k_embed


class KeyeSiglipAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You
    Need' paper."""

    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()
        self.config = config

        hidden_size = config.hidden_size
        self.hidden_size = config.hidden_size
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        use_data_parallel = is_vit_use_data_parallel()
        tp_size = 1 if use_data_parallel else get_tensor_model_parallel_world_size()
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        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = config.num_attention_heads
        if self.total_num_kv_heads >= tp_size:
            assert self.total_num_kv_heads % tp_size == 0
        else:
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = config.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scale = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.out_proj = RowParallelLinear(
            input_size=hidden_size,
            output_size=hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )

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        self.attn = MMEncoderAttention(
            num_heads=self.num_heads,
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            head_size=self.head_dim,
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            scale=self.scale,
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            num_kv_heads=self.num_kv_heads,
            prefix=f"{prefix}.attn",
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        )
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        self.apply_rotary_emb = ApplyRotaryEmb(
            enforce_enable=True,
            enable_fp32_compute=True,
        )

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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: torch.Tensor | None = None,
        output_attentions: bool | None = False,
        cu_seqlens: list[torch.Tensor] | None = None,
        rope_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
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    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split(
            [self.q_size, self.kv_size, self.kv_size],
            dim=-1,
        )

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        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
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        if rope_emb is None:
            q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
            k = k.view(
                *k.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )
            v = v.view(
                *v.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )
        else:
            if cu_seqlens is None:
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                raise ValueError("cu_seqlens cannot be None when rope_emb is not None.")
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            cos, sin = rope_emb
            q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
            k = k.view(
                *k.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )
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            q, k = apply_rotary_pos_emb_flashatt(q, k, cos, sin, self.apply_rotary_emb)
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            v = v.view(
                *v.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )

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        context_layer = self.attn(
            query=q,
            key=k,
            value=v,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
        )
        context_layer = rearrange(context_layer, "b s h d -> b s (h d)")
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        output, _ = self.out_proj(context_layer)
        return output


class SigLIPRotaryEmbedding(nn.Module):
    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
        self.rope_init()

    def rope_init(self):
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        inv_freq = 1.0 / (
            self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float) / self.dim)
        )
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        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, seqlen: int) -> torch.Tensor:
        seq = torch.arange(
            seqlen,
            device=self.inv_freq.device,
            dtype=self.inv_freq.dtype,
        )
        freqs = torch.outer(seq, self.inv_freq)
        return freqs


class KeyeSiglipEncoderLayer(nn.Module):
    def __init__(
        self,
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        config: PretrainedConfig,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()
        self.embed_dim = config.hidden_size
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        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
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        self.self_attn = KeyeSiglipAttention(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
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        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
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        self.mlp = SiglipMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
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        output_attentions: bool | None = False,
        cu_seqlens: list[torch.Tensor] | None = None,
        rope_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
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    ) -> tuple[torch.FloatTensor]:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            cu_seqlens=cu_seqlens,
            rope_emb=rope_emb,
        )

        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)

        hidden_states = residual + hidden_states

        return hidden_states


class KeyeSiglipEncoder(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size
        num_heads = config.num_attention_heads
        head_dim = embed_dim // num_heads
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        self.layers = nn.ModuleList(
            [
                KeyeSiglipEncoderLayer(
                    config,
                    quant_config=quant_config,
                    prefix=f"{prefix}.layers.{layer_idx}",
                )
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
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        self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)

    @staticmethod
    def flatten_list(image_grid_thw):
        tmp_image_grid_thw = list()
        for image_grid in image_grid_thw:
            if isinstance(image_grid, list):
                tmp_image_grid_thw.extend(image_grid)
            else:
                tmp_image_grid_thw.append(image_grid)
        return tmp_image_grid_thw

    def forward(
        self,
        inputs_embeds,
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        attention_mask: torch.Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        cu_seqlens: list[torch.Tensor] | None = None,
        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
        | None = None,
        height_position_ids: torch.Tensor | None = None,
        width_position_ids: torch.Tensor | None = None,
        use_rope: bool | None = False,
        window_size: bool | None = -1,
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        vision_or_text: str = "vision",
    ) -> BaseModelOutput:
        device = inputs_embeds.device
        hidden_states = inputs_embeds
        if use_rope is True:
            flatten_image_grid_thw = self.flatten_list(image_grid_thw)

