[Models] support HunYuanForCausalLM

vllm/config/model.py

@@ -1869,6 +1869,11 @@ def _get_and_verify_max_len(
             if rope_type == "yarn":
                 derived_max_model_len = rope_scaling[
                     "original_max_position_embeddings"]
+                
+            # see DynamicNTKAlphaRotaryEmbedding
+            if rope_scaling["type"] == "dynamic" and "alpha" in rope_scaling:
+                scaling_factor = 1
+                
             derived_max_model_len *= scaling_factor
 
     if encoder_config and "max_seq_length" in encoder_config:

vllm/model_executor/layers/rotary_embedding/__init__.py

@@ -137,9 +137,14 @@ def get_rope(
                     scaling_alpha, dtype)
             elif "factor" in rope_scaling:
                 scaling_factor = rope_scaling["factor"]
-                rotary_emb = DynamicNTKScalingRotaryEmbedding(
-                    head_size, rotary_dim, max_position, base, is_neox_style,
-                    scaling_factor, dtype)
+                if "alpha" in rope_scaling:
+                    rotary_emb = DynamicNTKAlphaRotaryEmbedding(
+                        head_size, rotary_dim, max_position, base, is_neox_style,
+                        rope_scaling["alpha"], dtype)
+                else:
+                    rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                        head_size, rotary_dim, max_position, base, is_neox_style,
+                        scaling_factor, dtype)
             else:
                 raise ValueError("Dynamic rope scaling must contain either "
                                  "'alpha' or 'factor' field")

