Merge branch 'develop' into feature/test_w2v_parallel.do

paddle/framework/CMakeLists.txt

@@ -26,7 +26,7 @@ nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
 
 cc_test(variable_test SRCS variable_test.cc)
 
-cc_library(threadpool SRCS threadpool.cc)
+cc_library(threadpool SRCS threadpool.cc DEPS enforce)
 cc_test(threadpool_test SRCS threadpool_test.cc DEPS threadpool)
 
 cc_library(scope SRCS scope.cc DEPS glog threadpool)
@@ -98,3 +98,5 @@ if(NOT WITH_C_API AND WITH_FLUID)
   install(FILES ${CMAKE_CURRENT_BINARY_DIR}/framework.pb.h DESTINATION include/paddle/framework)
   install(FILES details/cow_ptr.h details/op_registry.h DESTINATION include/paddle/framework/details)
 endif()
+
+cc_test(channel_test SRCS channel_test.cc)

paddle/framework/channel.h

@@ -13,75 +13,52 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include <condition_variable>
-#include <mutex>
-#include <queue>
+
+#include <stddef.h>  // for size_t
 
 namespace paddle {
 namespace framework {
 
+// Channel is the abstract class of buffered and un-buffered channels.
 template <typename T>
 class Channel {
  public:
-  explicit Channel(std::size_t capacity) : capacity_(capacity) {}
-
-  void Send(T* channel_element) {
-    std::unique_lock<std::mutex> lock(mu_);
-
-    if (IsBounded()) {
-      full_cond_var_.wait(lock, [this]() {
-        bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true;
-        return capacity_valid;
-      });
-    }
-    channel_.push_back(std::move(*channel_element));
-
-    lock.unlock();
-    empty_cond_var_.notify_one();
-  }
+  virtual void Send(T*) = 0;
+  virtual void Receive(T*) = 0;
+  virtual size_t Cap() = 0;
 
-  T* Receive() {
-    std::unique_lock<std::mutex> lock(mu_);
-    empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
-
-    T* channel_element = std::move(channel_.front());
-    channel_.pop_front();
-
-    NotifyAllSenders(&lock);
-    return channel_element;
-  }
-
-  size_t Size() {
-    std::unique_lock<std::mutex> lock(mu_);
-    return channel_.size();
-  }
+  // Don't delete channels; instead, call Channel::Close.
+ protected:
+  virtual ~Channel() {}
+};
 
-  void Clear() {
-    std::unique_lock<std::mutex> lock(mu_);
-    channel_.clear();
+// Forward declaration of channel implementations.
+namespace details {
+template <typename T>
+class Buffered;
+template <typename T>
+class UnBuffered;
+}  // namespace details
 
-    NotifyAllSenders(&lock);
+template <typename T>
+Channel<T>* MakeChannel(size_t buffer_size) {
+  if (buffer_size > 0) {
+    return new details::Buffered<T>(buffer_size);
   }
+  return new details::UnBuffered<T>();
+}
 
- private:
-  std::size_t capacity_;
-  std::mutex mu_;
-  std::condition_variable empty_cond_var_;
-  std::condition_variable full_cond_var_;
-  std::deque<T> channel_;
-
- private:
-  void NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
-    if (IsBounded()) {
-      lock->unlock();
-      full_cond_var_.notify_one();
-    }
+template <typename T>
+void CloseChannel(Channel<T>* ch) {
+  if (ch->Cap() > 0) {
+    delete dynamic_cast<details::Buffered<T>*>(ch);
+  } else {
+    delete dynamic_cast<details::UnBuffered<T>*>(ch);
   }
+}
 
-  bool IsBounded() const { return capacity_ > 0; }
-
-  bool IsCapacityFull() const { return channel_.size() >= capacity_; }
-};
-
-}  // namespace operator
+}  // namespace framework
 }  // namespace paddle
+
+#include "paddle/framework/details/buffered_channel.h"
+#include "paddle/framework/details/unbuffered_channel.h"

paddle/framework/channel_test.cc

+/* 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 "paddle/framework/channel.h"
+
+#include "gtest/gtest.h"
+
+TEST(Channel, MakeAndClose) {
+  using paddle::framework::Channel;
+  using paddle::framework::MakeChannel;
+  using paddle::framework::CloseChannel;
+
+  Channel<int>* ch = MakeChannel<int>(10);
+  CloseChannel(ch);
+}

