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

@@ -0,0 +1,21 @@
+# 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

@@ -164,10 +164,10 @@ func paddle_finish_init_params(client C.client) C.int {
 }
 
 //export paddle_send_grads
-func paddle_send_grads(client C.client, grads *C.paddle_gradient, total C.int) C.int {
+func paddle_send_grads(client C.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))
@@ -204,12 +204,14 @@ func paddle_get_params(client C.client, names **C.char, dst **C.paddle_parameter
 		}
 
 		p := ps[i]
-		param := *(**C.paddle_parameter)(unsafe.Pointer((uintptr(unsafe.Pointer(dst)) + uintptr(i)*unsafe.Sizeof(*dst))))
+		paramPtr := (**C.paddle_parameter)(unsafe.Pointer((uintptr(unsafe.Pointer(dst)) + uintptr(i)*unsafe.Sizeof(*dst))))
+		param := *paramPtr
 		nameReady := false
 		contentAllocated := false
 
 		if unsafe.Pointer(param) == nullPtr {
-			param = (*C.paddle_parameter)(C.calloc(1, C.size_t(unsafe.Sizeof(*param))))
+			*paramPtr = (*C.paddle_parameter)(C.calloc(1, C.size_t(unsafe.Sizeof(*param))))
+			param = *paramPtr
 		} else {
 			if unsafe.Pointer(param.name) != nullPtr {
 				if n := C.GoString(param.name); n != p.Name {

go/pserver/cclient/test/CMakeLists.txt

@@ -1,11 +1,20 @@
 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")
 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

@@ -1,6 +1,6 @@
 #include <stdio.h>
 
-#include "libclient.h"
+#include "libpaddle_pserver_cclient.h"
 
 void fail() {
   // TODO(helin): fix: gtest using cmake is not working, using this
@@ -40,31 +40,43 @@ int main() {
   }
 
   unsigned char content[] = {0x00, 0x11, 0x22};
-  paddle_gradient grads[2] = {
-      {"param_a", PADDLE_ELEMENT_TYPE_INT32, content, 3},
-      {"param_b", PADDLE_ELEMENT_TYPE_FLOAT32, content, 3}};
+  paddle_gradient** grads =
+      (paddle_gradient**)malloc(sizeof(paddle_gradient*) * 2);
+  grads[0] = (paddle_gradient*)malloc(sizeof(paddle_gradient));
+  grads[0]->name = "param_a";
+  grads[0]->content = content;
+  grads[0]->content_len = 3;
+  grads[0]->element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
 
-  if (!paddle_send_grads(c, grads, 2)) {
+  grads[1] = (paddle_gradient*)malloc(sizeof(paddle_gradient));
+  grads[1]->name = "param_b";
+  grads[1]->content = content;
+  grads[1]->content_len = 3;
+  grads[1]->element_type = PADDLE_ELEMENT_TYPE_INT32;
+
+  if (paddle_send_grads(c, grads, 2) != 0) {
     fail();
   }
 
   paddle_parameter* params[2] = {NULL, NULL};
   char* names[] = {"param_a", "param_b"};
-  if (!paddle_get_params(c, names, params, 2)) {
+  if (paddle_get_params(c, names, params, 2) != 0) {
     fail();
   }
 
   // get parameters again by reusing the allocated parameter buffers.
-  if (!paddle_get_params(c, names, params, 2)) {
+  if (paddle_get_params(c, names, params, 2) != 0) {
     fail();
   }
 
   paddle_release_param(params[0]);
   paddle_release_param(params[1]);
 
-  if (!paddle_save_model(c, "/tmp/")) {
+  if (paddle_save_model(c, "/tmp/") != 0) {
     fail();
   }
 
+  printf("test success!\n");
+
   return 0;
 }

go/pserver/cclient/test/mnist_test.py

@@ -0,0 +1,131 @@
+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()