Merge pull request #2413 from jacquesqiao/newparameterupdater

new parameterupdater use paddle pserver cclient of go

.travis.yml

@@ -50,6 +50,7 @@ before_install:
   # protobuf version.
   - pip install numpy wheel 'protobuf==3.1' sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit requests==2.9.2 LinkChecker
   - pip install rarfile
+  - eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
   - |
     function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
 script:

CMakeLists.txt

@@ -127,6 +127,7 @@ endif(WITH_GPU)
 add_subdirectory(proto)
 add_subdirectory(paddle)
 add_subdirectory(python)
+add_subdirectory(go/pserver/cclient)
 
 if(WITH_DOC)
     add_subdirectory(doc)

doc/design/cluster_train/remote_parameter_updater.md

+# Design Doc: Remote Parameter Updater for Cluster Train
+
+For an overview of distribute training, please refer to [distributed training design doc](README.md). In this design doc, we will discuss the parameter updater that will use parameter server cclient [The Client Library of Parameter Server Design Doc](pserver_client.md) to manage and update parameters.
+
+## Parameter Updater
+
+Parameter Updater is used by trainer to manage and update parameter, there are mainly two kind of parameter updater: local and remote, since this design is for cluster train, we will only discuss remote parameter updater here.
+
+### Remote Parameter Updater
+
+Remote Parameter Updater manage parameters through remote parameter server with the client that communicate with pserver([The Client Library of Parameter Server Design Doc](pserver_client.md))
+
+In PaddlePaddle Python V2 API, trainer is implemented in python, and the trainer will hold a instance of parameter updater and call it's functions directly. In this design, we will also expose the api of RemoteParameterUpdater to python with swig.
+
+#### Sparse Remote Parameter Updater
+
+Since we will only implement dense parameter management new, the mechanism for sparse parameter will be discussed in next stage.
+
+### Interface Design
+
+TBD

go/cmake/golang.cmake

@@ -17,7 +17,7 @@ function(GO_LIBRARY NAME BUILD_TYPE)
   endif()
 
   file(GLOB GO_SOURCE RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.go")
-  file(RELATIVE_PATH rel ${CMAKE_BINARY_DIR} ${CMAKE_CURRENT_SOURCE_DIR})
+  file(RELATIVE_PATH rel ${CMAKE_CURRENT_BINARY_DIR} ${CMAKE_CURRENT_SOURCE_DIR})
 
   # find Paddle directory.
   get_filename_component(PARENT_DIR ${CMAKE_CURRENT_SOURCE_DIR} DIRECTORY)
@@ -32,12 +32,14 @@ function(GO_LIBRARY NAME BUILD_TYPE)
   # will use the local changes in Paddle rather than checkout Paddle
   # in github.
   add_custom_target(copyPaddle
-    COMMAND ln -sf ${PADDLE_DIR} ${PADDLE_IN_GOPATH})
+    COMMAND rm -rf ${PADDLE_IN_GOPATH}/Paddle
+    COMMAND ln -sf ${PADDLE_DIR} ${PADDLE_IN_GOPATH}/Paddle)
   add_dependencies(goGet copyPaddle)
 
   add_custom_command(OUTPUT ${OUTPUT_DIR}/.timestamp
     COMMAND env GOPATH=${GOPATH} ${CMAKE_Go_COMPILER} build ${BUILD_MODE}
-    -o "${CMAKE_CURRENT_BINARY_DIR}/${LIB_NAME}"
+     -gcflags=-shared -asmflags=-shared -installsuffix=_shared -a
+     -o "${CMAKE_CURRENT_BINARY_DIR}/${LIB_NAME}"
     ${CMAKE_GO_FLAGS} ${GO_SOURCE}
     WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
 

go/pserver/cclient/CMakeLists.txt

@@ -9,5 +9,15 @@ project(cxx_go C Go)
 include(golang)
 include(flags)
 
