Support dygraph structuerd name (#21930)

* support dygraph structured name; test=develop

* add load static param unitest and fix save load; test=develop

* fix varBase import error; test=develop

* fix unitest error; test=develop

* add comment for parameter; test=develop

* fix uni test error; test=develop

* change parallel se-resnet; test=develop

* fix dygraph se resnext parallel test; test=develop

* remove useless code; test=develop

* remove useless code; test=develop

python/paddle/fluid/dygraph/checkpoint.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import os
 import collections
-from ..framework import Variable, default_main_program, in_dygraph_mode, dygraph_only, Parameter
+from ..framework import Variable, default_main_program, in_dygraph_mode, dygraph_only, Parameter, ParamBase
 import pickle
 from . import learning_rate_scheduler
 import warnings
@@ -68,20 +68,33 @@ def save_dygraph(state_dict, model_path):
     assert len(state_dict) > 0, "state_dict is empty, no need to save"
 
     for k, v in state_dict.items():
-        if not isinstance(v, Parameter):
+        if not isinstance(v, ParamBase):
             suffix = ".pdopt"
         break
 
-    core._save_dygraph_dict(model_path + suffix, state_dict)
+    model_dict = {}
+    name_table = {}
+    for k, v in state_dict.items():
+        if isinstance(v, (Variable, core.VarBase)):
+            model_dict[k] = v.numpy()
+        else:
+            model_dict[k] = v
+        name_table[k] = v.name
+    model_dict["StructuredToParameterName@@"] = name_table
+
+    with open(model_path + suffix, 'wb') as f:
+        pickle.dump(model_dict, f)
 
 
 @dygraph_only
-def load_dygraph(model_path):
+def load_dygraph(model_path, keep_name_table=False):
     '''
     Load parameter state_dict from disk.
 
     Args:
         model_path(str) : The file prefix store the state_dict. (The path should Not contain suffix '.pdparams') 
+        keep_name_table(bool, optional) : Whether keep structed name to parameter name conversion table in output dict. 
+                                          Default : False
 
     Returns:
         state_dict(dict) : the dict store the state_dict
@@ -111,45 +124,15 @@ def load_dygraph(model_path):
         raise RuntimeError("Parameter file [ {} ] not exists".format(
             params_file_path))
 
-    para_dict = core._load_dygraph_dict(params_file_path)
+    with open(params_file_path, 'rb') as f:
+        para_dict = pickle.load(f)
 
+    if not keep_name_table and "StructuredToParameterName@@" in para_dict:
+        del para_dict["StructuredToParameterName@@"]
     opti_dict = None
     opti_file_path = model_path + ".pdopt"
     if os.path.exists(opti_file_path):
-        opti_dict = core._load_dygraph_dict(opti_file_path)
+        with open(opti_file_path, 'rb') as f:
+            opti_dict = pickle.load(f)
 
     return para_dict, opti_dict
-
-
-@dygraph_only
-def load_optimizer(model_path):
-    '''
-    Load optimizer state_dict from disk.
-
-    Args:
-        model_path(str) : The file prefix store the state_dict. (The path should Not contain shuffix '.pdparams')
-
-    Returns:
-        state_dict(dict) : the dict store the state_dict
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-
-            with fluid.dygraph.guard():
-                adam = fluid.optimizer.Adam(0.001)
-
-                state_dict = adam.state_dict()
-                fluid.save_optimizer( state_dict, "opt_adam")
-
-                fluid.load_optimizer( "opt_adam")
-
-    '''
-
-    assert in_dygraph_mode(), "save_optimizer only work in dygraph mode"
-    opt_file_path = model_path + ".pdopt"
-    if not os.path.exists(opt_file_path):
-        raise RuntimeError("Optimizer file [ {} ] not exists".format(
-            opt_file_path))
-    return core._load_dygraph_dict(opt_file_path)

python/paddle/fluid/dygraph/layers.py

@@ -25,6 +25,8 @@ from .layer_object_helper import LayerObjectHelper
 from .base import program_desc_tracing_guard
 from paddle.fluid import framework
 from ..param_attr import ParamAttr
+import copy
+import warnings
 