paddle/framework/details/buffered_channel.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 <condition_variable>
+#include <deque>
+#include <mutex>
+
+#include "paddle/framework/channel.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+template <typename T>
+class Buffered : public paddle::framework::Channel<T> {
+  friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
+  friend void paddle::framework::CloseChannel<T>(Channel<T>*);
+
+ public:
+  virtual void Send(T*);
+  virtual void Receive(T*);
+  virtual size_t Cap() { return cap_; }
+
+ private:
+  size_t cap_;
+  std::mutex mu_;
+  std::condition_variable empty_cond_var_;
+  std::condition_variable full_cond_var_;
+  std::deque<T> channel_;
+
+  Buffered(size_t cap) : cap_(cap) {}
+  virtual ~Buffered();
+
+  void NotifyAllSenders(std::unique_lock<std::mutex>*);
+};
+
+template <typename T>
+void Buffered<T>::Send(T* item) {
+  std::unique_lock<std::mutex> lock(mu_);
+  full_cond_var_.wait(lock, [this]() { return channel_.size() < cap_; });
+  channel_.push_back(std::move(*item));
+  lock.unlock();
+  empty_cond_var_.notify_one();
+}
+
+template <typename T>
+void Buffered<T>::Receive(T* item) {
+  std::unique_lock<std::mutex> lock(mu_);
+  empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
+  *item = std::move(channel_.front());
+  channel_.pop_front();
+  NotifyAllSenders(&lock);
+}
+
+template <typename T>
+Buffered<T>::~Buffered() {
+  std::unique_lock<std::mutex> lock(mu_);
+  channel_.clear();
+  NotifyAllSenders(&lock);
+}
+
+template <typename T>
+void Buffered<T>::NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
+  lock->unlock();
+  full_cond_var_.notify_one();
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/framework/details/unbuffered_channel.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 <condition_variable>
+#include <deque>
+#include <mutex>
+
+#include "paddle/framework/channel.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+template <typename T>
+class UnBuffered : public paddle::framework::Channel<T> {
+  friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
+  friend void paddle::framework::CloseChannel<T>(Channel<T>*);
+
+ public:
+  virtual void Send(T*);
+  virtual void Receive(T*);
+  virtual size_t Cap() { return 0; }
+
+ private:
+  UnBuffered() {}
+  virtual ~UnBuffered();
+};
+
+template <typename T>
+void UnBuffered<T>::Send(T* channel_element) {}
+
+template <typename T>
+void UnBuffered<T>::Receive(T*) {}
+
+template <typename T>
+UnBuffered<T>::~UnBuffered() {}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/framework/prune.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <algorithm>
 #include <set>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include <glog/logging.h>
@@ -102,6 +103,32 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
       *op_field->Add() = input.blocks(block_id).ops(i);
     }
   }
+
+  // remove the VarDescs in BlockDesc that are not referenced in
+  // the pruned OpDescs
+  std::unordered_map<std::string, proto::VarDesc> var_map;
+  auto* var_field = output->mutable_blocks(block_id)->mutable_vars();
+  for (const auto& var : *var_field) {
+    var_map[var.name()] = var;
+  }
+
+  var_field->Clear();
+  for (const auto& op : *op_field) {
+    // add VarDescs of all input arguments for each OpDesc
+    auto& input_field = op.inputs();
+    for (auto& input_var : input_field) {
+      for (auto& arg : input_var.arguments()) {
+        *var_field->Add() = var_map[arg];
+      }
+    }
+    // add VarDescs of all output arguments for each OpDesc
+    auto& output_field = op.outputs();
+    for (auto& output_var : output_field) {
+      for (auto& arg : output_var.arguments()) {
+        *var_field->Add() = var_map[arg];
+      }
+    }
+  }
 }
 
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies

paddle/framework/threadpool.cc

@@ -14,6 +14,8 @@
 
 #include "paddle/framework/threadpool.h"
 
+#include "paddle/platform/enforce.h"
+
 namespace paddle {
 namespace framework {
 

paddle/framework/threadpool.h

@@ -22,7 +22,7 @@ limitations under the License. */
 #include <thread>
 #include <vector>
 
-#include "paddle/platform/enforce.h"
+#include "paddle/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
 
 namespace paddle {
 namespace framework {

paddle/operators/detail/grpc_client.cc

@@ -97,6 +97,21 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
   return true;
 }
 
+bool RPCClient::AsyncSendBatchBarrier(const std::string& ep, int64_t time_out) {
+  const auto ch = GetChannel(ep);
+
+  BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
+  s->Prepare(time_out);
+
+  sendrecv::VariableMessage req;
+  req.set_varname(BATCH_BARRIER_MESSAGE);
+  auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
+  rpc->Finish(&s->reply_, &s->status_, (void*)s);
+  req_count_++;
+
+  return true;
+}
+
 bool RPCClient::Wait() {
   if (req_count_ <= 0) {
     return true;

paddle/operators/detail/grpc_client.h

@@ -71,6 +71,15 @@ class ClientBase {
     context_->set_deadline(deadline);
   }
 
+  virtual void Prepare(int64_t time_out) {
+    context_.reset(new grpc::ClientContext());
+
+    std::chrono::system_clock::time_point deadline =
+        std::chrono::system_clock::now() + std::chrono::milliseconds(time_out);
+
+    context_->set_deadline(deadline);
+  }
+
   virtual void Process() = 0;
 
   std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
@@ -117,6 +126,17 @@ class GetProcessor : public ClientBase {
   RequestGetCallBack response_call_back_ = ProcGetResponse;
 };
 
+class BatchBarrierProcessor : public ClientBase {
+ public:
+  explicit BatchBarrierProcessor(std::shared_ptr<grpc::Channel> ch)
+      : ClientBase(ch) {}
+
+  virtual ~BatchBarrierProcessor() {}
+
+  virtual void Process() {}
+  sendrecv::VoidMessage reply_;
+};
+
 class RPCClient {
  public:
   bool AsyncSendVariable(const std::string& ep,
@@ -130,6 +150,10 @@ class RPCClient {
                         const framework::Scope& scope,
                         const std::string& var_name,
                         int64_t time_out = 600 * 1000);
+
+  bool AsyncSendBatchBarrier(const std::string& ep,
+                             int64_t time_out = 600 * 1000);
+
   bool Wait();
 
  private:

paddle/operators/detail/grpc_server.cc

@@ -132,6 +132,7 @@ void AsyncGRPCServer::RunSyncUpdate() {
 
   cq_send_ = builder.AddCompletionQueue();
   cq_get_ = builder.AddCompletionQueue();
+
   server_ = builder.BuildAndStart();
   LOG(INFO) << "Server listening on " << address_ << std::endl;
 
@@ -141,11 +142,11 @@ void AsyncGRPCServer::RunSyncUpdate() {
       std::bind(&AsyncGRPCServer::TryToRegisterNewGetOne, this);
 
   t_send_.reset(
-      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, false,
+      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
                                 cq_send_.get(), "cq_send", send_register)));
 
   t_get_.reset(
-      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, true,
+      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
                                 cq_get_.get(), "cq_get", get_register)));
 
   // wait server
@@ -174,7 +175,7 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() {
   }
   RequestSend* send =
       new RequestSend(&service_, cq_send_.get(), &var_recv_queue_);
-  VLOG(4) << "create RequestSend status:" << send->Status();
+  VLOG(4) << "Create RequestSend status:" << send->Status();
 }
 
 void AsyncGRPCServer::TryToRegisterNewGetOne() {
@@ -184,11 +185,11 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() {
   }
   RequestGet* get = new RequestGet(&service_, cq_get_.get(), scope_, dev_ctx_,
                                    &var_get_queue_);
-  VLOG(4) << "create Requestget status:" << get->Status();
+  VLOG(4) << "Create RequestGet status:" << get->Status();
 }
 
