Access partial_rotary_factor from rope_parameters (#29966)

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

tests/kernels/core/test_mrope.py

@@ -113,12 +113,10 @@ def test_mrope(
     is_neox_style = True
 
     max_position = config.max_position_embeddings
-    partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
-    rotary_dim = int(head_dim * partial_rotary_factor)
 
     mrope_helper_class = get_rope(
         head_size=head_dim,
-        rotary_dim=rotary_dim,
+        rotary_dim=head_dim,
         max_position=max_position,
         is_neox_style=is_neox_style,
         rope_parameters=config.rope_parameters,
@@ -184,12 +182,10 @@ def test_mrope_torch_compile_tracing(
     )
     is_neox_style = True
     max_position = config.max_position_embeddings
-    partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
-    rotary_dim = int(head_dim * partial_rotary_factor)
 
     mrope_helper_class = get_rope(
         head_size=head_dim,
-        rotary_dim=rotary_dim,
+        rotary_dim=head_dim,
         max_position=max_position,
         is_neox_style=is_neox_style,
         rope_parameters=config.rope_parameters,

vllm/model_executor/layers/rotary_embedding/__init__.py

@@ -30,7 +30,6 @@ def get_rope(
     is_neox_style: bool = True,
     rope_parameters: dict[str, Any] | None = None,
     dtype: torch.dtype | None = None,
-    partial_rotary_factor: float = 1.0,
     dual_chunk_attention_config: dict[str, Any] | None = None,
 ) -> RotaryEmbedding:
     if dtype is None:
@@ -55,6 +54,10 @@ def get_rope(
     else:
         dual_chunk_attention_args = None
 
+    partial_rotary_factor = 1.0
+    if rope_parameters is not None:
+        partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
+
     if partial_rotary_factor < 1.0:
         rotary_dim = int(rotary_dim * partial_rotary_factor)
     key = (

vllm/model_executor/models/apertus.py

@@ -148,8 +148,6 @@ class ApertusAttention(nn.Module):
         if head_dim is None:
             head_dim = self.hidden_size // self.total_num_heads
         self.head_dim = head_dim
-        # Phi models introduced a partial_rotary_factor parameter in the config
-        self.partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
         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
@@ -228,11 +226,10 @@ class ApertusAttention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=int(self.partial_rotary_factor * 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,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
 
 

vllm/model_executor/models/bailing_moe.py

@@ -127,8 +127,6 @@ class BailingAttention(nn.Module):
             prefix=f"{prefix}.dense",
         )
 
-        self.partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
-
         self.rotary_dim = getattr(config, "rotary_dim", self.head_dim)
 
         self.rotary_emb = get_rope(
@@ -137,7 +135,6 @@ class BailingAttention(nn.Module):
             max_position=config.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=True,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
 
         self.attn = Attention(

vllm/model_executor/models/bamba.py

@@ -178,9 +178,7 @@ class BambaAttentionDecoderLayer(nn.Module):
         self.scaling = self.head_dim**-0.5
         self.max_position_embeddings = max_position_embeddings
 
-        if hasattr(config, "partial_rotary_factor"):
-            rotary_dim = int(self.head_dim * config.partial_rotary_factor)
-        elif hasattr(config, "attn_rotary_emb"):
+        if hasattr(config, "attn_rotary_emb"):
             rotary_dim = config.attn_rotary_emb  # for backward compatibility
         else:
             rotary_dim = self.head_dim  # default

vllm/model_executor/models/config.py

@@ -8,7 +8,6 @@ import vllm.envs as envs
 from vllm.logger import init_logger
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import current_platform
-from vllm.transformers_utils.config import set_default_rope_theta
 from vllm.utils.math_utils import cdiv, round_up
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec, MLAAttentionSpec
@@ -78,8 +77,6 @@ class JinaRobertaModelConfig(VerifyAndUpdateConfig):
             if not model_config.enforce_eager:
                 max_position = round_up(max_position, 8)
 
-            set_default_rope_theta(config, default_theta=config.rotary_emb_base)
-
             config.rotary_kwargs = {
                 "head_size": head_dim,
                 "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
@@ -119,8 +116,6 @@ class NomicBertModelConfig(VerifyAndUpdateConfig):
         rotary_emb_dim = int(head_dim * config.rotary_emb_fraction)
         max_trained_positions = getattr(config, "max_trained_positions", 2048)
 
-        set_default_rope_theta(config, default_theta=config.rotary_emb_base)
-
         config.rotary_kwargs = {
             "head_size": head_dim,
             "rotary_dim": rotary_emb_dim,

vllm/model_executor/models/falcon_h1.py

@@ -242,9 +242,7 @@ class FalconH1AttentionDecoderLayer(nn.Module):
         self.scaling = self.head_dim**-0.5
         self.max_position_embeddings = max_position_embeddings
 
