golang pserver use OptimizerConfig.proto (#3358)

* golang pserver optimizer config for user

* update

* update

* update

* update

* update by comments

* fix errors

* fix errors

go/pserver/client/c/test/test_train.py

@@ -17,12 +17,10 @@ def main():
     # network config
     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
     y_predict = paddle.layer.fc(input=x,
-                                param_attr=paddle.attr.Param(
-                                    name='w', learning_rate=1e-3),
+                                param_attr=paddle.attr.Param(name='w'),
                                 size=1,
                                 act=paddle.activation.Linear(),
-                                bias_attr=paddle.attr.Param(
-                                    name='b', learning_rate=1e-3))
+                                bias_attr=paddle.attr.Param(name='b'))
     y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
     cost = paddle.layer.mse_cost(input=y_predict, label=y)
 

paddle/api/ParameterUpdater.cpp

@@ -41,7 +41,7 @@ ParameterUpdater *ParameterUpdater::createNewRemoteUpdater(
       config->m->getConfig(), pserverSpec, useEtcd));
   return updater;
 #else
-  throw UnsupportError();
+  throw UnsupportError("not compiled with WITH_GOLANG");
 #endif
 }
 

paddle/trainer/NewRemoteParameterUpdater.cpp

@@ -66,28 +66,92 @@ void NewRemoteParameterUpdater::init(
   // from parameter server
   if (paddle_begin_init_params(parameterClient_)) {
     LOG(INFO) << "paddle_begin_init_params start";
+    // NOTE: convert V1 OptimizatioinConfig proto to V2 OptimizerConfig.
+    // This makes golang pserver compatible with handy V1 demos.
+    // TODO: Refine or remove these ugly converting lines
+    OptimizerConfig optimizerConfigV2;
+    if (trainerConfig_.learning_method() == "momentum") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
+    } else if (trainerConfig_.learning_method() == "adagrad") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::Adagrad);
+      optimizerConfigV2.mutable_adagrad()->set_epsilon(
+          trainerConfig_.ada_epsilon());
+    } else if (trainerConfig_.learning_method() == "adadelta") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::Adagrad);
+      optimizerConfigV2.mutable_adadelta()->set_epsilon(
+          trainerConfig_.ada_epsilon());
+      optimizerConfigV2.mutable_adadelta()->set_rho(trainerConfig_.ada_rou());
+    } else if (trainerConfig_.learning_method() == "adam") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::Adam);
+      optimizerConfigV2.mutable_adam()->set_beta_1(trainerConfig_.adam_beta1());
+      optimizerConfigV2.mutable_adam()->set_beta_2(trainerConfig_.adam_beta2());
+      optimizerConfigV2.mutable_adam()->set_epsilon(
+          trainerConfig_.adam_epsilon());
+    } else {
+      LOG(ERROR) << "got unsupported v1 optimizer config: "
+                 << trainerConfig_.learning_method();
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
+    }
+
+    if (trainerConfig_.learning_rate_schedule() == "constant") {
+      optimizerConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
+      optimizerConfigV2.mutable_const_lr()->set_learning_rate(
+          trainerConfig_.learning_rate());
+    } else if (trainerConfig_.learning_rate_schedule() == "linear") {
+      optimizerConfigV2.set_lr_policy(paddle::OptimizerConfig::Linear);
+      optimizerConfigV2.mutable_linear_lr()->set_learning_rate(
+          trainerConfig_.learning_rate());
+      optimizerConfigV2.mutable_linear_lr()->set_lr_decay_a(
+          trainerConfig_.learning_rate_decay_a());
+      optimizerConfigV2.mutable_linear_lr()->set_lr_decay_b(
+          trainerConfig_.learning_rate_decay_b());
+    } else {
+      LOG(ERROR) << "got unsupported v1 learning_rate_schedule config: "
+                 << trainerConfig_.learning_rate_schedule() << ", set to const";
+      optimizerConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
+    }
+
+    // overwrite optimizerConfigV2 for per-parameter(layer) configs
