Add output threshold for ops that have several output activations, test=develop (#24726)

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

@@ -1156,14 +1156,13 @@ class OutScaleForTrainingPass(object):
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
         self._is_test = graph.is_test()
-        ops = graph.all_op_nodes()
-        for op_node in ops:
-            name = op_node.name()
-            if name in self._teller_set:
-                if len(op_node.output_arg_names()) != 1:
-                    continue
-                in_node = graph._find_node_by_name(
-                    op_node.outputs, op_node.output_arg_names()[0])
+        target_ops = []
+        for op in graph.all_op_nodes():
+            if op.name() in self._teller_set:
+                target_ops.append(op)
+        for op in target_ops:
+            for output_var_name in _get_op_output_var_names(op):
+                in_node = graph._find_node_by_name(op.outputs, output_var_name)
                 out_node = graph.create_var_node_from_desc(in_node.var())
                 scale_node = graph.create_persistable_node(
                     name=self._scale_name(in_node.name()),
@@ -1263,13 +1262,13 @@ class OutScaleForInferencePass(object):
         """
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
-        ops = graph.all_op_nodes()
-        for op_node in ops:
-            name = op_node.name()
-            if name in self._teller_set:
-                if len(op_node.output_arg_names()) != 1:
-                    continue
-                scale_name = self._scale_name(op_node.output_arg_names()[0])
+        op_nodes = graph.all_op_nodes()
+        for op_node in op_nodes:
+            if op_node.name() in self._teller_set:
+                output_var_name = _get_op_output_var_names(op_node)
+                assert len(output_var_name) == 1, "Only support collecting " \
+                    "output for op that only has an activation output for now."
+                scale_name = self._scale_name(output_var_name[0])
                 scale_v = np.array(
                     self._scope.find_var(scale_name).get_tensor())[0]
                 op_node.op()._set_attr("out_threshold", float(scale_v))