-go_library(client STATIC)
+go_library(paddle_pserver_cclient STATIC)
+
+if(PROJ_ROOT)
+  add_custom_command(OUTPUT ${PROJ_ROOT}/paddle/trainer/libpaddle_pserver_cclient.a
+          COMMAND cp ${CMAKE_BINARY_DIR}/go/pserver/cclient/libpaddle_pserver_cclient.h ${PROJ_ROOT}/paddle/trainer/
+          COMMAND cp ${CMAKE_BINARY_DIR}/go/pserver/cclient/libpaddle_pserver_cclient.a ${PROJ_ROOT}/paddle/trainer/
+          WORKING_DIRECTORY ${PROJ_ROOT}/paddle
+          DEPENDS paddle_pserver_cclient)
+  add_custom_target(paddle_pserver_cclient_lib ALL DEPENDS ${PROJ_ROOT}/paddle/trainer/libpaddle_pserver_cclient.a)
+endif(PROJ_ROOT)
+
 add_subdirectory(test)

go/pserver/cclient/cclient.go

@@ -162,10 +162,10 @@ func paddle_finish_init_params(client C.paddle_pserver_client) C.int {
 }
 
 //export paddle_send_grads
-func paddle_send_grads(client C.paddle_pserver_client, grads *C.paddle_gradient, total C.int) C.int {
+func paddle_send_grads(client C.paddle_pserver_client, grads **C.paddle_gradient, total C.int) C.int {
 	var gs []pserver.Gradient
 	for i := 0; i < int(total); i++ {
-		grad := (*C.paddle_gradient)(unsafe.Pointer((uintptr(unsafe.Pointer(grads)) + uintptr(i)*unsafe.Sizeof(*grads))))
+		grad := *(**C.paddle_gradient)(unsafe.Pointer((uintptr(unsafe.Pointer(grads)) + uintptr(i)*unsafe.Sizeof(*grads))))
 		et := pserver.ElementType(grad.element_type)
 		name := C.GoString(grad.name)
 		content := cArrayToSlice(unsafe.Pointer(grad.content), int(grad.content_len))

go/pserver/cclient/test/CMakeLists.txt

 cmake_minimum_required(VERSION 3.0)
 
-include_directories(${CMAKE_BINARY_DIR})
-
 add_executable(main main.c)
-add_dependencies(main client)
+add_dependencies(main paddle_pserver_cclient)
+add_executable(test_cclient test_cclient.c)
+add_dependencies(test_cclient paddle_pserver_cclient)
 
 if(APPLE)
   set(CMAKE_EXE_LINKER_FLAGS "-framework CoreFoundation -framework Security")
 else()
   set(CMAKE_EXE_LINKER_FLAGS "-pthread")  
 endif()
-target_link_libraries(main ${CMAKE_BINARY_DIR}/libclient.a)
+
+if(PROJ_ROOT)
+  include_directories(${CMAKE_BINARY_DIR}/go/pserver/cclient/)
+  target_link_libraries(main ${CMAKE_BINARY_DIR}/go/pserver/cclient/libpaddle_pserver_cclient.a pthread)
+  target_link_libraries(test_cclient ${CMAKE_BINARY_DIR}/go/pserver/cclient/libpaddle_pserver_cclient.a pthread)
+else(PROJ_ROOT)
+  include_directories(${CMAKE_BINARY_DIR})
+  target_link_libraries(main ${CMAKE_BINARY_DIR}/libpaddle_pserver_cclient.a pthread)
+  target_link_libraries(test_cclient ${CMAKE_BINARY_DIR}/libpaddle_pserver_cclient.a pthread)
+endif(PROJ_ROOT)

go/pserver/cclient/test/main.c

 #include <stdio.h>
 
-#include "libclient.h"
+#include "libpaddle_pserver_cclient.h"
 
