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>

benchmarks/kernels/benchmark_mrope.py

@@ -99,7 +99,6 @@ def benchmark_mrope(
     # the parameters to compute the q k v size based on tp_size
     mrope_helper_class = get_rope(
         head_size=head_dim,
-        rotary_dim=head_dim,
         max_position=max_position,
         is_neox_style=is_neox_style,
         rope_parameters=rope_parameters,

benchmarks/kernels/benchmark_rope.py

@@ -32,8 +32,8 @@ def get_benchmark(head_size, rotary_dim, is_neox_style, device):
     def benchmark(batch_size, seq_len, num_heads, provider):
         dtype = torch.bfloat16
         max_position = 8192
-        base = 10000
+        rope_parameters = {"partial_rotary_factor": rotary_dim / head_size}
-        rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style)
+        rope = get_rope(head_size, max_position, is_neox_style, rope_parameters)
         rope = rope.to(dtype=dtype, device=device)
         cos_sin_cache = rope.cos_sin_cache.to(dtype=torch.float, device=device)
 

tests/compile/test_functionalization.py

@@ -128,14 +128,12 @@ class TestFusedAddRMSNorm(torch.nn.Module):
 
 
 class TestRotaryEmbedding(torch.nn.Module):
-    def __init__(self, head_dim=64, rotary_dim=None, max_position=2048, base=10000):
+    def __init__(self, head_dim=64, max_position=2048, base=10000):
         super().__init__()
         self.head_dim = head_dim
-        self.rotary_dim = rotary_dim or head_dim
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.rotary_dim,
             max_position=max_position,
             rope_parameters={"rope_type": "default", "rope_theta": base},
         )
@@ -170,7 +168,6 @@ class TestRotaryEmbeddingSliceScatter(torch.nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters={"rope_type": "default", "rope_theta": base},
         )

tests/kernels/core/test_mrope.py

@@ -116,7 +116,6 @@ def test_mrope(
 
     mrope_helper_class = get_rope(
         head_size=head_dim,
-        rotary_dim=head_dim,
         max_position=max_position,
         is_neox_style=is_neox_style,
         rope_parameters=config.rope_parameters,
@@ -185,7 +184,6 @@ def test_mrope_torch_compile_tracing(
 
     mrope_helper_class = get_rope(
         head_size=head_dim,
-        rotary_dim=head_dim,
         max_position=max_position,
         is_neox_style=is_neox_style,
         rope_parameters=config.rope_parameters,

tests/kernels/core/test_pos_encoding.py

@@ -83,8 +83,12 @@ def test_rotary_embedding(
     torch.set_default_device(device)
     if rotary_dim is None:
         rotary_dim = head_size
-    rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+    rope_parameters = {
-    rope = get_rope(head_size, rotary_dim, max_position, is_neox_style, rope_parameters)
+        "rope_type": "default",
+        "rope_theta": rope_theta,
+        "partial_rotary_factor": rotary_dim / head_size,
+    }
+    rope = get_rope(head_size, max_position, is_neox_style, rope_parameters)
     rope = rope.to(dtype=dtype, device=torch.get_default_device())
 
     positions = torch.randint(0, max_position, (batch_size, seq_len))
@@ -150,9 +154,9 @@ def test_rope_module_cache():
         if rotary_dim is None:
             rotary_dim = head_size
         rope_parameters["rope_theta"] = rope_theta
+        rope_parameters["partial_rotary_factor"] = rotary_dim / head_size
         rope = get_rope(
             head_size,
-            rotary_dim,
             max_position,
             is_neox_style,
             rope_parameters,
@@ -177,9 +181,9 @@ def test_rope_module_cache():
         if rotary_dim is None:
             rotary_dim = head_size
         rope_parameters["rope_theta"] = rope_theta
+        rope_parameters["partial_rotary_factor"] = rotary_dim / head_size
         rope = get_rope(
             head_size,
-            rotary_dim,
             max_position,
             is_neox_style,
             rope_parameters,

vllm/config/utils.py

@@ -73,14 +73,28 @@ def get_field(cls: ConfigType, name: str) -> Field:
     )
 
