add api set_params/get_params in Model (#56)

* add api set_params/get_params in Model; add Interface of Network and LayerFunc to solve circular imports; refine parameter_names api of Model

* remove licence in third party code; remove interface of Network and LayerFunc; move get_parameter_pairs and get_parameter_names api to Network

* refine comment

* refine commment

.copyright.hook

@@ -103,6 +103,7 @@ def main(argv=None):
         first_line = fd.readline()
         second_line = fd.readline()
         if "COPYRIGHT (C)" in first_line.upper(): continue
+        if "THIRD PARTY" in first_line.upper(): continue
         original_contents = io.open(filename, encoding="utf-8").read()
         new_contents = generate_copyright(
             COPYRIGHT, lang_type(filename)) + original_contents

.teamcity/build.sh

@@ -55,13 +55,27 @@ function check_style() {
     trap : 0
 }
 
-function run_test() {
+function run_test_with_gpu() {
     mkdir -p ${REPO_ROOT}/build
     cd ${REPO_ROOT}/build
     cmake ..
     cat <<EOF
     ========================================
-    Running unit tests ...
+    Running unit tests with GPU...
+    ========================================
+EOF
+    ctest --output-on-failure
+}
+
+function run_test_with_cpu() {
+    export CUDA_VISIBLE_DEVICES=""
+
+    mkdir -p ${REPO_ROOT}/build
+    cd ${REPO_ROOT}/build
+    cmake ..
+    cat <<EOF
+    ========================================
+    Running unit tests with CPU...
     ========================================
 EOF
     ctest --output-on-failure
@@ -76,7 +90,8 @@ function main() {
           check_style
           ;;
         test)
-          run_test
+          run_test_with_gpu
+          run_test_with_cpu
           ;;
         *)
           print_usage

examples/DQN/atari.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+# Third party code
 #
-# 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.
+# The following code are copied or modified from:
+# https://github.com/tensorpack/tensorpack/blob/master/examples/DeepQNetwork/atari.py
 
 import cv2
 import gym
@@ -25,10 +16,6 @@ __all__ = ['AtariPlayer']
 
 ROM_URL = "https://github.com/openai/atari-py/tree/master/atari_py/atari_roms"
 _ALE_LOCK = threading.Lock()
-"""
-The following AtariPlayer are copied or modified from tensorpack/tensorpack:
-    https://github.com/tensorpack/tensorpack/blob/master/examples/DeepQNetwork/atari.py
-"""
 
 
 class AtariPlayer(gym.Env):

examples/DQN/atari_wrapper.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+# Third party code
 #
-# 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.
+# The following code are copied or modified from:
+# https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
 
 import gym
 import numpy as np
@@ -19,10 +10,6 @@ from gym import spaces
 
 _v0, _v1 = gym.__version__.split('.')[:2]
 assert int(_v0) > 0 or int(_v1) >= 10, gym.__version__
-"""
-The following wrappers are copied or modified from openai/baselines:
-https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
-"""
 
 
 class MapState(gym.ObservationWrapper):

examples/PPO/utils.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+# Third party code
 #
-# 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.
+# The following code are copied or modified from:
+# https://github.com/pat-coady/trpo
 
 import numpy as np
 import scipy.signal
 
 __all__ = ['calc_discount_sum_rewards', 'calc_gae', 'Scaler']
-"""
-The following code are copied or modified from:
-    https://github.com/pat-coady/trpo
-    Written by Patrick Coady (pat-coady.github.io)
-"""
 
 
 def calc_discount_sum_rewards(rewards, gamma):

parl/framework/model_base.py

@@ -17,19 +17,21 @@ Base class to define an Algorithm.
 
 import hashlib
 import paddle.fluid as fluid
-from abc import ABCMeta, abstractmethod
+from abc import ABCMeta
+from parl.layers.layer_wrappers import LayerFunc
+from parl.plutils import *
 
 __all__ = ['Network', 'Model']
 
 
 class Network(object):
     """
-    A Network is an unordered set of LayerFuncs or Networks.
+    A Network is a collection of LayerFuncs or Networks.
     """
 
     def sync_params_to(self,
                        target_net,
-                       gpu_id=0,
+                       gpu_id,
                        decay=0.0,
                        share_vars_parallel_executor=None):
         """
@@ -57,13 +59,10 @@ class Network(object):
             assert not target_net is self, "cannot copy between identical networks"
             assert isinstance(target_net, Network)
             assert self.__class__.__name__ == target_net.__class__.__name__, \
-                "must be the same class for para syncing!"
+                "must be the same class for params syncing!"
             assert (decay >= 0 and decay <= 1)
 