 __all__ = ['Layer']
 
@@ -99,11 +101,10 @@ class Layer(core.Layer):
         Returns:
             :ref:`api_guide_Variable_en` : created parameter.
         """
-        if isinstance(attr, ParamAttr) and (attr.name is not None):
+        temp_attr = copy.deepcopy(attr)
-            attr.name = ".".join([self._full_name, attr.name])
+        if isinstance(temp_attr, six.string_types) and temp_attr == "":
-        elif isinstance(attr, six.string_types):
+            temp_attr = None
-            attr = ".".join([self._full_name, attr])
+        return self._helper.create_parameter(temp_attr, shape, dtype, is_bias,
-        return self._helper.create_parameter(attr, shape, dtype, is_bias,
                                              default_initializer)
 
     # TODO: Add more parameter list when we need them
@@ -283,7 +284,10 @@ class Layer(core.Layer):
         else:
             object.__delattr__(self, name)
 
-    def state_dict(self, destination=None, include_sublayers=True):
+    def state_dict(self,
+                   destination=None,
+                   include_sublayers=True,
+                   structured_name_prefix=""):
         '''
         Get all parameters of current layer and its sub-layers. And set all the parameters into a dict
 
@@ -310,25 +314,31 @@ class Layer(core.Layer):
             destination = collections.OrderedDict()
         for name, data in self._parameters.items():
             if data is not None:
-                destination[data.name] = data
+                destination[structured_name_prefix + name] = data
 
         if include_sublayers:
             for layer_name, layer_item in self._sub_layers.items():
                 if layer_item is not None:
                     destination_temp = destination.copy()
                     destination_temp.update(
-                        layer_item.state_dict(destination_temp,
+                        layer_item.state_dict(
-                                              include_sublayers))
+                            destination_temp, include_sublayers,
+                            structured_name_prefix + layer_name + "."))
                     destination = destination_temp
         return destination
 
-    def set_dict(self, stat_dict, include_sublayers=True):
+    def set_dict(self,
+                 stat_dict,
+                 include_sublayers=True,
+                 use_structured_name=True):
         '''
         Set parameters from stat_dict. All the parameters will be reset by the tensor in the stat_dict
 
         Parameters:
             state_dict(dict) : Dict contains all the parameters
             include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True
+            use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter name as key. 
+                                                  Default: True
         Returns:
             None
 
@@ -347,9 +357,15 @@ class Layer(core.Layer):
                     emb.set_dict( para_state_dict )
 
         '''
-        self.load_dict(stat_dict, include_sublayers=include_sublayers)
+        self.load_dict(
-
+            stat_dict,
-    def load_dict(self, stat_dict, include_sublayers=True):
+            include_sublayers=include_sublayers,
+            use_structured_name=use_structured_name)
+
+    def load_dict(self,
+                  stat_dict,
+                  include_sublayers=True,
+                  use_structured_name=True):
         '''
         Set parameters from stat_dict. All the parameters will be reset by the tensor in the stat_dict
 
@@ -358,6 +374,8 @@ class Layer(core.Layer):
         Parameters:
             state_dict(dict) : Dict contains all the parameters
             include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True
+            use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter name as key.
+                                                  Default: True
         Returns:
             None
 
@@ -377,16 +395,22 @@ class Layer(core.Layer):
 
         '''
 
-        self._loaddict_holder = stat_dict
+        inner_state_dict = self.state_dict()
-        for name, item in self.__dict__.get('_parameters', None).items():
+
-            if item.name in stat_dict:
+        for name, para in inner_state_dict.items():
-                item.set_value(stat_dict[item.name])
+            key_name = name if use_structured_name else para.name
+            if key_name in stat_dict:
+                para.set_value(stat_dict[key_name])
             else:
                 raise RuntimeError(
-                    "Parameter not found, Can't not find [ {} ] in stat_dict".
+                    "Parameter not found, Can't not find [ {} ] in stat_dict"
-                    format(item.name))
+                    "use_structured_name is set to [{}]".format(
-
+                        key_name, use_structured_name))
-        if include_sublayers:
+        unused_para_list = []
-            for layer_name, layer_item in self._sub_layers.items():
+        for k, v in stat_dict.items():
-                if layer_item is not None:
+            if k not in inner_state_dict:
-                    layer_item.load_dict(stat_dict)
+                unused_para_list.append(k)
+        if len(unused_para_list) > 0:
+            warnings.warn(
+                "Varibale [ {} ] are not used, because not included in layers state_dict".
+                format(" ".join(unused_para_list)))

