glm4_1v.py 59.6 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/main/src/transformers/models/Glm4v/modeling_Glm4v.py
# Copyright 2025 The vLLM team.
# Copyright 2025 The ZhipuAI Team.
# Copyright 2025 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 GLM-4V model compatible with HuggingFace weights."""

import math
from collections.abc import Iterable, Mapping, Sequence
from functools import partial
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from typing import Annotated, Any, Callable, Literal, Optional, Union
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
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from packaging.version import Version
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from transformers import BatchFeature
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from transformers import __version__ as TRANSFORMERS_VERSION
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Yuxuan Zhang committed
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from transformers.models.glm4v.configuration_glm4v import Glm4vVisionConfig
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from transformers.models.glm4v.image_processing_glm4v import (
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    Glm4vImageProcessor,
    smart_resize,
)
from transformers.models.glm4v.video_processing_glm4v import Glm4vVideoProcessor
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from transformers.video_utils import VideoMetadata

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from vllm.attention.backends.registry import _Backend
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from vllm.attention.layer import (
    check_upstream_fa_availability,
    maybe_get_vit_flash_attn_backend,
)
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions
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from vllm.distributed import get_tensor_model_parallel_world_size, parallel_state
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from vllm.distributed import utils as dist_utils
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
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from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
    VideoItem,
)
from vllm.multimodal.parse import ImageSize, MultiModalDataItems, MultiModalDataParser
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from ..layers.activation import SiluAndMul
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from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
    SupportsMultiModal,
    SupportsPP,
)
from .qwen2_vl import _create_qwen2vl_field_factory, apply_rotary_pos_emb_vision
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    init_vllm_registered_model,
    maybe_prefix,
)
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from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model
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logger = init_logger(__name__)

# For profile run
_MAX_FRAMES_PER_VIDEO = 600

# === Vision Inputs === #


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class Glm4vImagePixelInputs(TensorSchema):
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    """
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    Dimensions:
        - np: Number of patches
        - cpp: Number of channels * patch_size * patch_size
        - ni: Number of images
        - g: Grid dimensions (3 for grid_t, grid_h, grid_w)
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    """
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    type: Literal["pixel_values"] = "pixel_values"
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    pixel_values: Annotated[torch.Tensor, TensorShape("np", "cpp")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
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class Glm4vImageEmbeddingInputs(TensorSchema):
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    """
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    Dimensions:
        - f: Number of image features (varies based on image resolution)
        - h: Hidden size (must match language model backbone)
        - n: Number of images
        - g: Grid dimensions (3 for grid_t, grid_h, grid_w)
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    """
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    type: Literal["image_embeds"] = "image_embeds"

    image_embeds: Annotated[torch.Tensor, TensorShape("f", "h")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("n", 3)]
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Glm4vImageInputs = Union[Glm4vImagePixelInputs, Glm4vImageEmbeddingInputs]


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class Glm4vVideoPixelInputs(TensorSchema):
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    """
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    Dimensions:
        - np: Number of patches
        - ctpp: Number of channels * temporal_patch_size *
            patch_size * patch_size
        - f: Number of frames
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        - g: Grid dimensions (3 for grid_t which is usually 1 for processed
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          video, grid_h, grid_w)
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    """
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    type: Literal["pixel_values_videos"] = "pixel_values_videos"
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    pixel_values_videos: Annotated[torch.Tensor, TensorShape("np", "ctpp")]
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    video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
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class Glm4vVideoEmbeddingInputs(TensorSchema):
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    """
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    Dimensions:
        - p: Number of video patches across all frames
        - h: Hidden size (must match language model backbone)
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        - f: Number of frames
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        - g: Grid dimensions (3 for grid_t which is usually 1 for processed
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          video, grid_h, grid_w)
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    """
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    type: Literal["video_embeds"] = "video_embeds"
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    video_embeds: Annotated[torch.Tensor, TensorShape("p", "h")]
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    video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
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Glm4vVideoInputs = Union[Glm4vVideoPixelInputs, Glm4vVideoEmbeddingInputs]

