[Model] Add Ernie4.5 VL Model Support (#22514)

Signed-off-by: wangyafeng <wangyafeng@baidu.com>

docs/models/supported_models.md

@@ -616,6 +616,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Cohere2VisionForConditionalGeneration` | Command A Vision | T + I<sup>+</sup> | `CohereLabs/command-a-vision-07-2025`, etc. | | ✅︎ | ✅︎ |
 | `DeepseekVLV2ForCausalLM`<sup>^</sup> | DeepSeek-VL2 | T + I<sup>+</sup> | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, etc. | | ✅︎ | ✅︎ |
 | `DonutForConditionalGeneration`<sup>^</sup> | Donut | T + I | `ByteDance/Dolphin`, `naver-clova-ix/donut-base-finetuned-docvqa`, etc. | | | |
+| `Ernie4_5_VLMoeForConditionalGeneration` | Ernie4.5-VL | T + I<sup>+</sup>/ V<sup>+</sup> | `baidu/ERNIE-4.5-VL-28B-A3B-PT`, `baidu/ERNIE-4.5-VL-424B-A47B-PT` | | ✅︎ | ✅︎ |
 | `Florence2ForConditionalGeneration` | Florence-2 | T + I | `microsoft/Florence-2-base`, `microsoft/Florence-2-large`, etc. | | | |
 | `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ | ✅︎ |
 | `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>+</sup> | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ | ⚠️ |

examples/offline_inference/vision_language.py

@@ -173,6 +173,37 @@ def run_deepseek_vl2(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# Ernie4.5-VL
+def run_ernie45_vl(questions: list[str], modality: str) -> ModelRequestData:
+    model_name = "baidu/ERNIE-4.5-VL-28B-A3B-PT"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        limit_mm_per_prompt={modality: 1},
+        trust_remote_code=True,
+    )
+
+    if modality == "image":
+        placeholder = "Picture 1:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>"
+    elif modality == "video":
+        placeholder = "Video 1:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>"
+
+    prompts = [
+        (
+            f"<|begin_of_sentence|>User: {question}{placeholder}\n"
+            "Assistant: <think></think>"
+        )
+        for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # Florence2
 def run_florence2(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1602,6 +1633,7 @@ model_example_map = {
     "chameleon": run_chameleon,
     "command_a_vision": run_command_a_vision,
     "deepseek_vl_v2": run_deepseek_vl2,
+    "ernie45_vl": run_ernie45_vl,
     "florence2": run_florence2,
     "fuyu": run_fuyu,
     "gemma3": run_gemma3,

requirements/test.in

@@ -54,3 +54,4 @@ runai-model-streamer-s3==0.11.0
 fastsafetensors>=0.1.10
 pydantic>=2.10 # 2.9 leads to error on python 3.10
 terratorch==1.1rc2 # required for PrithviMAE test
+decord==0.6.0

requirements/test.txt

@@ -156,6 +156,8 @@ datasets==3.0.2
     #   mteb
 decorator==5.1.1
     # via librosa
+decord==0.6.0
+    # via -r requirements/test.in
 dill==0.3.8
     # via
     #   datasets
@@ -493,6 +495,7 @@ numpy==1.26.4
     #   contourpy
     #   cupy-cuda12x
     #   datasets
+    #   decord
     #   einx
     #   encodec
     #   evaluate

tests/models/multimodal/processing/test_common.py

@@ -272,6 +272,7 @@ def _test_processing_correctness_one(
     "CohereLabs/command-a-vision-07-2025",
     "deepseek-ai/deepseek-vl2-tiny",
     "naver-clova-ix/donut-base-finetuned-docvqa",
+    "baidu/ERNIE-4.5-VL-28B-A3B-PT",
     "microsoft/Florence-2-base",
     "adept/fuyu-8b",
     "google/gemma-3-4b-it",

tests/models/registry.py

@@ -396,6 +396,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                 transformers_version_reason="HF model is not compatible.",  # noqa: E501
                                                 hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]}),  # noqa: E501
     "Emu3ForConditionalGeneration": _HfExamplesInfo("BAAI/Emu3-Chat-hf"),
+    "Ernie4_5_VLMoeForConditionalGeneration": _HfExamplesInfo("baidu/ERNIE-4.5-VL-28B-A3B-PT",  # noqa: E501
+                                                              trust_remote_code=True),
     "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
     "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it"),
     "Gemma3nForConditionalGeneration": _HfExamplesInfo("google/gemma-3n-E2B-it",    # noqa: E501

vllm/model_executor/layers/rotary_embedding/ernie45_vl_rope.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+from .common import apply_rotary_emb_dispatch
+from .mrope import MRotaryEmbedding
+
+
+class Ernie4_5_VLRotaryEmbedding(MRotaryEmbedding):
+    """3D rotary positional embedding. 3D is t:time h:height w:width"""
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        assert positions.ndim == 1 or positions.ndim == 2
+        assert key is not None
+
+        num_tokens = positions.shape[-1]
+        cos_sin = self.cos_sin_cache[positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if positions.ndim == 2:
+            assert self.mrope_section
+
+            section_h = self.mrope_section[0]  # 22
+            section_w = self.mrope_section[1]  # 22
+            section_t = self.mrope_section[2]  # 20
+            assert section_h == section_w
+            # Split according to [h w h w h w h w... t t t...]
+            section_cos_t = cos[..., -section_t:]
+            section_cos_h = cos[..., :section_h + section_w:2]
+            section_cos_w = cos[..., 1:section_h + section_w:2]
+
+            cos_t, cos_h, cos_w = section_cos_t[0], section_cos_h[
+                1], section_cos_w[2]
+            cos_hw = torch.stack([cos_h, cos_w],
+                                 dim=-1).reshape(cos_h.shape[:-1] +
+                                                 (cos_h.shape[-1] * 2, ))
+            cos = torch.cat([cos_hw, cos_t], dim=-1)
+
+            section_sin_t = sin[..., -section_t:]
+            section_sin_h = sin[..., :section_h + section_w:2]
+            section_sin_w = sin[..., 1:section_h + section_w:2]
+
+            sin_t, sin_h, sin_w = section_sin_t[0], section_sin_h[
+                1], section_sin_w[2]
+            sin_hw = torch.stack([sin_h, sin_w],
+                                 dim=-1).reshape(sin_h.shape[:-1] +
+                                                 (sin_h.shape[-1] * 2, ))
+            sin = torch.cat([sin_hw, sin_t], dim=-1)
+
+        query_shape = query.shape
+        query = query.view(num_tokens, -1, self.head_size)
+        query_rot = query[..., :self.rotary_dim]
+        query_pass = query[..., self.rotary_dim:]
+        query_rot = apply_rotary_emb_dispatch(query_rot, cos, sin,
+                                              self.is_neox_style)
+        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
+
+        key_shape = key.shape
+        key = key.view(num_tokens, -1, self.head_size)
+        key_rot = key[..., :self.rotary_dim]
+        key_pass = key[..., self.rotary_dim:]
+        key_rot = apply_rotary_emb_dispatch(key_rot, cos, sin,
+                                            self.is_neox_style)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -393,6 +393,15 @@ class MRotaryEmbedding(RotaryEmbedding):
                 context_len=context_len,
                 seq_len=seq_len,
             )
+        elif hf_config.model_type in ["ernie4_5_moe_vl", "ernie4_5_vl"]:
+            return cls._ernie_get_input_positions_tensor(
+                input_tokens=input_tokens,
+                hf_config=hf_config,
+                image_grid_thw=image_grid_thw,
+                video_grid_thw=video_grid_thw,
+                context_len=context_len,
+                seq_len=seq_len,
+            )
         else:
             return cls._vl_get_input_positions_tensor(
                 input_tokens=input_tokens,
@@ -513,6 +522,120 @@ class MRotaryEmbedding(RotaryEmbedding):
                                 len(input_tokens)).item()
         return llm_positions, mrope_position_delta
 