            if width_position_ids is None or height_position_ids is None:
                split_hids = list()
                split_wids = list()
                for t, h, w in flatten_image_grid_thw:
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                    image_pids = torch.arange(t * h * w, device=device) % (h * w)
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                    sample_hids = image_pids // w
                    sample_wids = image_pids % w
                    split_hids.append(sample_hids)
                    split_wids.append(sample_wids)
                width_position_ids = torch.concat(split_wids, dim=0)
                height_position_ids = torch.concat(split_hids, dim=0)

            pids = torch.stack(
                [height_position_ids, width_position_ids],
                dim=-1,
            )
            max_grid_size = pids.max() + 1
            rope_emb_max_grid = self.rotary_pos_emb(max_grid_size)
            rope_emb = rope_emb_max_grid[pids].flatten(1)
            rope_emb = rope_emb.repeat(1, 2)
            rope_emb = (rope_emb.cos(), rope_emb.sin())
        else:
            rope_emb = None

        attn_cu_seqlens = cu_seqlens
        hidden_states = inputs_embeds
        assert attention_mask is None

        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask,
                output_attentions=output_attentions,
                cu_seqlens=attn_cu_seqlens,
                rope_emb=rope_emb,
            )
        return hidden_states


class KeyeSiglipVisionTransformer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = KeyeVisionEmbeddings(config)
        self.encoder = KeyeSiglipEncoder(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.encoder",
        )
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        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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    def forward(
        self,
        pixel_values,
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        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        interpolate_pos_encoding: bool | None = False,
        attention_mask: torch.Tensor | None = None,
        sample_indices: torch.Tensor | None = None,
        image_indices: torch.Tensor | None = None,
        position_ids: torch.Tensor | None = None,
        height_position_ids: torch.Tensor | None = None,
        width_position_ids: torch.Tensor | None = None,
        cu_seqlens: list[torch.Tensor] | None = None,
        padding_mask: torch.Tensor | None = None,
        vision_return_embed_list: bool | None = False,
        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
        | None = None,
        return_pooler_output: bool | None = True,
        use_rope: bool | None = False,
        window_size: bool | None = -1,
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    ) -> BaseModelOutputWithPooling:
        hidden_states = self.embeddings(
            pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            position_ids=position_ids,
            image_grid_thw=image_grid_thw,
        )

        last_hidden_state = self.encoder(
            inputs_embeds=hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            attention_mask=attention_mask,
            cu_seqlens=cu_seqlens,
            image_grid_thw=image_grid_thw,
            use_rope=use_rope,
            height_position_ids=height_position_ids,
            width_position_ids=width_position_ids,
            window_size=window_size,
            vision_or_text="vision",
        )

        last_hidden_state = self.post_layernorm(last_hidden_state)

        sample_hidden_state = list()
        if cu_seqlens is None:
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            raise ValueError(
                "cu_seqlens cannot be None for "
                "SiglipVisionTransformer output processing."
            )
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        for i in range(cu_seqlens.shape[0] - 1):
            start = cu_seqlens[i]
            end = cu_seqlens[i + 1]
            tensor = last_hidden_state[:, start:end, :].squeeze(0)
            sample_hidden_state.append(tensor)

        return sample_hidden_state


class KeyeSiglipVisionModel(nn.Module):
    config_class = PretrainedConfig
    main_input_name = "pixel_values"

    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()

        self.vision_model = KeyeSiglipVisionTransformer(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.vision_model",
        )
        self.quant_config = quant_config

    @property
    def dtype(self) -> torch.dtype:
        return self.vision_model.embeddings.patch_embedding.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.vision_model.embeddings.patch_embedding.weight.device

    def get_input_embeddings(self) -> nn.Module:
        return self.vision_model.embeddings.patch_embedding

    def forward(
        self,
        pixel_values,
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        sample_indices: torch.Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
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        interpolate_pos_encoding: bool = False,
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        position_ids: torch.Tensor | None = None,
        vision_return_embed_list: bool | None = False,
        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
        | None = None,
        cu_seqlens: list[torch.Tensor] | None = None,
        return_pooler_output: bool | None = True,
        use_rope: bool | None = False,
        window_size: bool | None = -1,
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    ) -> BaseModelOutputWithPooling:
        return self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            position_ids=position_ids,
            vision_return_embed_list=vision_return_embed_list,
            image_grid_thw=image_grid_thw,
            sample_indices=sample_indices,
            cu_seqlens=cu_seqlens,
            return_pooler_output=return_pooler_output,
            use_rope=use_rope,
            window_size=window_size,
        )