-        if hasattr(config, "partial_rotary_factor"):
-            rotary_dim = self.head_dim * config.partial_rotary_factor
-        elif hasattr(config, "attn_rotary_emb"):
+        if hasattr(config, "attn_rotary_emb"):
             rotary_dim = config.attn_rotary_emb  # for backward compatibility
         else:
             rotary_dim = self.head_dim  # default

vllm/model_executor/models/glm.py

@@ -10,7 +10,8 @@ from .utils import PPMissingLayer
 
 class GlmForCausalLM(LlamaForCausalLM):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        vllm_config.model_config.hf_config.partial_rotary_factor = 0.5
+        hf_config = vllm_config.model_config.hf_config
+        hf_config.rope_parameters["partial_rotary_factor"] = 0.5
         super().__init__(vllm_config=vllm_config, prefix=prefix)
         # Hack Llama model to fit HF format GLM implementation
         # Attention difference between GLM and Llama:

vllm/model_executor/models/glm4.py

@@ -78,7 +78,7 @@ class Glm4Attention(nn.Module):
             # 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
-        partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+        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
@@ -106,7 +106,6 @@ class Glm4Attention(nn.Module):
             rotary_dim=self.rotary_dim,
             max_position=max_position,
             rope_parameters=config.rope_parameters,
-            partial_rotary_factor=partial_rotary_factor,
             is_neox_style=False,
         )
         self.attn = Attention(

vllm/model_executor/models/glm4_moe.py

@@ -282,13 +282,12 @@ class Glm4MoeAttention(nn.Module):
             prefix=f"{prefix}.o_proj",
         )
 
-        partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+        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,
-            partial_rotary_factor=partial_rotary_factor,
         )
         self.attn = Attention(
             self.num_heads,

vllm/model_executor/models/gpt_neox.py

@@ -89,16 +89,14 @@ class GPTNeoXAttention(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.dense",
         )
-        scaling = self.head_size**-0.5
-        rotary_dim = int(self.head_size * config.rotary_pct)
-        assert rotary_dim % 2 == 0
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.rotary_emb = get_rope(
             self.head_size,
-            rotary_dim=rotary_dim,
+            rotary_dim=self.head_size,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
         )
+        scaling = self.head_size**-0.5
         self.attn = Attention(
             self.num_heads,
             self.head_size,

vllm/model_executor/models/llama.py

@@ -149,8 +149,6 @@ class LlamaAttention(nn.Module):
         if head_dim is None:
             head_dim = self.hidden_size // self.total_num_heads
         self.head_dim = head_dim
-        # Phi models introduced a partial_rotary_factor parameter in the config
-        self.partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
         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
@@ -265,7 +263,6 @@ class LlamaAttention(nn.Module):
             max_position=self.max_position_embeddings,
             rope_parameters=getattr(config, "rope_parameters", None),
             is_neox_style=is_neox_style,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
 
 

vllm/model_executor/models/nemotron.py

@@ -178,7 +178,6 @@ class NemotronAttention(nn.Module):
         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.partial_rotary_factor = config.partial_rotary_factor
         self.max_position_embeddings = max_position_embeddings
 
         self.qkv_proj = QKVParallelLinear(
@@ -203,7 +202,6 @@ class NemotronAttention(nn.Module):
             rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=config.rope_parameters,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
         self.attn = Attention(
             self.num_heads,

vllm/model_executor/models/nemotron_nas.py

@@ -122,7 +122,6 @@ class DeciLMAttention(LlamaAttention):
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
 
 

vllm/model_executor/models/persimmon.py

@@ -106,7 +106,6 @@ class PersimmonAttention(nn.Module):
         self.num_heads = self.total_num_heads // tensor_parallel_world_size
         self.head_dim = self.hidden_size // self.total_num_heads
         self.max_position_embeddings = config.max_position_embeddings
-        self.partial_rotary_factor = config.partial_rotary_factor
         self.is_causal = True
 
         assert (self.head_dim * self.total_num_heads) == self.hidden_size
@@ -138,7 +137,6 @@ class PersimmonAttention(nn.Module):
             rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             rope_parameters=config.rope_parameters,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
         self.scaling = self.head_dim**-0.5
         self.attn = Attention(

vllm/model_executor/models/phi.py

@@ -109,10 +109,7 @@ class PhiAttention(nn.Module):
         )
 
         scaling = self.head_size**-0.5
-        rotary_dim = int(
-            config.partial_rotary_factor
-            * (config.hidden_size // config.num_attention_heads)
-        )
+        rotary_dim = config.hidden_size // config.num_attention_heads
         assert rotary_dim % 2 == 0
 
         max_position_embeddings = getattr(config, "max_position_embeddings", 2048)

vllm/model_executor/models/qwen3_next.py

@@ -750,7 +750,6 @@ class Qwen3NextAttention(nn.Module):
             rotary_dim=self.head_dim,
             max_position=config.max_position_embeddings,
             rope_parameters=config.rope_parameters,
-            partial_rotary_factor=config.partial_rotary_factor,
             dual_chunk_attention_config=self.dual_chunk_attention_config,
         )
 

