surport ernie module v2 (#442)

* surport ernie module v2

paddlehub/finetune/strategy.py

@@ -120,9 +120,7 @@ def get_depth_parameter(main_program):
     return updated_depth_params_dict
 
 
-def set_gradual_unfreeze(main_program, unfreeze_depths):
-    depth_params_dict = get_depth_parameter(main_program)
-
+def set_gradual_unfreeze(depth_params_dict, unfreeze_depths):
     for depth in unfreeze_depths:
         for index, param in enumerate(depth_params_dict[depth]):
             depth_params_dict[depth][index].stop_gradient = False
@@ -509,7 +507,7 @@ class CombinedStrategy(DefaultStrategy):
             if self.max_depth > 0 and self.epoch <= self.scheduler[
                     "gradual_unfreeze"]["blocks"]:
                 set_gradual_unfreeze(
-                    self.main_program,
+                    depth_params_dict=self.depth_params_dict,
                     unfreeze_depths=self.
                     sorted_depth[:self.max_depth * self.epoch //
                                  self.scheduler["gradual_unfreeze"]["blocks"]])

paddlehub/module/manager.py

@@ -76,7 +76,7 @@ class LocalModuleManager(object):
                             sys.modules[_item.__module__].__file__)
                         if issubclass(
                                 _item,
-                                hub.Module) and _file.startwith(module_file):
+                                hub.Module) and _file.startswith(module_file):
                             version = _item._version
                             break
                     sys.path.pop(0)

paddlehub/module/module.py

@@ -137,7 +137,8 @@ class Module(object):
 
         _run_func_name = self._get_func_name(self.__class__,
                                              _module_runnable_func)
-        self._run_func = getattr(self, _run_func_name)
+        self._run_func = getattr(self,
+                                 _run_func_name) if _run_func_name else None
         self._serving_func_name = self._get_func_name(self.__class__,
                                                       _module_serving_func)
         self._directory = directory

paddlehub/module/nlp_module.py

@@ -26,6 +26,7 @@ import six
 
 import numpy as np
 import paddle.fluid as fluid
+from paddlehub.common import paddle_helper
 from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
 import paddlehub as hub
 from paddlehub.common.logger import logger
@@ -265,6 +266,9 @@ class TransformerModule(NLPBaseModule):
         logger.info("Load pretraining parameters from {}.".format(
             pretraining_params_path))
 
+    def param_prefix(self):
+        return "@HUB_%s@" % self.name
+
     def context(
             self,
             max_seq_len=128,
@@ -330,8 +334,13 @@ class TransformerModule(NLPBaseModule):
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
 
+        # To be compatible with the module v1
+        vars = filter(lambda var: "tmp" not in var,
+                      list(module_program.global_block().vars.keys())[4:])
+        paddle_helper.add_vars_prefix(
+            program=module_program, prefix=self.param_prefix(), vars=vars)
         self.init_pretraining_params(
-            exe, self.params_path, main_program=startup_program)
+            exe, self.params_path, main_program=module_program)
 
         self.params_layer = {}
         for param in module_program.global_block().iter_parameters():