-            # Resolve Circular Imports
-            from parl.plutils import get_parameter_pairs, fetch_framework_var
-
-            param_pairs = get_parameter_pairs(self, target_net)
+            param_pairs = self._get_parameter_pairs(self, target_net)
 
             self._cached_sync_params_program = fluid.Program()
 
@@ -106,15 +105,120 @@ class Network(object):
     @property
     def parameter_names(self):
         """ param_attr names of all parameters in Network,
-            only parameter created by parl.layers included
+            only parameter created by parl.layers included.
+            The order of parameter names will be consistent between
+            different instances of same parl.Network.
 
         Returns:
             list of string, param_attr names of all parameters
         """
 
-        # Resolve Circular Imports
-        from parl.plutils import get_parameter_names
-        return get_parameter_names(self)
+        try:
+            return self._parameter_names
+        except AttributeError:
+            self._parameter_names = self._get_parameter_names(self)
+            return self._parameter_names
+
+    def get_params(self):
+        """ Get numpy arrays of parameters in this Network
+        
+        Returns:
+            List of numpy array.
+        """
+        params = []
+        for param_name in self.parameter_names:
+            param = fetch_value(param_name)
+            params.append(param)
+
+        return params
+
+    def set_params(self, params, gpu_id):
+        """ Set parameters in this Network with params
+        
+        Args:
+            params: List of numpy array.
+            gpu_id: gpu id where this Network in. (if gpu_id < 0, means in cpu.)
+        """
+        assert len(params) == len(self.parameter_names), \
+                'size of input params should be same as parameters number of current Network'
+        for (param_name, param) in list(zip(self.parameter_names, params)):
+            set_value(param_name, param, gpu_id)
+
+    def _get_parameter_names(self, obj):
+        """ Recursively get parameter names in obj,
+
+        Args:
+            obj (parl.Network/parl.LayerFunc/list/tuple/dict): input object
+
+        Returns:
+            parameter_names (list of string): all parameter names in obj
+        """
+
+        parameter_names = []
+        for attr in sorted(obj.__dict__.keys()):
+            val = getattr(obj, attr)
+            if isinstance(val, Network):
+                parameter_names.extend(self._get_parameter_names(val))
+            elif isinstance(val, LayerFunc):
+                for attr in val.attr_holder.sorted():
+                    if attr:
+                        parameter_names.append(attr.name)
+            elif isinstance(val, tuple) or isinstance(val, list):
+                for x in val:
+                    parameter_names.extend(self._get_parameter_names(x))
+            elif isinstance(val, dict):
+                for x in list(val.values()):
+                    parameter_names.extend(self._get_parameter_names(x))
+            else:
+                # for any other type, won't be handled. E.g. set
+                pass
+        return parameter_names
+
+    def _get_parameter_pairs(self, src, target):
+        """ Recursively gets parameters in source network and 
+        corresponding parameters in target network.
+
+        Args:
+            src (parl.Network/parl.LayerFunc/list/tuple/dict): source object
+            target (parl.Network/parl.LayerFunc/list/tuple/dict): target object
+
+        Returns:
+            param_pairs (list of tuple): all parameter names in source network
+                                         and corresponding parameter names in 
+                                         target network.
+        """
+
+        param_pairs = []
+        if isinstance(src, Network):
+            for attr in src.__dict__:
+                if not attr in target.__dict__:
+                    continue
+                src_var = getattr(src, attr)
+                target_var = getattr(target, attr)
+                param_pairs.extend(
+                    self._get_parameter_pairs(src_var, target_var))
+        elif isinstance(src, LayerFunc):
+            src_attrs = src.attr_holder.sorted()
+            target_attrs = target.attr_holder.sorted()
+            assert len(src_attrs) == len(target_attrs), \
+                    "number of ParamAttr between source layer and target layer should be same."
+            for (src_attr, target_attr) in zip(src_attrs, target_attrs):
+                if src_attr:
+                    assert target_attr, "ParamAttr between source layer and target layer is inconsistent."
+                    param_pairs.append((src_attr.name, target_attr.name))
+        elif isinstance(src, tuple) or isinstance(src, list):
+            for src_var, target_var in zip(src, target):
+                param_pairs.extend(
+                    self._get_parameter_pairs(src_var, target_var))
+        elif isinstance(src, dict):
+            for k in src.keys():
+                assert k in target
+                param_pairs.extend(
+                    self._get_parameter_pairs(src[k], target[k]))
+        else:
+            # for any other type, won't be handled. E.g. set
+            pass
+        return param_pairs
 