-// FIXME(typhoonzero): remove wait argument and change cq_name to enum.
-void AsyncGRPCServer::HandleRequest(bool wait, grpc::ServerCompletionQueue* cq,
+// FIXME(typhoonzero): change cq_name to enum.
+void AsyncGRPCServer::HandleRequest(grpc::ServerCompletionQueue* cq,
                                     std::string cq_name,
                                     std::function<void()> TryToRegisterNewOne) {
   TryToRegisterNewOne();

paddle/operators/detail/grpc_server.h

@@ -57,8 +57,7 @@ class AsyncGRPCServer final : public sendrecv::SendRecvService::Service {
   void ShutDown();
 
  protected:
-  void HandleRequest(bool wait, grpc::ServerCompletionQueue *cq,
-                     std::string cq_name,
+  void HandleRequest(grpc::ServerCompletionQueue *cq, std::string cq_name,
                      std::function<void()> TryToRegisterNewOne);
   void TryToRegisterNewSendOne();
   void TryToRegisterNewGetOne();

paddle/operators/detail/sendrecvop_utils.h

@@ -30,6 +30,9 @@ namespace paddle {
 namespace operators {
 namespace detail {
 
+#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
+#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
+
 void SerializeToMessage(const std::string& name, const framework::Variable* var,
                         const platform::DeviceContext& ctx,
                         sendrecv::VariableMessage* msg);

paddle/operators/recv_op.cc

@@ -29,8 +29,6 @@ limitations under the License. */
 #include "paddle/operators/detail/simple_block_queue.h"
 #include "paddle/string/printf.h"
 
-#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
-
 namespace paddle {
 namespace operators {
 
@@ -95,7 +93,6 @@ class RecvOp : public framework::OperatorBase {
     auto param_list = Attr<std::vector<std::string>>("ParamList");
     auto grad_list = Attr<std::vector<std::string>>("GradList");
     auto fan_in = Attr<int>("Fanin");
-    size_t param_count = param_list.size();
 
     auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
     auto *program = block->Program();
@@ -103,38 +100,50 @@ class RecvOp : public framework::OperatorBase {
 
     // TODO(typhoonzero): change this to a while_op for every cluster-batch.
     bool exit_flag = false;
-    size_t barrier_size = param_count * fan_in;
     while (!exit_flag) {
       // Get from multiple trainers, we don't care about the order in which
       // the gradients arrives, just add suffix 0~n and merge the gradient.
       rpc_service_->SetCond(0);
-      for (size_t i = 0; i < barrier_size; ++i) {
+      size_t recv_var_cnt = 0;
+      int batch_barrier = 0;
+      while (batch_barrier != fan_in) {
         const detail::MessageWithName &v = rpc_service_->Get();
         auto grad_var_name = v.first;
         if (grad_var_name == LISTEN_TERMINATE_MESSAGE) {
           LOG(INFO) << "received terminate message and exit";
           exit_flag = true;
           break;
-        }
-        auto it = std::find(grad_list.begin(), grad_list.end(), grad_var_name);
-        std::string param_var_name;
-        if (it != grad_list.end()) {
-          param_var_name = param_list[it - grad_list.begin()];
+        } else if (grad_var_name == BATCH_BARRIER_MESSAGE) {
+          VLOG(3) << "recv batch barrier message";
+          batch_barrier++;
+          continue;
         } else {
-          LOG(ERROR) << "grad has no paired param:" << grad_var_name;
-        }
-        VLOG(3) << "received grad: " << grad_var_name
-                << " updating param: " << param_var_name;
-        if (fan_in > 1) {
-          grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
-        }
-        auto *var = recv_scope.FindVar(grad_var_name);
-        if (var == nullptr) {
-          LOG(ERROR) << "Can not find server side var: " << grad_var_name;
-          PADDLE_THROW("Can not find server side var");
+          // receive a variable
+          recv_var_cnt++;
+          auto it =
+              std::find(grad_list.begin(), grad_list.end(), grad_var_name);
+          std::string param_var_name;
+          if (it != grad_list.end()) {
+            param_var_name = param_list[it - grad_list.begin()];
+          } else {
+            LOG(ERROR) << "grad has no paired param:" << grad_var_name;
+          }
+          VLOG(3) << "received grad: " << grad_var_name
+                  << " updating param: " << param_var_name;
+
+          if (fan_in > 1) {
+            grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
+          }
+          auto *var = recv_scope.FindVar(grad_var_name);
+          if (var == nullptr) {
+            LOG(ERROR) << "Can not find server side var: " << grad_var_name;
+            PADDLE_THROW("Can not find server side var");
+          }
+          detail::DeserializeFromMessage(v.second, dev_ctx, var);
         }
-        detail::DeserializeFromMessage(v.second, dev_ctx, var);
       }
+      VLOG(3) << "recv " << recv_var_cnt << " parmeters for one barrier.";
+      // TODO(Yancey1989): merge SelectedRows variables here
       if (exit_flag) {
         break;
       }
@@ -146,7 +155,7 @@ class RecvOp : public framework::OperatorBase {
         LOG(ERROR) << "run sub program error " << e.what();
       }
       rpc_service_->SetCond(1);
-      rpc_service_->WaitClientGet(barrier_size);
+      rpc_service_->WaitClientGet(recv_var_cnt);
       grads_counter_.clear();
     }  // while(true)
   }
@@ -161,7 +170,6 @@ class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   RecvOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("RX", "(Tensor) Input tensor to be optimized").AsDuplicable();
     AddComment(R"DOC(
 Recv operator
 

paddle/operators/reduce_op.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/reduce_op.h"
-#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace operators {
@@ -38,10 +37,14 @@ class ReduceOp : public framework::OperatorWithKernel {
         dim, x_rank,
         "The dim should be in the range [-rank(input), rank(input)).");
     bool reduce_all = ctx->Attrs().Get<bool>("reduce_all");
+    bool keep_dim = ctx->Attrs().Get<bool>("keep_dim");
     if (reduce_all) {
-      ctx->SetOutputDim("Out", {1});
+      if (keep_dim)
+        ctx->SetOutputDim(
+            "Out", framework::make_ddim(std::vector<int64_t>(x_rank, 1)));
+      else
+        ctx->SetOutputDim("Out", {1});
     } else {
-      bool keep_dim = ctx->Attrs().Get<bool>("keep_dim");
       auto dims_vector = vectorize(x_dims);
       if (keep_dim || x_rank == 1) {
         dims_vector[dim] = 1;

paddle/operators/send_op.cc

@@ -37,17 +37,25 @@ class SendOp : public framework::OperatorBase {
     auto ins = Inputs("X");
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
+    std::vector<std::string> endpoints =
+        Attr<std::vector<std::string>>("endpoints");
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
     for (size_t i = 0; i < ins.size(); i++) {
-      VLOG(3) << "sending " << ins[i];
+      VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
       client_.AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
     }
     PADDLE_ENFORCE(client_.Wait());
 
