Avoid Quant Weight Repeatedly (#47587)

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -1119,6 +1119,7 @@ class QuantizationFreezePass(object):
         self._op_input_rename_map = collections.OrderedDict()
         self._op_output_rename_map = collections.OrderedDict()
         self._quant_var_scale_map = collections.OrderedDict()
+        self._quantized_ops = set()
 
     def apply(self, graph):
         """
@@ -1173,6 +1174,8 @@ class QuantizationFreezePass(object):
                             quant_axis = 1
                         else:
                             quant_axis = 0
+                        if input_arg_name not in self._quantized_ops:
+                            self._quantized_ops.add(input_arg_name)
                             quantized_param_v = utils.quant_tensor(
                                 param_v.copy(),
                                 scale_v,
@@ -1191,6 +1194,7 @@ class QuantizationFreezePass(object):
                                 )
                                 quantized_param_v = np.round(quantized_param_v)
                             self._restore_var(input_arg_name, quantized_param_v)
+
                     self._remove_fake_quant_and_dequant_op(graph, op_node)
 
         # Remove all fake dequant op
@@ -3029,6 +3033,7 @@ class QuantWeightPass(object):
         self._save_int_weight = save_int_weight
         assert self._scope is not None, "scope must not be None."
         assert self._place is not None, "place must not be None."
+        self._quantized_ops = set()
 
     def apply(self, graph):
         assert isinstance(
@@ -3066,6 +3071,8 @@ class QuantWeightPass(object):
                 param_v = self._load_var(x_node.name())
                 quant_axis = _op.op().attr("quant_axis")
                 bits_length = _op.op().attr("bit_length")
+                if x_node.name() not in self._quantized_ops:
+                    self._quantized_ops.add(x_node.name())
                     quantized_param_v = utils.quant_tensor(
                         param_v.copy(),
                         scale_v,