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    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        stacked_params_mapping = [
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if "head.attention" in name or "head.layernorm" in name:
                continue
            if "head.mlp" in name or "head.probe" in name:
                continue
            if self.quant_config is not None and (
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                scale_name := self.quant_config.get_cache_scale(name)
            ):
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                param = params_dict[scale_name]
                weight_loader = getattr(
                    param,
                    "weight_loader",
                    default_weight_loader,
                )
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                loaded_weight = (
                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
                )
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                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue
            for (
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                param_name,
                weight_name,
                shard_id,
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            ) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                if name.endswith(".bias") and name not in params_dict:
                    continue
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = getattr(
                    param,
                    "weight_loader",
                    default_weight_loader,
                )
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Projector(nn.Module):
    def __init__(
        self,
        text_config: PretrainedConfig,
        vision_config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ):
        super().__init__()
        self.text_config = text_config
        self.vision_config = vision_config
        self.merge_kernel_size = (2, 2)

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        self.hidden_size = (
            self.vision_config.hidden_size
            * self.merge_kernel_size[0]
            * self.merge_kernel_size[1]
        )
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        self.pre_norm = torch.nn.LayerNorm(self.vision_config.hidden_size, eps=1e-05)
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        self.act = GELUActivation()

        self.linear_1 = ColumnParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.linear_1",
        )
        self.linear_2 = RowParallelLinear(
            self.hidden_size,
            self.text_config.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.linear_2",
        )

    def forward(
        self,
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        image_features: torch.Tensor | list[torch.Tensor],
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        image_grid_thw: list[tuple[int, int, int]],
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    ) -> torch.Tensor | list[torch.Tensor]:
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        m1, m2 = self.merge_kernel_size
        if isinstance(image_features, (list, tuple)):
            processed_features = list()
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            for image_feature, image_grid in zip(image_features, image_grid_thw):
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                image_feature = self.pre_norm(image_feature)
                t, h, w = image_grid

                image_feature = rearrange(
                    image_feature,
                    "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
                    t=t,
                    h=h // m1,
                    p1=m1,
                    w=w // m2,
                    p2=m2,
                )
                hidden_states, _ = self.linear_1(image_feature)
                hidden_states = self.act(hidden_states)
                hidden_states, _ = self.linear_2(hidden_states)
                processed_features.append(hidden_states)

            return processed_features

        dims = image_features.shape[:-1]
        dim = image_features.shape[-1]
        image_features = image_features.view(np.prod(dims), dim)
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        hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
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        hidden_states = self.linear_1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)

        return hidden_states.view(*dims, -1)


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def _keye_field_config(
    hf_inputs: Mapping[str, torch.Tensor],
):
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    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
    image_grid_sizes = image_grid_thw.prod(-1)

    video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
    video_grid_sizes = video_grid_thw.prod(-1)

    return dict(
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        pixel_values=MultiModalFieldConfig.flat_from_sizes("image", image_grid_sizes),
        image_embeds=MultiModalFieldConfig.flat_from_sizes("image", image_grid_sizes),
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        image_grid_thw=MultiModalFieldConfig.batched("image"),
        pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
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            "video", video_grid_sizes
        ),
        video_embeds=MultiModalFieldConfig.flat_from_sizes("video", video_grid_sizes),
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        video_grid_thw=MultiModalFieldConfig.batched("video"),
    )


class KeyeMultiModalDataParser(MultiModalDataParser):
    def _parse_image_data(
        self,
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        data: dict[str, torch.Tensor] | ModalityData[ImageItem],
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    ) -> ModalityDataItems[Any, Any] | None:
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        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="image",
                required_fields={
                    "image_embeds",
                    "image_grid_thw",
                },
                fields_factory=_keye_field_config,
            )

        return super()._parse_image_data(data)

    def _parse_video_data(
        self,
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        data: dict[str, torch.Tensor] | ModalityData[VideoItem],
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    ) -> ModalityDataItems[Any, Any] | None:
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        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="video",
                required_fields={
                    "video_embeds",
                    "video_grid_thw",
                },
                fields_factory=_keye_field_config,
            )

        return super()._parse_video_data(data)


class KeyeProcessingInfo(BaseProcessingInfo):
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    def get_max_image_size(self) -> int:
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        return 9999999  # _MAX_IMAGE_SIZE
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    def get_max_frame_per_video(self) -> int:
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        return 16  # _MAX_FRAMES_PER_VIDEO
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    def get_image_processor(self, **kwargs: object):
        return self.get_hf_processor(**kwargs).image_processor
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    def get_data_parser(self):
        return KeyeMultiModalDataParser(
            expected_hidden_size=self._get_expected_hidden_size(),
        )

