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qwen2_vl.py 65.9 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# 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 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."""
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from collections.abc import Iterable, Mapping, Sequence
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from functools import partial
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from typing import Annotated, Any, Callable, Literal, Optional, Union
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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
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from transformers import AutoConfig, BatchFeature, PretrainedConfig
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from transformers.models.qwen2_vl import (Qwen2VLImageProcessor,
                                          Qwen2VLProcessor)
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from transformers.models.qwen2_vl.configuration_qwen2_vl import (
    Qwen2VLConfig, Qwen2VLVisionConfig)
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from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
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from transformers.models.qwen2_vl.video_processing_qwen2_vl import (
    Qwen2VLVideoProcessor)
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from vllm.attention.layer import check_upstream_fa_availability
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from vllm.config import VllmConfig
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from vllm.distributed import parallel_state, tensor_model_parallel_all_gather
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from vllm.distributed import utils as dist_utils
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import QuickGELU
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               RowParallelLinear)
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.module_mapping import MultiModelKeys
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (ImageItem, ModalityData,
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                                    MultiModalDataDict, MultiModalFieldConfig,
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                                    MultiModalKwargsItems, VideoItem)
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from vllm.multimodal.parse import (DictEmbeddingItems, ImageSize,
                                   ModalityDataItems, MultiModalDataItems,
                                   MultiModalDataParser)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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                                        BaseProcessingInfo, PromptReplacement,
                                        PromptUpdate)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.platforms import _Backend, current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.config import uses_mrope
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE,
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                         SupportsMultiModal, SupportsPP)
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from .utils import (AutoWeightsLoader, WeightsMapper,
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                    init_vllm_registered_model, maybe_prefix,
                    merge_multimodal_embeddings)
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from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model
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import os
import re
from vllm import _custom_ops as ops
from vllm.model_executor.utils import pad_weight, gemm_bank_conf
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from vllm.platforms import current_platform
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logger = init_logger(__name__)

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# For profile run
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_MAX_FRAMES_PER_VIDEO = 14
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# === Vision Inputs === #


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class Qwen2VLImagePixelInputs(TensorSchema):
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    """
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    Dimensions:
        - np: The total number of patches over each image over each prompt in
              the batch
        - ni: Number of images
        - cps: Number of channels * patch_size * patch_size
    
    Historical context:
        - pixel_values shape: (num_patches, num_channels * patch_size * 
          patch_size)
        - image_grid_thw shape: (num_images, 3) in (grid_t, grid_h, grid_w)
          format
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    """
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    type: Literal["pixel_values"]
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    pixel_values: Annotated[
        torch.Tensor,
        TensorShape("np", "cps"),
    ]
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    image_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("ni", 3),
    ]


class Qwen2VLImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - nf: Number of image features
        - hs: Hidden size
        - ni: Number of images
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    Historical context:
        - image_embeds shape: (num_image_features, hidden_size)
        - num_image_features varies based on the number and resolution of the
          images.
        - hidden_size must match the hidden size of language model backbone.
        - image_grid_thw shape: (num_images, 3) in (grid_t, grid_h, grid_w)
          format
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    """
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    type: Literal["image_embeds"]
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    image_embeds: Annotated[
        torch.Tensor,
        TensorShape("nf", "hs"),
    ]

    image_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("ni", 3),
    ]
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Qwen2VLImageInputs = Union[Qwen2VLImagePixelInputs,
                           Qwen2VLImageEmbeddingInputs]


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class Qwen2VLVideoPixelInputs(TensorSchema):
    """
    Dimensions:
        - np: The total number of patches over each video over each prompt in
              the batch
        - ctps: Number of channels * temporal_patch_size * patch_size * 
          patch_size
        - nv: Number of videos
    
    Historical context:
        - pixel_values_videos shape: (num_patches, num_channels * 
          temporal_patch_size * patch_size * patch_size)
        - video_grid_thw shape: (num_videos, 3) in (grid_t, grid_h, grid_w)
          format
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    """
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    type: Literal["pixel_values_videos"]
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    pixel_values_videos: Annotated[
        torch.Tensor,
        TensorShape("np", "ctps"),
    ]
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    video_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("nv", 3),
    ]
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class Qwen2VLVideoEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - nf: Number of video features
        - hs: Hidden size
        - nv: Number of videos
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    Historical context:
        - video_embeds shape: (num_video_features, hidden_size)
        - num_video_features varies based on the number and resolution of the
          videos.
        - hidden_size must match the hidden size of language model backbone.
        - video_grid_thw shape: (num_videos, 3) in (grid_t, grid_h, grid_w)
          format
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    """
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    type: Literal["video_embeds"]
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    video_embeds: Annotated[
        torch.Tensor,
        TensorShape("nf", "hs"),
    ]

    video_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("nv", 3),
    ]
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Qwen2VLVideoInputs = Union[Qwen2VLVideoPixelInputs,
                           Qwen2VLVideoEmbeddingInputs]

