diff --git a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
index dc355fec0d362a4208ebb048a7b29925dabb6ead..90caee6c7a947023317f76874e8eac83c8b249f2 100644
--- a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
+++ b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
@@ -272,7 +272,8 @@ class QuantizationTransformPass(object):
     the quantized ops's inputs.
     """
     _supported_quantizable_op_type = [
-        'conv2d', 'depthwise_conv2d', 'conv2d_transpose', 'mul', 'matmul'
+        'conv2d', 'depthwise_conv2d', 'conv2d_transpose', 'mul', 'matmul',
+        'matmul_v2'
     ]
 
     def __init__(self,
@@ -520,6 +521,16 @@ class QuantizationTransformPass(object):
                     dequant_var_node = dequantized_vars[var_node.name()]
                     graph.update_input_link(var_node, dequant_var_node, op)
 
+        def _has_weight(op):
+            has_weight = False
+            for var_node in op.inputs:
+                if var_node.name() not in op.input_arg_names():
+                    continue
+                name = var_node.name()
+                if var_node.name() in persistable_vars:
+                    has_weight = True
+            return has_weight
+
         if not self._is_test:
             self._create_global_step(graph)
         ops = graph.all_op_nodes()
@@ -535,11 +546,11 @@ class QuantizationTransformPass(object):
         # The loop for transforming the forward graph:
         for op in ops:
             if op.name() in self._quantizable_ops:
-                if not self._is_skip_quant(graph, op):
+                if not self._is_skip_quant(graph, op) and _has_weight(op):
                     _transform_forward(graph, op)
         # The loop for renaming the inputs of backward op.
         for op in ops:
-            if op.name() in self._quantizable_grad_ops:
+            if op.name() in self._quantizable_grad_ops and _has_weight(op):
                 _transform_backward(graph, op)
         graph.resolve_hazard()
         return graph