 
 class Model(Network):

parl/framework/tests/agent_base_test.py

@@ -73,7 +73,7 @@ class AgentBaseTest(unittest.TestCase):
             self.assertIsNotNone(output_np)
 
     def test_agent_with_cpu(self):
-        agent = TestAgent(self.algorithm, gpu_id=0)
+        agent = TestAgent(self.algorithm, gpu_id=-1)
         obs = np.random.random([3, 10]).astype('float32')
         output_np = agent.predict(obs)
         self.assertIsNotNone(output_np)

parl/framework/tests/model_base_test.py

@@ -71,6 +71,19 @@ class TestModel3(Model):
         return out
 
 
+class TestModel4(Model):
+    def __init__(self):
+        self.fc1 = layers.fc(size=256)
+        self.fc2 = layers.fc(size=128)
+        self.fc3 = layers.fc(size=1)
+
+    def predict(self, obs):
+        out = self.fc1(obs)
+        out = self.fc2(out)
+        out = self.fc3(out)
+        return out
+
+
 class ModelBaseTest(unittest.TestCase):
     def setUp(self):
         self.model = TestModel()
@@ -80,8 +93,10 @@ class ModelBaseTest(unittest.TestCase):
         gpu_count = get_gpu_count()
         if gpu_count > 0:
             place = fluid.CUDAPlace(0)
+            self.gpu_id = 0
         else:
-            place = fluid.CPUPlace
+            place = fluid.CPUPlace()
+            self.gpu_id = -1
         self.executor = fluid.Executor(place)
 
     def test_network_copy(self):
@@ -121,6 +136,11 @@ class ModelBaseTest(unittest.TestCase):
             set(self.model.parameter_names),
             set(['fc1.w', 'fc1.b', 'fc2.w', 'fc2.b', 'fc3.w', 'fc3.b']))
 
+        # Second test for cache parameter_names
+        self.assertSetEqual(
+            set(self.model.parameter_names),
+            set(['fc1.w', 'fc1.b', 'fc2.w', 'fc2.b', 'fc3.w', 'fc3.b']))
+
     def test_sync_params_in_one_program(self):
         pred_program = fluid.Program()
         with fluid.program_guard(pred_program):
@@ -139,7 +159,7 @@ class ModelBaseTest(unittest.TestCase):
                 fetch_list=[model_output, target_model_output])
             self.assertNotEqual(outputs[0].flatten(), outputs[1].flatten())
 
-        self.model.sync_params_to(self.target_model)
+        self.model.sync_params_to(self.target_model, self.gpu_id)
 
         random_obs = np.random.random(size=(N, 4)).astype('float32')
         for i in range(N):
@@ -177,7 +197,7 @@ class ModelBaseTest(unittest.TestCase):
                 fetch_list=[target_model_output])
             self.assertNotEqual(outputs[0].flatten(), outputs_2[0].flatten())
 
-        self.model.sync_params_to(self.target_model)
+        self.model.sync_params_to(self.target_model, self.gpu_id)
 
         random_obs = np.random.random(size=(N, 4)).astype('float32')
         for i in range(N):
@@ -245,7 +265,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model, decay)
 
-        self.model.sync_params_to(self.target_model, decay=decay)
+        self.model.sync_params_to(self.target_model, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -278,7 +298,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model, decay)
 
-        self.model.sync_params_to(self.target_model, decay=decay)
+        self.model.sync_params_to(self.target_model, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -302,7 +322,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model, decay)
 
-        self.model.sync_params_to(self.target_model, decay=decay)
+        self.model.sync_params_to(self.target_model, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -335,7 +355,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model, decay)
 
-        self.model.sync_params_to(self.target_model, decay=decay)
+        self.model.sync_params_to(self.target_model, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -359,7 +379,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model, decay)
 
-        self.model.sync_params_to(self.target_model, decay=decay)
+        self.model.sync_params_to(self.target_model, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -393,7 +413,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model, decay)
 