vllm/model_executor/models/hunyuan.py

+# coding=utf-8
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# 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 HunYuan model compatible with HuggingFace weights."""
+import typing
+from collections.abc import Callable, Iterable
+from typing import Any, Optional, Union, Dict, List, Tuple
+
+import regex as re
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.distributed import (get_ep_group, get_pp_group,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+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 (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_layers, maybe_prefix)
+
+
+def _is_moe(config: PretrainedConfig) -> bool:
+    num_experts = getattr(config, "num_experts", None)
+    if isinstance(num_experts, int):
+        return num_experts > 1
+    if isinstance(num_experts, list) and num_experts:
+        # Ensure all elements are integers before calling max.
+        if all(isinstance(e, int) for e in num_experts):
+            return max(num_experts) > 1
+        else:
+            return False
+    return False
+
+
+def _get_cla_factor(config: PretrainedConfig) -> int:
+    if not getattr(config, "use_cla", False):
+        return 1
+    return getattr(config, "cla_share_factor", 1)
+
+
+class HunYuanMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+            reduce_results=reduce_results,
+        )
+        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 HunYuanSparseMoeBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        enable_eplb: bool = False,
+        layer_id: int = -1,
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = self.ep_group.rank()
+        self.ep_size = self.ep_group.size()
+        self.n_routed_experts = config.num_experts
+
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}.")
+            
+        # Get layer_id topk if config.moe_topk is a list
+        if isinstance(config.moe_topk, list):
+            assert layer_id >= 0
+            assert len(config.moe_topk) > layer_id
+            top_k = config.moe_topk[layer_id]
+        else:
+            top_k = config.moe_topk
+
+        # If it is moe, moe_intermediate_size is preferred
+        intermediate_size = config.intermediate_size
+        if config.moe_intermediate_size is not None:
+            intermediate_size = (config.moe_intermediate_size if isinstance(
+                config.moe_intermediate_size, int) else
+                                 config.moe_intermediate_size[layer_id])
+
+        # Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        eplb_config = vllm_config.parallel_config.eplb_config
+        self.enable_eplb = enable_eplb
+
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
+        self.n_physical_experts = (self.n_logical_experts +
+                                   self.n_redundant_experts)
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+        self.physical_expert_start = (self.ep_rank *
+                                      self.n_local_physical_experts)
+        self.physical_expert_end = (self.physical_expert_start +
+                                    self.n_local_physical_experts)
+
+        self.experts = FusedMoE(
+            num_experts=config.num_experts,
+            top_k=top_k,
+            hidden_size=config.hidden_size,
+            intermediate_size=intermediate_size,
+            reduce_results=False,
+            renormalize=top_k > 1,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+        )
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.num_experts,
+                                     bias=False,
+                                     quant_config=None)
+        if config.use_mixed_mlp_moe > 0:
+            # Get layer_id num_shared_expert if config.num_shared_expert is
+            # a list.
+            if isinstance(config.num_shared_expert, list):
+                assert layer_id >= 0
+                assert len(config.num_shared_expert) > layer_id
+                num_shared_expert = config.num_shared_expert[layer_id]
+            else:
+                num_shared_expert = config.num_shared_expert
+            self.shared_mlp = HunYuanMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size * config.num_shared_expert,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+            )
+        else:
+            self.shared_mlp = None
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        shared_output = None
+        if self.shared_mlp is not None:
+            shared_output = self.shared_mlp(hidden_states)
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(hidden_states=hidden_states,
+                                           router_logits=router_logits)
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+
+        return final_hidden_states.view(orig_shape)
+
+
+class HunYuanAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+        layer_id: int = -1,
+    ) -> None:
+        super().__init__()
+        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)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        if hasattr(config, "head_dim") and config.head_dim:
+            self.head_dim = config.head_dim
+        elif hasattr(config, "attention_head_dim"):
+            self.head_dim = config.attention_head_dim
+        else:
+            self.head_dim = self.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.use_qk_norm = getattr(config, "use_qk_norm", False)
+        self.layer_id = layer_id
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        is_neox_style = True
+        if quant_config is not None and quant_config.get_name() == "gguf":
+            is_neox_style = False
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+        )
+        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",
+        )
+
+        if self.use_qk_norm:
+            self.query_layernorm = RMSNorm(self.head_dim,
+                                           eps=config.rms_norm_eps)
+            self.key_layernorm = RMSNorm(self.head_dim,
+                                         eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_states: Optional[Tuple[torch.Tensor]] = None,
+    ) -> 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)
+        ori_k = k
+        if self.use_qk_norm:
+            q = self.query_layernorm(
+                q.view(-1, self.num_heads, self.head_dim).contiguous())
+            k = self.key_layernorm(
+                k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
+       
+        attn_output = self.attn(q, k, v)
+        # For o_proj
+        attn_output = attn_output.view(q.shape[0], -1)
+        output, _ = self.o_proj(attn_output)
+        return output, (ori_k, v)
+
+
+class HunYuanCrossAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+        layer_id: int = -1,
+    ) -> None:
+        super().__init__()
+        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)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        if hasattr(config, "head_dim"):
+            self.head_dim = config.head_dim
+        elif hasattr(config, "attention_head_dim"):
+            self.head_dim = config.attention_head_dim
+        else:
