aphrodite-engine/aphrodite/modeling/models/qwen2_vl.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
# Copyright 2024 The Qwen team.
# Copyright 2023 The PygmalionAI team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
from functools import lru_cache, partial
from typing import (Iterable, List, Mapping, Optional, Tuple, Type, TypedDict,
                    Union)

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from loguru import logger
from PIL import Image
from transformers.image_utils import (get_image_size,
                                      infer_channel_dimension_format,
                                      to_numpy_array)
from transformers.models.qwen2_vl.image_processing_qwen2_vl import (
    make_batched_images, make_batched_videos, smart_resize)

import aphrodite.common.envs as envs
from aphrodite.attention import AttentionMetadata
from aphrodite.attention.selector import (_Backend, backend_name_to_enum,
                                          get_global_forced_attn_backend)
from aphrodite.common.config import CacheConfig, MultiModalConfig
from aphrodite.common.logger import log_once
from aphrodite.common.sequence import IntermediateTensors, SequenceData
from aphrodite.distributed import get_pp_group, parallel_state
from aphrodite.distributed import utils as dist_utils
from aphrodite.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from aphrodite.modeling.layers.activation import QuickGELU
from aphrodite.modeling.layers.linear import (ColumnParallelLinear,
                                              RowParallelLinear)
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.sampler import Sampler, SamplerOutput
from aphrodite.modeling.layers.vocab_parallel_embedding import ParallelLMHead
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.models.interfaces import SupportsMultiModal
from aphrodite.modeling.models.qwen2 import Qwen2Model
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
                                  MultiModalInputs)
from aphrodite.multimodal.base import MultiModalData
from aphrodite.multimodal.image import cached_get_image_processor
from aphrodite.platforms import current_platform
from aphrodite.quantization import QuantizationConfig
from aphrodite.transformers_utils.configs import (Qwen2VLConfig,
                                                  Qwen2VLVisionConfig)
from aphrodite.transformers_utils.processor import get_processor

from .utils import (PPMissingLayer, is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory)


# === Vision Inputs === #
class Qwen2VLImageInputs(TypedDict):
    pixel_values: torch.Tensor
    """Shape: 
    `(num_patches, num_channels * patch_size * patch_size)`
    """
    image_grid_thw: torch.Tensor
    """Shape: `(num_images, 3)`
    
    This should be in `(grid_t, grid_h, grid_w)` format.
    """


class Qwen2VLVideoInputs(TypedDict):
    pixel_values_videos: torch.Tensor
    """Shape: 
    `(num_patches, 
      num_channels * temporal_patch_size * patch_size * patch_size)`
    """
    video_grid_thw: torch.Tensor
    """Shape: `(num_videos, 3)`
    
    This should be in `(grid_t, grid_h, grid_w)` format.
    """


# === Vision Encoder === #
class Qwen2VisionMLP(nn.Module):
    def __init__(
        self,
        in_features: int,
        hidden_features: int = None,
        act_layer: Type[nn.Module] = QuickGELU,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.fc1 = ColumnParallelLinear(
            in_features, hidden_features, quant_config=quant_config
        )
        self.act = act_layer()
        self.fc2 = RowParallelLinear(
            hidden_features, in_features, quant_config=quant_config
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x_parallel, _ = self.fc1(x)
        x_parallel = self.act(x_parallel)
        x, _ = self.fc2(x_parallel)
        return x


def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
    if not interleaved:
        x1, x2 = x.chunk(2, dim=-1)
        return torch.cat((-x2, x1), dim=-1)
    else:
        x1, x2 = x[..., ::2], x[..., 1::2]
        return rearrange(
            torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
        )


