[Quantization][Autoround][CPU] Add W4A16 Support (#38192)

Signed-off-by: Zhenzhong1 <zhenzhong.xu@intel.com>
Signed-off-by: Zhenzhong Xu <zhenzhong.xu@intel.com>

tests/quantization/test_cpu_wna16.py

@@ -12,6 +12,7 @@ MODELS = [
     "TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ",  # with g_idx
     "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int4",  # without g_idx
     "RedHatAI/Qwen3-1.7B-quantized.w4a16",  # with zp
+    "OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc",
 ]
 DTYPE = ["bfloat16"]
 

vllm/model_executor/layers/quantization/inc.py

@@ -414,6 +414,7 @@ class INCConfig(QuantizationConfig):
 
     def apply_xpu_w4a16_quant_layer(self, layer, prefix: str):
         weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
+
         if not self.check_quantized(weight_bits):
             if isinstance(layer, (LinearBase, ParallelLMHead)):
                 return UnquantizedLinearMethod()
@@ -437,6 +438,27 @@ class INCConfig(QuantizationConfig):
             )
         return None
 
+    def apply_cpu_w4a16_quant_layer(self, layer, prefix: str):
+        weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
+        if not self.check_quantized(weight_bits):
+            if isinstance(layer, (LinearBase, ParallelLMHead)):
+                return UnquantizedLinearMethod()
+            else:
+                return None
+
+        if weight_bits != 4:
+            raise NotImplementedError(
+                f"INC on CPU only supports 4-bit quantization, "
+                f"got weight_bits={weight_bits}."
+            )
+        if not sym:
+            raise NotImplementedError(
+                "INC W4A16 on CPU only supports symmetric quantization for now."
+            )
+        if isinstance(layer, (LinearBase, ParallelLMHead)):
+            return self.apply_gptq_quant_layer(layer, prefix)
+        return None
+
     def get_quant_method(self, layer: torch.nn.Module, prefix: str):
         if prefix and self.extra_config:
             for layer_name in self.extra_config:
@@ -446,11 +468,21 @@ class INCConfig(QuantizationConfig):
                     return UnquantizedLinearMethod()
         if current_platform.is_xpu():
             return self.apply_xpu_w4a16_quant_layer(layer, prefix)
-        if "gptq" in self.packing_format or "gptq" in self.backend:
+        is_gptq = "gptq" in self.packing_format or "gptq" in self.backend
+        if current_platform.is_cpu() and is_gptq:
+            return self.apply_cpu_w4a16_quant_layer(layer, prefix)
+        if is_gptq:
             return self.apply_gptq_quant_layer(layer, prefix)
         if "awq" in self.packing_format or "awq" in self.backend:
             return self.apply_awq_quant_layer(layer, prefix)
 
+        raise NotImplementedError(
+            f"Unsupported quantization configuration for layer '{prefix}'. "
+            f"Platform: CPU={current_platform.is_cpu()}. "
+            f"Platform: XPU={current_platform.is_xpu()}. "
+            f"Format: {self.packing_format}, Backend: {self.backend}."
+        )
+
     @classmethod
     def override_quantization_method(
         cls, hf_quant_cfg, user_quant, hf_config=None