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# === Vision Encoder === #


class Qwen2VisionMLP(nn.Module):

    def __init__(
        self,
        in_features: int,
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        hidden_features: int,
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        act_layer: type[nn.Module] = QuickGELU,
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        quant_config: Optional[QuantizationConfig] = None,
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        prefix: str = "",
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        use_data_parallel: bool = False,
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    ):
        super().__init__()
        self.fc1 = ColumnParallelLinear(in_features,
                                        hidden_features,
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                                        quant_config=quant_config,
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                                        prefix=f"{prefix}.fc1",
                                        disable_tp=use_data_parallel)
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        self.act = act_layer()
        self.fc2 = RowParallelLinear(hidden_features,
                                     in_features,
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                                     quant_config=quant_config,
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                                     prefix=f"{prefix}.fc2",
                                     disable_tp=use_data_parallel)
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    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,
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                                freqs: torch.Tensor) -> torch.Tensor:
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    t_ = t.float()
    cos = freqs.cos()
    sin = freqs.sin()
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    apply_rotary_emb = apply_rotary_emb_torch
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    if current_platform.is_cuda():
        from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
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    if current_platform.is_rocm():
        from flash_attn.layers.rotary import apply_rotary_emb
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    output = apply_rotary_emb(t_, cos, sin).type_as(t)
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    return output


class Qwen2VisionAttention(nn.Module):

    def __init__(
        self,
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        embed_dim: int,
        num_heads: int,
        projection_size: int,
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        quant_config: Optional[QuantizationConfig] = None,
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        prefix: str = "",
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        use_data_parallel: bool = False,
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    ) -> None:
        super().__init__()
        # Per attention head and per partition values.
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        self.tp_size = (1 if use_data_parallel else
                        parallel_state.get_tensor_model_parallel_world_size())
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        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
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        self.hidden_size_per_attention_head = dist_utils.divide(
            projection_size, num_heads)
        self.num_attention_heads_per_partition = dist_utils.divide(
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            num_heads, self.tp_size)
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        self.qkv = ColumnParallelLinear(input_size=embed_dim,
                                        output_size=3 * projection_size,
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                                        quant_config=quant_config,
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                                        prefix=f"{prefix}.qkv",
                                        disable_tp=use_data_parallel)
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        self.proj = RowParallelLinear(input_size=projection_size,
                                      output_size=embed_dim,
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                                      quant_config=quant_config,
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                                      prefix=f"{prefix}.proj",
                                      disable_tp=use_data_parallel)
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        # Detect attention implementation.
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        self.attn_backend = get_vit_attn_backend(
            head_size=self.hidden_size_per_attention_head,
            dtype=torch.get_default_dtype())
        self.use_upstream_fa = False
        if self.attn_backend != _Backend.FLASH_ATTN and \
            check_upstream_fa_availability(
                torch.get_default_dtype()):
            self.attn_backend = _Backend.FLASH_ATTN
            self.use_upstream_fa = True

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        if self.attn_backend not in {
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                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS,
                _Backend.ROCM_AITER_FA
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        }:
            raise RuntimeError(
                f"Qwen2-VL does not support {self.attn_backend} backend now.")
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        self.is_flash_attn_backend = self.attn_backend in {
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            _Backend.FLASH_ATTN, _Backend.ROCM_FLASH, _Backend.ROCM_AITER_FA
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        }
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    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
        # [s, b, 3 * head * head_dim]
        seq_len, bs, _ = qkv.shape
        if self.tp_size > 1:
            qkv = tensor_model_parallel_all_gather(qkv)

        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
        q, k, v = qkv.chunk(3, dim=2)

        # 3 * [s, b, head * head_dim]
        if self.tp_size > 1:
            splitter = partial(dist_utils.split_tensor_along_last_dim,
                               num_partitions=self.tp_size)
            q = splitter(q)[self.tp_rank]
            k = splitter(k)[self.tp_rank]
            v = splitter(v)[self.tp_rank]

        # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
        new_shape = (seq_len, bs, self.num_attention_heads_per_partition,
                     self.hidden_size_per_attention_head)
        q, k, v = (x.view(*new_shape) for x in (q, k, v))
        return q, k, v

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    def forward(
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            self,
            x: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor,
            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
            seqlens: Optional[list[int]] = None,  # Only used for xFormers
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    ) -> torch.Tensor:

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        # [s, b, c] --> [s, b, 3 * head * head_dim]
        x, _ = self.qkv(x)
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        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
        q, k, v = self.split_qkv(x)
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        batch_size = q.shape[1]