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# ==== Vision Encoder ==== #
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class Glm4vVisionMLP(nn.Module):
    def __init__(
        self,
        in_features: int,
        hidden_features: int,
        bias: bool = False,
        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__()
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        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=in_features,
            output_sizes=[hidden_features] * 2,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
            disable_tp=use_data_parallel,
        )
        self.down_proj = RowParallelLinear(
            hidden_features,
            in_features,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
            disable_tp=use_data_parallel,
        )
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        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor):
        x, _ = self.gate_up_proj(x)
        x = self.act_fn(x)
        x, _ = self.down_proj(x)
        return x


def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
    """All-gather the input tensor interleavely across model parallel group."""
    import torch.distributed as dist

    gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
    dist.all_gather(
        gathered_tensors,
        local_tensor,
        group=parallel_state.get_tp_group().device_group,
    )

    gathered_tensors_split = [
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        torch.split(tensor, hidden_size // tp_size, -1) for tensor in gathered_tensors
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    ]
    ordered_tensors = [
        tensor for pair in zip(*gathered_tensors_split) for tensor in pair
    ]
    result_tensor = torch.cat(ordered_tensors, dim=-1)
    return result_tensor


class Glm4vVisionAttention(nn.Module):
    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        projection_size: int,
        quant_config: Optional[QuantizationConfig] = None,
        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 get_tensor_model_parallel_world_size()
        )
        self.tp_rank = (
            0 if use_data_parallel else parallel_state.get_tensor_model_parallel_rank()
        )
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        self.hidden_size_per_attention_head = dist_utils.divide(
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            projection_size, num_heads
        )
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        self.num_attention_heads_per_partition = dist_utils.divide(
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            num_heads, self.tp_size
        )
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        self.qkv = QKVParallelLinear(
            hidden_size=embed_dim,
            head_size=self.hidden_size_per_attention_head,
            total_num_heads=num_heads,
            total_num_kv_heads=num_heads,
            bias=False,
            quant_config=quant_config,
            # Change qkv prefix to align with GLM-4.5V-FP8 quantization cfg
            prefix=f"{prefix}.qkv_proj" if quant_config else f"{prefix}.qkv",
            disable_tp=use_data_parallel,
        )
        self.proj = RowParallelLinear(
            input_size=projection_size,
            output_size=embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.proj",
            bias=False,
            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,
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            dtype=torch.get_default_dtype(),
        )
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        self.use_upstream_fa = False
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        self.attn_backend, self.flash_attn_varlen_func = (
            maybe_get_vit_flash_attn_backend(
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                self.attn_backend,
                self.use_upstream_fa,
            )
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        )
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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(
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                f"GLM-4V 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_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

        # [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] -> 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

    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:
        # [s, b, c] --> [s, b, head * 3 * head_dim]
        x, _ = self.qkv(x)

        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
        q, k, v = self.split_qkv(x)
        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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            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])

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            output = self.flash_attn_varlen_func(
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                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,
            )

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            context_layer = rearrange(
                output, "(b s) h d -> s b (h d)", b=batch_size
            ).contiguous()
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        elif self.attn_backend == _Backend.TORCH_SDPA:
            # Execute attention entry by entry for speed & less VRAM.
            outputs = []
            for i in range(1, len(cu_seqlens)):
                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]
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                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)
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                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:
            from xformers import ops as xops
            from xformers.ops.fmha.attn_bias import BlockDiagonalMask

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            attn_bias = BlockDiagonalMask.from_seqlens(
                q_seqlen=seqlens, kv_seqlen=None, device=q.device
            )
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            context_layer = xops.memory_efficient_attention_forward(
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                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()
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        output, _ = self.proj(context_layer)
        return output


class Glm4vVisionBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_hidden_dim: int,
        norm_layer: Optional[Callable[[int], nn.Module]] = None,
        quant_config: Optional[QuantizationConfig] = None,
        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)
        self.attn = Glm4vVisionAttention(
            embed_dim=dim,
            num_heads=num_heads,
            projection_size=dim,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
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            use_data_parallel=use_data_parallel,
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        )
        self.mlp = Glm4vVisionMLP(
            dim,
            mlp_hidden_dim,
            bias=False,
            quant_config=quant_config,
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            prefix=f"{prefix}.mlp",
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            use_data_parallel=use_data_parallel,
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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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        x_attn = self.attn(
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            self.norm1(x),
            cu_seqlens=cu_seqlens,
            rotary_pos_emb=rotary_pos_emb,
            max_seqlen=max_seqlen,
            seqlens=seqlens,
        )
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        x_fused_norm, residual = self.norm2(x, residual=x_attn)
        x = residual + self.mlp(x_fused_norm)
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        return x


class Glm4vVisionPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 1,
        in_channels: int = 3,
        hidden_size: int = 1536,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.hidden_size = hidden_size

        kernel_size = (temporal_patch_size, patch_size, patch_size)
        self.proj = nn.Conv3d(
            in_channels,
            hidden_size,
            kernel_size=kernel_size,
            stride=kernel_size,
            bias=True,
        )

