Fix some more Transformers nightly tests (#29872)

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

examples/offline_inference/vision_language.py

@@ -1801,7 +1801,10 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
-        hf_overrides={"architectures": ["Tarsier2ForConditionalGeneration"]},
+        hf_overrides={
+            "architectures": ["Tarsier2ForConditionalGeneration"],
+            "model_type": "tarsier2",
+        },
         limit_mm_per_prompt={modality: 1},
     )
 

examples/offline_inference/vision_language_multi_image.py

@@ -1222,7 +1222,10 @@ def load_tarsier2(question: str, image_urls: list[str]) -> ModelRequestData:
         trust_remote_code=True,
         max_model_len=32768,
         limit_mm_per_prompt={"image": len(image_urls)},
-        hf_overrides={"architectures": ["Tarsier2ForConditionalGeneration"]},
+        hf_overrides={
+            "architectures": ["Tarsier2ForConditionalGeneration"],
+            "model_type": "tarsier2",
+        },
     )
 
     prompt = (

tests/models/registry.py

@@ -831,7 +831,10 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "TarsierForConditionalGeneration": _HfExamplesInfo("omni-research/Tarsier-7b"),
     "Tarsier2ForConditionalGeneration": _HfExamplesInfo(
         "omni-research/Tarsier2-Recap-7b",
-        hf_overrides={"architectures": ["Tarsier2ForConditionalGeneration"]},
+        hf_overrides={
+            "architectures": ["Tarsier2ForConditionalGeneration"],
+            "model_type": "tarsier2",
+        },
     ),
     "VoxtralForConditionalGeneration": _HfExamplesInfo(
         "mistralai/Voxtral-Mini-3B-2507",

vllm/model_executor/models/qwen2_vl.py

@@ -1576,15 +1576,6 @@ class Tarsier2ForConditionalGeneration(Qwen2VLForConditionalGeneration):
         }
     )
 
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        # Tarsier2 uses llava as model_type, which will create a Qwen2VLConfig
-        # as text_config, we need to reconstruct Qwen2VLConfig from LlavaConfig.
-        config = vllm_config.model_config.hf_config
-        qwen2vl_config = config.text_config
-        qwen2vl_config.architectures = config.architectures
-        vllm_config.model_config.hf_config = qwen2vl_config
-        super().__init__(vllm_config=vllm_config, prefix=prefix)
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         skip_prefixes = []
         if self.visual is None:

vllm/tokenizers/mistral.py

@@ -14,13 +14,19 @@ if TYPE_CHECKING:
     )
     from mistral_common.tokens.tokenizers.tekken import Tekkenizer
     from transformers import BatchEncoding
-    from transformers.tokenization_mistral_common import (
-        MistralCommonTokenizer as TransformersMistralTokenizer,
-    )
 
     from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
     from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 
+    try:
+        # Transformers v5
+        from transformers.tokenization_mistral_common import MistralCommonBackend
+    except ImportError:
+        # Transformers v4
+        from transformers.tokenization_mistral_common import (
+            MistralCommonTokenizer as MistralCommonBackend,
+        )
+
 logger = init_logger(__name__)
 
 
@@ -208,11 +214,17 @@ class MistralTokenizer(TokenizerLike):
         **kwargs,
     ) -> "MistralTokenizer":
         from mistral_common.protocol.instruct.validator import ValidationMode
-        from transformers.tokenization_mistral_common import (
-            MistralCommonTokenizer as TransformersMistralTokenizer,
-        )
 
-        tokenizer = TransformersMistralTokenizer.from_pretrained(
+        try:
+            # Transformers v5
+            from transformers.tokenization_mistral_common import MistralCommonBackend
+        except ImportError:
+            # Transformers v4
+            from transformers.tokenization_mistral_common import (
+                MistralCommonTokenizer as MistralCommonBackend,
+            )
+
+        tokenizer = MistralCommonBackend.from_pretrained(
             path_or_repo_id,
             *args,
             mode=ValidationMode.test,
@@ -223,7 +235,7 @@ class MistralTokenizer(TokenizerLike):
 
         return cls(tokenizer)
 