 
-def getattr_iter(object: object, names: Iterable[str], default: Any) -> Any:
+def getattr_iter(
+    object: object, names: Iterable[str], default: Any, warn: bool = False
+) -> Any:
     """
     A helper function that retrieves an attribute from an object which may
     have multiple possible names. This is useful when fetching attributes from
     arbitrary `transformers.PretrainedConfig` instances.
+
+    In the case where the first name in `names` is the preferred name, and
+    any other names are deprecated aliases, setting `warn=True` will log a
+    warning when a deprecated name is used.
     """
-    for name in names:
+    for i, name in enumerate(names):
         if hasattr(object, name):
+            if warn and i > 0:
+                logger.warning_once(
+                    "%s contains a deprecated attribute name '%s'. "
+                    "Please use the preferred attribute name '%s' instead.",
+                    type(object).__name__,
+                    name,
+                    names[0],
+                )
             return getattr(object, name)
     return default
 

vllm/model_executor/layers/rotary_embedding/__init__.py

@@ -25,7 +25,6 @@ _ROPE_DICT: dict[tuple, RotaryEmbedding] = {}
 
 def get_rope(
     head_size: int,
-    rotary_dim: int,
     max_position: int,
     is_neox_style: bool = True,
     rope_parameters: dict[str, Any] | None = None,
@@ -54,12 +53,15 @@ def get_rope(
     else:
         dual_chunk_attention_args = None
 
-    partial_rotary_factor = 1.0
+    rope_parameters = rope_parameters or {}
-    if rope_parameters is not None:
+    base = rope_parameters.get("rope_theta", 10000)
-        partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
+    scaling_type = rope_parameters.get("rope_type", "default")
+    partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
+
+    if partial_rotary_factor <= 0.0 or partial_rotary_factor > 1.0:
+        raise ValueError(f"{partial_rotary_factor=} must be between 0.0 and 1.0")
+    rotary_dim = int(head_size * partial_rotary_factor)
 
-    if partial_rotary_factor < 1.0:
-        rotary_dim = int(rotary_dim * partial_rotary_factor)
     key = (
         head_size,
         rotary_dim,
@@ -72,7 +74,6 @@ def get_rope(
     if key in _ROPE_DICT:
         return _ROPE_DICT[key]
 