 // TODO(helin): Fix: gtest using cmake is not working, using this
 // hacky way for now.
@@ -11,10 +11,11 @@
 void sendGrads(paddle_pserver_client c) {
   unsigned char grad_a[2000] = {2};
   unsigned char grad_b[3000] = {3};
-  paddle_gradient grads[2] = {
-      {"param_a", PADDLE_ELEMENT_TYPE_FLOAT32, grad_a, 2000},
-      {"param_b", PADDLE_ELEMENT_TYPE_FLOAT32, grad_b, 3000}};
-
+  paddle_gradient grad1 = {
+      "param_a", PADDLE_ELEMENT_TYPE_FLOAT32, grad_a, 2000};
+  paddle_gradient grad2 = {
+      "param_b", PADDLE_ELEMENT_TYPE_FLOAT32, grad_b, 3000};
+  paddle_gradient* grads[2] = {&grad1, &grad2};
   if (paddle_send_grads(c, grads, 2)) {
     fail();
   }
@@ -77,7 +78,8 @@ retry:
     }
   }
 
-  for (int i = 0; i < 100; i++) {
+  int i;
+  for (i = 0; i < 100; i++) {
     sendGrads(c);
     getParams(c);
   }

go/pserver/cclient/test/test_cclient.c

+#include <stdio.h>
+#include <stdlib.h>
+
+#include "libpaddle_pserver_cclient.h"
+
+typedef float real;
+
+void fail() {
+  // TODO(helin): fix: gtest using cmake is not working, using this
+  // hacky way for now.
+  printf("test failed.\n");
+  exit(-1);
+}
+
+void print_parameter(paddle_gradient* param) {
+  if (param == NULL) {
+    printf("param is NULL!!\n");
+  } else {
+    printf("==== parameter ====\n");
+    printf("name: %s\n", param->name);
+    printf("content_len: %d\n", param->content_len);
+    printf("content_type: %d\n", param->element_type);
+    int i;
+    for (i = 0; i < param->content_len / (int)sizeof(real); ++i) {
+      printf("%f ", ((float*)param->content)[i]);
+    }
+    printf("\n\n");
+  }
+}
+
+int main() {
+  char addr[] = "localhost:3000";
+  paddle_pserver_client c = paddle_new_pserver_client(addr, 1);
+
+  char* names[] = {"param_a", "param_b"};
+
+retry:
+  printf("init parameter to pserver:\n");
+
+  real param_content1[] = {0.1, 0.2, 0.3};
+  real param_content2[] = {0.4, 0.5, 0.6};
+  paddle_parameter** params =
+      (paddle_parameter**)malloc(sizeof(paddle_parameter*) * 2);
+  params[0] = (paddle_parameter*)malloc(sizeof(paddle_parameter));
+  params[0]->name = names[0];
+  params[0]->content = (unsigned char*)param_content1;
+  params[0]->content_len = 3 * sizeof(real);
+  params[0]->element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+
+  params[1] = (paddle_parameter*)malloc(sizeof(paddle_parameter));
+  params[1]->name = names[1];
+  params[1]->content = (unsigned char*)param_content2;
+  params[1]->content_len = 3 * sizeof(real);
+  params[1]->element_type = PADDLE_ELEMENT_TYPE_INT32;
+
+  if (paddle_begin_init_params(c)) {
+    if (paddle_init_param(c, *params[0], NULL, 0) != 0) {
+      goto retry;
+    }
+    if (paddle_init_param(c, *params[1], NULL, 0) != 0) {
+      goto retry;
+    }
+    if (paddle_finish_init_params(c) != 0) {
+      goto retry;
+    }
+  } else {
+    fail();
+  }
+
+  printf("get inited parameters from pserver:\n");
+  // get parameters again by reusing the allocated parameter buffers.
+  if (paddle_get_params(c, params, 2) != 0) {
+    fail();
+  }
+  print_parameter(params[0]);
+  print_parameter(params[1]);
+
+  printf("send gradient to pserver:\n");
+  real gradient_content1[] = {0.01, 0.02, 0.03};
+  real gradinet_content2[] = {0.04, 0.05, 0.06};
+
+  paddle_gradient** grads =
+      (paddle_gradient**)malloc(sizeof(paddle_gradient*) * 2);
+  grads[0] = (paddle_gradient*)malloc(sizeof(paddle_gradient));
+  grads[0]->name = names[0];
+  grads[0]->content = (unsigned char*)gradient_content1;
+  grads[0]->content_len = 3 * sizeof(real);
+  grads[0]->element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+
+  grads[1] = (paddle_gradient*)malloc(sizeof(paddle_gradient));
+  grads[1]->name = names[1];
+  grads[1]->content = (unsigned char*)gradinet_content2;
+  grads[1]->content_len = 3 * sizeof(real);
+  grads[1]->element_type = PADDLE_ELEMENT_TYPE_INT32;
+
+  printf("print gradient sent to pserver:\n");
+  print_parameter(grads[0]);
+  print_parameter(grads[1]);
+
+  if (paddle_send_grads(c, grads, 2) != 0) {
+    fail();
+  }
+
+  printf("get updated parameters from pserver:\n");
+  // get parameters again by reusing the allocated parameter buffers.
+  if (paddle_get_params(c, params, 2) != 0) {
+    fail();
+  }
+  print_parameter(params[0]);
+  print_parameter(params[1]);
+
+  if (paddle_save_model(c, "/tmp/") != 0) {
+    fail();
+  }
+
+  return 0;
+}

go/pserver/cclient/test/test_mnist.py

+import paddle.v2 as paddle
+import gzip
+
+
+def softmax_regression(img):
+    predict = paddle.layer.fc(input=img,
+                              size=10,
+                              act=paddle.activation.Softmax())
+    return predict
+
+
+def multilayer_perceptron(img):
+    # The first fully-connected layer
+    hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu())
+    # The second fully-connected layer and the according activation function
+    hidden2 = paddle.layer.fc(input=hidden1,
+                              size=64,
+                              act=paddle.activation.Relu())
+    # The thrid fully-connected layer, note that the hidden size should be 10,
+    # which is the number of unique digits
+    predict = paddle.layer.fc(input=hidden2,
+                              size=10,
+                              act=paddle.activation.Softmax())
+    return predict
+
+
+def convolutional_neural_network(img):
+    # first conv layer
+    conv_pool_1 = paddle.networks.simple_img_conv_pool(
+        input=img,
+        filter_size=5,
+        num_filters=20,
+        num_channel=1,
+        pool_size=2,
+        pool_stride=2,
+        act=paddle.activation.Tanh())
+    # second conv layer
+    conv_pool_2 = paddle.networks.simple_img_conv_pool(
+        input=conv_pool_1,
+        filter_size=5,
+        num_filters=50,
+        num_channel=20,
+        pool_size=2,
+        pool_stride=2,
+        act=paddle.activation.Tanh())
+    # The first fully-connected layer
+    fc1 = paddle.layer.fc(input=conv_pool_2,
+                          size=128,
+                          act=paddle.activation.Tanh())
+    # The softmax layer, note that the hidden size should be 10,
+    # which is the number of unique digits
+    predict = paddle.layer.fc(input=fc1,
+                              size=10,
+                              act=paddle.activation.Softmax())
+    return predict
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    # define network topology
+    images = paddle.layer.data(
+        name='pixel', type=paddle.data_type.dense_vector(784))
+    label = paddle.layer.data(
+        name='label', type=paddle.data_type.integer_value(10))
+
+    # Here we can build the prediction network in different ways. Please
+    # choose one by uncomment corresponding line.
+    predict = softmax_regression(images)
+    #predict = multilayer_perceptron(images)
+    #predict = convolutional_neural_network(images)
+
+    cost = paddle.layer.classification_cost(input=predict, label=label)
+    parameters = paddle.parameters.create(cost)
+
+    optimizer = paddle.optimizer.Momentum(
+        learning_rate=0.1 / 128.0,
+        momentum=0.9,
+        regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
+
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer,
+                                 is_local=False,
+                                 pserver_spec="localhost:3000")
+
+    lists = []
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 1000 == 0:
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+
+        elif isinstance(event, paddle.event.EndPass):
+            result = trainer.test(reader=paddle.batch(
+                paddle.dataset.mnist.test(), batch_size=128))
+            print "Test with Pass %d, Cost %f, %s\n" % (
+                event.pass_id, result.cost, result.metrics)
+            lists.append((event.pass_id, result.cost,
+                          result.metrics['classification_error_evaluator']))
+
+    trainer.train(
+        reader=paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.mnist.train(), buf_size=8192),
+            batch_size=128),
+        event_handler=event_handler,
+        num_passes=100)
+
+    # find the best pass
+    best = sorted(lists, key=lambda list: float(list[1]))[0]
+    print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
+    print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
+
+    test_creator = paddle.dataset.mnist.test()
+    test_data = []
+    for item in test_creator():
+        test_data.append((item[0], ))
+        if len(test_data) == 100:
+            break
+
+    # output is a softmax layer. It returns probabilities.
+    # Shape should be (100, 10)
+    probs = paddle.infer(
+        output_layer=predict, parameters=parameters, input=test_data)
+    print probs.shape
+
+
+if __name__ == '__main__':
+    main()