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        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
                   for x in (q, k, v))
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        if rotary_pos_emb is not None:
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            # [2 * b, s, heads, head_dim]
            qk_concat = torch.cat([q, k], dim=0)
            qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
            q, k = torch.chunk(qk_rotated, 2, dim=0)
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        if self.is_flash_attn_backend:
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            # if self.attn_backend == _Backend.ROCM_AITER_FA:
            #     from aiter import flash_attn_varlen_func
            # else:
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            #     if self.use_upstream_fa:
            #         from flash_attn import flash_attn_varlen_func
            #     else:
            #         from vllm.vllm_flash_attn import flash_attn_varlen_func
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            from flash_attn import flash_attn_varlen_func
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            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
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            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,
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                                            dropout_p=0.0,
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                                            causal=False)

            context_layer = rearrange(output,
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                                      "(b s) h d -> s b (h d)",
                                      b=batch_size).contiguous()
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        elif self.attn_backend == _Backend.TORCH_SDPA:
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            # Execute attention entry by entry for speed & less VRAM.
            outputs = []
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            for i in range(1, len(cu_seqlens)):
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                start_idx = cu_seqlens[i - 1]
                end_idx = cu_seqlens[i]
                q_i = q[:, start_idx:end_idx]
                k_i = k[:, start_idx:end_idx]
                v_i = v[:, start_idx:end_idx]
                q_i, k_i, v_i = (rearrange(x, "b s h d -> b h s d")
                                 for x in [q_i, k_i, v_i])
                output_i = F.scaled_dot_product_attention(q_i,
                                                          k_i,
                                                          v_i,
                                                          dropout_p=0.0)
                output_i = rearrange(output_i, "b h s d -> b s h d ")
                outputs.append(output_i)
            context_layer = torch.cat(outputs, dim=1)
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            context_layer = rearrange(context_layer,
                                      "b s h d -> s b (h d)").contiguous()
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        elif self.attn_backend == _Backend.XFORMERS:
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            from xformers import ops as xops
            from xformers.ops.fmha.attn_bias import BlockDiagonalMask

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


class Qwen2VisionBlock(nn.Module):

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float,
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        act_layer: type[nn.Module] = QuickGELU,
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        norm_layer: Optional[Callable[[int], nn.Module]] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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        prefix: str = "",
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        use_data_parallel: bool = False,
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    ) -> 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,
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                                         quant_config=quant_config,
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                                         prefix=f"{prefix}.attn",
                                         use_data_parallel=use_data_parallel)
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        self.mlp = Qwen2VisionMLP(dim,
                                  mlp_hidden_dim,
                                  act_layer=act_layer,
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                                  quant_config=quant_config,
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                                  prefix=f"{prefix}.mlp",
                                  use_data_parallel=use_data_parallel)
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    def forward(
            self,
            x: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor,
            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
            seqlens: Optional[list[int]] = None,  # Only used for xFormers
    ) -> torch.Tensor:
        x = x + self.attn(
            self.norm1(x),
            cu_seqlens=cu_seqlens,
            rotary_pos_emb=rotary_pos_emb,
            max_seqlen=max_seqlen,
            seqlens=seqlens,
        )

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        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,
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        in_channels: int = 3,
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        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.embed_dim = embed_dim

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        kernel_size = (temporal_patch_size, patch_size, patch_size)
        self.proj = nn.Conv3d(in_channels,
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                              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)
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        if os.environ.get('PYTORCH_MIOPEN_SUGGEST_NDHWC') == '1':
            x = x.to(memory_format=torch.channels_last_3d)
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        x = self.proj(x).view(L, self.embed_dim)
        return x


class Qwen2VisionPatchMerger(nn.Module):

    def __init__(
        self,
        d_model: int,
        context_dim: int,
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        norm_layer: Optional[Callable[[int], nn.Module]] = None,
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        spatial_merge_size: int = 2,
        quant_config: Optional[QuantizationConfig] = None,
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        prefix: str = "",
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        use_data_parallel: bool = False,
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    ) -> 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,
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                                 quant_config=quant_config,
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                                 prefix=f"{prefix}.mlp.0",
                                 disable_tp=use_data_parallel),
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            nn.GELU(),
            RowParallelLinear(self.hidden_size,
                              d_model,
                              bias=True,
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                              quant_config=quant_config,
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                              prefix=f"{prefix}.mlp.2",
                              disable_tp=use_data_parallel),
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        ])

    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,
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        prefix: str = "",
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        use_data_parallel: bool = False,
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    ) -> None:
        super().__init__()

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        patch_size = vision_config.patch_size
        temporal_patch_size = vision_config.temporal_patch_size
        spatial_merge_size = vision_config.spatial_merge_size
        in_channels = vision_config.in_channels
        hidden_size = vision_config.hidden_size
        embed_dim = vision_config.embed_dim
        depth = vision_config.depth
        num_heads = vision_config.num_heads
        mlp_ratio = vision_config.mlp_ratio
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        self.use_data_parallel = use_data_parallel
        self.out_hidden_size = vision_config.hidden_size

