radio.py 28 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright (c) 2023-2024, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

import math
from collections.abc import Iterable
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from dataclasses import dataclass
from itertools import accumulate, repeat
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from typing import TypeAlias
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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from transformers import PretrainedConfig

from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.intern_vit import (
    InternParallelAttention,
    InternVisionEncoder,
    InternVisionEncoderLayer,
)
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input_dim_t: TypeAlias = int | tuple[int, int]
norm_t: TypeAlias = tuple[float, float, float] | torch.Tensor
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def _ntuple(n):
    def parse(x):
        if isinstance(x, Iterable) and not isinstance(x, str):
            return tuple(x)
        return tuple(repeat(x, n))

    return parse


to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple


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def calc_seq_len(size: tuple[int, int], patch_size: int) -> int:
    h, w = size
    return (h // patch_size) * (w // patch_size)


def calc_seq_lens(sizes: list[tuple[int, int]], patch_size: int) -> list[int]:
    return [calc_seq_len(size, patch_size) for size in sizes]


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class ClsToken(nn.Module):
    def __init__(
        self,
        ndim: int,
        num_tokens: int = 1,
        enabled: bool = True,
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        register_multiple: int | None = None,
        num_registers: int | None = None,
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    ):
        super().__init__()

        self.ndim = ndim
        self.enabled = enabled
        self.num_registers = 0
        self.num_tokens = num_tokens
        if enabled:
            if num_registers:
                self.num_registers = num_registers
            elif register_multiple:
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                self.num_registers = register_multiple - (
                    num_tokens % register_multiple
                )
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            scale = ndim**-0.5
            self.token = nn.Parameter(
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                torch.randn(num_tokens + self.num_registers, ndim) * scale
            )
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        else:
            self.token = None

        self.num_patches = self.num_tokens + self.num_registers

    def forward(self, x: torch.Tensor):
        if self.token is None:
            return x

        token = self.token.unsqueeze(0).expand(x.shape[0], -1, -1)
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        x = torch.cat(
            [
                token,
                x,
            ],
            dim=1,
        )
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        return x


class ViTPatchGenerator(nn.Module):
    def __init__(
        self,
        #  config: PretrainedConfig,
        patch_size: int,
        embed_dim: int,
        input_dims: input_dim_t,
        abs_pos: bool = True,
        normalize_patches: bool = False,
        cls_token: bool = False,
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        max_input_dims: input_dim_t | None = None,
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        pos_dropout: float = 0.0,
        return_pos_enc: bool = False,
        num_cls_tokens: int = 1,
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        register_multiple: int | None = None,
        num_registers: int | None = None,
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        patch_bias: bool = False,
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        temporal_patch_size: int = 1,
        separate_video_embedder: bool = True,
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        device=None,
        dtype=None,
    ):
        super().__init__()
        if isinstance(input_dims, int):
            input_dims = (input_dims, input_dims)

        if max_input_dims is None:
            max_input_dims = input_dims
        if isinstance(max_input_dims, int):
            max_input_dims = (max_input_dims, max_input_dims)

        max_input_dims = tuple(
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            int(math.ceil(d / patch_size) * patch_size) for d in max_input_dims
        )
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        self.cpe_mode = max_input_dims != input_dims
        self.pos_dropout = pos_dropout
        self.return_pos_enc = return_pos_enc

        factory = dict(device=device, dtype=dtype)

        self.patch_size = patch_size
        self.abs_pos = abs_pos
        self.embed_dim = embed_dim
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        self.temporal_patch_size = temporal_patch_size
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        self.num_rows = max_input_dims[0] // patch_size
        self.num_cols = max_input_dims[1] // patch_size
        self.input_dims = tuple(d // patch_size for d in input_dims)
        self.num_patches = self.num_rows * self.num_cols
        self.max_input_dims = max_input_dims

        self.im_to_patches = Im2Patches(patch_size)
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        self.embedder = ViTPatchLinear(
            patch_size, embed_dim, bias=patch_bias, **factory
        )
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        if temporal_patch_size > 1:
            if not separate_video_embedder:
                raise NotImplementedError(
                    "Only separate_video_embedder=True is supported for"
                    " temporal compression (temporal_patch_size > 1)"
                )
            self.video_embedder = ViTPatchLinear(
                patch_size,
                embed_dim,
                bias=patch_bias,
                temporal_patch_size=temporal_patch_size,
                **factory,
            )
            self._video_embedder_loaded = False