-    base = rope_parameters["rope_theta"] if rope_parameters else 10000
     if dual_chunk_attention_config is not None:
         extra_kwargs = {
             k: v
@@ -88,109 +89,76 @@ def get_rope(
             dtype,
             **extra_kwargs,
         )
-    elif not rope_parameters:
+    elif scaling_type == "default":
-        rotary_emb = RotaryEmbedding(
+        if "mrope_section" in rope_parameters:
+            rotary_emb = MRotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                is_neox_style,
+                dtype,
+                mrope_section=rope_parameters["mrope_section"],
+                mrope_interleaved=rope_parameters.get("mrope_interleaved", False),
+            )
+        else:
+            rotary_emb = RotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                is_neox_style,
+                dtype,
+            )
+    elif scaling_type == "llama3":
+        scaling_factor = rope_parameters["factor"]
+        low_freq_factor = rope_parameters["low_freq_factor"]
+        high_freq_factor = rope_parameters["high_freq_factor"]
+        original_max_position = rope_parameters["original_max_position_embeddings"]
+        rotary_emb = Llama3RotaryEmbedding(
+            head_size,
+            rotary_dim,
+            max_position,
+            base,
+            is_neox_style,
+            dtype,
+            scaling_factor,
+            low_freq_factor,
+            high_freq_factor,
+            original_max_position,
+        )
+    elif scaling_type == "mllama4":
+        rotary_emb = Llama4VisionRotaryEmbedding(
             head_size, rotary_dim, max_position, base, is_neox_style, dtype
         )
-    else:
+    elif scaling_type == "linear":
-        scaling_type = rope_parameters["rope_type"]
+        scaling_factor = rope_parameters["factor"]
-
+        rotary_emb = LinearScalingRotaryEmbedding(
-        if scaling_type == "llama3":
+            head_size,
-            scaling_factor = rope_parameters["factor"]
+            rotary_dim,
-            low_freq_factor = rope_parameters["low_freq_factor"]
+            max_position,
-            high_freq_factor = rope_parameters["high_freq_factor"]
+            base,
-            original_max_position = rope_parameters["original_max_position_embeddings"]
+            is_neox_style,
-            rotary_emb = Llama3RotaryEmbedding(
+            scaling_factor,
-                head_size,
+            dtype,
-                rotary_dim,
+        )
-                max_position,
+    elif scaling_type == "ntk":
-                base,
+        scaling_factor = rope_parameters["factor"]
-                is_neox_style,
+        mixed_b = rope_parameters.get("mixed_b")
-                dtype,
+        rotary_emb = NTKScalingRotaryEmbedding(
-                scaling_factor,
+            head_size,
-                low_freq_factor,
+            rotary_dim,
-                high_freq_factor,
+            max_position,
-                original_max_position,
+            base,
-            )
+            is_neox_style,
-        elif scaling_type == "mllama4":
+            scaling_factor,
-            rotary_emb = Llama4VisionRotaryEmbedding(
+            dtype,
-                head_size, rotary_dim, max_position, base, is_neox_style, dtype
+            mixed_b,
-            )
+        )
-        elif scaling_type == "default":
+    elif scaling_type == "dynamic":
-            if "mrope_section" in rope_parameters:
+        if "alpha" in rope_parameters:
-                rotary_emb = MRotaryEmbedding(
-                    head_size,
-                    rotary_dim,
-                    max_position,
-                    base,
-                    is_neox_style,
-                    dtype,
-                    mrope_section=rope_parameters["mrope_section"],
-                    mrope_interleaved=rope_parameters.get("mrope_interleaved", False),
-                )
-            else:
-                rotary_emb = RotaryEmbedding(
-                    head_size,
-                    rotary_dim,
-                    max_position,
-                    base,
-                    is_neox_style,
-                    dtype,
-                )
-        elif scaling_type == "linear":
-            scaling_factor = rope_parameters["factor"]
-            rotary_emb = LinearScalingRotaryEmbedding(
-                head_size,
-                rotary_dim,
-                max_position,
-                base,
-                is_neox_style,
-                scaling_factor,
-                dtype,
-            )
-        elif scaling_type == "ntk":
-            scaling_factor = rope_parameters["factor"]
-            mixed_b = rope_parameters.get("mixed_b")
-            rotary_emb = NTKScalingRotaryEmbedding(
-                head_size,
-                rotary_dim,
-                max_position,
-                base,
-                is_neox_style,
-                scaling_factor,
-                dtype,
-                mixed_b,
-            )
-        elif scaling_type == "dynamic":
-            if "alpha" in rope_parameters:
-                scaling_alpha = rope_parameters["alpha"]
-                rotary_emb = DynamicNTKAlphaRotaryEmbedding(
-                    head_size,
-                    rotary_dim,
-                    max_position,
-                    base,
-                    is_neox_style,
-                    scaling_alpha,
-                    dtype,
-                )
-            elif "factor" in rope_parameters:
-                scaling_factor = rope_parameters["factor"]
-                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"
-                )
-        elif scaling_type == "xdrope":
             scaling_alpha = rope_parameters["alpha"]
-            rotary_emb = XDRotaryEmbedding(
+            rotary_emb = DynamicNTKAlphaRotaryEmbedding(
                 head_size,
                 rotary_dim,
                 max_position,
@@ -198,67 +166,66 @@ def get_rope(
                 is_neox_style,
                 scaling_alpha,
                 dtype,
-                xdrope_section=rope_parameters["xdrope_section"],
             )
-        elif scaling_type == "yarn":
+        elif "factor" in rope_parameters:
             scaling_factor = rope_parameters["factor"]
-            original_max_position = rope_parameters["original_max_position_embeddings"]
+            rotary_emb = DynamicNTKScalingRotaryEmbedding(
-            extra_kwargs = {
+                head_size,
-                k: v
+                rotary_dim,
-                for k, v in rope_parameters.items()
+                max_position,
-                if k
+                base,