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    def get_supported_mm_limits(
        self,
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    ) -> Mapping[str, int | None]:
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        return {"image": None, "video": None}

    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:
        return {
            "image": self.get_max_image_tokens(),
            "video": self.get_max_video_tokens(seq_len),
        }

    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 1,
        do_resize: bool = True,
        image_processor,
    ) -> tuple[ImageSize, int]:
        if image_processor is None:
            image_processor = self.get_image_processor()

        hf_config = self.get_hf_config()
        vision_config = hf_config.vision_config
        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = 1

        if do_resize:
            resized_height, resized_width = smart_resize(
                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                min_pixels=image_processor.min_pixels,
                max_pixels=image_processor.max_pixels,
            )
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            preprocessed_size = ImageSize(width=resized_width, height=resized_height)
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        else:
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            preprocessed_size = ImageSize(width=image_width, height=image_height)
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        padded_num_frames = num_frames + num_frames % temporal_patch_size

        grid_t = max(padded_num_frames // temporal_patch_size, 1)
        grid_h = preprocessed_size.height // patch_size
        grid_w = preprocessed_size.width // patch_size

        num_patches = grid_t * grid_h * grid_w
        num_vision_tokens = num_patches // (merge_size**2)

        return preprocessed_size, num_vision_tokens

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        image_processor,
    ) -> int:
        _, num_image_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            image_processor=image_processor,
        )
        return num_image_tokens

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
        image_processor,
    ) -> int:
        _, num_video_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            num_frames=num_frames,
            image_processor=image_processor,
        )
        return num_video_tokens

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    def get_image_size_with_most_features(
        self,
    ) -> ImageSize:
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        max_image_size, _ = self._get_vision_info(
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            image_width=self.get_max_image_size(),
            image_height=self.get_max_image_size(),
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            image_processor=None,
        )
        return max_image_size

    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_image_tokens(
            image_width=target_width,
            image_height=target_height,
            image_processor=None,
        )

    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        num_frames = 0

        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
                image_processor=None,
            )

            if next_max_tokens > max_tokens:
                break

            num_frames = next_num_frames

        return num_frames

    def get_num_frames_with_most_features(self, seq_len: int) -> int:
        mm_config = self.ctx.get_mm_config()
        max_images = mm_config.get_limit_per_prompt("image")
        max_videos = mm_config.get_limit_per_prompt("video")

        max_image_tokens = self.get_max_image_tokens() * max_images
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        max_total_frames = self._get_max_video_frames(seq_len - max_image_tokens)
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        max_frames_per_video = min(
            max_total_frames // max(max_videos, 1),
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            self.get_max_frame_per_video(),
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        )

        return max(max_frames_per_video, 1)

    def get_max_video_tokens(self, seq_len: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_video_tokens(
            image_width=target_width,
            image_height=target_height,
            num_frames=self.get_num_frames_with_most_features(seq_len),
            image_processor=None,
        )


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_I = TypeVar("_I", bound=KeyeProcessingInfo)


class KeyeBaseDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
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    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        hf_processor = self.info.get_hf_processor()
        image_token: str = hf_processor.image_token
        video_token: str = hf_processor.video_token

        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
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        mm_options: Mapping[str, BaseDummyOptions] | None = None,
1162
        mm_processor_kwargs: Mapping[str, object] | None = None,
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    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

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        target_width, target_height = self.info.get_image_size_with_most_features()
        target_num_frames = self.info.get_num_frames_with_most_features(seq_len)
1169

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        image_overrides = mm_options.get("image") if mm_options else None
        video_overrides = mm_options.get("video") if mm_options else None