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        if abs_pos:
            scale = embed_dim**-0.5
            self.pos_embed = nn.Parameter(
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                torch.randn(1, self.num_patches, embed_dim, **factory) * scale
            )
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        self.cls_token = ClsToken(
            embed_dim,
            num_tokens=num_cls_tokens,
            enabled=cls_token,
            register_multiple=register_multiple,
            num_registers=num_registers,
        )

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        self.patch_normalizer = (
            nn.LayerNorm(embed_dim) if normalize_patches else nn.Identity()
        )
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    def forward(
        self, x: torch.Tensor, imgs_sizes: list[tuple[int, int]] | None = None
    ) -> torch.Tensor:
        if imgs_sizes is not None:
            patches = self.embedder(x)
            patches, pos_enc = self.apply_pos_enc_dynamic(
                patches, imgs_sizes=imgs_sizes
            )
            patches = self.cls_token_dynamic(patches, imgs_sizes=imgs_sizes)
        else:
            patches = self.embed_patches(x)
            patches, pos_enc = self.apply_pos_enc(patches, input_size=x.shape[2:])
            patches = self.cls_token(patches)
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        patches = self.patch_normalizer(patches)
        if self.return_pos_enc:
            return patches, pos_enc
        return patches

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    def forward_video(self, x: torch.Tensor) -> torch.Tensor:
        """Process video frames with temporal compression.

        Groups T consecutive frames into tubelets before embedding.

        Args:
            x: [num_frames, 3, H, W] tensor of video frames

        Returns:
            Embedded patches with temporal compression applied.
        """
        if not self._video_embedder_loaded:
            raise ValueError(
                "Temporal compression (video_temporal_patch_size > 1) requires "
                "video_embedder weights, but they were never loaded. "
                "Ensure the checkpoint was trained with temporal compression."
            )
        T = self.temporal_patch_size
        input_size = x.shape[2:]

        patches = self.im_to_patches(x)  # [N, num_patches, 3*P*P]
        num_frames, num_spatial, feat_dim = patches.shape

        # Pad to a multiple of T by repeating the last frame so that
        # all tubelets have exactly T frames.
        num_pad_frames = (-num_frames) % T
        if num_pad_frames > 0:
            last_frame_dup = patches[-1:].expand(num_pad_frames, -1, -1)
            patches = torch.cat([patches, last_frame_dup], dim=0)

        # Group T frames per tubelet: for each spatial position, concatenate
        #   features across T consecutive frames; order follows Megatron training
        num_frames_padded = patches.shape[0]
        num_tublets = num_frames_padded // T
        patches = rearrange(
            patches,
            "(tubelets frames) spatial feat -> tubelets spatial (frames feat)",
            tubelets=num_tublets,
            frames=T,
            spatial=num_spatial,
            feat=feat_dim,
        )

        patches = self.video_embedder(patches)

        patches, pos_enc = self.apply_pos_enc(patches, input_size=input_size)

        patches = self.cls_token(patches)

        patches = self.patch_normalizer(patches)
        if self.return_pos_enc:
            return patches, pos_enc
        return patches

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    def apply_pos_enc_dynamic(
        self, patches: torch.Tensor, imgs_sizes: list[tuple[int, int]]
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        if not self.abs_pos:
            return patches, None

        current_length = 0
        pos_enc_list = []

        for size in imgs_sizes:
            seq_length = calc_seq_len(size, self.patch_size)

            img_patches = patches[:, current_length : current_length + seq_length, :]
            pos_enc = self.get_pos_enc(patches.shape[0], input_size=size)
            img_patches_with_pos = img_patches + pos_enc

            patches = torch.cat(
                [
                    patches[:, :current_length, :],
                    img_patches_with_pos,
                    patches[:, current_length + seq_length :, :],
                ],
                dim=1,
            )
            pos_enc_list.append(pos_enc)
            current_length += seq_length

        full_pos_enc = torch.cat(pos_enc_list, dim=1) if pos_enc_list else None
        return patches, full_pos_enc

    def cls_token_dynamic(
        self, patches: torch.Tensor, imgs_sizes: list[tuple[int, int]]
    ) -> torch.Tensor:
        if not self.cls_token.enabled:
            return patches

        out = []
        current_length = 0

        for seq_len in calc_seq_lens(imgs_sizes, self.patch_size):
            class_token = self.cls_token.token.unsqueeze(0).expand(
                patches.shape[0], -1, -1
            )
            out.append(class_token)
            out.append(patches[:, current_length : current_length + seq_len, :])
            current_length += seq_len

        return torch.cat(out, dim=1)