-                in (
+                is_neox_style,
-                    "extrapolation_factor",
+                scaling_factor,
-                    "attn_factor",
+                dtype,
-                    "beta_fast",
+            )
-                    "beta_slow",
+        else:
-                    "apply_yarn_scaling",
+            raise ValueError(
-                    "truncate",
+                "Dynamic rope scaling must contain either 'alpha' or 'factor' field"
-                )
+            )
-            }
+    elif scaling_type == "xdrope":
-            if "mrope_section" in rope_parameters:
+        scaling_alpha = rope_parameters["alpha"]
-                extra_kwargs.pop("apply_yarn_scaling", None)
+        rotary_emb = XDRotaryEmbedding(
-                rotary_emb = MRotaryEmbedding(
+            head_size,
-                    head_size,
+            rotary_dim,
-                    rotary_dim,
+            max_position,
-                    original_max_position,
+            base,
-                    base,
+            is_neox_style,
-                    is_neox_style,
+            scaling_alpha,
-                    dtype,
+            dtype,
-                    mrope_section=rope_parameters["mrope_section"],
+            xdrope_section=rope_parameters["xdrope_section"],
-                    mrope_interleaved=rope_parameters.get("mrope_interleaved", False),
+        )
-                    scaling_factor=scaling_factor,
+    elif scaling_type == "yarn":
-                    **extra_kwargs,
+        scaling_factor = rope_parameters["factor"]
-                )
+        original_max_position = rope_parameters["original_max_position_embeddings"]
-            else:
+        extra_kwargs = {
-                rotary_emb = YaRNScalingRotaryEmbedding(
+            k: v
-                    head_size,
+            for k, v in rope_parameters.items()
-                    rotary_dim,
+            if k
-                    original_max_position,
+            in (
-                    base,
+                "extrapolation_factor",
-                    is_neox_style,
+                "attn_factor",
-                    scaling_factor,
+                "beta_fast",
-                    dtype,
+                "beta_slow",
-                    **extra_kwargs,
+                "apply_yarn_scaling",
-                )
+                "truncate",
-        elif scaling_type in ["deepseek_yarn", "deepseek_llama_scaling"]:
+            )
-            scaling_factor = rope_parameters["factor"]
+        }
-            original_max_position = rope_parameters["original_max_position_embeddings"]
+        if "mrope_section" in rope_parameters:
-            # assert max_position == original_max_position * scaling_factor
+            extra_kwargs.pop("apply_yarn_scaling", None)
-            extra_kwargs = {
+            rotary_emb = MRotaryEmbedding(
-                k: v
+                head_size,
-                for k, v in rope_parameters.items()
+                rotary_dim,
-                if k
+                original_max_position,
-                in (
+                base,
-                    "extrapolation_factor",
+                is_neox_style,
-                    "attn_factor",
+                dtype,
-                    "beta_fast",
+                mrope_section=rope_parameters["mrope_section"],
-                    "beta_slow",
+                mrope_interleaved=rope_parameters.get("mrope_interleaved", False),
-                    "mscale",
+                scaling_factor=scaling_factor,
-                    "mscale_all_dim",
+                **extra_kwargs,
-                )
+            )
-            }
+        else:
-            rotary_emb = DeepseekScalingRotaryEmbedding(
+            rotary_emb = YaRNScalingRotaryEmbedding(
                 head_size,
                 rotary_dim,
                 original_max_position,
@@ -268,28 +235,55 @@ def get_rope(
                 dtype,
                 **extra_kwargs,
             )
-        elif scaling_type == "longrope":
+    elif scaling_type in ["deepseek_yarn", "deepseek_llama_scaling"]:
-            short_factor = rope_parameters["short_factor"]
+        scaling_factor = rope_parameters["factor"]
-            long_factor = rope_parameters["long_factor"]
+        original_max_position = rope_parameters["original_max_position_embeddings"]
-            original_max_position = rope_parameters["original_max_position_embeddings"]
+        # assert max_position == original_max_position * scaling_factor
-            extra_kwargs = {
+        extra_kwargs = {
-                k: v
+            k: v
-                for k, v in rope_parameters.items()
+            for k, v in rope_parameters.items()
-                if k in ("short_mscale", "long_mscale")
+            if k
-            }
+            in (
-            rotary_emb = Phi3LongRoPEScaledRotaryEmbedding(
+                "extrapolation_factor",
-                head_size,
+                "attn_factor",
-                rotary_dim,
+                "beta_fast",
-                max_position,
+                "beta_slow",
-                original_max_position,
+                "mscale",
-                base,
+                "mscale_all_dim",
-                is_neox_style,
-                dtype,
-                short_factor,
-                long_factor,
-                **extra_kwargs,
             )
-        else:
+        }
-            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+        rotary_emb = DeepseekScalingRotaryEmbedding(
+            head_size,
+            rotary_dim,
+            original_max_position,
+            base,
+            is_neox_style,
+            scaling_factor,
+            dtype,
+            **extra_kwargs,
+        )
+    elif scaling_type == "longrope":
+        short_factor = rope_parameters["short_factor"]
+        long_factor = rope_parameters["long_factor"]
+        original_max_position = rope_parameters["original_max_position_embeddings"]
+        extra_kwargs = {
+            k: v
+            for k, v in rope_parameters.items()
+            if k in ("short_mscale", "long_mscale")
+        }
+        rotary_emb = Phi3LongRoPEScaledRotaryEmbedding(
+            head_size,
+            rotary_dim,
+            max_position,
+            original_max_position,
+            base,
+            is_neox_style,
+            dtype,
+            short_factor,
+            long_factor,
+            **extra_kwargs,
+        )
+    else:
+        raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
     _ROPE_DICT[key] = rotary_emb
     return rotary_emb