+    for (auto& ep : endpoints) {
+      VLOG(3) << "batch barrier, ep: " << ep;
+      client_.AsyncSendBatchBarrier(ep);
+    }
+    PADDLE_ENFORCE(client_.Wait());
+
     for (size_t i = 0; i < outs.size(); i++) {
-      VLOG(3) << "getting " << outs[i];
+      VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
       client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
 

paddle/operators/sequence_reshape_op.cc

@@ -30,8 +30,13 @@ class SequenceReshapeOp : public framework::OperatorWithKernel {
     auto x_numel = product(x_dims);
     PADDLE_ENFORCE_EQ(x_dims.size(), 2U, "Rank of Input(X) should be 2.");
     int new_dim = ctx->Attrs().Get<int>("new_dim");
-    ctx->SetOutputDim("Out",
-                      {x_numel / new_dim, static_cast<int64_t>(new_dim)});
+    if (ctx->IsRuntime()) {
+      ctx->SetOutputDim("Out",
+                        {x_numel / new_dim, static_cast<int64_t>(new_dim)});
+    } else {
+      // when compiling, the batch size is undetermined, just set to -1
+      ctx->SetOutputDim("Out", {-1, static_cast<int64_t>(new_dim)});
+    }
   }
 };
 

paddle/scripts/docker/build.sh

@@ -32,7 +32,7 @@ function cmake_gen() {
     cat <<EOF
     ========================================
     Configuring cmake in /paddle/build ...
-        -DCMAKE_BUILD_TYPE=Release
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:Release}
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
@@ -54,7 +54,7 @@ EOF
     # docker environment is fully controlled by this script.
     # See /Paddle/CMakeLists.txt, UNITTEST_USE_VIRTUALENV option.
     cmake .. \
-        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:Release} \
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \

python/paddle/trainer/config_parser.py

@@ -140,8 +140,13 @@ def init_config_environment(
         g_submodel_stack=[],
         g_add_submodel_suffix=False, ):
 
-    for k, v in locals().iteritems():
-        globals()[k] = copy.deepcopy(v)
+    # directly iterate through locals().iteritems() will change
+    # the size of locals() due to introducing k, v into scope
+    # which will break the process in some env
+
+    local_vars = copy.deepcopy(locals())
+    for k, v in local_vars.iteritems():
+        globals()[k] = v
 
 
 # Because type is widely used as a variable name in this code.

python/paddle/v2/fluid/clip.py

@@ -30,6 +30,9 @@ __all__ = [
 
 
 class BaseErrorClipAttr(object):
+    def __str__(self):
+        raise NotImplementedError()
+
     def append_clip_op(self, block, grad_name):
         raise NotImplementedError()
 
@@ -44,6 +47,9 @@ class ErrorClipByValue(BaseErrorClipAttr):
         self.max = max
         self.min = min
 
+    def __str__(self):
+        return "ByValue, min=%f, max=%f" % (self.min, self.max)
+
     def append_clip_op(self, block, grad_name):
         clip_op_desc = block.desc.append_op()
         clip_op_desc.set_type("clip")
@@ -71,6 +77,9 @@ def error_clip_callback(block, context):
 
 
 class BaseGradientClipAttr(object):
+    def __str__(self):
+        raise NotImplementedError()
+
     def process_context(self, context, param, grad):
         raise NotImplementedError()
 
@@ -79,6 +88,9 @@ class BaseGradientClipAttr(object):
 
 
 class NullGradientClipAttr(BaseGradientClipAttr):
+    def __str__(self):
+        return "Null"
+
     def process_context(self, context, param, grad):
         pass
 
@@ -96,6 +108,9 @@ class GradientClipByValue(BaseGradientClipAttr):
         self.max = max
         self.min = min
 
+    def __str__(self):
+        return "ByValue, min=%f, max=%f" % (self.min, self.max)
+
     def process_context(self, context, param, grad):
         pass
 
@@ -108,6 +123,9 @@ class GradientClipByNorm(BaseGradientClipAttr):
     def __init__(self, clip_norm):
         self.clip_norm = clip_norm
 
+    def __str__(self):
+        return "ByNorm, clip_norm=%f" % self.clip_norm
+
     def process_context(self, context, param, grad):
         pass
 
@@ -124,6 +142,10 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         self.clip_norm = clip_norm
         self.group_name = group_name
 
+    def __str__(self):
+        return "ByGlobalNorm, group_name=%s, clip_norm=%f" % (self.group_name,
+                                                              self.clip_norm)
+
     def process_context(self, context, param, grad):
         if self.group_name not in context:
             context[self.group_name] = []
@@ -160,6 +182,17 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
 
 
 def set_gradient_clip(clip, param_list=None, program=None):
+    """
+        To specify parameters that require gradient clip.
+        Args:
+            clip(BaseGradientClipAttr): An instance of some derived class of BaseGradientClipAttr, 
+                    which describes the type and detailed attributes of required gradient clip.
+            param_list(list, None by default): Parameters that require gradient clip. 
+                    It can be a list of parameter or a list of parameter's name. 
+                    When it's None, all parameters in the program will be included. 
+            program(Program, None by default): The program where parameters are. 
+                    Will be the default main program when assigned with None.
+    """
     if not isinstance(clip, BaseGradientClipAttr):
         raise TypeError(
             "'clip' should be an instance of BaseGradientClipAttr's derived class"
@@ -199,3 +232,5 @@ def append_gradient_clip_ops(param_grad):
 
 
 ClipByValue = GradientClipByValue
+ClipByNorm = GradientClipByNorm
+ClipByGlobalNorm = GradientClipByGlobalNorm

python/paddle/v2/fluid/distribute_transpiler.py

@@ -474,8 +474,7 @@ class DistributeTranspiler:
         # Append the recv op
         pserver_program.global_block().append_op(
             type="recv",
-            inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
-                    },  # grads to recv
+            inputs={},
             outputs={},
             attrs={
                 "OptimizeBlock": optimize_sub_program.global_block(),

python/paddle/v2/fluid/framework.py

@@ -14,6 +14,7 @@
 
 import collections
 import contextlib
+import re
 
 import numpy as np
 
@@ -239,20 +240,30 @@ class Variable(object):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
+    def to_string(self, throw_on_error, with_details=False):
         """
         Get debug string.
 