-        self.model.sync_params_to(self.target_model, decay=decay)
+        self.model.sync_params_to(self.target_model, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -417,7 +437,7 @@ class ModelBaseTest(unittest.TestCase):
          target_model_fc2_b, target_model_fc3_w,
          target_model_fc3_b) = self._numpy_update(self.target_model2, decay)
 
-        self.model.sync_params_to(self.target_model2, decay=decay)
+        self.model.sync_params_to(self.target_model2, self.gpu_id, decay=decay)
 
         N = 10
         random_obs = np.random.random(size=(N, 4)).astype('float32')
@@ -457,7 +477,7 @@ class ModelBaseTest(unittest.TestCase):
             self.assertNotEqual(
                 np.sum(outputs[0].flatten()), np.sum(outputs[1].flatten()))
 
-        model.sync_params_to(target_model)
+        model.sync_params_to(target_model, self.gpu_id)
 
         random_obs = np.random.random(size=(N, 100)).astype('float32')
         for i in range(N):
@@ -508,7 +528,7 @@ class ModelBaseTest(unittest.TestCase):
             x = np.expand_dims(random_obs[i], axis=0)
             self.executor.run(program1, feed={'obs': x})
 
-        model.sync_params_to(target_model)
+        model.sync_params_to(target_model, self.gpu_id)
 
         random_obs = np.random.random(size=(N, 32, 128, 128)).astype('float32')
         for i in range(N):
@@ -520,6 +540,124 @@ class ModelBaseTest(unittest.TestCase):
             self.assertEqual(
                 np.sum(outputs[0].flatten()), np.sum(outputs[1].flatten()))
 
+    def test_get_params(self):
+        pred_program = fluid.Program()
+        with fluid.program_guard(pred_program):
+            obs = layers.data(name='obs', shape=[4], dtype='float32')
+            model_output = self.model.predict(obs)
+
+        self.executor.run(fluid.default_startup_program())
+
+        expected_params = []
+        for param_name in [
+                'fc1.w', 'fc1.b', 'fc2.w', 'fc2.b', 'fc3.w', 'fc3.b'
+        ]:
+            expected_params.append(fetch_value(param_name))
+
+        params = self.model.get_params()
+        self.assertEqual(len(params), len(expected_params))
+        for param in params:
+            flag = False
+            for expected_param in expected_params:
+                if np.sum(param) - np.sum(expected_param) < 1e-5:
+                    flag = True
+                    break
+            self.assertTrue(flag)
+
+    def test_set_params(self):
+        pred_program = fluid.Program()
+        with fluid.program_guard(pred_program):
+            obs = layers.data(name='obs', shape=[4], dtype='float32')
+            model_output = self.model.predict(obs)
+
+        self.executor.run(fluid.default_startup_program())
+
+        params = self.model.get_params()
+        new_params = [x + 1.0 for x in params]
+
+        self.model.set_params(new_params, self.gpu_id)
+
+        for x, y in list(zip(new_params, self.model.get_params())):
+            self.assertEqual(np.sum(x), np.sum(y))
+
+    def test_set_params_between_different_models(self):
+        model1 = TestModel4()
+        model2 = TestModel4()
+
+        pred_program = fluid.Program()
+        with fluid.program_guard(pred_program):
+            obs = layers.data(name='obs', shape=[4], dtype='float32')
+            model1_output = model1.predict(obs)
+            model2_output = model2.predict(obs)
+
+        self.executor.run(fluid.default_startup_program())
+
+        N = 10
+        random_obs = np.random.random(size=(N, 4)).astype('float32')
+        for i in range(N):
+            x = np.expand_dims(random_obs[i], axis=0)
+            outputs = self.executor.run(
+                pred_program,
+                feed={'obs': x},
+                fetch_list=[model1_output, model2_output])
+            self.assertNotEqual(outputs[0].flatten(), outputs[1].flatten())
+
+        # pass parameters of self.model to model2
+        params = model1.get_params()
+        model2.set_params(params, self.gpu_id)
+
+        random_obs = np.random.random(size=(N, 4)).astype('float32')
+        for i in range(N):
+            x = np.expand_dims(random_obs[i], axis=0)
+            outputs = self.executor.run(
+                pred_program,
+                feed={'obs': x},
+                fetch_list=[model1_output, model2_output])
+            self.assertEqual(outputs[0].flatten(), outputs[1].flatten())
+
+    def test_set_params_with_wrong_params_num(self):
+        pred_program = fluid.Program()
+        with fluid.program_guard(pred_program):
+            obs = layers.data(name='obs', shape=[4], dtype='float32')
+            model_output = self.model.predict(obs)
+
+        self.executor.run(fluid.default_startup_program())
+
+        params = self.model.get_params()
+
+        try:
+            self.model.set_params(params[1:], self.gpu_id)
+        except:
+            # expected
+            return
+
+        assert False
+
+    def test_set_params_with_wrong_params_shape(self):
+        pred_program = fluid.Program()
+        with fluid.program_guard(pred_program):
+            obs = layers.data(name='obs', shape=[4], dtype='float32')
+            model_output = self.model.predict(obs)
+
+        self.executor.run(fluid.default_startup_program())
+
+        params = self.model.get_params()
+
+        params.reverse()
+
+        self.model.set_params(params, self.gpu_id)
+
+        x = np.random.random(size=(1, 4)).astype('float32')
+
+        try:
+            outputs = self.executor.run(
+                pred_program, feed={'obs': x}, fetch_list=[model_output])
+        except:
+            # expected
+            return
+
+        assert False
+
 