vllm/model_executor/models/afmoe.py

@@ -241,7 +241,6 @@ class AfmoeAttention(nn.Module):
         if self.is_local_attention:
             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/apertus.py

@@ -226,7 +226,6 @@ class ApertusAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,

vllm/model_executor/models/arctic.py

@@ -314,7 +314,6 @@ class ArcticAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/baichuan.py

@@ -189,7 +189,6 @@ class BaiChuanAttention(nn.Module):
         else:
             self.rotary_emb = get_rope(
                 self.head_dim,
-                rotary_dim=self.head_dim,
                 max_position=self.max_position_embeddings,
                 rope_parameters=rope_parameters,
             )

vllm/model_executor/models/bailing_moe.py

@@ -127,11 +127,11 @@ class BailingAttention(nn.Module):
             prefix=f"{prefix}.dense",
         )
 
-        self.rotary_dim = getattr(config, "rotary_dim", self.head_dim)
+        rotary_dim = getattr(config, "rotary_dim", self.head_dim)
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.rotary_dim,
             max_position=config.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/bamba.py

@@ -178,14 +178,11 @@ class BambaAttentionDecoderLayer(nn.Module):
         self.scaling = self.head_dim**-0.5
         self.max_position_embeddings = max_position_embeddings
 
-        if hasattr(config, "attn_rotary_emb"):
+        rotary_dim = getattr(config, "attn_rotary_emb", self.head_dim)
-            rotary_dim = config.attn_rotary_emb  # for backward compatibility
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
-        else:
-            rotary_dim = self.head_dim  # default
 
         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/chameleon.py

@@ -314,7 +314,6 @@ class ChameleonAttention(nn.Module):
         self.k_norm = ChameleonLayerNorm((self.num_kv_heads, self.head_dim))
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/chatglm.py

@@ -99,13 +99,16 @@ class GLMAttention(nn.Module):
         # https://huggingface.co/zai-org/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
         rope_ratio = getattr(config, "rope_ratio", 1.0)
         max_positions = getattr(config, "seq_length", 8192)
-        rope_parameters = {"rope_type": "default", "rope_theta": 10000 * rope_ratio}
+        rope_parameters = {
+            "rope_type": "default",
+            "rope_theta": 10000 * rope_ratio,
+            "partial_rotary_factor": 0.5,
+        }
         # NOTE: zai-org/cogagent-9b-20241220 uses original_rope=False,
         # which is equivalent to is_neox_style=True
         is_neox_style = not config.original_rope
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim // 2,
             max_position=max_positions,
             rope_parameters=rope_parameters,
             is_neox_style=is_neox_style,

vllm/model_executor/models/commandr.py

@@ -175,7 +175,6 @@ class CohereAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=False,

vllm/model_executor/models/config.py

@@ -42,9 +42,10 @@ class GteNewModelConfig(VerifyAndUpdateConfig):
         config.hidden_act = "geglu"
 
         head_dim = config.hidden_size // config.num_attention_heads
+        rotary_dim = getattr(config, "rotary_emb_dim", head_dim)
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / head_dim
         config.rotary_kwargs = {
             "head_size": head_dim,
-            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
             "max_position": config.max_position_embeddings,
             "rope_parameters": config.rope_parameters,
         }
@@ -77,9 +78,11 @@ class JinaRobertaModelConfig(VerifyAndUpdateConfig):
             if not model_config.enforce_eager:
                 max_position = round_up(max_position, 8)
 
+            rotary_dim = getattr(config, "rotary_emb_dim", head_dim)
+            config.rope_parameters["partial_rotary_factor"] = rotary_dim / head_dim
+
             config.rotary_kwargs = {
                 "head_size": head_dim,
-                "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
                 "max_position": max_position,
                 "rope_parameters": config.rope_parameters,
             }
@@ -113,12 +116,10 @@ class NomicBertModelConfig(VerifyAndUpdateConfig):
         config.num_hidden_layers = config.n_layer
 
         head_dim = config.hidden_size // config.num_attention_heads
-        rotary_emb_dim = int(head_dim * config.rotary_emb_fraction)
         max_trained_positions = getattr(config, "max_trained_positions", 2048)
 
         config.rotary_kwargs = {
             "head_size": head_dim,
-            "rotary_dim": rotary_emb_dim,
             "max_position": max_trained_positions,
             "rope_parameters": config.rope_parameters,
         }
@@ -240,9 +241,10 @@ class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
         config.hidden_act = "geglu"
 
         head_dim = config.hidden_size // config.num_attention_heads
+        rotary_dim = getattr(config, "rotary_emb_dim", head_dim)
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / head_dim
         config.rotary_kwargs = {
             "head_size": head_dim,
-            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
             "max_position": config.max_position_embeddings,
             "rope_parameters": config.rope_parameters,
         }

vllm/model_executor/models/dbrx.py

@@ -222,7 +222,6 @@ class DbrxAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position,
             rope_parameters=rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/deepseek_v2.py

@@ -156,7 +156,6 @@ class DeepseekAttention(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,
         )
@@ -499,7 +498,6 @@ class DeepseekV2Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             qk_rope_head_dim,
-            rotary_dim=qk_rope_head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=False,
@@ -1018,7 +1016,6 @@ class DeepseekV2MLAAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             qk_rope_head_dim,
-            rotary_dim=qk_rope_head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=False,
@@ -1038,7 +1035,6 @@ class DeepseekV2MLAAttention(nn.Module):
         if self.is_v32:
             self.indexer_rope_emb = get_rope(
                 qk_rope_head_dim,
-                rotary_dim=qk_rope_head_dim,
                 max_position=max_position_embeddings,
                 rope_parameters=config.rope_parameters,
                 is_neox_style=True,

vllm/model_executor/models/dots1.py

@@ -250,7 +250,6 @@ class Dots1Attention(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/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 = getattr(config, "attn_rotary_emb", self.head_dim)
-            rotary_dim = config.attn_rotary_emb  # for backward compatibility
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
-        else:
-            rotary_dim = self.head_dim  # default
 
         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,

vllm/model_executor/models/internlm2.py

@@ -140,7 +140,6 @@ class InternLM2Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/lfm2.py

@@ -143,7 +143,6 @@ class Lfm2Attention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/lfm2_moe.py

@@ -236,7 +236,6 @@ class Lfm2MoeAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/llama.py

@@ -259,7 +259,6 @@ class LlamaAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=getattr(config, "rope_parameters", None),
             is_neox_style=is_neox_style,

vllm/model_executor/models/llama4.py

@@ -243,7 +243,6 @@ class Llama4Attention(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/minicpm.py

@@ -277,7 +277,6 @@ class MiniCPMAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/minicpm3.py

@@ -120,7 +120,6 @@ class MiniCPM3Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.qk_rope_head_dim,
-            rotary_dim=self.qk_rope_head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/minimax_m2.py

@@ -199,9 +199,13 @@ class MiniMaxM2Attention(nn.Module):
             prefix=f"{prefix}.o_proj",
         )
 
+        if (
+            rope_parameters is not None
+            and "partial_rotary_factor" not in rope_parameters
+        ):
+            rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/minimax_text_01.py

@@ -187,7 +187,6 @@ class MiniMaxText01Attention(nn.Module):
         num_heads: int,
         head_dim: int,
         num_kv_heads: int,
-        rotary_dim: int,
         max_position: int = 4096 * 32,
         rope_parameters: dict | None = None,
         sliding_window: int | None = None,
@@ -245,7 +244,6 @@ class MiniMaxText01Attention(nn.Module):
         )
         self.rotary_emb = get_rope(
             head_size=self.head_dim,
-            rotary_dim=rotary_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
             is_neox_style=True,
@@ -290,6 +288,8 @@ class MiniMaxText01DecoderLayer(nn.Module):
         head_dim = getattr(config, "head_dim", None)
         if head_dim is None:
             head_dim = config.hidden_size // config.num_attention_heads
+        rotary_dim = getattr(config, "rotary_dim", head_dim)
+        config.rope_parameters["partial_rotary_factor"] = rotary_dim / head_dim
         if hasattr(config, "max_model_len") and isinstance(config.max_model_len, int):
             max_position_embeddings = min(
                 config.max_position_embeddings, config.max_model_len
@@ -321,9 +321,6 @@ class MiniMaxText01DecoderLayer(nn.Module):
                 hidden_size=self.hidden_size,
                 num_heads=config.num_attention_heads,
                 head_dim=head_dim,
-                rotary_dim=config.rotary_dim
-                if hasattr(config, "rotary_dim")
-                else head_dim,
                 num_kv_heads=config.num_key_value_heads,
                 max_position=max_position_embeddings,
                 rope_parameters=config.rope_parameters,

vllm/model_executor/models/mixtral.py

@@ -206,7 +206,6 @@ class MixtralAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/mllama4.py