         Args:
             throw_on_error(bool): True if raise an exception when self is not
                 intialized.
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
 
         Returns(str): The debug string.
 
         """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.VarDesc.FromString(str(protostr))
-        return _debug_string_(proto, throw_on_error)
+        res_str = _debug_string_(proto, throw_on_error)
+        if with_details:
+            additional_attr = ("error_clip", "stop_gradient")
+            for attr_name in additional_attr:
+                res_str += "%s: %s\n" % (attr_name,
+                                         str(getattr(self, attr_name)))
+        return res_str
 
     __repr__ = __str__
 
@@ -629,10 +640,36 @@ class Block(object):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
-        protostr = self.desc.serialize_to_string()
-        proto = framework_pb2.BlockDesc.FromString(str(protostr))
-        return _debug_string_(proto, throw_on_error)
+    def to_string(self, throw_on_error, with_details=False):
+        """
+        To debug string.
+        Args:
+            throw_on_error(bool): raise exception when self is not initialized
+                when throw_on_error is True
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+
+        Returns(str): The debug string.
+
+        """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
+        if with_details:
+            re_add_indent = re.compile(r"\n(.)")
+            res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
+                self.idx, self.parent_idx)
+            for var in self.vars.itervalues():
+                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
+                    r"\n    \1", var.to_string(throw_on_error, with_details))
+            for op in self.ops:
+                res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
+                    r"\n    \1", op.to_string(throw_on_error))
+            res_str += "\n}"
+        else:
+            protostr = self.desc.serialize_to_string()
+            proto = framework_pb2.BlockDesc.FromString(str(protostr))
+            res_str = _debug_string_(proto, throw_on_error)
+        return res_str
 
     __repr__ = __str__
 
@@ -796,10 +833,29 @@ class Program(object):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
-        protostr = self.desc.serialize_to_string()
-        proto = framework_pb2.ProgramDesc.FromString(str(protostr))
-        return _debug_string_(proto, throw_on_error)
+    def to_string(self, throw_on_error, with_details=False):
+        """
+        To debug string.
+        Args:
+            throw_on_error(bool): raise exception when self is not initialized
+                when throw_on_error is True
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+
+        Returns(str): The debug string.
+
+        """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
+        if with_details:
+            res_str = ""
+            for block in self.blocks:
+                res_str += block.to_string(throw_on_error, with_details)
+        else:
+            protostr = self.desc.serialize_to_string()
+            proto = framework_pb2.ProgramDesc.FromString(str(protostr))
+            res_str = _debug_string_(proto, throw_on_error)
+        return res_str
 
     def get_desc(self):
         return self.desc
@@ -950,6 +1006,36 @@ class Parameter(Variable):
 
         self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
 
+    def __str__(self):
+        return self.to_string(True)
+
+    def to_string(self, throw_on_error, with_details=False):
+        """
+        To debug string.
+        Args:
+            throw_on_error(bool): raise exception when self is not initialized
+                when throw_on_error is True
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+
+        Returns(str): The debug string.
+
+        """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
+        if with_details:
+            res_str = Variable.to_string(self, throw_on_error, True)
+            additional_attr = ("trainable", "optimize_attr", "regularizer",
+                               "gradient_clip_attr")
+            for attr_name in additional_attr:
+                res_str += "%s: %s\n" % (attr_name,
+                                         str(getattr(self, attr_name)))
+        else:
+            res_str = Variable.to_string(self, throw_on_error, False)
+        return res_str
+
+    __repr__ = __str__
+
 
 # program is a global instance.
 _main_program_ = Program()

python/paddle/v2/fluid/layer_helper.py

@@ -18,7 +18,7 @@ import itertools
 from framework import Variable, Parameter, default_main_program, default_startup_program, \
     unique_name, dtype_is_floating
 from paddle.v2.fluid.initializer import Constant, Xavier
-from param_attr import ParamAttr
+from param_attr import ParamAttr, WeightNormParamAttr
 
 
 class LayerHelper(object):
@@ -104,6 +104,177 @@ class LayerHelper(object):
                                  (dtype, each.dtype))
         return dtype
 
