modify util

x2paddle/core/program.py

@@ -275,7 +275,7 @@ class PaddleGraph(object):
                 
     def gen_dygraph_model(self, save_dir, jit_type=None):
         if jit_type == "trace":
-            from x2paddle.optimizer.code_optimizer import HierarchicalTree
+            from x2paddle.optimizer.pytorch_code_optimizer import HierarchicalTree
             hierarchical_tree = HierarchicalTree(self)
             for layer_id, layer in self.layers.items():
                 hierarchical_tree.insert(layer)
@@ -283,7 +283,7 @@ class PaddleGraph(object):
             self.dump_dygraph_parameter(save_dir)
         else:
             if self.source_type == "pytorch":
-                from x2paddle.optimizer.code_optimizer import ModuleGraph
+                from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph
                 module_graph = ModuleGraph(self)
                 module_graph.save_source_files(save_dir)
                 self.dump_dygraph_parameter(save_dir)
@@ -347,7 +347,8 @@ class PaddleGraph(object):
             ],
             indent=1)
         for layer_id, layer in self.layers.items():
-            remove_default_attrs(layer)
+            if layer.kernel.startswith("paddle"):
+                remove_default_attrs(layer.kernel, layer.attrs)
             edges_in = self.edges_in.get(layer_id, [])
             edges_out = self.edges_out.get(layer_id, [])
             if len(edges_in) == 0 and len(edges_out) == 0:
@@ -546,7 +547,8 @@ class PaddleGraph(object):
             gen_head()
 
         for layer_id, layer in self.layers.items():
-            remove_default_attrs(layer)
+            if layer.kernel.startswith("paddle"):
+                remove_default_attrs(layer.kernel, layer.attrs)
             if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel
                 ) or layer.kernel == "paddle.to_tensor" or \
                 layer.kernel.startswith("custom_layer") or \

x2paddle/core/util.py

@@ -28,7 +28,7 @@ def name_generator(nn_name, nn_name2id):
     real_nn_name = nn_name + str(nn_name2id[nn_name])
     return real_nn_name
 
-def remove_default_attrs(layer, diff_attrs=None):
+def remove_default_attrs(kernel, attrs):
     def get_default_args(func):
         signature = inspect.signature(func)
         return {
@@ -36,10 +36,6 @@ def remove_default_attrs(layer, diff_attrs=None):
             for k, v in signature.parameters.items()
             if v.default is not inspect.Parameter.empty
         }
-    kernel = layer.kernel
-    attrs = layer.attrs
-    if ":" in kernel or "prim" in kernel or "module" in kernel:
-        return 
     is_func = True
     if "paddle.nn" in kernel and "functional"not in kernel:
         is_func = False
@@ -61,9 +57,4 @@ def remove_default_attrs(layer, diff_attrs=None):
                 if len(set(attrs[default_k])) == 1:
                     attrs[default_k] = attrs[default_k][0]
             if default_v == attrs[default_k]:
-                if diff_attrs is None:
-                    attrs.pop(default_k)
+                attrs.pop(default_k)
\ No newline at end of file
-                else:
-                    key_name = "{}_{}".format(layer.outputs[0], default_k)
-                    if key_name not in diff_attrs:
-                        attrs.pop(default_k)

x2paddle/optimizer/code_optimizer/__init__.py → x2paddle/optimizer/pytorch_code_optimizer/__init__.py

@@ -13,5 +13,5 @@
 # limitations under the License.
 
 
-from x2paddle.optimizer.code_optimizer.hierachical_tree import HierarchicalTree
-from x2paddle.optimizer.code_optimizer.module_graph import ModuleGraph
+from x2paddle.optimizer.pytorch_code_optimizer.hierachical_tree import HierarchicalTree
+from x2paddle.optimizer.pytorch_code_optimizer.module_graph import ModuleGraph
\ No newline at end of file

x2paddle/optimizer/code_optimizer/hierachical_tree.py → x2paddle/optimizer/pytorch_code_optimizer/hierachical_tree.py

@@ -18,10 +18,10 @@ import copy
 import os.path as osp
 from treelib import Tree
 from queue import Queue
-from x2paddle.optimizer.code_optimizer.layer_code_generator import gen_layer_code, rename_layers, NN_KERNEL_WITH_PARAMS, NN_KERNEL_NAME
-from x2paddle.optimizer.code_optimizer.subgraphs_union import  distinguish_sequential, get_inputs_outputs
+from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import gen_layer_code, rename_layers, NN_KERNEL_WITH_PARAMS, NN_KERNEL_NAME
+from x2paddle.optimizer.pytorch_code_optimizer.subgraphs_union import  distinguish_sequential, get_inputs_outputs
 from x2paddle.core.program import PaddleLayer
-from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode, PamareterTree
+from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode, PamareterTree
 
 SEPARATOR_IN_SCOPE = "/"
 

x2paddle/optimizer/code_optimizer/layer_code_generator.py → x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py

@@ -16,7 +16,7 @@
 import copy
 import os.path as osp
 import x2paddle
-from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode
+from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode
 from x2paddle.core.util import *
 
 
@@ -128,7 +128,23 @@ def rename_layers(layers, param_tree=None, is_rename_module=False):
         return count
     rename_sub_layers(layers_cp, count)
     return layers_cp, nn_param_nodes, new_names
-    
+
+
+def _update_attrs(layer, different_attrs):
+    if "module" in layer.kernel or "prim" in layer.kernel:
+        return
+    common_attrs = copy.deepcopy(layer.attrs)
+    special_attrs = dict()
+    for k, v in layer.attrs.items():
+        if len(layer.outputs) < 1:
+            break
+        key_name = "{}_{}".format(layer.outputs[0], k)
+        if key_name in different_attrs:
+            common_attrs.pop(k)
+            special_attrs[k] = v
+    remove_default_attrs(layer.kernel, common_attrs)
+    common_attrs.update(special_attrs)
+    layer.attrs = common_attrs
 
 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。
@@ -224,7 +240,7 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()):
                 outputs.append(layer.outputs[0])
     no_output_count = 0
     for i, (layer_id, layer) in enumerate(sub_layers.items()):
-        remove_default_attrs(layer, different_attrs)
+        _update_attrs(layer, different_attrs)
         if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
                 layer.kernel.startswith("custom_layer"):
             line = "self.{}".format(layer.outputs[0])

x2paddle/optimizer/code_optimizer/module_graph.py → x2paddle/optimizer/pytorch_code_optimizer/module_graph.py

@@ -17,9 +17,9 @@ import copy
 import os
 import os.path as osp
 from x2paddle.core.program import PaddleLayer
-from x2paddle.optimizer.code_optimizer.subgraphs_union import construct_attrs_table, get_inputs_outputs
-from x2paddle.optimizer.code_optimizer.layer_code_generator import gen_layer_code, rename_layers
-from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode, PamareterTree
+from x2paddle.optimizer.pytorch_code_optimizer.subgraphs_union import construct_attrs_table, get_inputs_outputs
+from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import gen_layer_code, rename_layers
+from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode, PamareterTree
 
 
 NoModuleStart = ["paddle.nn.ReLU"]

x2paddle/optimizer/code_optimizer/subgraphs_union.py → x2paddle/optimizer/pytorch_code_optimizer/subgraphs_union.py

@@ -16,7 +16,7 @@
 
 import copy
 import pandas as pd
-from x2paddle.optimizer.code_optimizer.layer_code_generator import rename_layers
+from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import rename_layers
 
 
 def construct_attrs_table(sub_layers_list, node_name2sub_layers=None, module_name=None):