[Dy2Stat] Use Paddle2.0 api paddle.tensor.array_* (#30156)

python/paddle/fluid/dygraph/dygraph_to_static/list_transformer.py

@@ -126,7 +126,7 @@ class ListTransformer(gast.NodeTransformer):
             i = "paddle.cast(" \
                 "x=paddle.jit.dy2static.to_static_variable({})," \
                 "dtype='int64')".format(ast_to_source_code(slice_node))
-            assign_code = "{} = fluid.layers.array_write(x={}, i={}, array={})" \
+            assign_code = "{} = paddle.tensor.array_write(x={}, i={}, array={})" \
                 .format(target_name, value_code, i, target_name)
             assign_node = gast.parse(assign_code).body[0]
         return assign_node
@@ -168,7 +168,7 @@ class ListTransformer(gast.NodeTransformer):
         #         return False
         #     if NodeVarType.TENSOR not in var_type_set and NodeVarType.PADDLE_RETURN_TYPES not in var_type_set:
         #         return False
-        # # TODO: Consider that `arg` may be a gast.Call about Paddle Api. eg: list_a.append(fluid.layers.reshape(x))
+        # # TODO: Consider that `arg` may be a gast.Call about Paddle Api. eg: list_a.append(paddle.reshape(x))
         # # else:
         # # return True
         self.list_name_to_updated[value_name.strip()] = True
@@ -187,7 +187,7 @@ class ListTransformer(gast.NodeTransformer):
 
     def _create_tensor_array(self):
         # Although `dtype='float32'`, other types such as `int32` can also be supported
-        func_code = "fluid.layers.create_array(dtype='float32')"
+        func_code = "paddle.tensor.create_array(dtype='float32')"
         func_node = gast.parse(func_code).body[0].value
         return func_node
 
@@ -195,8 +195,8 @@ class ListTransformer(gast.NodeTransformer):
         assert isinstance(node, gast.Call)
         array = astor.to_source(gast.gast_to_ast(node.func.value))
         x = astor.to_source(gast.gast_to_ast(node.args[0]))
-        i = "fluid.layers.array_length({})".format(array)
-        func_code = "fluid.layers.array_write(x={}, i={}, array={})".format(
+        i = "paddle.tensor.array_length({})".format(array)
+        func_code = "paddle.tensor.array_write(x={}, i={}, array={})".format(
             x, i, array)
         return gast.parse(func_code).body[0].value