vllm/model_executor/models/stablelm.py

@@ -119,9 +119,6 @@ class StablelmAttention(nn.Module):
         self.num_key_value_heads = max(1, self.total_num_key_value_heads // tp_size)
         self.head_dim = self.hidden_size // self.total_num_heads
         self.max_position_embeddings = config.max_position_embeddings
-        self.partial_rotary_factor = getattr(
-            config, "rope_pct", getattr(config, "partial_rotary_factor", 1)
-        )
         self.scaling = self.head_dim**-0.5
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_key_value_heads * self.head_dim
@@ -154,7 +151,6 @@ class StablelmAttention(nn.Module):
             rotary_dim=self.head_dim,
             max_position=self.config.max_position_embeddings,
             rope_parameters=self.config.rope_parameters,
-            partial_rotary_factor=self.partial_rotary_factor,
         )
         self.attn = Attention(
             self.num_heads,

vllm/transformers_utils/config.py

@@ -25,6 +25,7 @@ from transformers.models.auto.tokenization_auto import get_tokenizer_config
 from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME
 
 from vllm import envs
+from vllm.config.utils import getattr_iter
 from vllm.logger import init_logger
 from vllm.transformers_utils.utils import parse_safetensors_file_metadata
 
@@ -304,7 +305,8 @@ def set_default_rope_theta(config: PretrainedConfig, default_theta: float) -> No
 
 def patch_rope_parameters(config: PretrainedConfig) -> None:
     """Provide backwards compatibility for RoPE."""
-    rope_theta = getattr(config, "rope_theta", None)
+    rope_theta_names = ("rope_theta", "rotary_emb_base")
+    rope_theta = getattr_iter(config, rope_theta_names, None)
     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:
@@ -313,6 +315,12 @@ 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
         config.standardize_rope_params()

vllm/transformers_utils/configs/nemotron.py

@@ -89,9 +89,14 @@ class NemotronConfig(PretrainedConfig):
         tie_word_embeddings (`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
         rope_parameters (`dict`, *optional*):
-            The parameters of the RoPE embeddings.
-        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
-            Percentage of the query and keys which will have rotary embedding.
+            The parameters of the RoPE embeddings. Expected contents:
+                `rope_theta` (`float`): The base period of the RoPE embeddings.
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear',
+                    'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the
+                    original RoPE implementation.
+                `partial_rotary_factor` (`float`, *optional*, defaults to 0.5):
+                    Percentage of the query and keys which will have rotary embedding.
         attention_bias (`bool`, *optional*, defaults to `False`):
             Whether to use a bias in the query, key, value and output
             projection layers during self-attention.
@@ -133,7 +138,6 @@ class NemotronConfig(PretrainedConfig):
         eos_token_id=3,
         tie_word_embeddings=False,
         rope_parameters=None,
-        partial_rotary_factor=0.5,
         attention_bias=False,
         attention_dropout=0.0,
         mlp_bias=False,
@@ -165,14 +169,16 @@ class NemotronConfig(PretrainedConfig):
         rope_theta = kwargs.pop("rope_theta", 10000.0)
         if "rope_theta" not in rope_parameters:
             rope_parameters["rope_theta"] = rope_theta
-        self.rope_parameters = rope_parameters
         # for backward compatibility
         partial_rotary_factor = (
             kwargs.get("rope_percent")
             or kwargs.get("rope_percentage")
-            or partial_rotary_factor
+            or kwargs.get("partial_rotary_factor")
+            or 0.5
         )
-        self.partial_rotary_factor = partial_rotary_factor
+        if "partial_rotary_factor" not in rope_parameters:
+            rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+        self.rope_parameters = rope_parameters
         self._rope_parameters_validation()
         self.attention_bias = attention_bias
         self.attention_dropout = attention_dropout

vllm/transformers_utils/configs/qwen3_next.py

@@ -103,8 +103,8 @@ class Qwen3NextConfig(PretrainedConfig):
                     Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                 `high_freq_factor` (`float`, *optional*):
                     Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        partial_rotary_factor (`float`, *optional*, defaults to 0.25):
-            Percentage of the query and keys which will have rotary embedding.
+                `partial_rotary_factor` (`float`, *optional*, defaults to 0.25):
+                    Percentage of the query and keys which will have rotary embedding.
         attention_bias (`bool`, *optional*, defaults to `False`):
             Whether to use a bias in the query, key, value and output projection layers during self-attention.
         attention_dropout (`float`, *optional*, defaults to 0.0):
@@ -198,7 +198,6 @@ class Qwen3NextConfig(PretrainedConfig):
         use_cache=True,
         tie_word_embeddings=False,
         rope_parameters=None,
-        partial_rotary_factor=0.25,
         attention_bias=False,
         attention_dropout=0.0,
         head_dim=256,
@@ -239,6 +238,9 @@ class Qwen3NextConfig(PretrainedConfig):
         rope_theta = kwargs.pop("rope_theta", 10000.0)
         if "rope_theta" not in rope_parameters:
             rope_parameters["rope_theta"] = rope_theta
+        partial_rotary_factor = kwargs.pop("partial_rotary_factor", 0.25)
+        if "partial_rotary_factor" not in rope_parameters:
+            rope_parameters["partial_rotary_factor"] = partial_rotary_factor
         self.rope_parameters = rope_parameters
         self.partial_rotary_factor = partial_rotary_factor
         self.attention_bias = attention_bias