[Cherry pick] fix quant scale name (#44903)

* fix quant scale name (#44116)

* fix acc diff problem caused by pr #44116 (#44311)
Co-authored-by: Nhandiz <35895648+ZhangHandi@users.noreply.github.com>

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

@@ -962,10 +962,10 @@ class PostTrainingQuantization(object):
         else:
             scale_dict = self._quantized_threshold
         for key, val in scale_dict.items():
-            utils.set_variable_data(self._scope, self._place, key + ".scale",
+            utils.set_variable_data(self._scope, self._place, key + "@scale",
                                     np.array([val], dtype=np.float32))
             utils.set_variable_data(self._scope, self._place,
-                                    key + ".quant_dequant.scale",
+                                    key + ".quant_dequant@scale",
                                     np.array([val], dtype=np.float32))
 
         if not self._onnx_format:

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

@@ -906,7 +906,7 @@ class QuantizationTransformPass(object):
         """
         Return the scale name of quantized variable for the input `var_name`.
         """
-        return "%s.scale" % (var_name)
+        return "%s@scale" % (var_name)
 
     def _is_skip_quant(self, graph, op_node):
         """
@@ -1246,8 +1246,8 @@ class QuantizationFreezePass(object):
             return var_name[:-len('.quantized')]
         if var_name.endswith('.dequantized'):
             return var_name[:-len('.dequantized')]
-        if var_name.endswith('.scale'):
-            return var_name[:-len('.scale')]
+        if var_name.endswith('@scale'):
+            return var_name[:-len('@scale')]
         else:
             return var_name
 
@@ -1440,11 +1440,18 @@ class OutScaleForTrainingPass(object):
                         [core.VarDesc.VarType.FP64, core.VarDesc.VarType.FP32]:
                         continue
 
+                    try:
+                        graph._find_node_by_name(
+                            graph.all_var_nodes(),
+                            self._scale_name(in_node.name()))
+                        continue
+                    except:
                         scale_node = graph.create_persistable_node(
                             name=self._scale_name(in_node.name()),
                             var_type=core.VarDesc.VarType.LOD_TENSOR,
                             shape=[1],
                             var_dtype=in_node.dtype())
+
                     data_type = 'float64' if in_node.dtype() \
                         == core.VarDesc.VarType.FP64 else 'float32'
                     _init_var_node(scale_node, np.ones([1], dtype=data_type),
@@ -1705,7 +1712,7 @@ class AddQuantDequantPass(object):
                                                shape=var_node.shape(),
                                                var_dtype=var_node.dtype())
         scale_in_node = graph.create_persistable_node(
-            name="{}.quant_dequant.scale".format(var_node.name()),
+            name="{}.quant_dequant@scale".format(var_node.name()),
             var_type=core.VarDesc.VarType.LOD_TENSOR,
             shape=[1],
             var_dtype=var_node.dtype())
@@ -1954,7 +1961,7 @@ class InsertQuantizeLinear(object):
         """
         Return the scale name of quantized variable for the input `var_name`.
         """
-        return "%s.scale" % (var_name)
+        return "%s@scale" % (var_name)
 
     def _zero_point_name(self, var_name):
         """