+            self.head_dim = self.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.use_qk_norm = getattr(config, "use_qk_norm", False)
+        self.layer_id = layer_id
+
+        self.q_proj = ColumnParallelLinear(
+            hidden_size,
+            hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.q_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        is_neox_style = True
+        if quant_config is not None and quant_config.get_name() == "gguf":
+            is_neox_style = False
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+        )
+        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",
+            attn_type=AttentionType.ENCODER_DECODER,
+        )
+
+        if self.use_qk_norm:
+            self.query_layernorm = RMSNorm(self.head_dim,
+                                           eps=config.rms_norm_eps)
+            self.key_layernorm = RMSNorm(self.head_dim,
+                                         eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_states: Optional[Tuple[torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        assert kv_states is not None
+        ori_k, v = kv_states # use last layer kv,
+        k = ori_k
+        q, _ = self.q_proj(hidden_states)
+        k_tmp = torch.empty_like(k) # Todo: reduant rotary embedding
+        q, _ = self.rotary_emb(positions, q, k_tmp)
+        if self.use_qk_norm:
+            q = self.query_layernorm(
+                q.view(-1, self.num_heads, self.head_dim).contiguous())
+            k = self.key_layernorm(
+                k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
+       
+        attn_output = self.attn(q, k, v)
+        # For o_proj
+        attn_output = attn_output.view(q.shape[0], -1)
+        output, _ = self.o_proj(attn_output)
+        return output, (ori_k, v)
+
+
+class HunYuanDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        layer_id: int = -1,
+        enable_eplb: bool = False,
+    ) -> None:
+        super().__init__()
+        assert layer_id >= 0
+        self.layer_id = layer_id
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = (config.intermediate_size if isinstance(
+            config.intermediate_size, int) else 
+                                config.intermediate_size[layer_id])
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        cla_factor = _get_cla_factor(config)
+        attention_type = (AttentionType.ENCODER_DECODER
+                          if layer_id >= 0 and layer_id % cla_factor != 0 else
+                          AttentionType.DECODER)
+        
+        if attention_type == AttentionType.DECODER:
+            self.self_attn = HunYuanAttention(
+                config=config,
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                num_kv_heads=getattr(config, "num_key_value_heads",
+                                     config.num_attention_heads),
+                rope_theta=rope_theta,
+                rope_scaling=rope_scaling,
+                max_position_embeddings=max_position_embeddings,
+                quant_config=quant_config,
+                bias=attention_bias,
+                cache_config=cache_config,
+                prefix=f"{prefix}.self_attn",
+                layer_id=layer_id,
+            )
+        elif attention_type == AttentionType.ENCODER_DECODER:
+            self.self_attn = HunYuanCrossAttention(
+                config=config,
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                num_kv_heads=getattr(config, "num_key_value_heads",
+                                     config.num_attention_heads),
+                rope_theta=rope_theta,
+                rope_scaling=rope_scaling,
+                max_position_embeddings=max_position_embeddings,
+                quant_config=quant_config,
+                bias=attention_bias,
+                cache_config=cache_config,
+                prefix=f"{prefix}.self_attn",
+                layer_id=layer_id,
+            )
+        else:
+            raise RuntimeError(f"Unsupported attention type: {attention_type}")
+
+        
+        if _is_moe(config):
+            self.mlp = HunYuanSparseMoeBlock(
+                config=config,
+                quant_config=quant_config,
+                layer_id=layer_id,
+                prefix=f"{prefix}.mlp",
+                enable_eplb=enable_eplb,
+            )
+        else:
+            self.mlp = HunYuanMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=self.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                bias=getattr(config, "mlp_bias", False),
+                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],
+        kv_states: Optional[Tuple[torch.Tensor]] = None,
+    ) -> Tuple[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, ori_kv_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_states=kv_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual, ori_kv_states
+
+
+@support_torch_compile
+class HunYuanModel(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
+        lora_config = vllm_config.lora_config
+        eplb_config = vllm_config.parallel_config.eplb_config
+        enable_eplb = vllm_config.parallel_config.enable_eplb
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+
+        self.config = config
+        self.quant_config = quant_config
+        self.padding_idx = config.pad_token_id
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: HunYuanDecoderLayer(
+                config=config,
+                layer_id=int(prefix.split(".")[-1]),
+                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()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        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"]
+
+        cla_factor = _get_cla_factor(self.config)
+        prev_kv_states = None
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual, kv_states = layer(
+                positions,
+                hidden_states,
+                residual,
+                prev_kv_states,
+            )
+
+            if (getattr(self.config, "use_cla", False)
+                    and (i - self.start_layer) % cla_factor == 0):
+                prev_kv_states = kv_states
+            else:
+                prev_kv_states = None
+
+        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
+
+    def _split_qkv_weight(self, qkv: torch.Tensor):
+        num_attention_heads = self.config.num_attention_heads
+        num_kv_heads = getattr(self.config, "num_key_value_heads",
+                               self.config.num_attention_heads)
+        num_key_value_groups = num_attention_heads // num_kv_heads
+        hidden_size = self.config.hidden_size
+
+        if hasattr(self.config, "head_dim"):
+            attention_head_dim = self.config.head_dim
+        elif hasattr(self.config, "attention_head_dim"):
+            attention_head_dim = self.config.attention_head_dim
+        else:
+            attention_head_dim = self.config.hidden_size // num_attention_heads
+
+        qkv = qkv.reshape(num_kv_heads, num_key_value_groups + 2,
+                          attention_head_dim, hidden_size)
+        q, k, v = torch.split(qkv, (num_key_value_groups, 1, 1), dim=1)
+        q = q.reshape(-1, hidden_size)
+        k = k.reshape(-1, hidden_size)
