Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into fix-beam_search-dev

cmake/external/grpc.cmake

@@ -40,12 +40,12 @@ ExternalProject_Add(
     # NOTE(wuyi):
     # this package is generated by following steps:
     # 1. git clone -b v1.8.x https://github.com/grpc/grpc.git
-    # 2. submodule update --init
+    # 2. git submodule update --init
     # 3. keep only zlib, cares, protobuf, boringssl under "third_party",
     #    checkout and clean other dirs under third_party
     # 4. remove .git, and package the directory.
-    URL "http://paddlepaddledeps.bj.bcebos.com/grpc-v1.8.x.tar.gz"
-    URL_MD5  "c9c58ee7d0e8929a63155af6a2ecdbd0"
+    URL "http://paddlepaddledeps.bj.bcebos.com/grpc-v1.10.x.tar.gz"
+    URL_MD5  "1f268a2aff6759839dccd256adcc91cf"
     PREFIX          ${GRPC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CONFIGURE_COMMAND ""

paddle/fluid/operators/distributed/grpc_client.cc

@@ -269,14 +269,15 @@ void GRPCClient::Proceed() {
 }
 
 std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
-  // TODO(Yancey1989): make grpc client completely thread-safe
   std::lock_guard<std::mutex> guard(chan_mutex_);
   auto it = channels_.find(ep);
   if (it != channels_.end()) {
     return it->second;
   }
 
+  // Channel configurations:
   grpc::ChannelArguments args;
+  args.SetInt(GRPC_ARG_MAX_RECONNECT_BACKOFF_MS, 2000);
   args.SetCompressionAlgorithm(GRPC_COMPRESS_NONE);
   args.SetMaxSendMessageSize(std::numeric_limits<int>::max());
   args.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());

paddle/fluid/operators/distributed/grpc_client.h

@@ -76,6 +76,7 @@ class BaseProcessor {
   virtual void Prepare(const VarHandle& var_info, int64_t time_out) {
     context_.reset(new grpc::ClientContext());
     var_h_ = var_info;
+    context_->set_wait_for_ready(true);
 
     std::chrono::system_clock::time_point deadline =
         std::chrono::system_clock::now() + std::chrono::milliseconds(time_out);
@@ -85,6 +86,7 @@ class BaseProcessor {
 
   virtual void Prepare(int64_t time_out) {
     context_.reset(new grpc::ClientContext());
+    context_->set_wait_for_ready(true);
 
     std::chrono::system_clock::time_point deadline =
         std::chrono::system_clock::now() + std::chrono::milliseconds(time_out);
@@ -176,26 +178,24 @@ class GRPCClient : public RPCClient {
 
   bool AsyncSendVar(const std::string& ep, const platform::DeviceContext& ctx,
                     const framework::Scope& scope, const std::string& var_name,
-                    int64_t time_out = RPCClient::rpc_time_out) override;
+                    int64_t time_out = FLAGS_grpc_deadline) override;
 
   bool AsyncGetVar(const std::string& ep, const platform::DeviceContext& ctx,
                    const framework::Scope& scope, const std::string& var_name,
-                   int64_t time_out = RPCClient::rpc_time_out) override;
+                   int64_t time_out = FLAGS_grpc_deadline) override;
 
   bool AsyncPrefetchVar(const std::string& ep,
                         const platform::DeviceContext& ctx,
                         const framework::Scope& scope,
                         const std::string& in_var_name,
                         const std::string& out_var_name,
-                        int64_t time_out = RPCClient::rpc_time_out) override;
+                        int64_t time_out = FLAGS_grpc_deadline) override;
 
-  void AsyncSendBatchBarrier(
-      const std::string& ep,
-      int64_t time_out = RPCClient::rpc_time_out) override;
+  void AsyncSendBatchBarrier(const std::string& ep,
+                             int64_t time_out = FLAGS_grpc_deadline) override;
 