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    @property
    def apply_cls_token(self):
        return self.cls_token.enabled

    @property
    def num_cls_tokens(self):
        return self.cls_token.num_tokens

    @property
    def num_cls_patches(self):
        return self.cls_token.num_patches

    @property
    def num_registers(self):
        return self.cls_token.num_registers

    @property
    def num_skip(self):
        return self.num_cls_tokens + self.num_registers

    def _load_embed(self, src_embed: torch.Tensor, targ_embed: nn.Parameter):
        if src_embed.shape != targ_embed.shape:
            src_size = int(math.sqrt(src_embed.shape[1]))

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            assert src_size**2 == src_embed.shape[1], (
                "Unable to interpolate non-square embedding"
            )

            src_embed = rearrange(
                src_embed, "b (h w) c -> b c h w", h=src_size, w=src_size
            )
            src_embed = F.interpolate(
                src_embed,
                size=(self.num_rows, self.num_cols),
                mode="bicubic",
                align_corners=True,
                antialias=False,
            )
            src_embed = rearrange(src_embed, "b c h w -> b (h w) c")
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        targ_embed.data.copy_(src_embed)

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    def _load_projection(
        self, src_proj_weight: torch.Tensor, targ_proj_weight: torch.Tensor
    ):
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        if src_proj_weight.shape != targ_proj_weight.shape:
            src_patch_size = int(math.sqrt(src_proj_weight.shape[1] // 3))

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            assert (src_patch_size**2) * 3 == src_proj_weight.shape[1], (
                "Unable to interpolate non-square patch size"
            )

            src_proj_weight = rearrange(
                src_proj_weight,
                "b (c h w) -> b c h w",
                c=3,
                h=src_patch_size,
                w=src_patch_size,
            )
            src_proj_weight = F.interpolate(
                src_proj_weight,
                size=(self.patch_size, self.patch_size),
                mode="bicubic",
                align_corners=True,
                antialias=False,
            )
            src_proj_weight = rearrange(src_proj_weight, "b c h w -> b (c h w)")
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        targ_proj_weight.data.copy_(src_proj_weight)

    def embed_patches(self, x: torch.Tensor) -> torch.Tensor:
        patches = self.im_to_patches(x)
        patches = self.embedder(patches)
        return patches

    def apply_pos_enc(
        self,
        patches: torch.Tensor,
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        patch_idxs: torch.Tensor | None = None,
        input_size: tuple[int, int] | None = None,
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    ) -> torch.Tensor:
        if not self.abs_pos:
            return patches

        pos_enc = self.get_pos_enc(patches.shape[0], patch_idxs, input_size)

        if self.training and self.pos_dropout > 0:
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            keeps = (
                torch.rand(
                    patches.shape[0], 1, 1, dtype=pos_enc.dtype, device=pos_enc.device
                )
                > self.pos_dropout
            )
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            pos_enc_drop = torch.where(keeps, pos_enc, 0)
        else:
            pos_enc_drop = pos_enc

        return patches + pos_enc_drop, pos_enc

    def get_pos_enc(
        self,
        batch_size: int,
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        patch_idxs: torch.Tensor | None = None,
        input_size: tuple[int, int] | None = None,
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    ) -> torch.Tensor:
        if input_size is None:
            input_dims = self.input_dims
        else:
            input_dims = tuple(d // self.patch_size for d in input_size)

        pos_embed = self._get_pos_embeddings(batch_size, input_dims)

        if patch_idxs is None:
            return pos_embed

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        exp_patch_idxs = patch_idxs.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1])
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        pos_embed = torch.gather(
            pos_embed.expand(patch_idxs.shape[0], -1, -1), dim=1, index=exp_patch_idxs
        )
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        return pos_embed

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    def _get_pos_embeddings(self, batch_size: int, input_dims: tuple[int, int]):
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        if (self.num_rows, self.num_cols) == input_dims:
            return self.pos_embed