@@ -295,11 +295,11 @@ class Llama4VisionAttention(nn.Module):
         rope_parameters = {
             "rope_type": "mllama4",
             "rope_theta": config.rope_parameters["rope_theta"],
+            "partial_rotary_factor": 0.5,
         }
 
         self.rotary_emb = get_rope(
             head_size=self.head_dim,
-            rotary_dim=config.hidden_size // config.num_attention_heads // 2,
             # number of image patches
             max_position=(config.image_size // config.patch_size) ** 2,
             rope_parameters=rope_parameters,

vllm/model_executor/models/modernbert.py

@@ -105,7 +105,6 @@ class ModernBertAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             head_size=self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=config.max_position_embeddings,
             rope_parameters=rope_parameters,
             dtype=torch.float16,

vllm/model_executor/models/molmo.py

@@ -433,7 +433,6 @@ class MolmoAttention(nn.Module):
         # Rotary embeddings.
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/nemotron.py

@@ -199,7 +199,6 @@ class NemotronAttention(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/nemotron_nas.py

@@ -118,7 +118,6 @@ class DeciLMAttention(LlamaAttention):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,

vllm/model_executor/models/olmo.py

@@ -102,7 +102,6 @@ class OlmoAttention(nn.Module):
         # Rotary embeddings.
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/olmo2.py

@@ -146,7 +146,6 @@ class Olmo2Attention(nn.Module):
             rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/olmoe.py

@@ -171,7 +171,6 @@ class OlmoeAttention(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/openpangu.py

@@ -352,7 +352,6 @@ class OpenPanguMLAAttention(nn.Module):
         }
         self.rotary_emb = get_rope(
             qk_rope_head_dim,
-            rotary_dim=qk_rope_head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             is_neox_style=False,
@@ -525,7 +524,6 @@ class OpenPanguEmbeddedAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,

vllm/model_executor/models/orion.py

@@ -135,7 +135,6 @@ class OrionAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/ouro.py

@@ -166,7 +166,6 @@ class OuroAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=config.rope_parameters,
             dual_chunk_attention_config=dual_chunk_attention_config,

vllm/model_executor/models/persimmon.py

@@ -134,7 +134,6 @@ class PersimmonAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/phi.py

@@ -84,19 +84,18 @@ class PhiAttention(nn.Module):
         prefix: str = "",
     ):
         super().__init__()
-        self.total_num_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
-        self.head_size = self.hidden_size // self.total_num_heads
+        self.head_size = self.hidden_size // config.num_attention_heads
 
         tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
-        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        assert config.num_attention_heads % tensor_model_parallel_world_size == 0
-        self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
+        self.num_heads = config.num_attention_heads // tensor_model_parallel_world_size
 
         # pylint: disable=C0103
         self.qkv_proj = QKVParallelLinear(
             self.hidden_size,
             self.head_size,
-            self.total_num_heads,
+            config.num_attention_heads,
             bias=True,
             quant_config=quant_config,
             prefix=f"{prefix}.qkv_proj",
@@ -109,13 +108,10 @@ class PhiAttention(nn.Module):
         )
 
         scaling = self.head_size**-0.5
-        rotary_dim = config.hidden_size // config.num_attention_heads
-        assert rotary_dim % 2 == 0
 
         max_position_embeddings = getattr(config, "max_position_embeddings", 2048)
         self.rotary_emb = get_rope(
             self.head_size,
-            rotary_dim=rotary_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/phimoe.py

@@ -352,7 +352,6 @@ class PhiMoEAttention(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/plamo2.py

@@ -574,7 +574,6 @@ class Plamo2AttentionMixer(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=config.rope_parameters,
         )

vllm/model_executor/models/plamo3.py

@@ -179,7 +179,6 @@ class Plamo3AttentionMixer(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/qwen.py

@@ -114,7 +114,6 @@ class QWenAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/qwen2.py

