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paddle/fluid/framework/parallel_executor.cc

@@ -80,7 +80,7 @@ ParallelExecutor::ParallelExecutor(
 
 // Bcast Parameters to all GPUs
 #ifdef PADDLE_WITH_CUDA
-  auto *nccl_id_var = scope->FindVar("NCCLID");
+  auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
   ncclUniqueId *nccl_id = nullptr;
   if (nccl_id_var != nullptr) {
     nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();

paddle/fluid/operators/CMakeLists.txt

@@ -187,7 +187,7 @@ if(WITH_DISTRIBUTE)
     if(WITH_GPU)
         op_library(gen_nccl_id_op DEPS nccl_common)
     else()
-    set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
+        set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
     endif()
     set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
     set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")

paddle/fluid/operators/detail/sendrecvop_utils.cc

@@ -162,8 +162,8 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   if (var->IsType<ncclUniqueId>()) {
     e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber,
                               NCCL_UNIQUE_ID_BYTES);
-    ncclUniqueId* uid = var->GetMutable<ncclUniqueId>();
-    e.WriteRawBytes(std::string(uid->internal, NCCL_UNIQUE_ID_BYTES));
+    ncclUniqueId& uid = var->Get<ncclUniqueId>();
+    e.WriteRawBytes(std::string(uid.internal, NCCL_UNIQUE_ID_BYTES));
 
     // for serialize NCCL_ID
     ::grpc::Slice slices(e.size());

paddle/fluid/operators/gen_nccl_id_op.cc

@@ -52,17 +52,17 @@ class GenNCCLIdOp : public framework::OperatorBase {
  private:
   void GenerateAndSend(framework::Scope* scope,
                        const platform::DeviceContext& dev_ctx) const {
-    auto var = scope->FindVar("NCCLID");
+    auto var = scope->FindVar(NCCL_ID_VARNAME);
     PADDLE_ENFORCE_NOT_NULL(var);
     auto id = var->GetMutable<ncclUniqueId>();
-    platform::dynload::ncclGetUniqueId(id);
+    PADDLE_ENFORCE(platform::dynload::ncclGetUniqueId(id));
 
     std::vector<std::string> endpoint_list =
         Attr<std::vector<std::string>>("endpoint_list");
     detail::RPCClient client;
     for (auto& ep : endpoint_list) {
       VLOG(3) << "sending nccl id to " << ep;
-      client.AsyncSendVariable(ep, dev_ctx, *scope, "NCCLID");
+      client.AsyncSendVariable(ep, dev_ctx, *scope, NCCL_ID_VARNAME);
     }
     client.Wait();
     VLOG(3) << "sending completed...";
@@ -71,6 +71,9 @@ class GenNCCLIdOp : public framework::OperatorBase {
   void GetIdByServer(framework::Scope* scope,
                      const platform::DeviceContext& dev_ctx) const {
     std::string endpoint = Attr<std::string>("endpoint");
+    // NOTE: Can not use unique_ptr here because the default
+    // deleter will call GRPC Server's base class's dtor and
+    // that will cause a wired crash.
     rpc_service_ = new detail::AsyncGRPCServer(endpoint, true);
     framework::ProgramDesc empty_program;
     framework::Executor executor(dev_ctx.GetPlace());

paddle/fluid/operators/test_send_nccl_id.cc

@@ -39,7 +39,7 @@ std::unique_ptr<detail::AsyncGRPCServer> rpc_service;
 void StartServer() {
   f::Scope scope;
   p::CPUPlace place;
-  scope.Var("NCCLID");
+  scope.Var(NCCL_ID_VARNAME);
   p::DeviceContextPool& pool = p::DeviceContextPool::Instance();
   auto& dev_ctx = *pool.Get(p::CPUPlace());
 
@@ -71,7 +71,7 @@ TEST(SendNcclId, Normal) {
   p::DeviceContextPool& pool = p::DeviceContextPool::Instance();
   auto& dev_ctx = *pool.Get(p::CPUPlace());
 