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        pos_embed = self.pos_embed.reshape(1, self.num_rows, self.num_cols, -1).permute(
            0, 3, 1, 2
        )
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        def window_select(pos_embed):
            if input_dims[0] < pos_embed.shape[-2]:
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                pos_embed = pos_embed[..., : input_dims[0], :]
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            if input_dims[1] < pos_embed.shape[-1]:
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                pos_embed = pos_embed[..., :, : input_dims[1]]
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            return pos_embed

        if self.cpe_mode:
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            max_dim = max(input_dims)
            pos_embed = F.interpolate(
                pos_embed.float(),
                size=(max_dim, max_dim),
                align_corners=False,
                mode="bilinear",
            ).to(pos_embed.dtype)
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            pos_embed = window_select(pos_embed)
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        else:
            pos_embed = window_select(pos_embed)

        if pos_embed.shape[-2:] != input_dims:
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            pos_embed = F.interpolate(
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                pos_embed.float(), size=input_dims, align_corners=False, mode="bilinear"
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            ).to(pos_embed.dtype)
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        pos_embed = pos_embed.flatten(2).permute(0, 2, 1)

        return pos_embed


class Im2Patches(nn.Module):
    def __init__(self, patch_size: int):
        super().__init__()
        self.patch_size = patch_size

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.patch_size == 1:
            patches = x.flatten(2)
            patches = patches.permute(0, 2, 1)
            return patches

        py = x.shape[-2] // self.patch_size
        px = x.shape[-1] // self.patch_size
        patches = rearrange(
            x,
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            "b c (py yy) (px xx) -> b (py px) (c yy xx)",
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            py=py,
            yy=self.patch_size,
            px=px,
            xx=self.patch_size,
        )
        return patches


class ViTPatchLinear(nn.Linear):
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    def __init__(
        self,
        patch_size: int,
        embed_dim: int,
        bias: bool = False,
        temporal_patch_size: int = 1,
        **factory,
    ):
        super().__init__(
            3 * temporal_patch_size * (patch_size**2), embed_dim, bias=bias, **factory
        )
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        self.patch_size = patch_size
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        self.temporal_patch_size = temporal_patch_size
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@dataclass(frozen=True, kw_only=True)
class MaskMetadata:
    cu_seqlens: torch.Tensor
    max_seqlen: torch.Tensor


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class RadioParallelAttention(InternParallelAttention):
    def forward(
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        self, x: torch.Tensor, mask_meta: MaskMetadata | None = None
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    ) -> torch.Tensor:
        qkv, _ = self.qkv(x)
        q, k, v = qkv.chunk(3, dim=-1)

        if self.qk_normalization:
            q, k = self._apply_qk_norm(q, k)

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        cu_seqlens, max_seqlen = None, None
        if mask_meta is not None:
            cu_seqlens = mask_meta.cu_seqlens
            max_seqlen = mask_meta.max_seqlen
        out = self.attn(q, k, v, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
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        out, _ = self.proj(out)
        return out


class RadioVisionEncoderLayer(InternVisionEncoderLayer):
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, attn_cls=RadioParallelAttention, **kwargs)

    def forward(
        self,
        hidden_states: torch.Tensor,
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        mask_meta: MaskMetadata | None = None,
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    ):
        hidden_states = (
            hidden_states
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            + self.attn(self.norm1(hidden_states), mask_meta=mask_meta) * self.ls1
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        )

        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) * self.ls2

        return hidden_states


class RadioVisionEncoder(InternVisionEncoder):
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, layer_cls=RadioVisionEncoderLayer, **kwargs)

    def forward(
        self,
        inputs_embeds: torch.Tensor,
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        mask_meta: MaskMetadata | None = None,
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    ):
        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
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            hidden_states = encoder_layer(hidden_states, mask_meta=mask_meta)
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        return hidden_states


