Add util function for checking nesting of rope parameters (#31146)

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

vllm/transformers_utils/config.py

@@ -15,7 +15,6 @@ from huggingface_hub import (
 )
 from packaging.version import Version
 from transformers import GenerationConfig, PretrainedConfig
-from transformers.configuration_utils import ALLOWED_LAYER_TYPES
 from transformers.models.auto.image_processing_auto import get_image_processor_config
 from transformers.models.auto.modeling_auto import (
     MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
@@ -44,6 +43,16 @@ from .repo_utils import (
     with_retry,
 )
 
+try:
+    # Transformers v5
+    from transformers.configuration_utils import ALLOWED_ATTENTION_LAYER_TYPES
+except ImportError:
+    # Transformers v4
+    from transformers.configuration_utils import (
+        ALLOWED_LAYER_TYPES as ALLOWED_ATTENTION_LAYER_TYPES,
+    )
+
+
 if envs.VLLM_USE_MODELSCOPE:
     from modelscope import AutoConfig
 else:
@@ -104,6 +113,14 @@ _AUTO_CONFIG_KWARGS_OVERRIDES: dict[str, dict[str, Any]] = {
 }
 
 
+def is_rope_parameters_nested(rope_parameters: dict[str, Any]) -> bool:
+    """Check if rope_parameters is nested by layer types."""
+    # Cannot be nested if rope_parameters is empty
+    if not rope_parameters:
+        return False
+    return set(rope_parameters.keys()).issubset(ALLOWED_ATTENTION_LAYER_TYPES)
+
+
 class HFConfigParser(ConfigParserBase):
     def parse(
         self,
@@ -346,7 +363,7 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
         config.rope_parameters["original_max_position_embeddings"] = ompe
 
     # Handle nested rope_parameters in interleaved sliding attention models
-    if set(config.rope_parameters.keys()).issubset(ALLOWED_LAYER_TYPES):
+    if is_rope_parameters_nested(config.rope_parameters):
         for rope_parameters_layer_type in config.rope_parameters.values():
             patch_rope_parameters_dict(rope_parameters_layer_type)
     else: