add optimizer factory (#230)

* add optimizer factory

* add file:optimizer_factory

* copyright

* remove dependence on algorithm.h

deepes/demo/paddle/cartpole_solver_parallel.cc

@@ -16,7 +16,6 @@
 #include <glog/logging.h>
 #include <omp.h>
 #include "cartpole.h"
-#include "gaussian_sampling.h"
 #include "es_agent.h"
 #include "paddle_api.h"
 
@@ -101,7 +100,7 @@ int main(int argc, char* argv[]) {
   noisy_keys.resize(ITER);
 
   omp_set_num_threads(10);
-  for (int epoch = 0; epoch < 10000; ++epoch) {
+  for (int epoch = 0; epoch < 1000; ++epoch) {
 #pragma omp parallel for schedule(dynamic, 1)
     for (int i = 0; i < ITER; ++i) {
       std::shared_ptr<ESAgent> sampling_agent = sampling_agents[i];

deepes/include/optimizer_factory.h

+//   Copyright (c) 2020 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.
+
+#ifndef OPTIMIZER_FACTORY_H
+#define OPTIMIZER_FACTORY_H
+
+#include <algorithm>
+#include <memory>
+#include "optimizer.h"
+#include "sgd_optimizer.h"
+#include "adam_optimizer.h"
+#include "deepes.pb.h"
+
+namespace DeepES{
+/* @brief: create an optimizer according to the configuration"
+ * @args: 
+ *    config: configuration for the optimizer
+ * 
+ */
+std::shared_ptr<Optimizer> create_optimizer(const OptimizerConfig& optimizer_config);
+
+}//namespace
+
+#endif

deepes/include/paddle/es_agent.h

@@ -16,7 +16,7 @@
 #define DEEPES_PADDLE_ES_AGENT_H_
 
 #include "paddle_api.h"
-#include "optimizer.h"
+#include "optimizer_factory.h"
 #include "utils.h"
 #include "gaussian_sampling.h"
 #include "deepes.pb.h"

deepes/include/torch/es_agent.h

@@ -17,7 +17,7 @@
 
 #include <memory>
 #include <string>
-#include "optimizer.h"
+#include "optimizer_factory.h"
 #include "utils.h"
 #include "gaussian_sampling.h"
 #include "deepes.pb.h"
@@ -49,7 +49,7 @@ public:
     load_proto_conf(config_path, *_config);
     _sampling_method = std::make_shared<GaussianSampling>();
     _sampling_method->load_config(*_config);
-    _optimizer = std::make_shared<SGDOptimizer>(_config->optimizer().base_lr());
+    _optimizer = create_optimizer(_config->optimizer());
     // Origin agent can't be used to sample, so keep it same with _model for evaluating.
     _sampling_model = model;
     _param_size = _calculate_param_size();
@@ -125,7 +125,7 @@ public:
     for (auto& param: params) {
       torch::Tensor tensor = param.value().view({-1});
       auto tensor_a = tensor.accessor<float,1>();
-      _optimizer->update(tensor_a, _neg_gradients+counter, tensor.size(0));
+      _optimizer->update(tensor_a, _neg_gradients+counter, tensor.size(0), param.key());
       counter += tensor.size(0);
     }
 

deepes/scripts/build.sh

@@ -14,7 +14,7 @@ if [ $1 = "paddle" ]; then
   fi
   
   # Initialization model
-  if [ ! -d ./demo/paddle/cartpole_init_model]; then
+  if [ ! -d ./demo/paddle/cartpole_init_model ]; then
     unzip ./demo/paddle/cartpole_init_model.zip -d ./demo/paddle/
   fi
 

deepes/src/optimizer_factory.cc

+//   Copyright (c) 2020 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.
+
+#include "optimizer_factory.h"
+
+namespace DeepES{
+
+std::shared_ptr<Optimizer> create_optimizer(const OptimizerConfig& optimizer_config) {
+  std::shared_ptr<Optimizer> optimizer;
+  std::string opt_type = optimizer_config.type();
+  std::transform(opt_type.begin(), opt_type.end(), opt_type.begin(), ::tolower);
+  if (opt_type == "sgd") {
+    optimizer = std::make_shared<SGDOptimizer>(optimizer_config.base_lr(), \
+                                                optimizer_config.momentum());
+  }else if (opt_type == "adam") {
+    optimizer = std::make_shared<AdamOptimizer>(optimizer_config.base_lr(), \
+                                                  optimizer_config.beta1(), \
+                                                  optimizer_config.beta2(), \
+                                                  optimizer_config.epsilon());
+  }else {
+    // TODO: NotImplementedError
+  }
+  return optimizer;
+}
+
+}//namespace

deepes/src/paddle/es_agent.cc

@@ -12,7 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <vector>
 #include "es_agent.h"
 
 namespace DeepES {
@@ -50,7 +49,7 @@ ESAgent::ESAgent(
   _sampling_method = std::make_shared<GaussianSampling>();
   _sampling_method->load_config(*_config);
 
-  _optimizer = std::make_shared<SGDOptimizer>(_config->optimizer().base_lr());
+  _optimizer = create_optimizer(_config->optimizer());
 
   _param_names = _predictor->GetParamNames();
   _param_size = _calculate_param_size();
@@ -108,7 +107,7 @@ bool ESAgent::update(
     std::unique_ptr<Tensor> tensor = _predictor->GetMutableTensor(param_name);
     float* tensor_data = tensor->mutable_data<float>();
     int64_t tensor_size = ShapeProduction(tensor->shape());
-    _optimizer->update(tensor_data, _neg_gradients + counter, tensor_size);
+    _optimizer->update(tensor_data, _neg_gradients + counter, tensor_size, param_name);
     counter += tensor_size;
   }
   return true;