-  auto var = scope.Var("NCCLID");
+  auto var = scope.Var(NCCL_ID_VARNAME);
   // var->SetType(f::proto::VarType_Type_RAW);
   auto id = var->GetMutable<ncclUniqueId>();
   p::dynload::ncclGetUniqueId(id);
@@ -80,7 +80,7 @@ TEST(SendNcclId, Normal) {
   std::string ep = string::Sprintf("127.0.0.1:%d", port);
   detail::RPCClient client;
 
-  client.AsyncSendVariable(ep, dev_ctx, scope, "NCCLID");
+  client.AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
   client.Wait();
   server_thread.join();
   auto* ptr = rpc_service.release();

paddle/fluid/platform/nccl_helper.h

@@ -21,6 +21,8 @@
 #include "paddle/fluid/platform/dynload/nccl.h"
 #include "paddle/fluid/platform/enforce.h"
 
+#define NCCL_ID_VARNAME "NCCLID"
+
 namespace paddle {
 namespace platform {
 
@@ -76,7 +78,7 @@ struct NCCLContextMap {
 
   explicit NCCLContextMap(const std::vector<platform::Place> &places,
                           ncclUniqueId *nccl_id = nullptr,
-                          size_t node_count = 0, size_t trainer_id = 0) {
+                          size_t num_trainers = 0, size_t trainer_id = 0) {
     PADDLE_ENFORCE(!places.empty());
     order_.reserve(places.size());
     for (auto &p : places) {
@@ -94,16 +96,14 @@ struct NCCLContextMap {
     std::unique_ptr<ncclComm_t[]> comms(new ncclComm_t[order_.size()]);
     // if pass nccl_id here, can assume we are doing multi node training
     if (nccl_id == nullptr) {
-      {
-        std::lock_guard<std::mutex> guard(NCCLGroupGuard::NCCLMutex());
-        PADDLE_ENFORCE(platform::dynload::ncclCommInitAll(
-            comms.get(), static_cast<int>(order_.size()), order_.data()));
-      }
+      std::lock_guard<std::mutex> guard(NCCLGroupGuard::NCCLMutex());
+      PADDLE_ENFORCE(platform::dynload::ncclCommInitAll(
+          comms.get(), static_cast<int>(order_.size()), order_.data()));
     } else {
-      PADDLE_ENFORCE_GT(node_count, 0);
+      PADDLE_ENFORCE_GT(num_trainers, 0);
       // TODO(wuyi): need to ensure each node have same number of GPUs
       {
-        int nranks = node_count * order_.size();
+        int nranks = num_trainers * order_.size();
         NCCLGroupGuard gurad;
         for (auto &gpu_id : order_) {
           int rank = trainer_id * order_.size() + gpu_id;

python/paddle/fluid/parallel_executor.py

@@ -31,7 +31,7 @@ class ParallelExecutor(object):
                  allow_op_delay=False,
                  share_vars_from=None,
                  use_default_grad_scale=True,
-                 num_nodes=0,
+                 num_trainers=0,
                  trainer_id=0):
         """
         ParallelExecutor can run program in parallel.
@@ -53,10 +53,10 @@ class ParallelExecutor(object):
                 gradients of each device and scaled gradients would be
                 aggregated. Otherwise, a customized scale value should be fed
                 to the network.
-            num_nodes(int, default 0): If greater than 0, NCCL will be
+            num_trainers(int, default 0): If greater than 0, NCCL will be
                 initialized with multpile rank of nodes, each node should have
                 same number of GPUs. Distributed training will be enabled then.
-            trainer_id(int, default 0): Must use together with num_nodes.
+            trainer_id(int, default 0): Must use together with num_trainers.
                 trainer_id is the "rank" of current node starts from 0.
 
         Returns:
@@ -137,7 +137,7 @@ class ParallelExecutor(object):
             local_scopes,
             allow_op_delay,
             use_default_grad_scale,
-            num_nodes,
+            num_trainers,
             trainer_id)
         self.scope = scope