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        self.spatial_merge_size = spatial_merge_size
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        self.num_heads = num_heads
        self.embed_dim = embed_dim
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        self.patch_embed = Qwen2VisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
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            in_channels=in_channels,
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            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([
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            Qwen2VisionBlock(dim=embed_dim,
                             num_heads=num_heads,
                             mlp_ratio=mlp_ratio,
                             norm_layer=norm_layer,
                             quant_config=quant_config,
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                             prefix=f"{prefix}.blocks.{layer_idx}",
                             use_data_parallel=use_data_parallel)
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            for layer_idx in range(depth)
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        ])
        self.merger = Qwen2VisionPatchMerger(
            d_model=hidden_size,
            context_dim=embed_dim,
            norm_layer=norm_layer,
            quant_config=quant_config,
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            prefix=f"{prefix}.merger",
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            use_data_parallel=use_data_parallel,
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        )
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        self.attn_backend = get_vit_attn_backend(
            head_size=head_dim, dtype=torch.get_default_dtype())
        if self.attn_backend != _Backend.FLASH_ATTN and \
            check_upstream_fa_availability(
                torch.get_default_dtype()):
            self.attn_backend = _Backend.FLASH_ATTN
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        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config
            
        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
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    @property
    def dtype(self) -> torch.dtype:
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        return self.patch_embed.proj.weight.dtype
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    @property
    def device(self) -> torch.device:
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        return self.patch_embed.proj.weight.device
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    def rot_pos_emb(self, grid_thw: list[list[int]]) -> torch.Tensor:
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        pos_ids = []
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        max_grid_size = 0
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        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))
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            max_grid_size = max(max_grid_size, h, w)
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        pos_ids = torch.cat(pos_ids, dim=0)
        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

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    def compute_attn_mask_seqlen(
            self, cu_seqlens: torch.Tensor
    ) -> tuple[Optional[int], Optional[list[int]]]:
        max_seqlen, seqlens = None, None
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        if (self.attn_backend == _Backend.FLASH_ATTN
                or self.attn_backend == _Backend.ROCM_AITER_FA):
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            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        elif self.attn_backend == _Backend.XFORMERS:
            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
        return max_seqlen, seqlens

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    def forward(
        self,
        x: torch.Tensor,
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        grid_thw: list[list[int]],
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    ) -> 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
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        grid_thw_ = torch.tensor(grid_thw,
                                device=self.device,
                                dtype=torch.int32
                                )
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        cu_seqlens = torch.repeat_interleave(grid_thw_[:, 1] * grid_thw_[:, 2],
                                             grid_thw_[:, 0]).cumsum(
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                                                 dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)

        # transformers
        x = x.unsqueeze(1)
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        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
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        for blk in self.blocks:
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            x = blk(
                x,
                cu_seqlens=cu_seqlens,
                rotary_pos_emb=rotary_pos_emb,
                max_seqlen=max_seqlen,
                seqlens=seqlens,
            )
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        # adapter
        x = self.merger(x)
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        return x

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    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
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        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
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        loaded_params: set[str] = set()
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        for name, loaded_weight in weights:
            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)

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
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        if self.use_llama_nn and self.quant_method is None:
            lay_key_words = [
                "attn.qkv.weight",
                "attn.proj.weight",
                "mlp.fc1.weight",
                "mlp.fc2.weight",
                "mlp.0.weight",
                "mlp.2.weight",
                "self_attn.qkv_proj.weight",
                "self_attn.o_proj.weight",
                "mlp.gate_up_proj.weight",
                "mlp.down_proj.weight",
                "lm_head.weight",
            ]
            combined_words = "|".join(lay_key_words)
            
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            # lay_qkv_words = ["attn.qkv.weight"]   
            # qkv_words = "|".join(lay_qkv_words)  
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            # lay_qkv_bias_words = ["attn.qkv.bias"]   
            # qkv_bias_words = "|".join(lay_qkv_bias_words) 
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            for layername in loaded_params:
                weight = params_dict[layername]
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                # if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
                #     weight.data = pad_weight(weight.data, 32)
                    
                matches = re.findall(combined_words, layername)
                if matches:   
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                    # if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
                    #     weight.data = pad_weight(weight.data, 32)  
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                    # if self.use_fa_pad and (re.findall(qkv_words, layername)):
                    #     if not gemm_bank_conf(weight.data.shape[0]):
                    #         weight.data = pad_weight(weight.data, 32)
                        
                    _weight = torch.zeros_like(weight.data)
                    ori_shape =_weight.shape
                    
                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
                    weight.data.copy_(_weight)
                    
                    weight.data=weight.data.reshape(ori_shape[1],-1)
                    
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        return loaded_params

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def _create_qwen2vl_field_factory(
    spatial_merge_size: int
) -> Callable[
    [Mapping[str, torch.Tensor]],
        Mapping[str, MultiModalFieldConfig],
]:

    def _qwen2vl_field_config(hf_inputs: Mapping[str, torch.Tensor]):
        image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
        image_pixel_grid_sizes = image_grid_thw.prod(-1)
        image_embed_grid_sizes = (image_pixel_grid_sizes //
                                  spatial_merge_size // spatial_merge_size)

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

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

    return _qwen2vl_field_config
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class Qwen2VLMultiModalDataParser(MultiModalDataParser):
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    def __init__(self, spatial_merge_size: int, *args, **kwargs):
        self._spatial_merge_size = spatial_merge_size
        super().__init__(*args, **kwargs)

