Fix TorchAOConfig skip layers

tests/quantization/test_torchao.py

@@ -60,5 +60,20 @@ def test_opt_125m_int4wo_model_per_module_quant(vllm_runner):
         print(output)
 
 
+@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
+def test_qwenvl_int8wo_model_loading_with_params(vllm_runner):
+    torch._dynamo.reset()
+    model_name = "mobicham/Qwen2.5-VL-3B-Instruct_int8wo_ao"
+    with vllm_runner(model_name=model_name,
+                     quantization="torchao",
+                     dtype="bfloat16",
+                     pt_load_map_location="cuda:0") as llm:
+        output = llm.generate_greedy(["The capital of France is"],
+                                     max_tokens=32)
+
+        assert output
+        print(output)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

vllm/model_executor/layers/quantization/torchao.py

@@ -17,11 +17,30 @@
 logger = init_logger(__name__)
 
 
+def should_skip(prefix: str, skip_modules: list[str]) -> bool:
+    """
+    Robust skipping logic:
+    should_skip("model.model.layers.1.q_proj",
+                ["model.model.layers.1.q_proj"])  # True
+    should_skip("model.model.layers.10.o_proj", ["o_proj"])  -> True
+    should_skip("visual.model.layers.1.q_proj", ["visual"])   -> True
+    should_skip("model.model.layers.1.q_proj", ["layers.1"])  -> True
+    should_skip("model.model.layers.11.q_proj", ["layers.1"]) -> False
+    """
+    for s in skip_modules:
+        if prefix == s:
+            return True
+        if f".{s}." in f".{prefix}.":
+            return True
+    return False
+
+
 class TorchAOConfig(QuantizationConfig):
     """Config class for torchao."""
 
-    def __init__(self, torchao_config) -> None:
-        self.torchao_config = torchao_config
+    def __init__(self,
+                 torchao_config,
+                 skip_modules: Optional[list[str]] = None) -> None:
         """
         # TorchAO quantization relies on tensor subclasses. In order,
         # to enable proper caching this needs standalone compile
@@ -36,6 +55,8 @@ def __init__(self, torchao_config) -> None:
             os.environ["VLLM_DISABLE_COMPILE_CACHE"] = "1"
             logger.info("Using TorchAO: Setting VLLM_DISABLE_COMPILE_CACHE=1")
         """
+        self.torchao_config = torchao_config
+        self.skip_modules = skip_modules or []
 
     def __repr__(self) -> str:
         return f"TorchAOConfig({self.torchao_config})"
@@ -67,11 +88,28 @@ def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig":
 
         hf_config = cls.get_from_keys_or(config, ["quant_type"], None)
         assert hf_config is not None, "quant_type must be specified"
-        assert (len(hf_config) == 1 and "default" in hf_config
-                ), "Expected only one key 'default' in quant_type dictionary"
+        assert len(hf_config) == 1 and "default" in hf_config, (
+            "Expected only one key 'default' in quant_type dictionary")
         quant_type = hf_config["default"]
         ao_config = config_from_dict(quant_type)
-        return cls(ao_config)
+
+        # Adds skipped modules defined in "modules_to_not_convert"
+        skip_modules = config.get("modules_to_not_convert", []) or []
+
+        # Adds skipped modules defined in "module_fqn_to_config"
+        _data = quant_type.get("_data", {})
+        if not isinstance(_data, dict):
+            _data = {}
+
+        module_fqn = _data.get("module_fqn_to_config", {})
+        if not isinstance(module_fqn, dict):
+            module_fqn = {}
+
+        for layer, layer_cfg in module_fqn.items():
+            if layer_cfg is None:
+                skip_modules.append(layer)
+
+        return cls(ao_config, skip_modules)
 
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["QuantizeMethodBase"]:
@@ -80,13 +118,16 @@ def get_quant_method(self, layer: torch.nn.Module,
 
         from torchao.quantization import ModuleFqnToConfig
 
+        if should_skip(prefix, self.skip_modules):
+            return UnquantizedLinearMethod()
+
         module_fqn = prefix
         if isinstance(self.torchao_config, ModuleFqnToConfig):
             module_fqn_to_config = self.torchao_config.module_fqn_to_config
             c = module_fqn_to_config.get(
                 module_fqn) or module_fqn_to_config.get("_default", None)
             if c is not None:
-                current_torchao_config = TorchAOConfig(c)
+                current_torchao_config = TorchAOConfig(c, self.skip_modules)
                 return TorchAOLinearMethod(current_torchao_config)
             else:
                 return UnquantizedLinearMethod()
@@ -108,8 +149,17 @@ def torchao_quantize_param_data(param: torch.Tensor,
     """
     from torchao.core.config import AOBaseConfig
     from torchao.quantization import quantize_
+
     assert isinstance(torchao_config, AOBaseConfig), f"{torchao_config}"
-    dummy_linear = torch.nn.Linear(param.shape[1], param.shape[0], bias=False)
+    """ 
+    Avoid real weight allocation for faster load, since we will 
+    end up setting it to param.
+    """
+    with torch.device("meta"):
+        dummy_linear = torch.nn.Linear(param.shape[1],
+                                       param.shape[0],
+                                       bias=False)
+
     dummy_linear.weight = param
     quantize_(dummy_linear, torchao_config)
     return dummy_linear.weight