+    @classmethod
+    def _ernie_get_input_positions_tensor(
+        cls,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Union[list[list[int]], torch.Tensor],
+        video_grid_thw: Union[list[list[int]], torch.Tensor],
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+    ) -> tuple[torch.Tensor, int]:
+        """Get mrope input positions and delta value for Ernie VL."""
+
+        image_token_id = hf_config.im_patch_id
+        video_start_token_id = hf_config.video_start_token_id
+        video_end_token_id = hf_config.video_end_token_id
+        spatial_conv_size = hf_config.spatial_conv_size
+        temporal_conv_size = hf_config.temporal_conv_size
+        llm_pos_ids_list: list = []
+
+        if not (image_grid_thw is None and video_grid_thw is None):
+            if isinstance(image_grid_thw, torch.Tensor):
+                image_grid_thw = image_grid_thw.tolist()
+
+            input_token_type: list[str] = []
+            video_check_flg = False
+            for token in input_tokens:
+                if token == video_start_token_id:
+                    video_check_flg = True
+                elif token == video_end_token_id:
+                    video_check_flg = False
+
+                if (token == image_token_id) and (video_check_flg is False):
+                    input_token_type.append("image")
+                elif (token == image_token_id) and (video_check_flg is True):
+                    input_token_type.append("video")
+                else:
+                    input_token_type.append("text")
+
+            input_type_group: list[tuple[str, int, int]] = []
+            for key, group_iter in itertools.groupby(
+                    enumerate(input_token_type), lambda x: x[1]):
+                group_list = list(group_iter)
+                start_index = group_list[0][0]
+                end_index = group_list[-1][0] + 1
+                input_type_group.append((key, start_index, end_index))
+
+            video_frame_num = 1
+            mm_data_idx = 0
+            for modality_type, start_idx, end_idx in input_type_group:
+                st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                    llm_pos_ids_list) > 0 else 0
+                if modality_type == "image":
+                    t, h, w = (
+                        image_grid_thw[mm_data_idx][0],
+                        image_grid_thw[mm_data_idx][1],
+                        image_grid_thw[mm_data_idx][2],
+                    )
+                    llm_grid_t, llm_grid_h, llm_grid_w = \
+                        t, h // spatial_conv_size, w // spatial_conv_size
+
+                    t_index = torch.arange(llm_grid_t).view(-1, 1).expand(
+                        -1, llm_grid_h * llm_grid_w).flatten()
+                    h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                        llm_grid_t, -1, llm_grid_w).flatten()
+                    w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                        llm_grid_t, llm_grid_h, -1).flatten()
+                    llm_pos_ids_list.append(
+                        torch.stack([t_index, h_index, w_index]) + st_idx)
+                    mm_data_idx += 1
+
+                elif modality_type == "video":
+                    t, h, w = (
+                        video_grid_thw[mm_data_idx][0],
+                        video_grid_thw[mm_data_idx][1],
+                        video_grid_thw[mm_data_idx][2],
+                    )
+                    llm_grid_t, llm_grid_h, llm_grid_w = (t //
+                                                          temporal_conv_size,
+                                                          h //
+                                                          spatial_conv_size,
+                                                          w //
+                                                          spatial_conv_size)
+
+                    for t_idx in range(llm_grid_t):
+                        t_index = torch.tensor(t_idx).view(-1, 1).expand(
+                            -1, llm_grid_h * llm_grid_w).flatten()
+                        h_index = torch.arange(llm_grid_h).view(
+                            1, -1, 1).expand(1, -1, llm_grid_w).flatten()
+                        w_index = torch.arange(llm_grid_w).view(
+                            1, 1, -1).expand(1, llm_grid_h, -1).flatten()
+                        llm_pos_ids_list.append(
+                            torch.stack([t_index, h_index, w_index]) + st_idx)
+
+                    mm_data_idx += 1
+                    video_frame_num += 1
+
+                else:
+                    text_len = end_idx - start_idx
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len).view(1, -1).expand(3, -1) +
+                        st_idx)
+                    video_frame_num = 1
+
+        else:
+            text_len = len(input_tokens)
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1))
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        llm_positions = llm_positions[:, context_len:seq_len]
+        mrope_position_delta = (llm_positions.max() + 1 -
+                                len(input_tokens)).item()
+        return llm_positions, mrope_position_delta
+
     @classmethod
     def _vl_get_input_positions_tensor(
         cls,

vllm/model_executor/models/ernie45_vl.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The Baidu 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 Erine VL model compatible with HuggingFace weights."""
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Any, Callable, Literal, Optional, TypedDict, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.distributed import parallel_state
+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.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargsItems)
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import _Backend, current_platform
+from vllm.sequence import IntermediateTensors
+
+from .ernie45_vl_moe import Ernie4_5_VLMoeForCausalLM
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .utils import (AutoWeightsLoader, WeightsMapper, maybe_prefix,
+                    merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+_MAX_FRAMES_PER_VIDEO = 16
+
+# === Vision Transformer === #
+
+
+def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        return rearrange(torch.stack((-x2, x1), dim=-1),
+                         "... d two -> ... (d two)",
+                         two=2)
+
+
+def apply_rotary_emb_torch(x: torch.Tensor,
+                           cos: torch.Tensor,
+                           sin: torch.Tensor,
+                           interleaved: bool = False) -> torch.Tensor:
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    cos = repeat(
+        cos,
+        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
+    sin = repeat(
+        sin,
+        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos +
+            rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:]
+        ],
+        dim=-1,
+    )
+
+
+def apply_rotary_pos_emb_vision(t: torch.Tensor,
+                                freqs: torch.Tensor) -> torch.Tensor:
+    t_ = t.float()
+    cos = freqs.cos()
+    sin = freqs.sin()
+    apply_rotary_emb = apply_rotary_emb_torch
+    if current_platform.is_cuda():
+        from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
+    output = apply_rotary_emb(t_, cos, sin).type_as(t)
+    return output
+
+
+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 = [
+        torch.split(tensor, hidden_size // tp_size, -1)
+        for tensor in gathered_tensors
+    ]
+    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 Ernie4_5_VisionAttention(nn.Module):
+    """VisionAttention using VLLM framework APIs"""
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # Per attention head and per partition values.
+        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, self.tp_size)
+
+        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=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv")
+        self.proj = RowParallelLinear(input_size=projection_size,
+                                      output_size=embed_dim,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.proj")
+
+        # Detect attention implementation.
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+        if self.attn_backend not in {
+                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS,
+                _Backend.ROCM_AITER_FA
+        }:
+            raise RuntimeError(
+                f"Ernie45-VL does not support {self.attn_backend} backend now."
+            )
+        self.is_flash_attn_backend = self.attn_backend in {
+            _Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA
+        }
+
+    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 = all_gather_interleave(qkv, self.qkv.hidden_size,
+                                        self.tp_size)
+
+        # [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
+
+    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:
+        # [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]
+
+        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
+                   for x in (q, k, v))
+        if rotary_pos_emb is not None:
+            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
+            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
+
+        if self.is_flash_attn_backend:
+            # from vllm_flash_attn.flash_attn_interface import (
+            #   flash_attn_varlen_func)
+            if self.attn_backend == _Backend.ROCM_AITER_FA:
+                from aiter import flash_attn_varlen_func
+            else:
+                from flash_attn import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
+            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=0.0,
+                                            causal=False)
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        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]
+                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)
+        elif self.attn_backend == _Backend.XFORMERS:
+            from xformers import ops as xops
+            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
+            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
+                                                       kv_seqlen=None,
+                                                       device=q.device)
+
+            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 Ernie4_5_VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        act_layer: type[nn.Module] = QuickGELU,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.fc1 = ColumnParallelLinear(in_features,
+                                        hidden_features,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
+        self.act = act_layer()
+        self.fc2 = RowParallelLinear(hidden_features,
+                                     in_features,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
+
+    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
+
+
+class Ernie4_5_VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float,
+        act_layer: type[nn.Module] = QuickGELU,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> 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 = Ernie4_5_VisionAttention(embed_dim=dim,
+                                             num_heads=num_heads,
+                                             projection_size=dim,
+                                             quant_config=quant_config,
+                                             prefix=f"{prefix}.attn")
+
+        self.mlp = Ernie4_5_VisionMLP(dim,
+                                      mlp_hidden_dim,
+                                      act_layer=act_layer,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.mlp")
+
+    def forward(
+            self,
+            hidden_states: 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:
+
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            max_seqlen=max_seqlen,
+            seqlens=seqlens,
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+class Ernie4_5_VisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        in_channels: int = 3,
+        embed_dim: int = 1280,
+        prefix="",
+    ) -> None:
+
+        super().__init__()
+        self.patch_size = patch_size
+        self.in_channels = in_channels
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Linear(in_channels * patch_size * patch_size,
+                              embed_dim,
+                              bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+
+        target_dtype = self.proj.weight.dtype
+        hidden_states = hidden_states.to(target_dtype)
+        hidden_states = self.proj(hidden_states)
+
+        return hidden_states
+
+
+class Ernie4_5_VisionRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.inv_freq = 1.0 / theta**(
+            torch.arange(start=0, end=dim, step=2, dtype=torch.float32) / dim)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(seqlen,
+                           device=self.inv_freq.device,
+                           dtype=self.inv_freq.dtype)
+        freqs = torch.outer(input=seq, vec2=self.inv_freq)
+        return freqs
+
+
+class Ernie4_5_VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+
+        super().__init__()
+        patch_size = vision_config.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
+
+        self.spatial_merge_size = spatial_merge_size
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+
+        self.patch_embed = Ernie4_5_VisionPatchEmbed(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            embed_dim=embed_dim,
+            prefix=f"{prefix}.patch_embed",
+        )
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = embed_dim // num_heads
+        self.rotary_pos_emb = Ernie4_5_VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList([
+            Ernie4_5_VisionBlock(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)
+        ])
+
+        assert (hidden_size == embed_dim
+                ), "vit's config.hidden must be equal to config.embed_dim"
+        self.ln = nn.LayerNorm(hidden_size, eps=1e-6)
+
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+
+    @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)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            pos_ids.append(
+                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def 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
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                grid_thw: torch.Tensor,
+                num_pad=0) -> torch.Tensor:
+
+        hidden_states = self.patch_embed(hidden_states)
+
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
+
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2],
+                                             grid_thw[:, 0]).cumsum(
+                                                 dim=0, dtype=torch.int32)
+
+        if num_pad > 0:
+            cu_seqlens = F.pad(cu_seqlens, (1, 1), value=0)
+            cu_seqlens[-1] = cu_seqlens[-2] + num_pad
+        else:
+            cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        # add batch size
+        if hidden_states.ndim == 2:
+            hidden_states = hidden_states.unsqueeze(dim=1)
+
+        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
+        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+
+        for i, blk in enumerate(self.blocks):
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb=rotary_pos_emb,
+                max_seqlen=max_seqlen,
+                seqlens=seqlens,
+            )
+
+        final_output = self.ln(hidden_states)
+
+        if final_output.ndim == 3:
+            final_output = final_output.squeeze(dim=1)
+
+        return final_output
+
+    def load_weights(self, weights) -> set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+# === Vision Inputs === #
+
+
+class Ernie4_5_VLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """Shape:
+    `(num_patches, num_channels * patch_size * patch_size)`
+    """
+
+    grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+Ernie4_5_VLImageInputs = Ernie4_5_VLImagePixelInputs
+
+
+class Ernie4_5_VLVideoPixelInputs(TypedDict):
+    type: Literal["pixel_values_videos"]
+    pixel_values_videos: torch.Tensor
+    """Shape:
+    `(num_patches,
+      num_channels * temporal_patch_size * patch_size * patch_size)`
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+Ernie4_5_VLVideoInputs = Ernie4_5_VLImagePixelInputs
+
+# === Vision Processor === #
+
+
+def round_by_factor(number: Union[int, float], factor: int) -> int:
+    return round(number / factor) * factor
+
+
+def ceil_by_factor(number: Union[int, float], factor: int) -> int:
+    return math.ceil(number / factor) * factor
+
+
+def floor_by_factor(number: Union[int, float], factor: int) -> int:
+    return math.floor(number / factor) * factor
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 4 * 28 * 28,
+    max_pixels: int = 16384 * 28 * 28,
+):
+    MAX_RATIO = 200
+    if max(height, width) / min(height, width) > MAX_RATIO:
+        if height > width:
+            new_width = max(factor, round_by_factor(width, factor))
+            new_height = floor_by_factor(new_width * MAX_RATIO, factor)
+        else:
+            new_height = max(factor, round_by_factor(height, factor))
+            new_width = floor_by_factor(new_height * MAX_RATIO, factor)
+
+        height = new_height
+        width = new_width
+
+    h_bar = max(factor, round_by_factor(height, factor))
+    w_bar = max(factor, round_by_factor(width, factor))
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = floor_by_factor(height / beta, factor)
+        w_bar = floor_by_factor(width / beta, factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = ceil_by_factor(height * beta, factor)
+        w_bar = ceil_by_factor(width * beta, factor)
+
+    if min_pixels > h_bar * w_bar or h_bar * w_bar > max_pixels:
+        raise ValueError(f"encounter invalid h_bar: {h_bar}, w_bar: {w_bar}")
+
+    return h_bar, w_bar
+
+
+class VariableResolutionResamplerModel(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 spatial_conv_size,
+                 temporal_conv_size,
+                 config,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+        self.config = config
+        self.spatial_conv_size = spatial_conv_size
+        self.temporal_conv_size = temporal_conv_size
+        self.use_temporal_conv = config.use_temporal_conv
+
+        # compress 2d conv(picture) to 1d
+        self.spatial_dim = (self.in_dim * self.spatial_conv_size *
+                            self.spatial_conv_size)
+        # compress 3d conv(video) to 1d
+        self.temporal_dim = (self.in_dim * self.spatial_conv_size *
+                             self.spatial_conv_size * self.temporal_conv_size)
+
+        self.spatial_linear1 = ColumnParallelLinear(
+            self.spatial_dim,
+            self.spatial_dim,
+            bias=True,
+            gather_output=True,
+            quant_config=getattr(config, 'quant_config', None),
+            prefix=f"{prefix}.spatial_linear1",
+        )
+
+        self.spatial_gelu = nn.GELU()
+
+        self.spatial_linear2 = ColumnParallelLinear(
+            self.spatial_dim,
+            self.spatial_dim,
+            bias=True,
+            gather_output=True,
+            quant_config=getattr(config, 'quant_config', None),
+            prefix=f"{prefix}.spatial_linear2",
+        )
+
+        self.spatial_norm = nn.LayerNorm(self.spatial_dim, eps=1e-6)
+
+        if self.use_temporal_conv:
+            self.temporal_linear1 = ColumnParallelLinear(
+                self.temporal_dim,
+                self.spatial_dim,
+                bias=True,
+                gather_output=True,
+                quant_config=getattr(config, 'quant_config', None),
+                prefix=f"{prefix}.temporal_linear1",
+            )
+
+            self.temporal_gelu = nn.GELU()
+
+            self.temporal_linear2 = ColumnParallelLinear(
+                self.spatial_dim,
+                self.spatial_dim,
+                bias=True,
+                gather_output=True,
+                quant_config=getattr(config, 'quant_config', None),
+                prefix=f"{prefix}.temporal_linear2",
+            )
+
+            self.temporal_norm = nn.LayerNorm(self.spatial_dim, eps=1e-6)
+
+        self.mlp = ColumnParallelLinear(
+            self.spatial_dim,
+            self.out_dim,
+            bias=True,
+            gather_output=True,
+            quant_config=getattr(config, 'quant_config', None),
+            prefix=f"{prefix}.mlp",
+        )
+
+        self.after_norm = RMSNorm(hidden_size=out_dim,
+                                  eps=getattr(config, 'rms_norm_eps', 1e-6))
+
+    def spatial_conv_reshape(self, x, spatial_conv_size):
+        S, C = x.shape
+        x = x.reshape([-1, C * (spatial_conv_size**2)])
+        return x
+
+    def forward(self, x, grid_thw):
+
+        def fwd_spatial(x):
+            x = self.spatial_conv_reshape(x, self.spatial_conv_size)
+
+            x, _ = self.spatial_linear1(x)
+            x = self.spatial_gelu(x)
+            x, _ = self.spatial_linear2(x)
+            x = self.spatial_norm(x)
+
+            return x
+
+        def fwd_placeholder(x, grid_thw, to_tensor=False):
+
+            grid_thw_cpu = grid_thw.cpu().numpy()
+            grid_t, grid_hw = grid_thw_cpu[:, 0], grid_thw_cpu[:, 1:]
+            grid_hw_after_conv = grid_hw.prod(-1) // (self.spatial_conv_size**
+                                                      2)
+
+            tokens_per_img_or_vid = grid_thw_cpu.prod(-1) // (
+                self.spatial_conv_size**2)
+            batch_offset = np.empty(tokens_per_img_or_vid.size,
+                                    dtype=tokens_per_img_or_vid.dtype)
+            batch_offset[0] = 0
+            batch_offset[1:] = tokens_per_img_or_vid.cumsum()[:-1]
+
+            slice_offsets = []
+            for temporoal_size, spatial_size, b_offset in zip(
+                    grid_t, grid_hw_after_conv, batch_offset):
+                for temp_offset in range(0, temporoal_size, 2):
+                    slice_offsets.append(
+                        np.arange(
+                            b_offset + (temp_offset) * spatial_size,
+                            b_offset + (temp_offset + 1) * spatial_size,
+                        ))
+            slice_offsets = torch.tensor(np.concatenate(slice_offsets,
+                                                        axis=-1)).to(x.device)
+
+            slice_offsets2 = []
+            for temporoal_size, spatial_size, b_offset in zip(
+                    grid_t, grid_hw_after_conv, batch_offset):
+                for temp_offset in range(1 if temporoal_size > 1 else 0,
+                                         temporoal_size, 2):
+                    slice_offsets2.append(
+                        np.arange(