 if __name__ == '__main__':
     unittest.main()

parl/layers/layer_wrappers.py

@@ -40,7 +40,6 @@ from paddle.fluid.executor import _fetch_var
 from paddle.fluid.framework import Variable
 from paddle.fluid.layers import *
 from paddle.fluid.param_attr import ParamAttr
-from parl.framework.model_base import Network
 from parl.layers.attr_holder import AttrHolder
 
 
@@ -128,22 +127,6 @@ class LayerFunc(object):
         return params_names
 
 
-def check_caller_name():
-    stack = inspect.stack()
-    ## we trace back to the call stack and make sure Network.__init__ is on the path
-    called_by_init = False
-    for s in stack:
-        try:
-            the_class = s[0].f_locals["self"].__class__
-            the_method = s[0].f_code.co_name
-            if issubclass(the_class, Network) and the_method == "__init__":
-                called_by_init = True
-        except:
-            pass
-
-    assert called_by_init, "parl.layers can only be called in Network.__init__()!"
-
-
 def fc(size,
        num_flatten_dims=1,
        param_attr=None,
@@ -156,7 +139,6 @@ def fc(size,
     default_name = "fc"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class FC_(LayerFunc):
         def __init__(self):
@@ -187,7 +169,6 @@ def embedding(size,
     Return a function that creates a paddle.fluid.layers.embedding.
     """
     param_attr = update_attr_name(name, "embedding", param_attr, False)
-    check_caller_name()
 
     class Embedding_(LayerFunc):
         def __init__(self):
@@ -222,7 +203,6 @@ def dynamic_lstm(size,
     default_name = "dynamic_lstm"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class DynamicLstm_(LayerFunc):
         def __init__(self):
@@ -265,7 +245,6 @@ def dynamic_lstmp(size,
     default_name = "dynamic_lstmp"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class DynamicLstmp_(LayerFunc):
         def __init__(self):
@@ -303,7 +282,6 @@ def dynamic_gru(size,
     default_name = "dynamic_gru"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class DynamicGru_(LayerFunc):
         def __init__(self):
@@ -353,7 +331,6 @@ def sequence_conv(num_filters,
     default_name = "sequence_conv"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class SequenceConv_(LayerFunc):
         def __init__(self):
@@ -391,7 +368,6 @@ def conv2d(num_filters,
     default_name = "conv2d"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class Conv2D_(LayerFunc):
         def __init__(self):
@@ -432,7 +408,6 @@ def conv2d_transpose(num_filters,
     default_name = "conv2d_transpose"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class Conv2DTranspose_(LayerFunc):
         def __init__(self):
@@ -463,7 +438,6 @@ def lstm_unit(forget_bias=0.0, param_attr=None, bias_attr=None, name=None):
     default_name = "lstm_unit"
     param_attr = update_attr_name(name, default_name, param_attr, False)
     bias_attr = update_attr_name(name, default_name, bias_attr, True)
-    check_caller_name()
 
     class LstmUnit_(LayerFunc):
         def __init__(self):
@@ -491,7 +465,6 @@ def row_conv(future_context_size, param_attr=None, act=None, name=None):
     Return a function that creates a paddle.fluid.layers.row_conv.
     """
     param_attr = update_attr_name(name, "row_conv", param_attr, False)
-    check_caller_name()
 
     class RowConv_(LayerFunc):
         def __init__(self):
@@ -534,7 +507,6 @@ def batch_norm(act=None,
                                         None, False)
     moving_variance_attr = update_attr_name(
         name, default_name + "_moving_variance", None, False)
-    check_caller_name()
 
     class BatchNorm_(LayerFunc):
         def __init__(self):
@@ -578,7 +550,6 @@ def create_parameter(shape,
 
