[Model] add Hunyuan V1 Dense Model support. (#21368)

Signed-off-by: Asher Zhang <asherszhang@tencent.com>

docs/models/supported_models.md

@@ -363,6 +363,7 @@ th {
 | `GraniteMoeSharedForCausalLM` | Granite MoE Shared | `ibm-research/moe-7b-1b-active-shared-experts` (test model) | ✅︎ | ✅︎ | ✅︎ |
 | `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ | |
 | `Grok1ModelForCausalLM` | Grok1 | `hpcai-tech/grok-1`. | ✅︎ | ✅︎ | ✅︎ |
+| `HunYuanDenseV1ForCausalLM` | Hunyuan-7B-Instruct-0124 | `tencent/Hunyuan-7B-Instruct-0124` | ✅︎ | | ✅︎ |
 | `HunYuanMoEV1ForCausalLM` | Hunyuan-80B-A13B | `tencent/Hunyuan-A13B-Instruct`, `tencent/Hunyuan-A13B-Pretrain`, `tencent/Hunyuan-A13B-Instruct-FP8`, etc. | ✅︎ | | ✅︎ |
 | `InternLMForCausalLM` | InternLM | `internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternLM2ForCausalLM` | InternLM2 | `internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc. | ✅︎ | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -199,6 +199,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                              trust_remote_code=True),
     "HunYuanMoEV1ForCausalLM": _HfExamplesInfo("tencent/Hunyuan-A13B-Instruct",
                                                trust_remote_code=True),
+    "HunYuanDenseV1ForCausalLM":_HfExamplesInfo("tencent/Hunyuan-7B-Instruct-0124",
+                                               trust_remote_code=True),
     "InternLMForCausalLM": _HfExamplesInfo("internlm/internlm-chat-7b",
                                            trust_remote_code=True),
     "InternLM2ForCausalLM": _HfExamplesInfo("internlm/internlm2-chat-7b",

vllm/model_executor/models/hunyuan_v1_moe.py → vllm/model_executor/models/hunyuan_v1.py

@@ -61,6 +61,19 @@ from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
                     make_layers)
 
 
+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
@@ -140,8 +153,8 @@ class HunYuanAttention(nn.Module):
             # 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"):
+
+        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
@@ -490,12 +503,23 @@ class HunYuanDecoderLayer(nn.Module):
         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",
             )
+        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,
@@ -642,7 +666,7 @@ class HunYuanModel(nn.Module):
         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(
@@ -651,6 +675,8 @@ class HunYuanModel(nn.Module):
                 ckpt_up_proj_name="up_proj",
                 num_experts=self.config.num_experts,
             )
+        else:
+            return []
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         cla_factor = _get_cla_factor(self.config)
@@ -815,7 +841,7 @@ class HunYuanModel(nn.Module):
         return loaded_params
 
 
-class HunYuanMoEV1ForCausalLM(nn.Module, SupportsLoRA):
+class HunYuanV1Base(nn.Module, SupportsLoRA):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -901,3 +927,11 @@ class HunYuanMoEV1ForCausalLM(nn.Module, SupportsLoRA):
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         return self.model.get_expert_mapping()
+
+
+class HunYuanDenseV1ForCausalLM(HunYuanV1Base):
+    pass
+
+
+class HunYuanMoEV1ForCausalLM(HunYuanV1Base):
+    pass

vllm/model_executor/models/registry.py

@@ -79,7 +79,8 @@ _TEXT_GENERATION_MODELS = {
     "GraniteMoeSharedForCausalLM": ("granitemoeshared", "GraniteMoeSharedForCausalLM"),   # noqa: E501
     "GritLM": ("gritlm", "GritLM"),
     "Grok1ModelForCausalLM": ("grok1", "Grok1ForCausalLM"),
-    "HunYuanMoEV1ForCausalLM": ("hunyuan_v1_moe", "HunYuanMoEV1ForCausalLM"),
+    "HunYuanMoEV1ForCausalLM": ("hunyuan_v1", "HunYuanMoEV1ForCausalLM"),
+    "HunYuanDenseV1ForCausalLM": ("hunyuan_v1", "HunYuanDenseV1ForCausalLM"),
     "InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
     "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
     "InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"),