def apply_rotary_emb_torch(
    x: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
    interleaved: bool = False,
) -> torch.Tensor:
    """
    x: (batch_size, seqlen, nheads, headdim)
    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
    """
    ro_dim = cos.shape[-1] * 2
    assert ro_dim <= x.shape[-1]
    cos = repeat(
        cos,
        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)",
    )
    sin = repeat(
        sin,
        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)",
    )
    return torch.cat(
        [
            x[..., :ro_dim] * cos
            + rotate_half(x[..., :ro_dim], interleaved) * sin,
            x[..., ro_dim:],
        ],
        dim=-1,
    )


def apply_rotary_pos_emb_vision(
    t: torch.Tensor, freqs: torch.Tensor
) -> torch.Tensor:
    t_ = t.float()
    cos = freqs.cos()
    sin = freqs.sin()
    output = apply_rotary_emb_torch(t_, cos, sin).type_as(t)
    return output


class Qwen2VisionAttention(nn.Module):
    def __init__(
        self,
        embed_dim: Optional[int] = None,
        num_heads: Optional[int] = None,
        projection_size: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        # Per attention head and per partition values.
        world_size = parallel_state.get_tensor_model_parallel_world_size()
        self.hidden_size_per_attention_head = dist_utils.divide(
            projection_size, num_heads
        )
        self.num_attention_heads_per_partition = dist_utils.divide(
            num_heads, world_size
        )
        self.qkv = ColumnParallelLinear(
            input_size=embed_dim,
            output_size=3 * projection_size,
            quant_config=quant_config,
        )
        self.proj = RowParallelLinear(
            input_size=projection_size,
            output_size=embed_dim,
            quant_config=quant_config,
        )
        # Detect attention implementation.
        selected_backend: Optional[_Backend] = get_global_forced_attn_backend()
        if selected_backend is None:
            backend_by_env_var: Optional[str] = envs.APHRODITE_ATTENTION_BACKEND
            if backend_by_env_var is not None:
                selected_backend = backend_name_to_enum(backend_by_env_var)
        if selected_backend is None:
            # For Volta and Turing GPUs, use xformers instead.
            device_available = current_platform.get_device_capability()[0] >= 8
            if device_available:
                from transformers.utils import is_flash_attn_2_available

                if is_flash_attn_2_available():
                    self._use_flash_attn = True
                else:
                    log_once(
                    level="WARNING",
                    message=
                        "Current Qwen2-VL implementation has a bug with "
                        "`aphrodite-flash-attn` inside vision module, so we use"
                        " xformers backend instead. You can run `pip install "
                        "flash-attn to use flash-attention backend."
                    )
                    self._use_flash_attn = False
            else:
                self._use_flash_attn = False
        else:
            if selected_backend == _Backend.FLASH_ATTN:
                self._use_flash_attn = True
            elif selected_backend == _Backend.XFORMERS:
                self._use_flash_attn = False
            else:
                raise RuntimeError(
                    f"Qwen2-VL does not support {selected_backend} backend now."
                )

    def forward(
        self,
        x: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: torch.Tensor = None,
    ) -> torch.Tensor:
        # [s, b, c] --> [s, b, head * 3 * head_dim]
        x, _ = self.qkv(x)
        # [s, b, head * 3 * head_dim] --> [s, b, head, 3 * head_dim]
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads_per_partition,
            3 * self.hidden_size_per_attention_head,
        )
        x = x.view(*new_x_shape)
        # [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim]
        q, k, v = dist_utils.split_tensor_along_last_dim(x, 3)
        batch_size = q.shape[1]
        q, k, v = [
            rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
        ]
        if rotary_pos_emb is not None:
            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
        if self._use_flash_attn:
            # from aphrodite_flash_attn.flash_attn_interface import (
            #   flash_attn_varlen_func)
            from flash_attn import flash_attn_varlen_func

            q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
            output = flash_attn_varlen_func(
                q,
                k,
                v,
                cu_seqlens_q=cu_seqlens,
                cu_seqlens_k=cu_seqlens,
                max_seqlen_q=max_seqlen,
                max_seqlen_k=max_seqlen,
                dropout_p=0,
                causal=False,
            )
            context_layer = rearrange(
                output, "(b s) ... -> b s ...", b=batch_size
            )
        else:
            from xformers import ops as xops
            from xformers.ops.fmha.attn_bias import BlockDiagonalMask

