[Model] Add Ernie4.5 and Ernie4.5MoE Model Support (#20220)

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

docs/models/supported_models.md

@@ -330,6 +330,8 @@ Specified using `--task generate`.
 | `DeepseekV2ForCausalLM`                           | DeepSeek-V2                                         | `deepseek-ai/DeepSeek-V2`, `deepseek-ai/DeepSeek-V2-Chat` etc.                                                                                                               |                        | ✅︎                          | ✅︎                     |
 | `DeepseekV3ForCausalLM`                           | DeepSeek-V3                                         | `deepseek-ai/DeepSeek-V3-Base`, `deepseek-ai/DeepSeek-V3` etc.                                                                                                               |                        | ✅︎                          | ✅︎                     |
 | `Dots1ForCausalLM`                                | dots.llm1                                           | `rednote-hilab/dots.llm1.base`, `rednote-hilab/dots.llm1.inst` etc.                                                                                                               |                        | ✅︎                          | ✅︎                     |
+| `Ernie4_5_ForCausalLM`                            | Ernie4.5                                            | `baidu/ERNIE-4.5-0.3B-PT`,etc.                                                                                                               |                        | ✅︎                          | ✅︎                     |
+| `Ernie4_5_MoeForCausalLM`                         | Ernie4.5MoE                                         | `baidu/ERNIE-4.5-21B-A3B-PT`,  `baidu/ERNIE-4.5-300B-A47B-PT`, etc.                                                                                                               |                        | ✅︎                          | ✅︎                     |
 | `ExaoneForCausalLM`                               | EXAONE-3                                            | `LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc.                                                                                                                                 | ✅︎                     | ✅︎                          | ✅︎                     |
 | `FalconForCausalLM`                               | Falcon                                              | `tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.                                                                                                         |                        | ✅︎                          | ✅︎                     |
 | `FalconMambaForCausalLM`                          | FalconMamba                                         | `tiiuae/falcon-mamba-7b`, `tiiuae/falcon-mamba-7b-instruct`, etc.                                                                                                            |                        | ✅︎                          | ✅︎                     |

tests/models/registry.py

@@ -162,6 +162,10 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                          trust_remote_code=True),
     "DeepseekV3ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3",  # noqa: E501
                                          trust_remote_code=True),
+    "Ernie4_5_ForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-0.3B-PT",
+                                        trust_remote_code=True),
+    "Ernie4_5_MoeForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-21B-A3B-PT",
+                                        trust_remote_code=True),
     "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
     "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),  # noqa: E501
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),

vllm/model_executor/models/ernie45.py

@@ -0,0 +1,43 @@
+# 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 model compatible with HuggingFace weights."""
+from vllm.config import VllmConfig
+from vllm.model_executor.models.llama import LlamaForCausalLM
+
+from .utils import PPMissingLayer
+
+
+class Ernie4_5_ForCausalLM(LlamaForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        # Hack Llama model to fit HF format Ernie4.5 dense implementation
+        # Attention difference between Ernie and Llama:
+        # 1. rotary_dim and no Neox style.
+        # 2. There is no bias for o_proj in attention
+        for layer in self.model.layers:
+            if not isinstance(layer, PPMissingLayer):
+                layer.self_attn.rotary_emb.is_neox_style = False
+                layer.self_attn.o_proj.bias = None
+                layer.self_attn.o_proj.skip_bias_add = True

vllm/model_executor/models/ernie45_moe.py

@@ -0,0 +1,583 @@
+# 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 ErineMoE 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.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               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 import get_rope
+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 .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_MoeMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        use_bias: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=use_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=use_bias,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Ernie4_5_MoeMoE(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.moe_num_shared_experts = getattr(config, "moe_num_shared_experts",
+                                              None)
+
+        if self.tp_size > config.moe_num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.moe_num_experts}.")
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.moe_num_experts,
+                                     bias=False,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+
+        self.experts = FusedMoE(num_experts=config.moe_num_experts,
+                                top_k=config.moe_k,
+                                hidden_size=config.hidden_size,
+                                intermediate_size=config.moe_intermediate_size,
+                                reduce_results=False,
+                                renormalize=True,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.experts")
+
+        if self.moe_num_shared_experts is not None:
+            intermediate_size = (config.moe_intermediate_size *
+                                 config.moe_num_shared_experts)
+            self.shared_experts = Ernie4_5_MoeMLP(
+                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.experts.must_reduce_shared_expert_outputs(
+                ))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        if self.moe_num_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+
+        router_logits, _ = self.gate(hidden_states)
+
+        final_hidden_states = self.experts(hidden_states=hidden_states,
+                                           router_logits=router_logits)
+
+        if self.moe_num_shared_experts is not None and \
+              shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+
+        if self.tp_size > 1:
+            final_hidden_states = (
+                self.experts.maybe_all_reduce_tensor_model_parallel(
+                    final_hidden_states))
+
+        return final_hidden_states.view(orig_shape)
+
+
+class Ernie4_5_MoeAttention(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,
+        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")
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=False,
+            rope_scaling=rope_scaling,
+        )
+        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_MoeDecoderLayer(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)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          131072)
+        self.self_attn = Ernie4_5_MoeAttention(
+            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,
+            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_num_experts = getattr(config, "moe_num_experts", 0)
+        moe_layer_start_index = getattr(config, "moe_layer_start_index", 0)
+        moe_layer_end_index = getattr(config, "moe_layer_end_index",
+                                      config.num_hidden_layers - 1)
+        moe_layer_interval = getattr(config, "moe_layer_interval", 1)
+        use_moe = getattr(config, "use_moe", moe_num_experts > 0)
+
+        if (use_moe and ((layer_idx + 1) % moe_layer_interval == 0)
+                and layer_idx >= moe_layer_start_index
+                and layer_idx <= moe_layer_end_index):
+            self.mlp = Ernie4_5_MoeMoE(config=config,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = Ernie4_5_MoeMLP(
+                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],
+    ) -> 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)
+
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Ernie4_5_MoeModel(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
+
+        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_MoeDecoderLayer(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,
+    ) -> 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)
+
+        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
+
+
+class Ernie4_5_MoeForCausalLM(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_MoeModel(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,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        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=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"):
+                continue
+            # MTP will be supported soon.
+            if "mtp" 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:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+
+                    if weight_name not in name:
+                        continue
+
+                    name = name.replace(weight_name, param_name)
+                    # 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:
+                    # 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

@@ -53,6 +53,8 @@ _TEXT_GENERATION_MODELS = {
     "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
     "DeepseekV3ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),
     "Dots1ForCausalLM": ("dots1", "Dots1ForCausalLM"),
+    "Ernie4_5_ForCausalLM": ("ernie45", "Ernie4_5_ForCausalLM"),
+    "Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"),
     "ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"),
     "FalconForCausalLM": ("falcon", "FalconForCausalLM"),
     "Fairseq2LlamaForCausalLM": ("fairseq2_llama", "Fairseq2LlamaForCausalLM"),