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


class Glm4vPatchMerger(nn.Module):
    def __init__(
        self,
        d_model: int,
        context_dim: int,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
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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 = d_model
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        self.proj = ColumnParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=bias,
            gather_output=True,
            quant_config=quant_config,
            prefix=f"{prefix}.proj",
            disable_tp=use_data_parallel,
        )
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        self.post_projection_norm = nn.LayerNorm(self.hidden_size)
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        self.gate_up_proj = MergedColumnParallelLinear(
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            input_size=self.hidden_size,
            output_sizes=[context_dim] * 2,
            bias=bias,
            quant_config=quant_config,
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            prefix=f"{prefix}.gate_up_proj",
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            disable_tp=use_data_parallel,
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        )
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        self.down_proj = RowParallelLinear(
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            context_dim,
            self.hidden_size,
            bias=bias,
            quant_config=quant_config,
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            prefix=f"{prefix}.down_proj",
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            disable_tp=use_data_parallel,
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        )
        self.act_fn = SiluAndMul()
        self.extra_activation_func = nn.GELU()

    def forward(self, x: torch.Tensor):
        x, _ = self.proj(x)
        x = self.extra_activation_func(self.post_projection_norm(x))
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Glm4vVisionEmbeddings(nn.Module):
    def __init__(self, config: Glm4vVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

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        self.num_patches = (self.image_size // self.patch_size) ** 2
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        self.num_positions = self.num_patches
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        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions).expand((1, -1)),
            persistent=False,
        )

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    def forward(
        self, embeddings, lengths, image_shapes, h_coords, w_coords
    ) -> torch.Tensor:
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        pos_embed_weight = self.position_embedding.weight
        hidden_size = pos_embed_weight.shape[1]
        total_seq = h_coords.shape[0]
        device = pos_embed_weight.device

        # Move coordinates to correct device
        h_coords, w_coords = h_coords.to(device), w_coords.to(device)

        # Handle empty sequence case
        if total_seq == 0:
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            adapted_pos_embed = torch.empty(
                0, hidden_size, device=device, dtype=pos_embed_weight.dtype
            )
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        else:
            # Convert inputs to tensors if needed
            if isinstance(lengths, list):
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                lengths = torch.tensor(lengths, device=device, dtype=torch.long)
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            if not isinstance(image_shapes, torch.Tensor):
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                image_shapes = torch.tensor(
                    image_shapes, device=device, dtype=torch.long
                )
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            # Prepare 2D position embedding
            orig_size_sq = pos_embed_weight.shape[0]
            orig_size = int(orig_size_sq**0.5)
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            pos_embed_2d = (
                pos_embed_weight.view(orig_size, orig_size, hidden_size)
                .permute(2, 0, 1)
                .unsqueeze(0)
                .to(device=device, dtype=torch.float32)
            )
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            # Calculate target dimensions for each patch
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            # Add bounds checking for data parallel mode
            if len(lengths) > image_shapes.shape[0]:
                # In data parallel mode, some GPUs might not have all
                # image shapes
                # Use available image shapes, cycling if necessary
                target_h_list = []
                target_w_list = []
                for i in range(len(lengths)):
                    # Cycle through available shapes
                    shape_idx = i % image_shapes.shape[0]
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                    target_h_list.append(image_shapes[shape_idx, 1].repeat(lengths[i]))
                    target_w_list.append(image_shapes[shape_idx, 2].repeat(lengths[i]))
                target_h = torch.cat(target_h_list).to(
                    device=device, dtype=torch.float32
                )
                target_w = torch.cat(target_w_list).to(
                    device=device, dtype=torch.float32
                )
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            else:
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                target_h = torch.cat(
                    [image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]
                ).to(device=device, dtype=torch.float32)
                target_w = torch.cat(
                    [image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]
                ).to(device=device, dtype=torch.float32)
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            # Normalize coordinates to [-1, 1] range for grid_sample
            h_coords = h_coords.to(device=device, dtype=torch.float32)
            w_coords = w_coords.to(device=device, dtype=torch.float32)
            norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
            norm_h = ((h_coords + 0.5) / target_h) * 2 - 1

            # Create sampling grid
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            grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2)
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            # Perform bicubic interpolation
            interpolated_embed_fp32 = F.grid_sample(
                pos_embed_2d,
                grid,
                mode="bicubic",
                align_corners=False,
                padding_mode="border",
            )

            # Reshape and convert back to original dtype
            adapted_pos_embed_fp32 = (
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                interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0)
            )
            adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(
                embeddings.device
            )
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        # Add adapted position encoding to embeddings
        embeddings = embeddings + adapted_pos_embed
        return embeddings


class Glm4vVisionRotaryEmbedding(nn.Module):
    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
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        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
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        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
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            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
            )
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            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 Glm4vVisionTransformer(nn.Module):
    def __init__(
        self,
        vision_config: Glm4vVisionConfig,
        norm_eps: float = 1e-6,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
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        use_data_parallel: bool = False,
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    ) -> None:
        super().__init__()

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

        self.patch_embed = Glm4vVisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
            in_channels=in_channels,
            hidden_size=self.hidden_size,
        )

        norm_layer = partial(RMSNorm, eps=norm_eps)
        head_dim = self.hidden_size // self.num_heads
        self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
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        self.blocks = nn.ModuleList(
            [
                Glm4vVisionBlock(
                    dim=self.hidden_size,
                    num_heads=self.num_heads,
                    mlp_hidden_dim=vision_config.out_hidden_size,
                    norm_layer=norm_layer,
                    quant_config=quant_config,
                    prefix=f"{prefix}.blocks.{layer_idx}",
                    use_data_parallel=self.use_data_parallel,
                )
                for layer_idx in range(depth)
            ]
        )
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        self.merger = Glm4vPatchMerger(
            d_model=vision_config.out_hidden_size,
            context_dim=vision_config.intermediate_size,
            quant_config=quant_config,
            bias=False,
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            prefix=f"{prefix}.merger",
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            use_data_parallel=self.use_data_parallel,
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        )
        self.embeddings = Glm4vVisionEmbeddings(vision_config)

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        self.post_conv_layernorm = RMSNorm(
            vision_config.hidden_size, eps=vision_config.rms_norm_eps
        )
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        self.downsample = nn.Conv2d(
            in_channels=vision_config.hidden_size,
            out_channels=vision_config.out_hidden_size,
            kernel_size=vision_config.spatial_merge_size,
            stride=vision_config.spatial_merge_size,
        )
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        self.post_layernorm = RMSNorm(
            vision_config.hidden_size, eps=vision_config.rms_norm_eps
        )
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        self.attn_backend = get_vit_attn_backend(
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            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()
        ):
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            self.attn_backend = _Backend.FLASH_ATTN
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    @property
    def dtype(self) -> torch.dtype:
        return self.patch_embed.proj.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.patch_embed.proj.weight.device

    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
        pos_ids = []
        for t, h, w in grid_thw:
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
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            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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        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, pos_ids

    def compute_attn_mask_seqlen(
        self,
        cu_seqlens: torch.Tensor,
    ) -> tuple[Optional[int], Optional[list[int]]]:
        max_seqlen, seqlens = None, None
        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
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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()
        return max_seqlen, seqlens

    def forward(
        self,
        x: torch.Tensor,
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        grid_thw: list[list[int]],
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    ) -> torch.Tensor:
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        # Convert grid_thw to tensor (always expecting list format now)
        grid_thw = torch.tensor(grid_thw, device=x.device, dtype=torch.long)

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        # patchify
        x = x.to(device=self.device, dtype=self.dtype)
        x = self.patch_embed(x)
        x = self.post_conv_layernorm(x)

        # compute position embedding
        rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
        # compute cu_seqlens
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        cu_seqlens = torch.repeat_interleave(
            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
        ).cumsum(dim=0, dtype=torch.int32)
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        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)