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    def _parse_image_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
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    ) -> Optional[ModalityDataItems[Any, Any]]:
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        if isinstance(data, dict):
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            return DictEmbeddingItems(
                data,
                modality="image",
                required_fields={"image_embeds", "image_grid_thw"},
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                fields_factory=_create_qwen2vl_field_factory(
                    self._spatial_merge_size),
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            )
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        return super()._parse_image_data(data)
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    def _parse_video_data(
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        self,
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        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
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    ) -> Optional[ModalityDataItems[Any, Any]]:
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        if isinstance(data, dict):
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            return DictEmbeddingItems(
                data,
                modality="video",
                required_fields={"video_embeds", "video_grid_thw"},
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                fields_factory=_create_qwen2vl_field_factory(
                    self._spatial_merge_size),
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            )
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        return super()._parse_video_data(data)

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class Qwen2VLProcessingInfo(BaseProcessingInfo):
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    def get_hf_config(self):
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        return self.ctx.get_hf_config(Qwen2VLConfig)

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    def get_hf_processor(self, **kwargs: object) -> Qwen2VLProcessor:
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        return self.ctx.get_hf_processor(
            Qwen2VLProcessor,
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            use_fast=kwargs.pop("use_fast", True),
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            **kwargs,
        )

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    def get_image_processor(self, **kwargs: object) -> Qwen2VLImageProcessor:
        return self.get_hf_processor(**kwargs).image_processor
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    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None, "video": None}

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

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    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 1,
        do_resize: bool = True,
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        image_processor: Optional[Qwen2VLImageProcessor],
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    ) -> tuple[ImageSize, int]:
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        if image_processor is None:
            image_processor = self.get_image_processor()

        hf_config = self.get_hf_config()
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        vision_config = hf_config.vision_config
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        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = vision_config.temporal_patch_size
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        if do_resize:
            resized_height, resized_width = smart_resize(
                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                min_pixels=image_processor.min_pixels,
                max_pixels=image_processor.max_pixels,
            )
            preprocessed_size = ImageSize(width=resized_width,
                                          height=resized_height)
        else:
            preprocessed_size = ImageSize(width=image_width,
                                          height=image_height)

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        # NOTE: Frames are padded to be divisible by `temporal_patch_size`
        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294
        padded_num_frames = num_frames + num_frames % temporal_patch_size

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

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

        return preprocessed_size, num_vision_tokens

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    def get_num_image_tokens(
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        self,
        *,
        image_width: int,
        image_height: int,
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        image_processor: Optional[Qwen2VLImageProcessor],
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    ) -> int:
        _, num_image_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
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            num_frames=1,
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            image_processor=image_processor,
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        )
        return num_image_tokens

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    def get_num_video_tokens(
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        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
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        image_processor: Optional[Qwen2VLImageProcessor],
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    ) -> int:
        _, num_video_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            num_frames=num_frames,
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            image_processor=image_processor,
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        )
        return num_video_tokens

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    def get_image_size_with_most_features(self) -> ImageSize:
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        max_image_size, _ = self._get_vision_info(
            image_width=9999999,
            image_height=9999999,
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            num_frames=1,
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            image_processor=None,
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        )
        return max_image_size

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    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
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        return self.get_num_image_tokens(
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            image_width=target_width,
            image_height=target_height,
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            image_processor=None,
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        )
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    def _get_max_video_frames(self,
                              max_tokens: int,
                              start_num_frames: int = 1) -> int:
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        target_width, target_height = self.get_image_size_with_most_features()
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        num_frames = start_num_frames
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        while True:
            next_num_frames = num_frames + 1
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            next_max_tokens = self.get_num_video_tokens(
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                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
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                image_processor=None,
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            )
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            if next_max_tokens > max_tokens:
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                break

            num_frames = next_num_frames

        return num_frames

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    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
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        max_frames_per_video: int = _MAX_FRAMES_PER_VIDEO,
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    ) -> int:
        max_videos = mm_counts.get("video", 0)
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        max_total_frames = self._get_max_video_frames(seq_len)
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        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
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                                   max_frames_per_video)
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        return max(max_frames_per_video, 1)
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    def get_max_video_tokens(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
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        target_width, target_height = self.get_image_size_with_most_features()
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        return self.get_num_video_tokens(
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            image_width=target_width,
            image_height=target_height,
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            num_frames=self.get_num_frames_with_most_features(
                seq_len, mm_counts),
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            image_processor=None,
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        )

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class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]):

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

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        hf_processor = self.info.get_hf_processor()
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        image_token: str = hf_processor.image_token
        video_token: str = hf_processor.video_token
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        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

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        target_width, target_height = \
            self.info.get_image_size_with_most_features()
        target_num_frames = \
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            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
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        return {
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            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images),
            "video":
            self._get_dummy_videos(
                width=target_width,
                height=target_height,
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                num_frames=target_num_frames,
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                num_videos=num_videos,
            )
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        }

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class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo]
                                 ):
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    def _get_data_parser(self) -> MultiModalDataParser:
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        return Qwen2VLMultiModalDataParser(
            self.info.get_hf_config().vision_config.spatial_merge_size)
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    def _get_prompt_updates(
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        self,
        mm_items: MultiModalDataItems,
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        hf_processor_mm_kwargs: Mapping[str, Any],
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        out_mm_kwargs: MultiModalKwargsItems,
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    ) -> Sequence[PromptUpdate]:
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        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_processor = self.info.get_image_processor(
            **hf_processor_mm_kwargs)
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        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()
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        placeholder = {
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            "image": vocab[hf_processor.image_token],
            "video": vocab[hf_processor.video_token],
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        }
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        merge_length = image_processor.merge_size**2

        def get_replacement_qwen2vl(item_idx: int, modality: str):
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            out_item = out_mm_kwargs[modality][item_idx]
            grid_thw = out_item[f"{modality}_grid_thw"].data
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            assert isinstance(grid_thw, torch.Tensor)

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            num_tokens = int(grid_thw.prod()) // merge_length
            return [placeholder[modality]] * num_tokens
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        return [
            PromptReplacement(
                modality=modality,
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                target=[placeholder[modality]],
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                replacement=partial(get_replacement_qwen2vl,
                                    modality=modality),
            ) for modality in ("image", "video")
        ]
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    def _get_mm_fields_config(
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        self,
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        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
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        return _create_qwen2vl_field_factory(
            self.info.get_hf_config().vision_config.spatial_merge_size)(
                hf_inputs)
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@MULTIMODAL_REGISTRY.register_processor(Qwen2VLMultiModalProcessor,
                                        info=Qwen2VLProcessingInfo,
                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
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class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
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                                      SupportsLoRA, SupportsPP, SupportsMRoPE):
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    # To ensure correct weight loading and mapping.
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    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            # mapping for new names in checkpoint saved after transformers v4.52
            "model.language_model.": "language_model.model.",
            "model.visual.": "visual.",
            # mapping for original checkpoint
            "lm_head.": "language_model.lm_head.",
            "model.": "language_model.model.",
        })
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    supports_encoder_tp_data = True

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    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
        hf_config: PretrainedConfig,
        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
        second_per_grid_ts: Optional[list[float]] = None,
        context_len: int = 0,
        seq_len: Optional[int] = None,
        audio_feature_lengths: Optional[torch.Tensor] = None,
        use_audio_in_video: bool = False,
    ) -> tuple[torch.Tensor, int]:
        """Get M-RoPE input positions for Qwen2-VL model."""
        if image_grid_thw is None:
            image_grid_thw = []
        if video_grid_thw is None:
            video_grid_thw = []
        if second_per_grid_ts is None:
            second_per_grid_ts = []

        image_token_id = hf_config.image_token_id
        video_token_id = hf_config.video_token_id
        vision_start_token_id = hf_config.vision_start_token_id
        spatial_merge_size = hf_config.vision_config.spatial_merge_size
        tokens_per_second = getattr(hf_config.vision_config,
                                    "tokens_per_second", 1.0)

        input_tokens_tensor = torch.tensor(input_tokens)
        vision_start_indices = torch.argwhere(
            input_tokens_tensor == vision_start_token_id).squeeze(1)
        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
        image_nums = (vision_tokens == image_token_id).sum()
        video_nums = (vision_tokens == video_token_id).sum()
        llm_pos_ids_list: list = []

        st = 0
        remain_images, remain_videos = image_nums, video_nums

        image_index, video_index = 0, 0
        for _ in range(image_nums + video_nums):
            video_second_per_grid_t = 0.0
            if remain_images > 0:
                try:
                    ed_image = input_tokens.index(image_token_id, st)
                except ValueError:
                    ed_image = len(input_tokens) + 1
            else:
                ed_image = len(input_tokens) + 1
            if remain_videos > 0:
                try:
                    ed_video = input_tokens.index(video_token_id, st)
                except ValueError:
                    ed_video = len(input_tokens) + 1
            else:
                ed_video = len(input_tokens) + 1
            if ed_image < ed_video:
                t, h, w = (
                    image_grid_thw[image_index][0],
                    image_grid_thw[image_index][1],
                    image_grid_thw[image_index][2],
                )
                image_index += 1
                remain_images -= 1
                ed = ed_image
            else:
                t, h, w = (
                    video_grid_thw[video_index][0],
                    video_grid_thw[video_index][1],
                    video_grid_thw[video_index][2],
                )
                video_second_per_grid_t = 1.0
                if second_per_grid_ts:
                    video_second_per_grid_t = second_per_grid_ts[video_index]
                video_index += 1
                remain_videos -= 1
                ed = ed_video

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

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

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

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

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

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

        return llm_positions, mrope_position_delta

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    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<|vision_start|><|image_pad|><|vision_end|>"
        if modality.startswith("video"):
            return "<|vision_start|><|video_pad|><|vision_end|>"

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

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config: Qwen2VLConfig = vllm_config.model_config.hf_config
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        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
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        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
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        self.config = config
        self.multimodal_config = multimodal_config