     """
     param_attr = update_attr_name(name, "create_parameter", attr, False)
-    check_caller_name()
 
     class CreateParameter_(LayerFunc):
         def __init__(self):

parl/plutils/common.py

@@ -17,13 +17,8 @@ Common functions of PARL framework
 
 import paddle.fluid as fluid
 from paddle.fluid.executor import _fetch_var
-from parl.layers.layer_wrappers import LayerFunc
-from parl.framework.model_base import Network
 
-__all__ = [
-    'fetch_framework_var', 'fetch_value', 'get_parameter_pairs',
-    'get_parameter_names'
-]
+__all__ = ['fetch_framework_var', 'fetch_value', 'set_value']
 
 
 def fetch_framework_var(attr_name):
@@ -57,77 +52,15 @@ def fetch_value(attr_name):
     return _fetch_var(attr_name, return_numpy=True)
 
 
-def get_parameter_pairs(src, target):
-    """ Recursively get pairs of parameter names between src and target
+def set_value(attr_name, value, gpu_id):
+    """ Given name of ParamAttr, set numpy value to the parameter in global_scope
     
     Args:
-        src: parl.Network/parl.LayerFunc/list/tuple/set/dict
-        target: parl.Network/parl.LayerFunc/list/tuple/set/dict
-
-    Returns:
-        param_pairs: list of all tuple(src_param_name, target_param_name, is_bias)
-                     between src and target
-    """
-
-    param_pairs = []
-    if isinstance(src, Network):
-        for attr in src.__dict__:
-            if not attr in target.__dict__:
-                continue
-            src_var = getattr(src, attr)
-            target_var = getattr(target, attr)
-            param_pairs.extend(get_parameter_pairs(src_var, target_var))
-    elif isinstance(src, LayerFunc):
-        src_attrs = src.attr_holder.sorted()
-        target_attrs = target.attr_holder.sorted()
-        assert len(src_attrs) == len(target_attrs), \
-                "number of ParamAttr between source layer and target layer should be same."
-        for (src_attr, target_attr) in zip(src_attrs, target_attrs):
-            if src_attr:
-                assert target_attr, "ParamAttr between source layer and target layer is inconsistent."
-                param_pairs.append((src_attr.name, target_attr.name))
-    elif isinstance(src, tuple) or isinstance(src, list) or isinstance(
-            src, set):
-        for src_var, target_var in zip(src, target):
-            param_pairs.extend(get_parameter_pairs(src_var, target_var))
-    elif isinstance(src, dict):
-        for k in src.keys():
-            assert k in target
-            param_pairs.extend(get_parameter_pairs(src[k], target[k]))
-    else:
-        # for any other type, won't be handled
-        pass
-    return param_pairs
-
-
-def get_parameter_names(obj):
-    """ Recursively get parameter names in obj,
-        mainly used to get parameter names of a parl.Network
-
-    Args:
-        obj: parl.Network/parl.LayerFunc/list/tuple/set/dict
-
-    Returns:
-        parameter_names: list of string, all parameter names in obj
+        attr_name: ParamAttr name of parameter
+        value: numpy array
+        gpu_id: gpu id where the parameter in
     """
-
-    parameter_names = []
-    for attr in obj.__dict__:
-        val = getattr(obj, attr)
-        if isinstance(val, Network):
-            parameter_names.extend(get_parameter_names(val))
-        elif isinstance(val, LayerFunc):
-            for attr in val.attr_holder.tolist():
-                if attr:
-                    parameter_names.append(attr.name)
-        elif isinstance(val, tuple) or isinstance(val, list) or isinstance(
-                val, set):
-            for x in val:
-                parameter_names.extend(get_parameter_names(x))
-        elif isinstance(val, dict):
-            for x in list(val.values()):
-                parameter_names.extend(get_parameter_names(x))
-        else:
-            # for any other type, won't be handled
-            pass
-    return parameter_names
+    place = fluid.CPUPlace() if gpu_id < 0 \
+            else fluid.CUDAPlace(gpu_id)
+    var = _fetch_var(attr_name, return_numpy=False)
+    var.set(value, place)