[ROCm][Quantization] make quark ocp mx dtype parser robust for weight-only quantization (#36232)

Signed-off-by: xuebwang-amd <xuebwang@amd.com>

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -92,7 +92,8 @@ class QuarkMoEMethod(FusedMoEMethodBase):
                 rocm_aiter_ops.is_fused_moe_enabled()
             )
             if (
-                input_config.get("dtype") == "fp8_e4m3"
+                input_config is not None
+                and input_config.get("dtype") == "fp8_e4m3"
                 and not input_config.get("is_dynamic")
                 and not emulate
             ):

vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py

@@ -176,7 +176,7 @@ class QuarkOCP_MX(QuarkScheme):
     def __init__(
         self,
         weight_quant_spec: dict[str, Any],
-        input_quant_spec: dict[str, Any],
+        input_quant_spec: dict[str, Any] | None,
         dynamic_mxfp4_quant: bool = False,
     ):
         self.out_dtype = torch.get_default_dtype()
@@ -185,7 +185,13 @@ class QuarkOCP_MX(QuarkScheme):
         self.input_quant_spec = input_quant_spec
         self.dynamic_mxfp4_quant = dynamic_mxfp4_quant
         self.weight_dtype = weight_quant_spec["dtype"].replace("fp", "mxfp")
-        self.input_dtype = input_quant_spec["dtype"].replace("fp", "mxfp")
+        self.input_dtype: str | None = None
+        if input_quant_spec is not None:
+            input_quant = input_quant_spec["dtype"]
+            if input_quant == "fp8_e4m3":
+                self.input_dtype = "fp8"
+            else:
+                self.input_dtype = input_quant.replace("fp", "mxfp")
 
         self.ocp_mx_scheme = OCP_MX_Scheme.from_quant_dtype(
             self.input_dtype, self.weight_dtype
@@ -200,14 +206,21 @@ class QuarkOCP_MX(QuarkScheme):
                 dequant_mxfp6, quant_dtype=self.weight_dtype.replace("mx", "")
             )
 
-        if self.input_dtype == "mxfp4":
+        if self.input_dtype is None:
+            self.quant_dequant_func: Callable[[torch.Tensor], torch.Tensor] = (
+                lambda x: x
+            )  # no input Q/DQ for weight-only
+        elif self.input_dtype == "mxfp4":
             self.quant_dequant_func = quant_dequant_mxfp4
         else:
             self.quant_dequant_func = partial(
                 quant_dequant_mxfp6, quant_dtype=self.input_dtype.replace("mx", "")
             )
 
-        self.static_input_scales = not input_quant_spec.get("is_dynamic")
+        if input_quant_spec is None:
+            self.static_input_scales = False
+        else:
+            self.static_input_scales = not input_quant_spec.get("is_dynamic")
 
         if self.static_input_scales:
             raise NotImplementedError(