-    def __init__(self, tokenizer: "TransformersMistralTokenizer") -> None:
+    def __init__(self, tokenizer: "MistralCommonBackend") -> None:
         super().__init__()
 
         from mistral_common.protocol.instruct.validator import ValidationMode

vllm/transformers_utils/config.py

@@ -89,6 +89,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     step3_text="Step3TextConfig",
     qwen3_next="Qwen3NextConfig",
     lfm2_moe="Lfm2MoeConfig",
+    tarsier2="Tarsier2Config",
 )
 
 _CONFIG_ATTRS_MAPPING: dict[str, str] = {
@@ -127,6 +128,9 @@ class HFConfigParser(ConfigParserBase):
                 if config_dict.get("speculators_config") is not None
                 else model_type
             )
+        # Allow hf_overrides to override model_type before checking _CONFIG_REGISTRY
+        if (hf_overrides := kwargs.pop("hf_overrides", None)) is not None:
+            model_type = hf_overrides.get("model_type", model_type)
 
         if model_type in _CONFIG_REGISTRY:
             config_class = _CONFIG_REGISTRY[model_type]
@@ -310,7 +314,7 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
             config.rope_parameters["rope_theta"] = rope_theta
 
     # No RoPE parameters to patch
-    if not hasattr(config, "rope_parameters"):
+    if getattr(config, "rope_parameters", None) is None:
         return
 
     # Add original_max_position_embeddings if present
@@ -351,7 +355,10 @@ def patch_rope_parameters_dict(rope_parameters: dict[str, Any]) -> None:
         rope_parameters["rope_type"] = "longrope"
         logger.warning("Replacing legacy rope_type 'su' with 'longrope'")
     elif rope_parameters["rope_type"] == "mrope":
-        assert "mrope_section" in rope_parameters
+        if "mrope_section" not in rope_parameters:
+            raise ValueError(
+                "Legacy rope_type 'mrope' requires 'mrope_section' in rope_parameters"
+            )
         rope_parameters["rope_type"] = "default"
         logger.warning("Replacing legacy rope_type 'mrope' with 'default'")
 
@@ -584,6 +591,7 @@ def get_config(
         trust_remote_code=trust_remote_code,
         revision=revision,
         code_revision=code_revision,
+        hf_overrides=hf_overrides_kw,
         **kwargs,
     )
     # Special architecture mapping check for GGUF models
@@ -915,11 +923,13 @@ def get_hf_text_config(config: PretrainedConfig):
     """
     text_config = config.get_text_config()
 
-    if text_config is not config:
-        # The code operates under the assumption that text_config should have
-        # `num_attention_heads` (among others). Assert here to fail early
-        # if transformers config doesn't align with this assumption.
-        assert hasattr(text_config, "num_attention_heads")
+    if text_config is not config and not hasattr(text_config, "num_attention_heads"):
+        raise ValueError(
+            "The text_config extracted from the model config does not have "
+            "`num_attention_heads` attribute. This indicates a mismatch "
+            "between the model config and vLLM's expectations. Please "
+            "ensure that the model config is compatible with vLLM."
+        )
 
     return text_config
 

vllm/transformers_utils/configs/__init__.py

@@ -48,6 +48,7 @@ from vllm.transformers_utils.configs.step3_vl import (
     Step3VisionEncoderConfig,
     Step3VLConfig,
 )
+from vllm.transformers_utils.configs.tarsier2 import Tarsier2Config
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
 
 __all__ = [
@@ -81,4 +82,5 @@ __all__ = [
     "Step3VisionEncoderConfig",
     "Step3TextConfig",
     "Qwen3NextConfig",
+    "Tarsier2Config",
 ]

vllm/transformers_utils/configs/tarsier2.py

@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from transformers import Qwen2VLConfig
+
+
+class Tarsier2Config(Qwen2VLConfig):
+    """
+    Tarsier2's config.json is written such that AutoConfig.from_pretrained will create
+    a deeply nested config consisting of:
+
+    - LlavaConfig
+      - Qwen2VLConfig
+        - Qwen2VLTextConfig
+        - Qwen2VLVisionConfig
+      - Qwen2VLConfig
+        - Qwen2VLTextConfig
+        - Qwen2VLVisionConfig
+
+    When it should really just be a single Qwen2VLConfig.
+
+    This class is a hack to stop AutoConfig from creating the nested config structure.
+    """
+
+    model_type = "tarsier2"