        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
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        x = self.embeddings(
            x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
        )
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        # transformers
        x = x.unsqueeze(1)
        for blk in self.blocks:
            x = blk(
                x,
                cu_seqlens=cu_seqlens,
                rotary_pos_emb=rotary_pos_emb,
                max_seqlen=max_seqlen,
                seqlens=seqlens,
            )

        # adapter
        x = self.post_layernorm(x)

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        x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size, x.shape[-1])
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        x = x.permute(0, 3, 1, 2)
        x = self.downsample(x).view(-1, self.out_hidden_size)
        x = self.merger(x)

        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)
            ("attn.qkv.", "attn.q.", "q"),
            ("attn.qkv.", "attn.k.", "k"),
            ("attn.qkv.", "attn.v.", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        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]
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                weight_loader = getattr(param, "weight_loader", default_weight_loader)
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                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Glm4vProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
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        return self.ctx.get_hf_config()
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    def get_tokenizer(self):
        return self.ctx.tokenizer

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None, "video": 1}

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    def get_image_processor(self, **kwargs: object) -> Glm4vImageProcessor:
        return self.get_hf_processor(**kwargs).image_processor
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    def get_video_processor(self, **kwargs: object) -> Glm4vVideoProcessor:
        return self.get_hf_processor(**kwargs).video_processor
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    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 16,
        do_resize: bool = True,
        max_image_pixels: int = 28 * 28 * 2 * 30000,
    ) -> tuple[ImageSize, int]:
        hf_config = self.get_hf_config()
        vision_config = hf_config.vision_config
        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = vision_config.temporal_patch_size
        if do_resize:
            resized_height, resized_width = smart_resize(
                num_frames=num_frames
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                if num_frames > temporal_patch_size
                else temporal_patch_size,
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                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                max_pixels=max_image_pixels,
            )
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            preprocessed_size = ImageSize(width=resized_width, height=resized_height)
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        else:
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            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)
        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

    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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        return max_image_size

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        _, num_image_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            max_image_pixels=28 * 28 * 2 * 6144,
        )
        return num_image_tokens

    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_image_tokens(
            image_width=target_width,
            image_height=target_height,
        )

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
    ) -> int:
        _, num_video_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            num_frames=num_frames,
            max_image_pixels=28 * 28 * 2 * 30000,
        )
        return num_video_tokens

    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        num_frames = 0

        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
            )
            if next_max_tokens > max_tokens or next_max_tokens == 0:
                break

            num_frames = next_num_frames

        return num_frames

    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)

        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)
        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_video_second_idx(
        self, metadata: dict[str, Any], total_frames: int
    ) -> list[int]:
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        video_processor = self.get_video_processor()

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        video_fps = metadata.get("fps", video_processor.fps)
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        meta_frames = metadata.get("total_num_frames", total_frames)
        max_frame_idx = meta_frames - 1
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        duration = metadata.get("duration", round(max_frame_idx / video_fps) + 1)
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        do_sample_frames = metadata["do_sample_frames"]
        if not do_sample_frames:
            frame_indices = metadata["frames_indices"]
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        else:
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            if duration <= video_processor.max_duration:
                n = int(math.floor(duration * video_processor.fps))
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                frame_indices = [
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                    min(
                        max_frame_idx,
                        int(math.ceil(i * video_fps / video_processor.fps)),
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                    )
                    for i in range(n)
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                ]
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            else:
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                num_samples = int(video_processor.max_duration * video_processor.fps)
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                if num_samples >= meta_frames:
                    frame_indices = list(range(meta_frames))
                else:
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                    target_seconds = np.linspace(
                        0, duration, num_samples, endpoint=True
                    )
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                    frame_indices = [
                        min(max_frame_idx, int(math.ceil(t * video_fps)))
                        for t in target_seconds
                    ]
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        seen, uniq = set(), []
        for idx in frame_indices:
            if idx not in seen:
                seen.add(idx)
                uniq.append(idx)
        if len(uniq) & 1:
            uniq.append(uniq[-1])
        frame_indices = uniq

        full_second_idxs = [int(idx / video_fps) for idx in frame_indices]
        timestamps_list = full_second_idxs[::2]
        selected_timestamps = []
        for idx in range(0, len(timestamps_list)):
            selected_timestamps.append(timestamps_list[idx])
        return selected_timestamps