@@ -164,7 +164,6 @@ class Qwen2Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
             dual_chunk_attention_config=dual_chunk_attention_config,

vllm/model_executor/models/qwen2_5_vl.py

@@ -624,9 +624,9 @@ class Qwen2_5_VisionTransformer(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.attn_backend = get_vit_attn_backend(

vllm/model_executor/models/qwen2_moe.py

@@ -244,7 +244,6 @@ class Qwen2MoeAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             dual_chunk_attention_config=dual_chunk_attention_config,

vllm/model_executor/models/qwen2_vl.py

@@ -621,9 +621,9 @@ class Qwen2VisionTransformer(nn.Module):
         head_dim = embed_dim // 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/qwen3.py

@@ -111,7 +111,6 @@ class Qwen3Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
             dual_chunk_attention_config=dual_chunk_attention_config,

vllm/model_executor/models/qwen3_moe.py

@@ -269,7 +269,6 @@ class Qwen3MoeAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=rope_parameters,
             dual_chunk_attention_config=dual_chunk_attention_config,

vllm/model_executor/models/qwen3_next.py

@@ -747,7 +747,6 @@ class Qwen3NextAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             head_size=self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=config.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             dual_chunk_attention_config=self.dual_chunk_attention_config,

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -333,9 +333,9 @@ class Qwen3Omni_VisionTransformer(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/qwen3_vl.py

@@ -340,9 +340,9 @@ class Qwen3_VisionTransformer(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.merger = Qwen3_VisionPatchMerger(

vllm/model_executor/models/seed_oss.py

@@ -161,7 +161,6 @@ class SeedOssAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/solar.py

@@ -160,7 +160,6 @@ class SolarAttention(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/stablelm.py

@@ -148,7 +148,6 @@ class StablelmAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.config.max_position_embeddings,
             rope_parameters=self.config.rope_parameters,
         )

vllm/model_executor/models/starcoder2.py

@@ -112,7 +112,6 @@ class Starcoder2Attention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,

vllm/model_executor/models/step3_text.py

@@ -196,7 +196,6 @@ class Step3TextAttention(nn.Module):
         )
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embedding,
             rope_parameters=rope_parameters,
         )

vllm/model_executor/models/zamba2.py

@@ -230,7 +230,6 @@ class Zamba2Attention(nn.Module):
         if config.use_mem_rope:
             self.rotary_emb = get_rope(
                 head_size=self.attention_head_dim,
-                rotary_dim=self.attention_head_dim,
                 max_position=config.max_position_embeddings,
                 rope_parameters=config.rope_parameters,
                 is_neox_style=True,

vllm/transformers_utils/config.py

@@ -306,8 +306,13 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
     """Provide backwards compatibility for RoPE."""
     from vllm.config.utils import getattr_iter
 
-    rope_theta_names = ("rope_theta", "rotary_emb_base")
+    # Older custom models may use non-standard field names
-    rope_theta = getattr_iter(config, rope_theta_names, None)
+    # which need patching for both Transformers v4 and v5.
+    names = ["rope_theta", "rotary_emb_base"]
+    rope_theta = getattr_iter(config, names, None, warn=True)
+    names = ["partial_rotary_factor", "rotary_pct", "rotary_emb_fraction"]
+    partial_rotary_factor = getattr_iter(config, names, None, warn=True)
+
     if Version(version("transformers")) < Version("5.0.0.dev0"):
         # Transformers v4 installed, legacy config fields may be present
         if (rope_scaling := getattr(config, "rope_scaling", None)) is not None:
@@ -316,14 +321,18 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
             if not hasattr(config, "rope_parameters"):
                 config.rope_parameters = {"rope_type": "default"}
             config.rope_parameters["rope_theta"] = rope_theta
-        partial_rotary_factor_names = ("partial_rotary_factor", "rotary_pct")
-        partial_rotary_factor = getattr_iter(config, partial_rotary_factor_names, None)
         if partial_rotary_factor is not None:
             if not hasattr(config, "rope_parameters"):
                 config.rope_parameters = {"rope_type": "default"}
             config.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
     elif rope_theta is not None or hasattr(config, "rope_parameters"):
         # Transformers v5 installed
+        # Patch these fields in case they used non-standard names
+        if rope_theta is not None:
+            config.rope_theta = rope_theta
+        if partial_rotary_factor is not None:
+            config.partial_rotary_factor = partial_rotary_factor
+        # Standardize and validate RoPE parameters
         config.standardize_rope_params()
         config.validate_rope()