-  void AsyncSendFetchBarrier(
-      const std::string& ep,
-      int64_t time_out = RPCClient::rpc_time_out) override;
+  void AsyncSendFetchBarrier(const std::string& ep,
+                             int64_t time_out = FLAGS_grpc_deadline) override;
 
   void Wait() override;
 
@@ -211,7 +211,7 @@ class GRPCClient : public RPCClient {
   void Proceed();
 
   void AsyncSendComplete(const std::string& ep,
-                         int64_t time_out = RPCClient::rpc_time_out);
+                         int64_t time_out = FLAGS_grpc_deadline);
 
   std::shared_ptr<grpc::Channel> GetChannel(const std::string& ep);
 

paddle/fluid/operators/distributed/grpc_server.cc

@@ -97,7 +97,7 @@ class RequestSend final : public RequestBase {
 
   void Process() override {
     std::string varname = GetReqName();
-    VLOG(3) << "RequestSend var_name:" << varname;
+    VLOG(4) << "RequestSend var_name:" << varname;
 
     auto scope = request_->GetMutableLocalScope();
     auto invar = request_->GetVar();
@@ -132,7 +132,7 @@ class RequestGet final : public RequestBase {
   void Process() override {
     // proc request.
     std::string varname = request_.varname();
-    VLOG(3) << "RequestGet " << varname;
+    VLOG(4) << "RequestGet " << varname;
 
     auto scope = request_handler_->scope();
     auto invar = scope->FindVar(varname);
@@ -178,7 +178,7 @@ class RequestPrefetch final : public RequestBase {
     // prefetch process...
     std::string in_var_name = request_->Varname();
     std::string out_var_name = request_->OutVarname();
-    VLOG(3) << "RequestPrefetch, in_var_name: " << in_var_name
+    VLOG(4) << "RequestPrefetch, in_var_name: " << in_var_name
             << " out_var_name: " << out_var_name;
 
     auto scope = request_->GetMutableLocalScope();
@@ -201,10 +201,10 @@ class RequestPrefetch final : public RequestBase {
 };
 
 void AsyncGRPCServer::WaitServerReady() {
-  VLOG(3) << "AsyncGRPCServer is wait server ready";
+  VLOG(4) << "AsyncGRPCServer is wait server ready";
   std::unique_lock<std::mutex> lock(this->mutex_ready_);
   condition_ready_.wait(lock, [=] { return this->ready_ == 1; });
-  VLOG(3) << "AsyncGRPCServer WaitSeverReady";
+  VLOG(4) << "AsyncGRPCServer WaitSeverReady";
 }
 
 void AsyncGRPCServer::StartServer() {
@@ -243,7 +243,7 @@ void AsyncGRPCServer::StartServer() {
     for (int i = 0; i < threadnum; i++) {
       rpc_threads_[rpc_name].emplace_back(new std::thread(std::bind(
           &AsyncGRPCServer::HandleRequest, this, cq.get(), rpc_name, f)));
-      VLOG(3) << t.first << " creates threads!";
+      VLOG(4) << t.first << " creates threads!";
     }
   }
 
@@ -260,7 +260,7 @@ void AsyncGRPCServer::StartServer() {
     auto& threads = t.second;
     for (size_t i = 0; i < threads.size(); ++i) {
       threads[i]->join();
-      VLOG(3) << t.first << " threads ends!";
+      VLOG(4) << t.first << " threads ends!";
     }
   }
 }
@@ -268,7 +268,7 @@ void AsyncGRPCServer::StartServer() {
 void AsyncGRPCServer::ShutdownQueue() {
   for (auto& t : rpc_cq_) {
     t.second->Shutdown();
-    VLOG(3) << t.first << " shutdown!";
+    VLOG(4) << t.first << " queue shutdown!";
   }
 }
 
@@ -277,7 +277,7 @@ void AsyncGRPCServer::ShutDownImpl() {
   is_shut_down_ = true;
   ShutdownQueue();
 
