Use Transformers v5 WeightRenaming for Transformers modeling backend (#31545)

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

tests/models/multimodal/test_mapping.py

@@ -5,9 +5,10 @@ from collections.abc import Iterable
 import pytest
 import torch
 import transformers
-from transformers import AutoConfig, PreTrainedModel
+from transformers import AutoConfig, AutoModel, PreTrainedModel
 
 from vllm.config import ModelConfig
+from vllm.model_executor.models.transformers.base import Base as TransformersBase
 from vllm.model_executor.models.utils import WeightsMapper
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.transformers_utils.config import try_get_safetensors_metadata
@@ -23,6 +24,16 @@ def create_repo_dummy_weights(repo: str) -> Iterable[tuple[str, torch.Tensor]]:
         return ((name, torch.empty(0)) for name in weight_names)
 
 
+def create_dummy_base_model(repo: str, model_arch: str) -> PreTrainedModel:
+    """
+    Create weights from a dummy meta deserialized hf base model with name conversion
+    """
+    config = AutoConfig.from_pretrained(repo)
+    with torch.device("meta"):
+        model = AutoModel.from_config(config)
+    return model
+
+
 def create_dummy_model(repo: str, model_arch: str) -> PreTrainedModel:
     """
     Create weights from a dummy meta deserialized hf model with name conversion
@@ -79,6 +90,19 @@ def test_hf_model_weights_mapper(model_arch: str):
         dtype=model_info.dtype,
     )
     model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
+    if issubclass(model_cls, TransformersBase):
+        # Transformers backend models create their mapper during __init__
+        # by inspecting the HF model instance. We simulate this by calling
+        # _create_hf_to_vllm_mapper with a minimal proxy object.
+        model_cls = type(
+            "ProxyModelCls",
+            (),
+            {
+                "model": create_dummy_base_model(model_id, model_arch),
+                "_maybe_apply_model_mapping": lambda self: None,
+            },
+        )()
+        TransformersBase._create_hf_to_vllm_mapper(model_cls)
 
     original_weights = create_repo_dummy_weights(model_id)
     hf_dummy_model = create_dummy_model(model_id, model_arch)
@@ -102,9 +126,12 @@ def test_hf_model_weights_mapper(model_arch: str):
     # after they are tied in the model, so the mapper will not be able to map them.
     # We exclude them from the reference weight names for this test.
     if isinstance(tied := getattr(hf_dummy_model, "_tied_weights_keys", None), dict):
-        mapped_tied_weights = mapper.apply((k, None) for k in tied)
-        tied_weight_names = set(map(lambda x: x[0], mapped_tied_weights))
-        ref_weight_names -= tied_weight_names
+        config = hf_dummy_model.config
+        key = "tie_word_embeddings"
+        if getattr(config.get_text_config(), key, False) or getattr(config, key, False):
+            mapped_tied_weights = mapper.apply((k, None) for k in tied)
+            tied_weight_names = set(map(lambda x: x[0], mapped_tied_weights))
+            ref_weight_names -= tied_weight_names
 
     weights_missing = ref_weight_names - weight_names
     weights_unmapped = weight_names - ref_weight_names