qwen2_vl.py 55.4 KB
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# SPDX-License-Identifier: Apache-2.0

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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 cached_property, partial
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from typing import (Any, Callable, Literal, Optional, Set, Tuple, TypedDict,
                    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 BatchFeature
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 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 import SamplingMetadata
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
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from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.gptq_marlin import (
    GPTQMarlinConfig)
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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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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                                    MultiModalFieldConfig, MultiModalKwargs,
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                                    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, ProcessorInputs
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from vllm.platforms import _Backend
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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.processor import (
    cached_image_processor_from_config)
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from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                         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
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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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logger = init_logger(__name__)

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# For profile run
_MAX_FRAMES_PER_VIDEO = 16

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# === Vision Inputs === #


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class Qwen2VLImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
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    pixel_values: torch.Tensor
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    """Shape:
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    `(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.
    """


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class Qwen2VLImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
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    image_embeds: torch.Tensor
    """Supported types:
    - List[`torch.Tensor`]: A list of tensors holding all images' features.
        Each tensor holds an image's features.
    - `torch.Tensor`: A tensor holding all images' features
        (concatenation of all images' feature tensors).
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    Tensor 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: torch.Tensor
    """Shape: `(num_images, 3)`
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    This should be in `(grid_t, grid_h, grid_w)` format.
    """


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Qwen2VLImageInputs = Union[Qwen2VLImagePixelInputs,
                           Qwen2VLImageEmbeddingInputs]


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class Qwen2VLVideoPixelInputs(TypedDict):
    type: Literal["pixel_values_videos"]
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    pixel_values_videos: torch.Tensor
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    """Shape:
    `(num_patches,
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      num_channels * temporal_patch_size * patch_size * patch_size)`
    """

    video_grid_thw: torch.Tensor
    """Shape: `(num_videos, 3)`
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    This should be in `(grid_t, grid_h, grid_w)` format.
    """


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class Qwen2VLVideoEmbeddingInputs(TypedDict):
    type: Literal["video_embeds"]
    video_embeds: torch.Tensor
    """Supported types:
    - List[`torch.Tensor`]: A list of tensors holding all videos' features.
        Each tensor holds an video's features.
    - `torch.Tensor`: A tensor holding all videos' features
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        (concatenation of all videos' feature tensors).
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    Tensor shape: `(num_image_features, hidden_size)`
    - `num_image_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: torch.Tensor
    """Shape: `(num_videos, 3)`
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    This should be in `(grid_t, grid_h, grid_w)` format.
    """


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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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    ):
        super().__init__()
        self.fc1 = ColumnParallelLinear(in_features,
                                        hidden_features,
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                                        quant_config=quant_config,
                                        prefix=f"{prefix}.fc1")
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        self.act = act_layer()
        self.fc2 = RowParallelLinear(hidden_features,
                                     in_features,
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                                     quant_config=quant_config,
                                     prefix=f"{prefix}.fc2")
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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,
                                use_flash_attn=False) -> 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
    if use_flash_attn:
        from flash_attn.layers.rotary import apply_rotary_emb
    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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    ) -> None:
        super().__init__()
        # Per attention head and per partition values.
        world_size = parallel_state.get_tensor_model_parallel_world_size()
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        self.tp_size = world_size
        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(
            num_heads, world_size)

        self.qkv = ColumnParallelLinear(input_size=embed_dim,
                                        output_size=3 * projection_size,
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                                        quant_config=quant_config,
                                        prefix=f"{prefix}.qkv")
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        self.proj = RowParallelLinear(input_size=projection_size,
                                      output_size=embed_dim,
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                                      quant_config=quant_config,
                                      prefix=f"{prefix}.proj")
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        # Detect attention implementation.
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        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
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        if self.attn_backend not in {
                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS
        }:
            raise RuntimeError(
                f"Qwen2-VL does not support {self.attn_backend} backend now.")
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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:
            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)

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        if self.attn_backend == _Backend.FLASH_ATTN:
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            # from vllm_flash_attn.flash_attn_interface import (
            #   flash_attn_varlen_func)
            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,
                                            dropout_p=0,
                                            causal=False)

            context_layer = rearrange(output,
                                      "(b s) ... -> b s ...",
                                      b=batch_size)
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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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        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)
        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,
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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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    ) -> 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,
                                         prefix=f"{prefix}.attn")
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        self.mlp = Qwen2VisionMLP(dim,
                                  mlp_hidden_dim,
                                  act_layer=act_layer,
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                                  quant_config=quant_config,
                                  prefix=f"{prefix}.mlp")
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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)
        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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    ) -> 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,
                                 prefix=f"{prefix}.mlp.0"),
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            nn.GELU(),
            RowParallelLinear(self.hidden_size,
                              d_model,
                              bias=True,
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                              quant_config=quant_config,
                              prefix=f"{prefix}.mlp.2"),
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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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    ) -> 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.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,
                             prefix=f"{prefix}.blocks.{layer_idx}")
            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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        )
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        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
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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: 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

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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
        if self.attn_backend == _Backend.FLASH_ATTN:
            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,
        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)
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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]:
        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))
        loaded_params: Set[str] = set()

        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 _qwen2vl_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
    image_grid_sizes = image_grid_thw.prod(-1)