+    def _create_weight_normalize(self, attr, shape, dtype):
+        from .layers import elementwise_mul, elementwise_div, reshape
+
+        # Remove these ops when LayerHelper and layers support indicating
+        # program and block.
+        def __norm_op(x,
+                      out=None,
+                      p=2,
+                      dim=None,
+                      keep_dim=False,
+                      block=self.startup_program.global_block()):
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join([self.name, 'weight_norm_norm'])),
+                    dtype=dtype,
+                    persistable=False)
+            abs_out = block.create_var(
+                name=unique_name(".".join([self.name, 'weight_norm_abs'])),
+                dtype=dtype,
+                persistable=False)
+            block.append_op(
+                type='abs', inputs={'X': x}, outputs={'Out': abs_out})
+            pow_out = block.create_var(
+                name=unique_name(".".join([self.name, 'weight_norm_pow'])),
+                dtype=dtype,
+                persistable=False)
+            block.append_op(
+                type='pow',
+                inputs={'X': abs_out},
+                outputs={'Out': pow_out},
+                attrs={'factor': float(p)})
+            sum_out = block.create_var(
+                name=unique_name(".".join([self.name, 'weight_norm_sum'])),
+                dtype=dtype,
+                persistable=False)
+            block.append_op(
+                type='reduce_sum',
+                inputs={'X': pow_out},
+                outputs={'Out': sum_out},
+                attrs={
+                    'dim': dim,
+                    'keep_dim': keep_dim,
+                    'reduce_all': True if dim is None else False
+                })
+            block.append_op(
+                type='pow',
+                inputs={'X': sum_out},
+                outputs={'Out': out},
+                attrs={'factor': 1. / p})
+            return out
+
+        def __reshape_op(x,
+                         shape,
+                         out=None,
+                         block=self.startup_program.global_block()):
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join(
+                        [self.name, 'weight_norm_reshape'])),
+                    dtype=dtype,
+                    persistable=False)
+            block.append_op(
+                type='reshape',
+                inputs={'X': x},
+                outputs={'Out': out},
+                attrs={'shape': shape})
+            return out
+
+        def __transpose_op(x,
+                           axis,
+                           out=None,
+                           block=self.startup_program.global_block()):
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join(
+                        [self.name, 'weight_norm_transpose'])),
+                    dtype=dtype,
+                    persistable=False)
+            block.append_op(
+                type='transpose',
+                inputs={'X': x},
+                outputs={'Out': out},
+                attrs={'axis': axis})
+            return out
+
+        def __norm_except_dim(x,
+                              out=None,
+                              dim=None,
+                              block=self.startup_program.global_block()):
+            """Computes the norm over all dimensions except dim"""
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join([self.name, 'weight_norm_norm'])),
+                    dtype=dtype,
+                    persistable=False)
+            if dim is None:
+                __norm_op(x, out, dim=dim, block=block)
+            elif dim == 0:
+                out_shape = [x.shape[0]] + [1] * (len(x.shape) - 1)
+                reshape = __reshape_op(x, shape=[x.shape[0], -1], block=block)
+                norm = __norm_op(reshape, dim=1, block=block)
+                __reshape_op(norm, out=out, shape=out_shape, block=block)
+            elif dim == len(x.shape) - 1:
+                out_shape = [1] * (len(x.shape) - 1) + [x.shape[-1]]
+                reshape = __reshape_op(x, shape=[-1, x.shape[-1]], block=block)
+                norm = __norm_op(reshape, dim=0, block=block)
+                __reshape_op(norm, out=out, shape=out_shape, block=block)
+            else:
+                perm = range(len(x.shape))
+                perm[0], perm[dim] = dim, 0
+                transpose = __transpose_op(x, perm, block=block)
+                norm = __norm_op(transpose, dim=0, block=block)
+                __transpose_op(norm, perm, out=out, block=block)
+            return out
+
+        def __weight_normalize(g, v, dim):
+            """Calculations for weight normalization"""
+            norm = __norm_except_dim(
+                v, dim=dim, block=self.main_program.current_block())
+            scale = elementwise_div(
+                x=g, y=norm)  # The shapes of g and norm are the same.
+            # Currently, elementwise_mul only support broadcast when the shape
+            # of y is a subset of the shape of x. Thus, we reshape y to squeeze
+            # to achive the subset.
+            w = elementwise_mul(
+                x=v,
+                y=scale if dim is None else reshape(
+                    x=scale, shape=[v.shape[dim]]),
+                axis=-1 if dim is None else dim)
+            # To serialize the original parameter for inference, maybe a
+            # parameter rather than a variable should be returned.
+            return w
+
+        g_param_attr = copy.deepcopy(attr)
+        g_param_attr.name = attr.name + '_g'
+        g_param_shape = [1] * len(shape)
+        if attr.dim is not None:
+            g_param_shape[attr.dim] = shape[attr.dim]
+        v_param_attr = copy.deepcopy(attr)
+        v_param_attr.name = attr.name + '_v'
+        v_param_shape = shape
+
+        # Add to startup_program to initialize g and v.
+        # Try to reconstruct the initializer of w by initializing g and v.
+        # Set the initializers of g and v as below, then the distribution
+        # of w is the same as initializing w with the given initializer.
+        # For Data-Dependent Initialization, please compute the init-values
+        # of g and v in external and then feed the values to g and v by
+        # executing an extra program.
+        g_param = self.startup_program.global_block().create_parameter(
+            dtype=dtype,
+            shape=g_param_shape,
+            **g_param_attr.to_kwargs(with_initializer=False))
+        v_param = self.startup_program.global_block().create_parameter(
+            dtype=dtype,
+            shape=v_param_shape,
+            **v_param_attr.to_kwargs(with_initializer=True))
+        __norm_except_dim(
+            x=v_param,
+            out=g_param,
+            dim=attr.dim,
+            block=self.startup_program.global_block())
+
+        # Add weight normalization to main_program
+        g_param = self.main_program.global_block().create_parameter(
+            dtype=dtype, shape=g_param_shape, **g_param_attr.to_kwargs())
+        v_param = self.main_program.global_block().create_parameter(
+            dtype=dtype, shape=v_param_shape, **v_param_attr.to_kwargs())
+        w_param = __weight_normalize(g_param, v_param, dim=attr.dim)
+        return w_param
+
     def create_parameter(self,
                          attr,
                          shape,
@@ -114,16 +285,23 @@ class LayerHelper(object):
         attr = copy.deepcopy(attr)
         assert isinstance(attr, ParamAttr)
         suffix = 'b' if is_bias else 'w'
+        if attr.name is None:
+            attr.name = unique_name(".".join([self.name, suffix]))
 
-        if default_initializer is None:
+        if default_initializer is None and attr.initializer is None:
             if is_bias:
                 attr.set_default_bias_initializer()
             else:
                 attr.set_default_param_initializer()
         else:
             attr.set_default_initializer(default_initializer)
-        if attr.name is None:
-            attr.name = unique_name(".".join([self.name, suffix]))
+
+        # If weight normalization is set, insert extra parameters and ops.
+        # Refer to https://arxiv.org/pdf/1602.07868.pdf
+        if isinstance(attr, WeightNormParamAttr):
+            param = self._create_weight_normalize(attr, shape, dtype)
+            WeightNormParamAttr.params_with_weight_norm.append(param)
+            return param
 
         self.startup_program.global_block().create_parameter(
             dtype=dtype, shape=shape, **attr.to_kwargs(with_initializer=True))

python/paddle/v2/fluid/layers/io.py

@@ -14,8 +14,10 @@
 
 from .. import core
 from ..layer_helper import LayerHelper
+from control_flow import BlockGuard
+from ..layer_helper import LayerHelper
 
