[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

@@ -0,0 +1,72 @@
+# 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_moe.py

@@ -0,0 +1,723 @@
+# 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