update distribute transformer for adam and adamax optimizer

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -377,11 +377,6 @@ class DistributeTranspiler(object):
         # append it into the sub program.
 
         global_ops = []
-        # HACK: optimization global ops only used to scale beta1 and beta2
-        # replace it with dependency engine.
-        for op in self.optimize_ops:
-            if self._is_adam_connected_op(op):
-                global_ops.append(op)
 
         def __append_optimize_op__(op, block, grad_to_block_id, merged_var,
                                    lr_ops):
@@ -1289,22 +1284,16 @@ class DistributeTranspiler(object):
         # If one op's input is another op's output or
         # one op's output is another op's input, we say
         # the two operator is connected.
-        def _append_inname_remove_beta(varname_list):
+        def _append_inname(varname_list):
             op_input_names = []
             for in_name in varname_list:
-                # HACK: remove beta1 and beta2 to avoid let all
-                # ops connected.
-                if in_name.startswith("beta2_pow_acc") or \
-                    in_name.startswith("beta1_pow_acc"):
-                    continue
-                else:
                 op_input_names.append(in_name)
             return op_input_names
 
-        op1_input_names = _append_inname_remove_beta(op1.desc.input_arg_names())
+        op1_input_names = _append_inname(op1.desc.input_arg_names())
         op1_output_names = op1.desc.output_arg_names()
 
-        op2_input_names = _append_inname_remove_beta(op2.desc.input_arg_names())
+        op2_input_names = _append_inname(op2.desc.input_arg_names())
         op2_output_names = op2.desc.output_arg_names()
 
         if set(op1_output_names) & set(op2_input_names) or \
@@ -1413,7 +1402,7 @@ class DistributeTranspiler(object):
 
     def _get_optimize_pass(self):
         """
-        Get optimizer operators, paramters and gradients from origin_program
+        Get optimizer operators, parameters and gradients from origin_program
         Returns:
             opt_ops (list): optimize operators.
             params_grads (dict): paramter->gradient.
@@ -1436,20 +1425,6 @@ class DistributeTranspiler(object):
                             origin_var_dict[param_name],
                             origin_var_dict[input_name]
                         ])
-            elif self._is_adam_connected_op(op):
-                opt_ops.append(op)
             else:
                 pass
         return opt_ops, params_grads
-
-    def _is_adam_connected_op(self, op):
-        """
-        A hack function to determinate whether the input operator
-        is connected to optimize operator.
-        """
-        if op.type == "scale":
-            for in_name in op.input_arg_names:
-                if in_name.startswith("beta1_pow_acc") or \
-                        in_name.startswith("beta2_pow_acc"):
-                    return True
-        return False