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class RadioInternVisionModel(nn.Module):
    packed_modules_mapping = {
        "qkv": ["qkv"],
    }

    def __init__(
        self,
        config: PretrainedConfig = None,
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        quant_config: QuantizationConfig | None = None,
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        *,
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        num_hidden_layers_override: int | None = None,
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        num_dummy_heads: int = 0,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.img_size, self.grid_size, self.num_patches = self._init_img_size(
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            to_2tuple(config.patch_size), config.image_size
        )
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        max_img_size = int(
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            round(config.cpe_max_size / config.patch_size) * config.patch_size
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        )
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        self.temporal_patch_size = config.video_temporal_patch_size
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        unique_teachers = set(t["name"] for t in config.teachers)
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        self.patch_generator = ViTPatchGenerator(
            config.patch_size,
            config.hidden_size,
            input_dims=self.img_size,
            max_input_dims=max_img_size,
            cls_token=True,
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            num_cls_tokens=len(unique_teachers) if config.cls_token_per_teacher else 1,
            register_multiple=config.register_multiple,
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            temporal_patch_size=self.temporal_patch_size,
            separate_video_embedder=config.separate_video_embedder,
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        )
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        self.encoder = RadioVisionEncoder(
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            config=config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            num_dummy_heads=num_dummy_heads,
            prefix=f"{prefix}.encoder",
        )

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    def _init_img_size(self, patch_size, img_size: int | tuple[int, int]):
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        if img_size is None:
            return None, None, None
        img_size = to_2tuple(img_size)
        grid_size = tuple([s // p for s, p in zip(img_size, patch_size)])
        num_patches = grid_size[0] * grid_size[1]
        return img_size, grid_size, num_patches

    def get_input_embeddings(self):
        return self.embeddings

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    def inter_image_mask_metadata(
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        self, imgs_sizes: list[tuple[int, int]], device: torch.device
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    ) -> MaskMetadata:
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        """Build mask metadata from image pixel sizes. Adds num_skip to each
        sequence length (cls/register tokens) to match patch generator output."""
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        patch_size = self.patch_generator.patch_size
        num_skip = self.patch_generator.num_skip

        seq_lens = calc_seq_lens(imgs_sizes, patch_size)
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        adjusted = [s + num_skip for s in seq_lens]
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        return self._inter_image_mask_metadata_from_seq_lens(adjusted, device=device)

    def _inter_image_mask_metadata_from_seq_lens(
        self, seq_lens: list[int], device: torch.device
    ) -> MaskMetadata:
        """Build mask metadata from actual sequence lengths (already including
        cls/register tokens, i.e. patch_count + num_skip per item).
        Use inter_image_mask_metadata() when you only have imgs_sizes."""
        assert len(seq_lens) > 0
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        cu_seqlens = torch.tensor(
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            list(accumulate(seq_lens, initial=0)), dtype=torch.int32, device=device
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        )
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        # Keep max_seqlen on CPU to avoid .item() sync
        # See: https://github.com/vllm-project/vllm/blob/20b6b01/vllm/v1/attention/ops/vit_attn_wrappers.py#L48
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        max_seqlen = torch.tensor(max(seq_lens), dtype=torch.int32)
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        return MaskMetadata(cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
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    def forward(
        self,
        x: torch.Tensor,
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        imgs_sizes: list[tuple[int, int]] | None = None,
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        num_frames: int | None = None,
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    ) -> torch.FloatTensor:
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        T = self.temporal_patch_size

        # Build packed-sequence metadata for MMEncoderAttention when needed.
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        mask_meta = None
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        packed_batch_size = None  # Original batch size before packing

        if num_frames is not None and T > 1:
            # Conv3d video: all tubelets have the same sequence length.
            # Pack [num_tubelets, seq_per_tubelet, hidden] → [1, total, hidden]
            hidden_states = self.patch_generator.forward_video(x)
            packed_batch_size, seq_per_tubelet, hidden_dim = hidden_states.shape
            hidden_states = hidden_states.reshape(1, -1, hidden_dim)
            mask_meta = self._inter_image_mask_metadata_from_seq_lens(
                [seq_per_tubelet] * packed_batch_size, device=hidden_states.device
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            )
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        else:
            # Images for any model, or video for non-conv3d model
            hidden_states = self.patch_generator(x, imgs_sizes=imgs_sizes)
            if imgs_sizes is not None and len(imgs_sizes) > 1:
                # Dynamic resolution w/ > 1 image, create attn mask
                mask_meta = self.inter_image_mask_metadata(
                    imgs_sizes, device=hidden_states.device
                )

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        encoder_outputs = self.encoder(inputs_embeds=hidden_states, mask_meta=mask_meta)
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        # Unpack back to original batch shape if we packed for video
        if packed_batch_size is not None:
            encoder_outputs = encoder_outputs.reshape(
                packed_batch_size, seq_per_tubelet, -1
            )