            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
            attn_bias = BlockDiagonalMask.from_seqlens(
                q_seqlen=seqlens, kv_seqlen=None
            )
            context_layer = xops.memory_efficient_attention_forward(
                q, k, v, attn_bias=attn_bias, p=0, scale=None
            )
        context_layer = rearrange(
            context_layer, "b s h d -> s b (h d)"
        ).contiguous()
        output, _ = self.proj(context_layer)
        return output


class Qwen2VisionBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float,
        act_layer: Type[nn.Module] = QuickGELU,
        norm_layer: Type[nn.Module] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.norm1 = norm_layer(dim)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.attn = Qwen2VisionAttention(
            embed_dim=dim,
            num_heads=num_heads,
            projection_size=dim,
            quant_config=quant_config,
        )
        self.mlp = Qwen2VisionMLP(
            dim, mlp_hidden_dim, act_layer=act_layer, quant_config=quant_config
        )

    def forward(
        self,
        x: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: torch.Tensor,
    ) -> torch.Tensor:
        x = x + self.attn(
            self.norm1(x), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
        )
        x = x + self.mlp(self.norm2(x))
        return x


class Qwen2VisionPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
        in_chans: int = 3,
        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.embed_dim = embed_dim
        kernel_size = [temporal_patch_size, patch_size, patch_size]
        self.proj = nn.Conv3d(
            in_chans,
            embed_dim,
            kernel_size=kernel_size,
            stride=kernel_size,
            bias=False,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        L, C = x.shape
        x = x.view(
            L, -1, self.temporal_patch_size, self.patch_size, self.patch_size
        )
        x = self.proj(x).view(L, self.embed_dim)
        return x


class Qwen2VisionPatchMerger(nn.Module):
    def __init__(
        self,
        d_model: int,
        context_dim: int,
        norm_layer: Type[nn.Module] = None,
        spatial_merge_size: int = 2,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.ln_q = norm_layer(context_dim)
        self.mlp = nn.ModuleList(
            [
                ColumnParallelLinear(
                    self.hidden_size,
                    self.hidden_size,
                    bias=True,
                    quant_config=quant_config,
                ),
                nn.GELU(),
                RowParallelLinear(
                    self.hidden_size,
                    d_model,
                    bias=True,
                    quant_config=quant_config,
                ),
            ]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.ln_q(x)
        x = x.view(-1, self.hidden_size)
        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
        x_parallel, _ = mlp_fc1(x)
        x_parallel = mlp_act(x_parallel)
        out, _ = mlp_fc2(x_parallel)
        return out


class Qwen2VisionRotaryEmbedding(nn.Module):
    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
        inv_freq = 1.0 / (
            theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)
        )
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._seq_len_cached = 0
        self._freqs_cached = None

    def update_freqs_cache(self, seqlen: int) -> None:
        if seqlen > self._seq_len_cached:
            seqlen *= 2
            self._seq_len_cached = seqlen
            self.inv_freq = 1.0 / (
                self.theta
                ** (
                    torch.arange(
                        0,
                        self.dim,
                        2,
                        dtype=torch.float,
                        device=self.inv_freq.device,
                    )
                    / self.dim
                )
            )
            seq = torch.arange(
                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
            )
            freqs = torch.outer(seq, self.inv_freq)
            self._freqs_cached = freqs

    def forward(self, seqlen: int) -> torch.Tensor:
        self.update_freqs_cache(seqlen)
        return self._freqs_cached[:seqlen]