+        v = v.reshape(-1, hidden_size)
+        return torch.concat((q, k, v))
+    
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        if _is_moe(self.config):
+            # Params for weights, fp8 weight scales, fp8 activation scales
+            # (param_name, weight_name, expert_id, shard_id)
+            return 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.num_experts,
+                num_redundant_experts=self.num_redundant_experts,
+            )
+        else:
+            return []
+        
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        cla_factor = _get_cla_factor(self.config)
+        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),
+        ]
+
+        num_attention_heads = self.config.num_attention_heads
+        num_kv_heads = getattr(self.config, "num_key_value_heads",
+                               self.config.num_attention_heads)
+        split_params_mapping = [
+            (".gate_up_proj", ".gate_and_up_proj", 2, [(1, 1), (0, 1)], None),
+            (
+                ".qkv_proj",
+                ".qkv_proj",
+                num_attention_heads + num_kv_heads * 2,
+                [("q", num_attention_heads), ("k", num_kv_heads),
+                 ("v", num_kv_heads)],
+                self._split_qkv_weight,
+            ),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "gate_proj_bias" in name:
+                name = name.replace("gate_proj_bias", "gate_proj.bias")
+            if "up_proj_bias" in name:
+                name = name.replace("up_proj_bias", "up_proj.bias")
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            # With tie_word_embeddings, we can skip lm_head.weight
+            # The weight might appear unnecessarily in the files if the model is
+            # processed with quantization, LoRA, fine-tuning, etc.
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            if self.quant_config is not None and (
+                    scale_name := self.quant_config.get_cache_scale(name)):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                continue
+
+            is_found = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    continue
+                # cross layer only have q_proj, skip qkv pack
+                if weight_name == ".q_proj":
+                    match = re.search(r"layers\.\d+", name)
+                    if match:
+                        layer_id = int(match.group(0).split(".")[-1])
+                        if cla_factor > 1 and layer_id % cla_factor != 0:
+                            continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                is_found = True
+                break
+            if is_found:
+                continue
+
+            for (
+                    param_name,
+                    weight_name,
+                    den,
+                    split_param,
+                    func,
+            ) in split_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                assert loaded_weight.shape[0] % den == 0
+                units = loaded_weight.shape[0] // den
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                offset = 0
+                for shard_id, num in split_param:
+                    new_offset = offset + num * units
+                    if func:
+                        weight_loader(param,
+                                      func(loaded_weight)[offset:new_offset],
+                                      shard_id)
+                    else:
+                        weight_loader(param, loaded_weight[offset:new_offset],
+                                      shard_id)
+                    offset = new_offset
+
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                is_expert_weight = False
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    # this is an expert weight and should not be
+                    # attempted to load as other weights later
+                    is_expert_weight = True
+
+                    # Do not modify `name` since the loop may continue here
+                    # Instead, create a new variable
+                    name_mapped = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name_mapped, self):
+                        continue
+                    param = params_dict[name_mapped]
+                    # We should ask the weight loader to return success or not
+                    # here since otherwise we may skip experts with other
+                    # available replicas.
+                    weight_loader = typing.cast(Callable[..., bool],
+                                                param.weight_loader)
+                    success = weight_loader(
+                        param,
+                        loaded_weight,
+                        name_mapped,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                        return_success=True,
+                    )
+                    if success:
+                        name = name_mapped
+                        break
+                else:
+                    if is_expert_weight:
+                        # We've checked that this is an expert weight
+                        # However it's not mapped locally to this rank
+                        # So we simply skip it
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    if "mlp.gate.wg." in name:
+                        name = name.replace("wg.", "")
+
+                    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 HunYuanForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    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 = HunYuanModel(vllm_config=vllm_config, prefix="model")
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if config.tie_word_embeddings:
+                self.lm_head.weight = self.model.embed_tokens.weight
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+        else:
+            self.lm_head = PPMissingLayer()
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        for layer_idx, layer in enumerate(self.moe_layers):
+            self.expert_weights.append(layer.get_expert_weights())
+            # Register the expert weights.
+            layer.set_eplb_state(
+                moe_layer_idx=layer_idx,
+                expert_load_view=expert_load_view,
+                logical_to_physical_map=logical_to_physical_map,
+                logical_replica_count=logical_replica_count,
+            )
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = (num_physical_experts -
+                                      self.num_logical_experts)
+        for layer in self.model.layers:
+            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
+                moe = layer.mlp
+                moe.n_local_physical_experts = num_local_physical_experts
+                moe.n_physical_experts = num_physical_experts
+                moe.n_redundant_experts = self.num_redundant_experts
+                moe.experts.update_expert_map()
+                
+    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]:
+        model_output = self.model(input_ids, positions, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+    
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    
\ No newline at end of file