-__all__ = ['data']
+__all__ = ['data', 'BlockGuardServ', 'ListenAndServ', 'Send']
 
 
 def data(name,
@@ -74,3 +76,123 @@ def data(name,
         type=type,
         stop_gradient=stop_gradient,
         lod_level=lod_level)
+
+
+class BlockGuardServ(BlockGuard):
+    """
+    BlockGuardServ class.
+
+    BlockGuardServ class is used to create an op with a block in a program.
+    """
+
+    def __init__(self, server):
+        if not (isinstance(server, ListenAndServ)):
+            raise TypeError("BlockGuardServ takes a ListenAndServ")
+        super(BlockGuardServ, self).__init__(server.helper.main_program)
+        self.server = server
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if exc_type is not None:
+            return False
+
+        self.server.complete_op()
+        return super(BlockGuardServ, self).__exit__(exc_type, exc_val, exc_tb)
+
+
+class ListenAndServ(object):
+    """
+    ListenAndServ class.
+
+    ListenAndServ class is used to wrap listen_and_serv op to create a server
+    which can receive variables from clients and run a block.
+    """
+
+    def __init__(self, endpoint, fan_in=1, optimizer_mode=True):
+        self.helper = LayerHelper("recv")
+        self.inputs = []
+        self.outputs = []
+        self.endpoint = endpoint
+        self.fan_in = fan_in
+        # FIXME(typhoonzero): add optimizer_mode is stupid, should make it more
+        # general.
+        self.optimizer_mode = optimizer_mode
+
+    def do(self):
+        return BlockGuardServ(self)
+
+    def get_params_and_grads(self):
+        main_program = self.helper.main_program
+        current_block = main_program.current_block()
+        parent_block = self.parent_block()
+        # params and grads in the same order.
+        params = list()
+        grads = list()
+        for op in current_block.ops:
+            # FIXME(typhoonzero): op.inputs is None if it's cloned.
+            if self.optimizer_mode:
+                if "Grad" in op.inputs and "Param" in op.inputs:
+                    params.append(op.inputs["Param"].name)
+                    grads.append(op.inputs["Grad"].name)
+            else:
+                # simple recv mode, recv operators inputs.
+                for iname in op.input_names:
+                    for in_var_name in op.input(iname):
+                        params.append(parent_block.var(in_var_name))
+                        grads.append(parent_block.var(in_var_name))
+
+        return params, grads
+
+    def parent_block(self):
+        prog = self.helper.main_program
+        parent_idx = prog.current_block().parent_idx
+        assert parent_idx >= 0
+        parent_block = prog.block(parent_idx)
+        return parent_block
+
+    def complete_op(self):
+        main_program = self.helper.main_program
+        current_block = main_program.current_block()
+        parent_block = self.parent_block()
+
+        params, grads = self.get_params_and_grads()
+        param_names = [p.name for p in params]
+        grad_names = [g.name for g in grads]
+        parent_block.append_op(
+            type='recv',
+            inputs={},
+            outputs={},
+            attrs={
+                'endpoint': self.endpoint,
+                'Fanin': self.fan_in,
+                'ParamList': param_names,
+                'GradList': grad_names,
+                'OptimizeBlock': current_block
+            })
+
+
+def Send(endpoints, send_vars, get_vars):
+    """
+    Send layer
+
+    Args:
+        endpoints: comma seperated IP:PORT pairs in the order
+                   of send_vars to send
+        send_vars: vars to send
+        get_vars: vars to get from server after send completes.
+
+    Send variables to the server side, and get vars from server
+    side when server have finished running server side program.
+    """
+    assert (type(send_vars) == list)
+    assert (type(get_vars) == list)
+
+    epmap = endpoints.split(",")
+    endpoints = list(set(epmap))
+
+    helper = LayerHelper("Send", **locals())
+    helper.append_op(
+        type="send",
+        inputs={"X": send_vars},
+        outputs={"Out": get_vars},
+        attrs={"endpoints": endpoints,
+               "epmap": epmap})

python/paddle/v2/fluid/param_attr.py

@@ -15,7 +15,10 @@
 from initializer import Initializer, Xavier, Constant
 from regularizer import WeightDecayRegularizer
 
-__all__ = ['ParamAttr']
+__all__ = [
+    'ParamAttr',
+    'WeightNormParamAttr',
+]
 
 
 class ParamAttr(object):
@@ -82,3 +85,20 @@ class ParamAttr(object):
         if with_initializer:
             kwargs['initializer'] = self.initializer
         return kwargs
+
+
+class WeightNormParamAttr(ParamAttr):
+    """
+    Used for weight normalization. Any field in ParamAttr can also be set here.
+    Besides, an extra field dim can be set to indicate the dimension except 
+    which to normalize.
+    """
+    # List to record the parameters reparameterized by weight normalization.
+    # If these parameters are treated as Variable rather than Parameter,
+    # it can be used to discriminate these parameters and help to serialize
+    # these paramters for inference.
+    params_with_weight_norm = []
+
+    def __init__(self, dim=None, **kwargs):
+        super(WeightNormParamAttr, self).__init__(**kwargs)
+        self.dim = dim

python/paddle/v2/fluid/regularizer.py

@@ -87,6 +87,11 @@ class WeightDecayRegularizer(object):
         """
         raise NotImplementedError()
 
+    def __str__(self):
+        """Debug string
+        """
+        raise NotImplementedError()
+
 
 class L2DecayRegularizer(WeightDecayRegularizer):
     """Implements the L2 Weight Decay Regularization
@@ -123,6 +128,9 @@ class L2DecayRegularizer(WeightDecayRegularizer):
 
         return decay
 
+    def __str__(self):
+        return "L2Decay, regularization_coeff=%f" % self._regularization_coeff
+
 
 class L1DecayRegularizer(WeightDecayRegularizer):
     """Implements the L1 Weight Decay Regularization
@@ -163,6 +171,9 @@ class L1DecayRegularizer(WeightDecayRegularizer):
 
         return decay
 
+    def __str__(self):
+        return "L1Decay, regularization_coeff=%f" % self._regularization_coeff
+
 