-  VLOG(3) << "server_ shutdown!";
+  VLOG(4) << "server_ shutdown!";
   server_->Shutdown();
 }
 
@@ -285,7 +285,7 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
                                           int req_id) {
   std::unique_lock<std::mutex> lock(cq_mutex_);
   if (is_shut_down_) {
-    LOG(WARNING) << "shutdown, do not TryToRegisterNewSendOne";
+    VLOG(4) << "shutdown, do not TryToRegisterNewSendOne";
     return;
   }
 

paddle/fluid/operators/distributed/rpc_client.cc

@@ -13,6 +13,10 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/distributed/rpc_client.h"
+#include "gflags/gflags.h"
+
+// default to 3min to avoid temprary network failures.
+DEFINE_int32(grpc_deadline, 180000, "deadline timeouts for grpc");
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/distributed/rpc_client.h

@@ -15,11 +15,14 @@
 #pragma once
 
 #include <string>
+#include "gflags/gflags.h"
 
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 
+DECLARE_int32(grpc_deadline);
+
 namespace paddle {
 namespace operators {
 namespace distributed {
@@ -32,26 +35,26 @@ class RPCClient {
                             const platform::DeviceContext& ctx,
                             const framework::Scope& scope,
                             const std::string& var_name,
-                            int64_t time_out = rpc_time_out) = 0;
+                            int64_t time_out = FLAGS_grpc_deadline) = 0;
 
   virtual bool AsyncGetVar(const std::string& ep,
                            const platform::DeviceContext& ctx,
                            const framework::Scope& scope,
                            const std::string& var_name,
-                           int64_t time_out = rpc_time_out) = 0;
+                           int64_t time_out = FLAGS_grpc_deadline) = 0;
 
   virtual bool AsyncPrefetchVar(const std::string& ep,
                                 const platform::DeviceContext& ctx,
                                 const framework::Scope& scope,
                                 const std::string& in_var_name,
                                 const std::string& out_var_name,
-                                int64_t time_out = rpc_time_out) = 0;
+                                int64_t time_out = FLAGS_grpc_deadline) = 0;
 
-  virtual void AsyncSendBatchBarrier(const std::string& ep,
-                                     int64_t time_out = rpc_time_out) = 0;
+  virtual void AsyncSendBatchBarrier(
+      const std::string& ep, int64_t time_out = FLAGS_grpc_deadline) = 0;
 
-  virtual void AsyncSendFetchBarrier(const std::string& ep,
-                                     int64_t time_out = rpc_time_out) = 0;
+  virtual void AsyncSendFetchBarrier(
+      const std::string& ep, int64_t time_out = FLAGS_grpc_deadline) = 0;
 
   // SendComplete tells all the server that current trainer have no more data
   // to train, so that the pserver can reduce it's barrier count, and continue
@@ -60,8 +63,6 @@ class RPCClient {
 
   virtual void Wait() = 0;
 
-  static constexpr int64_t rpc_time_out = 120 * 1000;
-
   template <typename T>
   static RPCClient* GetInstance() {
     std::call_once(init_flag_, &RPCClient::Init<T>);

paddle/fluid/operators/distributed/rpc_server.cc

@@ -47,11 +47,12 @@ void RPCServer::WaitBarrier(const std::string& rpc_name) {
     return (barrier_counter_[rpc_name] >= client_num_ || exit_flag_.load());
   });
 
