Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into rename_rpc_ops

doc/api/v2/fluid/layers.rst

@@ -18,6 +18,11 @@ dynamic_lstm
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
     :noindex:
 
+dynamic_lstmp
+-------------
+..  autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
+    :noindex:
+
 dynamic_gru
 -----------
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_gru

doc/design/support_new_device.md

@@ -174,7 +174,7 @@ class MaxOutFunctor {
 };
 ```
 
-CPU implemention is in .cc file
+CPU implementation is in .cc file
 
 ```
 template <typename T>
@@ -188,7 +188,7 @@ class MaxOutFunctor<platform::CPUDeviceContext, T> {
 };
 ```
 
-CUDA implemention is in .cu file
+CUDA implementation is in .cu file
 
 ```
 template <typename T>
@@ -203,9 +203,9 @@ class MaxOutFunctor<platform::CUDADeviceContext, T> {
 ```
 
 
-We first obtain the computing handle from a concrete DeviceContext, and then compute on tensors.
+We first obtain the computing handle from a concrete DeviceContext and then compute on tensors.
 
-The implemention of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
+The implementation of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
 
 Fluid provides different register interfaces in op_registry.h
 

paddle/framework/CMakeLists.txt

@@ -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/data_type.h

@@ -79,5 +79,33 @@ inline void VisitDataType(proto::DataType type, Visitor visitor) {
   }
 }
 
+inline std::string DataTypeToString(const proto::DataType type) {
+  using namespace paddle::framework::proto;
+  switch (type) {
+    case DataType::FP16:
+      return "float16";
+    case DataType::FP32:
+      return "float32";
+    case DataType::FP64:
+      return "float64";
+    case DataType::INT16:
+      return "int16";
+    case DataType::INT32:
+      return "int32";
+    case DataType::INT64:
+      return "int64";
+    case DataType::BOOL:
+      return "bool";
+    default:
+      PADDLE_THROW("Not support type %d", type);
+  }
+}
+
+inline std::ostream& operator<<(std::ostream& out,
+                                const proto::DataType& type) {
+  out << DataTypeToString(type);
+  return out;
+}
+
 }  // namespace framework
 }  // namespace paddle

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/op_kernel_type_test.cc

@@ -26,9 +26,9 @@ TEST(OpKernelType, ToString) {
   OpKernelType op_kernel_type(DataType::FP32, CPUPlace(), DataLayout::kNCHW,
                               LibraryType::kCUDNN);
 
-  ASSERT_EQ(
-      paddle::framework::KernelTypeToString(op_kernel_type),
-      "data_type[5]:data_layout[NCHW]:place[CPUPlace]:library_type[CUDNN]");
+  ASSERT_EQ(paddle::framework::KernelTypeToString(op_kernel_type),
+            "data_type[float32]:data_layout[NCHW]:place[CPUPlace]:library_type["
+            "CUDNN]");
 }
 
 TEST(OpKernelType, Hash) {

paddle/operators/CMakeLists.txt

@@ -122,9 +122,11 @@ if(WITH_DISTRIBUTE)
     set_source_files_properties(send_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     op_library(recv_op DEPS ${DISTRIBUTE_DEPS})
     set_source_files_properties(recv_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS send_op recv_op sum_op executor)
+    op_library(listen_and_serv_op DEPS ${DISTRIBUTE_DEPS})
+    set_source_files_properties(listen_and_serv_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS send_op listen_and_serv_op sum_op executor)
 else()
-    set(DEPS_OPS ${DEPS_OPS} send_op recv_op)
+    set(DEPS_OPS ${DEPS_OPS} send_op recv_op listen_and_serv_op)
 endif()
 
 op_library(cond_op DEPS framework_proto tensor net_op)
@@ -147,6 +149,7 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
 op_library(sequence_conv_op DEPS context_project)
 op_library(sequence_pool_op DEPS sequence_pooling)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
+op_library(lstmp_op DEPS sequence2batch lstm_compute)
 op_library(gru_op DEPS sequence2batch gru_compute)
 op_library(recurrent_op DEPS executor)
 op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale math_function)

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

@@ -25,9 +25,9 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class SendOp : public framework::OperatorBase {
+class RecvOp : public framework::OperatorBase {
  public:
-  SendOp(const std::string& type, const framework::VariableNameMap& inputs,
+  RecvOp(const std::string& type, const framework::VariableNameMap& inputs,
          const framework::VariableNameMap& outputs,
          const framework::AttributeMap& attrs)
       : OperatorBase(type, inputs, outputs, attrs) {}
@@ -44,7 +44,6 @@ class SendOp : public framework::OperatorBase {
       VLOG(3) << "getting " << outs[i];
       client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
-
     PADDLE_ENFORCE(client_.Wait());
   }
 
@@ -52,21 +51,22 @@ class SendOp : public framework::OperatorBase {
   mutable detail::RPCClient client_;
 };
 
-class SendOpMaker : public framework::OpProtoAndCheckerMaker {
+class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  SendOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+  RecvOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "(Tensor) Input tensor to be sent").AsDuplicable();
     AddOutput("Out", "(Tensor) Output tensor to be received from server")
         .AsDuplicable();
     AddComment(R"DOC(
-Send operator
+Recv operator
 
-This operator will send tensor to recv_op at the parameter server.
+This operator can get variables from server side.
 )DOC");
     AddAttr<std::vector<std::string>>("endpoints",
                                       "(string vector, default 127.0.0.1:6164)"
-                                      "Server endpoints to send variables to.")