+                            b_offset + (temp_offset) * spatial_size,
+                            b_offset + (temp_offset + 1) * spatial_size,
+                        ))
+            slice_offsets2 = torch.tensor(
+                np.concatenate(slice_offsets2, axis=-1)).to(x.device)
+
+            x_timestep_1 = torch.index_select(x, dim=0, index=slice_offsets)
+            x_timestep_2 = torch.index_select(x, dim=0, index=slice_offsets2)
+            x = torch.concat([x_timestep_1, x_timestep_2], dim=-1)
+            return x
+
+        def fwd_temporal(x):
+            x, _ = self.temporal_linear1(x)
+            x = self.temporal_gelu(x)
+            x, _ = self.temporal_linear2(x)
+            x = self.temporal_norm(x)
+            return x
+
+        def fwd_mlp(x):
+            x, _ = self.mlp(x)
+            x = self.after_norm(x)
+            return x
+
+        x = fwd_spatial(x)
+        if self.use_temporal_conv:
+            x = fwd_placeholder(x, grid_thw)
+            x = fwd_temporal(x)
+        x = fwd_mlp(x)
+        return x
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if name not in params_dict:
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Ernie4_5_VLProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.model_config.hf_config
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(use_fast=True, **kwargs)
+
+    def get_image_processor(self, **kwargs: object):
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": None}
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 1,
+        do_resize: bool = True,
+        image_processor: Optional[Any],
+    ) -> tuple[ImageSize, int]:
+        if image_processor is None:
+            image_processor = self.get_image_processor()
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+
+        patch_size = vision_config.patch_size
+        spatial_conv_size = hf_config.spatial_conv_size
+        temporal_conv_size = hf_config.temporal_conv_size
+
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                height=image_height,
+                width=image_width,
+                factor=patch_size * spatial_conv_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)
+
+        grid_t = max(num_frames // temporal_conv_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 // (spatial_conv_size**2)
+
+        return preprocessed_size, num_vision_tokens
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        image_processor: Optional[Any],
+    ) -> int:
+        _, num_image_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            image_processor=image_processor,
+        )
+        return num_image_tokens
+
+    def get_num_video_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int,
+        image_processor: Optional[Any],
+    ) -> int:
+        _, num_video_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=num_frames,
+            image_processor=image_processor,
+        )
+        return num_video_tokens
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        max_image_size, _ = self._get_vision_info(
+            image_width=9999999,
+            image_height=9999999,
+            image_processor=None,
+        )
+        return max_image_size
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        num_image_tokens = self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            image_processor=None,
+        )
+        return num_image_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,
+                image_processor=None,
+            )
+
+            if next_max_tokens > max_tokens:
+                break
+
+            num_frames = next_num_frames
+
+        # If the number of frames is odd, discard one frame.
+        if num_frames % 2 != 0:
+            num_frames -= 1
+
+        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
+        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)
+
+        return max(max_frames_per_video, 2)
+
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_video_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            num_frames=self.get_num_frames_with_most_features(
+                seq_len, mm_counts),
+            image_processor=None,
+        )
+
+
+class Ernie4_5VLMultiModalProcessor(
+        BaseMultiModalProcessor[Ernie4_5_VLProcessingInfo]):
+
+    def _pixel_values_norm(
+        self,
+        pixel_values: torch.Tensor,
+        mm_kwargs: object,
+    ) -> torch.Tensor:
+        hf_config = self.info.get_hf_config()
+        vision_config = hf_config.vision_config
+        image_processor = self.info.get_image_processor(**mm_kwargs)
+        image_mean_tensor = torch.tensor(image_processor.image_mean,
+                                         dtype=torch.float32).reshape(
+                                             [1, 3, 1, 1])
+        image_std_tensor = torch.tensor(image_processor.image_std,
+                                        dtype=torch.float32).reshape(
+                                            [1, 3, 1, 1])
+        rescale_factor = torch.tensor(image_processor.rescale_factor,
+                                      dtype=torch.float32)
+        patch_size_squared = vision_config.patch_size**2
+
+        image_mean_tensor = (image_mean_tensor.squeeze(
+            [-2, -1]).repeat_interleave(patch_size_squared, -1))
+        image_std_tensor = (image_std_tensor.squeeze(
+            [-2, -1]).repeat_interleave(patch_size_squared, -1))
+
+        if not image_mean_tensor.is_contiguous():
+            image_mean_tensor = image_mean_tensor.contiguous()
+        if not image_std_tensor.is_contiguous():
+            image_std_tensor = image_std_tensor.contiguous()
+
+        pixel_values = (rescale_factor * pixel_values.to(torch.float32) -
+                        image_mean_tensor) / image_std_tensor
+        pixel_values = pixel_values.to(hf_config.torch_dtype)
+        return pixel_values
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        # when the prompt is not empty but the multimodal data is empty,
+        # directly invoke the tokenizer.
+        if "images" not in mm_data and "videos" not in mm_data and prompt != "":
+            tokenizer = self.info.get_tokenizer()
+            prompt_ids = tokenizer.encode(prompt)
+            tokenizer_output = BatchFeature(dict(input_ids=[prompt_ids]),
+                                            tensor_type="pt")
+            return tokenizer_output
+
+        if "images" not in mm_data:
+            mm_data["images"] = []
+        if "videos" not in mm_data:
+            mm_data["videos"] = []
+        processor_output = self.info.ctx.call_hf_processor(
+            self.info.get_hf_processor(**mm_kwargs),
+            dict(text=[prompt],
+                 images=mm_data["images"],
+                 videos=mm_data["videos"]),
+            dict(**mm_kwargs, **tok_kwargs),
+        )
+
+        # Divide the processor_output into two modalities: image and video.
+        if processor_output is not None:
+            pixel_values = processor_output['images']
+            if pixel_values is not None:
+                processor_output['images'] = self._pixel_values_norm(
+                    pixel_values, mm_kwargs)
+            for key in list(processor_output.keys()):
+                if processor_output[key] is None:
+                    del processor_output[key]
+                    continue
+                if key == "grid_thw":
+                    grid_thw = processor_output['grid_thw']
+                    pixel_values_all = processor_output['images']
+                    # Identify elements where the first
+                    # dimension is greater than 1 and
+                    # treat them as the video modality
+                    mask = grid_thw[:, 0] > 1
+                    processor_output["video_grid_thw"] = grid_thw[mask]
+                    processor_output["image_grid_thw"] = grid_thw[~mask]
+                    image_patch_num = processor_output["image_grid_thw"].prod(
+                        dim=1).sum()
+                    processor_output[
+                        'pixel_values'] = pixel_values_all[:image_patch_num]
+                    processor_output['pixel_values_videos'] = pixel_values_all[
+                        image_patch_num:]
+                    del processor_output['images']
+
+        return processor_output
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        before_placeholder = {
+            "image": "<|image@placeholder|>",
+            "video": "<|video@placeholder|>"
+        }
+
+        after_placeholder = {
+            # image and video have same placeholder
+            "image": "<|IMAGE_PLACEHOLDER|>",
+            "video": "<|IMAGE_PLACEHOLDER|>"
+        }
+
+        merge_length = hf_processor.spatial_conv_size**2
+
+        def get_replacement_ernie45vl(item_idx: int, modality: str):
+            out_item = out_mm_kwargs[modality][item_idx]