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    def _construct_video_placeholder(
        self,
        video_array: np.ndarray,
        metadata: dict[str, Any],
        grid_thw: torch.Tensor,
    ) -> str:
        hf_processor = self.get_hf_processor()
        tokenizer = self.get_tokenizer()
        image_processor = hf_processor.image_processor

        hf_config = self.get_hf_config()
        boi_token_id = hf_config.image_start_token_id
        eoi_token_id = hf_config.image_end_token_id
        bov_token_id = hf_config.video_start_token_id
        eov_token_id = hf_config.video_end_token_id
        merge_length = image_processor.merge_size**2

        assert isinstance(grid_thw, torch.Tensor)
        timestamps = self._get_video_second_idx(metadata, len(video_array))
        frames_idx_token = [
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            tokenizer.encode(str(i), add_special_tokens=False) for i in timestamps
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        ]
        T, H, W = grid_thw
        num_tokens_per_frame = int(H * W) // merge_length
        placeholder = []
        placeholder.append(bov_token_id)
        for frame_idx in frames_idx_token:
            placeholder.append(boi_token_id)
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            placeholder.extend([hf_processor.video_token_id] * num_tokens_per_frame)
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            placeholder.append(eoi_token_id)
            placeholder.extend(frame_idx)
        placeholder.append(eov_token_id)

        return placeholder

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class Glm4vDummyInputsBuilder(BaseDummyInputsBuilder[Glm4vProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        hf_config = self.info.get_hf_config()
        hf_processor = self.info.get_hf_processor()
        tokenizer = self.info.get_tokenizer()

        image_token: str = hf_processor.image_token
        video_token_ids = [
            hf_config.video_start_token_id,
            hf_processor.video_token_id,
            hf_config.video_end_token_id,
        ]
        video_token = tokenizer.decode(video_token_ids)

        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
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        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
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    ) -> 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()
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        target_num_frames = self.info.get_num_frames_with_most_features(
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            seq_len, mm_counts
        )
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        image_overrides = mm_options.get("image") if mm_options else None
        video_overrides = mm_options.get("video") if mm_options else None

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        return {
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            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            ),
            "video": self._get_dummy_videos(
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                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
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                overrides=video_overrides,
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            ),
        }

    def _get_dummy_videos(
        self,
        *,
        width: int,
        height: int,
        num_frames: int,
        num_videos: int,
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        overrides: Optional[VideoDummyOptions] = None,
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    ) -> list[VideoItem]:
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        if overrides:
            if overrides.num_frames:
                if overrides.num_frames > num_frames:
                    logger.warning(
                        "video.num_frames override (%d) exceeds model's "
                        "maximum number of frames (%d), will be ignored",
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                        overrides.num_frames,
                        num_frames,
                    )
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                num_frames = min(num_frames, overrides.num_frames)
            if overrides.width:
                if overrides.width > width:
                    logger.warning(
                        "video.width override (%d) exceeds model's "
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                        "maximum width (%d), will be ignored",
                        overrides.width,
                        width,
                    )
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                width = min(width, overrides.width)
            if overrides.height:
                if overrides.height > height:
                    logger.warning(
                        "video.height override (%d) exceeds model's "
                        "maximum height (%d), will be ignored",
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                        overrides.height,
                        height,
                    )
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                height = min(height, overrides.height)
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        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
        video_items = []
        for i in range(num_videos):
            video_metadata = {
                "fps": 2.0,
                "duration": num_frames / 2.0,
                "total_num_frames": num_frames,
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                "frames_indices": [i for i in range(num_frames)],
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                "video_backend": "opencv",
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                "do_sample_frames": False,
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            }
            video_item = (video.copy(), video_metadata)
            video_items.append(video_item)

        return video_items


class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
    def _get_data_parser(self) -> MultiModalDataParser:
        return MultiModalDataParser(video_needs_metadata=True)

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        mm_data = dict(mm_data)
        processor = self.info.get_hf_processor(**mm_kwargs)

        # GLM-4.1V use `image_token_id` as video placeholder, we need to
        # replace it with `video_token_id` for video processing. So we
        # separate video processing from image processing.
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        if (
            "videos" in mm_data
            and isinstance(mm_data["videos"], list)
            and len(mm_data["videos"]) > 0
        ):
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            video_grid_thw_lst = []
            pixel_values_videos_lst = []
            for item in mm_data.pop("videos", []):
                video_array, metadata = item

