[Hunyuan]: Fix Dense Model Support

python/sglang/srt/models/hunyuan.py

@@ -206,6 +206,42 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return final_hidden_states.view(orig_shape)
 
 
+def get_head_dim(config):
+    if hasattr(config, "head_dim"):
+        return int(config.head_dim)
+    if hasattr(config, "attention_head_dim"):
+        return int(config.attention_head_dim)
+
+    # since some hunyuan model don't follow the self.hidden_size // self.total_num_heads rule
+    # wrong setting may cause runtime error, just throw error if this field is missing.
+    raise ValueError("Missing head dim config, try set head_dim in config.json")
+
+
+def check_head_dim(config):
+    # Some models may lack `head_dim` and use `attention_head_dim` instead.
+    # This attribute is also used by flashinfer_backend.py, so we check for
+    # consistency and raise an error if it's not met to avoid silent failures.
+    # Although we could adapt the HunYuan model to use `attention_head_dim`,
+    # flashinfer expects `head_dim`, so we enforce its presence for correctness.
+    calc_head_dim = config.hidden_size // config.num_attention_heads
+
+    if hasattr(config, "attention_head_dim"):
+        if calc_head_dim != config.attention_head_dim and not hasattr(
+            config, "head_dim"
+        ):
+            # in this case, flash infer(and other components may calculate wrong value.)
+            raise ValueError(
+                f"HunYuan model config error: calculated head_dim {calc_head_dim} != attention_head_dim {config.attention_head_dim}"
+                + f"\nPlease Add head_dim:{config.attention_head_dim} in config.json to make sure correctly inference."
+            )
+
+        if hasattr(config, "head_dim") and config.attention_head_dim != config.head_dim:
+            raise ValueError(
+                f"HunYuan model config error: head_dim({config.head_dim}) != attention_head_dim({config.attention_head_dim})"
+                + f"\nPlease change head_dim:{config.attention_head_dim} in config.json to make sure correctly inference."
+            )
+
+
 class HunYuanAttention(nn.Module):
 
     def __init__(
@@ -240,9 +276,11 @@ def __init__(
             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
-        self.head_dim = getattr(
-            config, "head_dim", self.hidden_size // self.total_num_heads
-        )
+        # Prioritize `head_dim` but fall back to `attention_head_dim` for Hunyuan models.
+        self.head_dim = get_head_dim(config)
+
+        check_head_dim(config)
+
         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
@@ -493,7 +531,6 @@ def forward(
             hidden_states = self.get_input_embeddings(input_ids)
         residual = None
 
-        cla_factor = _get_cla_factor(self.config)
         prev_kv_states = None
         for i in range(len(self.layers)):
             layer = self.layers[i]
@@ -560,6 +597,11 @@ def __init__(
         if config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
 
+        self.hidden_size = config.hidden_size
+        self.head_dim = get_head_dim(config)
+
+        check_head_dim(config)
+
         logit_scale = getattr(config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(config, logit_scale=logit_scale)
         self.sampler = Sampler()
@@ -582,16 +624,14 @@ def _split_qkv_weight(self, qkv: torch.Tensor):
             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
-        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
+            num_kv_heads, num_key_value_groups + 2, self.head_dim, self.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)
+        q = q.reshape(-1, self.hidden_size)
+        k = k.reshape(-1, self.hidden_size)
+        v = v.reshape(-1, self.hidden_size)
         return torch.concat((q, k, v))
         # return qkv.reshape((num_kv_heads, num_key_value_groups+2 , attention_head_dim, hidden_size)).permute((1,0,2,3)).reshape((-1, hidden_size)),
 
@@ -768,4 +808,8 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None:
                 )
 
 
-EntryClass = HunYuanMoEV1ForCausalLM
+class HunYuanDenseV1ForCausalLM(HunYuanMoEV1ForCausalLM):
+    pass
+
+
+EntryClass = [HunYuanMoEV1ForCausalLM, HunYuanDenseV1ForCausalLM]