     for (int i = 0; i < parameterSize(); ++i) {
       auto paramConfig = parameters_[i]->getConfig();
-      LOG(INFO) << "old param config: " << paramConfig.DebugString();
-      // FIXME(typhoonzero): convert old paramConfig to optimizerConfig
-      OptimizerConfig optimizeConfigV2;
-      auto sgdConfigV2 = optimizeConfigV2.mutable_sgd();
-      sgdConfigV2->set_momentum(paramConfig.momentum());
-      sgdConfigV2->set_decay(paramConfig.decay_rate());
-      optimizeConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
-      auto constlr = optimizeConfigV2.mutable_const_lr();
+      if (paramConfig.has_momentum() &&
+          trainerConfig_.learning_method() == "momentum") {
+        optimizerConfigV2.mutable_sgd()->set_momentum(paramConfig.momentum());
+      }
       if (paramConfig.has_learning_rate()) {
-        constlr->set_learning_rate(paramConfig.learning_rate());
-      } else {
-        constlr->set_learning_rate(trainerConfig_.learning_rate());
+        switch (optimizerConfigV2.lr_policy()) {
+          case 0:
+            optimizerConfigV2.mutable_const_lr()->set_learning_rate(
+                paramConfig.learning_rate());
+            break;
+          case 1:
+            optimizerConfigV2.mutable_linear_lr()->set_learning_rate(
+                paramConfig.learning_rate());
+            break;
+        }
+      }
+      if (paramConfig.has_decay_rate()) {
+        switch (optimizerConfigV2.optimizer()) {
+          case 1:  // SGD
+            optimizerConfigV2.mutable_sgd()->set_decay(
+                paramConfig.decay_rate());
+            break;
+          case 2:  // Adadelta
+            optimizerConfigV2.mutable_adadelta()->set_decay(
+                paramConfig.decay_rate());
+            break;
+          case 3:  // Adagrad
+            optimizerConfigV2.mutable_adagrad()->set_decay(
+                paramConfig.decay_rate());
+            break;
+          case 4:  // Adam
+            optimizerConfigV2.mutable_adam()->set_decay(
+                paramConfig.decay_rate());
+            break;
         }
-      if (trainerConfig_.algorithm() == "sgd") {
-        optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
-        // FIXME: config all algorithms
-      } else {
-        optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
       }
-      std::string bytes = optimizeConfigV2.SerializeAsString();
+      // send param and config to pserver
+      std::string bytes = optimizerConfigV2.SerializeAsString();
       const char *array = bytes.data();
       int size = (int)bytes.size();
       paddle_init_param(

python/paddle/v2/optimizer.py

-import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
-import paddle.trainer_config_helpers.optimizers as v1_optimizers
+# Copyright (c) 2016 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.
 """
 Optimizers(update equation) for SGD method.
 
-TODO(zhihong) : create new optimizer with proto config, add new optimizer here
-
 TODO(yuyang18): Complete comments.
 """
 
+import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
+import paddle.trainer_config_helpers.optimizers as v1_optimizers
+from paddle.proto.OptimizerConfig_pb2 import OptimizerConfig
+
 __all__ = [
     'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
     'RMSProp', 'ModelAverage', 'L2Regularization'
@@ -70,7 +83,8 @@ class Optimizer(object):
                         gradient_machine.prefetch(in_args)
                         parameter_updater.getParametersRemote()
 
-        :param pserver_spec: pserver location, eg: localhost:3000
+        :param pserver_spec: pserver location, eg: localhost:3000, if use etcd,
+        pserver_spec should be the etcd endpoints, eg: http://localhost:2379
         :return: parameter_updater
         """
         if is_local:

python/paddle/v2/parameters.py

+# Copyright (c) 2016 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 numpy as np
 from paddle.proto.ParameterConfig_pb2 import ParameterConfig
 import paddle.trainer.config_parser as cp