class Qwen2VisionTransformer(nn.Module):
    def __init__(
        self,
        vision_config: Qwen2VLVisionConfig,
        norm_eps: float = 1e-6,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        patch_size: int = vision_config.patch_size
        temporal_patch_size: int = vision_config.temporal_patch_size
        spatial_merge_size: int = vision_config.spatial_merge_size
        in_chans: int = vision_config.in_chans
        hidden_size: int = vision_config.hidden_size
        embed_dim: int = vision_config.embed_dim
        depth: int = vision_config.depth
        num_heads: int = vision_config.num_heads
        mlp_ratio: float = vision_config.mlp_ratio
        self.spatial_merge_size = spatial_merge_size
        self.patch_embed = Qwen2VisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )
        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
        head_dim = embed_dim // num_heads
        self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
        self.blocks = nn.ModuleList(
            [
                Qwen2VisionBlock(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    norm_layer=norm_layer,
                    quant_config=quant_config,
                )
                for _ in range(depth)
            ]
        )
        self.merger = Qwen2VisionPatchMerger(
            d_model=hidden_size,
            context_dim=embed_dim,
            norm_layer=norm_layer,
            quant_config=quant_config,
        )

    @property
    def dtype(self) -> torch.dtype:
        return self.blocks[0].mlp.fc2.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.blocks[0].mlp.fc2.weight.device

    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
        pos_ids = []
        for t, h, w in grid_thw:
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            hpos_ids = (
                hpos_ids.reshape(
                    h // self.spatial_merge_size,
                    self.spatial_merge_size,
                    w // self.spatial_merge_size,
                    self.spatial_merge_size,
                )
                .permute(0, 2, 1, 3)
                .flatten()
            )
            wpos_ids = (
                wpos_ids.reshape(
                    h // self.spatial_merge_size,
                    self.spatial_merge_size,
                    w // self.spatial_merge_size,
                    self.spatial_merge_size,
                )
                .permute(0, 2, 1, 3)
                .flatten()
            )
            pos_ids.append(
                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
            )
        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_thw[:, 1:].max()
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
        return rotary_pos_emb

    def forward(
        self,
        x: torch.Tensor,
        grid_thw: torch.Tensor,
    ) -> torch.Tensor:
        # patchify
        x = x.to(device=self.device, dtype=self.dtype)
        x = self.patch_embed(x)
        # compute position embedding
        rotary_pos_emb = self.rot_pos_emb(grid_thw)
        # compute cu_seqlens
        cu_seqlens = torch.repeat_interleave(
            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
        ).cumsum(dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
        # transformers
        x = x.unsqueeze(1)
        for blk in self.blocks:
            x = blk(x, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)
        # adapter
        x = self.merger(x)
        return x


# === Vision input helpers === #
cached_get_processor = lru_cache(get_processor)


def mm_input_mapper_for_qwen2_vl(
    ctx: InputContext,
    data: MultiModalData[object],
    data_type_key: str,
) -> MultiModalInputs:
    """Input mapper for Qwen2-VL."""
    model_config = ctx.model_config
    image_processor = cached_get_image_processor(
        model_config.model, trust_remote_code=model_config.trust_remote_code
    )
    if image_processor is None:
        raise RuntimeError(
            "No HuggingFace processor is available "
            "to process the image object"
        )
    images = None
    videos = None
    if data_type_key == "image":
        images = data
    else:
        assert data_type_key == "video"
        videos = data
    try:
        batch_data = image_processor.preprocess(
            images=images, videos=videos, return_tensors="pt"
        ).data
    except Exception:
        logger.error(f"Failed to process image ({data})")
        raise
    return MultiModalInputs(batch_data)


image_input_mapper_for_qwen2_vl = partial(
    mm_input_mapper_for_qwen2_vl, data_type_key="image"
)
video_input_mapper_for_qwen2_vl = partial(
    mm_input_mapper_for_qwen2_vl, data_type_key="video"
)