go/pserver/cclient/test/test_train.py

+import paddle.v2 as paddle
+import paddle.v2.dataset.uci_housing as uci_housing
+
+
+def main():
+    # init
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    # 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'),
+                                size=1,
+                                act=paddle.activation.Linear(),
+                                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)
+
+    # create parameters
+    parameters = paddle.parameters.create(cost)
+
+    # create optimizer
+    optimizer = paddle.optimizer.Momentum(momentum=0)
+
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer,
+                                 is_local=False,
+                                 pserver_spec="localhost:3000")
+
+    # event_handler to print training and testing info
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                print "Pass %d, Batch %d, Cost %f" % (
+                    event.pass_id, event.batch_id, event.cost)
+
+        if isinstance(event, paddle.event.EndPass):
+            if (event.pass_id + 1) % 10 == 0:
+                result = trainer.test(
+                    reader=paddle.batch(
+                        uci_housing.test(), batch_size=2),
+                    feeding={'x': 0,
+                             'y': 1})
+                print "Test %d, %.2f" % (event.pass_id, result.cost)
+
+    # training
+    trainer.train(
+        reader=paddle.batch(
+            paddle.reader.shuffle(
+                uci_housing.train(), buf_size=500),
+            batch_size=2),
+        feeding={'x': 0,
+                 'y': 1},
+        event_handler=event_handler,
+        num_passes=30)
+
+
+if __name__ == '__main__':
+    main()

go/pserver/optimizer.c

@@ -32,7 +32,13 @@ int update_SGD(void* optimizer,
                const void* gradient,
                int num_bytes) {
   SGD_optimizer* o = (SGD_optimizer*)optimizer;
-  // TODO
+  float* parameter = (float*)buffer;
+  float* grad = (float*)gradient;
+
+  int i;
+  for (i = 0; i < num_bytes / sizeof(float); ++i) {
+    parameter[i] -= o->learning_rate * grad[i];
+  }
   return 0;
 }
 

go/pserver/service.go

@@ -51,7 +51,7 @@ type Service struct {
 
 // NewService creates a new service.
 func NewService() *Service {
-	s := &Service{opt: newOptimizer(sgd, 0.01)}
+	s := &Service{opt: newOptimizer(sgd, 0.005)}
 	s.paramMap = make(map[string]Parameter)
 	s.initialized = make(chan struct{})
 	return s

paddle/api/CMakeLists.txt

@@ -16,7 +16,7 @@ set(API_HEADER
     Internal.h)
 
 add_library(paddle_api STATIC ${API_SOURCES})
-add_dependencies(paddle_api gen_proto_cpp)
+add_dependencies(paddle_api gen_proto_cpp paddle_pserver_cclient_lib)
 
 INCLUDE(${SWIG_USE_FILE})
 INCLUDE_DIRECTORIES(${PROJ_ROOT}/paddle)
@@ -45,7 +45,7 @@ SET(SWIG_MODULE_swig_paddle_EXTRA_DEPS
 )
 
 IF(APPLE)
-    SET(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load")
+    SET(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load -framework CoreFoundation -framework Security")
 ELSE(APPLE)
     SET(START_GROUP "-Xlinker -start-group")
     SET(END_GROUP "-Xlinker -end-group")

paddle/api/Paddle.i

@@ -179,6 +179,7 @@ namespace std {
 %newobject ParameterOptimizer::needSpecialTraversal;
 %newobject ParameterUpdater::createLocalUpdater;
 %newobject ParameterUpdater::createRemoteUpdater;
+%newobject ParameterUpdater::createNewRemoteUpdater;
 