+                                      "Server endpoints to recv variables"
+                                      "from.")
         .SetDefault({});
     AddAttr<std::vector<std::string>>("epmap",
                                       "(string vector, default 127.0.0.1:6164)"
@@ -81,4 +81,4 @@ This operator will send tensor to recv_op at the parameter server.
 
 namespace ops = paddle::operators;
 
-REGISTER_OPERATOR(send, ops::SendOp, ops::SendOpMaker);
+REGISTER_OPERATOR(recv, ops::RecvOp, ops::RecvOpMaker);

paddle/operators/send_op.cc

@@ -38,26 +38,34 @@ class SendOp : public framework::OperatorBase {
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
     bool do_get = Attr<bool>("DoGet");
+    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());
+
     if (do_get) {
       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_ENFORCE(client_.Wait());
     }
-
-    PADDLE_ENFORCE(client_.Wait());
   }
 
  private:
+  // TODO(typhoonzero): put RPCClient in a Variable.
   mutable detail::RPCClient client_;
 };
 

paddle/operators/send_recv_op_test.cc

@@ -132,7 +132,7 @@ void StartServerNet(bool is_sparse) {
   attrs.insert({"GradList", std::vector<std::string>({"x1"})});
   attrs.insert({"OptimizeBlock", block});
   listen_and_serv_op =
-      f::OpRegistry::CreateOp("listen_and_serv", {{"RX", {"x1"}}}, {}, attrs);
+      f::OpRegistry::CreateOp("listen_and_serv", {}, {}, attrs);
   listen_and_serv_op->Run(scope, place);
 }
 

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/v2/fluid/distribute_transpiler.py

@@ -471,11 +471,10 @@ class DistributeTranspiler:
             else:
                 self._append_pserver_non_opt_ops(optimize_sub_program,
                                                  pserver_program, opt_op)
-        # Append the recv op
+        # Append the listen_and_serv op
         pserver_program.global_block().append_op(
-            type="recv",
-            inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
-                    },  # grads to recv
+            type="listen_and_serv",
+            inputs={},
             outputs={},
             attrs={
                 "OptimizeBlock": optimize_sub_program.global_block(),

python/paddle/v2/fluid/framework.py

@@ -478,7 +478,7 @@ class Operator(object):
         no_kernel_op_set = {
             'feed', 'fetch', 'save', 'load', 'recurrent',
             'rnn_memory_helper_grad', 'conditional_block', 'while', 'send',
-            'recv', 'parallel_do'
+            'recv', 'listen_and_serv', 'parallel_do'
         }
         if type not in no_kernel_op_set:
             self.desc.infer_var_type(self.block.desc)

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 +76,151 @@ 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("listen_and_serv")
+        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='listen_and_serv',
+            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})
+
+
+def Recv(endpoints, get_vars):
+    """
+    Recv 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("Recv", **locals())
+    helper.append_op(
+        type="recv",
+        inputs={"X": get_vars},
+        outputs={"Out": get_vars},
+        attrs={"endpoints": endpoints,
+               "epmap": epmap})

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

@@ -26,6 +26,7 @@ __all__ = [
     'fc',
     'embedding',
     'dynamic_lstm',
+    'dynamic_lstmp',
     'dynamic_gru',
     'gru_unit',
     'linear_chain_crf',
@@ -256,7 +257,8 @@ def dynamic_lstm(input,
                  gate_activation='sigmoid',
                  cell_activation='tanh',
                  candidate_activation='tanh',
-                 dtype='float32'):
+                 dtype='float32',
+                 name=None):
     """
     **Dynamic LSTM Layer**
 
@@ -282,7 +284,7 @@ def dynamic_lstm(input,
     W_{fc}, W_{oc}` are diagonal weight matrices for peephole connections. In
     our implementation, we use vectors to reprenset these diagonal weight
     matrices. The :math:`b` terms denote bias vectors (:math:`b_i` is the input
-    gate bias vector), :math:`\sigma` is the non-line activations, such as
+    gate bias vector), :math:`\sigma` is the non-linear activations, such as
     logistic sigmoid function, and :math:`i, f, o` and :math:`c` are the input
     gate, forget gate, output gate, and cell activation vectors, respectively,
     all of which have the same size as the cell output activation vector :math:`h`.
@@ -308,25 +310,25 @@ def dynamic_lstm(input,
                          (T X 4D), where T is the total time steps in this
                          mini-batch, D is the hidden size.
         size(int): 4 * hidden size.
-        param_attr(ParamAttr): The parameter attribute for the learnable
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
                                hidden-hidden weights.
 
-                               - The shape is (D x 4D), where D is the hidden
-                                 size.
                                - Weights = {:math:`W_{ch}, W_{ih}, \
                                                 W_{fh}, W_{oh}`}
-        bias_attr(ParamAttr): The bias attribute for the learnable bias
+                               - The shape is (D x 4D), where D is the hidden
+                                 size.
+        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
                               weights, which contains two parts, input-hidden
                               bias weights and peephole connections weights if
                               setting `use_peepholes` to `True`.
 
                               1. `use_peepholes = False`
-                                - The shape is (1 x 4D).
                                 - Biases = {:math:`b_c, b_i, b_f, b_o`}.