def _get_vision_info(
    image_processor,
    height: int,
    width: int,
    min_pixels: int,
    max_pixels: int,
    do_resize: bool = True,
    data_type_key: str = "image",
    mm_count: int = 1,
):
    """Get information (resized height / width and number of vision tokens)
    of input image / video frame."""
    if do_resize:
        resized_height, resized_width = smart_resize(
            height=height,
            width=width,
            factor=image_processor.patch_size * image_processor.merge_size,
            min_pixels=min_pixels,
            max_pixels=max_pixels,
        )
    else:
        resized_height, resized_width = height, width
    if data_type_key == "image":
        grid_t = mm_count
    else:
        assert data_type_key == "video"
        grid_t = max(mm_count // image_processor.temporal_patch_size, 1)
    grid_h = resized_height // image_processor.patch_size
    grid_w = resized_width // image_processor.patch_size
    vision_tokens = grid_t * grid_h * grid_w
    llm_num_vision_tokens = (
        vision_tokens
        // image_processor.merge_size
        // image_processor.merge_size
    )
    return resized_height, resized_width, llm_num_vision_tokens


def _get_max_image_info(
    image_processor,
    data_type_key: str = "image",
    mm_count: int = 1,
):
    return _get_vision_info(
        image_processor,
        height=9999999,
        width=9999999,
        # Limit min / max pixels.
        min_pixels=max(image_processor.min_pixels, 28 * 28),
        max_pixels=min(image_processor.max_pixels, 1280 * 28 * 28),
        data_type_key=data_type_key,
        mm_count=mm_count,
    )


def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int:
    image_processor = cached_get_image_processor(ctx.model_config.model)
    (
        max_resized_height,
        max_resized_width,
        max_llm_image_tokens,
    ) = _get_max_image_info(
        image_processor, data_type_key=data_type_key, mm_count=1
    )
    return max_llm_image_tokens


get_max_qwen2_vl_image_tokens = partial(
    get_max_qwen2_vl_mm_tokens, data_type_key="image"
)
get_max_qwen2_vl_video_tokens = partial(
    get_max_qwen2_vl_mm_tokens, data_type_key="video"
)


def dummy_data_for_qwen2_vl(
    ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]
) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:
    image_processor = cached_get_image_processor(ctx.model_config.model)
    num_images = mm_counts["image"]
    (
        max_resized_height,
        max_resized_width,
        max_llm_image_tokens,
    ) = _get_max_image_info(
        image_processor, data_type_key="image", mm_count=num_images
    )
    if seq_len - max_llm_image_tokens - 2 < 0:
        raise RuntimeError(
            f"Qwen2-VL cannot process {num_images} images in a prompt, "
            "please increase max_model_len or reduce image limit by "
            "--limit-mm-per-prompt."
        )
    # Check video counts.
    num_videos = mm_counts["video"]
    (
        max_resized_height,
        max_resized_width,
        max_llm_video_tokens,
    ) = _get_max_image_info(
        image_processor, data_type_key="video", mm_count=num_videos
    )
    if seq_len - max_llm_video_tokens - 2 < 0:
        raise RuntimeError(
            f"Qwen2-VL cannot process {num_images} videos in a prompt, "
            "please increase max_model_len or reduce video limit by "
            "--limit-mm-per-prompt."
        )
    hf_config = ctx.get_hf_config(Qwen2VLConfig)
    dummy_seqdata = SequenceData.from_token_counts(
        (hf_config.vision_start_token_id, 1),
        (hf_config.image_token_id, max_llm_image_tokens),
        (hf_config.vision_end_token_id, 1),
        (0, seq_len - max_llm_image_tokens - 2),
    )
    dummy_image = Image.new(
        "RGB", (max_resized_width, max_resized_height), color=0
    )
    return dummy_seqdata, {
        "image": dummy_image if num_images == 1 else [dummy_image] * num_images
    }