 %feature("director") UpdateCallback;
 %feature("autodoc", 1); // To generate method stub, for code hint in ide

paddle/api/PaddleAPI.h

@@ -841,6 +841,8 @@ public:
   static ParameterUpdater* createRemoteUpdater(OptimizationConfig* config,
                                                int passCount,
                                                bool useSparseUpdater);
+  static ParameterUpdater* createNewRemoteUpdater(
+      OptimizationConfig* config, const std::string pserverSpec);
   ~ParameterUpdater();
 
   /**

paddle/api/ParameterUpdater.cpp

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "PaddleAPI.h"
 
 #include "PaddleAPIPrivate.h"
+#include "paddle/trainer/NewRemoteParameterUpdater.h"
 #include "paddle/trainer/RemoteParameterUpdater.h"
 #include "paddle/trainer/ThreadParameterUpdater.h"
 
@@ -28,6 +29,14 @@ ParameterUpdater *ParameterUpdater::createLocalUpdater(
   return updater;
 }
 
+ParameterUpdater *ParameterUpdater::createNewRemoteUpdater(
+    OptimizationConfig *config, const std::string pserverSpec) {
+  auto updater = new ParameterUpdater();
+  updater->m->updater.reset(new paddle::NewRemoteParameterUpdater(
+      config->m->getConfig(), pserverSpec));
+  return updater;
+}
+
 ParameterUpdater *ParameterUpdater::createRemoteUpdater(
     OptimizationConfig *config, int passCount, bool useSparseUpdater) {
   auto updater = new ParameterUpdater();

paddle/trainer/CMakeLists.txt

@@ -4,6 +4,7 @@ set(TRAINER_SOURCES
         ParameterUpdater.cpp
         ParamUtil.cpp
         RemoteParameterUpdater.cpp
+        NewRemoteParameterUpdater.cpp
         Tester.cpp
         Trainer.cpp
         TrainerInternal.cpp
@@ -16,6 +17,7 @@ set(TRAINER_HEADERS
         ParameterUpdater.h
         ParamUtil.h
         RemoteParameterUpdater.h
+        NewRemoteParameterUpdater.h
         Tester.h
         TesterConfig.h
         Trainer.h
@@ -32,7 +34,7 @@ add_style_check_target(paddle_trainer_lib
 add_style_check_target(paddle_trainer_lib
     ${TRAINER_HEADERS})
 add_dependencies(paddle_trainer_lib
-    gen_proto_cpp)
+    gen_proto_cpp paddle_pserver_cclient_lib)
 
 macro(add_paddle_exe TARGET_NAME)
   add_executable(${TARGET_NAME} ${ARGN})
@@ -56,3 +58,10 @@ install(TARGETS paddle_trainer paddle_merge_model
 
 set_target_properties(paddle_trainer PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
 set_target_properties(paddle_merge_model PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
+
+if(APPLE)
+  set(CMAKE_EXE_LINKER_FLAGS "-framework CoreFoundation -framework Security")
+endif()
+
+target_link_libraries(paddle_trainer ${CMAKE_CURRENT_SOURCE_DIR}/libpaddle_pserver_cclient.a)
+target_link_libraries(paddle_trainer_lib ${CMAKE_CURRENT_SOURCE_DIR}/libpaddle_pserver_cclient.a)