1173
        mm_data = {
1174
            "image": self._get_dummy_images(
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                width=target_width,
                height=target_height,
                num_images=num_images,
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                overrides=image_overrides,
1179
            ),
1180
            "video": self._get_dummy_videos(
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                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
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                overrides=video_overrides,
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            ),
        }

        return mm_data


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class KeyeDummyInputsBuilder(KeyeBaseDummyInputsBuilder[KeyeProcessingInfo]):
    pass
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class KeyeMultiModalProcessor(BaseMultiModalProcessor[KeyeProcessingInfo]):
    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
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        out_mm_kwargs: MultiModalKwargsItems,
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    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
1204
        image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
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        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        placeholder = {
            "image": vocab[hf_processor.image_token],
            "video": vocab[hf_processor.video_token],
        }

        merge_length = image_processor.merge_size**2

        def get_replacement_keye(item_idx: int, modality: str):
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            out_item = out_mm_kwargs[modality][item_idx]
            grid_thw = out_item[f"{modality}_grid_thw"].data
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            assert isinstance(grid_thw, torch.Tensor)

            num_tokens = int(grid_thw.prod()) // merge_length
            return [placeholder[modality]] * num_tokens

        return [
            PromptReplacement(
                modality=modality,
                target=[placeholder[modality]],
                replacement=partial(get_replacement_keye, modality=modality),
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            )
            for modality in ("image", "video")
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        ]

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _keye_field_config(hf_inputs)


1240
class BaseKeyeModule(nn.Module, SupportsMultiModal):
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    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

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    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "lm_head.": "language_model.lm_head.",
            "model.": "language_model.model.",
        }
    )
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    @classmethod
1261
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
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        if modality.startswith("image"):
            return "<|vision_start|><|image_pad|><|vision_end|>"
        if modality.startswith("video"):
            return "<|vision_start|><|video_pad|><|vision_end|>"

        raise ValueError("Only image or video modality is supported")

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: PretrainedConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config

        self.config = config

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        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual = KeyeSiglipVisionModel(
                config.vision_config,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "visual"),
            )
            self.mlp_AR = self._build_projector(
                config,
                config.vision_config,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "mlp_AR"),
            )
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        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                prefix=maybe_prefix(prefix, "language_model"),
                architectures=["Qwen3ForCausalLM"],
            )
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        self.make_empty_intermediate_tensors = (
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            self.language_model.make_empty_intermediate_tensors
        )
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    @abstractmethod
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    def _build_projector(
        self,
        text_config: PretrainedConfig,
        vision_config: PretrainedConfig,
1305
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ) -> nn.Module:
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        raise NotImplementedError("Need projector")
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    def _process_image_input(self, image_input: Any) -> tuple[torch.Tensor, ...]:
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        siglip_position_ids = list()
        image_grid_hws = list()
        sample_indices = list()
        cu_seqlens = [0]

        image_grid_thw = image_input["image_grid_thw"]
        assert image_grid_thw.ndim == 2

        for idx, thaw in enumerate(image_grid_thw):
            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
            numel = np.prod(thw_tuple)
            image_grid_hws.append(thw_tuple)
            image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
            siglip_position_ids.append(image_position_ids)
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            sample_indices.append(torch.full((numel,), idx, dtype=torch.int64))
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            cu_seqlens.append(cu_seqlens[-1] + numel)

        if image_input["type"] == "image_embeds":
            raise ValueError(
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                "Image embeddings are not supported for this processing path."
            )
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        else:
            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
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            siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
                pixel_values.device
            )
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            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
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                pixel_values.device
            )
            sample_indices = torch.concat(sample_indices, dim=0).to(pixel_values.device)
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            image_embeds = self.visual(
                pixel_values=pixel_values,
                image_grid_thw=image_grid_hws,
                position_ids=siglip_position_ids,
                vision_return_embed_list=False,
                interpolate_pos_encoding=True,
                sample_indices=sample_indices,
                cu_seqlens=cu_seqlens,
                use_rope=True,
                window_size=-1,
            )
            image_embeds = tuple(self.mlp_AR(image_embeds, image_grid_thw))
            return image_embeds

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    def _process_video_embeds(
        self,
        video_type: Literal["video_embeds", "pixel_values_videos"],
        video_grid_thw: list[torch.Tensor],
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        pixel_values_videos: torch.Tensor | None = None,
    ) -> torch.Tensor | list[torch.Tensor]:
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        siglip_position_ids = list()
        video_grid_hws = list()
        sample_indices = list()
        cu_seqlens = [0]

        assert video_grid_thw.ndim == 2
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        for idx, sub_thw in enumerate(video_grid_thw):
            thw_tuple = tuple(sub_thw.detach().cpu().numpy().tolist())
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            numel = np.prod(thw_tuple)

            video_grid_hws.append(thw_tuple)
            video_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
            siglip_position_ids.append(video_position_ids)
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            sample_indices.append(torch.full((numel,), idx, dtype=torch.int64))
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            cu_seqlens.append(cu_seqlens[-1] + numel)