python/paddle/fluid/dygraph/nn.py

@@ -19,9 +19,11 @@ from .. import core
 from ..layers import utils
 from ..dygraph import dygraph_utils
 from . import layers
-from ..framework import Variable, in_dygraph_mode, OpProtoHolder, Parameter, _dygraph_tracer_
+from ..framework import Variable, in_dygraph_mode, OpProtoHolder, Parameter
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant, NumpyArrayInitializer
+from .. import unique_name
+from .layer_object_helper import LayerObjectHelper
 import numpy as np
 import numbers
 import logging
@@ -1287,6 +1289,9 @@ class BatchNorm(layers.Layer):
         self._bias_attr = bias_attr
         self._act = act
 
+        self._full_name = unique_name.generate("batch_norm")
+        self._helper = LayerObjectHelper(self._full_name)
+
         assert bias_attr is not False, "bias_attr should not be False in batch_norm."
 
         if dtype == "float16":
@@ -1618,6 +1623,10 @@ class LayerNorm(layers.Layer):
         super(LayerNorm, self).__init__()
         if isinstance(normalized_shape, numbers.Integral):
             normalized_shape = [normalized_shape]
+
+        self._full_name = unique_name.generate("layer_norm")
+        self._helper = LayerObjectHelper(self._full_name)
+
         self._normalized_shape = list(normalized_shape)
         self._scale = scale
         self._shift = shift

python/paddle/fluid/param_attr.py

@@ -71,6 +71,9 @@ class ParamAttr(object):
                  gradient_clip=None,
                  do_model_average=True):
         self.name = name
+        if isinstance(self.name, six.string_types) and self.name == "":
+            raise ValueError("name of ParamAttr can not be empty str")
+
         self.initializer = initializer
         self.learning_rate = learning_rate
         self.regularizer = regularizer

python/paddle/fluid/tests/unittests/parallel_dygraph_se_resnext.py

@@ -101,8 +101,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
-            bias_attr=False,
+            bias_attr=False)
-            param_attr=fluid.ParamAttr(name="weights"))
 
         # disable BatchNorm in multi-card. disable LayerNorm because of complex input_shape
         # self._batch_norm = BatchNorm(num_filters, act=act)