+                                - The shape is (1 x 4D).
                               2. `use_peepholes = True`
-                                - The shape is (1 x 7D).
                                 - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                  W_{fc}, W_{oc}`}.
+                                - The shape is (1 x 7D).
         use_peepholes(bool): Whether to enable diagonal/peephole connections,
                              default `True`.
         is_reverse(bool): Whether to compute reversed LSTM, default `False`.
@@ -339,6 +341,8 @@ def dynamic_lstm(input,
                               Choices = ["sigmoid", "tanh", "relu", "identity"],
                               default "tanh".
         dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
 
     Returns:
         tuple: The hidden state, and cell state of LSTM. The shape of both \
@@ -353,6 +357,7 @@ def dynamic_lstm(input,
             forward, _ = fluid.layers.dynamic_lstm(
                 input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
     """
+
     helper = LayerHelper('lstm', **locals())
     size = size / 4
     weight = helper.create_parameter(
@@ -389,6 +394,192 @@ def dynamic_lstm(input,
     return hidden, cell
 
 
+def dynamic_lstmp(input,
+                  size,
+                  proj_size,
+                  param_attr=None,
+                  bias_attr=None,
+                  use_peepholes=True,
+                  is_reverse=False,
+                  gate_activation='sigmoid',
+                  cell_activation='tanh',
+                  candidate_activation='tanh',
+                  proj_activation='tanh',
+                  dtype='float32',
+                  name=None):
+    """
+    **Dynamic LSTMP Layer**
+
+    LSTMP (LSTM with recurrent projection) layer has a separate projection 
+    layer after the LSTM layer, projecting the original hidden state to a 
+    lower-dimensional one, which is proposed to reduce the number of total 
+    parameters and furthermore computational complexity for the LSTM, 
+    espeacially for the case that the size of output units is relative 
+    large (https://research.google.com/pubs/archive/43905.pdf). 
+
+    The formula is as follows:
+
+    .. math::
+
+        i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
+
+        f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
+
+        \\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
+
+        o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
+
+        c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
+
+        h_t & = o_t \odot act_h(c_t)
+
+        r_t & = \overline{act_h}(W_{rh}h_t)
+
+    In the above formula:
+
+    * :math:`W`: Denotes weight matrices (e.g. :math:`W_{xi}` is \
+          the matrix of weights from the input gate to the input).
+    * :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight \
+          matrices for peephole connections. In our implementation, \
+          we use vectors to reprenset these diagonal weight matrices. 
+    * :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
+          bias vector). 
+    * :math:`\sigma`: The activation, such as logistic sigmoid function.
+    * :math:`i, f, o` and :math:`c`: The input gate, forget gate, output \
+          gate, and cell activation vectors, respectively, all of which have \
+          the same size as the cell output activation vector :math:`h`. 
+    * :math:`h`: The hidden state.
+    * :math:`r`: The recurrent projection of the hidden state. 
+    * :math:`\\tilde{c_t}`: The candidate hidden state, whose \
+          computation is based on the current input and previous hidden state.
+    * :math:`\odot`: The element-wise product of the vectors. 
+    * :math:`act_g` and :math:`act_h`: The cell input and cell output \
+          activation functions and `tanh` is usually used for them. 
+    * :math:`\overline{act_h}`: The activation function for the projection \
+          output, usually using `identity` or same as :math:`act_h`.
+
+    Set `use_peepholes` to `False` to disable peephole connection. The formula
+    is omitted here, please refer to the paper
+    http://www.bioinf.jku.at/publications/older/2604.pdf for details.
+    
+    Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
+    operations on the input :math:`x_{t}` are NOT included in this operator.
+    Users can choose to use fully-connected layer before LSTMP layer.
+
+    Args:
+        input(Variable): The input of dynamic_lstmp layer, which supports
+                         variable-time length input sequence. The underlying
+                         tensor in this Variable is a matrix with shape
+                         (T X 4D), where T is the total time steps in this
+                         mini-batch, D is the hidden size.
+        size(int): 4 * hidden size.
+        proj_size(int): The size of projection output.
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
+                               hidden-hidden weight and projection weight.
+
+                               - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
+                                                W_{fh}, W_{oh}`}.
+                               - The shape of hidden-hidden weight is (P x 4D), 
+                                 where P is the projection size and D the hidden 
+                                 size.
+                               - Projection weight = {:math:`W_{rh}`}.
+                               - The shape of projection weight is (D x P).
+        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
+                              weights, which contains two parts, input-hidden