-  VLOG(3) << "batch_barrier_:" << barrier_counter_[rpc_name];
+  VLOG(3) << "batch_barrier_: " << rpc_name << " "
+          << barrier_counter_[rpc_name];
 }
 
 void RPCServer::IncreaseBatchBarrier(const std::string rpc_name) {
-  VLOG(3) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
+  VLOG(4) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
   int b = 0;
   std::unique_lock<std::mutex> lock(mutex_);
   b = ++barrier_counter_[rpc_name];
@@ -100,7 +101,7 @@ void RPCServer::SetCond(const std::string& rpc_name) {
 }
 
 void RPCServer::WaitCond(const std::string& rpc_name) {
-  VLOG(3) << "RPCServer WaitCond " << rpc_name;
+  VLOG(4) << "RPCServer WaitCond " << rpc_name;
   int cond = 0;
   {
     std::unique_lock<std::mutex> lock(mutex_);

paddle/fluid/operators/listen_and_serv_op.cc

@@ -164,7 +164,6 @@ void ListenAndServOp::RunSyncLoop(
 
 void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
                                    framework::ProgramDesc *program) const {
-  VLOG(3) << "RunAsyncLoop in";
   // grad name to block id
   std::unordered_map<std::string, int32_t> grad_to_block_id;
   std::unordered_map<int32_t, std::string> id_to_grad;
@@ -202,7 +201,6 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
   request_get_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
   request_prefetch_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
 
-  VLOG(3) << "RunAsyncLoop into while";
   while (true) {
     if (rpc_service_->IsExit()) {
       LOG(INFO) << "get exit!rpc_processor break!";

python/paddle/fluid/layers/nn.py

@@ -4318,14 +4318,18 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
                                 say :attr:`actual_shape` has a higher priority
                                 than :attr:`shape`.
         act (str): The non-linear activation to be applied to output variable.
-        inplace(bool): If this flag is set true, a new output tensor is created
-                       whose data is copied from input x, otherwise the output
-                       shares data with input without copying.
+        inplace(bool): If this flag is set true, the output
+                       shares data with input without copying, otherwise
+                       a new output tensor is created
+                       whose data is copied from input x.
         name (str): The name of this layer. It is optional.
 
     Returns:
         Variable: The output tensor.
 
+    Raises:
+        TypeError: if actual_shape is neither Variable nor None.
+
     Examples:
         .. code-block:: python
 
@@ -4337,6 +4341,11 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
 
     if not (isinstance(shape, list) or isinstance(shape, tuple)):
         raise ValueError("Input shape must be a python lsit or tuple.")
+    inputs = {"X": x}
+    if isinstance(actual_shape, Variable):
+        inputs["Shape"] = actual_shape
+    elif actual_shape is not None:
+        raise TypeError("actual_shape should either be Variable or None")
 
     # Validate the shape
     unk_dim_idx = -1
@@ -4357,9 +4366,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
     reshaped = helper.create_tmp_variable(dtype=x.dtype)
     helper.append_op(
         type="reshape",
-        inputs={"X": x,
-                "Shape": actual_shape}
-        if isinstance(actual_shape, Variable) else {"X": x},
+        inputs=inputs,
         attrs={"shape": shape,
                "inplace": inplace},
         outputs={"Out": reshaped})
@@ -4978,10 +4985,10 @@ def log(x):
 
     .. math::
 
-        Out = \\ln(input)
+        Out = \\ln(x)
 
     Args:
-        input (Variable): Input tensor.
+        x (Variable): Input tensor.
 
     Returns:
         Variable: The natural log of the input tensor computed element-wise.
@@ -5002,7 +5009,7 @@ def log(x):
 def relu(x):
     """
     Relu takes one input data (Tensor) and produces one output data (Tensor)
-    where the rectified linear function, y = max(0, input), is applied to
+    where the rectified linear function, y = max(0, x), is applied to
     the tensor elementwise.
 
     .. math::
@@ -5010,7 +5017,7 @@ def relu(x):
         Out = \\max(0, x)
 
     Args:
-        input (Variable): The input tensor.
+        x (Variable): The input tensor.
 
     Returns:
         Variable: The output tensor with the same shape as input.
@@ -5019,7 +5026,7 @@ def relu(x):
 
         .. code-block:: python
 
-            output = fluid.layers.relu(input)
+            output = fluid.layers.relu(x)
     """
     helper = LayerHelper('relu', **locals())
     dtype = helper.input_dtype(input_param_name='x')