1378
        if video_type == "video_embeds":
1379
            raise ValueError(
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                "Video embeddings are not supported for this processing path."
            )
1382
        else:
1383
            pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
1384
            siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
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                pixel_values_videos.device
            )
1387
            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
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                pixel_values_videos.device
            )
            sample_indices = torch.concat(sample_indices, dim=0).to(
                pixel_values_videos.device
            )
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            video_embeds = self.visual(
                pixel_values=pixel_values_videos,
                image_grid_thw=video_grid_hws,
                position_ids=siglip_position_ids,
                vision_return_embed_list=True,
                interpolate_pos_encoding=True,
                sample_indices=sample_indices,
                cu_seqlens=cu_seqlens,
                use_rope=True,
                window_size=-1,
            )
1405
            video_embeds = self.mlp_AR(video_embeds, video_grid_thw)
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            return video_embeds

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        for input_key in kwargs:
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            if (
                input_key in ("pixel_values", "image_embeds")
                and "images" not in modalities
            ):
                modalities["images"] = self._parse_and_validate_image_input(**kwargs)
            if (
                input_key in ("pixel_values_videos", "video_embeds")
                and "videos" not in modalities
            ):
                modalities["videos"] = self._parse_and_validate_video_input(**kwargs)
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        return modalities

1425
    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
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        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return None

        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
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                image_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
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            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_video_input(video_input)
1440
                multimodal_embeddings += tuple(video_embeddings)
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        return multimodal_embeddings

    def forward(
        self,
1445
        input_ids: torch.Tensor | None,
1446
        positions: torch.Tensor,
1447
1448
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
1449
        **kwargs: object,
1450
    ) -> torch.Tensor | IntermediateTensors:
1451
        """Run forward pass for Keye-VL.
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        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
                opensource models), the shape will be `(3, seq_len)`,
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                otherwise it will be `(seq_len,)`.
            intermediate_tensors: Intermediate tensors from prior forward pass.
            inputs_embeds: Optional tensor of input embeddings.
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        """
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
1473

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        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
1479
    ) -> torch.Tensor | None:
1480
        return self.language_model.compute_logits(hidden_states)
1481

1482
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def get_mm_mapping(self) -> MultiModelKeys:
        """Get the module prefix in multimodal models."""
        return MultiModelKeys.from_string_field(
            language_model="language_model",
1490
1491
            connector="mlp_AR.",
            tower_model="visual.",
1492
        )
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1497
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1499


@MULTIMODAL_REGISTRY.register_processor(
    KeyeMultiModalProcessor,
    info=KeyeProcessingInfo,
    dummy_inputs=KeyeDummyInputsBuilder,
)
1500
class KeyeForConditionalGeneration(
1501
    BaseKeyeModule, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
1502
1503
1504
1505
1506
):
    def _build_projector(
        self,
        text_config: PretrainedConfig,
        vision_config: PretrainedConfig,
1507
        quant_config: QuantizationConfig | None = None,
1508
1509
        prefix: str = "",
    ) -> nn.Module:
1510
1511
1512
        return Projector(text_config, vision_config, quant_config, prefix)

    def _parse_and_validate_image_input(
1513
        self, **kwargs: object
1514
    ) -> KeyeImageInputs | None:
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        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)
        image_grid_thw = kwargs.pop("image_grid_thw", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            return KeyeImagePixelInputs(
                type="pixel_values",
                pixel_values=pixel_values,
                image_grid_thw=image_grid_thw,
            )

        if image_embeds is not None:
            return KeyeImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
                image_grid_thw=image_grid_thw,
            )

    def _parse_and_validate_video_input(
1537
        self, **kwargs: object
1538
    ) -> KeyeVideoInputs | None:
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1560
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
        video_embeds = kwargs.pop("video_embeds", None)
        video_grid_thw = kwargs.pop("video_grid_thw", None)

        if pixel_values_videos is None and video_embeds is None:
            return None

        if pixel_values_videos is not None:
            return KeyeVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            return KeyeVideoEmbeddingInputs(
                type="video_embeds",
                video_embeds=video_embeds,
                video_grid_thw=video_grid_thw,
            )

    def _process_video_input(
1561
1562
        self, video_input: KeyeVideoInputs
    ) -> tuple[torch.Tensor, ...]:
1563
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1567
        video_type = video_input["type"]
        video_grid_thw = video_input["video_grid_thw"]
        pixel_values_videos = video_input.get("pixel_values_videos", None)

        return tuple(
1568
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            self._process_video_embeds(video_type, video_grid_thw, pixel_values_videos)
        )
1570
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1572
1573