python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py

@@ -0,0 +1,200 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+from paddle.fluid.dygraph.nn import *
+import numpy as np
+
+print("11")
+
+
+class TestDygraphLoadStatic(unittest.TestCase):
+    def testLoadStaticModel(self):
+        # static mode
+        a = fluid.data(name="a", shape=[10, 10])
+        conv_in = fluid.data(name="conv_in", shape=[None, 10, 10, 10])
+
+        fc_out1 = fluid.layers.fc(a, 10)
+        fc_out2 = fluid.layers.fc(a, 20)
+
+        conv_out_1 = fluid.layers.conv2d(
+            conv_in, num_filters=10, filter_size=5, act="relu")
+        conv_out_2 = fluid.layers.conv2d(
+            conv_in, num_filters=10, filter_size=5, act="relu")
+
+        conv3d_in = fluid.data(
+            name='conv3d_in', shape=[None, 3, 12, 32, 32], dtype='float32')
+        conv3d_out_1 = fluid.layers.conv3d(
+            input=conv3d_in, num_filters=2, filter_size=3, act="relu")
+        conv3d_out_2 = fluid.layers.conv3d(
+            input=conv3d_in, num_filters=2, filter_size=3, act="relu")
+
+        batchnorm_in = fluid.data(
+            name="batchnorm_in", shape=[None, 10], dtype='float32')
+        batchnorm_out_1 = fluid.layers.batch_norm(batchnorm_in)
+        batchnorm_out_2 = fluid.layers.batch_norm(batchnorm_in)
+
+        emb_in = fluid.data(name='emb_in', shape=[None, 10], dtype='int64')
+        emb_out_1 = fluid.embedding(emb_in, [1000, 100])
+        emb_out_2 = fluid.embedding(emb_in, [2000, 200])
+
+        layernorm = fluid.data(name="ln", shape=[None, 10], dtype='float32')
+        layernorm_1 = fluid.layers.layer_norm(layernorm)
+        layernorm_2 = fluid.layers.layer_norm(layernorm)
+
+        nce_in = fluid.data(name="nce_in", shape=[None, 100], dtype='float32')
+        nce_label = fluid.data(
+            name="nce_label", shape=[None, 10], dtype='int64')
+        nce_out_1 = fluid.layers.nce(nce_in, nce_label, 10000)
+        nce_out_2 = fluid.layers.nce(nce_in, nce_label, 10000)
+
+        prelu_in = fluid.data(
+            name="prelu_in", shape=[None, 5, 10, 10], dtype='float32')
+        prelu_out_1 = fluid.layers.prelu(prelu_in, "channel")
+        prelu_out_2 = fluid.layers.prelu(prelu_in, "channel")
+
+        bilinear_tensor_pro_x = fluid.data(
+            "t1", shape=[None, 5], dtype="float32")
+        bilinear_tensor_pro_y = fluid.data(
+            "t2", shape=[None, 4], dtype="float32")
+
+        bilinear_tensor_pro_out_1 = fluid.layers.bilinear_tensor_product(
+            x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000)
+        bilinear_tensor_pro_out_2 = fluid.layers.bilinear_tensor_product(
+            x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000)
+
+        conv2d_trans_in = fluid.data(
+            name="conv2d_trans_in", shape=[None, 10, 10, 10])
+
+        conv2d_trans_out_1 = fluid.layers.conv2d_transpose(
+            conv2d_trans_in, num_filters=10, filter_size=5, act="relu")
+        conv2d_trans_out_2 = fluid.layers.conv2d_transpose(
+            conv2d_trans_in, num_filters=10, filter_size=5, act="relu")
+
+        conv3d_trans_in = fluid.data(
+            name='conv3d_trans_in',
+            shape=[None, 3, 12, 32, 32],
+            dtype='float32')
+        conv3d_trans_out_1 = fluid.layers.conv3d_transpose(
+            input=conv3d_trans_in, num_filters=2, filter_size=3, act="relu")
+        conv3d_trans_out_2 = fluid.layers.conv3d_transpose(
+            input=conv3d_trans_in, num_filters=2, filter_size=3, act="relu")
+
+        groupnorm_in = fluid.data(
+            name='groupnorm_in', shape=[None, 8, 32, 32], dtype='float32')
+        groupnorm_out1 = fluid.layers.group_norm(input=groupnorm_in, groups=4)
+        groupnorm_out2 = fluid.layers.group_norm(input=groupnorm_in, groups=4)
+        '''
+        spec_norm = fluid.data(name='spec_norm', shape=[2, 8, 32, 32], dtype='float32')
+        spe_norm_out_1 = fluid.layers.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
+        spe_norm_out_2 = fluid.layers.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