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                # don't update mm_kwargs inplace
                video_mm_kwargs = dict(**mm_kwargs)
                video_mm_kwargs["do_sample_frames"] = metadata.get(
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                    "do_sample_frames", True
                )
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                video_mm_data = dict()
                video_mm_data["videos"] = [[video_array]]
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                # backward compatibility for Transformers 4.55
                unuse_metadata = ["do_sample_frames"]
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                if (
                    not hasattr(VideoMetadata, "frames_indices")
                    and "frames_indices" in metadata
                ):
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                    unuse_metadata.append("frames_indices")

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                video_mm_data["video_metadata"] = [
                    [
                        VideoMetadata(
                            **{
                                k: metadata[k]
                                for k in metadata
                                if k not in unuse_metadata
                            }
                        )
                    ]
                ]
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                video_outputs = super()._call_hf_processor(
                    prompt="<|begin_of_video|><|video|><|end_of_video|>",
                    mm_data=video_mm_data,
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                    mm_kwargs=video_mm_kwargs,
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                    tok_kwargs=tok_kwargs,
                )
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                if not video_mm_kwargs["do_sample_frames"] and Version(
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                    TRANSFORMERS_VERSION
                ) < Version("4.56.0"):
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                    # Transformers v4.55 has incorrect timestamps issue for
                    # skip sampling. We construct the placeholder manually to
                    # get placeholders with correct timestamps.
                    placeholder = self.info._construct_video_placeholder(
                        video_array,
                        metadata,
                        video_outputs["video_grid_thw"].squeeze(0),
                    )
                    video_placeholder = processor.tokenizer.decode(placeholder)
                else:
                    input_ids = video_outputs.pop("input_ids")
                    input_ids[input_ids == processor.image_token_id] = (
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                        processor.video_token_id
                    )
                    video_placeholder = processor.tokenizer.batch_decode(input_ids)[0]
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                prompt = prompt.replace(
                    "<|begin_of_video|><|video|><|end_of_video|>",
                    video_placeholder,
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                    1,
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                )

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                video_grid_thw_lst.append(video_outputs["video_grid_thw"])
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                pixel_values_videos_lst.append(video_outputs["pixel_values_videos"])
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            video_outputs = dict(
                pixel_values_videos=torch.cat(pixel_values_videos_lst),
                video_grid_thw=torch.cat(video_grid_thw_lst),
            )
        else:
            video_outputs = dict()

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )
        combined_outputs = dict(
            processed_outputs,
            **video_outputs,
        )
        return BatchFeature(combined_outputs)

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
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        return _create_qwen2vl_field_factory(
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            self.info.get_hf_config().vision_config.spatial_merge_size
        )(hf_inputs)
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    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
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        out_mm_kwargs: MultiModalKwargsItems,
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    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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        image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
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        merge_length = image_processor.merge_size**2

        def get_image_replacement_glm4v(item_idx: int):
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            out_item = out_mm_kwargs["image"][item_idx]
            grid_thw = out_item["image_grid_thw"].data
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            assert isinstance(grid_thw, torch.Tensor)

            num_tokens = int(grid_thw.prod()) // merge_length
            return [hf_processor.image_token_id] * num_tokens

        def get_video_replacement_glm4v(item_idx: int):
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            out_item = out_mm_kwargs["video"][item_idx]
            grid_thw = out_item["video_grid_thw"].data
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            assert isinstance(grid_thw, torch.Tensor)

            video, metadata = mm_items["video"][item_idx]
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            placeholder = self.info._construct_video_placeholder(
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                video, metadata, grid_thw
            )
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            return PromptUpdateDetails.select_token_id(
                placeholder,
                embed_token_id=hf_processor.video_token_id,
            )
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        return [
            PromptReplacement(
                modality="image",
                target=hf_processor.image_token,
                replacement=get_image_replacement_glm4v,
            ),
            PromptReplacement(
                modality="video",
                target="<|begin_of_video|><|video|><|end_of_video|>",
                replacement=get_video_replacement_glm4v,
            ),
        ]