    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
1574
        mm_features: list[MultiModalFeatureSpec],
1575
    ) -> tuple[torch.Tensor, int]:
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1582
        kwargs = MultiModalFeatureSpec.gather_kwargs(
            mm_features,
            {"image_grid_thw", "video_grid_thw"},
        )
        image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])]
        video_grid_thw = [item.tolist() for item in kwargs.get("video_grid_thw", [])]

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        if isinstance(video_grid_thw, list) and len(video_grid_thw) > 0:
            video_grid_thw = video_grid_thw[0]

        def split_thw(grid_thw: torch.Tensor | list[int]) -> list[list[int]]:
            """
            Split grid_thw along the t dimension.

            Args:
                grid_thw: shape [N, 3] tensor or nested list of [t, h, w].

            Returns:
                List of [1, h, w] rows, repeated t times for each original row.
            """

            if isinstance(grid_thw, list):
                grid_thw = torch.tensor(grid_thw, dtype=torch.long)

            if grid_thw.numel() == 0:
                return []

            t, hw = grid_thw[:, 0], grid_thw[:, 1:]
            ones = torch.ones_like(hw[:, :1])  # [N,1]
            out = torch.cat([ones, hw], dim=1).repeat_interleave(t, dim=0)
            return out.tolist()

        video_grid_thw = split_thw(video_grid_thw)

1610
        hf_config = self.config
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        image_token_id = hf_config.image_token_id
        video_token_id = hf_config.video_token_id
        spatial_merge_size = hf_config.vision_config.spatial_merge_size

        image_nums = len(image_grid_thw)
        frame_nums = len(video_grid_thw)
        llm_pos_ids_list: list = []

        st = 0
        remain_images, remain_frames = image_nums, frame_nums

        image_index, video_index = 0, 0
        for _ in range(image_nums + frame_nums):
            if remain_images > 0:
                try:
                    ed_image = input_tokens.index(image_token_id, st)
                except ValueError:
                    ed_image = len(input_tokens) + 1
            else:
                ed_image = len(input_tokens) + 1
            if remain_frames > 0:
                try:
                    ed_video = input_tokens.index(video_token_id, st)
                except ValueError:
                    ed_video = len(input_tokens) + 1
            else:
                ed_video = len(input_tokens) + 1

            if ed_image < ed_video:
1640
                t, h, w = image_grid_thw[image_index]
1641
1642
1643
1644
                image_index += 1
                remain_images -= 1
                ed = ed_image
            else:
1645
                t, h, w = video_grid_thw[video_index]
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                video_index += 1
                remain_frames -= 1
                ed = ed_video

            llm_grid_t, llm_grid_h, llm_grid_w = (
                t,
                h // spatial_merge_size,
                w // spatial_merge_size,
            )
            text_len = ed - st

            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
            llm_pos_ids_list.append(
                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
            )

            t_index = (
                (
                    torch.arange(llm_grid_t)
                    .view(-1, 1)
                    .expand(-1, llm_grid_h * llm_grid_w)
                )
                .long()
                .flatten()
            )

            h_index = (
                torch.arange(llm_grid_h)
                .view(1, -1, 1)
                .expand(llm_grid_t, -1, llm_grid_w)
                .flatten()
            )
            w_index = (
                torch.arange(llm_grid_w)
                .view(1, 1, -1)
                .expand(llm_grid_t, llm_grid_h, -1)
                .flatten()
            )
            llm_pos_ids_list.append(
                torch.stack([t_index, h_index, w_index]) + text_len + st_idx
            )
            st = ed + llm_grid_t * llm_grid_h * llm_grid_w

        if st < len(input_tokens):
            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
            text_len = len(input_tokens) - st
            llm_pos_ids_list.append(
                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
            )

        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
        mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()

        return llm_positions, mrope_position_delta