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        if multimodal_config.get_limit_per_prompt("image") or \
            multimodal_config.get_limit_per_prompt("video"):
            self.visual = Qwen2VisionTransformer(
                config.vision_config,
                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
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                quant_config=quant_config,
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                prefix=maybe_prefix(prefix, "visual"),
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                use_data_parallel=self.use_data_parallel,
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            )
        else:
            self.visual = None
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        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "language_model"),
            architectures=["Qwen2ForCausalLM"],
        )
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        self.make_empty_intermediate_tensors = (
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            self.language_model.make_empty_intermediate_tensors)
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    def _validate_and_reshape_mm_tensor(self, mm_input: object,
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                                        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. "
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                                 f"Got ndim: {mm_input.ndim} "
                                 f"(shape={mm_input.shape})")
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            return mm_input.reshape(-1, mm_input.shape[-1])
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        else:
            return torch.concat(mm_input)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[Qwen2VLImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
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        image_embeds = kwargs.pop("image_embeds", None)
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        image_grid_thw = kwargs.pop("image_grid_thw", None)

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        if pixel_values is None and image_embeds is None:
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            return None

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        if pixel_values is not 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")
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            return Qwen2VLImagePixelInputs(type="pixel_values",
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                                           pixel_values=pixel_values,
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                                           image_grid_thw=image_grid_thw)

        if image_embeds is not None:
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            image_embeds = self._validate_and_reshape_mm_tensor(
                image_embeds, "image embeds")
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            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")
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            return Qwen2VLImageEmbeddingInputs(type="image_embeds",
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                                               image_embeds=image_embeds,
                                               image_grid_thw=image_grid_thw)
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    def _parse_and_validate_video_input(
            self, **kwargs: object) -> Optional[Qwen2VLVideoInputs]:
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
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        video_embeds = kwargs.pop("video_embeds", None)
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        video_grid_thw = kwargs.pop("video_grid_thw", None)

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        if pixel_values_videos is None and video_embeds is None:
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            return None

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        if pixel_values_videos is not 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")
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            return Qwen2VLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            video_embeds = self._validate_and_reshape_mm_tensor(
                video_embeds, "video embeds")
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")

            return Qwen2VLVideoEmbeddingInputs(type="video_embeds",
                                               video_embeds=video_embeds,
                                               video_grid_thw=video_grid_thw)
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    def _process_image_input(
            self, image_input: Qwen2VLImageInputs) -> tuple[torch.Tensor, ...]:

        grid_thw = image_input["image_grid_thw"]
        assert grid_thw.ndim == 2
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        grid_thw_list = grid_thw.tolist()
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        if image_input["type"] == "image_embeds":
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            image_embeds = image_input["image_embeds"]
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        else:
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            pixel_values = image_input["pixel_values"]
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            if self.use_data_parallel:
                return run_dp_sharded_mrope_vision_model(self.visual,
                                                         pixel_values,
                                                         grid_thw_list,
                                                         rope_type="rope_3d")
            else:
                image_embeds = self.visual(pixel_values,
                                           grid_thw=grid_thw_list)
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        # Split concatenated embeddings for each image item.
        merge_size = self.visual.spatial_merge_size
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        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
                 (merge_size * merge_size)).tolist()
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        return image_embeds.split(sizes)
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    def _process_video_input(
            self, video_input: Qwen2VLVideoInputs) -> tuple[torch.Tensor, ...]:
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        grid_thw = video_input["video_grid_thw"]
        assert grid_thw.ndim == 2
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        grid_thw_list = grid_thw.tolist()
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        if video_input["type"] == "video_embeds":
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            video_embeds = video_input["video_embeds"]
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        else:
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            pixel_values_videos = video_input["pixel_values_videos"]
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            if self.use_data_parallel:
                return run_dp_sharded_mrope_vision_model(self.visual,
                                                         pixel_values_videos,
                                                         grid_thw_list,
                                                         rope_type="rope_3d")
            else:
                video_embeds = self.visual(pixel_values_videos,
                                           grid_thw=grid_thw_list)
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        # Split concatenated embeddings for each video item.
        merge_size = self.visual.spatial_merge_size
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        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
                 (merge_size * merge_size)).tolist()
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        return video_embeds.split(sizes)
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    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
            if input_key in ("pixel_values",
                             "image_embeds") and "images" not in modalities:
                modalities["images"] = self._parse_and_validate_image_input(
                    **kwargs)
            if input_key in ("pixel_values_videos",
                             "video_embeds") and "videos" not in modalities:
                modalities["videos"] = self._parse_and_validate_video_input(
                    **kwargs)

        return modalities
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    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

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    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
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        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
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            return []
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        # The result multimodal_embeddings is tuple of tensors, with each
        # tensor correspoending to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                vision_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += vision_embeddings
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_video_input(video_input)
                multimodal_embeddings += video_embeddings
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        return multimodal_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
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        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
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    ) -> torch.Tensor:
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        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
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        if multimodal_embeddings is not None \
            and len(multimodal_embeddings) != 0:
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            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                [self.config.image_token_id, self.config.video_token_id])
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        return inputs_embeds