+            grid_thw = out_item[f"{modality}_grid_thw"].data
+            assert isinstance(grid_thw, torch.Tensor)
+            if modality == "video":
+                num_tokens = int(grid_thw.prod(
+                )) // hf_processor.temporal_conv_size // merge_length
+            else:
+                num_tokens = int(grid_thw.prod()) // merge_length
+            return after_placeholder[modality] * num_tokens
+
+        return [
+            PromptReplacement(
+                modality=modality,
+                target=before_placeholder[modality],
+                replacement=partial(get_replacement_ernie45vl,
+                                    modality=modality),
+            ) for modality in ("image", "video")
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+
+        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_grid_thw=MultiModalFieldConfig.batched("image"),
+            pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
+                "video", video_grid_sizes),
+            video_grid_thw=MultiModalFieldConfig.batched("video"),
+        )
+
+
+class Ernie4_5_VLDummyInputsBuilder(
+        BaseDummyInputsBuilder[Ernie4_5_VLProcessingInfo]):
+
+    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)
+        prompt = ""
+        for i in range(num_images):
+            prompt += (f"Picture {i+1}:"
+                       "<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>")
+
+        for i in range(num_videos):
+            prompt += (f"Video {i+1}:"
+                       "<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>")
+        return prompt
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        target_num_frames = \
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
+
+        return {
+            "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,
+                                   num_frames=target_num_frames,
+                                   num_videos=num_videos)
+        }
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Ernie4_5VLMultiModalProcessor,
+    info=Ernie4_5_VLProcessingInfo,
+    dummy_inputs=Ernie4_5_VLDummyInputsBuilder)
+class Ernie4_5_VLMoeForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                             SupportsLoRA, SupportsPP):
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # 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.",
+            # model.resampler_model.-> language_model.model.resampler_model.
+            # language_model.model.resampler_model. -> resampler_model.
+            "language_model.model.resampler_model.": "resampler_model.",
+        },
+        # resampler_weight_mappings
+        orig_to_new_substr={
+            "spatial_linear.0.": "spatial_linear1.",
+            "spatial_linear.2.": "spatial_linear2.",
+            "spatial_linear.3.": "spatial_norm.",
+            "temporal_linear.0.": "temporal_linear1.",
+            "temporal_linear.2.": "temporal_linear2.",
+            "temporal_linear.3.": "temporal_norm.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>"
+        if modality.startswith("video"):
+            return "<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.vision_model = Ernie4_5_VisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.language_model = Ernie4_5_VLMoeForCausalLM(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.resampler_model = VariableResolutionResamplerModel(
+            self.config.pixel_hidden_size,
+            self.config.hidden_size,
+            self.config.spatial_conv_size,
+            self.config.temporal_conv_size,
+            config=self.config,
+            prefix=maybe_prefix(prefix, "resampler_model"))
+
+        self.visual_token_mask = None
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        """compute logits"""
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def _vision_forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        if grid_thw is not None:
+            grid_thw = grid_thw[grid_thw > 0]
+            if grid_thw.numel() % 3 != 0:
+                raise ValueError(
+                    f"grid_thw has {grid_thw.numel()} elements after filtering,"
+                    "which is not divisible by 3.")
+            grid_thw = grid_thw.reshape(-1, 3)
+            # example: [[1,64,64],[2,80,80]] -> [[1,64,64],[1,80,80],[1,80,80]]
+            grid_thw = F.pad(
+                torch.repeat_interleave(grid_thw[:, 1:], grid_thw[:, 0], 0),
+                [1, 0, 0, 0],
+                value=1,
+            )
+        image_features = self.vision_model(pixel_values, grid_thw)
+        return image_features
+
+    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
+        if getattr(self.config, "im_patch_id", None) is not None:
+            self.visual_token_mask = (
+                input_ids == self.config.im_patch_id).reshape(-1, 1)
+        else:
+            self.visual_token_mask = None
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: object,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim} "
+                                 f"(shape={mm_input.shape})")
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Ernie4_5_VLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None:
+            return None
+
+        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")
+
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            return Ernie4_5_VLImagePixelInputs(type="pixel_values",
+                                               pixel_values=pixel_values,
+                                               image_grid_thw=image_grid_thw)
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Ernie4_5_VLVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None:
+            return None
+
+        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")
+
+            return Ernie4_5_VLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+            )
+
+    def _process_image_input(
+            self,
+            image_input: Ernie4_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        pixel_values = image_input["pixel_values"].type(
+            self.vision_model.dtype)
+        image_features = self._vision_forward(pixel_values=pixel_values,
+                                              grid_thw=grid_thw)
+        image_embeds = self.resampler_model(image_features, grid_thw)
+
+        merge_size = self.vision_model.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return image_embeds.split(sizes.tolist())
+
+    def _process_video_input(
+            self,
+            video_input: Ernie4_5_VLVideoInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        pixel_values_videos = video_input["pixel_values_videos"].type(
+            self.vision_model.dtype)
+        video_features = self._vision_forward(pixel_values=pixel_values_videos,
+                                              grid_thw=grid_thw)
+        video_embeds = self.resampler_model(video_features, grid_thw)
+
+        merge_size = self.vision_model.spatial_merge_size
+        sizes = (grid_thw.prod(-1) //
+                 self.config.temporal_conv_size) // merge_size // merge_size
+
+        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
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return None
+
+        # 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
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+
+        if multimodal_embeddings is None:
+            return inputs_embeds
+
+        self._set_visual_token_mask(input_ids)
+        inputs_embeds = merge_multimodal_embeddings(input_ids, inputs_embeds,
+                                                    multimodal_embeddings,
+                                                    [self.config.im_patch_id])
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+
+        forward_kwargs = {
+            "input_ids": input_ids,
+            "positions": positions,
+            "intermediate_tensors": intermediate_tensors,
+            "inputs_embeds": inputs_embeds,
+        }
+
+        if self.visual_token_mask is not None:
+
+            if self.visual_token_mask.shape[0] != inputs_embeds.shape[0]:
+                padding_len = inputs_embeds.shape[
+                    0] - self.visual_token_mask.shape[0]
+                # right pad False
+                pad = torch.zeros(
+                    (padding_len, self.visual_token_mask.shape[1]),
+                    dtype=self.visual_token_mask.dtype,
+                    device=self.visual_token_mask.device)
+                self.visual_token_mask = torch.cat(
+                    [self.visual_token_mask, pad], dim=0)
+
+            forward_kwargs.update(
+                {"visual_token_mask": self.visual_token_mask})
+            self.visual_token_mask = None
+
+        hidden_states = self.language_model.model(
+            **forward_kwargs,
+            **kwargs,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