def _get_llm_num_vision_tokens(
    mm_inputs: list,
    data_type_key: str,
    image_processor,
):
    """Get number of vision tokens of multimodal inputs.
    This method is derived from `transformers.models.qwen2_vl.
    image_processing_qwen2_vl.Qwen2VLImageProcessor._preprocess`.
    """
    image = to_numpy_array(mm_inputs[0])
    input_data_format = infer_channel_dimension_format(image)
    height, width = get_image_size(image, channel_dim=input_data_format)
    _, _, llm_num_vision_tokens = _get_vision_info(
        image_processor,
        height=height,
        width=width,
        min_pixels=image_processor.min_pixels,
        max_pixels=image_processor.max_pixels,
        do_resize=image_processor.do_resize,
        data_type_key=data_type_key,
        mm_count=len(mm_inputs),
    )
    return llm_num_vision_tokens


def input_processor_for_qwen2_vl(
    ctx: InputContext, llm_inputs: LLMInputs
) -> LLMInputs:
    multi_modal_data = llm_inputs.get("multi_modal_data", None)
    if multi_modal_data is None:
        return llm_inputs
    image_inputs = multi_modal_data.get("image", None)
    video_inputs = multi_modal_data.get("video", None)
    processor = cached_get_processor(ctx.model_config.model)
    image_processor = processor.image_processor
    hf_config = ctx.get_hf_config(Qwen2VLConfig)
    # To avoid redundant processing of vision objects (resize, rescale, etc.),
    # we extract code of calculating number of vision tokens from
    # `transformers.models.qwen2_vl.processing_qwen2_vl.Qwen2VLProcessor`.
    #
    # The following code is equivalent to:
    #    prompt = llm_inputs["prompt"]
    #    inputs = processor(text=[prompt],
    #                       images=image_inputs,
    #                       videos=video_inputs,
    #                       padding=True,
    #                       return_tensors="pt")
    #    prompt_token_ids = inputs["input_ids"][0].tolist()
    prompt_token_ids = llm_inputs.get("prompt_token_ids", None)
    if prompt_token_ids is None:
        prompt = llm_inputs["prompt"]
        prompt_token_ids = processor.tokenizer(
            prompt,
            padding=True,
            return_tensors=None,
        )["input_ids"]
    # Expand image pad tokens.
    if image_inputs is not None:
        image_indices = [
            idx
            for idx, token in enumerate(prompt_token_ids)
            if token == hf_config.image_token_id
        ]
        image_inputs = make_batched_images(image_inputs)
        assert len(image_indices) == len(image_inputs)
        prompt_token_ids_with_image = []
        for image_cnt, image in enumerate(image_inputs):
            num_image_tokens = _get_llm_num_vision_tokens(
                [image],
                data_type_key="image",
                image_processor=image_processor,
            )
            if image_cnt == 0:
                non_image_tokens = prompt_token_ids[: image_indices[image_cnt]]
            else:
                non_image_tokens = prompt_token_ids[
                    image_indices[image_cnt - 1] + 1 : image_indices[image_cnt]
                ]
            prompt_token_ids_with_image.extend(non_image_tokens)
            prompt_token_ids_with_image.extend(
                hf_config.image_token_id for _ in range(num_image_tokens)
            )
        prompt_token_ids_with_image.extend(
            prompt_token_ids[image_indices[-1] + 1 :]
        )
        prompt_token_ids = prompt_token_ids_with_image
    # Expand video pad tokens.
    if video_inputs is not None:
        video_indices = [
            idx
            for idx, token in enumerate(prompt_token_ids)
            if token == hf_config.video_token_id
        ]
        video_inputs = make_batched_videos(video_inputs)
        assert len(video_indices) == len(video_inputs)
        prompt_token_ids_with_video = []
        for video_cnt, video in enumerate(video_inputs):
            num_video_tokens = _get_llm_num_vision_tokens(
                video,
                data_type_key="video",
                image_processor=image_processor,
            )
            if video_cnt == 0:
                non_video_tokens = prompt_token_ids[: video_indices[video_cnt]]
            else:
                non_video_tokens = prompt_token_ids[
                    video_indices[video_cnt - 1] + 1 : video_indices[video_cnt]
                ]
            prompt_token_ids_with_video.extend(non_video_tokens)
            prompt_token_ids_with_video.extend(
                hf_config.video_token_id for _ in range(num_video_tokens)
            )
        prompt_token_ids_with_video.extend(
            prompt_token_ids[video_indices[-1] + 1 :]
        )
        prompt_token_ids = prompt_token_ids_with_video
    return LLMInputs(
        prompt_token_ids=prompt_token_ids,
        prompt=llm_inputs["prompt"],
        multi_modal_data=multi_modal_data,
    )