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    def get_input_embeddings_v0(
        self,
        input_ids: torch.Tensor,
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        image_input: Optional[Qwen2VLImagePixelInputs] = None,
        video_input: Optional[Qwen2VLVideoPixelInputs] = None,
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    ) -> torch.Tensor:
        inputs_embeds = self.get_input_embeddings(input_ids)
        if image_input is not None:
            image_embeds = self._process_image_input(image_input)
            inputs_embeds = 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 = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                video_embeds,
                placeholder_token_id=self.config.video_token_id,
            )
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        return inputs_embeds

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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
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        inputs_embeds: Optional[torch.Tensor] = None,
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        **kwargs: object,
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    ) -> Union[torch.Tensor, IntermediateTensors]:
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        """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)`,
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                otherwise it will be `(seq_len,)`.
            intermediate_tensors: Intermediate tensors from prior forward pass.
            inputs_embeds: Optional tensor of input embeddings.
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        """

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        if intermediate_tensors is not None:
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            inputs_embeds = None
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        # NOTE: In v1, inputs_embeds is always generated at model runner from
        # `get_multimodal_embeddings` and `get_input_embeddings`, this
        # condition is only for v0 compatibility.
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        elif inputs_embeds is None:
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            image_input = self._parse_and_validate_image_input(**kwargs)
            video_input = self._parse_and_validate_video_input(**kwargs)
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            if image_input is None and video_input is None:
                inputs_embeds = None
            else:
                if uses_mrope(self.config):
                    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.get_input_embeddings_v0(
                    input_ids,
                    image_input=image_input,
                    video_input=video_input)
                input_ids = None
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        hidden_states = self.language_model.model(
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            input_ids=input_ids,
            positions=positions,
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            intermediate_tensors=intermediate_tensors,
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            inputs_embeds=inputs_embeds,
        )
        return hidden_states

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    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> Optional[torch.Tensor]:
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        return self.language_model.compute_logits(hidden_states)
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    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
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        skip_prefixes = []
        if self.visual is None:
            skip_prefixes.extend(["visual."])
        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
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        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
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            connector="visual.merger.",
            tower_model="visual.",
        )
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class Tarsier2MultiModalProcessor(Qwen2VLMultiModalProcessor):
    pass


class Tarsier2ImageProcessor(Qwen2VLImageProcessor):

    def __init__(
        self,
        size: Optional[dict[str, int]] = None,
        **kwargs,
    ) -> None:
        if size is not None and "min_pixels" in size and "max_pixels" in size:
            # Remap if Tarsier2-specific format is provided
            remapped_size = {
                "shortest_edge": size["min_pixels"],
                "longest_edge": size["max_pixels"]
            }
            super().__init__(size=remapped_size, **kwargs)
        else:
            super().__init__(size=size, **kwargs)


class Tarsier2Processor(Qwen2VLProcessor):

    def __init__(
        self,
        vision_config: dict,
        tokenizer: AnyTokenizer,
        **kwargs,
    ):
        self.image_processor = Tarsier2ImageProcessor(**vision_config)
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        super().__init__(
            image_processor=self.image_processor,
            tokenizer=tokenizer,
            video_processor=Qwen2VLVideoProcessor(**vision_config),
            chat_template=None,
            **kwargs)
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class Tarsier2ProcessingInfo(Qwen2VLProcessingInfo):

    def get_hf_config(self) -> Qwen2VLConfig:
        model_path = self.ctx.model_config.model
        original_config = AutoConfig.from_pretrained(model_path)
        config_dict = original_config.to_dict()
        correct_config = Qwen2VLConfig.from_dict(config_dict)

        return correct_config

    def get_hf_processor(self, **kwargs: object) -> Tarsier2Processor:
        return Tarsier2Processor(
            vision_config=self.ctx.get_hf_image_processor_config(),
            tokenizer=self.get_tokenizer(),
            **kwargs,
        )

    def get_image_processor(self) -> Tarsier2ImageProcessor:
        return Tarsier2ImageProcessor(
            **self.ctx.get_hf_image_processor_config())


@MULTIMODAL_REGISTRY.register_processor(Tarsier2MultiModalProcessor,
                                        info=Tarsier2ProcessingInfo,
                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
class Tarsier2ForConditionalGeneration(Qwen2VLForConditionalGeneration):
    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
        "vision_tower.": "visual.",
    })

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        # Tarsier2 uses llava as model_type, which will create a Qwen2VLConfig
        # as text_config, we need to reconstruct Qwen2VLConfig from LlavaConfig.
        config = vllm_config.model_config.hf_config
        qwen2vl_config = config.text_config
        qwen2vl_config.architectures = config.architectures
        vllm_config.model_config.hf_config = qwen2vl_config
        super().__init__(vllm_config=vllm_config, prefix=prefix)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:

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        skip_prefixes = []
        if self.visual is None:
            skip_prefixes.extend(["visual."])
        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
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        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)