+                              bias weights and peephole connections weights if
+                              setting `use_peepholes` to `True`.
+
+                              1. `use_peepholes = False`
+                                - Biases = {:math:`b_c, b_i, b_f, b_o`}.
+                                - The shape is (1 x 4D).
+                              2. `use_peepholes = True`
+                                - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
+                                                 W_{fc}, W_{oc}`}.
+                                - The shape is (1 x 7D).
+        use_peepholes(bool): Whether to enable diagonal/peephole connections,
+                             default `True`.
+        is_reverse(bool): Whether to compute reversed LSTM, default `False`.
+        gate_activation(str): The activation for input gate, forget gate and
+                              output gate. Choices = ["sigmoid", "tanh", "relu",
+                              "identity"], default "sigmoid".
+        cell_activation(str): The activation for cell output. Choices = ["sigmoid",
+                              "tanh", "relu", "identity"], default "tanh".
+        candidate_activation(str): The activation for candidate hidden state.
+                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              default "tanh".
+        proj_activation(str): The activation for projection output.
+                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              default "tanh".
+        dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
+
+    Returns:
+        tuple: The projection of hidden state, and cell state of LSTMP. The \
+               shape of projection is (T x P), for the cell state which is \
+               (T x D), and both LoD is the same with the `input`.
+
+    Examples:
+        .. code-block:: python
+
+            hidden_dim, proj_dim = 512, 256
+            fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                                     act=None, bias_attr=None)
+            proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out, 
+                                                     size=hidden_dim * 4, 
+                                                     proj_size=proj_dim, 
+                                                     use_peepholes=False,
+                                                     is_reverse=True,
+                                                     cell_activation="tanh",
+                                                     proj_activation="tanh")
+    """
+
+    helper = LayerHelper('lstmp', **locals())
+    size = size / 4
+    weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[proj_size, 4 * size], dtype=dtype)
+    proj_weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[size, proj_size], dtype=dtype)
+    bias_size = [1, 7 * size]
+    if not use_peepholes:
+        bias_size[1] = 4 * size
+    bias = helper.create_parameter(
+        attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
+
+    projection = helper.create_tmp_variable(dtype)
+    cell = helper.create_tmp_variable(dtype)
+    ordered_proj0 = helper.create_tmp_variable(dtype)
+    batch_hidden = helper.create_tmp_variable(dtype)
+    batch_gate = helper.create_tmp_variable(dtype)
+    batch_cell_pre_act = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='lstmp',
+        inputs={
+            'Input': input,
+            'Weight': weight,
+            'ProjWeight': proj_weight,
+            'Bias': bias
+        },
+        outputs={
+            'Projection': projection,
+            'Cell': cell,
+            'OrderedP0': ordered_proj0,
+            'BatchHidden': batch_hidden,
+            'BatchGate': batch_gate,
+            'BatchCellPreAct': batch_cell_pre_act
+        },
+        attrs={
+            'use_peepholes': use_peepholes,
+            'is_reverse': is_reverse,
+            'gate_activation': gate_activation,
+            'cell_activation': cell_activation,
+            'candidate_activation': candidate_activation,
+            'proj_activation': proj_activation
+        })
+    return projection, cell
+
+
 def dynamic_gru(input,
                 size,
                 param_attr=None,

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/book_distribute/notest_dist_image_classification.py

-#Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 #
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
+# 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
+#     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.
+# 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.
 
 from __future__ import print_function
 

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

@@ -202,6 +202,18 @@ class TestBook(unittest.TestCase):
                     x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
         print(str(program))
 
+    def test_dynamic_lstmp(self):
+        program = Program()
+        with program_guard(program):
+            hidden_dim, proj_dim = 16, 8
+            seq_data = layers.data(
+                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
+            fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
+            self.assertIsNotNone(
+                layers.dynamic_lstmp(
+                    input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
+        print(str(program))
+
     def test_sequence_softmax(self):
         program = Program()
         with program_guard(program):

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
+import time
+
+
+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()
+        time.sleep(5)
+        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()