 # We short the class name, since users will use the regulaizer with the package
 # name. The sample code:

python/paddle/v2/fluid/tests/CMakeLists.txt

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+if(NOT WITH_DISTRIBUTE)
+    list(REMOVE_ITEM TEST_OPS test_recv_op)
+endif(NOT WITH_DISTRIBUTE)
+
 foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()

python/paddle/v2/fluid/tests/test_recv_op.py

+#   Copyright (c) 2018 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.
+
+import unittest
+
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as layers
+import numpy
+from multiprocessing import Process
+import os, sys
+
+
+class TestRecvOp(unittest.TestCase):
+    def test_send(self):
+        # Run init_serv in a thread
+        place = fluid.CPUPlace()
+        p = Process(target=self.init_serv, args=(place, ))
+        p.daemon = True
+        p.start()
+        self.init_client(place)
+        # FIXME(typhoonzero): find a way to gracefully shutdown the server.
+        os.system("kill -9 %d" % p.pid)
+        p.join()
+
+    def init_serv(self, place):
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            x = layers.data(
+                shape=[32, 32],
+                dtype='float32',
+                name="X",
+                append_batch_size=False)
+            fluid.initializer.Constant(value=1.0)(x, main.global_block())
+            serv = layers.ListenAndServ("127.0.0.1:6174", optimizer_mode=False)
+            with serv.do():
+                o = layers.scale(x=x, scale=10.0)
+            main.global_block().create_var(
+                name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)
+        exe = fluid.Executor(place)
+        exe.run(main)
+
+    def init_client(self, place):
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            x = layers.data(
+                shape=[32, 32],
+                dtype='float32',
+                name='X',
+                append_batch_size=False)
+            fluid.initializer.Constant(value=1.0)(x, main.global_block())
+            layers.Send("127.0.0.1:6174", [x], [x])
+        exe = fluid.Executor(place)
+        exe.run(main)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/v2/fluid/tests/test_weight_normalization.py

+#   Copyright (c) 2018 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.
+
+import unittest
+import numpy
+import collections
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+from paddle.v2.fluid.initializer import ConstantInitializer
+from paddle.v2.fluid.param_attr import WeightNormParamAttr
+
+
+class TestWeightNormalization(unittest.TestCase):
+    batch_size = 3
+    hidden_size = 5
+    data_desc = (['x', [10], 0], )
+
+    @classmethod
+    def setUpClass(cls):
+        cls.set_program()
+
+    @classmethod
+    def set_program(cls):
+        data = fluid.layers.data(
+            name=cls.data_desc[0][0], shape=cls.data_desc[0][1])
+        out = fluid.layers.fc(input=data,
+                              size=cls.hidden_size,
+                              param_attr=WeightNormParamAttr(
+                                  dim=None,
+                                  name='weight_norm_param',
+                                  initializer=ConstantInitializer(1.0)),
+                              bias_attr=False,
+                              act=None)
+        loss = fluid.layers.reduce_sum(out)
+        fluid.backward.append_backward(loss=loss)
+        cls.fetch_list = [
+            'weight_norm_param_g', 'weight_norm_param_v',
+            'weight_norm_param_g@GRAD'
+        ]
+
+    def run_program(self):
+        outputs = []
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        for place in places:
+            self.set_inputs(place)
+            exe = fluid.Executor(place)
+            exe.run(fluid.default_startup_program())
+            output = exe.run(fluid.default_main_program(),
+                             feed=self.inputs,
+                             fetch_list=self.fetch_list,
+                             return_numpy=False)
+            outputs.append(output)
+        self.actual_outputs = outputs
+
+    def set_data(self):
+        self.data = collections.OrderedDict()
+        for desc in self.data_desc:
+            data_name = desc[0]
+            data_shape = desc[1]
+            data_lod_level = desc[2]
+            data_lod = []
+            for i in range(data_lod_level):
+                lod_level_i = numpy.random.randint(
+                    low=1,
+                    high=5,
+                    size=self.batch_size if i == 0 else lod_level_i[-1])
+                lod_level_i = [0] + numpy.cumsum(lod_level_i).tolist()
+                data_lod.append(lod_level_i)
+            data_value = numpy.random.random(
+                size=[data_lod[-1][-1] if data_lod else self.batch_size
+                      ] + data_shape).astype('float32')
+            self.data[data_name] = (data_value, data_lod)
+
+    def set_inputs(self, place):
+        self.inputs = {}
+        for desc in self.data_desc:
+            tensor = fluid.Tensor()
+            tensor.set(self.data[desc[0]][0], place)
+            if self.data[desc[0]][1]:
+                tensor.set_lod(self.data[desc[0]][1])
+            self.inputs[desc[0]] = tensor
+
+    def weight_normalize(self):
+        v = numpy.ones((self.data[self.data_desc[0][0]][0].shape[-1],
+                        self.hidden_size))
+        g = numpy.linalg.norm(v, axis=None, keepdims=True)
+        w = g * v / numpy.linalg.norm(v, axis=None, keepdims=True)
+        x = self.data[self.data_desc[0][0]][0]
+        out = numpy.dot(x, w)
+        g_grad = (numpy.dot(x.T, numpy.ones_like(out)) * (v / numpy.linalg.norm(
+            v, axis=None, keepdims=True))).sum(axis=None, keepdims=True)
+        return g, v, g_grad
+
+    def test_weight_normalization(self):
+        self.set_data()
+        self.run_program()
+        expect_output = self.weight_normalize()
+        for actual_output in self.actual_outputs:
+            [
+                self.assertTrue(
+                    numpy.allclose(
+                        numpy.array(actual), expect, atol=0.001))
+                for expect, actual in zip(expect_output, actual_output)
+            ]
+
+
+if __name__ == '__main__':
+    unittest.main()