Access `partial_rotary_factor` from `rope_parameters` (#29966)

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

vllm/transformers_utils/configs/qwen3_next.py

@@ -103,7 +103,7 @@ 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):
+                `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.
@@ -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