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

    return dict(
        pixel_values=MultiModalFieldConfig.flat_from_sizes(
            "image", image_grid_sizes),
        image_embeds=MultiModalFieldConfig.flat_from_sizes(
            "image", image_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_grid_sizes),
        video_grid_thw=MultiModalFieldConfig.batched("video"),
    )
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class Qwen2VLMultiModalDataParser(MultiModalDataParser):
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    def _parse_image_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
    ) -> ModalityDataItems[Any, Any]:
        if isinstance(data, dict):
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            return DictEmbeddingItems(
                data,
                modality="image",
                required_fields={"image_embeds", "image_grid_thw"},
                fields_factory=_qwen2vl_field_config,
            )
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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]],
    ) -> ModalityDataItems[Any, Any]:
        if isinstance(data, dict):
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            return DictEmbeddingItems(
                data,
                modality="video",
                required_fields={"video_embeds", "video_grid_thw"},
                fields_factory=_qwen2vl_field_config,
            )
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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(
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        self,
        *,
        min_pixels: Optional[int] = None,
        max_pixels: Optional[int] = None,
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        size: Optional[dict[str, int]] = None,
        **kwargs: object,
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    ) -> Qwen2VLProcessor:
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        return self.ctx.get_hf_processor(
            Qwen2VLProcessor,
            image_processor=self.get_image_processor(min_pixels=min_pixels,
                                                     max_pixels=max_pixels,
                                                     size=size),
            **kwargs,
        )

    def _get_image_processor_kwargs(
        self,
        *,
        min_pixels: Optional[int] = None,
        max_pixels: Optional[int] = None,
        size: Optional[dict[str, int]] = None,
        **kwargs: object,
    ):
        if self.ctx.model_config.mm_processor_kwargs:
            kwargs.update(self.ctx.model_config.mm_processor_kwargs)

        if min_pixels is not None:
            kwargs["min_pixels"] = min_pixels

            if size is None:
                size = {"shortest_edge": min_pixels}
            else:
                size["shortest_edge"] = min_pixels

        if max_pixels is not None:
            kwargs["max_pixels"] = max_pixels

            if size is None:
                size = {"longest_edge": max_pixels}
            else:
                size["longest_edge"] = max_pixels

        if size is not None:
            kwargs["size"] = size

        return kwargs
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    def get_image_processor(
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        self,
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        *,
        min_pixels: Optional[int] = None,
        max_pixels: Optional[int] = None,
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        size: Optional[dict[str, int]] = None,
        **kwargs: object,
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    ) -> Qwen2VLImageProcessor:
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        return cached_image_processor_from_config(
            self.ctx.model_config,
            **self._get_image_processor_kwargs(min_pixels=min_pixels,
                                               max_pixels=max_pixels,
                                               size=size,
                                               **kwargs),
        )
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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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        return {
            "image": self.get_max_image_tokens(),
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            "video": self.get_max_video_tokens(seq_len, mm_counts),
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        }

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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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            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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            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) -> int:
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        target_width, target_height = self.get_image_size_with_most_features()
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        num_frames = 0

        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],
    ) -> int:
        max_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)
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        max_image_tokens = self.get_max_image_tokens() * max_images
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        max_total_frames = self._get_max_video_frames(seq_len -
                                                      max_image_tokens)
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        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
                                   _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_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        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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        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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        mm_data = {
            "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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        return ProcessorInputs(
            prompt_text=image_token * num_images + video_token * num_videos,
            mm_data=mm_data,
        )
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class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo]
                                 ):
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    def _get_data_parser(self) -> MultiModalDataParser:
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        return Qwen2VLMultiModalDataParser()
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    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        return self.info.ctx.call_hf_processor(
            self.info.get_hf_processor(**mm_kwargs),
            dict(text=prompt, **mm_data),
            self.info._get_image_processor_kwargs(**mm_kwargs),
        )

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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: MultiModalKwargs,
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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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            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
            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 _qwen2vl_field_config(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):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }
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    # To ensure correct weight loading and mapping.
    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
        "lm_head.": "language_model.lm_head.",
        "model.": "language_model.model.",
    })
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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.config = config
        self.multimodal_config = multimodal_config

        self.visual = Qwen2VisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
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            quant_config=self._maybe_ignore_quant_config(quant_config),
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            prefix=maybe_prefix(prefix, "visual"),
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        )

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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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    @cached_property
    def sampler(self):
        if hasattr(self.language_model, "sampler"):
            return self.language_model.sampler
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        return get_sampler()
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    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
        # seems to avoid vision encoder sections for some models.
        # See: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4
        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
            return None
        return quant_config
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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 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)
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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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            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image pixel values. "
                                 f"Got type: {type(pixel_values)}")
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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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            if not isinstance(image_embeds, torch.Tensor):
                raise ValueError("Incorrect type of image embeddings. "
                                 f"Got type: {type(image_embeds)}")
            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")

            if not isinstance(video_embeds, torch.Tensor):
                raise ValueError("Incorrect type of video embeddings. "
                                 f"Got type: {type(video_embeds)}")
            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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        if image_input["type"] == "image_embeds":
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            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
        else:
            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
            image_embeds = self.visual(pixel_values, grid_thw=grid_thw)

        # Split concatenated embeddings for each image item.
        merge_size = self.visual.spatial_merge_size
        sizes = grid_thw.prod(-1) // merge_size // merge_size
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        return image_embeds.split(sizes.tolist())

    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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        if video_input["type"] == "video_embeds":
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            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
        else:
            pixel_values_videos = video_input["pixel_values_videos"].type(
                self.visual.dtype)
            video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
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        # Split concatenated embeddings for each video item.
        merge_size = self.visual.spatial_merge_size
        sizes = grid_thw.prod(-1) // merge_size // merge_size
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        return video_embeds.split(sizes.tolist())

    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_multimodal_embeddings(
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            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
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        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
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            return None

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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:
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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)`,
                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.
        """

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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,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)
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    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
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        return self.language_model.sample(logits, sampling_metadata)
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    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
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        loader = AutoWeightsLoader(self)
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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",
            connector="visual.",
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            tower_model="visual.merger.")