paddle/trainer/NewRemoteParameterUpdater.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "NewRemoteParameterUpdater.h"
+#include "Trainer.h"
+#include "paddle/utils/Stat.h"
+
+DECLARE_int32(trainer_id);
+DECLARE_string(save_dir);
+
+namespace paddle {
+NewRemoteParameterUpdater::NewRemoteParameterUpdater(
+    const OptimizationConfig &config, const std::string pserverSpec)
+    : parameterClient_(-1),
+      newParameters_(nullptr),
+      newGradients_(nullptr),
+      pserverSpec_(pserverSpec) {}
+
+void NewRemoteParameterUpdater::init(
+    const std::vector<ParameterPtr> &parameters) {
+  ParameterUpdater::init(parameters);
+
+  for (auto &para : parameters_) {
+    para->getBuf(PARAMETER_VALUE)->zeroMem();
+    para->getBuf(PARAMETER_GRADIENT)->zeroMem();
+  }
+
+  // create parameter server client.
+  parameterClient_ = paddle_new_pserver_client((char *)pserverSpec_.c_str(),
+                                               FLAGS_trainer_id == 0);
+
+  // init new parameter and gradient.
+  newParameters_ = initNewParameter(PARAMETER_VALUE);
+  newGradients_ = initNewParameter(PARAMETER_GRADIENT);
+
+  // init parameter, one trainer will get the opportunity to int parameter and
+  // send them to parameter server. Others will get the initialized parameter
+  // from parameter server
+  if (paddle_begin_init_params(parameterClient_)) {
+    LOG(INFO) << "paddle_begin_init_params start";
+    for (int i = 0; i < parameterSize(); ++i) {
+      auto paramConfig = parameters_[i]->getConfig();
+      std::string bytes = paramConfig.SerializeAsString();
+      const char *array = bytes.data();
+      int size = (int)bytes.size();
+      paddle_init_param(
+          parameterClient_, *newParameters_[i], (void *)array, size);
+    }
+    paddle_finish_init_params(parameterClient_);
+    LOG(INFO) << "paddle_begin_init_params done";
+  } else {
+    paddle_get_params(parameterClient_, newParameters_, parameterSize());
+  }
+
+  LOG(INFO) << "NewRemoteParameterUpdater initialized";
+}
+
+void NewRemoteParameterUpdater::updateImpl(Parameter *para) {}
+
+void NewRemoteParameterUpdater::finishBatch(real cost) {
+  // send gradient to parameter server.
+  paddle_send_grads(parameterClient_, newGradients_, parameterSize());
+  // get the updated parameter from parameterClient.
+  paddle_get_params(parameterClient_, newParameters_, parameterSize());
+
+  // clear gradient after update parameter.
+  for (auto &para : parameters_) {
+    para->getBuf(PARAMETER_GRADIENT)->zeroMem();
+  }
+}
+
+void NewRemoteParameterUpdater::startPass() {}
+
+bool NewRemoteParameterUpdater::finishPass() { return true; }
+}

paddle/trainer/NewRemoteParameterUpdater.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+
+#include <functional>
+#include <thread>
+#include "ParameterUpdater.h"
+#include "libpaddle_pserver_cclient.h"
+#include "paddle/pserver/ParameterClient2.h"
+#include "paddle/utils/Queue.h"
+#include "paddle/utils/Util.h"
+
+namespace paddle {
+
+/**
+ * New remote parameter updater for dense parameters that use cclient of go.
+ */
+class NewRemoteParameterUpdater : public ParameterUpdater {
+public:
+  NewRemoteParameterUpdater(const OptimizationConfig& config,
+                            const std::string pserverSpec);
+  ~NewRemoteParameterUpdater() {
+    releaseNewParameter(newParameters_);
+    releaseNewParameter(newGradients_);
+    if (parameterClient_ >= 0) paddle_pserver_client_release(parameterClient_);
+  }
+
+  /**
+   * initialize the internal parameter client and itself.
+   */
+  virtual void init(const std::vector<ParameterPtr>& parameters);
+  /**
+   * @brief start batch
+   *
+   * @note  one batch training exhibits stateful feature to help
+   *        to do performance tuning, sgd optimization if necessary.
+   */
+  virtual PassType startBatch(int64_t batchSize) { return PASS_TRAIN; }
+
+  /**
+   * send parameters to pservers and get returned parameters
+   * from all pservers if necessary.
+   */
+  virtual void finishBatch(real cost);
+  virtual void startPass();
+  virtual bool finishPass();
+
+protected:
+  /**
+   * work need to do after finishBatch
+   */
+  virtual void updateImpl(Parameter* para);
+
+private:
+  int parameterSize() { return (int)parameters_.size(); }
+
+  /**
+   * init parameter of go paddle pserver cclient.
+   * @param new_params
+   * @param type
+   */
+  paddle_parameter** initNewParameter(ParameterType type) {
+    paddle_parameter** new_params =
+        (paddle_parameter**)malloc(sizeof(paddle_parameter*) * parameterSize());
+    for (int i = 0; i < parameterSize(); ++i) {
+      new_params[i] = (paddle_parameter*)malloc(sizeof(paddle_parameter));
+      memset(new_params[i], 0, sizeof(paddle_parameter));
+    }
+
+    for (int i = 0; i < parameterSize(); ++i) {
+      ParameterPtr param = parameters_[i];
+      new_params[i]->element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+      new_params[i]->name = (char*)param->getName().c_str();
+      new_params[i]->content =
+          (unsigned char*)(param->getBuf(type).get()->getData());
+      new_params[i]->content_len =
+          (int)param->getBuf(type).get()->getSize() * sizeof(real);
+    }
+    return new_params;
+  }
+
+  void releaseNewParameter(paddle_parameter** newParams) {
+    if (newParams != nullptr) {
+      for (int i = 0; i < parameterSize(); ++i) {
+        free(newParams[i]);
+      }
+      free(newParams);
+    }
+  }
+
+protected:
+  /// internal parameter client object for exchanging data with pserver
+  paddle_pserver_client parameterClient_;
+  /// the parameters for new pserver client
+  paddle_parameter** newParameters_;
+  /// the gradinets for new pserver client
+  paddle_parameter** newGradients_;
+  /// the specification of parameter server "host1:port,host1:port"
+  std::string pserverSpec_;
+};
+
+}  // namespace paddle

python/paddle/v2/optimizer.py

@@ -45,7 +45,12 @@ class Optimizer(object):
         return swig_api.ParameterUpdater.createRemoteUpdater(
             self.__opt_conf__, pass_num, use_sparse_updater)
 
-    def create_updater(self, is_local, num_passes, use_sparse_updater):
+    def __create_new_remote_updater__(self, pserver_spec):
+        return swig_api.ParameterUpdater.createNewRemoteUpdater(
+            self.__opt_conf__, pserver_spec)
+
+    def create_updater(self, is_local, num_passes, use_sparse_updater,
+                       pserver_spec):
         """
         create proper parameter_updater by configuration.
         :param is_local: create local or remote parameter updater
@@ -64,8 +69,12 @@ class Optimizer(object):
         if is_local:
             parameter_updater = self.__create_local_updater__()
         else:
-            parameter_updater = self.__create_remote_updater__(
-                num_passes, use_sparse_updater)
+            if pserver_spec is None:
+                parameter_updater = self.__create_remote_updater__(
+                    num_passes, use_sparse_updater)
+            else:
+                parameter_updater = self.__create_new_remote_updater__(
+                    pserver_spec)
         return parameter_updater
 
 

python/paddle/v2/trainer.py

@@ -49,7 +49,8 @@ class SGD(object):
                  parameters,
                  update_equation,
                  extra_layers=None,
-                 is_local=True):
+                 is_local=True,
+                 pserver_spec=None):
 
         if not isinstance(parameters, v2_parameters.Parameters):
             raise TypeError('parameters should be parameters')
@@ -63,6 +64,7 @@ class SGD(object):
         self.__parameters__ = parameters
         self.__topology_in_proto__ = topology.proto()
         self.__is_local__ = is_local
+        self.__pserver_spec__ = pserver_spec
 
         self.__use_sparse_updater__ = self.__topology__.use_sparse_updater()
         # # In local mode, disable sparse_remote_update.
@@ -126,7 +128,8 @@ class SGD(object):
         __check_train_args__(**locals())
 
         self.__parameter_updater__ = self.__optimizer__.create_updater(
-            self.__is_local__, num_passes, self.__use_sparse_updater__)
+            self.__is_local__, num_passes, self.__use_sparse_updater__,
+            self.__pserver_spec__)
         self.__parameter_updater__.init(self.__gradient_machine__)
 
         self.__gradient_machine__.start()