fix conflicts

paddleslim/nas/darts/search_space/conv_bert/cls.py

@@ -91,19 +91,6 @@ class AdaBERTClassifier(Layer):
             use_fixed_gumbel=self.use_fixed_gumbel,
             gumbel_alphas=gumbel_alphas)
 
-        for s_emb, t_emb in zip(self.student.emb_names(),
-                                self.teacher.emb_names()):
-            t_emb.stop_gradient = True
-            if fix_emb:
-                s_emb.stop_gradient = True
-            print(
-                "Assigning embedding[{}] from teacher to embedding[{}] in student.".
-                format(t_emb.name, s_emb.name))
-            fluid.layers.assign(input=t_emb, output=s_emb)
-            print(
-                "Assigned embedding[{}] from teacher to embedding[{}] in student.".
-                format(t_emb.name, s_emb.name))
-
         fix_emb = False
         for s_emb, t_emb in zip(self.student.emb_names(),
                                 self.teacher.emb_names()):
@@ -173,4 +160,3 @@ class AdaBERTClassifier(Layer):
         total_loss = (1 - self._gamma) * ce_loss + self._gamma * kd_loss
 
         return total_loss, accuracy, ce_loss, kd_loss, s_logits
-

paddleslim/nas/darts/search_space/conv_bert/model/transformer_encoder.py

@@ -262,7 +262,6 @@ class EncoderLayer(Layer):
             default_initializer=NormalInitializer(
                 loc=0.0, scale=1e-3))
 
-
         self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
         self.bns = []
         self.outs = []
@@ -305,21 +304,6 @@ class EncoderLayer(Layer):
 
     def forward(self, enc_input_0, enc_input_1, epoch, flops=[],
                 model_size=[]):
-=======
-            self.outs.append(out)
-
-        self.use_fixed_gumbel = use_fixed_gumbel
-        self.gumbel_alphas = gumbel_softmax(self.alphas)
-        if gumbel_alphas is not None:
-            self.gumbel_alphas = np.array(gumbel_alphas).reshape(
-                self.alphas.shape)
-        else:
-            self.gumbel_alphas = gumbel_softmax(self.alphas)
-            self.gumbel_alphas.stop_gradient = True
-
-        print("gumbel_alphas: {}".format(self.gumbel_alphas))
-
-    def forward(self, enc_input_0, enc_input_1, flops=[], model_size=[]):
         alphas = self.gumbel_alphas if self.use_fixed_gumbel else gumbel_softmax(
             self.alphas, epoch)