vllm/model_executor/models/hunyuan_v1.py

@@ -889,7 +889,7 @@ class HunYuanModel(nn.Module):
         return loaded_params
 
 
-class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
+class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -931,30 +931,6 @@ class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
         else:
             self.lm_head = PPMissingLayer()
 
-        # Set MoE hyperparameters
-        self.expert_weights = []
-        self.num_expert_groups = 1
-        self.moe_layers: list[FusedMoE] = []
-        example_layer = None
-        for layer in self.model.layers:
-            if isinstance(layer, PPMissingLayer):
-                continue
-
-            assert isinstance(layer, HunYuanDecoderLayer)
-            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
-                example_layer = layer.mlp
-                self.moe_layers.append(layer.mlp.experts)
-
-        if example_layer is None:
-            raise RuntimeError("No HunYuanMoE layer found in model.layers.")
-
-        self.num_moe_layers = len(self.moe_layers)
-        self.num_logical_experts = example_layer.n_logical_experts
-        self.num_physical_experts = example_layer.n_physical_experts
-        self.num_local_physical_experts = example_layer.n_local_physical_experts
-        self.num_routed_experts = example_layer.n_routed_experts
-        self.num_redundant_experts = example_layer.n_redundant_experts
-
     def set_eplb_state(
         self,
         expert_load_view: torch.Tensor,
@@ -1030,13 +1006,120 @@ class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
         )
         return loader.load_weights(weights)
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+class HunYuanMoEV1Base(HunYuanV1Base, MixtureOfExperts):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = HunYuanModel(vllm_config=vllm_config, prefix="model")
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if config.tie_word_embeddings:
+                self.lm_head.weight = self.model.embed_tokens.weight
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        # Set MoE hyperparameters
+        self.expert_weights = []
+        self.num_expert_groups = 1
+        self.moe_layers: list[FusedMoE] = [] # list[SharedFusedMoE] = []
+        example_layer = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, HunYuanDecoderLayer)
+            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
+                example_layer = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_layer is None:
+            raise RuntimeError("No HunYuanMoE layer found in model.layers.")
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_logical_experts = example_layer.n_logical_experts
+        self.num_physical_experts = example_layer.n_physical_experts
+        self.num_local_physical_experts = example_layer.n_local_physical_experts
+        self.num_routed_experts = example_layer.n_routed_experts
+        self.num_redundant_experts = example_layer.n_redundant_experts
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        for layer_idx, layer in enumerate(self.moe_layers):
+            self.expert_weights.append(layer.get_expert_weights())
+            # Register the expert weights.
+            layer.set_eplb_state(
+                moe_layer_idx=layer_idx,
+                expert_load_view=expert_load_view,
+                logical_to_physical_map=logical_to_physical_map,
+                logical_replica_count=logical_replica_count,
+            )
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = (num_physical_experts -
+                                      self.num_logical_experts)
+        for layer in self.model.layers:
+            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
+                moe = layer.mlp
+                moe.n_local_physical_experts = num_local_physical_experts
+                moe.n_physical_experts = num_physical_experts
+                moe.n_redundant_experts = self.num_redundant_experts
+                moe.experts.update_expert_map()
+
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         return self.model.get_expert_mapping()
 
 
-class HunYuanDenseV1ForCausalLM(HunYuanV1Base):
+class HunYuanDenseV1Base(HunYuanV1Base):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        
+class HunYuanDenseV1ForCausalLM(HunYuanDenseV1Base):
     pass
 
 
-class HunYuanMoEV1ForCausalLM(HunYuanV1Base):
-    pass
+class HunYuanMoEV1ForCausalLM(HunYuanMoEV1Base):
+    pass
\ No newline at end of file

vllm/model_executor/models/registry.py

@@ -100,6 +100,7 @@ _TEXT_GENERATION_MODELS = {
     "Grok1ModelForCausalLM": ("grok1", "Grok1ForCausalLM"),
     "HunYuanMoEV1ForCausalLM": ("hunyuan_v1", "HunYuanMoEV1ForCausalLM"),
     "HunYuanDenseV1ForCausalLM": ("hunyuan_v1", "HunYuanDenseV1ForCausalLM"),
+    "HunYuanForCausalLM": ("hunyuan", "HunYuanForCausalLM"),
     "HCXVisionForCausalLM": ("hyperclovax_vision", "HCXVisionForCausalLM"),
     "InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
     "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),