@MULTIMODAL_REGISTRY.register_image_input_mapper(
    image_input_mapper_for_qwen2_vl
)
@MULTIMODAL_REGISTRY.register_input_mapper(
    "video", video_input_mapper_for_qwen2_vl
)
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_qwen2_vl_image_tokens)
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
    "video", get_max_qwen2_vl_video_tokens
)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen2_vl)
@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen2_vl)
class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal):
    def __init__(
        self,
        config: Qwen2VLConfig,
        multimodal_config: MultiModalConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        assert (
            not cache_config.enable_prefix_caching
        ), "Qwen2-VL currently does not support prefix caching"
        self.config = config
        self.multimodal_config = multimodal_config
        self.visual = Qwen2VisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
            # NOTE: Qwen2-VL vision encoder does not support any
            # quantization method now.
            quant_config=None,
        )
        self.model = Qwen2Model(config, cache_config, quant_config)
        if get_pp_group().is_last_rank:
            if config.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
                self.lm_head = ParallelLMHead(config.vocab_size,
                                              config.hidden_size,
                                              quant_config=quant_config)
        else:
            self.lm_head = PPMissingLayer()
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.sampler = Sampler()
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

    def _validate_and_reshape_mm_tensor(
        self, mm_input: Union[torch.Tensor, List[torch.Tensor]], name: str
    ) -> torch.Tensor:
        if not isinstance(mm_input, (torch.Tensor, list)):
            raise ValueError(
                f"Incorrect type of {name}. " f"Got type: {type(mm_input)}"
            )
        if isinstance(mm_input, torch.Tensor):
            if mm_input.ndim == 2:
                return mm_input
            if mm_input.ndim != 3:
                raise ValueError(
                    f"{name} should be 2D or batched 3D tensor. "
                    f"Got ndim: {mm_input.ndim}"
                )
            return torch.concat(list(mm_input))
        else:
            return torch.concat(mm_input)

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> Optional[Qwen2VLImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_grid_thw = kwargs.pop("image_grid_thw", None)
        if pixel_values is None:
            return None
        pixel_values = self._validate_and_reshape_mm_tensor(
            pixel_values, "image pixel values"
        )
        image_grid_thw = self._validate_and_reshape_mm_tensor(
            image_grid_thw, "image grid_thw"
        )
        if not isinstance(pixel_values, (torch.Tensor, list)):
            raise ValueError(
                "Incorrect type of image pixel values. "
                f"Got type: {type(pixel_values)}"
            )
        return Qwen2VLImageInputs(
            pixel_values=pixel_values, image_grid_thw=image_grid_thw
        )

    def _parse_and_validate_video_input(
        self, **kwargs: object
    ) -> Optional[Qwen2VLVideoInputs]:
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
        video_grid_thw = kwargs.pop("video_grid_thw", None)
        if pixel_values_videos is None:
            return None
        pixel_values_videos = self._validate_and_reshape_mm_tensor(
            pixel_values_videos, "video pixel values"
        )
        video_grid_thw = self._validate_and_reshape_mm_tensor(
            video_grid_thw, "video grid_thw"
        )
        return Qwen2VLVideoInputs(
            pixel_values_videos=pixel_values_videos,
            video_grid_thw=video_grid_thw,
        )

    def _process_image_input(
        self, image_input: Qwen2VLImageInputs
    ) -> torch.Tensor:
        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
        image_embeds = self.visual(
            pixel_values, grid_thw=image_input["image_grid_thw"]
        )
        return image_embeds