+        '''
+
+        nodes_vector = fluid.data(
+            name='vectors', shape=[None, 10, 5], dtype='float32')
+        edge_set = fluid.data(
+            name='edge_set', shape=[None, 10, 2], dtype='float32')
+        tree_conv_out1 = fluid.contrib.layers.tree_conv(nodes_vector, edge_set,
+                                                        6, 1, 2)
+        tree_conv_out2 = fluid.contrib.layers.tree_conv(nodes_vector, edge_set,
+                                                        6, 1, 2)
+
+        para1 = fluid.layers.create_parameter(
+            [100, 100], 'float32', name="weight_test_1")
+        para2 = fluid.layers.create_parameter(
+            [20, 200], 'float32', name="weight_test_2")
+
+        para_list = fluid.default_main_program().list_vars()
+
+        exe = fluid.Executor(fluid.CPUPlace(
+        ) if not fluid.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+        out = exe.run(framework.default_startup_program())
+
+        fluid.save(framework.default_main_program(), "./test_1")
+
+        para_dict = fluid.load_program_state("./test_1")
+
+        new_dict = {}
+        for k, v in para_dict.items():
+            #print( k, v.shape )
+            if k.startswith("fc"):
+                name = k.replace("fc", "linear", 1)
+                new_dict[name] = v
+            else:
+                new_dict[k] = v
+
+        with fluid.dygraph.guard():
+
+            class MyTest(fluid.dygraph.Layer):
+                def __init__(self):
+                    super(MyTest, self).__init__()
+
+                    self.linear1 = Linear(10, 10)
+                    self.lienar2 = Linear(10, 20)
+
+                    self.conv2d_1 = Conv2D(
+                        num_channels=10,
+                        num_filters=10,
+                        filter_size=5,
+                        act="relu")
+                    self.conv2d_2 = Conv2D(
+                        num_channels=10,
+                        num_filters=10,
+                        filter_size=5,
+                        act="relu")
+
+                    self.conv3d_1 = Conv3D(
+                        num_channels=3,
+                        num_filters=2,
+                        filter_size=3,
+                        act="relu")
+                    self.conv3d_2 = Conv3D(
+                        num_channels=3,
+                        num_filters=2,
+                        filter_size=3,
+                        act="relu")
+
+                    self.batch_norm_1 = BatchNorm(10)
+                    self.batch_norm_2 = BatchNorm(10)
+
+                    self.emb1 = Embedding([1000, 100])
+                    self.emb2 = Embedding([2000, 200])
+
+                    self.layer_norm_1 = LayerNorm([10])
+                    self.layer_norm_2 = LayerNorm(10)
+
+                    self.nce1 = NCE(10000, 100)
+                    self.nce2 = NCE(10000, 100)
+
+                    self.prelu1 = PRelu("channel", [-1, 5, 10, 10])
+                    self.prelu2 = PRelu("channel", [-1, 5, 10, 10])
+
+                    self.group_norm1 = GroupNorm(8, 4)
+                    self.gourp_norm2 = GroupNorm(8, 4)
+
+                    self.w_1 = self.create_parameter(
+                        [100, 100], dtype='float32', attr="weight_test_1")
+                    self.w_2 = self.create_parameter(
+                        [20, 200], dtype='float32', attr="weight_test_2")
+
+            my_test = MyTest()
+            my_test.set_dict(new_dict, use_structured_name=False)
+            for k, v in my_test.state_dict().items():
+                self.assertTrue(np.array_equal(v.numpy(), new_dict[v.name]))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_imperative_save_load.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import unittest
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid.dygraph.nn import Embedding, FC
+from paddle.fluid.dygraph.nn import Embedding, Linear
 import paddle.fluid.framework as framework
 from paddle.fluid.optimizer import Adam
 from paddle.fluid.dygraph.base import to_variable
@@ -29,13 +29,12 @@ import six
 
