add option DefaultTensorType to specify the default tensor type to quantize

onnxruntime/python/tools/quantization/onnx_quantizer.py

@@ -446,6 +446,19 @@
             return False
         return self.parent.is_valid_quantize_weight(weight_name)
 
+    def _get_default_tensor_type(self, tensor_name):
+        if "DefaultTensorType" in self.extra_options:
+            logging.info(f"get_tensor_type returns DefaultTensorType for tensor name %r, use %d", tensor_name, self.extra_options["DefaultTensorType"])
+            return self.extra_options["DefaultTensorType"]
+        raise RuntimeError(
+            f"Unable to find data type for weight_name={tensor_name!r}. "
+            f"shape_inference failed to return a type probably this node is "
+            f"from a different domain or using an input produced by such an operator. "
+            f"This may happen if you quantize a model already quantized. "
+            f"You may use extra_options `DefaultTensorType` to indicate "
+            f"the default weight type, usually `onnx.TensorProto.FLOAT`."
+        )
+
     def get_tensor_type(self, tensor_name, mandatory=False):
         weight = find_by_name(tensor_name, self.model.initializer())
         if weight is not None:
@@ -454,11 +467,11 @@
             vi = self.value_infos[tensor_name]
             if vi.type.HasField("tensor_type"):
                 if mandatory and vi.type.tensor_type.elem_type == 0:
-                    raise RuntimeError(f"Unable to find data type for weight_name={tensor_name!r}")
+                    return self._get_default_tensor_type(tensor_name)
                 return vi.type.tensor_type.elem_type
         if (not self.enable_subgraph_quantization) or (self.parent is None):
             if mandatory:
-                raise RuntimeError(f"Unable to find data type for weight_name={tensor_name!r}")
+                return self._get_default_tensor_type(tensor_name)
             return None
         otype = self.parent.is_valid_quantize_weight(tensor_name)
         if otype is not None:
@@ -468,7 +481,7 @@
             if res is not None:
                 return res
         if mandatory:
-            raise RuntimeError(f"Unable to find data type for weight_name={tensor_name!r}")
+            return self._get_default_tensor_type(tensor_name)
         return None
 
     def is_float_tensor(self, tensor_name):

onnxruntime/python/tools/transformers/quantize_helper.py

@@ -69,6 +69,7 @@ def quantize_onnx_model(onnx_model_path, quantized_model_path, use_external_data
             onnx_model_path,
             quantized_model_path,
             use_external_data_format=use_external_data_format,
+            extra_options={"DefaultTensorType": onnx.TensorProto.FLOAT},
         )
         logger.info(f"quantized model saved to:{quantized_model_path}")
         # TODO: inlcude external data in total model size.