update 1.8 rlnas (#294) (#295)

* fix

* update

demo/nas/rl_nas_mobilenetv2.py

@@ -65,8 +65,10 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
             is_sync=False,
             server_addr=(args.server_address, args.port),
             controller_batch_size=1,
+            controller_decay_steps=1000,
+            controller_decay_rate=0.8,
             lstm_num_layers=1,
-            hidden_size=100,
+            hidden_size=10,
             temperature=1.0)
     else:
         ### start a client
@@ -78,6 +80,9 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
             lstm_num_layers=1,
             hidden_size=10,
             temperature=1.0,
+            controller_batch_size=1,
+            controller_decay_steps=1000,
+            controller_decay_rate=0.8,
             is_server=False)
 
     image_shape = [3, image_size, image_size]

docs/zh_cn/api_cn/nas_api.rst

@@ -194,6 +194,9 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习
     - decay(float, optional): LSTM中记录rewards的baseline的平滑率。默认：0.99.
     - weight_entropy(float, optional): 在更新controller参数时是否为接收到的rewards加上计算token过程中的带权重的交叉熵值。默认：None。
     - controller_batch_size(int, optional): controller的batch_size，即每运行一次controller可以拿到几组token。默认：1.
+    - controller_lr(float, optional): controller的学习率，默认：1e-4。
+    - controller_decay_steps(int, optional): controller学习率下降步长，设置为None的时候学习率不下降。默认：None。
+    - controller_decay_rate(float, optional): controller学习率衰减率，默认：None。
 
 
   - **`DDPG`算法的附加参数：**

paddleslim/common/rl_controller/__init__.py

@@ -20,7 +20,7 @@ try:
     from .ddpg import *
 except ImportError as e:
     _logger.warn(
-        "If you want to use DDPG in RLNAS, please pip intall parl first. Now states: {}".
+        "If you want to use DDPG in RLNAS, please pip install parl first. Now states: {}".
         format(e))
 
 from .lstm import *

paddleslim/common/rl_controller/lstm/lstm_controller.py

@@ -63,6 +63,8 @@ class LSTM(RLBaseController):
         self.hidden_size = kwargs.get('hidden_size') or 100
         self.temperature = kwargs.get('temperature') or None
         self.controller_lr = kwargs.get('controller_lr') or 1e-4
+        self.decay_steps = kwargs.get('controller_decay_steps') or None
+        self.decay_rate = kwargs.get('controller_decay_rate') or None
         self.tanh_constant = kwargs.get('tanh_constant') or None
         self.decay = kwargs.get('decay') or 0.99
         self.weight_entropy = kwargs.get('weight_entropy') or None
@@ -198,11 +200,15 @@ class LSTM(RLBaseController):
             fluid.layers.assign(self.baseline - (1.0 - self.decay) *
                                 (self.baseline - self.rewards), self.baseline)
             self.loss = self.sample_log_probs * (self.rewards - self.baseline)
-            fluid.clip.set_gradient_clip(
-                clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0))
+            clip = fluid.clip.GradientClipByNorm(clip_norm=5.0)
+            if self.decay_steps is not None:
                 lr = fluid.layers.exponential_decay(
-                self.controller_lr, decay_steps=1000, decay_rate=0.8)
-            optimizer = fluid.optimizer.Adam(learning_rate=lr)
+                    self.controller_lr,
+                    decay_steps=self.decay_steps,
+                    decay_rate=self.decay_rate)
+            else:
+                lr = self.controller_lr
+            optimizer = fluid.optimizer.Adam(learning_rate=lr, grad_clip=clip)
             optimizer.minimize(self.loss)
 
     def _create_input(self, is_test=True, actual_rewards=None):