 class SimpleLSTMRNN(fluid.Layer):
     def __init__(self,
-                 name_scope,
                  hidden_size,
                  num_steps,
                  num_layers=2,
                  init_scale=0.1,
                  dropout=None):
-        super(SimpleLSTMRNN, self).__init__(name_scope)
+        super(SimpleLSTMRNN, self).__init__()
         self._hidden_size = hidden_size
         self._num_layers = num_layers
         self._init_scale = init_scale
@@ -44,8 +43,6 @@ class SimpleLSTMRNN(fluid.Layer):
         self._num_steps = num_steps
         self.cell_array = []
         self.hidden_array = []
-
-    def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
         self.weight_1_arr = []
         self.weight_2_arr = []
         self.bias_arr = []
@@ -149,7 +146,6 @@ class PtbModel(fluid.Layer):
         self.num_steps = num_steps
         self.dropout = dropout
         self.simple_lstm_rnn = SimpleLSTMRNN(
-            self.full_name(),
             hidden_size,
             num_steps,
             num_layers=num_layers,
@@ -164,9 +160,7 @@ class PtbModel(fluid.Layer):
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale)))
 
-        self.out_project = FC(self.full_name(),
+        self.out_project = Linear(self.hidden_size, self.vocab_size)
-                              self.vocab_size,
-                              num_flatten_dims=2)
 
     def forward(self, input, label, init_hidden, init_cell):
         init_h = fluid.layers.reshape(
@@ -277,10 +271,11 @@ class TestDygraphPtbRnn(unittest.TestCase):
             fluid.save_dygraph(self.opti_dict, "./test_dy")
 
             self.state_dict = ptb_model.state_dict()
+
             self.model_base = {}
             for k, v in self.state_dict.items():
                 np_t = v.numpy()
-                self.model_base[v.name] = np_t
+                self.model_base[k] = np_t
 
             fluid.save_dygraph(self.state_dict, "./test_dy")
 
@@ -386,7 +381,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             for k, v in state_dict.items():
                 new_t = v.numpy()
 
-                base_t = self.model_base[v.name]
+                base_t = self.model_base[k]
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
@@ -491,7 +486,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             for k, v in state_dict.items():
                 new_t = v.numpy()
 
-                base_t = self.model_base[v.name]
+                base_t = self.model_base[k]
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
@@ -588,7 +583,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             np_state_dict = {}
             for k, v in state_dict.items():
                 np_t = v.numpy()
-                np_state_dict[v.name] = np_t
+                np_state_dict[k] = np_t
                 var = v.value().get_tensor()
 
                 var.set(np.zeros_like(np_t), place)
@@ -600,7 +595,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             for k, v in state_dict.items():
                 new_t = v.numpy()
 
-                base_t = self.model_base[v.name]
+                base_t = self.model_base[k]
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
@@ -626,20 +621,10 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 num_steps=num_steps,
                 init_scale=init_scale)
 
-            bd = []
-            lr_arr = [1.0]
-            # this a fake lr decay strategy
-            for i in range(1, 10):
-                bd.append(100 * i)
-                #set lr to 0.0, not udpate parameter
-                new_lr = 0.0
-                lr_arr.append(new_lr)
-
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
             adam = Adam(
-                learning_rate=fluid.layers.piecewise_decay(
+                learning_rate=0.0,
-                    boundaries=bd, values=lr_arr),
                 beta1=0.8,
                 beta2=0.6,
                 parameter_list=ptb_model.parameters())
@@ -686,14 +671,12 @@ class TestDygraphPtbRnn(unittest.TestCase):
                         np.array_equal(v.numpy(), self.base_opti[v.name] *
                                        adam._beta2))
 
-            # check parameter
-
             state_dict = ptb_model.state_dict()
 
             for k, v in state_dict.items():
                 new_t = v.numpy()
 
-                base_t = self.model_base[v.name]
+                base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
     def testLoadAndSetVarBaseBeforeTrain(self):
@@ -719,7 +702,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 init_scale=init_scale)
 
             bd = []
-            lr_arr = [1.0]
+            lr_arr = [0.0]
             # this a fake lr decay strategy
             for i in range(1, 10):
                 bd.append(100 * i)
@@ -730,8 +713,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
             adam = Adam(
-                learning_rate=fluid.layers.piecewise_decay(
+                learning_rate=0.0,
-                    boundaries=bd, values=lr_arr),
                 beta1=0.8,
                 beta2=0.6,
                 parameter_list=ptb_model.parameters())
@@ -786,7 +768,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             for k, v in state_dict.items():
                 new_t = v.numpy()
 
-                base_t = self.model_base[v.name]
+                base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
     def testSetNumpyBeforeTrain(self):
@@ -812,7 +794,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 init_scale=init_scale)
 
             bd = []
-            lr_arr = [1.0]
+            lr_arr = [0.0]
             # this a fake lr decay strategy
             for i in range(1, 10):
                 bd.append(100 * i)
@@ -841,11 +823,10 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 np_opti_dict[v.name] = v.numpy()
 
             for k, v in self.state_dict.items():
-                np_state_dict[v.name] = v.numpy()
+                np_state_dict[k] = v.numpy()
 
             adam.set_dict(np_opti_dict)
             ptb_model.set_dict(np_state_dict)
-
             for i in range(1):
                 x_data = np.arange(12).reshape(4, 3).astype('int64')
                 y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
@@ -887,7 +868,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             for k, v in state_dict.items():
                 new_t = v.numpy()
 
-                base_t = self.model_base[v.name]
+                base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
     def testOnlyLoadParams(self):