@MULTIMODAL_REGISTRY.register_processor(
    Glm4vMultiModalProcessor,
    info=Glm4vProcessingInfo,
    dummy_inputs=Glm4vDummyInputsBuilder,
)
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class Glm4vForConditionalGeneration(
    nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP
):
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    merge_by_field_config = True

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    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
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        "gate_up_proj": ["gate_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.": "language_model.model.",
            "model.visual.": "visual.",
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        }
    )
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    supports_encoder_tp_data = True

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    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<|begin_of_image|><|image|><|end_of_image|>"
        if modality.startswith("video"):
            return "<|begin_of_video|><|video|><|end_of_video|>"

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

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

        self.config = config
        self.multimodal_config = multimodal_config
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        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
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        self.visual = Glm4vVisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-5),
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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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        )

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        if config.model_type == "glm4v":
            architectures = ["Glm4ForCausalLM"]
        elif config.model_type == "glm4v_moe":
            architectures = ["Glm4MoeForCausalLM"]
        else:
            architectures = None

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        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
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            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
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            architectures=architectures,
        )
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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 _parse_and_validate_image_input(
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        self, **kwargs: object
    ) -> Optional[Glm4vImageInputs]:
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        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)
        image_grid_thw = kwargs.pop("image_grid_thw", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            return Glm4vImagePixelInputs(
                type="pixel_values",
                pixel_values=pixel_values,
                image_grid_thw=image_grid_thw,
            )

        if image_embeds is not None:
            return Glm4vImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
                image_grid_thw=image_grid_thw,
            )

    def _parse_and_validate_video_input(
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        self, **kwargs: object
    ) -> Optional[Glm4vVideoInputs]:
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        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
        video_embeds = kwargs.pop("video_embeds", None)
        video_grid_thw = kwargs.pop("video_grid_thw", None)
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        if pixel_values_videos is None and video_embeds is None:
            return None
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        if pixel_values_videos is not None:
            return Glm4vVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            return Glm4vVideoEmbeddingInputs(
                type="video_embeds",
                video_embeds=video_embeds,
                video_grid_thw=video_grid_thw,
            )

    def _process_image_input(
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        self, image_input: Glm4vImageInputs
    ) -> tuple[torch.Tensor, ...]:
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        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":
            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
        else:
            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
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            if self.use_data_parallel:
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                return run_dp_sharded_mrope_vision_model(
                    self.visual, pixel_values, grid_thw.tolist(), rope_type="rope_3d"
                )
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            else:
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                image_embeds = self.visual(pixel_values, grid_thw=grid_thw.tolist())
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        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(
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        self, video_input: Glm4vVideoInputs
    ) -> 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":
            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
        else:
            pixel_values_videos = video_input["pixel_values_videos"].type(
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                self.visual.dtype
            )
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            if self.use_data_parallel:
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                return run_dp_sharded_mrope_vision_model(
                    self.visual,
                    pixel_values_videos,
                    grid_thw.tolist(),
                    rope_type="rope_3d",
                )
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            else:
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                video_embeds = self.visual(
                    pixel_values_videos, grid_thw=grid_thw.tolist()
                )
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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:
        mm_input_by_modality = {}

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

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

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

        # The result multimodal_embeddings is tuple of tensors, with each
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        # tensor corresponding to a multimodal data item (image or video).
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        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 mm_input_by_modality:
            multimodal_input = mm_input_by_modality[modality]
            if modality == "image":
                vision_embeddings = self._process_image_input(multimodal_input)
                multimodal_embeddings += vision_embeddings
            if modality == "video":
                video_embeddings = self._process_video_input(multimodal_input)
                multimodal_embeddings += video_embeddings
        return multimodal_embeddings

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        """Run forward pass for GLM-4V.

        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 GLM-4V
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
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            intermediate_tensors: Optional intermediate tensors for pipeline
                parallelism.
            inputs_embeds: Optional pre-computed input embeddings.
            **kwargs: Additional keyword arguments.
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        """
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    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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        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
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            language_model="language_model.model",
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            connector="visual.merger.",
            tower_model="visual.",
        )
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@MULTIMODAL_REGISTRY.register_processor(
    Glm4vMultiModalProcessor,
    info=Glm4vProcessingInfo,
    dummy_inputs=Glm4vDummyInputsBuilder,
)
class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }