add remove default param

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -929,7 +929,7 @@ def aten_constant_pad_nd(mapper, graph, node):
             outputs=[inputs_name[0] + "_list"],
             scope_name=scope_name)
         block.add_layer(
-            "paddle.tensor.unsqueeze",
+            "paddle.unsqueeze",
             inputs={"x": inputs_name[0],
                     "axis": inputs_name[0] + "_list"},
             outputs=[inputs_name[0] + "_var"],
@@ -941,7 +941,7 @@ def aten_constant_pad_nd(mapper, graph, node):
             scope_name=scope_name,
             **layer_attrs)
         block.add_layer(
-            "paddle.tensor.squeeze",
+            "paddle.squeeze",
             inputs={"x": output_name,
                     "axis": inputs_name[0] + "_list"},
             outputs=[output_name],
@@ -1703,7 +1703,7 @@ def aten_expand_as(mapper, graph, node):
         outputs=[inputs_name[1] + "_type"],
         scope_name=scope_name)
     block.add_layer(
-        "fluid.layers.cast",
+        "paddle.cast",
         inputs={"x": inputs_name[0]},
         outputs=[inputs_name[0]],
         scope_name=scope_name,
@@ -1722,7 +1722,7 @@ def aten_expand_as(mapper, graph, node):
     if_layer = graph.layers[list(graph.layers.keys())[-1]]
     block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
     block.add_layer(
-        "fluid.layers.cast",
+        "paddle.cast",
         inputs={"x": layer_outputs[0]},
         outputs=copy.deepcopy(layer_outputs),
         scope_name=scope_name,
@@ -4074,7 +4074,7 @@ def aten_squeeze(mapper, graph, node):
         layer_inputs["axis"] = inputs_name[1]
         current_inputs.append(inputs_name[1])
     graph.add_layer(
-        "paddle.tensor.squeeze",
+        "paddle.squeeze",
         inputs=layer_inputs,
         outputs=layer_outputs,
         scope_name=scope_name,

x2paddle/optimizer/code_optimizer/hierachical_tree.py

@@ -67,7 +67,10 @@ class HierarchicalTree(Tree):
                         else:
                             len1 = len(min_scope_name.split("/"))
                             len2 = len(scope_name.split("/"))
-                            if len1 > len2 and scope_name in self.scope_name_list:
+                            if scope_name not in self.scope_name_list:
+                                min_scope_name = scope_name
+                                continue
+                            if len1 > len2:
                                 min_scope_name = scope_name
                     if min_scope_name == "":
                         self.create_node(tag=layer.id, 

x2paddle/optimizer/code_optimizer/layer_code_generator.py

@@ -14,6 +14,9 @@
 # limitations under the License.
 
 import copy
+import yaml
+import os.path as osp
+import x2paddle
 from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode
 
 NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
@@ -125,15 +128,34 @@ def rename_layers(layers, param_tree=None, is_rename_module=False):
     rename_sub_layers(layers_cp, count)
     return layers_cp, nn_param_nodes, new_names
 
+def load_default_parameter():
+    path = x2paddle.__file__
+    path = path.replace("__init__.py", "core")
+    yaml_dir = osp.join(path, "paddle_default_parameter.yml")
+    with open(yaml_dir, "rb") as fr:
+        default_parameter = yaml.load(fr.read())
+    return default_parameter
 
-def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
+def is_abandon(default_parameter, layer_kernel, param_key, param_value):
+    if layer_kernel not in default_parameter:
+        return False
+    params = default_parameter[layer_kernel]
+    if param_key not in params:
+        return False
+    if params[param_key] == param_value:
+        return True
+    else:
+        return False
+    
+
+def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()):
     """ 根据sub_layers生成对应的Module代码。
     
     Args:
         graph (x2paddle.core.program.PaddleGraph): 整个Paddle图。
         sub_layers (dict): 子图的id和其对应layer组成的字典。
         sub_layers_name (str): 子图的名字。
-        different_attrs (list): 属性列表，这些属性表明在被调用时赋予不同值。
+        different_attrs (dict/list): 属性字典/列表，这些属性表明在被调用时赋予不同值。
     """
     def gen_codes(code_list, indent=0):
         """ 根据code_list生成代码段。
@@ -158,6 +180,12 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
         # 生成Layer的头部代码
         head = gen_codes(["class {}(paddle.nn.Layer):".format(sub_layers_name)], indent=0)
         # 生成init函数的头部代码
+        diff_str_list = list()
+        if isinstance(different_attrs, dict):
+            for k, v in different_attrs.items():
+                diff_str_list.append("{}={}".format(k, v))
+            attrs_str = ", ".join(diff_str_list)
+        else:
             attrs_str = ", ".join(different_attrs)
         init_func_head = \
             gen_codes(["def __init__(self, {}):".format(attrs_str)], indent=1) + \
@@ -213,6 +241,7 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
             if is_set_item:
                 outputs.append(layer.outputs[0])
     no_output_count = 0
+    default_parameter = load_default_parameter()
     for i, (layer_id, layer) in enumerate(sub_layers.items()):
         if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
                 layer.kernel.startswith("custom_layer"):
@@ -226,6 +255,8 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 if key_name in different_attrs:
                     line += "{}={}, ".format(k, key_name)
                 else:
+                    if is_abandon(default_parameter, layer.kernel, k, v):
+                        continue
                     line += "{}={}, ".format(k, v)
             line = line.strip(", ")
             line += ")"
@@ -267,7 +298,7 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                     init_func=init_func,
                     forward_func=forward_func,
                     layer_id=layer_id, 
-                    different_attrs=different_attrs)
+                    different_attrs=list(different_attrs.keys()) if isinstance(different_attrs, dict) else different_attrs)
                 cur_outputs.extend(layer.outputs)
             else:
                 raise Exception(
@@ -327,6 +358,8 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                     line += "{}=self.{}, ".format(k, key_name)
                     init_func.extend(gen_codes(["self.{} = {}".format(key_name, key_name)], indent=2))
                 else:
+                    if is_abandon(default_parameter, layer.kernel, k, v):
+                        continue
                     line += "{}={}, ".format(k, v)
             line = line.strip(", ")
             line += ")"

x2paddle/optimizer/code_optimizer/module_graph.py

@@ -179,16 +179,27 @@ class ModuleGraph(object):
     def analyze_attrs_table(self, attrs_table):
         """ 分析属性表格，哪些属性取值不一致。
         """
-        diff_attrs_column = list()
+        diff_attrs_column = dict()
         for column in list(attrs_table.columns):
             elements = list(attrs_table.get(column))
-            base = elements[0]
-            for element in elements[1:]:
-                if isinstance(base, str) and "'" not in base:
-                    break
-                if element != base:
-                    diff_attrs_column.append(column)
+            elements_list = list()
+            count_list = list()
+            for element in elements:
+                if isinstance(element, str) and "'" not in element:
                     break
+                if element not in elements_list:
+                    count_list.append(1)
+                    elements_list.append(element)
+                else:
+                    index = elements_list.index(element)
+                    count_list[index] += 1
+            if len(elements_list) > 1:
+                max_ct = 0
+                for k, v in zip(elements_list, count_list):
+                    if v > max_ct:
+                        max_ele = k
+                        max_ct = v
+                diff_attrs_column[column] = max_ele
         return diff_attrs_column
     
     def analyze_graph(self, sub_layers_list):
@@ -258,8 +269,10 @@ class ModuleGraph(object):
             outputs = ["{}_{}".format(mn, index)] + outputs
             node_name = "{}_{}".format(module_name, index)
             diff_attrs = dict() 
-            for column in diff_attrs_column:
-                diff_attrs[column] = attrs_table.get(column).loc[node_name]
+            for column, element in diff_attrs_column.items():
+                current_element = attrs_table.get(column).loc[node_name]
+                if current_element != element:
+                    diff_attrs[column] = current_element
             new_layer = PaddleLayer(id=list(sub_layers.keys())[-1],
                                     kernel="module",
                                     inputs=inputs_dict,