    def _process_video_input(
        self, video_input: Qwen2VLVideoInputs
    ) -> torch.Tensor:
        pixel_values_videos = video_input["pixel_values_videos"].type(
            self.visual.dtype
        )
        video_embeds = self.visual(
            pixel_values_videos, grid_thw=video_input["video_grid_thw"]
        )
        return video_embeds

    def _merge_multimodal_embeddings(
        self,
        input_ids: torch.Tensor,
        inputs_embeds: torch.Tensor,
        multimodal_embeddings: torch.Tensor,
        placeholder_token_id: int,
    ) -> torch.Tensor:
        mask = input_ids == placeholder_token_id
        inputs_embeds[mask, :] = multimodal_embeddings
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        **kwargs: object,
    ) -> SamplerOutput:
        """Run forward pass for Qwen2-VL.
        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)`,
                otherwise it will be `(seq_len,).
            pixel_values: Pixel values to be fed to a model.
                `None` if no images are passed.
            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
                `None` if no images are passed.
            pixel_values_videos: Pixel values of videos to be fed to a model.
                `None` if no videos are passed.
            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
                `None` if no videos are passed.
        """
        image_input = self._parse_and_validate_image_input(**kwargs)
        video_input = self._parse_and_validate_video_input(**kwargs)
        if (image_input is None
                and video_input is None) or not get_pp_group().is_first_rank:
            inputs_embeds = None
        else:
            if (
                getattr(self.config, "rope_scaling", {}).get("type", None)
                == "mrope"
            ):
                assert positions.ndim == 2 and positions.size(0) == 3, (
                    "multimodal section rotary embedding requires "
                    f"(3, seq_len) positions, but got {positions.size()}"
                )
            inputs_embeds = self.model.embed_tokens(input_ids)
            if image_input is not None:
                image_embeds = self._process_image_input(image_input)
                inputs_embeds = self._merge_multimodal_embeddings(
                    input_ids,
                    inputs_embeds,
                    image_embeds,
                    placeholder_token_id=self.config.image_token_id,
                )
            if video_input is not None:
                video_embeds = self._process_video_input(video_input)
                inputs_embeds = self._merge_multimodal_embeddings(
                    input_ids,
                    inputs_embeds,
                    video_embeds,
                    placeholder_token_id=self.config.video_token_id,
                )
            input_ids = None
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            kv_caches=kv_caches,
            attn_metadata=attn_metadata,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata
    ) -> torch.Tensor:
        logits = self.logits_processor(
            self.lm_head, hidden_states, sampling_metadata
        )
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "up_proj", 1),
            ("gate_up_proj", "gate_proj", 0),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if self.config.tie_word_embeddings and "lm_head.weight" in name:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                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 "visual" in name and "qkv.weight" in name:
                    visual_num_heads = self.config.vision_config.num_heads
                    visual_embed_dim = self.config.vision_config.embed_dim
                    head_size = visual_embed_dim // visual_num_heads
                    loaded_weight = loaded_weight.view(
                        3, visual_num_heads, head_size, visual_embed_dim
                    )
                    loaded_weight = loaded_weight.transpose(0, 1)
                    loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
                elif "visual" in name and "qkv.bias" in name:
                    visual_num_heads = self.config.vision_config.num_heads
                    visual_embed_dim = self.config.vision_config.embed_dim
                    head_size = visual_embed_dim // visual_num_heads
                    loaded_weight = loaded_weight.view(
                        3, visual_num_heads, head_size
                    )
                    loaded_weight = loaded_weight.transpose(0, 1)
                    loaded_weight = loaded_weight.reshape(-1)
                try:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    if is_pp_missing_parameter(name, self):
                        continue
                    param = params_dict[name]
                except KeyError:
                    raise ValueError(f"Unexpected weight: {name}") from None
                weight_loader = getattr(
                    param, "weight_loader", default_weight_loader
                )
                weight_loader(param, loaded_weight)