vllm/model_executor/models/ernie45_vl_moe.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The Baidu 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 Erine VL model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention
+# from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding.ernie45_vl_rope import (
+    Ernie4_5_VLRotaryEmbedding)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .ernie45_moe import Ernie4_5_MoeMLP
+from .interfaces import SupportsPP
+from .utils import (PPMissingLayer, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Ernie4_5_VLMoeMLP(Ernie4_5_MoeMLP):
+    pass
+
+
+class Ernie4_5_VLMoeAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: Optional[int] = None,
+        rope_theta: float = 500000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        freq_allocation: int = 20,
+        max_position_embeddings: int = 131072,
+        rms_norm_eps: float = 1e-05,
+        qkv_bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        layer_idx = extract_layer_index(prefix) if len(prefix) > 0 else 0
+        self.layer_idx = layer_idx
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or (hidden_size // self.total_num_heads)
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(hidden_size,
+                                          self.head_dim,
+                                          self.total_num_heads,
+                                          self.total_num_kv_heads,
+                                          bias=qkv_bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.qkv_proj")
+
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.o_proj")
+
+        t_rope = freq_allocation
+        h_rope = (self.head_dim // 2 - freq_allocation) // 2
+        w_rope = (self.head_dim // 2 - freq_allocation) // 2
+
+        self.rotary_emb = Ernie4_5_VLRotaryEmbedding(
+            head_size=self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position_embeddings=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=False,
+            dtype=torch.get_default_dtype(),
+            mrope_section=[h_rope, w_rope, t_rope])
+
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+
+        qkv, _ = self.qkv_proj(hidden_states)
+
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+
+        # Attention
+        attn_output = self.attn(q, k, v)
+        # Output projection
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Ernie4_5_VLMoeMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        layer_idx = extract_layer_index(prefix)
+        self.layer_idx = layer_idx
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.has_shared_experts = (getattr(config, "moe_num_shared_experts", 0)
+                                   > 0)
+        self.hidden_size = config.hidden_size
+
+        moe_num_experts = config.moe_num_experts
+        max_moe_num_experts = max(moe_num_experts)
+
+        if self.tp_size > max_moe_num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {moe_num_experts}.")
+
+        moe_layer_start_index = config.moe_layer_start_index
+        text_moe_layer_start_index = moe_layer_start_index[0]
+        vision_moe_layer_start_index = moe_layer_start_index[1]
+        moe_layer_end_index = config.moe_layer_end_index
+        moe_layer_end_index = getattr(
+            config, "moe_layer_end_index",
+            [config.num_hidden_layers - 1, config.num_hidden_layers - 1])
+        text_moe_layer_end_index = moe_layer_end_index[0]
+        vision_moe_layer_end_index = moe_layer_end_index[1]
+
+        assert config.moe_num_experts[0] == config.moe_num_experts[1]
+        self.e_score_correction_bias = nn.Parameter(
+            torch.empty(2, config.moe_num_experts[0]))
+
+        assert text_moe_layer_start_index <= text_moe_layer_end_index
+
+        if layer_idx >= text_moe_layer_start_index and \
+            layer_idx <= text_moe_layer_end_index:
+            self.text_experts_gate = ReplicatedLinear(
+                config.hidden_size,
+                config.moe_num_experts[0],
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.text_experts_gate")
+
+            self.text_experts = FusedMoE(
+                num_experts=config.moe_num_experts[0],
+                top_k=config.moe_k,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.moe_intermediate_size[0],
+                reduce_results=False,
+                renormalize=True,
+                quant_config=quant_config,
+                e_score_correction_bias=self.e_score_correction_bias[0],
+                prefix=f"{prefix}.text_experts")
+        else:
+            self.text_experts = Ernie4_5_VLMoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                use_bias=getattr(config, 'use_bias', False),
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp")
+
+        assert vision_moe_layer_start_index <= vision_moe_layer_end_index
+        if layer_idx >= vision_moe_layer_start_index and \
+            layer_idx <= vision_moe_layer_end_index:
+            self.vision_experts_gate = ReplicatedLinear(
+                config.hidden_size,
+                config.moe_num_experts[1],
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.vision_experts_gate")
+
+            self.vision_experts = FusedMoE(
+                num_experts=config.moe_num_experts[1],
+                top_k=config.moe_k,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.moe_intermediate_size[1],
+                reduce_results=False,
+                renormalize=True,
+                quant_config=quant_config,
+                e_score_correction_bias=self.e_score_correction_bias[1],
+                prefix=f"{prefix}.vision_experts")
+        else:
+            self.vision_experts = Ernie4_5_VLMoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                use_bias=getattr(config, 'use_bias', False),
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp")
+
+        if self.has_shared_experts:
+            intermediate_size = (config.moe_intermediate_size[0] *
+                                 config.moe_num_shared_experts)
+            self.shared_experts = Ernie4_5_VLMoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.shared_experts",
+                reduce_results=self.text_experts.
+                must_reduce_shared_expert_outputs())
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        visual_token_mask: torch.Tensor,
+        **kwargs: object,
+    ) -> torch.Tensor:
+
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        if self.has_shared_experts:
+            shared_output = self.shared_experts(hidden_states)
+
+        if visual_token_mask is not None and visual_token_mask.any():
+            # assert visual_token_mask.shape[0] != hidden_states.shape[0]
+            visual_token_mask = visual_token_mask.repeat(
+                1, self.hidden_size).bool()
+            text_token_mask = ~visual_token_mask
+            final_hidden_states = torch.zeros_like(hidden_states)
+
+            text_hidden_states = hidden_states[text_token_mask].reshape(
+                -1, self.hidden_size)
+            vision_hidden_states = hidden_states[visual_token_mask].reshape(
+                -1, self.hidden_size)
+
+            text_router_logits, _ = self.text_experts_gate(text_hidden_states)
+            final_hidden_states[text_token_mask] = self.text_experts(
+                hidden_states=text_hidden_states,
+                router_logits=text_router_logits).flatten()
+
+            vision_router_logits, _ = self.vision_experts_gate(
+                vision_hidden_states)
+            final_hidden_states[visual_token_mask] = self.vision_experts(
+                hidden_states=vision_hidden_states,
+                router_logits=vision_router_logits).flatten()
+        else:
+            # text modal input processing directly
+            text_router_logits, _ = self.text_experts_gate(hidden_states)
+
+            final_hidden_states = self.text_experts(
+                hidden_states=hidden_states, router_logits=text_router_logits)
+
+        if self.has_shared_experts and \
+              shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+
+        if self.tp_size > 1:
