Standardise `get_rope` to use `rope_parameters["partial_rotary_factor"]`, not `rotary_dim` (#30389)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/models/ernie45_moe.py

@@ -288,7 +288,6 @@ class Ernie4_5_MoeAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             is_neox_style=False,

vllm/model_executor/models/exaone.py

@@ -167,7 +167,6 @@ class ExaoneAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,

vllm/model_executor/models/exaone4.py

@@ -176,7 +176,6 @@ class Exaone4Attention(nn.Module):
         set_default_rope_theta(config, default_theta=1000000)
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,

vllm/model_executor/models/falcon.py

@@ -167,7 +167,6 @@ class FalconAttention(nn.Module):
             max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
             self.rotary_emb = get_rope(
                 self.head_dim,
-                rotary_dim=self.head_dim,
                 max_position=max_position_embeddings,
                 rope_parameters=config.rope_parameters,
             )

vllm/model_executor/models/falcon_h1.py

@@ -242,14 +242,11 @@ class FalconH1AttentionDecoderLayer(nn.Module):
         self.scaling = self.head_dim**-0.5
         self.max_position_embeddings = max_position_embeddings
 
-        if hasattr(config, "attn_rotary_emb"):
-            rotary_dim = config.attn_rotary_emb  # for backward compatibility
-        else:
-            rotary_dim = self.head_dim  # default
+        rotary_dim = getattr(config, "attn_rotary_emb", self.head_dim)
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
 
         self.rotary_emb = get_rope(
             head_size=self.head_dim,
-            rotary_dim=rotary_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/gemma.py

@@ -174,7 +174,6 @@ class GemmaAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/gemma2.py

@@ -152,7 +152,6 @@ class Gemma2Attention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/gemma3.py

@@ -176,7 +176,6 @@ class Gemma3Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/gemma3n.py

@@ -384,7 +384,6 @@ class Gemma3nAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/glm4.py

@@ -81,7 +81,6 @@ class Glm4Attention(nn.Module):
         config.rope_parameters.setdefault("partial_rotary_factor", 0.5)
         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.rotary_dim = self.head_dim
         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
@@ -103,7 +102,6 @@ class Glm4Attention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.rotary_dim,
             max_position=max_position,
             rope_parameters=config.rope_parameters,
             is_neox_style=False,

vllm/model_executor/models/glm4_1v.py

@@ -678,9 +678,9 @@ class Glm4vVisionTransformer(nn.Module):
         head_dim = self.hidden_size // self.num_heads
         self.rotary_pos_emb = get_rope(
             head_size=head_dim,
-            rotary_dim=head_dim // 2,
             max_position=8192,
             is_neox_style=True,
+            rope_parameters={"partial_rotary_factor": 0.5},
         )
         self.blocks = nn.ModuleList(
             [

vllm/model_executor/models/glm4_moe.py

@@ -285,7 +285,6 @@ class Glm4MoeAttention(nn.Module):
         config.rope_parameters.setdefault("partial_rotary_factor", 0.5)
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/gpt_j.py

@@ -95,12 +95,13 @@ class GPTJAttention(nn.Module):
         scaling = self.head_size**-0.5
         assert getattr(config, "rotary", True)
         assert config.rotary_dim % 2 == 0
+        rope_parameters = getattr(config, "rope_parameters", {})
+        rope_parameters["partial_rotary_factor"] = config.rotary_dim / self.head_size
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.rotary_emb = get_rope(
             self.head_size,
-            rotary_dim=config.rotary_dim,
             max_position=max_position_embeddings,
-            rope_parameters=getattr(config, "rope_parameters", None),
+            rope_parameters=rope_parameters,
             is_neox_style=False,
         )
         self.attn = Attention(

vllm/model_executor/models/gpt_neox.py

@@ -92,7 +92,6 @@ class GPTNeoXAttention(nn.Module):
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.rotary_emb = get_rope(
             self.head_size,
-            rotary_dim=self.head_size,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/gpt_oss.py

@@ -67,7 +67,6 @@ class OAIAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=config.max_position_embeddings,
             dtype=torch.float32,
             rope_parameters={

vllm/model_executor/models/granite.py

@@ -160,7 +160,6 @@ class GraniteAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/granitemoe.py

@@ -190,7 +190,6 @@ class GraniteMoeAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/granitemoehybrid.py

@@ -271,7 +271,6 @@ class GraniteMoeHybridAttention(nn.Module):
         if config.position_embedding_type == "rope":
             self.rotary_emb = get_rope(
                 self.head_dim,
-                rotary_dim=self.head_dim,
                 max_position=config.max_position_embeddings,
                 rope_parameters=config.rope_parameters,
                 is_neox_style=True,

vllm/model_executor/models/grok1.py

@@ -181,7 +181,6 @@ class Grok1Attention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/hunyuan_v1.py

@@ -199,7 +199,6 @@ class HunYuanAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,
@@ -305,7 +304,6 @@ class HunYuanCrossAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,