radio.py

# 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
from dataclasses import dataclass
from itertools import accumulate, repeat
from typing import TypeAlias

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
from vllm.model_executor.models.intern_vit import (
    InternParallelAttention,
    InternVisionEncoder,
    InternVisionEncoderLayer,
)

input_dim_t: TypeAlias = int | tuple[int, int]
norm_t: TypeAlias = tuple[float, float, float] | torch.Tensor


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


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]


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

            scale = ndim**-0.5
            self.token = nn.Parameter(
                torch.randn(num_tokens + self.num_registers, ndim) * scale
            )

        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)
        x = torch.cat(
            [
                token,
                x,
            ],
            dim=1,
        )

        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,
        max_input_dims: input_dim_t | None = None,
        pos_dropout: float = 0.0,
        return_pos_enc: bool = False,
        num_cls_tokens: int = 1,
        register_multiple: int | None = None,
        num_registers: int | None = None,
        patch_bias: bool = False,
        temporal_patch_size: int = 1,
        separate_video_embedder: bool = True,
        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(
            int(math.ceil(d / patch_size) * patch_size) for d in max_input_dims
        )

        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
        self.temporal_patch_size = temporal_patch_size

        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)
        self.embedder = ViTPatchLinear(
            patch_size, embed_dim, bias=patch_bias, **factory
        )

        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,
            )

        if abs_pos:
            scale = embed_dim**-0.5
            self.pos_embed = nn.Parameter(
                torch.randn(1, self.num_patches, embed_dim, **factory) * scale
            )

        self.cls_token = ClsToken(
            embed_dim,
            num_tokens=num_cls_tokens,
            enabled=cls_token,
            register_multiple=register_multiple,
            num_registers=num_registers,
        )

        self.patch_normalizer = (
            nn.LayerNorm(embed_dim) if normalize_patches else nn.Identity()
        )

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

    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.
        """
        assert self.temporal_patch_size > 1
        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

    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)

    @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]))

            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")
        targ_embed.data.copy_(src_embed)

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

            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)")
        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,
        patch_idxs: torch.Tensor | None = None,
        input_size: tuple[int, int] | None = None,
    ) -> 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:
            keeps = (
                torch.rand(
                    patches.shape[0], 1, 1, dtype=pos_enc.dtype, device=pos_enc.device
                )
                > self.pos_dropout
            )
            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,
        patch_idxs: torch.Tensor | None = None,
        input_size: tuple[int, int] | None = None,
    ) -> 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

        exp_patch_idxs = patch_idxs.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1])

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

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

        pos_embed = self.pos_embed.reshape(1, self.num_rows, self.num_cols, -1).permute(
            0, 3, 1, 2
        )

        def window_select(pos_embed):
            if input_dims[0] < pos_embed.shape[-2]:
                pos_embed = pos_embed[..., : input_dims[0], :]
            if input_dims[1] < pos_embed.shape[-1]:
                pos_embed = pos_embed[..., :, : input_dims[1]]
            return pos_embed

        if self.cpe_mode:
            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)

            pos_embed = window_select(pos_embed)
        else:
            pos_embed = window_select(pos_embed)

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

        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,
            "b c (py yy) (px xx) -> b (py px) (c yy xx)",
            py=py,
            yy=self.patch_size,
            px=px,
            xx=self.patch_size,
        )
        return patches


class ViTPatchLinear(nn.Linear):
    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
        )
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size


@dataclass(frozen=True, kw_only=True)
class MaskMetadata:
    cu_seqlens: torch.Tensor
    max_seqlen: torch.Tensor


class RadioParallelAttention(InternParallelAttention):
    def forward(
        self, x: torch.Tensor, mask_meta: MaskMetadata | None = None
    ) -> 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)

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

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


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

    def __init__(
        self,
        config: PretrainedConfig = None,
        quant_config: QuantizationConfig | None = None,
        *,
        num_hidden_layers_override: int | None = None,
        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(
            to_2tuple(config.patch_size), config.image_size
        )
        max_img_size = int(
            round(config.cpe_max_size / config.patch_size) * config.patch_size
        )
        self.temporal_patch_size = config.video_temporal_patch_size
        unique_teachers = set(t["name"] for t in config.teachers)
        self.patch_generator = ViTPatchGenerator(
            config.patch_size,
            config.hidden_size,
            input_dims=self.img_size,
            max_input_dims=max_img_size,
            cls_token=True,
            num_cls_tokens=len(unique_teachers) if config.cls_token_per_teacher else 1,
            register_multiple=config.register_multiple,
            temporal_patch_size=self.temporal_patch_size,
            separate_video_embedder=config.separate_video_embedder,
        )

        self.encoder = RadioVisionEncoder(
            config=config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            num_dummy_heads=num_dummy_heads,
            prefix=f"{prefix}.encoder",
        )

    def _init_img_size(self, patch_size, img_size: int | tuple[int, int]):
        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

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

        seq_lens = calc_seq_lens(imgs_sizes, patch_size)
        adjusted = [s + num_skip for s in seq_lens]
        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
        cu_seqlens = torch.tensor(
            list(accumulate(seq_lens, initial=0)), dtype=torch.int32, device=device
        )
        # 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
        max_seqlen = torch.tensor(max(seq_lens), dtype=torch.int32)
        return MaskMetadata(cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)

    def forward(
        self,
        x: torch.Tensor,
        imgs_sizes: list[tuple[int, int]] | None = None,
        num_frames: int | None = None,
    ) -> torch.FloatTensor:
        T = self.temporal_patch_size

        # Build packed-sequence metadata for MMEncoderAttention when needed.
        mask_meta = None
        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
            )
        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
                )

        encoder_outputs = self.encoder(inputs_embeds=hidden_states, mask_meta=mask_meta)

        # 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
            )

        return encoder_outputs


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

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig | None = None,
        *,
        num_hidden_layers_override: int | None = None,
        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,
            prefix=prefix,
        )

        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

    def forward(
        self,
        pixel_values: torch.Tensor | None = None,
        pixel_embeds: torch.Tensor | None = None,
        *,
        imgs_sizes: list[tuple[int, int]] | None = None,
        num_frames: int | None = None,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
        y = self.model(
            pixel_values,
            imgs_sizes=imgs_sizes,
            num_frames=num_frames,
        )
        return self._extract_final(y, imgs_sizes=imgs_sizes)

    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

            sub = name[len("radio_model.") :]  # drop "radio_model." prefix

            # Skip buffers not used in vLLM
            if sub in {"summary_idxs"}:
                continue
            if sub.startswith("input_conditioner."):
                # we normalize in the input processor,
                # based on norm and std values from the config
                continue

            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
                    if suffix in {"ls1", "ls2"} or suffix.startswith(("ls1.", "ls2.")):
                        continue
                    vllm_key = f"model.encoder.layers.{layer_idx}.{suffix}"

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

        return loaded_params

    def _extract_final(
        self, y: torch.Tensor, imgs_sizes: list[tuple[int, int]] | None = None
    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
        # Remove CLS + REGISTERS tokens
        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
        return bb_summary.flatten(1), all_feat