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        return encoder_outputs


class RadioModel(nn.Module):
    packed_modules_mapping = {
        "qkv": ["qkv"],
    }

    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None = None,
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        *,
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        num_hidden_layers_override: int | None = None,
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        num_dummy_heads: int = 0,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.model = RadioInternVisionModel(
            config=config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            num_dummy_heads=num_dummy_heads,
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            prefix=prefix,
        )
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        summary_idxs = None
        if config.teachers:
            summary_idxs = torch.tensor(
                [i for i, t in enumerate(config.teachers) if t.get("use_summary", True)]
            )
            if summary_idxs.numel() > 0:
                self.register_buffer("summary_idxs", summary_idxs)
        self.summary_idxs = summary_idxs

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    def forward(
        self,
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        pixel_values: torch.Tensor | None = None,
        pixel_embeds: torch.Tensor | None = None,
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        *,
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        imgs_sizes: list[tuple[int, int]] | None = None,
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        num_frames: int | None = None,
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    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
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        y = self.model(
            pixel_values,
            imgs_sizes=imgs_sizes,
            num_frames=num_frames,
        )
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        return self._extract_final(y, imgs_sizes=imgs_sizes)
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    def load_weights(self, weights) -> set[str]:
        loaded_params: set[str] = set()
        params_dict = dict(self.named_parameters())

        if isinstance(weights, dict):
            weights_list = list(weights.items())
        else:
            weights_list = list(weights)

        for name, weight in weights_list:
            if not name.startswith("radio_model."):
                # Skip non-radio weights
                continue

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            sub = name[len("radio_model.") :]  # drop "radio_model." prefix
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            # Skip buffers not used in vLLM
            if sub in {"summary_idxs"}:
                continue
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            if sub.startswith("input_conditioner."):
                # we normalize in the input processor,
                # based on norm and std values from the config
                continue
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            vllm_key = None
            if sub.startswith("model.patch_generator."):
                vllm_key = f"model.patch_generator.{sub.split('.', 2)[-1]}"
            elif sub.startswith("input_conditioner."):
                vllm_key = f"input_conditioner.{sub.split('.', 1)[-1]}"
            elif sub.startswith("model.blocks."):
                # Encoder blocks: HF 'model.blocks.{i}.' ->
                # vLLM 'model.encoder.layers.{i}.'
                parts = sub.split(".")
                if len(parts) >= 4:
                    layer_idx = parts[2]
                    suffix = ".".join(parts[3:])
                    # Skip layer-scale entries that vLLM doesn't use
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                    if suffix in {"ls1", "ls2"} or suffix.startswith(("ls1.", "ls2.")):
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                        continue
                    vllm_key = f"model.encoder.layers.{layer_idx}.{suffix}"

            if vllm_key and vllm_key in params_dict:
                param = params_dict[vllm_key]
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                weight_loader = getattr(param, "weight_loader", default_weight_loader)
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                weight_loader(param, weight)
                loaded_params.add(vllm_key)

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        if "model.patch_generator.video_embedder.weight" in loaded_params:
            self.model.patch_generator._video_embedder_loaded = True

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        return loaded_params

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    def _extract_final(
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        self, y: torch.Tensor, imgs_sizes: list[tuple[int, int]] | None = None
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    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
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        # Remove CLS + REGISTERS tokens
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        num_skip = self.model.patch_generator.num_skip
        patch_size = self.model.patch_generator.patch_size
        num_cls_tokens = self.model.patch_generator.num_cls_tokens
        if imgs_sizes is None:
            all_summary = y[:, :num_cls_tokens]
            all_feat = y[:, num_skip:]
        else:
            all_patches = []
            summaries = []
            current_pos = 0
            for num_patches in calc_seq_lens(imgs_sizes, patch_size):
                patches = y[
                    :, current_pos + num_skip : current_pos + num_skip + num_patches, :
                ]
                all_patches.append(patches)
                summary = y[:, current_pos : current_pos + num_cls_tokens, :]
                summaries.append(summary)
                current_pos += num_skip + num_patches
            all_summary = torch.cat(summaries, dim=1)
            all_feat = torch.cat(all_patches, dim=1)

        if self.summary_idxs is not None:
            bb_summary = all_summary[:, self.summary_idxs]
        else:
            bb_summary = all_summary
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        return bb_summary.flatten(1), all_feat