+            final_hidden_states = (
+                self.text_experts.maybe_all_reduce_tensor_model_parallel(
+                    final_hidden_states))
+
+        return final_hidden_states.view(orig_shape)
+
+
+class Ernie4_5_VLMoeDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 500000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        freq_allocation = getattr(config, "freq_allocation", 20)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          131072)
+
+        self.self_attn = Ernie4_5_VLMoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=getattr(config, 'head_dim', None),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            freq_allocation=freq_allocation,
+            max_position_embeddings=max_position_embeddings,
+            rms_norm_eps=config.rms_norm_eps,
+            qkv_bias=getattr(config, 'use_bias', False),
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        layer_idx = extract_layer_index(prefix)
+        self.layer_idx = layer_idx
+
+        # MoE
+        moe_layer_start_index = config.moe_layer_start_index
+        min_moe_layer_start_index = min(moe_layer_start_index)
+        moe_layer_end_index = getattr(
+            config, "moe_layer_end_index",
+            [config.num_hidden_layers - 1, config.num_hidden_layers - 1])
+        max_moe_layer_end_index = max(moe_layer_end_index)
+        assert min_moe_layer_start_index <= max_moe_layer_end_index
+        moe_num_experts = config.moe_num_experts
+        max_moe_num_experts = max(moe_num_experts)
+        moe_layer_interval = getattr(config, "moe_layer_interval", 1)
+        use_moe = getattr(config, "use_moe", max_moe_num_experts > 0)
+
+        if (use_moe and ((layer_idx + 1) % moe_layer_interval == 0)
+                and layer_idx >= min_moe_layer_start_index
+                and layer_idx <= max_moe_layer_end_index):
+            self.mlp = Ernie4_5_VLMoeMoE(config=config,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = Ernie4_5_VLMoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                use_bias=getattr(config, 'use_bias', False),
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp")
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        visual_token_mask: Optional[torch.Tensor],
+        **kwargs: object,
+    ) -> torch.Tensor:
+
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+
+        if isinstance(self.mlp, Ernie4_5_VLMoeMoE):
+            hidden_states = self.mlp(hidden_states, visual_token_mask,
+                                     **kwargs)
+        else:
+            hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+# Since Ernie VL distinguishes between text experts and vision experts,
+# enabling torch.compile will cause errors.
+# @support_torch_compile(
+#     dynamic_arg_dims={
+#         "input_ids": 0,
+#         "positions": -1,
+#         "intermediate_tensors": 0,
+#         "inputs_embeds": 0,
+#         "visual_token_mask": 0,
+#     })
+class Ernie4_5_VLMoeModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.im_patch_id = config.im_patch_id
+
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=f"{prefix}.embed_tokens")
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Ernie4_5_VLMoeDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        visual_token_mask: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states, residual,
+                                            visual_token_mask, **kwargs)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        return hidden_states
+
+
+# only used as text backbone for ernie4.5-vl
+class Ernie4_5_VLMoeForCausalLM(nn.Module, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    fall_back_to_pt_during_load = False
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Ernie4_5_VLMoeModel(vllm_config=vllm_config,
+                                         prefix=maybe_prefix(prefix, "model"))
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(config.vocab_size,
+                                          config.hidden_size,
+                                          quant_config=quant_config)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    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]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds, **kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    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"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=max(self.config.moe_num_experts))
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if self.config.tie_word_embeddings and name.endswith(
+                    "lm_head.weight"):
+                loaded_params.add("lm_head.weight")
+                continue
+            # MTP will be supported soon.
+            if "mtp" in name or \
+               "vision_model" in name or \
+               "resampler_model" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+
+                if (("mlp.experts." in name) and name not in params_dict):
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if ((name.endswith(".bias") or name.endswith("_bias"))
+                        and name not in params_dict):
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Distinguish between vision experts and text experts
+                if "mlp.experts" in name:
+                    moe_offset = int(name.split(".")[-3])
+                    vision_expert_start_idx = self.config.moe_num_experts[0]
+                    is_text_expert = \
+                        moe_offset <= vision_expert_start_idx - 1
+                    if is_text_expert:
+                        name = name.replace(".experts.", ".text_experts.")
+                    else:
+                        name = name.replace(
+                            f".experts.{moe_offset}",
+                            f".vision_experts.{moe_offset-vision_expert_start_idx}"
+                        )
+
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+
+                    if weight_name not in name:
+                        continue
+
+                    # Distinguish between vision experts and text experts
+                    moe_offset = int(name.split(".")[-3])
+                    is_text_expert = \
+                        moe_offset <= self.config.moe_num_experts[0] - 1
+
+                    name = name.replace(weight_name, param_name)
+                    if is_text_expert:
+                        name = name.replace(".experts.", ".text_experts.")
+                    else:
+                        name = name.replace(".experts.", ".vision_experts.")
+
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Distinguish between vision expert gate
+                    # and text expert gate
+                    if name.endswith("mlp.gate.weight"):
+                        name = name.replace("gate.weight",
+                                            "text_experts_gate.weight")
+                        loaded_weight = loaded_weight.T
+                    elif name.endswith("mlp.gate.weight_1"):
+                        name = name.replace("gate.weight_1",
+                                            "vision_experts_gate.weight")
+                        loaded_weight = loaded_weight.T
+
+                    if "e_score_correction_bias" in name:
+                        name = name.replace(".moe_statics.", ".")
+
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    param = params_dict[name]
+
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params

vllm/model_executor/models/registry.py

@@ -206,6 +206,7 @@ _MULTIMODAL_MODELS = {
     "ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"),  # noqa: E501
     "Cohere2VisionForConditionalGeneration": ("cohere2_vision", "Cohere2VisionForConditionalGeneration"),  # noqa: E501
     "DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
+    "Ernie4_5_VLMoeForConditionalGeneration": ("ernie45_vl", "Ernie4_5_VLMoeForConditionalGeneration"),  # noqa: E501
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
     "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
     "Gemma3nForConditionalGeneration": ("gemma3n_mm", "Gemma3nForConditionalGeneration"),    # noqa: E501