[Bugfix] Ignore GPTQ quantization of Qwen2-VL visual module (#10169)

Signed-off-by: mgoin <michael@neuralmagic.com>

vllm/model_executor/models/qwen2_vl.py

@@ -51,7 +51,9 @@ from vllm.model_executor.layers.activation import QuickGELU
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization import (GPTQConfig,
+                                                     GPTQMarlinConfig,
+                                                     QuantizationConfig)
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -982,7 +984,7 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.visual = Qwen2VisionTransformer(
             config.vision_config,
             norm_eps=getattr(config, "rms_norm_eps", 1e-6),
-            quant_config=quant_config,
+            quant_config=self._maybe_ignore_quant_config(quant_config),
             prefix="visual",
         )
 
@@ -1008,6 +1010,14 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             make_empty_intermediate_tensors_factory(
                 ["hidden_states", "residual"], config.hidden_size))
 
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
+        # seems to avoid vision encoder sections for some models.
+        # See: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
+
     def _validate_and_reshape_mm_tensor(self,
                                         mm_input: Union[torch.Tensor,
                                                         List[torch.Tensor]],