Fix GPTQ model loading in Transformers backend (#25770)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Signed-off-by: yewentao256 <zhyanwentao@126.com>

tests/models/test_transformers.py

@@ -100,10 +100,9 @@ def test_distributed(
                          kwargs_test=kwargs)
 
 
-@pytest.mark.skipif(
-    current_platform.is_rocm(),
-    reason="bitsandbytes quantization is currently not supported in rocm.")
 @pytest.mark.parametrize("model, quantization_kwargs", [
+    ("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {}),
+    ("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", {}),
     (
         "meta-llama/Llama-3.2-1B-Instruct",
         {
@@ -121,6 +120,11 @@ def test_quantization(
     max_tokens: int,
     num_logprobs: int,
 ) -> None:
+    if (current_platform.is_rocm()
+            and quantization_kwargs.get("quantization", "") == "bitsandbytes"):
+        pytest.skip(
+            "bitsandbytes quantization is currently not supported in rocm.")
+
     with vllm_runner(
             model, model_impl="auto", enforce_eager=True,
             **quantization_kwargs) as vllm_model:  # type: ignore[arg-type]

vllm/model_executor/models/transformers.py

@@ -447,7 +447,8 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
         self.device_config: DeviceConfig = vllm_config.device_config
         self.model_config: ModelConfig = vllm_config.model_config
         self.parallel_config: ParallelConfig = vllm_config.parallel_config
-        self.quant_config: QuantizationConfig = vllm_config.quant_config
+        self.quant_config: Optional[
+            QuantizationConfig] = vllm_config.quant_config
 
         self.pp_group = get_pp_group()
         self.pp_size = self.pp_group.world_size
@@ -456,7 +457,18 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
 
         # Weights to skip in `self.load_weights`
         self.skip_prefixes: list[str] = []
+        """Skip loading weights whose qualname starts with these prefixes."""
         self.skip_substrs: list[str] = []
+        """Skip loading weights whose qualname contains these substrings."""
+        self.ignore_unexpected_prefixes: list[str] = []
+        """Ignore unexpected weights whose qualname starts with these prefixes.
+        """
+        self.ignore_unexpected_suffixes: list[str] = []
+        """Ignore unexpected weights whose qualname ends with these suffixes."""
+
+        # Skip loading extra bias for GPTQ models.
+        if self.quant_config and "gptq" in self.quant_config.get_name():
+            self.ignore_unexpected_suffixes.append(".bias")
 
         # Set correct attn and init on "meta" to delay allocating GPU tensors
         # TODO: @raushan, use the public `model.set_attn_implementation()`
@@ -563,9 +575,7 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
             raise ValueError(
                 f"{type(self.model)} does not support tensor parallel. {tip}")
 
-        def _tensor_parallel(module: nn.Module,
-                             prefix: str = "",
-                             tp_plan=None):
+        def _tensor_parallel(module: nn.Module, prefix: str, tp_plan=None):
             tp_plan = tp_plan or {}
 
             # If the current module is a PreTrainedModel, set the tp_plan for
@@ -597,7 +607,7 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
                                      prefix=qual_name,
                                      tp_plan=tp_plan)
 
-        _tensor_parallel(self.model)
+        _tensor_parallel(self.model, prefix="model")
 
     def create_attention_instances(
         self,
@@ -696,6 +706,8 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
             self,
             skip_prefixes=self.skip_prefixes,
             skip_substrs=self.skip_substrs,
+            ignore_unexpected_prefixes=self.ignore_unexpected_prefixes,
+            ignore_unexpected_suffixes=self.ignore_unexpected_suffixes,
         )
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 

vllm/model_executor/models/utils.py

@@ -109,6 +109,7 @@ class AutoWeightsLoader:
         skip_prefixes: Optional[list[str]] = None,
         skip_substrs: Optional[list[str]] = None,
         ignore_unexpected_prefixes: Optional[list[str]] = None,
+        ignore_unexpected_suffixes: Optional[list[str]] = None,
     ) -> None:
         super().__init__()
 
@@ -116,6 +117,7 @@ class AutoWeightsLoader:
         self.skip_prefixes = skip_prefixes or []
         self.skip_substrs = skip_substrs or []
         self.ignore_unexpected_prefixes = ignore_unexpected_prefixes or []
+        self.ignore_unexpected_suffixes = ignore_unexpected_suffixes or []
         # update default skip_substrs
         self.skip_substrs += self.ROTARY_EMBEDS_UNUSED_WEIGHTS
 
@@ -149,8 +151,9 @@ class AutoWeightsLoader:
                 or any(substr in qualname for substr in self.skip_substrs))
 
     def _can_ignore_unexpected(self, qualname: str) -> bool:
-        return any(
-            qualname.startswith(p) for p in self.ignore_unexpected_prefixes)
+        iup = (qualname.startswith(p) for p in self.ignore_unexpected_prefixes)
+        ius = (qualname.endswith(s) for s in self.ignore_unexpected_suffixes)
+        return any(iup) or any(ius)
 
     def _load_param(
         self,